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title: Sex-Specific Predictors of Long-Term Mortality in Elderly Patients with Ischemic
Cardiomyopathy
authors:
- Hyun Ju Yoon
- Kye Hun Kim
- Nuri Lee
- Hyukjin Park
- Hyung Yoon Kim
- Jae Yeong Cho
- Youngkeun Ahn
- Myung Ho Jeong
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10003953
doi: 10.3390/jcm12052012
license: CC BY 4.0
---
# Sex-Specific Predictors of Long-Term Mortality in Elderly Patients with Ischemic Cardiomyopathy
## Abstract
Ischemic heart failure (HF) is one of the most common causes of morbidity and mortality in the world-wide, but sex-specific predictors of mortality in elderly patients with ischemic cardiomyopathy (ICMP) have been poorly studied. A total of 536 patients with ICMP over 65 years-old (77.8 ± 7.1 years, 283 males) were followed for a mean of 5.4 years. The development of death during clinical follow up was evaluated, and predictors of mortality were compared. Death was developed in 137 patients ($25.6\%$); 64 females ($25.3\%$) vs. 73 males ($25.8\%$). Low-ejection fraction was only an independent predictor of mortality in ICMP, regardless of sex (HR 3.070 CI = 1.708–5.520 in female, HR 2.011, CI = 1.146–3.527 in male). Diabetes (HR 1.811, CI = 1.016–3.229), elevated e/e’ (HR 2.479, CI = 1.201–5.117), elevated pulmonary artery systolic pressure (HR 2.833, CI = 1.197–6.704), anemia (HR 1.860, CI = 1.025–3.373), beta blocker non-use (HR2.148, CI = 1.010–4.568), and angiotensin receptor blocker non-use (HR 2.100, CI = 1.137–3.881) were bad prognostic factors of long term mortality in female, whereas hypertension (HR 1.770, CI = 1.024–3.058), elevated Creatinine (HR 2.188, CI = 1.225–3.908), and statin non-use (HR 3.475, CI = 1.989–6.071) were predictors of mortality in males with ICMP independently. Systolic dysfunction in both sexes, diastolic dysfunction, beta blocker and angiotensin receptor blockers in female, and statins in males have important roles for long-term mortality in elderly patients with ICMP. For improving long-term survival in elderly patients with ICMP, it may be necessary to approach sex specifically.
## 1. Introduction
The prevalence of heart failure showed an increase due to the increase in the aging population. Ischemic cardiomyopathy (ICMP) is the most common cardiomyopathy. It is a condition when the heart muscle is weakened as a result of acute ischemic syndrome [1,2]. ICMP is most often presented with dilated morphology with wall motion defects and a history of previous myocardial infarction or confirmed coronary artery disease. Uncontrolled ischemia is a frequent cause of heart failure (HF) exacerbation after myocardial infarction in the case of progressive remodeled heart. Ischemic heart disease (IHD) has been the most significant cause of death in developed countries for several decades [3]. Women with IHD experience relatively worse outcomes compared to men. IHD and heart failure represent the leading causes of death in women, especially elderly people. IHD accounts for a third of all female deaths globally and affects nearly 48 million women in the United States [4,5]. A previous report noted that once women develop IHD, the risk of HF is high [6,7]. An expanded view of the multifactorial epidemiology of IHD and/or HF in women has identified important risk factors, including age, race, culture, ethnicity, socioeconomic status, lifestyle, and educational level influences that adversely impact cardiovascular outcomes. These disparities reflect our limited understanding of the sex differences in physiology, which are substantially related to lack of elderly female-specific data. Until now, there were few sex-specific predictors of ICMP. Therefore, we investigated sex-specific predictors on long-term mortality in elderly patients with ischemic cardiomyopathy (ICMP).
## 2.1. Study Design and Population
The present study was a single-center retrospective observational study, and the study protocol was approved by our institutional review board (No. 2015-05-092).
From January 2007 to December 2017, a total of 1200 ischemic HF patients with echocardiographic findings with both left ventricular (LV) ejection fraction < $45\%$ and LV end diastolic dimension > 55 mm were identified. Among them, 536 patients with ICMP more than 65 years old (77.8 ± 7.1 years, 283 males) were enrolled. In the present study, ICMP was defined as LV ejection fraction < $40\%$ and LV end diastolic dimension > 55 mm with one or more of the following findings: [1] a history of prior myocardial infarction or revascularization, [2] more than $75\%$ stenosis of left main or proximal left anterior descending coronary artery, or [3] more than $75\%$ stenosis of two or more epicardial coronary arteries [8]. In this study, for the purpose of detecting predictor of mortality, we included patients with ejection fraction ranging from 40–$45\%$ ($$n = 53$$). The reasons of exclusion were as follows: [1] age < 65 years ($$n = 552$$), [2] no imaging studies for coronary artery ($$n = 60$$), [3] acute myocardial infarction ($$n = 14$$), or [4] severe aortic or mitral valve disease ($$n = 11$$), [5] others ($$n = 27$$) (Figure 1).
The development of death during clinical follow up was evaluated, and predictors of mortality according to sex were evaluated.
## 2.2. Echocardiographic Examination
Comprehensive two-dimensional and Doppler echocardiographic examinations were performed in accordance with the recommendation of the current guideline [9]. Echocardiographic images from various echocardiographic windows were obtained by using a digital ultrasonographic equipment system (Vivid 7, GE Vingmed Ultrasound, Horten, Norway). Digital cine loops were obtained for subsequent offline analysis. All of the data were analyzed by using the computerized offline software package (EchoPAC PC 6.0.0, GE Vingmed Ultrasound, Horten, Norway). Chamber quantification was performed according to the current recommendations and included the measurement of LV end-systolic and end-diastolic dimensions or volumes, interventricular septal and posterior wall thicknesses, LV mass, left atrial diameter or volume, and LVEF. Early (E) and late (A) diastolic velocities of the mitral inflow (E wave) were measured by pulsed-wave Doppler from the apical four-chamber view, with the sample volume located at the tip of the mitral leaflets. Deceleration time (DT) of the E wave was measured as the time between the peak early diastolic velocity and the point at which the steepest deceleration slope was extrapolated to the zero line. Early diastolic (e’), late diastolic (a’), and systolic (s’) velocities of the septal mitral annulus were obtained by tissue Doppler imaging in the apical four-chamber view. Right ventricular systolic pressure (RVSP) was measured by the maximal velocity of the tricuspid regurgitation jet using a modified Bernoulli’s equation.
Global longitudinal strain (GLS) of the LV was measured by automate function imaging (AFI) at a frame rate of 65.2 ± 10.5 frames/sec. After selecting the optimal two-dimensional image, the timing of aortic valve closure was derived from the pulse wave Doppler of the aortic valve, and the three-point click method in three apical planes (apical four-chamber, two-chamber, and long axis view) was used. AFI non-invasively tracked and analyzed GLS based on the two-dimensional speckle tracking method and displayed the combined results of GLS of the three planes in a single bull’s eye summary. The mean value of GLS was calculated by dividing the sum of the GLS of each segment by 18 [10].
## 2.3. Statistical Analyses
The Statistical Package for Social Sciences, version 18.0 for Windows (SPSS Inc., Chicago, IL, USA) was used for the statistical analysis. Data are presented as percentages or mean ± standard deviation. The differences in the categorical variables were evaluated by using the chi-square test, and the continuous variables were compared by using the independent t test. Event-free survival rate was evaluated by using the Kaplan-Meier analysis, and event rates were compared by using the log-rank test. To identify the independent predictor of mortality, a multivariate Cox regression model was used for each of the cut-offs, with covariates that had $p \leq 0.05$ on univariate analysis. A p value of <0.05 was considered as statistically significant.
## 3.1. Baseline Clinical Characteristics
During 5.4 years of clinical follow-up, death was developed in 137 patients ($25.6\%$), and the mortality rate was not different between males ($$n = 73$$, $25.8\%$) and females ($$n = 64$$, $25.3\%$) (p = ns).
Comparisons of baseline characteristics by sex between the survived and the dead are summarized in Table 1.
In females, hypertension, diabetes, chronic kidney disease, and peripheral artery disease were significantly frequent in the dead than in the survived.
In males, the average age of the population was higher, and body weight and body mass index were significantly lower in the dead than in the survived group. Hypertension and chronic kidney disease were significantly more frequent in the dead than in the survived.
Hypertension and chronic kidney disease were significantly associated with mortality in both sexes.
## 3.2. Laboratory Findings
Comparisons of laboratory findings by sex between the survived and the dead are summarized in Table 2. The level of hemoglobin was significantly lower in death group in both sexes. Total cholesterol and HDL cholesterol were lower in female death group. Creatinine, hs-CRP, and NT-pro BNP were higher in the female death group. Triglyceride was higher in the death group than in the alive group of males. Other laboratory findings were not different between the groups.
## 3.3. Echocardiographic and Coronary Angiographic Findings
Comparisons of echocardiographic findings by sex between the survived and the dead are summarized in the Table 3.
LVEF was lower in death group in both sexes. LV end systolic dimension, RVSP, and E/E’ were higher in female death group. Male specific echocardiographic parameter was not shown in ICMP.
Coronary angiographic findings revealed involved vessel number was higher in the male death group (Table 4).
## 3.4. Medication
The differences of medication are summarized in Table 5. The user of beta blocker and angiotensin receptor blocker (ARB) was more frequent in female alive group. Nitrate was more used in female death group. Statin was frequently prescribed in male alive group compared with the death group or the female group.
## 3.5. Clinical Outcomes
A multivariate Cox regression model was used for each of the cut-offs, which defined from median value or normal limit with covariates that had $p \leq 0.05$ on univariate analysis. Low-ejection fraction was only an independent predictor of mortality in ICMP regardless of sex (HR 3.070 CI = 1.708–5.520 in female, HR 2.011, CI = 1.146–3.527 in male). Diabetes (HR 1.811, CI = 1.016–3.229), elevated e/e’ (HR 2.479, CI = 1.201–5.117), elevated pulmonary artery systolic pressure (HR 2.833, CI = 1.197–6.704), anemia (HR 1.860, CI = 1.025–3.373), beta blocker non-use (HR2.148, CI = 1.010–4.568), and angiotensin receptor blocker non-use (HR 2.100,CI = 1.137–3.881) were bad prognostic factors of long-term mortality in female, but use of beta blocker (HR 0.466, CI = 0.219–0.990) and angiotensin receptor blocker (HR 0.476, CI = 0.258–0.880) were good prognostic factors. Hypertension (HR 1.770, CI = 1.024–3.058), BMI (HR0.897, CI = 0.806–0.998), multi-vessel involved ICMP (HR 1.455, CI = 1.093–1.910), elevated creatinine (HR 2.188, CI = 1.225–3.908), and statin non-use (HR 3.475, CI = 1.989–6.071) were predictors of mortality in males, and statin (HR 0.600, CI = 0.260–1.387) was an improving factor of mortality in males with ICMP, independently (Figure 2).
Kaplan-Maier survival analysis showed a poorer outcome, which has multiple risk factors in ICMP (Figure 3). The progress was worse as the number of risk factors increased in both sexes. The fewer the predictors of mortality, the better the long-term prognosis.
## 4. Discussion
In this present study, we investigated the sex-specific prognostic factors of long-term mortality in patients with elderly ICMP.
Firstly, regardless of sex, low ejection fraction was a significantly bad prognostic factor of mortality in ICMP.
Second, Diabetes, anemia, non-use of beta blocker, and non-use of ARB were female-specific risk factors in ICMP. Advanced diastolic dysfunction, such as elevated e/e’ and elevated RVSP, was also a female-specific predictor of mortality in elderly females, but not in males, in ICMP.
Third, hypertension, elevated Cr, and non-use of statin were predictors of mortality in males with ICMP independently. For improving long term survival in ICMP, it may be necessary to approach sex specifically, especially in elderly patients.
HF is a disease with a poor prognosis, and it appears frequently at the last phase of various heart diseases. Despite the advances in drug and device therapy for HF, the mortality rate in patients with HF remains high. It is comparable to that of the most common cancers, with <$50\%$ four-year survival [11]. One of the most prominent etiologies is IHD [12,13], ischemia exacerbation-progressed ICMP. ICMP is a morbid condition with a 10-year mortality rate of $60\%$. These patients have a multitude of comorbidities, including LV systolic dysfunction, impaired coronary hemodynamics, abnormal myocardial energetics, increased myocardial oxygen consumption, and altered myocardial lactate metabolism [14].
Elderly status is also related with many risk factors and comorbid conditions that may increase the risk of death. Therefore, elderly patients with ICMP were high risks in themselves, but there were some differences among them according to sex. Previous reports showed that sex differences in ICMP epidemiology depend on the age of the patient as the effect of sex on the outcome changes across the lifespan. In middle age, the rates of ICMP begin to increase in women, concomitant with the onset of menopause and loss of female sex hormone [15]. After middle age, event rates continually rise in women, with some reports of higher mortality in elderly women (85 years) compared with elderly men [16].
Traditional cardiovascular prognostic factors affect differently according to sex on long term mortality in this study. Decreased LV EF was only common prognostic factor on long term mortality in both sexes with ICMP. Diabetes was a female-specific, whereas hypertension was a male-specific predictor of long-term mortality in ICMP. Previous studies demonstrated that the excess risk of diabetes-related HF is significantly greater in women with diabetes than in men with diabetes [17]. Since women generally develop cardiovascular disease later in life than men, the age-adjusted relative risk is higher in women than in men, but some evidence suggests that diabetes confers a higher absolute risk in women than it does in men [18]. Prognosis is much worse among those women with diabetes than among women without diabetes, although the prognoses for women and men with diabetes are similar [19]. Hypertension is more prevalent among females than males with HF, as was shown in previous reports. This could be explained by a higher augmentation index between peripheral and central blood pressure in women compared with men, which contributes to greater end organ damage, including LV hypertrophy [20]. In our elderly population, incidence was similar with previous reports, and mortality prediction was male-specific.
In this study, anemia was the female-specific risk factor of mortality in ICMP. Anemia can cause worse symptoms and prognosis in heart failure. It may worsen ischemia in patients who already have ICMP, and uncontrolled ischemia can deteriorate prognosis in this group of patients.
Even though systolic function was a common risk factor of mortality, diastolic dysfunction was a susceptibility for long-term mortality in female rather than male patients with ICMP. Generally, diastolic dysfunction, especially relaxation abnormality, was regarded as phenotype of cardiac aging, since, in the case of our elderly population, diastolic dysfunction was considered somewhat natural. According to ischemic cascade flow, diastolic dysfunction was located in front of systolic dysfunction in ischemic cascade flow [21]. From this data, females may be friable for diastolic function in ICMP.
Angiotensin converting enzyme inhibitors/ARB, beta blocker, diuretics, antiplatelets, and statins are included in the medical therapies used in patients with ICMP. Selected cases consider vasodilators, ivabradine, aldosterone antagonists, and/or the angiotensin receptor neprilysin inhibitors (ARNI) nowadays. Because our population consists of chronic patients who were diagnosed several years ago, new drug data were not included in this study, unfortunately.
There are currently no therapeutic guidelines regarding sex-based treatments. A previous study of hospitalized chronic HF patients revealed that women were equally likely to receive diuretics, but less likely to receive vasodilators or to be treated with evidence-based therapy than men [22].
Beta blockers may improve the function of viable, but hibernating, myocardium by reducing myocardial oxygen consumption and increasing diastolic perfusion [23].
Inhibition of renin angiotensin system decreases specific neurohumoral activation in chronic HF patients, at least partly, by slowing disease progression. From this study, beta blockers and ARB were more important for long-term prognosis in females, which suggests that autonomic nerve system and neurohormonal regulation were more sensitive in females than in males.
Statin use is a critical factor of long term survival in ICMP, especially in males, but not in females. All individuals with CAD have high risk of cardiovascular events and should be treated with statins according to the recommendations of the ESC/European Atherosclerosis Society guidelines for the management of dyslipidemia, regardless of low-density lipoprotein cholestestol (LDL-C) levels [24]. The mechanism was unclear, but statins were essential for male ICMP for improving long-term outcome.
## Study Limitations
There are several potential limitations in this study. Firstly, the present study was a single-center, retrospective, observational study with a relatively small number of patients and thus has all limitations of retrospective analysis, including selection bias in study subjects. Secondly, the duration of ICMP was not exactly clear in the present study, as it may influence long-term MACE. Thirdly, the present study is mostly based on previous diagnosed ICMP. Therefore, we did not consider new medications, such as ARNI or SGLT2I. Recently diagnosed and recovered ICMP treated with ARNI takes only a relatively small portion of this study. Thus, we have no long-term data about ICMP related to ARNI.
## 5. Conclusions
Despite these potential limitations, the results of the present study demonstrated that low-ejection fraction was the only independent predictor of mortality in ICMP, regardless sex.
Diabetes and impaired diastolic function, non-use of beta blockers, and ARB were female-specific predictors of long-term mortality in patients with ICMP.
Hypertension and non-use of statins were male-specific predictors of long-term mortality in patients with ICMP.
For improving long term survival in ICMP, it may be necessary to approach sex specifically when we meet with elderly HFs with ICMP. Prospective long-term study for effect and mechanism of medication, including new drugs, will give us more customized information in the near future.
## References
1. Vedin O., Lam C.S.P., Koh A.S., Bemson L., Teng T.H.K., Tay W.T., Braun O.Ö., Savarese G., Dahlström U., Lund L.H.. **Significance of ischemic heart disease in patients with heart failure and preserved, midrange, and reduced ejection fraction A nationwide cohort study**. *Circ. Heart Fail.* (2017) **10** e003875. DOI: 10.1161/CIRCHEARTFAILURE.117.003875
2. Sanchis-Gomar F., Perez-Quilis C., Leischik R., Lucia A.. **Epidemiology of coronary heart disease and acute coronary syndrome**. *Ann. Transl. Med.* (2016) **4** 256. DOI: 10.21037/atm.2016.06.33
3. Finegold J.A., Asaria P., Francis D.P.. **Mortality from ischemic heart disease by contry, region, and age: Statistics from World Health Organisation and United Nations**. *Intern. J. Cardiolol.* (2013) **168** 934-945. DOI: 10.1016/j.ijcard.2012.10.046
4. Peters S.A.E., Muntner P., Woodward M.. **Sex differences in the prevalence of, and trends in, cardiovascular risk factors, treatment, and control in the United States, 2001 to 2016**. *Circulation* (2019) **139** 1025-1035. DOI: 10.1161/CIRCULATIONAHA.118.035550
5. Gottlieb S.S., Khatta M., Friedmann E., Einbinder L., Katzen S., Baker B., Marshall J., Minshall S., Robinson S., Fisher M.L.. **The influence of age, gender, and race on the prevalence of depression in heart failure patients**. *J. Am. Coll. Cardiol.* (2004) **43** 1542-1549. DOI: 10.1016/j.jacc.2003.10.064
6. Masoudi F.A., Havranek E.P., Smith G., Fish R.H., Steiner J.F., Ordin D.L., Krumholz H.M.. **Gender, age, and heart failure with preserved left ventricular systolic function**. *J. Am. Coll. Cardiol.* (2003) **41** 217-223. DOI: 10.1016/S0735-1097(02)02696-7
7. Bucholz E.M., Strait K.M., Dreyer R.P., Lindau S.T., D’Onofrio G., Geda M., Spatz E.S., Beltrame J.F., Lichtman J.H., Lorenze N.P.. **Editor’s choice-sex differences in young patients with acute myocardial infarction: A VIRGO study analysis**. *Eur. Heart J. Acute Cardiovasc. Care* (2017) **6** 610-622. DOI: 10.1177/2048872616661847
8. Felker G.M., Shaw L.K., O’Connor C.M.. **A Standardized Definition of Ischemic Cardiomyopathy for Use in Clinical Research**. *J. Am. Coll. Cardiol.* (2002) **39** 210-218. DOI: 10.1016/S0735-1097(01)01738-7
9. Lang R.M., Badano L.P., Mor-Avi V., Afilalo J., Armstrong A., Ernande L., Flachskampf F.A., Foster E., Goldstein S.A., Kuznetsova T.. **Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging**. *Eur. Heart J. Cardiovasc. Imaging* (2015) **16** 233-271. DOI: 10.1093/ehjci/jev014
10. Marwick T.H., Leano R.L., Brown J., Sun J.P., Hoffmann R., Lysyansky P., Becker M., Thomas J.D.. **Myocardial strain measurement with 2-dimensional speckle-tracking echocardiography: Definition of normal range**. *JACC Cardiovasc. Imaging* (2009) **2** 80-84. DOI: 10.1016/j.jcmg.2007.12.007
11. Henkel M., Redfield M., Weston S.A., Gerber Y., Roger V.L.. **Death in heart failure: A community perspective**. *Circ. Heart Fail.* (2008) **1** 91-97. DOI: 10.1161/CIRCHEARTFAILURE.107.743146
12. Goldberg R.J., Ciampa J., Lessard D., Meyer T.E., Spencer F.A.. **Long-term survival after heart failure: A contemporary population-based perspective**. *Arch. Intern. Med.* (2007) **167** 490-496. DOI: 10.1001/archinte.167.5.490
13. Chin M., Goldman L.. **Factors contributing to the hospitalization of patients with congestive heart failure**. *Am. J. Public Health* (1997) **87** 643-648. DOI: 10.2105/AJPH.87.4.643
14. Sapontis J., Marso S.P., Cohen D.J., Lombardi W., Karmpaliotis D., Moses J., Nicholson W.J., Pershad A., Wyman R.M., Spaedy A.. **The outcomes, patient health status, and efficiency in chronic total occlusion hybrid procedures registry: Rationale and Design**. *Coron. Artery Dis.* (2017) **28** 110-119. DOI: 10.1097/MCA.0000000000000439
15. Merz A.A., Cheng S.. **Sex differences in cardiovascular ageing**. *Heart* (2016) **102** 825-831. DOI: 10.1136/heartjnl-2015-308769
16. Bots S.H., Peters S.A.E., Woodward M.. **Sex differences in coronary heart disease and stroke mortality: A global assessment of the effect of ageing between 1980 and 2010**. *BMJ Glob. Health* (2017) **2** e000298. DOI: 10.1136/bmjgh-2017-000298
17. Ohkuma T., Komorita Y., Peters S.A.E., Woodward M.. **Diabetes as a risk factor for heart failure in women and men: A systematic review and meta-analysis of 47 cohorts including 12 million individuals**. *Diabetologia* (2019) **62** 1550-1560. DOI: 10.1007/s00125-019-4926-x
18. Peters S.A., Huxley R.R., Woodward M.. **Diabetes as risk factor for incident coronary heart disease in women compared with men: A systematic review and meta-analysis of 64 cohorts including 858,507 individuals and 28,203 coronary events**. *Diabetologia* (2014) **57** 1542-1551. DOI: 10.1007/s00125-014-3260-6
19. Johansson I., Dahlström U., Edner M., Näsman P., Rydén L., Norhammar A.. **Risk factors, treatment and prognosis in men and women with heart failure with and without diabetes**. *Heart* (2015) **101** 1139-1148. DOI: 10.1136/heartjnl-2014-307131
20. Levy D., Larson M.G., Vasan R.S., Kannel W.B., Ho K.K.. **The progression from hypertension to congestive heart failure**. *JAMA* (1996) **275** 1557-1562. DOI: 10.1001/jama.1996.03530440037034
21. Maznyczka A., Sen S., Cook C., Francis D.P.. **The ischemic constellation: An alternative to the ischemic cascade—Implications for the validation of new ischemic tests**. *Open Heart* (2015) **2** e000178. DOI: 10.1136/openhrt-2014-000178
22. Yancy C.W., Jessup M., Bozkurt B., Butler J., Casey D.E.J., Drazner M.H., Fonarow G.C., Geraci S.A., Horwich T., Januzzi J.L.. **2013 ACCF/AHA guideline for the management of heart failure: A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines**. *J. Am. Coll. Cardiol.* (2013) **62** e147. DOI: 10.1016/j.jacc.2013.05.019
23. Cleland J.G., Pennell D.J., Ray S.G., Coats A.J., Macfarlane P.W., Murray G.D., Mule J.D., Vered Z., Lahiri A.. **Myocardial viability as a determinant of the ejection fraction response to carvedilol in patients with heart failure (CHRISTMAS trial): Randomised controlled trial**. *Lancet* (2003) **362** 14-21. DOI: 10.1016/S0140-6736(03)13801-9
24. Windecker S., Kolh P., Alfonso F., Collet J.P., Cremer J., Falk V., Filippatos G., Hamm C., Head S.J., Jüni P.. **2014 ESC/EACTS Guidelines on myocardial revascularization: The Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS) Developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI)**. *Eur. Heart J.* (2014) **35** 2541-2619. PMID: 25173339
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title: Evaluation of the Accuracy of Cr and BUN Using the ABL90 FLEX PLUS Blood Gas
Analyzer and the Equivalence of Candidate Specimens for Assessment of Renal Function
authors:
- Ha-Jin Lim
- Seung-Yeob Lee
- Hyun-Jung Choi
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10003958
doi: 10.3390/jcm12051940
license: CC BY 4.0
---
# Evaluation of the Accuracy of Cr and BUN Using the ABL90 FLEX PLUS Blood Gas Analyzer and the Equivalence of Candidate Specimens for Assessment of Renal Function
## Abstract
Background: The ABL90 FLEX PLUS (Radiometer) is a blood gas analyzer that also provides creatinine (Cr) and blood urea nitrogen (BUN) results. We assessed the accuracy of the ABL90 FLEX PLUS to measure Cr and BUN and find suitable candidate specimens against primary specimens (heparinized whole-blood (H-WB)). Methods: Paired H-WB, serum, and sodium-citrated whole-blood (C-WB) samples [105] were collected. The Cr and BUN levels in the H-WB using the ABL90 FLEX PLUS were compared with those of the serum using four automated chemistry analyzers. The suitability of the candidate specimens was assessed at each medical decision level according to the CLSI guideline EP35-ED1. Results: The respective mean differences of the ABL90 FLEX PLUS for the Cr and BUN were below −0.10 and −3.51 mg/dL compared to the other analyzers. The systematic differences between the serum and the H-WB at the low, medium, and high medical decision levels were all $0\%$ for Cr, but those of the C-WB were −$12.96\%$, −$11.81\%$, and −$11.30\%$, respectively. Regarding imprecision, the SDserum/SDH-WB ratios at each level were 0.14, 1.41, and 0.68, whereas the SDC-WB/SDH-WB ratios were 0.35, 2.00, and 0.73, respectively. Conclusions: The ABL90 FLEX PLUS provided Cr and BUN results comparable with the four widely used analyzers. Among the candidates, the serum was suitable for Cr testing using the ABL90 FLEX PLUS, while the C-WB did not satisfy the acceptance criteria.
## 1. Introduction
Creatinine (Cr) and blood urea nitrogen (BUN) are two commonly measured parameters used to assess kidney function in clinical laboratories. The most frequently used devices for measuring Cr and BUN levels are automated chemistry analyzers, and point-of-care (POC) testing devices, including blood gas analyzers. Automated chemistry analyzers use chemical reactions to measure the concentration of Cr and BUN in blood samples. These analyzers are commonly found in clinical laboratories in hospitals and can analyze a wide range of other blood markers in addition to Cr and BUN. Beckman Coulter, Hitachi, Roche, and Siemens are widely recognized manufacturers of automated chemistry analyzers used in clinical laboratories [1].
The ABL90 FLEX PLUS analyzer (Radiometer, Copenhagen, Denmark) is primarily designed to measure blood gas levels but can also measure Cr and BUN levels, as well as other blood markers such as electrolytes, glucose, and lactate on heparinized (H-WB) [2,3,4]. It is easy to use and provides fast results, with built-in quality control features to ensure accuracy and reliability [5]. Point-of-care (POC) testing with the ABL FLEX PLUS for kidney function is a useful method for providing rapid results, particularly in emergency departments, acute medical units, or critical care settings where there is a need to make immediate decisions regarding treatment [6]. Ensuring patient safety before the administration of contrast media is particularly important, as highlighted by several studies [7,8,9,10,11]. However, the literature reveals both disparities in the clinical concordance with the central laboratory and in the clinical utility of POC in clinical practice, and therefore, its adoption has been limited [6,12]. These inconsistencies in Cr and BUN results have been caused by different sample types and measuring methods or analyzers [13,14], which may have largely affected the uncertainty of the estimated glomerular filtration rate (eGFR) at the medical decision level [15]. Therefore, providing precise and continuous Cr and BUN data is important for clinicians to determine the delta value for making medical decisions in individual patients even though the measurement equipment is different or changed. In addition, it is necessary and important to know the equivalence between different specimen types for Cr and BUN tests in order to respond quickly to various clinical situations [4,5,6,7,8,9,10]. However, there have been no comparative studies of Cr and BUN testing between two or more automated chemistry analyzers simultaneously with the ABL90 FLEX PLUS.
To address the unmet need for the evaluation of the ABL90 FLEX PLUS, this study evaluates the accuracy of Cr and BUN testing of the ABL90 FLEX PLUS by comparing these measurements to four automated chemistry analyzers widely used in clinical laboratories. Furthermore, the expandability of the sample selection was explored according to the up-to-date Clinical and Laboratory Standards Institute (CLSI) guideline EP35-ED1 for the first time by confirming the suitability of an alternative candidate specimen for Cr and BUN measurement using the ABL90 FLEX PLUS [16].
## 2.1. Patients and Sample Collection
A total of 105 patients were enrolled at Chonnam National University Hwasun Hospital in Korea. The male-to-female ratio was approximately 1.84:1. The age distribution ranged from 12 to 84 years old, and the interquartile range was between 57 and 72 years. H-WB, C-WB, and serum samples were collected simultaneously from each patient using a heparin syringe, sodium citrate tube, and serum-separating tube, respectively, and were tested on the same day without being stored. This study was conducted with the approval of the Institutional Review Board of Chonnam National University Hwasun Hospital (CNUHH-2018-096).
## 2.2. Testing Analyzers, Reagents, and Measurements
The ABL90 FLEX PLUS (Radiometer Medical ApS) is a robust benchtop blood gas analyzer that is also available for Cr and BUN measurement [3,4,5]. High precision (<$3\%$ of the coefficient of variation (CV) at low and high levels of quality control materials) and linearity (R2 > 0.99 at the five levels) were previously validated for Cr and BUN testing using the ABL90 FLEX PLUS by the CLSI guidelines EP05-A3 [17] and EP06-A [18], respectively (Supplementary Table S1 and Supplementary Figure S1). The CLSI EP05 recommends running at least two levels (low and high) of QC materials five times per day for five days, with each run consisting of a minimum of two replicates for each level of QC material. In addition, the linearity evaluation was performed using at least five different concentrations of the Cr and BUN in accordance with CLSI guideline EP06. H-WB is the established primary specimen for the ABL90 FLEX PLUS, according to the manufacturer [5]. For the assessment of the suitability of the candidate specimens, the ABL90 FLEX PLUS was tested using the H-WB (primary specimen), C-WB, and serum samples using the manufacturer’s reagent.
Four automated chemistry analyzers were used, including the ADVIA 1800 (Siemens Healthcare GmbH, Erlangen, Germany), the AU5822 (Beckman Coulter, Brea, CA, USA), the Cobas 8000 c702 (Roche Diagnostics, Basel, Switzerland), and the Hitachi 7600-210 (Hitachi, Tokyo, Japan), to evaluate the performance of the Cr and BUN testing of the ABL90 FLEX PLUS. The external quality assessment program organized by the Korean Association of External Quality Assessment Service assessed the automated chemistry analyzers, especially the Cr measurement, through accuracy-based proficiency testing. For the Hitachi 7600-210, L-Type UN (Wako Pure Chemical Industries, Ltd., Osaka, Japan) and Clinimate® CRE Reagent (SEKISUI MEDICAL CO., Tokyo, Japan) were used for the BUN and Cr measurements, respectively. For the other instruments, reagents exclusive to the manufacturers were used. The Cr and BUN levels were measured in duplicate according to each manufacturer’s instructions. Serum, a standard specimen for chemistry in our institution, was used for the four automated chemistry analyzers.
## 2.3. Data and Statistical Analysis
The means and standard deviations (SDs) of the Cr and BUN levels were calculated for each analyzer and specimen type. A one-way analysis of variance was used to compare the mean values among the different analyzers and specimen types. A p-value of less than 0.05 was considered a statistically significant level. For comparison between the test methods or specimen types, each automated chemistry analyzer or H-WB was considered a comparator, respectively.
In accordance with the CLSI guideline EP09C-ED3 for a method comparison study [19], a Bland–Altman plot was visually inspected, and a Passing–Bablok regression was analyzed with a $95\%$ confidence interval (CI) calculated by the bootstrap method using the ‘mcr’ package of R version 1.2.2 [20]. According to the CLSI guideline EP35-ED1 for assessing specimen suitability [16], the equivalence between the primary and candidate specimen types was assessed in terms of systematic difference and imprecision at each medical decision level. The systematic difference at each medical decision level was calculated using the Passing–Bablok regression equation and compared to the total allowable error (TEa) criteria to evaluate the systematic differences between the primary and candidate specimens. The Cr and BUN results of the ABL90 FLEX PLUS were subdivided into three subinterval groups (<0.7, 0.7–1.7, and >1.7 mg/dL for Cr; <20, 20–30, and >30 mg/dL for BUN), which included each medical decision point, to compare the precision of the primary and candidate specimen types. The precision of each specimen type was obtained from the results of two replicates and calculated using the following formula:[1]SDspecimen type2=1K∑$i = 1$K(Xi,2−Xi,1)22 where SDspecimen type is the average SD of the two replicates of the sample of a specific specimen type, K is the sample number of each specimen type, and X1 and X2 are the replicates of the sample. The calculated ratio of SDcandidate/SDprimary (SD ratio) was considered clinically acceptable for precision when the following criteria were satisfied: (i) the SD ratio is less than or equal to 1.00; (ii) the SD ratio is over 1.00, but that of the $95\%$ CI includes 1.00; or (iii) the SD ratio and the lower bound of the $95\%$ CI is over 1.00, but the imprecision for the medical decision level are within the allowable limits [16]. Rounding was performed only on the final results.
The medical decision levels were determined as 0.6, 1.6, and 6.0 mg/dL for Cr, and 6, 26, and 50 mg/dL for BUN [21,22]. The allowable CV ($2.3\%$ for Cr; $7.0\%$ for BUN) and TEa criteria ($7.4\%$ for Cr; $17.8\%$ for BUN) were set using the desirable specification from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database [23].
## 3.1. Comparative Analysis According to Analyzers or Sample Types
The Cr and BUN results of the ABL90 FLEX PLUS, compared with the four automated chemistry analyzers, are illustrated in Table 1 and Figure 1. The mean values of the Cr and BUN are not statistically different between the ABL90 FLEX PLUS and the other four chemistry analyzers (Table 1A). The correlation coefficients exceeded 0.994 (range: 0.994 to 0.995) and 0.974 (range 0.974 to 0.991) for Cr and BUN, respectively, which indicate high correlations between the ABL90 FLEX PLUS and each automated chemistry analyzer (Figure 1A). The slopes of the Passing–Bablok regression between the ABL90 FLEX PLUS and the four chemistry analyzers were all 1.00 for Cr and ranged from 0.89 to 0.91 for BUN. The intercepts of Cr and BUN ranged from −0.10 to 0.00 and from 0.31 to 0.53, respectively. The mean differences between the ABL90 FLEX PLUS and the ADVIA 1800, AU5822, Cobas 8000 c702, and Hitachi 7600-210 were −0.01, −0.06, −0.10, and −0.08 mg/dL for the Cr testing, and −2.70, −3.51, −1.73, and −2.49 mg/dL for the BUN testing, respectively (Table 1A and Figure 1B).
## 3.2.1. Evaluation of the Systematic Difference among Specimen Types
There were no significant differences in the mean values of Cr and BUN results among the specimen types using the ABL90 FLEX PLUS (Table 1B). The correlation coefficients of the serum and C-WB compared to that of the H-WB were 0.995 and 0.996 for Cr, and 0.981 and 0.987 for BUN, respectively, using the ABL90 FLEX PLUS (Figure 2A). The slopes of the serum and C-WB compared to that of the H-WB were 1.00 and 0.89 for Cr, and 0.98 and 0.83 for BUN, respectively, in the Passing–Bablok regression analysis (Table 2), and the intercepts of the serum and C-WB compared to that of the H-WB were 0.00 and −0.01 for Cr, and −1.12 and −0.18 for BUN, respectively. The mean difference (% mean difference) in the serum and C-WB compared to the H-WB were 0.04 ($4.26\%$) and −0.14 mg/dL (−$12.77\%$) for Cr, and −0.58 (−$9.22\%$) and −4.00 mg/dL (−$18.72\%$) for BUN, respectively (Table 1B and Figure 2B).
Systematic differences at each medical decision level were investigated for the Cr and BUN results according to the CLSI EP35-ED1. Compared to the H-WB results using the ABL90 FLEX PLUS for Cr, the systematic difference in the serum was $0\%$ at all medical decision levels, and those of the C-WB were −$12.96\%$, −$11.81\%$, and −$11.30\%$ at the 0.6, 1.6, and 6.0 mg/dL medical decision levels, respectively (Table 2). The systematic differences for BUN at the 6, 26, and 50 mg/dL medical decision levels were −$21.03\%$, −$6.72\%$, and −$4.66\%$ in the serum, respectively, and −$19.87\%$, −$17.57\%$, and −$17.24\%$ in the C-WB, respectively, compared to the H-WB. When the TEa criteria ($7.4\%$ for Cr; $17.8\%$ for BUN) were applied, the systematic differences compared to the H-WB were acceptable when a serum specimen was used on the ABL90 FLEX PLUS (except for a low level of BUN testing), whereas the C-WB had inequivalent results at all medical decision levels for both the Cr and BUN testing. In addition, the serum Cr results using the ABL90 FLEX PLUS showed a high correlation ($r = 0.995$ to 0.996) and no systematic difference at all medical decision levels compared to those from each automated chemistry analyzer (Figure 3 and Table 3). For the serum BUN results of the ABL90 FLEX PLUS, the slope of the Passing–*Bablok analysis* was 0.85 to 0.88 ($r = 0.975$ to 0.991) and the systemic differences ranged from −$28.56\%$ to −$21.73\%$, −$17.12\%$ to −$14.68\%$, and −$15.82\%$ to −$13.50\%$ at the 6, 26, and 50 mg/dL medical decision levels, respectively.
## 3.2.2. Comparison of Precision among Specimen Types
For the subintervals of Cr testing within the ranges of <0.7, 0.7–1.7, and >1.7 mg/dL, the SDserum/SDH-WB ratios ($95\%$ CI) were 0.14 (0.10 to 0.20), 1.41 (1.09 to 1.84), and 0.68 (0.40 to 1.15), respectively, and the SDC-WB/SDH-WB ratios ($95\%$ CI) were 0.35 (0.25 to 0.49), 2.00 (1.54 to 2.61), and 0.73 (0.43 to 1.24), respectively (Table 4). The SD ratios of the BUN testing among the specimen types were below 1.00 at all three subintervals (range: 0.05 to 0.51 for SDserum/SDH-WB; 0.06 to 0.33 for SDC-WB/SDH-WB). Evaluating the precision in the candidate specimens for Cr, the SD ratios of the candidate specimens were all below 1.00 except for those of the serum (1.41) and C-WB (2.00) at the subinterval range of 0.7–1.7 mg/dL. However, the percent CV of only the serum specimen was $1.89\%$, and it satisfied the acceptable criteria (<$2.3\%$) at that subinterval.
## 4. Discussion
In this study, Cr and BUN tests by the ABL90 FLEX PLUS showed comparable performance with four automated chemistry analyzers: the ADVIA 1800 (Siemens), the AU5822 (Beckman Coulter), the Cobas 8000 c702 (Roche Diagnostics), and the Hitachi 7600-210 (Hitachi). In addition, an evaluation of equivalence between the blood sample types for Cr and BUN testing of the ABL90 FLEX PLUS in accordance with the latest CLSI guidelines EP35-ED1 showed that only serum was suitable. Given that serum *Cr is* used for eGFR calculation [15,24,25], these findings provide useful information for interpreting Cr results in situations where an institution is using the ABL90 FLEX PLUS concurrently with one or more chemistry analyzers to measure Cr for the diagnosis and monitoring of renal impairment. To our knowledge, this is the first study to show the equivalence of serum to H-WB, which is the primary recommended sample for Cr and BUN testing by the ABL90 FLEX PLUS, according to the up-to-date CLSI guideline [16].
There have been many studies comparing and evaluating POC devices and one automated chemical device in a central laboratory in various Cr-measuring POC devices, including the ABL90 FLEX PLUS and i-STAT (Abbott) [3,4,6,26,27]. However, each performance of POC Cr testing has varied according to the study cohort or any automated analyzers used for comparison study, making the interpretation of these results difficult [2,3]. To address this issue, 105 paired samples, including H-WB, serum, and C-WB, were used in this study and measured for Cr and BUN in duplicate by four automated chemistry analyzers.
According to the results from accuracy-based proficiency testing of Cr measurements from 2011 to 2017 in Korea [1], the bias found according to the chemistry analyzers was as follows: The Roche and Beckman instruments had a bias close to zero, and the Siemens instrument had a slightly negative bias without statistical significance, while the Hitachi instrument using the Sekisui reagent showed a positive bias over total error. Similarly, our study showed a higher mean value of Cr from the Hitachi 7600-210, and a lower mean value from the ADVIA 1800 among the four chemistry analyzers tested, though there were no statistical differences. The serum Cr levels using the ABL90 FLEX PLUS were in a range between those of the Hitachi 7600-210 and ADVIA 1800 and disclosed no systematic differences among the four automated analyzers at the medical decision levels (Table 3). As a result, in addition to equivalent use with H-WB, the Cr measurements from serum using the ABL90 FLEX PLUS showed results comparable with the other automated chemistry analyzers used worldwide.
The serum specimens showed concordant results with H-WB for the accuracy and precision of the Cr results, whereas the serum BUN level using the ABL90 FLEX PLUS was slightly lower compared to that of the H-WB and even compared to the four chemistry analyzers. These results may also have been influenced by the concentration distribution of the collected samples, considering that negative differences and higher systematic differences were observed mostly at the lower BUN levels (Figure 3A and Table 3). At the low medical decision point (6 mg/dL) for BUN, the systematic differences between the serum and H-WB were unacceptable (Table 2). Hence, a further study involving more patients with higher levels (>40 mg/dL) of BUN may be needed. In addition, if the above finding shows consistency in a future study, careful interpretation of the BUN testing in serum by the ABL90 FLEX PLUS at a lower level and equivalent use with the other automated analyzers may require additional calibration procedures.
WB samples for Cr testing enable a rapid turnaround time with a minimal pre-analytic process. In contrast, serum was originally used for the Modification of Diet in Renal Disease (MDRD) Study equation of eGFR and provides relatively stable results over time [15]. Regarding the imprecision in each specimen compared to H-WB, the primary specimen, the SDserum/SDH-WB were all less than 1.00, except for the subinterval of 0.7 to 1.7 mg/dL in the Cr testing. The precision of the WB specimens was similar to that of the serum. Using duplicates of patient WB samples or the WB matrix for Cr testing, a few studies have reported CVs of approximately 3–$6\%$ at all concentrations [2,28,29], which is consistent with our findings and exceeds the desirable criteria for imprecision based on biological variability. Moreover, the percent CV of the WB specimens (H-WB and C-WB) was above the allowable criteria only in the subinterval with a low Cr concentration (<0.7 mg/dL). According to the database of biological variability for Cr testing [23], the existing allowable CVs and TEa goals are derived from studies based on serum and plasma. In this context, the accumulation of studies regarding imprecisions based on WB may be helpful for managing Cr testing in POC analyzers. However, C-WB, as another type of WB specimen, consistently showed negative bias compared to H-WB for both Cr and BUN. The negative bias of Cr and BUN testing in C-WB by the ABL90 FLEX PLUS was more prominent when compared to the four other automated analyzers (Supplementary Figure S2). These results may partly explain the bias due to the dilution effect of the anticoagulant, which accounts for one-tenth of the total volume, or the innate negative effects of the citrate solution [30,31]. However, the bias, especially for the Cr results, might be problematic because the eGFR could be overestimated, possibly overlooking some patients with impaired kidney function. In addition, the C-WB showed an unallowable CV ($3.17\%$) and clinically unacceptable SD ratios (2.00 with $95\%$ CI of 1.54 to 2.61) compared to the H-WB at the subinterval range of 0.7–1.7 mg/dL for Cr testing using the ABL90 FLEX PLUS. This result is notable because the range of 0.7–1.7 mg/dL in the Cr testing included an eGFR level of 60 mL/min/1.73 m2, considering the age, gender, and race of the patient [15]. Consequently, C-WB is not recommended for Cr and BUN testing using the ABL90 FLEX PLUS.
## 5. Conclusions
In conclusion, the Cr testing in serum and H-WB by the ABL90 FLEX PLUS was comparable with that by four automated chemistry analyzers commonly used in clinical laboratories. Therefore, they can be equivalently used in situations where the ABL90 FLEX PLUS is used simultaneously with one or more chemistry analyzers in one institute. However, C-WB is not recommended.
## References
1. Jeong T.D., Lee H.A., Lee K., Yun Y.M.. **Accuracy-based proficiency testing of creatinine measurement: 7 years’ experience in Korea**. *J. Lab. Med. Qual. Assur.* (2019.0) **41** 13-23. DOI: 10.15263/jlmqa.2019.41.1.13
2. Shephard M.D.. **Point-of-Care Testing and Creatinine Measurement**. *Clin. Biochem. Rev.* (2011.0) **32** 109-114. PMID: 21611085
3. Corbett M., Duarte A., Llewellyn A., Altunkaya J., Harden M., Harris M., Walker S., Palmer S., Dias S., Soares M.. **Point-of-care creatinine tests to assess kidney function for outpatients requiring contrast-enhanced CT imaging: Systematic reviews and economic evaluation**. *Health Technol. Assess.* (2020.0) **24** 1-248. DOI: 10.3310/hta24390
4. Bargnoux A.S., Kuster N., Sutra T., Laroche L., Rodriguez A., Morena M., Chenine L., Chalabi L., Dupuy A.M., Badiou S.. **Evaluation of a new point-of-care testing for creatinine and urea measurement**. *Scand J. Clin. Lab. Investig.* (2021.0) **81** 290-297. DOI: 10.1080/00365513.2021.1914344
5. 5.
Radiometer Medical ApS
ABL90 FLEX PLUS: Instructions for UseRadiometer Medical ApSBrønshøj, Denmark2018. *ABL90 FLEX PLUS: Instructions for Use* (2018.0)
6. Snaith B., Harris M.A., Shinkins B., Jordaan M., Messenger M., Lewington A.. **Point-of-care creatinine testing for kidney function measurement prior to contrast-enhanced diagnostic imaging: Evaluation of the performance of three systems for clinical utility**. *Clin. Chem. Lab. Med.* (2018.0) **56** 1269-1276. DOI: 10.1515/cclm-2018-0128
7. Korpi-Steiner N.L., Williamson E.E., Karon B.S.. **Comparison of three whole blood creatinine methods for estimation of glomerular filtration rate before radiographic contrast administration**. *Am. J. Clin. Pathol.* (2009.0) **132** 920-926. DOI: 10.1309/AJCPTE5FEY0VCGOZ
8. Lee-Lewandrowski E., Chang C., Gregory K., Lewandrowski K.. **Evaluation of rapid point-of-care creatinine testing in the radiology service of a large academic medical center: Impact on clinical operations and patient disposition**. *Clin. Chim. Acta* (2012.0) **413** 88-92. DOI: 10.1016/j.cca.2011.05.006
9. Dimeski G., Tilley V., Jones B.W., Brown N.N.. **Which point-of-care creatinine analyser for radiology: Direct comparison of the i-Stat and StatStrip creatinine methods with different sample types**. *Ann. Clin. Biochem.* (2013.0) **50** 47-52. DOI: 10.1258/acb.2012.012081
10. Karamasis G.V., Hampton-Till J., Al-Janabi F., Mohdnazri S., Parker M., Ioannou A., Jagathesan R., Kabir A., Sayer J.W., Robinson N.M.. **Impact of point-of-care pre-procedure creatinine and eGFR testing in patients with ST segment elevation myocardial infarction undergoing primary PCI: The pilot STATCREAT study**. *Int. J. Cardiol.* (2017.0) **240** 8-13. DOI: 10.1016/j.ijcard.2017.03.147
11. Carden A.J., Salcedo E.S., Tran N.K., Gross E., Mattice J., Shepard J., Galante J.M.. **Prospective observational study of point-of-care creatinine in trauma**. *Trauma Surg. Acute Care Open* (2016.0) **1** e000014. DOI: 10.1136/tsaco-2016-000014
12. Harris M.A., Snaith B., Clarke R.. **Strategies for assessing renal function prior to outpatient contrast-enhanced CT: A UK survey**. *Br. J. Radiol.* (2016.0) **89** 20160077. DOI: 10.1259/bjr.20160077
13. Schnabl K.L., Bagherpoor S., Diker P., Cursio C., Dubois J., Yip P.M.. **Evaluation of the analytical performance of the Nova StatSensor creatinine meter and reagent strip technology for whole blood testing**. *Clin. Biochem.* (2010.0) **43** 1026-1029. DOI: 10.1016/j.clinbiochem.2010.04.055
14. **Whole Blood Protocol: National Institute of Diabetes and Digestive and Kidney Diseases**
15. Myers G.L., Miller W.G., Coresh J., Fleming J., Greenberg N., Greene T., Hostetter T., Levey A.S., Panteghini M., Welch M.. **Recommendations for improving serum creatinine measurement: A report from the Laboratory Working Group of the National Kidney Disease Education Program**. *Clin. Chem.* (2006.0) **52** 5-18. DOI: 10.1373/clinchem.2005.0525144
16. 16.
CLSI
Assessment of Equivalence or Suitability of Specimen Types for Medical Laboratory Measurement Procedures EP351st ed.Clinical and Laboratory Standards InstituteWayne, PA, USA2019. *Assessment of Equivalence or Suitability of Specimen Types for Medical Laboratory Measurement Procedures EP35* (2019.0)
17. 17.
CLSI
Evaluation of Precision of Quantitative Measurement Procedures; Approved Guideline EP053rd ed.Clinical and Laboratory Standards InstituteWayne, PA, USA2019. *Evaluation of Precision of Quantitative Measurement Procedures; Approved Guideline EP05* (2019.0)
18. Tholen D.W., Kroll M., Astles J.R., Caffo A.L., Happe T.M., Krouwer J., Lasky F.. *Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline EP06* (2003.0)
19. 19.
CLSI
Measurement Procedure Comparison and Bias Estimation Using Patient Samples EP09c3rd ed.Clinical and Laboratory Standards InstituteWayne, PA, USA2018. *Measurement Procedure Comparison and Bias Estimation Using Patient Samples EP09c* (2018.0)
20. **R Package Version 1.2.2**
21. Statland B.E.. *Clinical Decision Levels for Laboratory Tests* (1987.0)
22. Kume T., Saglam B., Ergon C., Sisman A.R.. **Evaluation and comparison of Abbott Jaffe and enzymatic creatinine methods: Could the old method meet the new requirements?**. *J. Clin. Lab. Anal.* (2018.0) **32** e22168. DOI: 10.1002/jcla.22168
23. **EFLM Biological Variation Database**
24. **ESUR Guidelines on Contrast Media. Version. 10.0**
25. **ACR Manual on Contrast Media**
26. Kost G.J., Vu H.T., Inn M., DuPlantier R., Fleisher M., Kroll M.H., Spinosa J.C.. **Multicenter study of whole-blood creatinine, total carbon dioxide content, and chemistry profiling for laboratory and point-of-care testing in critical care in the United States**. *Crit. Care Med.* (2000.0) **28** 2379-2389. DOI: 10.1097/00003246-200007000-00033
27. Salvagno G.L., Pucci M., Demonte D., Gelati M., Lippi G.. **Analytical evaluation of Radiometer ABL90 FLEX PLUS enzymatic creatinine assay**. *J. Lab. Precis. Med.* (2019.0) **4** 26. DOI: 10.21037/jlpm.2019.07.01
28. McPherson R.A., Pincus M.R.. *Henry’s Clinical Diagnosis and Management by Laboratory Methods—Electronic* (2016.0)
29. Nichols J.H., Bartholomew C., Bonzagi A., Garb J.L., Jin L.. **Evaluation of the IRMA TRUpoint and i-STAT creatinine assays**. *Clin. Chim. Acta* (2007.0) **377** 201-205. DOI: 10.1016/j.cca.2006.09.024
30. Kennedy A.D., Ford L., Wittmann B., Conner J., Wulff J., Mitchell M., Evans A.M., Toal D.R.. **Global biochemical analysis of plasma, serum and whole blood collected using various anticoagulant additives**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0249797
31. Mohri M., Rezapoor H.. **Effects of heparin, citrate, and EDTA on plasma biochemistry of sheep: Comparison with serum**. *Res. Vet. Sci.* (2009.0) **86** 111-114. DOI: 10.1016/j.rvsc.2008.05.010
|
---
title: Effect of Peptides from Plasma of Patients with Coronary Artery Disease on
the Vascular Endothelial Cells
authors:
- Marko Kozyk
- Kateryna Strubchevska
- Tetiana Marynenko
- Alena Zlatska
- Tetiana Halenova
- Nataliia Raksha
- Olexii Savchuk
- Tetyana Falalyeyeva
- Oleksandr Kovalchuk
- Ludmyla Ostapchenko
journal: Medicina
year: 2023
pmcid: PMC10003965
doi: 10.3390/medicina59020238
license: CC BY 4.0
---
# Effect of Peptides from Plasma of Patients with Coronary Artery Disease on the Vascular Endothelial Cells
## Abstract
Background and Objectives: Coronary artery disease (CAD) is the foremost cause of adult disability and mortality. There is an urgent need to focus on the research of new approaches for the prevention and treatment of CAD. Materials and Methods: The effects of peptides isolated from the blood plasma of CAD patients on endothelial cell secretion using the in vitro model have been tested. Human endothelial progenitor cells (HEPCs) were incubated for 24 h with peptides isolated from the plasma of healthy subjects or patients with stable angina, progressive unstable angina, and myocardial infarction. The contents of some soluble anticoagulant as well as procoagulant mediators in HEPC culture treated with peptide pools were then compared. Results and Conclusion: The results show that peptides from the plasma of patients with myocardial infarction promote endothelial cells to release both von Willebrand factor and endothelin-1, increasing vasoconstriction and shifting hemostatic balance toward a prothrombotic state. In contrast, peptides from the plasma of patients with progressive unstable angina suppress the secretion of endothelin-1 by HEPCs, while the secretion of both von Willebrand factor and tissue plasminogen activator was increased. As can be seen from the results obtained, disease derived peptides may contribute to the homeostasis of living organisms or the progression of pathological processes.
## 1. Introduction
Coronary artery disease (CAD) is the most common type of heart disease all over the world [1]. CAD is caused by plaque buildup in the inner lining of coronary arteries that supply blood to the heart. Plaque is made up of cholesterol deposits and other substances including cellular waste products, calcium, and fibrin. As the plaque builds up in the process known as atherosclerosis, the artery wall becomes thickened and the artery lumen narrows over time, which can partially or totally block the blood flow [1,2]. To date, there are numerous risk factors that have been identified to be responsible for causing atherosclerosis. These factors include dyslipidemia, diabetes, obesity, smoking, family history, sedentary lifestyle, etc., [ 3,4]. Despite a significant focus of many researchers on atherosclerosis, the mechanisms of the formation of atherosclerotic lesions are still not fully understood. However, numerous studies have shown that coronary endothelial dysfunction contributes to both the initiation and progression of atherosclerotic plaques [5,6].
Over the last two decades, it has been shown that vascular endothelium is an active autocrine, paracrine, and endocrine organ that lines the entire circulatory system from the heart to the smallest capillaries, and plays an essential role in the regulation of vascular tone and the maintenance of vascular homeostasis [7]. Under normal conditions, the endothelium not only provides a highly selective physical barrier to control the vascular permeability, but it also releases a large number of vasoprotective and thromboresistant molecules, which maintain the state of vasodilatation over vasoconstriction [7,8]. In contrast, during vascular injury, the release of endothelium-derived constricting and prothrombotic factors has a pivotal role in all phases of thrombus formation. The constitutive synthesis and secretion of vasoactive substances by endothelial cells appear to be under tight regulation as their levels are fairly constant in healthy subjects [8,9]. However, due to mechanisms which are not yet fully understood, the alteration of the normal homeostatic properties of the endothelium may occur and lead to unfavorable physiological vascular changes. Several studies have shown that alterations in endothelial properties can be induced by some endogenous substances such as cytokines, modified proteins, and antibodies, indicating the potential relevance of these mechanisms to cardiovascular events [2,10]. The research of molecules that may affect endothelial function promises a proper approach to find new biomarkers for the prediction of cardiovascular events.
Endothelial cells are constantly exposed to the influence of substances present in blood. One hypothesis is that peptides, among other molecules present in the circulation, are more likely to affect the endothelial function. It is known that the amount and repertoire of peptides in biological fluids change dynamically according to the physiological or pathological state of an individual [11,12]. The appearance of specific plasma peptides in CAD may induce the release of vasoactive molecules by endothelial cells, which are able to shift homeostatic balance towards the pro-coagulant phenotype. On the other hand, as a part of compensatory response under thrombogenic condition, such peptides may affect endothelium to release pro-fibrinolytic molecules to maintain a hemostatic balance. Thus, the aim of this study was to characterize the effects of peptides collected from the blood plasma of healthy subjects and patients with angina or myocardial infarction on endothelial cell secretion using the in vitro model.
## 2.1. Study Groups
The study included 70 patients who were hospitalized to the cardiology department of Kyiv City Hospital #12 with a preliminary diagnosis of CAD. To establish the diagnosis, the guidelines of European Society of Cardiology 2020 were followed [13]. A set of routine laboratory tests, including lipid profile, and total protein, was performed by the laboratory of the hospital. Cardiac troponin I (cTnI) was analyzed by immunoassay (MyBioSource Inc., San Diego, CA, USA; the upper reference limit (99th percentile) was determined at 14 pg/mL, reference ranges: <7 pg/mL for males, and <5 pg/mL—for females). Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. After the full set investigation was completed, we defined three groups: patients with stable angina (SA, $$n = 25$$), patients with progressive unstable angina (PUA, $$n = 28$$), patients with myocardial infarction (MI, $$n = 17$$). The control group included 20 healthy volunteers, who did not have cardiovascular or any chronic disease, and were well matched with age, gender, and other basic parameters with experimental patients’ groups. The Ethical Committees of both the Kyiv City Hospital #12 (Kyiv, Ukraine) and Taras Shevchenko University of Kyiv (Kyiv, Ukraine) approved the study and all patients gave informed consent to participate according to the Declaration of Helsinki.
## 2.2. Blood Sampling and Plasma Preparation
Blood samples were drawn from the cubital vein on the day of admission, prior to beginning treatment. Blood samples were collected in standard plastic vacuum tubes with $3.8\%$ sodium citrate. The volume ratio of blood to citrate was equal to 9:1. Plasma was separated by centrifugation at 2500× g for 25 min. After centrifugation, all plasma samples were aliquoted, and frozen at −80 °C until used.
## 2.3. Plasma Peptide Pool Isolation
The peptide pool was obtained from plasma according to the procedure described by Nikolaichyk et al., as cited in Katrii et al. [ 14]. Cold 1.2 M HClO4 was added to the 1 mL plasma sample (1:1, v/v), the mixture was placed on ice for 15 min and centrifuged at 10,000× g for 20 min 4 °C. The protein precipitate was discarded. The resultant supernatant was neutralized by 5 N KOH to pH 7.0 and was kept on ice for 15 min, followed by centrifugation. The remaining protein in the resultant supernatant was precipitated with four volumes of cold $96\%$ ethanol and was kept at 4 °C for 30 min. Then, the sample was subjected to centrifugation step again. The supernatant was obtained and its optical density (OD) was measured at 210 nm using the spectrophotometer (Smart SpecTMPlus, BioRad, Hercules, CA, USA). The peptide concentration was calculated using calibration curve prepared with CBZ-glycylglycine dipeptide (0.26 kDa) as a standard.
The purity of peptide fraction was estimated by $15\%$ polyacrylamide gel electrophoresis [15]. Gels were stained with $0.125\%$ solution of Coomassie Brilliant Blue G-250 (Thermo Fisher Scientific, Waltham, MA, USA) in $25\%$ isopropanol and $10\%$ acetic acid.
Peptide fractions separated from 1 mL of the blood plasma of all patients and healthy donors were lyophilized (LyoQuest, Terrassa, Spain) and were kept at 4 °C. Before assay, dry peptide material was dissolved in 0.2 mL of 0.05 M Tris-HCl, pH 7.4, containing 0.13 M NaCl.
The further analysis of effects of peptides on endothelial cell secretion was repeated at least three times for each experimental group. Every time we used freshly prepared peptide pool solution obtained by mixing of five different peptide fractions isolated from individuals of the same experimental group.
## 2.4. Human Endothelial Progenitor Cell (HEPC) Culture
All experiments with cell culture were performed in accordance with the bioethics and biological safety norms confirmed by the permission of medical company “Good cells” (Kyiv, Ukraine).
Mononuclear cells (MNCs) from human peripheral blood samples were isolated [16]. 20 mL of heparinized venous blood was centrifuged in Histopaque®-1077 density gradient (Sigma, St. Louis, MO, USA) at 400× g for 30 min at 4 °C. MNCs were washed twice in phosphate-buffered saline (PBS), pH 7.4. The obtained MNCs were seeded onto collagen-coated 75 cm2 culture flasks (SPL, Pocheon-si, Korea) in endothelial growing medium with the following composition: MCDB 131 medium (Gibco, Loughborough, UK), $5\%$ fetal bovine serum (Sigma), 2 U/mL heparin sodium (Indar, Kyiv, Ukraine), 1 ng/mL vascular endothelial growth factor, 10 ng/mL epidermal growth factor, 2 ng/mL basic fibroblastic growth factor, 20 ng/mL insulin-like growth factor; 0.2 μg/mL hydrocortisone, and 1 μg/mL ascorbate-2-phosphate (all manufactured by Sigma). Cells were subcultured with a 0.1/$0.02\%$ trypsin/ethylenediaminetetraacetic acid mixture in PBS (Sigma).
Studies were performed on secondary cultures grown in 24-well cell culture plates (Becton, Dickinson and Company, Franklin Lakes, NJ, USA) at a density of 3 × 103 cells/cm2 under the same conditions as primary cultures. The lyophilized peptide pools from all studied groups were diluted with culture medium. Cells were treated with peptides for 24 h. The final concentration of peptides in culture medium was the same for all experiments (300 µg/mL). The aliquots of culture medium were collected after the appropriate incubation period with peptides (1 h and 24 h), centrifuged at 15,000× g to remove cell debris, and frozen at −70 °C until use for immunoassay. The samples of control culture medium that was not exposed to any treatment were collected at the same time periods to analyze the basal level of endothelial cells secretion. The integrity of endothelial cells after incubation with peptide fractions was verified using a commercial kit for measurement of lactate dehydrogenase (LDH) activity (Felicit Diagnostics, Dnipro, Ukraine).
## 2.5. Enzyme-Linked Immunosorbent Assay (ELISA)
Samples of HEPC culture medium in volume of 100 µL were incubated in wells of 96-well plate overnight at 4 °C until analysis the next day. The coated plate was washed three times with wash buffer—PBS, pH 7.4 that contained $0.05\%$ Tween-20. Then, the microtiter plate was incubated with a blocking buffer—PBS, pH 7.4, containing $5\%$ non-fat milk at 37 °C for 60 min to inactivate unspecific binding sites. Monoclonal mouse antibodies, namely, anti-tissue plasminogen activator, anti-plasminogen activator inhibitor-1, anti-von Willebrand factor, and anti-endothelin-1 (all manufactured by Santa Cruz Biotechnology, Dallas, TX, USA) at a dilution of 1:3000 was added to determine corresponding antigens. Anti-mouse secondary antibody conjugated to horseradish peroxidase (Bio-Rad) was used at a dilution of 1:7000. Substrate development was performed with chromogenic mixture—o-phenylenediamine (Sigma) and H2O2 in 0.1 M sodium citrate buffer, pH 5.0. The reaction was terminated by the addition of 1 N H2SO4. Photometrical evaluation took place with the help of a computer-controlled microplate reader (μQuantTM, BioTek Instruments, Inc., Winooski, VT, USA) at a wavelength of 492 nm.
The levels of studied molecules in culture medium that was not exposed to peptide treatment (the volume of 0.05 M Tris-HCl, pH 7.4, containing 0.13 M NaCl, was added instead of peptide solution) at a time point of 1 h were used as a standard, defined as $100\%$ value, for each substance in our experiments. The data obtained at 24 hours’ time point from untreated culture as well as the data for all peptide-treated cultures at 1 h and 24 h were compared to the data from untreated culture at 1 h (Supplementary Figure S1).
## 2.6. Statistical Analysis
Statistical analysis was performed with Statistica 8.0 software. All experiments were performed by utilizing parallel design and repeated at a minimum of three times each. The data from experiments were expressed as mean ± standard deviation (SD). Data distribution was analyzed using the Kolmogorov-Smirnov test. Data with a normal distribution was analyzed using one-way analysis of variance (ANOVA). Nonparametric data was analyzed using the Kruskal-Wallis test followed by Dunn’s post-test. Differences were statistically significant when p-value was less than 0.05.
## 3.1. Peptide Concentrations in Plasma of Patients with CAD and Healthy Subjects
The basic clinical characteristics of the controls and patients with CAD are shown in Table 1. As can be seen from the table below, all four groups were well matched with respect to age, gender, smoking status, as well as BMI. It should be noted that patients younger than 50 years and older than 75 years old, with chronic inflammatory diseases, autoimmune diseases, acute infectious diseases, chronic liver, and kidney diseases, were not enrolled in this research. No significant differences in common biochemical characteristics were observed between the healthy subjects and patients either with stable or unstable angina pectoris. At the same time, patients with MI had a higher plasma concentration of total glycerides and a lower concentration of HDL cholesterol than did the controls and the other two CAD groups. The plasma concentration of total protein was also significantly elevated in the MI group, compared to both healthy subjects and patients with angina. The cardiac troponin I level, which is the biomarker of choice for the detection of cardiac injury, was notably increased in the plasma of patients with MI (Table 1).
The greatest peptide concentration was observed in the plasma of patients with MI. Thus, the plasma peptide level was almost 15 times higher in the MI group compared to the group of healthy subjects. We also found that plasma peptide concentrations were remarkably increased in patients with different angina pectoris states, compared to healthy volunteers. The plasma peptide level in patients with progressive unstable angina was six times higher than in control subjects, and almost 1.5 times higher than in patients with stable angina (Table 2).
## 3.2. The Levels of Some Soluble Anticoagulant As Well As Procoagulant Mediators Synthetized by Endothelial Cells Treated with Peptides Isolated from Plasma of Patients with CAD and Control Subjects
One of the major functions of endothelial cells is to produce physiologically important molecules, which have vasoprotective action, and are able to suppress platelet activation under physiological conditions [7,8,9]. Some molecules are expressed on the endothelial cell surface, while others are released in circulation. In this study, the levels of some soluble anticoagulant as well as procoagulant mediators synthetized by endothelial cells treated with peptides isolated from the plasma of patients with CAD and control subjects were examined. Since it has recently been shown that the HEPC culture can be considered as a robust and valid in vitro model suitable for studying endothelial cell function, we used the HEPC culture to evaluate the effects of peptide pools accumulated in the plasma of patients with CAD on endothelium. Postconfluent monolayers of HEPC were incubated with the peptide pools for 24 h. The possible cytolysis of endothelial cells in culture was measured by the release of cytosolic LDH into the culture supernatant. LDH release by the HEPC was not increased after 24 h exposure to peptides from both CAD patients and healthy volunteers.
## 3.2.1. The Levels of Tissue Plasminogen Activator and Plasminogen Activator Inhibitor-1 Synthetized by Endothelial Cells Treated with Peptides Isolated from Plasma of Patients with CAD and Control Subjects
Endothelial cells are the major site of synthesis of tissue plasminogen activator (tPA), a 68-kDa glycoprotein, and a major mediator of endogenous fibrinolysis. The endothelium produces plasminogen activator inhibitor-1 (PAI-1), which serves to neutralize tPA activity [17,18]. Since these two compounds are involved in the maintenance of homeostatic balance and may play a role in cardiovascular disease development, we analyzed the levels of tPA and PAI-1 in endothelial cell cultures that were treated with peptides isolated from patients with CAD, and healthy volunteers. The results obtained are shown in Figure 1.
The tPA secretion level by endothelial cells exposed to treatment with peptides derived from the PUA group (after a 1 h incubation period) was slightly higher than that of the control group. When endothelial cells were treated with peptides derived from patients with MI, the tPA secretion level was slightly lower than in the control culture. The peptide pools isolated from healthy subjects and patients with SA did not cause changes of tPA secretion within 1 h. The stimulation of endothelial cells with peptides derived from the SA-group for 24 h was accompanied by a reduced amount of released tPA (Figure 1A). In addition, the level of secretion of PAI-1 was not influenced by the treatment of HEPCs with peptides isolated from both CAD patients and healthy volunteers (Figure 1B).
## 3.2.2. The Levels of von Willebrand factor and Endothelin-1 Synthetized by Endothelial Cells Treated with Peptides Isolated from Plasma of Patients with CAD and Control Subjects
The von Willebrand factor (vWF) is one of the most important endothelium-derived molecules involved in controlling coagulation [19,20]. It plays a pivotal role in hemostatic plug formation, serving as a ligand for platelet adhesion to denuded vascular endothelium, and functions as a bridge molecule to support agonist-induced platelet aggregation. Thus, we explored the possible effect of peptides on vWF secretion by endothelial cells in vitro. The results obtained are shown in Figure 2A. The incubation of HEPCs with peptide pools isolated from either healthy subjects or patients with SA, revealed no effect on the vWF secretion level. However, when the HEPC culture was exposed to peptides derived from patients with PUA or MI, the secretion level of vWF was significantly increased within 24 h after stimulation (Figure 2A).
Endothelin-1 (ET-1) is one of the most potent endogenous vasoconstrictors secreted by endothelial cells, which acts as the natural counterpart of the vasodilator molecules. Apart from participating in the vascular tone regulation, ET-1 is involved in vascular remodeling, the development of inflammatory processes in the vascular wall, and contributes to both cell proliferation and apoptosis. Under physiological conditions, the effects of ET-1 are tightly regulated through the inhibition or stimulation of ET-1 release from endothelium [21]. Hemodynamic disorders are usually accompanied by imbalance in the production of vasodilator and vasoconstrictor agents. We examined the secretion of ET-1 by cultures of HEPCs treated with peptides isolated from healthy subjects and patients with CAD. The results obtained are shown in Figure 2B. The secretion of ET-1 decreased significantly within a 1 h treatment of endothelial cells with peptides derived from patients with CAD, compared to untreated culture (control). Thus, within 24 h of treatment with peptides derived from patients with SA, the level of ET-1 in the culture medium was in the same range as its level in unstimulated HEPC. However, treatment with peptides derived from patients with PUS caused a significant decrease in ET-1 level, and treatment with peptides derived from patients with MI was associated with a significant increase in ET-1 level (Figure 2B).
## 4. Discussion
There has been an increased interest in the study of peptides as possible effector molecules in many diseases and physiological conditions. Scientists have focused on the qualitative and quantitative analysis of the peptide spectrum in sera, or other biological fluids [22,23]. It has been established that peptides are synthesized in almost all organs and tissues of living organisms. The first group of peptides are intact small molecules (oxytocin, vasopressin, opioid peptides, ACTH, etc.,) that are released from larger precursor molecules during protein processing [24]. The second group of peptides represents “degradation products” that are formed as a result of the protein cleavage by the proteolytic enzymes [25].
The first group of peptides has been studied for decades, and it is now clear that their synthesis is usually highly regulated, since almost all of them have unique physiological functions and are involved in the maintenance of organism homeostasis, regulating processes such as proliferation, differentiation, cell death, and many others [22,26,27]. In contrast, the second peptide group has been considered to be a set of the degradation products of proteins, that have no specific function. However, recent experimental data have provided additional information on the proteolytic processes involved in the generation of components of “tissue-specific peptide pools” [14,28]. In the literature, several suggestions on ways of tissue specific peptide pool generation were described. Firstly, that peptides can be formed inside living cells by means of the cell specific proteinases, and further released into extracellular medium [29]. Secondly, that peptides can be formed extracellularly by means of the tissue specific proteolytic enzymes present in the extracellular matrix, such as matrix proteinases [30]. Finally, that peptides can be formed in dying cells and further released into surrounding fluids after cytolysis.
Recently, the development of peptide extraction methods and analytical technologies created an opportunity to identify numerous previously unknown peptides [31]. Despite the large repertoire of peptides that exist, the composition of tissue specific peptide pools is relatively stable under physiological conditions and does not show individual differences. The pathological processes and the use of medications affect the composition of peptide pools in target tissue. Therefore, peptides are convenient substances that can serve as biomarkers for early diagnosis in symptomatic patients, estimating disease progression, or monitoring responses to therapy.
During our experiments, we used platelet-depleted citrate plasma for the analysis of low molecular weight proteome [32]. The analysis of whole plasma is analytically challenging due to the wide range of protein and peptide concentrations. For example, albumin, the most abundant plasma protein, is present in the range of mg/mL, whereas peptides are more likely to be found in the pg/mL. Thus, it is important to remove high molecular weight proteins, such as albumin, immunoglobulins, transferrin, and lipoproteins, from plasma prior to the further analysis of plasma peptidome. Therefore, a unique technique described by Nikolaichyk for the analysis of peptide concentrations in the plasma of patients with CAD, and control subjects, has been employed [14].
We found that plasma peptide concentrations were remarkably increased in patients with cardiovascular disorders, in comparison with those in healthy donors (Table 2). *In* general, the elevated peptide levels in the plasma of CAD patients can be explained either by the alteration of protein metabolism, or by the intensification of processes associated with cell disruption. Many of the peptides formed under the pathological condition could be synthesized as byproducts of protein metabolism. Proteases, as well as specific cellular mechanisms such as protein ubiquitination, induce proteins to limited digestion generating intermediate peptides [33]. Cells can also produce peptides by directly translating small mRNA sequences [34]. The biological significance of peptides could be broad, modulating cell signaling from inside and outside the cells. Such untypical peptides can be formed specifically for some purpose, and act as modulating agents that could contribute to the maintenance of the homeostasis of living organisms, or may be recognized as risk factors involved in the progression of pathological processes.
The aim of this study was to investigate the effect of peptides derived from patients with CAD on HEPC. The study examined and compared the secretion levels of some molecules involved in the maintenance of vascular homeostasis by the cultures of human endothelial cells treated with peptides isolated from healthy subjects, and patients with CAD, namely, stable angina, progressive unstable angina, or myocardial infarction.
According to the results obtained, peptides isolated from healthy patients showed different effects on endothelial cell secretion compared to peptides isolated from patients with CAD. Moreover, peptides isolated from patients with SA, PUA, or MI, also had diverse effects (Figure 1 and Figure 2).
The most pronounced effects were observed under the incubation of HEPCs with peptides derived from patient with PUA or MI. Thus, peptides from patients with MI stimulated endothelial cells, resulting in the increased secretion of both vWF and endotelin-1. In contrast, the release of tPA by endothelial cells was inhibited by the presence of peptides derived from patients with MI in culture medium. The release of endothelin-1 may promote vasoconstriction, whereas the production of von Willebrand factor (vWF) shifts the hemostatic balance towards a procoagulant state, resulting in coronary arteries’ thrombosis, and subsequent ischemia. The treatment of endothelial cells with peptides derived from patients with PUA was accompanied by an increased secretion of tPA and vWF, while the amount of released ET-1 was decreased at the same time. The suppressive effect of peptides derived from patients with PUA on endotelin-1 production may be a part of the compensatory responses of organism to cardiovascular events.
## 5. Conclusions
Peptides from the plasma of patients with myocardial infarction promote endothelial cells to release both von Willebrand factor and endothelin-1; however, peptides from the plasma of patients with progressive unstable angina suppress the secretion of endothelin-1, and increase the secretion of von Willebrand factor and tissue plasminogen activator.
The molecular basis for these processes is not entirely clear, but it is reasonable to presume that the plasma peptides in patients with CAD may play a role in endothelial cell function.
We believe that the comparative analysis of the plasma peptides of patients with MI or angina, and healthy subjects, is a proper strategy for detecting either CAD biomarkers or triggers of cardiovascular events. Although several challenges will have to be met, the circulatory peptides are usually present in very low concentration, and there is high complexity involved due to the heterogeneity of CAD. Thus, there is a compelling need to develop sensitive, specific, and easily performed extracting and analytical techniques for plasma peptides analysis. Our immediate plans include using currently available approaches for cataloguing human blood plasma peptides, as well as detecting the individual variability, and the features of the peptides, related to cardiovascular diseases.
## References
1. Malakar A.K., Choudhury D., Halder B., Paul P., Uddin A., Chakraborty S.. **A review on coronary artery disease, its risk factors, and therapeutics**. *J. Cell Physiol.* (2019) **234** 16812-16823. DOI: 10.1002/jcp.28350
2. Nguyen M., Fernando S., Schwarz N., Tan J., Bursill C., Psaltis P.. **Inflammation as a Therapeutic Target in Atherosclerosis**. *J. Clin. Med.* (2019) **8**. DOI: 10.3390/jcm8081109
3. Zmysłowski A., Szterk A.. **Current knowledge on the mechanism of atherosclerosis and pro-atherosclerotic properties of oxysterols**. *Lipids Health Dis.* (2017) **16** 188. DOI: 10.1186/s12944-017-0579-2
4. Alfarisi H.A.H., Mohamed Z.B.H., Ibrahim M.B.. **Basic pathogenic mechanisms of atherosclerosis**. *Egypt. J. Basic Appl. Sci.* (2020) **7** 116-125. DOI: 10.1080/2314808X.2020.1769913
5. Gutierrez E., Flammer A.J., Lerman L.O., Elizaga J., Lerman A., Fernandez-Aviles F.. **Endothelial dysfunction over the course of coronary artery disease**. *Eur. Heart J.* (2013) **34** 3175-3181. DOI: 10.1093/eurheartj/eht351
6. Dengel D.R., Bronas U.G.. **The Role of Endothelial Dysfunction on Development and Progression of Atherosclerosis and Methods to Assess Vascular Function and Structure**. *Am. J. Lifestyle Med.* (2010) **4** 445-456. DOI: 10.1177/1559827610368773
7. Galley H.F., Webster N.R.. **Physiology of the endothelium**. *Br. J. Anaesth.* (2004) **93** 105-113. DOI: 10.1093/bja/aeh163
8. Sandoo A., Veldhuijzen van Zanten J.J.C., Metsios G.S., Carroll D., Kitas G.D.. **The Endothelium and Its Role in Regulating Vascular Tone**. *Open Cardiovasc. Med. J.* (2010) **4** 302-312. DOI: 10.2174/1874192401004010302
9. Félétou M., Köhler R., Vanhoutte P.M.. **Endothelium-derived Vasoactive Factors and Hypertension: Possible Roles in Pathogenesis and as Treatment Targets**. *Curr. Hypertens. Rep.* (2010) **12** 267-275. DOI: 10.1007/s11906-010-0118-2
10. Radder C.M., Beekhuizen H., Kanhai H., Brand A.. **Effect of maternal anti-HPA-1a antibodies and polyclonal IVIG on the activation status of vascular endothelial cells**. *Clin. Exp. Immunol.* (2004) **137** 216-222. DOI: 10.1111/j.1365-2249.2004.02496.x
11. Chuhray S.M., Lavrynenko V.E., Kaminsky R.F., Dzevulska I.V., Malikov O.V., Matkivska R.M., Shobat L.B., Kovalchuk O.I., Sokurenko L.M.. **Morphofunctional status of cardio-vascular system of rats with arterial hypertension**. *Wiad. Lek.* (2020) **73** 355-359. DOI: 10.36740/WLek202002128
12. Chuhray S.M., Lavrynenko V.E., Kaminsky R.F., Dzevulska I.V., Malikov O.V., Kovalchuk O.I., Sokurenko L.M.. **Morphofunctional status of cardio-vascular system of rats with congenital hypothyreosis**. *Wiad. Lek.* (2019) **72** 229-233. DOI: 10.36740/WLek201902116
13. Collet J.P., Thiele H., Barbato E., Barthélémy O., Bauersachs J., Bhatt D.L., Dendale P., Dorobantu M., Edvardsen T., Folliguet T.. **2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation**. *Eur. Heart J.* (2021) **42** 1289-1367. DOI: 10.1093/eurheartj/ehaa575
14. Katrii T., Raksha N., Halenova T., Vovk T., Savchuk O., Ostapchenko L., Tomchuk V., Melnyk V.. **Influence of peptide pools from the plasma of ischemic stroke patients on hemostasis**. *Curr. Top. Pept. Protein Res.* (2020) **21** 21-29
15. Ostapchenko L., Savchuk O., Burlova-Vasilieva N.. **Enzyme electrophoresis method in analysis of active components of haemostasis system**. *Adv. Biosci. Biotechnol.* (2011) **2** 20-26. DOI: 10.4236/abb.2011.21004
16. Gubar O.S., Rodnichenko A.E., Zlatska O.V., Zubov D.O., Litvinova L.S., Shupletsova V.V., Vasyliev R.G.. **Isolation and Large Scale Expansion of Human Endothelial Progenitor Cells from Peripheral Blood**. *Probl. Cryobiol. Cryomedicine* (2018) **28** 29-33. DOI: 10.15407/cryo28.01.029
17. Sillen M., Declerck P.J.. **A Narrative Review on Plasminogen Activator Inhibitor-1 and Its (Patho)Physiological Role: To Target or Not to Target?**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22052721
18. Jung R.G., Motazedian P., Ramirez F.D., Simard T., Di Santo P., Visintini S., Faraz M.A., Labinaz A., Jung Y., Hibbert B.. **Association between plasminogen activator inhibitor-1 and cardiovascular events: A systematic review and meta-analysis**. *Thromb. J.* (2018) **16** 12. DOI: 10.1186/s12959-018-0166-4
19. Rayner S.G., Scholl Z., Mandrycky C.J., Chen J., LaValley K.N., Leary P.J., Altemeier W.A., Liles W.C., Chung D.W., López J.A.. **Endothelial-derived von Willebrand factor accelerates fibrin clotting within engineered microvessels**. *J. Thromb. Haemost.* (2022) **20** 1627-1637. DOI: 10.1111/jth.15714
20. Marchi R., Rojas H.. **Effect of von Willebrand factor on clot structure and lysis**. *Blood Coagul. Fibrinolysis* (2015) **26** 533-536. DOI: 10.1097/MBC.0000000000000284
21. Marasciulo F.L., Montagnani M., Potenza M.A.. **Endothelin-1: The yin and yang on vascular function**. *Curr. Med. Chem.* (2006) **13** 1655-1665. DOI: 10.2174/092986706777441968
22. Sanders E.J., Harvey S.. **Peptide hormones as developmental growth and differentiation factors**. *Dev. Dyn.* (2008) **237** 1537-1552. DOI: 10.1002/dvdy.21573
23. Fosgerau K., Hoffmann T.. **Peptide therapeutics: Current status and future directions**. *Drug Discov. Today* (2015) **20** 122-128. DOI: 10.1016/j.drudis.2014.10.003
24. Fricker L.D.. **Neuropeptide-processing enzymes: Applications for drug discovery**. *AAPS J.* (2005) **7** E449-E455. DOI: 10.1208/aapsj070244
25. Glickman M.H., Ciechanover A.. **The Ubiquitin-Proteasome Proteolytic Pathway: Destruction for the Sake of Construction**. *Physiol. Rev.* (2002) **82** 373-428. DOI: 10.1152/physrev.00027.2001
26. Silveira P., Gil J., Casis L., Irazusta J.. **Peptide Metabolism and the Control of Body Fluid Homeostasis**. *Curr. Med. Chem. Hematol. Agents* (2004) **2** 219-238. DOI: 10.2174/1568016043356264
27. Miller G.D.. **Appetite Regulation: Hormones, Peptides, and Neurotransmitters and Their Role in Obesity**. *Am. J. Lifestyle Med.* (2019) **13** 586-601. DOI: 10.1177/1559827617716376
28. Raksha N., Halenova T., Vovk T., Kharchenko O., Savchuk O., Samborska I., Zaichko N., Ostapchenko L., Maievskyi O.. **Protein-peptide composition in the lungs of rats with hyperhomocysteinemia**. *Boll. Soc. Ital. Biol. Sper.* (2021) **94** 67-71. DOI: 10.4081/jbr.2021.9858
29. De Araujo C.B., Heimann A.S., Remer R.A., Russo L.C., Colquhoun A., Forti F.L., Ferro E.S.. **Intracellular Peptides in Cell Biology and Pharmacology**. *Biomolecules* (2019) **9**. DOI: 10.3390/biom9040150
30. Shiryaev S.A., Cieplak P., Aleshin A.E., Sun Q., Zhu W., Motamedchaboki K., Sloutsky A., Strongin A.Y.. **Matrix metalloproteinase proteolysis of the mycobacterial HSP65 protein as a potential source of immunogenic peptides in human tuberculosis**. *FEBS J.* (2011) **278** 3277-3286. DOI: 10.1111/j.1742-4658.2011.08244.x
31. Apostolopoulos V., Bojarska J., Chai T.-T., Elnagdy S., Kaczmarek K., Matsoukas J., New R., Parang K., Lopez O.P., Parhiz H.. **A Global Review on Short Peptides: Frontiers and Perspectives**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26020430
32. Tammen H., Schulte I., Hess R., Menzel C., Kellmann M., Mohring T., Schulz-Knappe P.. **Peptidomic analysis of human blood specimens: Comparison between plasma specimens and serum by differential peptide display**. *Proteomics* (2005) **5** 3414-3422. DOI: 10.1002/pmic.200401219
33. Li Y., Li S., Wu H.. **Ubiquitination-proteasome system (UPS) and autophagy two main protein degradation machineries in response to cell stress**. *Cells* (2022) **11**. DOI: 10.3390/cells11050851
34. Apcher S., Millot G., Daskalogianni C., Scherl A., Manoury B., Fåhraeus R.. **Translation of pre-spliced RNAs in the nuclear compartment generates peptides for the MHC class I pathway**. *Proc. Natl. Acad. Sci. USA* (2013) **110** 17951-17956. DOI: 10.1073/pnas.1309956110
|
---
title: Quantitative Proteomic Analysis Reveals the Mechanisms of Sinapine Alleviate
Macrophage Foaming
authors:
- Aiyang Liu
- Bin Liao
- Shipeng Yin
- Zhan Ye
- Mengxue He
- Xue Li
- Yuanfa Liu
- Yongjiang Xu
journal: Molecules
year: 2023
pmcid: PMC10003987
doi: 10.3390/molecules28052012
license: CC BY 4.0
---
# Quantitative Proteomic Analysis Reveals the Mechanisms of Sinapine Alleviate Macrophage Foaming
## Abstract
Rapeseed polyphenols have cardiovascular protective effects. Sinapine, one main rapeseed polyphenol, possesses antioxidative, anti-inflammatory, and antitumor properties. However, no research has been published about the role of sinapine in alleviating macrophage foaming. This study aimed to reveal the macrophage foaming alleviation mechanism of sinapine by applying quantitative proteomics and bioinformatics analyses. A new approach was developed to retrieve sinapine from rapeseed meals by using hot-alcohol-reflux-assisted sonication combined with anti-solvent precipitation. The sinapine yield of the new approach was significantly higher than in traditional methods. Proteomics was performed to investigate the effects of sinapine on foam cells, and it showed that sinapine can alleviate foam cell formation. Moreover, sinapine suppressed CD36 expression, enhanced the CDC42 expression, and activated the JAK2 and the STAT3 in the foam cells. These findings suggest that the action of sinapine on foam cells inhibits cholesterol uptake, activates cholesterol efflux, and converts macrophages from pro-inflammatory M1 to anti-inflammatory M2. This study confirms the abundance of sinapine in rapeseed oil by-products and elucidates the biochemical mechanisms of sinapine that alleviates macrophage foaming, which may provide new perspectives for reprocessing rapeseed oil by-products.
## 1. Introduction
Rapeseeds are the second-largest source of edible oil production and one of the most important oil crops in the world [1]. Sinapine is the most abundant phenolic compound in rapeseeds, accounting for up to $80\%$ of the total phenolic content [2], and is mainly found in rapeseed meals [3]. Sinapine has some bioactivities, such as anti-inflammatory [4], antioxidant [5], and anti-angiogenic properties [6]. Various polyphenols, such as gallic acid [7] and chlorogenic acid [8], alleviate foam cells and prevent atherosclerosis. Though sinapine is the main polyphenol in rapeseed meals, there are few studies on its alleviation effect on foam cells.
Macrophages are immunological and metabolic cells involved in atherosclerosis occurrence and development [9]. Macrophages take up modified LDL in the form of oxidized LDL particles (ox-LDL) through uptake receptors [10]. Lipid metabolism in macrophages involves three distinct processes: cholesterol uptake, esterification, and efflux. The dysregulation of these lipid processes leads to the formation of lipid-dense macrophages, known as “foam cells”, which are defined as a characteristic of early atherosclerosis [11]. Thus, finding ways to effectively inhibit the formation of foam cells and exploring the mechanism of inhibiting foam cells are significant for the prevention and treatment of atherosclerosis.
In this study, we developed a new extraction method to retrieve sinapine from rapeseed meals by using hot-alcohol-reflux-assisted sonication combined with anti-solvent precipitation. We found a significant ameliorative effect of sinapine on foam cell formation. Through label-free quantitative proteomics (LFQ) and bioinformatics analysis of differential proteins and the pathways involved, we found that sinapine can activate the CD36/CDC42-JAK2-STAT3 pathway in foam cells. We achieved the efficient utilization of rapeseed-oil by-products and revealed the main plant polyphenols in rapeseed meals, sinapine, to alleviate macrophage foaming.
## 2.1. The Effect of Extraction Methods on Sinapine
The total content of sinapine was measured according to the equation of the standard curve: $y = 0.0515$x − 0.0047. The sinapine contents in rapeseed meals prepared from HE, HE + UE, and HE + UE + AP were analyzed. The sinapine yields of the three methods were 6.69 ± 0.072, 10.92 ± 0.007, and 15.76 ± 0.015 mg/g, respectively. The sinapine content in rapeseed meals prepared from HE + UE + AP was significantly higher than those of the other methods (Figure 1).
## 2.2. Regulatory Effects of Sinapine on Cholesterol Accumulation in Foam Cells
Foam cells were treated with different concentrations of sinapine (0–640 μM) for 24 or 48 h. After that, cell viability was detected with a CCK8 kit. The concentration range of sinapine that did not significantly affect the cell viability of macrophages was determined. In the range of 0–80 μM, the cell viability was not significantly affected when macrophages were treated with sinapine for 24 h (Figure 2A). Therefore, the effect of sinapine on macrophages was further investigated at a concentration of 80 μM after 24 h of incubation. The Oil Red O staining results demonstrated the intracellular lipid content greatly decreased after adding sinapine (80 μM) for 24 h (Figure 2B). The semi-quantitative Oil Red O assay showed that treatment with sinapine significantly reduced lipid droplet content in foam cells (Figure 2C).
After a treatment under optimal conditions, the amounts of TC, FC, and CE/TC were measured in foam cells. In the sinapine group, the cells contained significantly more FC than the ox-LDL group did (3.607 ± 0.041 vs. 1.037 ± 0.174 mg/g). The TC and CE/TC were lower in the AST group than in the ox-LDL group (5.187 ± 0.256 vs. 11.163 ± 0.184 mg/g, and 29.795 ± $3.523\%$ vs. 85.2 ± $1.671\%$, respectively) ($p \leq 0.05$) (Figure 2D).
## 2.3. Differences in Differentially Expressed Proteins (DEPs) between Different Treatments
In total, 3554 proteins were found to be differentially expressed among the control, the ox-LDL, and the sinapine groups by screening DEPs in foam cells with LFQ-based quantitative proteomics. A principal component analysis (PCA) was used to examine the protein expression changes induced by ox-LDL and sinapine interventions. The protein profiles of the control, the ox-LDL, and the sinapine groups differed significantly. The protein expressions were significantly different between the ox-LDL group and the control group. In addition, the sinapine intervention altered the protein expression profile compared to the ox-LDL group (Figure 3A,B). To further compare the control and ox-LDL groups with the sinapine group, we screened out the significantly different proteins by using the following criteria: fold change (FC) > 1.2, $p \leq 0.05$ for upregulated proteins; FC < 0.83, $p \leq 0.05$ for downregulated proteins. In comparing the ox-LDL group and the control group, a total of 2390 significant DEPs and 512 DEPs proteins were identified in the sinapine group compared to the ox-LDL group. Thus, sinapine treatment significantly affected protein expression in foam cells. Specifically, as seen in the volcano plot, when comparing the ox-LDL group to the control group, 2368 DEPs were downregulated significantly, and 20 DEPs were significantly upregulated, whereas 271 DEPs were downregulated significantly. Sinapine induced the upregulation of 214 DEPs compared with the ox-LDL group (Figure 3C). As a result, the sinapine group had more upregulated proteins than downregulated proteins. These data indicate that the main biological effects of sinapine are the upregulation of proteins and the downregulation of foam cells. The above results suggest that protein expression in foam cells affected by ox-LDL is compensated by sinapine treatment and that sinapine significantly alleviates macrophage foaming.
## 2.4. DEPs Enriched in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
In total, 2390 DEPs in the control group versus the ox-LDL group, and 512 DEPs in the ox-LDL group versus the sinapine group were identified (Figure 4A). Among them, 344 proteins were common in the control group, the ox-LDL group, and the AST group (Figure 4A). A bioinformatics analysis of these DEPs was performed to find the proteins that play important roles in the sinapine-treating process. GO and KEGG databases were accessed for the 344 DEPs among the control, the ox-LDL, and the sinapine groups. The MF, CC, and BP are shown according to GO annotations (Figure 4B). The DEPs of MF included binding, catalytic, structural molecule, molecular function regulator, transporter, ATP-dependent, transcription regulator, molecular transducer, translation regulator, cytoskeletal motor, and low-density lipoprotein particle receptor activities. In the CC class, the cellular, anatomical entity, protein-containing complex, and BP categorization activities revealed that these DEPs were involved primarily in the cellular process, metabolic process, biological regulation, localization, response to stimulus, multicellular organismal process, immune system process, the biological process involved in interspecies interaction between organisms, developmental process, locomotion, biological adhesion, biological phase, and the reproductive process.
Based on the shared DEPs among the control, ox-LDL, and sinapine groups, an enrichment analysis of the 9 KEGG pathway and foam cell cholesterol metabolism pathway was conducted (Figure 4C).
## 2.5. Effects of Sinapine on the Cholesterol Metabolism in Foam Cells
Whether sinapine regulated cholesterol metabolism in foam cells was investigated. The KEGG pathway analysis revealed that some proteins were involved in those pathways, and the STRING analysis provided evidence of interactions in these proteins. Those proteins interacted and were closely linked, and the expression trends of these proteins in the sinapine group were opposite to those in the ox-LDL group (Figure 5A,B). Among them, CD36, CDC42, JAK2, and STAT3 proteins were closely associated with atherosclerosis and foam cells, and their protein expressions were opposite to those of the ox-LDL group and converged with those of the control group after the sinapine base intervention (Figure 5C,D). Those proteins are key proteins in cholesterol metabolism and foam cell cholesterol efflux, respectively.
## 2.6. Western Blot Validation of Key Proteins
A Western blot of key proteins was conducted to validate the LFQ-based quantitative proteomics. CD36, CDC42, JAK2, and STAT3 DEPs participated in the KEGG pathways and were all involved in the cholesterol metabolism pathway. Consequently, proteins related to sinapine that prevented foam cell formation were verified by Western blot. Western blot (Figure 6A) showed that the relative expression level of CD36 in the ox-LDL group was higher than in the control group. The relative expression levels of CDC42, JAK2, and STAT3 were lower than in the control group, and the relative expression level of CD36 in the sinapine group was lower than in the ox-LDL group. The relative expression levels of CDC42, JAK2, and STAT3 were higher than in the ox-LDL group. These results were confirmed by proteomic analysis. The expressions of those proteins were strikingly higher in the sinapine group than in the ox-LDL group, and this is consistent with the proteomic analysis.
## 3. Discussion
Although sinapine can significantly improve several chronic diseases, limited research has focused on its beneficial effects on the prevention and intervention of atherosclerosis. Experiments here first suggest that sinapine can prevent and intervene with atherosclerosis by modulating lipid accumulation in foam cells induced by ox-LDL. In addition, CD36, JAK2, STAT3, and CDC42 are essential in reducing lipid accumulation and atherosclerosis in foam cells.
In the arteries of early atherosclerotic lesions, macrophages accumulate under the endothelium [12]. Moreover, macrophages are rich in lipids due to the differentiation of monocytes into macrophages, which are taken up by modified lipoproteins [10]. Lipid metabolism in macrophages involves three distinct processes: absorption, esterification, and excretion of cholesterol. These dysregulated lipid pathways lead to foam cell formation, which results in various atherosclerotic effects, including matrix degradation. When located in this stage, macrophages lead to plaque rupture [13]. Research with animal models shows that foam cell death can lead to atherosclerosis. Moreover, atherosclerosis occurs in the cerebral arteries, coronary arteries, and aorta of humans [14]. Thus, the formation of foam cells is one of the targets for the treatment of atherosclerosis. According to the lipid and inflammation hypothesis, anti-inflammatory therapies and lipid-modulating strategies remain the backbone of atherosclerosis treatment.
Several types of cells express CD36, including monocytes and macrophages. CD36 has been implicated in atherosclerosis by promoting foam cell formation in the intima of blood vessels [15,16,17]. CD36 has several functions in regulating modified LDL binding, inflammatory processes, lipid metabolism, fatty acid transport, and immunity [15]. In atherosclerosis, ox-LDL can be taken up by macrophages through CD36 and transformed into foam cells, secreting inflammatory cytokines and chemokines [18]. Specifically, a decrease in CD36 protein expression reduces the uptake of ox-LDL and further inhibits the formation of foam cells and the development of atherosclerotic plaques in ApoE-/-mice [19]. The expression of CD36 in macrophages is upregulated by several proatherogenic stimuli, such as ox-LDL. Moreover, ox-LDL enhances the interaction of CD36 with JAK2, induces phosphorylation of JAK2, and subsequently activates STAT3 signaling [20]. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) is involved in regulating mammalian cell proliferation and differentiation and various physiological functions [21]. Receptor binding triggers autophosphorylation to activate the Janus kinase (JAK), which activates the STAT3 transcription factor (signal transducer and activator of transcription), translocates to the nucleus and binds DNA to regulate transcription [22]. The JAK2/STAT3 signaling pathway can regulate lipid metabolism disorders caused by environmental pollutants [23]. The JAK2/STAT3 signaling pathway is also closely related to lipid metabolism. Mice deficient in JAK2 have lipolysis and impaired insulin resistance [24]. Moreover, activating the JAK2/STAT3 pathway in macrophages can reduce macrophage lipid levels [25], suggesting that the JAK2/STAT3 pathway may play a role in reducing intracellular lipid accumulation. Therefore, the addition of sinapine reduces the expressions of CD36, JAK2, and STAT3, thereby reducing the ox-LDL uptake and lipid accumulation in macrophages.
Additionally, the protein expression of the cell-division cycle 42 (CDC42) in foam cells treated with sinapine is significantly increased. CDC42 affects cytoskeletal rearrangement and lipid metabolism in macrophages. It promotes actin polymerization and disrupts lipid rafts [26,27]. The CDC42 protein directly interacts with ABCA1 to direct lipid transport out of cells [28]. The small GTPase CDC42 is central to the rearrangement of cytoskeletons and lipid metabolism [29]. AIBP activates CDC42 expression to increase cholesterol efflux [30], and CDC42 may be involved in cholesterol vesicle transport across the trans-Golgi network and plasma membrane, suggesting that CDC42 activation induced by APOA-I enhances vesicular transport, prompting ABCA1 to export cholesterol from cells [31]. The activation of CDC42 causes lipid raft abundance and structure changes and the rearrangement of actin filaments during actin remodeling, resulting in raft clustering. Lipid raft changes create regions of the plasma membrane that can bind apoA-I. The increased apoA-I binding leads to the intracellular redistribution of cholesterol to the plasma membrane and, together with increased access to cholesterol, can enhance cholesterol efflux [30]. Therefore, after the foam cells were treated with sinapine, the CDC42 protein expression significantly increased, indicating that sinapine can promote the efflux of cholesterol in foam cells.
Interestingly, CDC42 represents a critical source of signals that promote STAT3 activation [32]. Human cells expressing mutationally activated CDC42 can activate STAT3 due to phosphorylation at tyr705 and ser727 [33]. Inhibition experiments indicate that CDC42 activate STAT3 though JAK2 [34]. Activation of CDC42 affects the protein expressions of JAK2 and STAT3, which is consistent with our study. Macrophages found in atherosclerotic plaques can alter their phenotypes in response to various stimuli. Modified LDL and cholesterol crystals stimulate pro-inflammatory M1 macrophages [35]. M1 macrophages produce and secrete pro-inflammatory cytokines, such as TNF-α, IL-1β, IL-6, NO, and ROS [36]. M1 macrophages also express different chemokine receptor ligands, such as CXC chemokine ligand CXCL-9, CXCL-10, and CXCL-5. CXCL-5 promotes the recruitment of Th1 and natural killer cells, which are important in killing intracellular pathogens [37]. Thus, M1 macrophages have potent antimicrobial effects. However, in the aseptic inflammatory setting of AS, pro-inflammatory M1 macrophages lead to a sustained inflammatory response, resulting in peripheral tissue damage [38]. M2 macrophages are activated by IL-4 and IL-13 and secrete IL-10, TNF-α, CCL-17, and CCL-22. Therefore, the main roles of M2 macrophages are to prevent tissue damage, exert anti-inflammatory effects and promote tissue repair [39]. CD36 is the main scavenger receptor for ox-LDL phagocytosis by macrophages [40], suggesting that M1 macrophages can phagocytose large amounts of ox-LDL and facilitate lipid accumulation. This finding is consistent with the present study, whereas M2 macrophages lowly expressed LXRα, ABCA1, and ApoE B. The above results suggest that M1 macrophages are more prone to foam cell formation than M2 macrophages are [35]. In addition to anti-hemorrhagic properties, M2 macrophages can prevent foam cell formation. Reportedly, the activation of JAK/STAT1 pathway may lead to the polarization of pro-inflammatory macrophages into M2 [41]. Moreover, the JAK/STAT3 pathway can shift the macrophage phenotype from M1 to M2, inhibiting atherosclerotic lesions in early and late development stages [42,43]. Therefore, sinapine induces the conversion from M1 to M2 macrophages, thereby inhibiting foam cell formation.
## 4.1. Materials
Sinapine was purchased from Solarbio (purity ≥ $96\%$, Beijing Solar Science Technology Co., Ltd., Beijing, China). Ox-LDL was bought from Yiyuan (Yiyuan Co. Ltd., Guangzhou, China). Antibodies were offered by Proteintech (Proteintech Group, Inc., Wuhan, China). The bicinchoninic acid (BCA) protein quantification kit was purchased from Beyotime (Beyotime Institute of Biotechnology, Shanghai, China).
## 4.2. Preparation of Rapeseed-Meal Samples and Extraction of Sinapine
Rapeseed-meal samples were prepared by removing any visible debris, crushed, passed through a 60-mesh sieve, and dried in an oven at 40 °C to a constant weight. The rapeseed meal produced in the third step of the process was divided into three equal parts with a weight of 1.25 g, compacted in a filter paper sleeve, and immersed in a round-bottom flask with a Soxhlet extractor (Soxtec-2055, FOSS, Eden Prairie, MN, USA) containing petroleum ether. Then the three parts were defatted at reflux in a water bath at 70 °C to a constant weight and processed in three separate ways. ( A) Hot ethanol extraction (HE): The oiled rapeseed meal was extracted four times (one hour each time), at 90 °C, with 130 mL of $80\%$ ethanol, under hot reflux, and the resulting ethanol extract was poured into a round-bottom flask and concentrated to 10 mL under vacuum to obtain the final extract. ( B) HE with ultrasonic extraction (HE + UE): The de-oiled rapeseed meal was extracted under hot reflux, at 90 °C, with 130 mL of $80\%$ ethanol four times (1 h each time) and ultrasonicated with an ultrasonic power of 600 W during the last 40 min. The resulting ethanol extract was poured into a round-bottom flask and concentrated to 10 mL under vacuum to obtain the final extract. ( C) HE + UE combined with anti-solvent precipitation (HE + UE + AP): The de-oiled rapeseed meal was extracted under hot reflux, at 90 °C, with 130 mL of $80\%$ ethanol four times (1 h each time) and ultrasonicated with an ultrasonic power of 600 W during the last 40 min. The resulting ethanol extract (petroleum ether) was poured into a round bottom flask and concentrated to 10 mL under vacuum to obtain the final extract. The mustard bases were precipitated with the anti-solvent method. Next, 50 mL of a counter solvent was put in a beaker and placed on a magnetic stirrer at the speed of 700–800 rpm. Then 10 mL of the extraction solution was taken at a counter solvent to an extraction solution ratio of 5:1 and dropped into the beaker at a rate of 4 mL/min to obtain a mixed suspension, which was then centrifuged at 4000 rpm for 10 min. The sample was prepared after the supernatant was removed. Standard sinapine was used to authenticate the UV-visible spectrum (UV-1801, Rayleigh, Beijing, China) absorption spectra and calibration curves. Sinapine was detected at 326 nm, and the sinapine extraction yield was calculated. Experiments were repeated three times. The sinapine yield (mg/grapeseed meal) was calculated from Equation [1]:[1]Yield=Csinapine×Vsolventmrapeseed meal where mrapeseed meal is the mass of dry matter in rapeseed meal (g).
## 4.3. Foam Cell Formation of Macrophages
THP-1 cells were purchased from Procell Life Technology Co., Ltd. (Wuhan, China)). Under standard culture conditions ($5\%$ CO2 and 37 °C), the cells were cultured in an RPMI-1640 medium that contained $0.1\%$ non-essential amino acids, $10\%$ fetal bovine serum, and $1\%$ penicillin/streptomycin. Before the experiment, the THP-1 cells were treated with phorbol-12-millistate-13-acetate (Beyotime Institute of Biotechnology, Shanghai, China) for 48 h to differentiate into macrophages.
The THP-1 macrophages were treated with 50 g mL−1 ox-LDL and cultured in a 1640 medium containing $5\%$ fetal bovine serum for 48 h to establish a macrophage foam model.
## 4.4. Confirmation of Sinapine Effects on Macrophages
Sinapine was added to foam cells at 20, 40, 80, 160, 320, and 640 μM. After successfully incubating the induced foam cells at 37 °C for 24 or 48 h, cell viability was measured using a CCK8 kit. Afterward, to study the effects of sinapine on the lipid content of macrophages, sinapine (80 μM) was added and cultured with foam cells at 37 °C for 24 h. Cells were harvested after culture, fixed with paraformaldehyde, and stained with Oil Red O-kit. Then Oil Red O staining was measured semi-quantitatively by extracting Oil Red O stain with isopropanol ($100\%$, Alading Biotechnology Co., Ltd., Shanghai, China) for 10 min. After gentle rocking, the absorbance at 492 nm was read [44]. After the treatment with the optimal conditions, the TC, FC, and the ratio of cholesterol ester and total cholesterol (CE/TC) in the cells were determined; the CE was calculated by subtracting FC from TC.
## 4.5. Preparation Method for Proteomic Samples
Three groups of cell samples, namely the control, ox-LDL, and AST groups, were used. Cell pellets were washed three times with a Hanks solution (Thermo Fisher Scientific, Waltham, MA, USA), and cells were scraped and stored at −80 °C for proteomic analysis. Each set of samples was repeated five times.
## 4.6. Protein Identification, Quantitation, and Bioinformatic Analysis
Cells were lysed for 10 min in an ice bath, using a radio immunoprecipitation assay (RIPA) lysis buffer containing 1 mM phenylmethanesulfonyl fluoride (PMSF) (Beyotime Institute of Biotechnology, Shanghai, China) protease inhibitor. After centrifugation (20,000× g) at 4 °C for 30 min, a BCA protein quantification kit was used to measure the protein levels in the supernatant.
A sample of protein (about 200 μg) was collected. The volume of precooled acetonitrile(ACN) (MREDA, Beijing, China) was added five times, and the sample was stored at −20 °C for 3 h. The sample was centrifuged to extract the precipitate, and 100 μL of guanidine hydrochloride (Sangon Biotech, Shanghai, China) was added. Then the proteins were reduced with dithiothreitol (DTT) (20 mM, Alading Biotechnology Co., Ltd., Shanghai, China) for 30 min at 56 °C and added with iodoacetamide (IAA) (50 mM, Alading Biotechnology Co., Ltd., Shanghai, China). The reaction proceeded in the dark for 30 min. The sample was transferred to a 10 kDa ultrafiltration tube where 50 μL NH4HCO3 (50 mM, Alading Biotechnology Co., Ltd., Shanghai, China) and trypsin were added at an enzyme: protein ratio of 1:50. The sample was left overnight to react at 37 °C. After 24 h, the sample was centrifuged at 14,000× g, and the filtrate was collected, washed once with a $0.1\%$ aqueous solution, and centrifuged again. Next, 50 μg of each peptide sample was collected, combined into a mixed sample, fractionated using HPLC (AB SCIEX, Foster, CA, USA) (pH = 10.0), and divided into 12 fractions. IDA acquisition was used to identify the fractionated samples for library building and SWATH acquisition mode for data collection of samples. The peptides were resuspended in $0.1\%$ FA and separated using an Acclaim PepMap C18 analytical column in an Ekisgent. The peptide was eluted using a gradient solution comprised of solvent A ($98\%$ H2O/$0.1\%$ FA) and solvent B ($98\%$ ACN/$0.1\%$ FA). Solutions of $5\%$, $6\%$, $27\%$, $50\%$, $80\%$, $80\%$, $5\%$, and $5\%$ of solvent B were combined with corresponding volumes of solvent A and applied at 0, 1, 50, 65, 65.5, 75, 75.5, and 90 min. The flow rate was 5 μL/min. A Triple TOF 6600+ (AB SCIEX, Danaher, Washington, DC, USA) was set to 320 °C, and 2.2 kV DDA (data-dependent mode) was selected to switch automatically between MS and MS/MS to collect the spectrum. The MS1 scan ranged from 300 to 1300 m/z. The scanning range of MS/MS was from 100 to 1500 m/z.
The graded peptide samples were subjected to IDA acquisition, using a 90 min gradient, and the obtained data were merged on ProteinPilot (http://www.absciex.com/products/software/proteinpilot-software, accessed on 6 January 2023) for a library search to construct the SWATH database. The mixed samples were subjected to IDA data acquisition, and dynamic windows were constructed according to the ion distribution density. The SWATH acquisition method was established for each sample according to the constructed dynamic windows. The results of the ProteinPilot search were used as a library for the analysis of SWATH data, and the relative quantitative information of all the identified proteins was obtained in total.
## 4.7. Western Blotting
Western blotting was performed using the method of Nie [45]. Extracted protein samples (1 mg·mL−1), loading buffer, and reducing agent (Thermo Fisher Scientific Inc., Waltham, MA, USA) were mixed and heated at 70 °C for 10 min to denature the protein. The protein samples (20 μL) were separated electrophoretically (NuPAGE 4–$12\%$ Bis-Tis GEL; Thermo Fisher Scientific, Waltham, MA, USA) and electrotransferred onto polyvinylidene difluoride membranes (IBLOT2, Thermo Fisher Scientific, Waltham, MA, USA). The membranes were blocked with TBST containing bovine serum albumin ($5\%$, Calbiochem, Billerica, MA, USA) for 2 h at room temperature. The samples were incubated with a primary antibody solution of anti-β-actin (1:1000, Abcam, Cambridge, MA, USA), anti-CD36 (1:1000, Abcam), anti-CDC42 (1:1000, Abcam), anti-JAK2 (1:1000, Abcam), and anti-STAT3 (1:1000, Abcam) overnight at 4 °C. The second antibody (1:1000, Abcam) solution was kept at room temperature for 2 h in a dark room. All the antibodies were diluted with TBST-$0.5\%$ BSA dilute solution. β-actin was used as an internal reference protein. An ECL working solution was then applied for 2 min to each blot. ImageJ was used to analyze the protein content’s luminous intensity (Bethesda Softworks LLT, Bethesda, MD, USA).
## 4.8. Data Analysis
Gene Ontology enrichment analysis was used to categorize DEPs into BP, MF, or CC. Metabolic pathways were identified by KEGG (KEGG, http://www.genome.jp/kegg/, accessed on 6 January 2023), Graphpad Prism (Graphpad Prism 8.3.0, Graphpad Software, Boston, MA, USA), or TB tools (v1.068, http://www.tbtools.com/, accessed on 6 January 2023), and MetaboAnalyst (https://www.metaboanalyst.com, accessed on 6 January 2023) visualized changes in proteins after different treatments. The PPI network of atherosclerosis-related DEPs was constructed using the String database (https://string-db.org/, accessed on 6 January 2023), with DEPs as the node and interactions as the edge. For routine data analysis, one-way analysis of variance (ANOVA) ($p \leq 0.05$) was used to determine statistical significance between processes on SPSS, and we averaged data from five measurements. We also present the data as mean ± standard deviation. For DEPs, it was concluded that there was a fold change > 1.2 or < 0.83 at a $p \leq 0.05.$
## 5. Conclusions
The ultrasonic hot alcohol reflux method combined with the anti-solvent method was used to extract sinapine from rapeseed for the first time. The sinapine yield of this method was significantly higher than that of the traditional hot alcohol reflux method, and the high-efficiency utilization of rapeseed by-products was realized. Our proteomic findings showed that sinapine reduced foam cell formation in human macrophages by reducing the expression of the scavenger receptor CD36. In addition, sinapine could increase the protein expression of CDC42, which promoted cholesterol efflux in foam cells. In addition, sinapine could also block the formation of foam cells by activating the JAK/STAT3 pathway, converting pro-inflammatory M1 to anti-inflammatory M2 macrophages. Therefore, sinapine is effective at alleviating macrophage foaming and may play a significant role in preventing heart disease in the future.
## References
1. He M., Nian B., Shi J., Sun X., Du R., Tan C.P., Xu Y.J., Liu Y.. **Influence of extraction technology on rapeseed oil functional quality: A study on rapeseed polyphenols**. *Food Funct.* (2022) **13** 270-279. DOI: 10.1039/D1FO01507A
2. Zago E., Lecomte J., Barouh N., Aouf C., Carré P., Fine F., Villeneuve P.. **Influence of rapeseed meal treatments on its total phenolic content and composition in sinapine, sinapic acid and canolol**. *Ind. Crop. Prod.* (2015) **76** 1061-1070. DOI: 10.1016/j.indcrop.2015.08.022
3. Li J., Guo Z.. **Concurrent extraction and transformation of bioactive phenolic compounds from rapeseed meal using pressurized solvent extraction system**. *Ind. Crops Prod.* (2016) **94** 152-159. DOI: 10.1016/j.indcrop.2016.08.045
4. Bhinu V.S., Schäfer U.A., Li R., Huang J., Hannoufa A.. **Targeted modulation of sinapine biosynthesis pathway for seed quality improvement in Brassica napus**. *Transgenic Res.* (2009) **18** 31-44. DOI: 10.1007/s11248-008-9194-3
5. Dubie J., Stancik A., Morra M., Nindo C.. **Antioxidant extraction from mustard (Brassica juncea) seed meal using high-intensity ultrasound**. *J. Food Sci.* (2013) **78** E542-E548. DOI: 10.1111/1750-3841.12085
6. Guo Y., Ding Y., Zhang T., An H.. **Sinapine reverses multi-drug resistance in MCF-7/dox cancer cells by downregulating FGFR4/FRS2α-ERK1/2 pathway-mediated NF-κB activation**. *Phytomedicine* (2016) **23** 267-273. DOI: 10.1016/j.phymed.2015.12.017
7. Gu Y., Zhang Y., Li M., Huang Z., Jiang J., Chen Y., Chen J., Jia Y., Zhang L., Zhou F.. **Ferulic Acid Ameliorates Atherosclerotic Injury by Modulating Gut Microbiota and Lipid Metabolism**. *Front. Pharmacol.* (2021) **12** 621339. DOI: 10.3389/fphar.2021.621339
8. Marino M., Del Bo C., Tucci M., Venturi S., Mantegazza G., Taverniti V., Møller P., Riso P., Porrini M.. **A mix of chlorogenic and caffeic acid reduces C/EBPß and PPAR-γ1 levels and counteracts lipid accumulation in macrophages**. *Eur. J. Nutr.* (2022) **61** 1003-1014. DOI: 10.1007/s00394-021-02714-w
9. Koelwyn G.J., Corr E.M., Erbay E., Moore K.J.. **Regulation of macrophage immunometabolism in atherosclerosis**. *Nat. Immunol.* (2018) **19** 526-537. DOI: 10.1038/s41590-018-0113-3
10. Bobryshev Y.V., Ivanova E.A., Chistiakov D.A., Nikiforov N.G., Orekhov A.N.. **Macrophages and Their Role in Atherosclerosis: Pathophysiology and Transcriptome Analysis**. *BioMed Res. Int.* (2016) **2016** 9582430. DOI: 10.1155/2016/9582430
11. Burgos V., Paz C., Saavedra K., Saavedra N., Foglio M.A., González-Chavarría I., Salazar L.A.. **Drimys winteri and isodrimeninol decreased foam cell formation in THP-1 derived macrophages**. *Food Chem. Toxicol.* (2020) **146** 111842. DOI: 10.1016/j.fct.2020.111842
12. Kobiyama K., Ley K.. **Atherosclerosis**. *Circ. Res.* (2018) **123** 1118-1120. DOI: 10.1161/CIRCRESAHA.118.313816
13. Maguire E.M., Pearce S.W.A., Xiao Q.. **Foam cell formation: A new target for fighting atherosclerosis and cardiovascular disease**. *Vasc. Pharmacol.* (2019) **112** 54-71. DOI: 10.1016/j.vph.2018.08.002
14. Tamminen M., Mottino G., Qiao J.H., Breslow J.L., Frank J.S.. **Ultrastructure of early lipid accumulation in ApoE-deficient mice**. *Arter. Thromb. Vasc. Biol.* (1999) **19** 847-853. DOI: 10.1161/01.ATV.19.4.847
15. Febbraio M., Hajjar D.P., Silverstein R.L.. **CD36: A class B scavenger receptor involved in angiogenesis, atherosclerosis, inflammation, and lipid metabolism**. *J. Clin. Investig.* (2001) **108** 785-791. DOI: 10.1172/JCI14006
16. Rahaman S.O., Lennon D.J., Febbraio M., Podrez E.A., Hazen S.L., Silverstein R.L.. **A CD36-dependent signaling cascade is necessary for macrophage foam cell formation**. *Cell Metab.* (2006) **4** 211-221. DOI: 10.1016/j.cmet.2006.06.007
17. Silverstein R.L., Febbraio M.. **CD36, a scavenger receptor involved in immunity, metabolism, angiogenesis, and behavior**. *Sci. Signal.* (2009) **2** re3. DOI: 10.1126/scisignal.272re3
18. Moore K.J., Sheedy F.J., Fisher E.A.. **Macrophages in atherosclerosis: A dynamic balance**. *Nat. Rev. Immunol.* (2013) **13** 709-721. DOI: 10.1038/nri3520
19. Febbraio M., Podrez E.A., Smith J.D., Hajjar D.P., Hazen S.L., Hoff H.F., Sharma K., Silverstein R.L.. **Targeted disruption of the class B scavenger receptor CD36 protects against atherosclerotic lesion development in mice**. *J. Clin. Investig.* (2000) **105** 1049-1056. DOI: 10.1172/JCI9259
20. Yang L., Sun J., Li M., Long Y., Zhang D., Guo H., Huang R., Yan J.. **Oxidized Low-Density Lipoprotein Links Hypercholesterolemia and Bladder Cancer Aggressiveness by Promoting Cancer Stemness**. *Cancer Res.* (2021) **81** 5720-5732. DOI: 10.1158/0008-5472.CAN-21-0646
21. Xin P., Xu X., Deng C., Liu S., Wang Y., Zhou X., Ma H., Wei D., Sun S.. **The role of JAK/STAT signaling pathway and its inhibitors in diseases**. *Int. Immunopharmacol.* (2020) **80** 106210. DOI: 10.1016/j.intimp.2020.106210
22. Razidlo G.L., Burton K.M., McNiven M.A.. **Interleukin-6 promotes pancreatic cancer cell migration by rapidly activating the small GTPase CDC42**. *J. Biol. Chem.* (2018) **293** 11143-11153. DOI: 10.1074/jbc.RA118.003276
23. Ferrer B., Prince L.M., Tinkov A.A., Santamaria A., Farina M., Rocha J.B., Bowman A.B., Aschner M.. **Chronic exposure to methylmercury enhances the anorexigenic effects of leptin in C57BL/6J male mice**. *Food Chem. Toxicol.* (2021) **147** 111924. DOI: 10.1016/j.fct.2020.111924
24. Bowe B., Gibson A.K., Xie Y., Yan Y., Donkelaar A.V., Martin R.V., Al-Aly Z.. **Ambient Fine Particulate Matter Air Pollution and Risk of Weight Gain and Obesity in United States Veterans: An Observational Cohort Study**. *Environ. Health Perspect.* (2021) **129** 47003. DOI: 10.1289/EHP7944
25. Chen K., Zhao Z., Wang G., Zou J., Yu X., Zhang D., Zeng G., Tang C.. **Interleukin-5 promotes ATP-binding cassette transporter A1 expression through miR-211/JAK2/STAT3 pathways in THP-1-dervied macrophages**. *Acta Biochim. Biophys. Sin.* (2020) **52** 832-841. DOI: 10.1093/abbs/gmaa071
26. Chadda R., Howes M.T., Plowman S.J., Hancock J.F., Parton R.G., Mayor S.. **Cholesterol-sensitive Cdc42 activation regulates actin polymerization for endocytosis via the GEEC pathway**. *Traffic* (2007) **8** 702-717. DOI: 10.1111/j.1600-0854.2007.00565.x
27. Mukhamedova N., Hoang A., Dragoljevic D., Dubrovsky L., Pushkarsky T., Low H., Ditiatkovski M., Fu Y., Ohkawa R., Meikle P.J.. **Exosomes containing HIV protein Nef reorganize lipid rafts potentiating inflammatory response in bystander cells**. *PLoS Pathog.* (2019) **15**. DOI: 10.1371/journal.ppat.1007907
28. Diederich W., Orsó E., Drobnik W., Schmitz G.. **Apolipoprotein AI and HDL(3) inhibit spreading of primary human monocytes through a mechanism that involves cholesterol depletion and regulation of CDC42**. *Atherosclerosis* (2001) **159** 313-324. DOI: 10.1016/S0021-9150(01)00518-4
29. Heasman S.J., Ridley A.J.. **Mammalian Rho GTPases: New insights into their functions from in vivo studies**. *Nat. Rev. Mol. Cell Biol.* (2008) **9** 690-701. DOI: 10.1038/nrm2476
30. Low H., Mukhamedova N., Capettini L., Xia Y., Carmichael I., Cody S.H., Huynh K., Ditiatkovski M., Ohkawa R., Bukrinsky M.. **Cholesterol Efflux-Independent Modification of Lipid Rafts by AIBP (Apolipoprotein A-I Binding Protein)**. *Arter. Thromb. Vasc. Biol.* (2020) **40** 2346-2359. DOI: 10.1161/ATVBAHA.120.315037
31. Liu Y., Tang C.. **Regulation of ABCA1 functions by signaling pathways**. *Biochim. Biophys. Acta* (2012) **1821** 522-529. DOI: 10.1016/j.bbalip.2011.08.015
32. Raptis L., Arulanandam R., Geletu M., Turkson J.. **The R(h)oads to Stat3: Stat3 activation by the Rho GTPases**. *Exp. Cell Res.* (2011) **317** 1787-1795. DOI: 10.1016/j.yexcr.2011.05.008
33. Aznar S., Valerón P.F., del Rincon S.V., Pérez L.F., Perona R., Lacal J.C.. **Simultaneous tyrosine and serine phosphorylation of STAT3 transcription factor is involved in Rho A GTPase oncogenic transformation**. *Mol. Biol. Cell* (2001) **12** 3282-3294. DOI: 10.1091/mbc.12.10.3282
34. Arulanandam R., Geletu M., Feracci H., Raptis L.. **Activated Rac1 requires gp130 for Stat3 activation, cell proliferation and migration**. *Exp. Cell Res.* (2010) **316** 875-886. DOI: 10.1016/j.yexcr.2009.10.017
35. Yang S., Yuan H.Q., Hao Y.M., Ren Z., Qu S.L., Liu L.S., Wei D.H., Tang Z.H., Zhang J.F., Jiang Z.S.. **Macrophage polarization in atherosclerosis**. *Clin. Chim. Acta* (2020) **501** 142-146. DOI: 10.1016/j.cca.2019.10.034
36. Petit V., Parcelier A., Mathé C., Barroca V., Torres C., Lewandowski D., Ferri F., Gallouët A.S., Dalloz M., Dinet O.. **TRIM33 deficiency in monocytes and macrophages impairs resolution of colonic inflammation**. *EBioMedicine* (2019) **44** 60-70. DOI: 10.1016/j.ebiom.2019.05.037
37. Shirai T., Hilhorst M., Harrison D.G., Goronzy J.J., Weyand C.M.. **Macrophages in vascular inflammation--From atherosclerosis to vasculitis**. *Autoimmunity* (2015) **48** 139-151. DOI: 10.3109/08916934.2015.1027815
38. Colin S., Chinetti-Gbaguidi G., Staels B.. **Macrophage phenotypes in atherosclerosis**. *Immunol. Rev.* (2014) **262** 153-166. DOI: 10.1111/imr.12218
39. Shapouri-Moghaddam A., Mohammadian S., Vazini H., Taghadosi M., Esmaeili S.A., Mardani F., Seifi B., Mohammadi A., Afshari J.T., Sahebkar A.. **Macrophage plasticity, polarization, and function in health and disease**. *J. Cell. Physiol.* (2018) **233** 6425-6440. DOI: 10.1002/jcp.26429
40. Wang Y., Liu J., Chen X., Sun H., Peng S., Kuang Y., Pi J., Zhuang T., Zhang L., Yu Z.. **Dysfunctional endothelial-derived microparticles promote inflammatory macrophage formation via NF-кB and IL-1β signal pathways**. *J. Cell. Mol. Med.* (2019) **23** 476-486. DOI: 10.1111/jcmm.13950
41. Martinez F.O., Gordon S.. **The M1 and M2 paradigm of macrophage activation: Time for reassessment**. *F1000Prime Rep.* (2014) **6** 13. DOI: 10.12703/P6-13
42. Wang N., Liang H., Zen K.. **Molecular mechanisms that influence the macrophage m1-m2 polarization balance**. *Front. Immunol.* (2014) **5** 614. DOI: 10.3389/fimmu.2014.00614
43. Hutchins A.P., Diez D., Miranda-Saavedra D.. **The IL-10/STAT3-mediated anti-inflammatory response: Recent developments and future challenges**. *Brief. Funct. Genom.* (2013) **12** 489-498. DOI: 10.1093/bfgp/elt028
44. Liu W., Meridew J.A., Aravamudhan A., Ligresti G., Tschumperlin D.J., Tan Q.. **Targeted regulation of fibroblast state by CRISPR-mediated CEBPA expression**. *Respir. Res.* (2019) **20** 281. DOI: 10.1186/s12931-019-1253-1
45. Nie C., Wang B., Fan M., Wang Y., Sun Y., Qian H., Li Y., Wang L.. **Highland Barley Tea Polyphenols Extract Alleviates Skeletal Muscle Fibrosis in Mice by Reducing Oxidative Stress, Inflammation, and Cell Senescence**. *J. Agric. Food Chem.* (2023) **71** 739-748. DOI: 10.1021/acs.jafc.2c05246
|
---
title: 'Silica-Fiber-Reinforced Composites for Microelectronic Applications: Effects
of Curing Routes'
authors:
- Imran Haider
- Iftikhar Hussain Gul
- Malik Adeel Umer
- Mutawara Mahmood Baig
journal: Materials
year: 2023
pmcid: PMC10003988
doi: 10.3390/ma16051790
license: CC BY 4.0
---
# Silica-Fiber-Reinforced Composites for Microelectronic Applications: Effects of Curing Routes
## Abstract
For curing of fiber-reinforced epoxy composites, an alternative to thermal heating is the use of microwave energy, which cures quickly and consumes less energy. Employing thermal curing (TC) and microwave (MC) curing methods, we present a comparative study on the functional characteristics of fiber-reinforced composite for microelectronics. The composite prepregs, prepared from commercial silica fiber fabric/epoxy resin, were separately cured via thermal and microwave energy under curing conditions (temperature/time). The dielectric, structural, morphological, thermal, and mechanical properties of composite materials were investigated. Microwave cured composite showed a $1\%$ lower dielectric constant, $21.5\%$ lower dielectric loss factor, and $2.6\%$ lower weight loss, than thermally cured one. Furthermore, the dynamic mechanical analysis (DMA) revealed a $20\%$ increase in the storage and loss modulus along with a $15.5\%$ increase in the glass transition temperature (Tg) of microwave-cured compared to thermally cured composite. The fourier transformation infrared spectroscopy (FTIR) showed similar spectra of both the composites; however, the microwave-cured composite exhibited higher tensile ($15.4\%$), and compression strength ($4.3\%$) than the thermally cured composite. These results illustrate that microwave-cured silica-fiber-reinforced composite exhibit superior electrical performance, thermal stability, and mechanical properties compared to thermally cured silica fiber/epoxy composite in a shorter time and the expense of less energy.
## 1. Introduction
Fiber-reinforced composites (FRC) are superior to other structural materials, due to their high specific strength and stiffness, high temperature, and fatigue resistance [1,2]. Glass-fiber-reinforced composites have outstanding properties, easy manufacturing, and reasonable cost compared to other fiber-reinforced composites [3,4]. The mechanical properties depend on the fiber as well as the matrix properties. The physical and mechanical properties of the thermoset resins, such as epoxy, depend on curing conditions [5]. The mechanical performance of FRC is influenced by the properties of the phases and their interactions. The interface and fiber-matrix bonding are quite important for enhancing the effective mechanical properties of FRC [6,7]. Amorphous materials (polymers, glasses, and metals) change their structural state below Tg, as physical aging or structural relaxation affect the properties of the material (mechanical, dielectric, and thermal behavior) [8]. The epoxy resin in a B-level semi-cured state can be cured under specific conditions to obtain high-quality composite products [9]. The curing degree of the resin has a very large effect on the properties of the composite [10,11,12].
In adhesively bonded structures, the quality of bonded composites has a strong relationship with the variabilities caused by process parameters, such as temperature, curing duration, and rate [13,14]. Usually, ambient, thermal curing, or a combination of both is used, depending on the requirement. As the demand for lighter, cheaper, and more compact electronic devices increases, there is a greater need to develop innovative and fast processing techniques with higher energy efficiency and reduced cure time. Longer curing times in thermal curing reduce the production throughput. In the electronics industry especially, the curing of thermoset systems has become a limiting factor in the production time; other processing methods are needed to be explored to reduce manufacturing costs or increase energy efficiency. One such method makes use of microwave radiation to heat epoxy resins [15]. The microwave curing process (MCP) can reduce production costs [16]. The MCP mechanism operates through dipolar loss, conduction loss, hysteresis loss, and Eddy current loss [17]. For thermosetting polymers, microwave curing involves the microwave radiation to heat the sample directly, which leads to efficient, fast, and selective heating, compared to conventional thermal curing, where the samples are heated indirectly. Microwave radiation also heats in a volumetric manner, thus leading to less temperature disparity and therefore a less severe temperature gradient within the material, leading potentially to less internal residual stress [18,19,20]. Microwave curing produces composites with mechanical properties comparable to those produced with the autoclave process while reducing $45\%$ processing time and $3\%$ energy consumption [21]. The curing kinetics has a significant influence on the macroscopic mechanical properties of composite materials [22,23,24]. Comparative studies of microwave-cured and thermally cured composites have reported contradictory results, while many authors have shown a reaction rate enhancement for microwave curing compared to thermal curing [25,26].
For many applications, the advantages of microwave curing must not be outweighed by a loss of thermal, chemical, or mechanical properties. Microwave heating is known as the most efficient volume-heating process due to its excellent depth of penetration in polymers [27]. Its process can be tuned with the use of highly efficient dielectric nanomaterials in polymers to boost micro-level heating at the molecular scale [28,29,30]. Dielectric nanomaterials can efficiently absorb radiation and convert it to molecular vibrations/rotations via dipole moments, mainly because of dipolar polarization. The vibrations can then add to the heating level in the polymer surrounding the nanomaterials by the friction mechanism [29].
The mechanical properties of certain microwave-cured materials are similar or even increased compared to conventional cures [27,28,29,30,31]. Generally, an increase in the glass transition temperature, Tg, has been reported [32]. However, depending on the type of curing agent used, Tg can be seen to decrease compared to thermally cured samples [33]. When composites are used as interconnections, printed circuit boards, and airplane skin materials, their dielectric properties become very important and must be determined before they can be used in these applications [34]. For polymer composites, the dielectric properties are associated with the component fractions [35,36] and they are widely used in electronics applications because of their good dielectric and mechanical properties. Silica fibers are used as reinforcement due to their small coefficient of thermal expansion, low thermal conductivity, superior mechanical strength, and excellent dielectric properties [37]. Compression molding, hand lay-up, spray-up, vacuum infusion, vacuum bagging, resin transfer molding (RTM), autoclave molding, filament winding, automated fiber placement (AFP), pultrusion, injection molding, vacuum forming, and stamp forming are most of the manufacturing techniques used in composite production. Prepegging produces a semi-finished composite by controlling the curing to fulfill the final requirement [38]. The curing process parameters have a crucial influence on the quality of the composite products [39]. Effects of curing degree, of Quartz fiber-boron phenolic composites, were investigated by the mechanical properties test, scanning electron microscope (SEM), and thermogravimetric analysis [40]. Fourier-transform infrared analysis shows no significant difference between the conventional and microwave cured samples [41]. The chemorheology of a filled epoxy system declares isothermal DSC measurements to be inadequate in the case of fast-curing thermosets [42]. Generally, a comparison between literature data is difficult due to the variety of curing agents used and their effects on the curing of the epoxy resin [43]. As preferred by many researchers, one of the most common methods is the mechanical testing of composite properties on different cure regimes, which can optimize the curing parameters [44].
Different composite curing studies have investigated various aspects of functional fiber-reinforced composites, but in microelectronics, low dielectric constant and loss factor of material is a matter of interest. In this study, multiple proportions of silica fiber/epoxy composite (prepregs) were cured thermally, via microwave energy, and their dielectric, structural, morphological, thermal, and mechanical properties were investigated.
## 2.1. Materials
Commercial silica fiber fabric BWT260-82 (ρ = 2.25 g/cc, avg. fiber dia = 6.86 μm, UTS = 1.8 GPa, SiO2 ~$95\%$, thickness = 0.21 mm) was obtained from Business and Engineering Trends, Punjab, Pakistan (BET Pakistan). The bonding adhesive used was a two-component commercial Epoxy RER160 and curing agent REH160 (RESSICHEM, Karachi, Pakistan). Commercial ethanol was used as a solvent in the resin matrix preparation.
## 2.2. Preparation of Composite Prepregs
Unidirectional woven silica fabric was cut into fabric plies (200 × 200 mm), washed with commercial ethanol, air-dried (30 min), and then oven-dried (30 min) at 120 °C. Lab environmental conditions were 25 °C and $36\%$ RH. In a 1000 mL glass beaker, epoxy (500 gm) and hardener (50 gm) were weighed (SHIMADZU-UW 3400 g, Kyoto, Japan), and the resin matrix was prepared with dropwise addition ethanol (30 mL). The resin matrix was poured on fabric plies and distributed with an applicator. By hand lay-up, the impregnated plies were stacked layer by layer between clean surfaces of mold plates and retained for 2 h. Stacked laminates (2 No.) were simultaneously processed to form composite prepregs (S0.3E0.7, S0.4E0.6, S0.5E0.5, S0.6E0.4, S0.7E0.3) where “S” refers to silica fabric, “E” refers to epoxy resin and the subscript refers to the wt. fraction. The fabrication process (raw material to prepreg to composite) is shown in Figure 1.
## 2.3. Curing of Composite Prepregs
Curing is an irreversible time-dependent and progressive molecular reaction process that requires heat energy, either by conventional heating or radiation, to raise molecular mobility [45]. Epoxy curing is an exothermic process that strengthens the material by cross-linking polymer chains [46]. Curing can be achieved by conventional heating, electron beams, chemical additives, or accelerated curing (e.g., microwave, radiofrequency, and ultra-violet radiation) [47]. The electromagnetic radiation cure mechanisms differ from thermal mechanisms in that curing is initiated by ionic or free radical intermediates formed by high-energy electromagnetic radiation [41].
The mechanism of microwave heating is shown in Figure 2. Epoxy resin is electrically neutral but dipolar in nature, as it possesses partial (+δ, −δ) charges. Microwave energy penetrated in a volumetric manner and increased the molecular mobility of resin due to periodic changing electrical fields (Figure 2). In this manner, the kinetic energy of molecules was increased and created a temperature gradient. Keeping in view the previous studies, the curing time and temperature were experimentally designed.
Five composite prepregs (of each proportion) were cured. “ T″ refers to thermally cured, “M” to microwave cured, and “tc” to cure time. In the thermal cure method, the heating oven was raised to 40 °C then composite prepregs were put in (heating rate was 10 °C/min) to reach the curing temperature (160 °C). Curing times were 240 s, 300 s, 360 s, 420 s, 480 s, 600 s, and 660 s. In the microwave cure cycle, a set of composite prepregs was heated (300 W) for 20 s, 30 s 45 s, 60 s, 75 s, 90 s, and 100 s.
## 2.4. Characterization
The dielectric constant (Ɛr) and dielectric loss (δ) were measured (S-band) at PNA Network 8362B (Agilent) with a 3 mm inner bore circular disc (2 × 6 mm). The spectrums of thermally and microwave-cured silica fiber/epoxy composites (5 mg) were recorded at a resolution of 4 cm−1 using an FTIR spectrometer (Spectrum 100, Perkin-Elmer, Waltham, MA, USA). The microscopic morphology was obtained through scanning electron microscopy (SEM) (JSM-6490A, EOL, Tokyo, Japan) at an accelerating voltage of 20 kV. Calorimetric measurements were conducted on a DSC 6000, Perkin-Elmer, USA differential scanning calorimeter (DSC). 7 mg powder was placed in hermetic sample pan in the DSC cell which was raised from 50 °C to 250 °C (nitrogen atmosphere and heating rate of 10 °C/min). The storage modulus (E′), loss modulus (E″), and damping factor (Tan D) were obtained through a dynamic mechanical analyzer Q800 DMA (1 Hz and the heating rate at 5 °C/min. The weight loss was determined (30–800 °C) by thermogravimetric analysis on TGA Q600 SDT, (TA instruments, SHIMADZU, Kyoto, Japan at a heating rate of 20 °C under nitrogen atmosphere. The tensile and compression strength were measured (ASTM D3039) on the universal testing machine AGX-Plus (SHIMADZU, Kyoto, Japan) test speed of 2 mm/min using a 50 N load.
## 3. Results and Discussion
Dielectric performance, structural, morphology, thermal properties, and mechanical properties are discussed in this section.
## 3.1. Dielectric Properties
The reflection of electromagnetic (EM) waves on the surface and the energy loss inside the material is due to the loss of EM waves. Equations [1] and [2] determine the relative dielectric constant (Ɛ) and dielectric loss (δ) of dielectric material toward an electromagnetic field. ƐI = Ct/ƐoA[1] δ = C/Coω[2] where ƐI = dielectric constant, δ = dielectric loss C = capacitance with dielectric, t = sample thickness, Ɛo = permittivity of air (8.85 × 10–12 F/m), A = cross-sectional area of sample, Co = capacitance without dielectric, and ω = angular frequency.
Dielectric permittivity describes how fast an electrical signal can transmit through a dielectric material and a low dielectric constant facilitates signal propagation across it. Dielectric constant and dielectric loss factors (with standard deviation, SD) of thermally cured and microwave-cured composites are mentioned in Table 1 and Table 2, respectively.
The Ɛr and δ of thermally cured composite were 3.90 and 0.053 (S0.3E0.7), 3.89 and 0.054 (S0.4E0.6), 3.80 and 0.050 (S0.5E0.5), 3.80 and 0.051 (S0.6E0.4), and 3.78 and 0.052 (S0.7E0.3). The dielectric properties (Ɛr and δ) decrease as the extent of the cure increases. From the results, both Ɛr and δ were decreased with the increase in cure time and decreased as the reaction progressed; the changes in the dielectric properties are related to the decreasing number of the dipolar groups in the reactants and the increasing viscosity [48].
The Ɛr and δ of microwave-cured composite were 3.81 and 0.046 (S0.3E0.7), 3.80 and 0.045 (S0.4E0.6), 3.77 and 0.043 (S0.5E0.5), 3.79 and 0.043 (S0.6E0.4) and 3.80 and 0.043 (S0.7E0.3). Since microwaves are high-energy waves, they increased the molecular mobility of epoxy molecules and raised the temperate. The rapid curing of epoxy resin was due to the epoxy–amine reaction progress to a greater extent than the epoxy–hydroxyl reaction [32]. Compared to the thermally cured composite, microwave-cured composite exhibited $1\%$ lower Ɛr and $21.5\%$ lower δ. It was due to the fast curing of thermosetting polymer with the progress of the epoxy–amine reaction.
Figure 3 shows the dielectric constant and loss of S0.5E0.5, which was considered an optimum among all proportions. During curing, dielectric properties change due to the disappearance of epoxy, amine groups, and charge migration of dipolar groups [32]. Remarkable dielectric properties of silica fiber ($x = 50$%) composites were noted (i.e., dielectric loss of microwave cured). Adding silica fiber (x > $50\%$), the dielectric properties were insignificantly varied; however, decreasing x < $50\%$, the Ɛr, and δ were marginally increased. Considering the dielectric properties, S0.5E0.5, the structure, morphology, and thermal and mechanical properties were analyzed. Samples were ethanol washed, cleaned, air dried (2 h), and then kept in a desiccator at room temperature until required for testing.
## 3.2.1. FTIR Analysis
The FTIR spectral analysis of cured silica fiber/epoxy composites is shown in Figure 4. The spectrum shows characteristic absorption peaks of the epoxide ring between 400 cm−1 and 4000 cm−1. The peak around 916 cm−1 assigned to the C–O deformation of the oxirane ring while the second band located at 1002 cm−1 represents the C–O–C stretching of the epoxy group and another band at 2922 cm−1, which is attributed to the C–H stretching of methylene group in oxirane. Reference peaks around 1631 cm−1 and 1504 cm−1 correspond to the C–C stretching vibration of aromatics, and the C=C stretching vibrations of -CH3, 2922 cm−1 are related to the C–H stretching vibration of CH2 and C–H stretching of –CH3, respectively [27,32]. A hydroxyl linkage is due to the -OH stretching band at 3426 cm−1. There is a decrease in the epoxy ring at 905 cm−1 and shows N–H compression at 1580 cm−1 confirming the reaction of epoxy resin through crosslinking of end epoxy groups with the hardener during curing [38]. The presence of an absorption peak at 1247 cm−1 represents the stretching of the C–N formed by cross-linking of the epoxy ring with an amine group hardener [18]. The peaks at 890 cm−1, 975 cm−1 and 1002 cm−1 are attributed to the presence of Si–OH compression, and Si–O–Si stretching vibrations. The spectra reveal the opening of the epoxide ring by an amine to form OH and CN groups and, conversion of epoxy groups. Comparable IR spectra of thermally and microwave-cured composites are found with similar functional groups, irrespective of the curing route.
## 3.2.2. Morphology
SEM images in Figure 5 represent the surface morphology, fabric–matrix interaction, and fracture propagation, of S0.5E0.5-T600 (thermally cured). In the thermally cured composite Figure 5a–c, the resin is attached to fiber surfaces; however, fewer fibers are detached. In thermal curing, the heat is transferred through conduction from the outward surface to the inward, and in some portions, there might be different energy available for cure.
The SEM of the S0.5E0.5-M90 composite, as shown in Figure 5d–f, were similar but had a better fiber-matrix interaction than the thermally cured composite. During microwave curing, however, the irradiation and convection result in localized curing (cross-linking) of the thermoset resin. Despite the few voids there, the fibers were seen to be firmly intact with epoxy.
## 3.3.1. Thermogravimetric Analysis (TGA)
Figure 6 shows the TGA curves where the red line shows a weight loss of thermally cured S0.5E0.5-T600 and the blue line represents microwave cured composite. The degradation was compared to $20\%$ weight loss of the cured composite samples. In the thermally cured composite, degradation temperature was around 535 °C. An abundant weight loss ($16.9\%$) was observed between 285 °C and 538 °C, which refers to pyrolysis and the maximum weight loss, was $27.1\%$. In the microwave-cured composite (S0.5E0.5-M90) there is a significant mass degradation that begins at 260 °C with a major weight loss ($15.4\%$) from 268 °C to 548 °C. With the final wt. loss of $24.5\%$, the microwave-cured composite required more degradation energy than thermally cured composites. In a sense, the degradation temperature of microwave cured was slightly higher than that of the thermally cured composite.
Due to high energy radiation, the microwave energy efficiently cured composite than thermal heating. In thermal curing, mainly the heat was transferred to the composite surface, which was reached inside through conduction. Silica fibers are heat-resistance materials in nature that also affect conduction. However, microwave irradiations and convection end in quicker cross-linking of the epoxy in the composite. Due to this ($2.6\%$) lower weight loss, microwave curing can be claimed as a superior route in manufacturing thermally stable composite.
## 3.3.2. Differential Scanning Calorimeter (DSC)
DSC evaluated the thermal stability and phase transitions in silica fiber/epoxy composites, represented in Figure 7, where an increase in the glass transition temperature (Tg) was observed. Exothermic transitions appeared due to the polymerization of epoxy-amine. In a high-temperature region, more energy was available for the etherification of -OH and epoxy groups, destruction of weak bonds, and homo-polymerization of epoxide rings [26]. DSC curves of S0.5E0.5-T300, S0.5E0.5-T420, and S0.5E0.5-T600 are shown in Figure 7a, where transitions around 80 °C and near 200 °C indicate curing of thermosetting resin conversion of low mol. wt. monomers into a macromolecular cross-linked network through complex transformations [39]. With increased curing time, the height of exothermic transition peaks is more reduced than S0.5E0.5-T300 and S0.5E0.5-T420. The energy evolution is expected due to the possibility of side reactions and homo-polymerization of residual epoxide groups. S0.5E0.5-T600 (10 min cured) is found with the highest degree of increase in Tg, expected to have intermolecular interactions.
DSC of microwave-cured composite, Figure 7b, shows an increasing Tg, where the polymerization is initiated, propagated, and completed rapidly. The thermogram [S0.5E0.5 -M30 and S0.5E0.5 -M60] illustrates transitions around 204 °C to 208 °C, where the composite was cured in a short time as microwave irradiations boosted the reaction rate by more energy penetration and rapid increase in the system’s viscosity [33]. Microwave radiation has a more complex effect on the curing process than the temperature increase. Quick epoxy-amine crosslinking in a short time and microwave energy restricted the mobility of polymer chains [13]. From the thermogram of S0.5E0.5-M90 (cured for 90 s), it looks fully cured, with good dielectric properties, like that epoxy composites with low concentrations of primary amines [36].
## 3.4. Mechanical Properties
Viscoelastic behavior, ultimate tensile strength (UTS), and ultimate compression strength (UCS) of silica fiber/epoxy composite are discussed in this section.
## 3.4.1. Dynamic Mechanical Analysis (DMA)
Viscoelastic properties of cured thermosetting polymers are often investigated using DMA as a standard technique suitable for application in a wide temperature range [48]. The thermomechanical spectra (cured composites) were obtained as shown in Figure 8a,b. Storage modulus (E′), loss moduli (E″), and mechanical damping (Tan D) as a function of temperature were presented. Storage moduli evaluate the material’s resistance to deformation while loss modulus quantifies the energy dissipation in the composites [49]. Tg of thermally cured composite (S0.5E0.5-T600) was 72.1 °C, storage modulus started dropping from 51.5 °C, and loss modulus from 64.0 °C.
Glass transition marks the thermal transition between glass and leathery regimes based on the peak of loss modulus [49]. The storage modulus also dropped before Tg; this referred to polymer chain mobility due to the increased thermal energy from the rising temperature. The chains in the epoxy matrix began to slide with higher degrees of freedom than below the first transition, usually referred to as beta transition. An increase in storage modulus, due to a change in Tβ, refers to an increase in the energy dissipation ability of the composite.
DMA of microwave cured S0.5E0.5-M90, in Figure 8b shows $15.5\%$ higher Tg (85.4 °C), and $20\%$ modulus (storage and loss) than thermally cured composite. The EM waves in the microwave oven periodically changed the electric field, which raised the molecular mobility. This molecular motion increased the kinetic energy and the temperature of epoxy resin, where the quick progression of epoxy–amine polymerization caused a good, cross-linked composite. The DMA refers to the fact that the microwave cured offers better resistance against the applied stresses, which revealed superior stability than the thermally cured composite.
## 3.4.2. Ultimate Tensile Strength (UTS)
Tensile stress–strain curves of thermally and microwave composites are shown in Figure 9. The UTS of S0.5E0.5-T600 was 75.79 MPa and that of S0.5E0.5-M90 was 89.68 MPa. UTS of the thermally cured was $15.4\%$ lower than the microwave-cured composite. From the stress–strain curves in Figure 9, the tough nature of the composite is evident, as the microwave-cured specimen fractured at higher stress. This difference occurred due to the curing method. The strength of the epoxy becomes greater and reaches the maximum upon efficient crosslinking during curing [32]. The higher UTS indicates that the microwave composite structure became comparatively tough compared to the thermally cured composite due to the increased cross-linked composite density.
Composites fracture mechanism describes that epoxy resin transmitted the resistive forces on fibers and fractured as seen in Figure 10. SEM images of the thermally cured composite are shown in the Figure 10a and the microwave-cured composite is shown in Figure 10b. The applied load started cracks from the bonded epoxy resin on the surface, then the fiber–matrix interface, and finally the fibers. This scheme contributed to the final fracture of the composite structure.
The microwave-cured composite broke at a comparatively higher tensile load which can be seen in the Figure 10b that the fracture originated from the area where the fiber-to-matrix bonding was weak. However, a fractured specimen of the microwave-cured specimen showed an even load distribution, which resulted in higher tensile strength, the benefit of microwave curing over thermal heating.
## 3.4.3. Ultimate Compression Strength (UCS)
In the compression test, the magnitude of opposing forces pushes inward on the specimen. The stress–strain curves showed a linear increase in the compression stress revealing a tough composite structure. The compression strength of S0.5E0.5-T600 (thermally cured) was 201.1 MPa and of S0.5E0.5-M90 (microwave cured) composite, was 210.25 MPa as shown in Figure 11. The compression strength of microwave composite was seen as $4.3\%$ higher than that of the thermally cured composite. Initially, up to $5\%$ strain, the thermally cured specimen offered more resistive forces, but then true stress–strain curves showed similar increasing trends for both samples. Microwave-cured specimens broke at a higher load than thermally cured composite.
Initially, S0.5E0.5-T600 seemed to be tougher than S0.5E0.5-M90 but as the curve progressed, its behavior was slightly changed. The applied force put a load on the surface and then transmitted it inward to the composite structure. In thermal curing, due to conduction, the composite outer surface was better cured than its inner structure. The strength of the epoxy became greater and reached maximum upon efficient crosslinking during curing [32]. Microwave-cured (S0.5E0.5-M90) was found with an improved compact structure than the thermally cured composite, as shown by the results and images.
SEM images of fractured specimens during the compression test, are shown in the Figure 12a,b. The low compression strength of thermally cured composite indicated that the fracture mechanism changed due to the lowering of the fiber–matrix interfacial shear in this specimen. The higher compression strength of S0.5E0.5-M90 revealed that microwave energy increased penetration and better cross-linking. It is clear from the SEM images that the thermally cured structure was more damaged than the microwave-cured composite. These results showed that microwave-assisted localized heating can improve the tensile and compressive strength of fiber-reinforced composites. However, change in curing conditions can improve mechanical properties with further experimentation.
## 4. Conclusions
The present work compared the effect of the curing route on the properties of silica fiber/epoxy composites. FTIR spectra revealed a similar chemical structure irrespective of the cure mechanism; however, microwave curing took a shorter time and less energy than thermal heating. A compact composite structure was seen where silica fibers were firmly embedded in epoxy resin. Composite cured through microwave energy was obtained with superior dielectric properties ($1\%$ lower dielectric constant, $21.5\%$ lower dielectric loss factor), higher thermal stability ($2.6\%$ lower wt. loss), and better mechanical properties (higher Tg, storage, loss modulus, $15.4\%$ higher tensile strength, and $4.3\%$ higher compression strength) than thermally cured composite. In microwave heating, high-energy electromagnetic radiations increase the molecular mobility of dipolar epoxy molecules and facilitate efficient curing. The increased kinetic energy of molecules provides a high temperature to put a complex effect on curing compared to conduction in thermal curing. This study suggests that microwave curing is a superior alternative to thermal curing for achieving low dielectric constant, dielectric loss factor, fair thermal stability, and mechanical properties of silica fiber/epoxy composites for microelectronics. Further research is recommended to optimize microwave curing conditions to attain even better results.
## References
1. Kistaiah N., Kiran C.U., Reddy G.R., Rao M.S.. **Mechanical characterization of hybrid composites: A review**. *J. Reinf. Plast. Compos.* (2014) **33** 1364-1372. DOI: 10.1177/0731684413513050
2. Sathishkumar T.P., Satheeshkumar S., Naveen J.. **Glass fiber-reinforced polymer composites—A review**. *J. Reinf. Plast. Compos.* (2014) **33** 1258-1275. DOI: 10.1177/0731684414530790
3. Stickel J.M., Nagarajan M.. **Glass Fiber-Reinforced Composites: From Formulation to Application**. *Int. J. Appl. Glass Sci.* (2012) **3** 122-136. DOI: 10.1111/j.2041-1294.2012.00090.x
4. Yıldız S., Karaağaç B., Güzeliş S.G.. **Utilization of glass fiber reinforced polymer wastes**. *Polym. Compos.* (2021) **42** 412-423. DOI: 10.1002/pc.25834
5. Benedetti A., Fernandes P., Granja J.L., Sena-Cruz J., Azenha M.. **Influence of temperature on the curing of an epoxy adhesive and its influence on bond behaviour of NSM-CFRP systems**. *Compos. Part B Eng.* (2016) **89** 219-229. DOI: 10.1016/j.compositesb.2015.11.034
6. Rutz B.H., Berg J.C.. **Electrostatic deposition of silica nanoparticles between E-glass fibers and an epoxy resin**. *J. Appl. Polym. Sci.* (2015) **132** 41516. DOI: 10.1002/app.41516
7. Godara A., Gorbatikh L., Kalinka G., Warrier A., Rochez O., Mezzo L., Luizi F., van Vuure A.W., Lomov S.V., Verpoest I.. **Interfacial shear strength of a glass fiber/epoxy bonding in composites modified with carbon nanotubes**. *Compos. Sci. Technol.* (2010) **70** 1346-1352. DOI: 10.1016/j.compscitech.2010.04.010
8. Quinson R., Perez J., Rink M., Pavan A.. **Components of non-elastic deformation in amorphous glassy polymers**. *J. Mater. Sci.* (1996) **31** 4387-4394. DOI: 10.1007/BF00356465
9. Budelmann D., Schmidt C., Meiners D.. **Prepreg tack: A review of mechanisms, measurement, and manufacturing implication**. *Polym. Compos.* (2020) **41** 3440-3458. DOI: 10.1002/pc.25642
10. Wachsman E.D., Frank C.W.. **Effect of cure history on the morphology of polyimide: Fluorescence spectroscopy as a method for determining the degree of cure**. *Polymer* (1988) **29** 1191-1197. DOI: 10.1016/0032-3861(88)90043-2
11. Uddin M.A., Alam M.O., Chan Y.C., Chan H.P.. **Adhesion strength and contact resistance of flip chip on flex packages––effect of curing degree of anisotropic conductive film**. *Microelectron. Reliab.* (2004) **44** 505-514. DOI: 10.1016/S0026-2714(03)00185-9
12. Montserrat S., Flaqué C., Pagès P., Málek J.. **Effect of the crosslinking degree on curing kinetics of an epoxy–anhydride system**. *J. Appl. Polym. Sci.* (1995) **56** 1413-1421. DOI: 10.1002/app.1995.070561104
13. Hart-Smith L.J., De Silva L.F.M., Öchsner A., Adams R.D.. **Adhesively bonded joints in aircraft structures**. *Handbook of Adhesion Technology* (2011) 1103-1149
14. So S., Rudin A.. **Effects of resin and curing parameters on the degree of cure of resole phenolic resins and woodflour composites**. *J. Appl. Polym. Sci.* (1990) **40** 2135-2149. DOI: 10.1002/app.1990.070401126
15. Benson R.C., Miragliotta J.A., Farrar D.. **Polymer adhesives and encapsulants for microelectronic applications**. *Johns Hopkins Apl Tech. Dig.* (2008) **28** 58-71
16. Park E.-T., Lee Y., Kim J., Kang B.-S., Song W.. **Experimental study on microwave-based curing process with thermal expansion pressure of PTFE for manufacturing carbon fiber/epoxy composites**. *Materials* (2019) **12**. DOI: 10.3390/ma12223737
17. Mishra R.R., Sharma A.K.. **Microwave–material interaction phenomena: Heating mechanisms, challenges and opportunities in material processing**. *Compos. Part A Appl. Sci. Manuf.* (2016) **81** 78-97. DOI: 10.1016/j.compositesa.2015.10.035
18. Liu L., Yi S., Ong L.S., Chian K.S.. **Finite element analysis for microwave cure of underfill in flip chip packaging**. *Thin Solid Film.* (2004) **462** 436-445. DOI: 10.1016/j.tsf.2004.05.060
19. Malik S., Gul I.H., Baig M.M.. **Hierarchical MnNiCo ternary metal oxide/graphene nanoplatelets composites as high rated electrode material for supercapacitors**. *Ceram. Int.* (2021) **47** 17008-17014. DOI: 10.1016/j.ceramint.2021.02.272
20. Baig M.M., Gul I.H.. **Transformation of wheat husk to 3D activated carbon/NiCo2S4 frameworks for high-rate asymmetrical supercapacitors**. *J. Energy Storage* (2021) **37** 102477. DOI: 10.1016/j.est.2021.102477
21. Li N., Li Y., Jelonnek J., Link G., Gao J.. **A new process control method for microwave curing of carbon fibre reinforced composites in aerospace applications**. *Compos. Part B Eng.* (2017) **122** 61-70. DOI: 10.1016/j.compositesb.2017.04.009
22. Mphahlele K., Ray S.S., Kolesnikov A.. **Cure kinetics, morphology development, and rheology of a high-performance carbon-fiber-reinforced epoxy composite**. *Compos. Part B Eng.* (2019) **176** 107300. DOI: 10.1016/j.compositesb.2019.107300
23. Baig M.M., Gul I.H., Ahmad R., Baig S.M., Khan M.Z., Iqbal N.. **One-step sonochemical synthesis of NiMn-LDH for supercapacitors and overall water splitting**. *J. Mater. Sci.* (2021) **56** 18636-18649. DOI: 10.1007/s10853-021-06431-x
24. Elizbit U., Liaqat Z., Hussain M.M., Baig M.A., Khan D.. **Preparation of porous ZIF-67 network interconnected by MWCNTs and decorated with Ag nanoparticles for improved non-enzymatic electrochemical glucose sensing**. *J. Korean Ceram. Soc.* (2021) **58** 598-605. DOI: 10.1007/s43207-021-00132-6
25. Joshi S.C., Bhudolia S.K.. **Microwave-thermal technique for energy and time efficient curing of carbon fiber reinforced polymer prepreg composites**. *J. Compos. Mater.* (2014) **48** 3035-3048. DOI: 10.1177/0021998313504606
26. De Vergara U.L., Sarrionandia M., Gondra K., Aurrekoetxea J.. **Polymerization and curing kinetics of furan resins under conventional and microwave heating**. *Thermochim. Acta* (2014) **581** 92-99. DOI: 10.1016/j.tca.2014.02.017
27. Rangari V., Bhuyan M., Jeelani S.. **Microwave curing of CNFs/EPON-862 nanocomposites and their thermal and mechanical properties**. *Compos. Part A Appl. Sci. Manuf.* (2011) **42** 849-858. DOI: 10.1016/j.compositesa.2011.03.014
28. Li A., Li W., Ling Y., Gan W., Brady M.A., Wang C.. **Effects of silica-coated carbon nanotubes on the curing behavior and properties of epoxy composites**. *RSC Adv.* (2016) **6** 23318-23326. DOI: 10.1039/C5RA25182F
29. Zhao B., Hardiman M., Ryan K.M., O’Reilly E., McCarthy C.. **Formation of reworkable nanocomposite adhesives by dielectric heating of epoxy resin embedded Fe**. *CrystEngComm* (2016) **18** 6096-6101. DOI: 10.1039/C6CE01359G
30. Kim N.D., Metzger A., Hejazi V., Li Y., Kovalchuk A., Lee S.-K., Ye R., Mann J.A., Kittrell C., Shahsavari R.. **Microwave heating of functionalized graphene nanoribbons in thermoset polymers for wellbore reinforcement**. *ACS Appl. Mater. Interfaces* (2016) **8** 12985-12991. DOI: 10.1021/acsami.6b01756
31. Alfano M., Pagnotta L.. **An investigation of the mechanical behaviour of vinyl ester resins cured by microwave irradiation**. *Mater. Des.* (2009) **30** 4537-4542. DOI: 10.1016/j.matdes.2009.05.002
32. Yarlagadda K.P., Hsu S.-H.. **Experimental studies on comparison of microwave curing and thermal curing of epoxy resins used for alternative mould materials**. *J. Mater. Process. Technol.* (2004) **155** 1532-1538. DOI: 10.1016/j.jmatprotec.2004.04.248
33. Boey F., Yap B.. **Microwave curing of an epoxy-amine system: Effect of curing agent on the glass-transition temperature**. *Polym. Test.* (2001) **20** 837-845. DOI: 10.1016/S0142-9418(00)00070-2
34. Yao L., Wang X., Liang F., Wu R., Hu B., Feng Y., Qiu Y.. **Modeling and experimental verification of dielectric constants for three-dimensional woven composites**. *Compos. Sci. Technol.* (2008) **68** 1794-1799. DOI: 10.1016/j.compscitech.2008.01.014
35. Mäder E., Gao S.-L., Plonka R., Wang J.. **Investigation on adhesion, interphases, and failure behaviour of cyclic butylene terephthalate (CBT®)/glass fiber composites**. *Compos. Sci. Technol.* (2007) **67** 3140-3150. DOI: 10.1016/j.compscitech.2007.04.014
36. Ramajo L., Reboredo M., Castro M.. **Dielectric response and relaxation phenomena in composites of epoxy resin with BaTiO**. *Compos. Part A Appl. Sci. Manuf.* (2005) **36** 1267-1274. DOI: 10.1016/j.compositesa.2005.01.026
37. Xie H.Q., Zhang S., Xie D.. **An efficient way to improve the mechanical properties of polypropylene/short glass fiber composites**. *J. Appl. Polym. Sci.* (2005) **96** 1414-1420. DOI: 10.1002/app.21575
38. Liu Z., Zhang J., Tang L., Zhou Y., Lin Y., Wang R., Kong J., Tang Y., Gu J.. **Improved wave-transparent performances and enhanced mechanical properties for fluoride-containing PBO precursor modified cyanate ester resins and their PBO fibers/cyanate ester composites**. *Compos. Part B Eng.* (2019) **178** 107466. DOI: 10.1016/j.compositesb.2019.107466
39. Zhu L., Wang Z., Rahman M.B., Shen W., Zhu C.. **The Curing kinetics of e-glass fiber/epoxy resin prepreg and the bending properties of its products**. *Materials* (2021) **14**. DOI: 10.3390/ma14164673
40. Fu H., Qin Y., He X., Meng X., Zhong Y., Zou Z.. **Effect of curing degree on mechanical and thermal properties of 2.5 D quartz fiber reinforced boron phenolic composites**. *e-Polymers* (2019) **19** 462-469. DOI: 10.1515/epoly-2019-0048
41. Johnston K., Pavuluri S., Leonard M., Desmulliez M., Arrighi V.. **Microwave and thermal curing of an epoxy resin for microelectronic applications**. *Thermochim. Acta* (2015) **616** 100-109. DOI: 10.1016/j.tca.2015.08.010
42. Spoelstra A., Peters G., Meijer H.. **Chemorheology of a highly filled epoxy compound**. *Polym. Eng. Sci.* (1996) **36** 2153-2162. DOI: 10.1002/pen.10612
43. Boey F., Yap B., Chia L.. **Microwave curing of epoxy-amine system—Effect of curing agent on the rate enhancement**. *Polym. Test.* (1999) **18** 93-109. DOI: 10.1016/S0142-9418(98)00014-2
44. Koushyar H., Alavi-Soltani S., Minaie B., Violette M.. **Effects of variation in autoclave pressure, temperature, and vacuum-application time on porosity and mechanical properties of a carbon fiber/epoxy composite**. *J. Compos. Mater.* (2012) **46** 1985-2004. DOI: 10.1177/0021998311429618
45. Michels J., Cruz J.S., Christen R., Czaderski C., Motavalli M.. **Mechanical performance of cold-curing epoxy adhesives after different mixing and curing procedures**. *Compos. Part B Eng.* (2016) **98** 434-443. DOI: 10.1016/j.compositesb.2016.05.054
46. Ellis B.. *Chemistry and Technology of Epoxy Resins* (1993)
47. Endruweit A., Johnson M., Long A.. **Curing of composite components by ultraviolet radiation: A review**. *Polym. Compos.* (2006) **27** 119-128. DOI: 10.1002/pc.20166
48. Rusmirović J., Galović J., Kluz M., Perković S., Brzić S., Bogosavljević M., Milojković A., Kovačević T.. **Using potential of filament-wound carbon/glass polymeric composites as rocket motor thermal insulation**. *Polym. Compos.* (2021) **29** S1541-S1554. DOI: 10.1177/09673911211056787
49. Youssef G., Pessoa G., Nacy S.. **Effect of elevated operating temperature on the dynamic mechanical performance of E-glass/epoxy composite**. *Compos. Part B Eng.* (2019) **173** 106937. DOI: 10.1016/j.compositesb.2019.106937
|
---
title: Early Gastrointestinal Neuropathy Assessed by Wireless Motility Capsules in
Adolescents with Type 1 Diabetes
authors:
- Vinni Faber Rasmussen
- Mathilde Thrysøe
- Páll Karlsson
- Esben Thyssen Vestergaard
- Kurt Kristensen
- Ann-Margrethe Rønholt Christensen
- Jens Randel Nyengaard
- Astrid Juhl Terkelsen
- Christina Brock
- Klaus Krogh
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10003990
doi: 10.3390/jcm12051925
license: CC BY 4.0
---
# Early Gastrointestinal Neuropathy Assessed by Wireless Motility Capsules in Adolescents with Type 1 Diabetes
## Abstract
Background: To assess the prevalence of objective signs of gastrointestinal (GI) autonomic neuropathy (AN) in adolescents with type 1 diabetes (T1D). In addition, to investigate associations between objective GI findings and self-reported symptoms or other findings of AN. Methods: Fifty adolescents with T1D and 20 healthy adolescents were examined with a wireless motility capsule to assess the total and regional GI transit times and motility index. GI symptoms were evaluated with the GI Symptom Rating Scale questionnaire. AN was evaluated with cardiovascular and quantitative sudomotor axon reflex tests. Results: There was no difference in GI transit times in adolescents with T1D and healthy controls. Adolescents with T1D had a higher colonic motility index and peak pressure than the controls, and GI symptoms were associated with low gastric and colonic motility index (all $p \leq 0.05$). Abnormal gastric motility was associated with the duration of T1D, while a low colonic motility index was inversely associated with “time in target range” for blood glucose (all $p \leq 0.01$). No associations were found between signs of GI neuropathy and other measures of AN. Conclusions: Objective signs of GI neuropathy are common in adolescents with T1D and it seems to require early interventions in patients at high risk of developing GI neuropathy.
## 1. Introduction
Gastrointestinal (GI) symptoms are prevalent in individuals with type 1 diabetes (T1D) and negatively impair their quality of life [1,2]. Common symptoms include abdominal pain, dyspepsia, reflux, poor appetite, postprandial fullness, swallowing difficulties, nausea, vomiting, diarrhea, chronic constipation, and fecal incontinence [1,3,4]. Usually, severe GI symptoms appear after a minimum of 10 years of having the disease, making early intervention crucial for preventing further progression and worsening of symptoms. Although GI symptoms are also common in healthy adolescents, they are not more frequent in adolescents with T1D [1,4]. Autonomic neuropathy and hyperglycemia are the most widely recognized contributing factors to GI symptoms in T1D [5,6]. Therefore, the identification of subclinical signs of GI neuropathy could be of clinical importance for the individual patient. By identifying risk factors for subclinical GI neuropathy, targeted investigations can be performed in high-risk adolescents without exposing the entire population of young individuals with T1D to time-consuming clinical tests. In adolescents with T1D, GI symptoms are associated with increased HbA1c, duration of diabetes, insulin requirement, body mass index (BMI), poor socioeconomic status, daily cigarette smoking, and an irregular meal pattern [1,4]. The relationship between these factors and early objective signs of GI neuropathy is yet to be determined. Potential early interventions such as improved blood glucose control and gastroparesis treatment may have positive outcomes [7].
Up to $75\%$ of adolescents with T1D may show signs of autonomic dysfunction, depending on the diagnostic methods and definitions used [8,9,10]. However, it is unclear how accurately tests for neuropathy in other organs can predict neuropathy in the GI tract, which also includes damage to the enteric nervous system. Because GI neuropathy in T1D potentially affects all regions of the GI tract, it is essential to perform a panenteric assessment of GI function [11,12]. While several methods qualify for this [13], few have been applied to adolescents with T1D. Perano et al. performed a [13]C-octanoate breath test to evaluate the relationship between gastric emptying time and postprandial glycemia, but not for evaluating the signs of neuropathy [14].
Capsule-based methods are generally considered the most appropriate for studying GI neuropathy in diabetes and other severe motility disorders [11,13]. They provide a minimally invasive assessment of pan-enteric motility, including total and regional GI transit times and contractility patterns [11,13]. The wireless motility capsule (WMC) (Smartpill Monitoring System, Medtronic, MN, USA) has been tested in people with diabetes and is commercially available. It is easy to use and has robust normative data for healthy adults [11,15]. This argues for the use of WMC as a suitable method for assessing GI neuropathy in both research and clinical settings for adolescents with T1D.
The aims of the present study were to [1] investigate the prevalence of signs of GI neuropathy in a selected group of adolescents with T1D and to compare them to healthy age-matched participants; [2] investigate the association between GI transit times and motility index to self-reported GI symptoms, as well as findings on tests for cardiovascular and sudomotor function; and [3] identify potential risk factors for early GI autonomic neuropathy in adolescents with T1D.
## 2.1. Study Population
The study was a part of the T1DANES study. Adolescents aged 15 to less than 19 years old with T1D and a history of diabetes for at least five years were recruited from outpatient clinics at Danish hospitals in Randers, Aarhus, and Aalborg, as well as the Steno Diabetes Centre Aarhus and North Denmark, from August 2020 to December 2021. Exclusion criteria were participants who were prescribed medication or who had other diseases that could affect the central or peripheral nervous system. Additionally, a negative COVID-19 test result within 72 h before the test day was required. Participants with well-treated autoimmune disorders such as thyroid disease or celiac disease, or complications to diabetes such as microalbuminuria were accepted and included in the study. Healthy age-matched controls were recruited through notices at boarding and secondary schools.
Information about age, gender, diabetes duration, total daily insulin dose, basal insulin dose, time-in-range, glucose-monitoring system, HbA1c values over the last five years, events of severe hypoglycemia and ketoacidosis during the last year, and the last test results of retinopathy and nephropathy (urine albumin/creatinine ratio) was obtained from the patients’ clinical electronic records covering all outpatient visits.
Informed oral and written consent was obtained from each participant and the accompanying parents. All procedures in the study protocol were approved by the Danish Ethics Committee (Project ID M-2019-211-19) and Legal Office, Central Denmark Region [1-16-02-42-21]. Data were safely stored in REDCap, a secure web application for online surveys and databases.
## 2.2. Clinical and Biochemical Data Collection
All of the available clinical and biochemical data were extracted from the electronic hospital records of the adolescents, and any missing data were collected on the test day. A blood sample was taken from each participant for later analysis. The blood from healthy controls was analyzed for HbA1c and lipid profile. Participants arrived at the research facility at Aarhus University Hospital in the morning, after fasting for at least 6 h for food and nutrient-containing liquid (milk) and 2 h for water. Caffeine (coffee and cola) and alcohol were not allowed for 12 h before the 08:00 a.m. meeting time. The weight and height of each participant were measured, and the BMI (kg/m2) was calculated. Hip and waist measurements were taken using a measuring tape, and blood pressure and heart rate were recorded using an automatic blood pressure monitor. The puberty stage was assessed by showing the participants pictures of different Tanner stages and asking them to point to the relevant stage. The participants also self-reported their activity levels, alcohol consumption, and smoking status.
## 2.3. Questionnaires
The GI Symptom Rating Scale (GSRS), a 15-item instrument with questions into five symptom domains, reflux, abdominal pain, indigestion, diarrhea, and constipation, was filled out online, at home, before the study day [16].
## 2.4. Wireless Motility Capsule
WMC was used to describe gastric motility and regional GI transit times. Prior to the test day, the adolescents with T1D were informed to take their regular basal insulin dose in the morning and bring their blood glucose levels within the target range (4–8 mmol/L).
On the test day, the participants consumed a standard meal (SmartBar), and they were allowed to drink a maximum of 200 mL of water to swallow the capsule. When ingested, the capsule travelled through the GI tract and transmitted information about temperature, pH, and pressure to a receiver worn on the abdomen. The patients carried the receiver until the capsule was expelled, normally within one to five days. The imported data provided valid information about the segmental transit times and motility index [13,15,17,18]. Data from the adolescents with T1D were compared to previously published normal ranges for transit times in adults (upper limits: gastric emptying 5 h, small bowel transit 8 h, colonic transit 50.5 h [19]) and to the motility index in adults [11]. In addition, data from the adolescents with T1D were compared to data from healthy adolescents in the present study.
## 2.5. Autonomic Tests
The evaluation of cardiovagal function was carried out using the following cardiovascular reflex tests (CARTs) [20]: [1] deep breathing test, which measures the delta heart rate and the difference in heart rate between expiration and inspiration; [2] the Valsalva maneuver (VM) ratio, obtained from forcefully exhaling with expiratory pressure of 40 mmHg for 15 s in a 20-degree tilt position; and [3] the response to standing, measured using the 30:15 ratio. The autonomic tests were performed in a standardized manner using a Task Force Monitor® (CNSystems Medizintechnik AG, Graz, Austria), obtained from a three-channel electrocardiogram. Real-time respiratory pressure and volume were measured by blowing into a mouthpiece connected to a digital transducer.
A quantitative sudomotor reflex test (QSART) [21] was performed on the right side of the body at four locations: the forearm, proximal leg, distal leg, and on the foot, under a heat lamp so as to maintain a constant temperature. The nerves were stimulated with acetylcholine, and the test was conducted using WR TestWorks Q-Sweat Quantitative Sweat Measurement System (WR Medical Electronics Co., Maplewood, MN, USA).
The data from the autonomic testing will be presented in a separate publication.
## 2.6. Statistical Analysis
All statistical analyses were conducted using the software program R (R Core Team [2022], Vienna, Austria). The normality of the variables in Table 1 were tested using the Shapiro–Wilk test and QQ plots. Descriptive data are presented as the mean (SD) for normally distributed continuous variables, median (range) for non-normally distributed continuous variables, and number (%) for categorical variables. The groups were compared using Student’s t-test for continuous variables with normal distribution, Wilcoxon rank-sum test for non-parametric continuous variables, and Fisher’s exact test for categorical variables. p-Values of less than 0.05 were considered statistically significant. The abnormality of a diagnostic test was defined as below the 5th (or above the 95th) percentile of the data obtained from the control subjects. Linear regression (lm() function in R) was applied to analyze the associations, and ROC analysis with area under the curve (AUC) was used to evaluate the usefulness of the tests as screening methods, with AUC values of 0.5 indicating no discrimination, 0.5–0.7 poor discrimination, 0.7–0.8 acceptable discrimination, 0.8–0.9 excellent discrimination, and greater than 0.9 outstanding discrimination.
## 3. Results
Fifty-five adolescents with T1D and twenty-one healthy adolescents were included in this study. A flowchart of the selection process is shown in Figure 1. One healthy adolescent was excluded due to having a whole gut transit time exceeding five days. The median duration of T1D among 55 adolescents was 8.5 (range: 5–17 years), and their HbA1c was 61 mmol/mol (range: 41–93 mmol/mol). See Table 1 for additional details on the characteristics of the two groups.
Overall, there was no difference in the total or regional GI transit times between healthy adolescents and those with T1D (Table 2). Four adolescents with T1D ($8\%$) had one or more segmental transit times that exceeded the upper $95\%$ percentile range of the data collected from our healthy controls in the study. This included two with a prolonged gastric emptying time, one with a prolonged small intestinal transit time, and one with a prolonged colonic transit time.
Peak pressure amplitude ($$p \leq 0.010$$) and motility index ($$p \leq 0.022$$) in the colon were higher in the adolescents with T1D than in the controls (as shown in Table 2). Six adolescents had a low motility index of the small intestine (below the 5th percentile of healthy adolescents), and four adolescents had a low motility index in the colon.
When comparing healthy adolescents to those with T1D, there was no difference in total GSRS score (median (range) 1.35 (1–2.87) vs. 1.24 (1–2.97), $$p \leq 0.09$$).
A low colonic motility index was associated with both severe diarrhea ($$p \leq 0.042$$) and indigestion ($$p \leq 0.038$$), as assessed by GSRS. Additionally, a higher total GSRS score was associated with both a low gastric motility index ($$p \leq 0.047$$) and low colon motility index ($$p \leq 0.033$$). The ROC analysis of the total GSRS score as a screening tool for predicting abnormal GI motility showed an AUC of 0.67, which was considered “acceptable”.
No associations were found between the GI parameters (transit times, motility index, and GSRS scores) and autonomic test results (CARTs and QSART) (data not shown, all $p \leq 0.05$).
When evaluating the clinical factors, we found that diabetes duration and “time in range” for blood glucose were risk factors. A longer gastric emptying time ($$p \leq 0.004$$) and higher gastric motility index ($$p \leq 0.009$$) were associated with the time since diagnosis of T1D. The colon motility index was inversely associated with “time in range” for blood glucose ($$p \leq 0.003$$).
Of the four included adolescents with T1D who had microvascular complications (as listed in Table 1), only one had an abnormal finding (prolonged colonic transit time) on the WMC. The only included adolescents with T1D and well-treated celiac disease had a prolonged gastric emptying time.
## 4. Discussion
GI symptoms are a major concern for many individuals with T1D [22,23]. The present study is the first to show that objective signs of gastroenteric neuropathy are prevalent in adolescents with T1D and a history of diabetes for at least five years. Typically, major changes in GI motility are indicated by abnormal gastric emptying or altered small intestinal or colonic transit times, but this was not observed in this study. Instead, discrete changes in contractility patterns were found more in adolescents with T1D, suggesting early stages of GI neuropathy. This is supported by the association we found between objective signs of GI neuropathy, the length of time since the onset of T1D, and poor metabolic control.
The neuronal control of GI motility is complex. The frequency of contractions is controlled by the interstitial cells of the Cajal, also named GI pacemaker cells. Interneurons within the enteric nervous system connect the Cajal cells with the smooth muscle cells of the GI tract. Most excitatory interneurons are cholinergic. The sympathetic nervous system inhibits GI motility in non-sphincteric regions, and the parasympathetic nervous system enhances it via the vagal nerves or sacral segments of the spinal cord. This difference in innervation is probably important for the understanding of our results. *In* general, GI motility indices were higher in our group of adolescents with T1D compared with the healthy controls. This could indicate a loss of inhibitory control from the sympathetic nervous system. Conversely, a lower “time in range” for blood glucose was associated with a higher motility index in the colon, potentially due to elevated blood glucose on the day of investigation or a progression of GI neuropathy affecting parasympathetic nerves. Progression to neuropathy of cholinergic nerves seems likely and may be of clinical relevance, as diarrhea and indigestion were associated with reduced motility in the colon. This would either imply reduced activity of the Cajal cells or neuropathy of cholinergic nerves. In addition to neuropathy, factors such as morphological changes in the gut wall and disturbed blood glucose control likely contribute to symptoms [23].
## 4.1. Methods for Assessment of Enteric Neuropathy
Our findings, along with previous studies, suggest that the GSRS questionnaire could serve as a screening tool for gastroenteric neuropathy. Although several objective methods exist for evaluating gastroenteric neuropathy, most focus on gastric emptying [13]. However, as diabetes can impact the entire GI tract, a pan-enteric evaluation is necessary [24]. Our study supports this by showing that different segments of the GI tract can be affected and abnormalities can vary from patient to patient. One important limitation of WMC as a diagnostic method for autonomic neuropathy is that it only provides information on intestinal pressure rather than detecting peristaltic waves and responses to nerve stimuli [13]. However, WMC has been proven to be highly sensitive in symptomatic pediatric patients [25].
The 11C-donepezil PET/CT scan visualizes cholinergic innervation of the GI tract and has shown reduced cholinergic activity in the gut of patients with T1D [13]. Unfortunately, this test is only available at a few centers and is not suitable for children as a result of radiation exposure.
Another option for adults is high-resolution manometry, which assesses antro-duodenal or colonic motor function. This test involves the placement of a catheter through colonoscopy, which can disrupt normal physiology. The Motilis 3D-Transit system is a superior alternative, as it provides exact information about the location of the capsule in the GI tract though an ambulatory electromagnetic wireless capsule system. Although the Motilis 3D-Transit system has been trialed in children [26], it is not yet clinically available and is only offered at specialist centers [27].
Vagal nerve function can also be assessed by measuring the pancreatic polypeptide and ghrelin in response to Sham feeding. Previous studies have shown impaired pancreatic polypeptide response in diabetic gastroparesis, indicating dysfunction of the vagal nerve [28].
## 4.2. Autonomic Evaluation
In clinical practice, tests of cardiovascular reflexes are sometimes taken as a surrogate for tests of enteric neuropathy, but the scientific validity of this approach is questionable. While some data have shown an association between cardiac vagal tone and GI function, others have questioned this relationship [15,29,30]. Our study found that cardiac vagal function, as assessed by CARTs, was not associated with either GI transit times or the motility index. Despite the fact that the heart and the GI tract are regulated by sympathetic and parasympathetic nervous systems, there are differences in the length and physiology of the neurons responsible for their regulation [31]. This may lead to differences in how these nerves are affected. In addition, the sympathetic and parasympathetic nerves exert their effects on the GI tract indirectly through the enteric nervous system.
## 4.3. Clinical Implications of the Study
GI symptoms are common in people with T1D, but their correlation with measurable GI parameters is inconsistent [15]. Previous studies found that a longer colonic transit was correlated with constipation and postprandial fullness, while a decreased colonic motility index was correlated with diarrhea and decreased bloating in adults [15]. Our study also found that a low colonic motility index was associated with severe diarrhea and indigestion. The association between objective signs of gastroenteric neuropathy and time since the onset of T1D or suboptimal control of blood glucose suggests that early intervention is possible. As objective signs of GI neuropathy were not associated with other measures of autonomic neuropathy, patients at risk should be evaluated with specific GI tract methods. As a relatively simple, valid, and minor invasive method, WMC could serve that purpose. From adult patients with T1D, it is known that gastrointestinal dysmotility is common in the absence of GI symptoms [30].
Performing diagnostic tests on adolescents with T1D might be time-consuming and costly, and for that reason only recommended for adolescents with high-risk profiles such as long diabetes duration, poor metabolic control, abnormal screening tests, and/or GI symptoms (questionnaires). Annual screening may help with early detection. A widely recognized tool for monitoring bowel function is the Bristol Stool Chart (BSC), which has been shown to be associated with colonic transit in both healthy people and people with functional constipation [32]. However, to the best of our knowledge, it has not yet been shown in people with diabetes, who very often have extremely variable stool consistency within the same patient. Further research into screening methods and interventions is still needed. Early intervention will mainly be the optimization of glycemic controls, but detailed dietary recommendations could also be offered to some people with diabetes [7,33]. Likewise, improving gastric emptying time in patients with gastroparesis can potentially improve glycemic control [34].
Our findings on the high occurrence of GI symptoms among adolescents with T1D emphasize the need for addressing these symptoms in their treatment. Further research is necessary to explore the effectiveness of pharmacological interventions, such as prokinetics, laxatives, enzyme supplements, and antidiarrheal products, for managing motility dysfunction. Longitudinal studies evaluating the effect of tight glycemic control and lifestyle changes on GI symptoms are also needed. It has been reported that more pronounced acidity is present in people with T1D and peripheral neuropathy [35]. However, in our study, no differences in mean pH values in the different GI segments were found. The treatment of *Helicobacter pylori* gastritis has been attempted in children with T1D, but it was not found to improve metabolic control [36]. Therefore, this issue seemed to not require a special focus.
Gastroparesis increases glucose variability, especially during the night [37]. Thus, unexplained changes in glucose profiles should lead to investigations of GI function. Continuous glucose monitoring (CGM) has been suggested as a possible useful screening tool for detecting delayed gastric emptying [37], but further research is needed. Our results suggest that dysregulated adolescents with a reduced time in range and a longer diabetes duration seem to require additional attention. Further research of risk profiles for GI neuropathy, i.e., genetic, immunological, and microbiota profiles, should be performed [29]. Another subgroup that requires extra attention is adolescents with both T1D and celiac disease, as celiac disease increases risk for autonomic neuropathy by four times compared with the general population [38,39]. Our only participant with T1D and celiac disease supported this by having prolonged gastric emptying time.
## 4.4. Strengths and Limitations
The strength of our study is that it is the first to assess pan-intestinal function in adolescents with T1D. All tests were performed in a standardized manner by only two healthcare professionals, and our main findings were built on objective measurements rather than subjective reporting.
The limited population size and the higher proportions of female participants in the control group were limitations in our study. In addition, blood glucose levels were not checked during the test days, which could have affected the results, as hyperglycemia is known to slow gastric emptying [40]. However, the adolescents with T1D were informed to keep their blood glucose level in the target range, and they corrected their blood glucose during the test day if their CGM or insulin pump sounded an alarm, which reduced the impact of missing blood glucose measurements. Although HbA1c is not the correct factor to use, we did not find any association between HbA1c and gastric emptying. A limitation of WMC is that it lacks the capacity to detect abnormally rapid gastric emptying, which is a problem in the context of other methods, showing that rapid gastric emptying is frequently observed in adolescents with T1D [14], with important implications for postprandial glycaemic control. In addition, WMC gastric emptying times showed $52.8\%$ agreement with scintigraphy, which is regarded as the gold-standard test [41].
In conclusion, objective signs of GI neuropathy are common in adolescents with T1D and are associated with the duration of disease and poor control of blood glucose. This calls for early intervention in patients at high risk of developing GI neuropathy.
## References
1. Lodefalk M., Åman J.. **Gastrointestinal symptoms in adolescents with type 1 diabetes**. *Pediatr. Diabetes* (2010) **11** 265-270. DOI: 10.1111/j.1399-5448.2010.00664.x
2. Talley N.J., Young L., Bytzer P., Hammer J., Leemon M., Jones M., Horowitz M.. **Impact of chronic gastrointestinal symptoms in diabetes mellitus on health-related quality of life**. *Am. J. Gastroenterol.* (2001) **96** 71-76. DOI: 10.1111/j.1572-0241.2001.03350.x
3. Selbuz S., Buluş A.D.. **Gastrointestinal symptoms in pediatric patients with type 1 diabetes mellitus**. *J. Pediatr. Endocrinol. Metab.* (2020) **33** 185-190. DOI: 10.1515/jpem-2019-0350
4. Vazeou A., Papadopoulou A., Booth I.W., Bartsocas C.S.. **Prevalence of gastrointestinal symptoms in children and adolescents with type 1 diabetes**. *Diabetes Care* (2001) **24** 962-964. DOI: 10.2337/diacare.24.5.962
5. Halland M., Bharucha A.E.. **Relationship Between Control of Glycemia and Gastric Emptying Disturbances in Diabetes Mellitus**. *Clin. Gastroenterol. Hepatol.* (2016) **14** 929-936. DOI: 10.1016/j.cgh.2015.11.021
6. Kuźnik E., Dudkowiak R., Adamiec R., Poniewierka E.. **Diabetic autonomic neuropathy of the gastrointestinal tract**. *Prz. Gastroenterol.* (2020) **15** 89-93. DOI: 10.5114/pg.2020.95554
7. Camilleri M., Parkman H.P., Shafi M.A., Abell T.L., Gerson L.. **Clinical guideline: Management of gastroparesis**. *Am. J. Gastroenterol.* (2013) **108** 18-37. DOI: 10.1038/ajg.2012.373
8. Rasmussen V.F., Jensen T.S., Tankisi H., Karlsson P., Vestergaard E.T., Kristensen K., Nyengaard J.R., Terkelsen A.J.. **Large fibre, small fibre and autonomic neuropathy in adolescents with type 1 diabetes: A systematic review**. *J. Diabetes Complicat.* (2021) **35** 108027. DOI: 10.1016/j.jdiacomp.2021.108027
9. Franceschi R., Mozzillo E., Di Candia F., Rosanio F.M., Leonardi L., Liguori A., Micheli F., Cauvin V., Franzese A., Piona C.A.. **A systematic review of the prevalence, risk factors and screening tools for autonomic and diabetic peripheral neuropathy in children, adolescents and young adults with type 1 diabetes**. *Acta Diabetol.* (2022) **59** 293-308. DOI: 10.1007/s00592-022-01850-x
10. Graves L.E., Donaghue K.C.. **Vascular Complication in Adolescents With Diabetes Mellitus**. *Front. Endocrinol.* (2020) **11** 370. DOI: 10.3389/fendo.2020.00370
11. Farmer A.D., Wegeberg A.L., Brock B., Hobson A.R., Mohammed S.D., Scott S.M., Bruckner-Holt C.E., Semler J.R., Hasler W.L., Hellström P.M.. **Regional gastrointestinal contractility parameters using the wireless motility capsule: Inter-observer reproducibility and influence of age, gender and study country**. *Aliment Pharm.* (2018) **47** 391-400. DOI: 10.1111/apt.14438
12. Rouphael C., Arora Z., Thota P.N., Lopez R., Santisi J., Funk C., Cline M.. **Role of wireless motility capsule in the assessment and management of gastrointestinal dysmotility in patients with diabetes mellitus**. *Neurogastroenterol. Motil.* (2017) **29** e13087. DOI: 10.1111/nmo.13087
13. Kornum D.S., Terkelsen A.J., Bertoli D., Klinge M.W., Høyer K.L., Kufaishi H.H.A., Borghammer P., Drewes A.M., Brock C., Krogh K.. **Assessment of Gastrointestinal Autonomic Dysfunction: Present and Future Perspectives**. *J. Clin. Med.* (2021) **10**. DOI: 10.3390/jcm10071392
14. Perano S.J., Rayner C.K., Kritas S., Horowitz M., Donaghue K., Mpundu-Kaambwa C., Giles L., Couper J.J.. **Gastric Emptying Is More Rapid in Adolescents With Type 1 Diabetes and Impacts on Postprandial Glycemia**. *J. Clin. Endocrinol. Metab.* (2015) **100** 2248-2253. DOI: 10.1210/jc.2015-1055
15. Wegeberg A.-M.L., Brock C., Ejskjaer N., Karmisholt J.S., Jakobsen P.-E., Drewes A.M., Brock B., Farmer A.D.. **Gastrointestinal symptoms and cardiac vagal tone in type 1 diabetes correlates with gut transit times and motility index**. *Neurogastroenterol. Motil.* (2021) **33** e13885. DOI: 10.1111/nmo.13885
16. Revicki D.A., Wood M., Wiklund I., Crawley J.. **Reliability and validity of the Gastrointestinal Symptom Rating Scale in patients with gastroesophageal reflux disease**. *Q. Life Res.* (1998) **7** 75-83. DOI: 10.1023/A:1008841022998
17. Brock C., Brock B., Pedersen A.G., Drewes A.M., Jessen N., Farmer A.D.. **Assessment of the cardiovascular and gastrointestinal autonomic complications of diabetes**. *World J. Diabetes* (2016) **7** 321-332. DOI: 10.4239/wjd.v7.i16.321
18. Kant R., Yadav P.. **Role of wireless motility capsule in diagnosing diabetic gut autonomic neuropathy: A narrative review**. *J. Diabetol.* (2021) **12** 263-269. DOI: 10.4103/jod.Jod_97_20
19. Wang Y.T., Mohammed S.D., Farmer A.D., Wang D., Zarate N., Hobson A.R., Hellström P.M., Semler J.R., Kuo B., Rao S.S.. **Regional gastrointestinal transit and pH studied in 215 healthy volunteers using the wireless motility capsule: Influence of age, gender, study country and testing protocol**. *Aliment. Pharmacol. Ther.* (2015) **42** 761-772. DOI: 10.1111/apt.13329
20. Spallone V.. **Update on the Impact, Diagnosis and Management of Cardiovascular Autonomic Neuropathy in Diabetes: What Is Defined, What Is New, and What Is Unmet**. *Diabetes Metab. J.* (2019) **43** 3-30. DOI: 10.4093/dmj.2018.0259
21. Illigens B.M., Gibbons C.H.. **Sweat testing to evaluate autonomic function**. *Clin. Auton. Res.* (2009) **19** 79-87. DOI: 10.1007/s10286-008-0506-8
22. Bytzer P., Talley N.J., Leemon M., Young L.J., Jones M.P., Horowitz M.. **Prevalence of Gastrointestinal Symptoms Associated With Diabetes Mellitus: A Population-Based Survey of 15,000 Adults**. *Arch. Intern. Med.* (2001) **161** 1989-1996. DOI: 10.1001/archinte.161.16.1989
23. Klinge M.W., Haase A.M., Mark E.B., Sutter N., Fynne L.V., Drewes A.M., Schlageter V., Lund S., Borghammer P., Krogh K.. **Colonic motility in patients with type 1 diabetes and gastrointestinal symptoms**. *Neurogastroenterol. Motil.* (2020) **32** e13948. DOI: 10.1111/nmo.13948
24. Kornum D.S., Klinge M.W., Fassov J., Brock C., Bjerregård N.C., Lund S., Drewes A.M., Krogh K.. **Symptoms of diabetic gastroenteropathy in patients with diabetes**. *Ugeskr Laeger* (2020) **182** V6200462. PMID: 33280642
25. Green A.D., Belkind-Gerson J., Surjanhata B.C., Mousa H., Kuo B., Di Lorenzo C.. **Wireless Motility Capsule Test in Children with Upper Gastrointestinal Symptoms**. *J. Pediatr.* (2013) **162** 1181-1187. DOI: 10.1016/j.jpeds.2012.11.040
26. Brinck C.E., Mark E.B., Ejerskov C., Johansen K.B., Sutter N., Schlageter V., Drewes A.M., Krogh K.. **Regional Gastrointestinal Motility in Healthy Children**. *J. Pediatr. Gastroenterol. Nutr.* (2021) **73** 306-313. DOI: 10.1097/MPG.0000000000003198
27. Sutter N., Klinge M.W., Mark E.B., Nandhra G., Haase A.M., Poulsen J., Knudsen K., Borghammer P., Schlageter V., Birch M.. **Normative values for gastric motility assessed with the 3D-transit electromagnetic tracking system**. *Neurogastroenterol. Motil.* (2020) **32** e13829. DOI: 10.1111/nmo.13829
28. Gaddipati K., Simonian H., Kresge K., Boden G.. **Abnormal Ghrelin and Pancreatic Polypeptide Responses in Gastroparesis**. *Dig. Dis. Sci.* (2006) **51** 1339-1346. DOI: 10.1007/s10620-005-9022-z
29. Meldgaard T., Keller J., Olesen A.E., Olesen S.S., Krogh K., Borre M., Farmer A., Brock B., Brock C., Drewes A.M.. **Pathophysiology and management of diabetic gastroenteropathy**. *Ther. Adv. Gastroenterol.* (2019) **12** 1756284819852047. DOI: 10.1177/1756284819852047
30. Wegeberg A.M., Liao D., Jensen S.L., Sørensen P.S., Wigh I.M., Zaugg V.S., Brock C.. **Gastrocolic Reflex Is Delayed and Diminished in Adults with Type 1 Diabetes**. *Dig. Dis. Sci.* (2022) **67** 4827-4833. DOI: 10.1007/s10620-021-07346-y
31. Brock C.. **Are measures of enteric and autonomic nervous system associated?**. *J. Intern. Med.* (2021) **290** 1105-1107. DOI: 10.1111/joim.13360
32. Jaruvongvanich V., Patcharatrakul T., Gonlachanvit S.. **Prediction of Delayed Colonic Transit Using Bristol Stool Form and Stool Frequency in Eastern Constipated Patients: A Difference From the West**. *J. Neurogastroenterol. Motil.* (2017) **23** 561-568. DOI: 10.5056/jnm17022
33. Young C.F., Moussa M., Shubrook J.H.. **Diabetic Gastroparesis: A Review**. *Diabetes Spectr.* (2020) **33** 290-297. DOI: 10.2337/ds19-0062
34. Mihai B.M., Mihai C., Cijevschi-Prelipcean C., Grigorescu E.D., Dranga M., Drug V., Sporea I., Lăcătușu C.M.. **Bidirectional Relationship between Gastric Emptying and Plasma Glucose Control in Normoglycemic Individuals and Diabetic Patients**. *J. Diabetes Res.* (2018) **2018** 1736959. DOI: 10.1155/2018/1736959
35. Wegeberg A.L., Brock C., Brock B., Farmer A.D., Hobson A.R., Semler J.R., Scott S.M.. **Regional gastrointestinal pH profile is altered in patients with type 1 diabetes and peripheral neuropathy**. *Neurogastroenterol. Motil.* (2018) **30** e13407. DOI: 10.1111/nmo.13407
36. Burghen G.A., Murrell L.R., Whitington G.L., Klyce M.K., Burstein S.. **Acid peptic disease in children with type I diabetes mellitus. A complicating relationship**. *Am J Dis Child* (1992) **146** 718-722. DOI: 10.1001/archpedi.1992.02160180078021
37. Cichosz S.L., Hejlesen O.. **Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study**. *J. Diabetes Sci. Technol.* (2021) **16** 1190-1195. DOI: 10.1177/19322968211015206
38. Thawani S.P., Brannagan T.H., Lebwohl B., Green P.H., Ludvigsson J.F.. **Risk of Neuropathy Among 28,232 Patients With Biopsy-Verified Celiac Disease**. *JAMA Neurol.* (2015) **72** 806-811. DOI: 10.1001/jamaneurol.2015.0475
39. Thawani S., Brannagan T.H., Lebwohl B., Mollazadegan K., Green P.H.R., Ludvigsson J.F.. **Type 1 Diabetes, Celiac Disease, and Neuropathy-A Nationwide Cohort Study**. *J. Clin. Neuromuscul. Dis.* (2017) **19** 12-18. DOI: 10.1097/CND.0000000000000174
40. Goyal R.K., Cristofaro V., Sullivan M.P.. **Rapid gastric emptying in diabetes mellitus: Pathophysiology and clinical importance**. *J. Diabetes Complicat.* (2019) **33** 107414. DOI: 10.1016/j.jdiacomp.2019.107414
41. Hasler W.L., May K.P., Wilson L.A., Van Natta M., Parkman H.P., Pasricha P.J., Koch K.L., Abell T.L., McCallum R.W., Nguyen L.A.. **Relating gastric scintigraphy and symptoms to motility capsule transit and pressure findings in suspected gastroparesis**. *Neurogastroenterol. Motil.* (2018) **30** e13196. DOI: 10.1111/nmo.13196
|
---
title: A CBCT Investigation of the Sella Turcica Dimension and Sella Turcica Bridging
in Different Vertical Growth Patterns
authors:
- Shiyi Yan
- Sheng Huang
- Zuping Wu
- Ying Liu
- Yanling Men
- Xiuping Nie
- Jie Guo
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10003992
doi: 10.3390/jcm12051890
license: CC BY 4.0
---
# A CBCT Investigation of the Sella Turcica Dimension and Sella Turcica Bridging in Different Vertical Growth Patterns
## Abstract
This study aimed to compare the sella turcica dimensions and sella turcica bridging (STB) via cone-beam computed tomography in different vertical patterns and then analyze the link between the sella turcica and vertical growth patterns. The CBCT images of 120 skeletal Class I subjects (an equal proportion of females and males; mean age of 21.46 years) were divided into three vertical growth skeletal groups. Student’s t tests and Mann–Whitney U tests were used to assess the possible diversity in genders. The link between sella turcica dimensions and different vertical patterns was explored by one-way analysis of variance, as well as Pearson and Spearman correlation tests. The prevalence of STB was compared using the chi-square test. Sella turcica shapes were not linked to gender, but statistical differences were observed among different vertical patterns. In the low-angle group, a larger posterior clinoid distance and smaller posterior clinoid height, tuberculum sellae height, and dorsum sellae height were determined, and the incidence of STB was higher ($p \leq 0.01$). Sella turcica shapes were linked to vertical growth patterns, mainly involving the posterior clinoid process and STB, which could be used as an index to assess vertical growth trends.
## 1. Introduction
The saddle-shaped structure situated on the intracranial surface of the sphenoid bone is called the sella turcica, and it is three-dimensional. Its anterior margin is known as the tuberculum sellae, and the posterior margin, which surrounds the pituitary gland, is known as the dorsum sellae [1]. Its center point, the sella point, is of vital importance in orthodontic cephalometry landmarks and plays a crucial part in image analysis [2]. The anterior and posterior sections of the sella turcica are separately developed from neural crest cells and the para-axial mesoderm. Neural crest cells were found to have nothing to do with the notochord, while the para-axial mesoderm relied heavily notochordal induction, which has been confirmed previously [3,4]. Studies have shown that sella turcica shapes were relevant to the development of the pituitary gland and neural crest cells [5]. An abnormal pituitary gland affects the secretion of a growth hormone, which is not conducive to the development of bone and body. Moreover, the mutations of the homeobox genes in cranial neural crest cells may have an impact on the development of the dentition and midface through signaling conduction [6]. This suggests that the sella turcica is closely related to the growth and development of the craniofacial region.
Two factors showed the importance of evaluating the dimensions and abnormal shapes of the sella turcica: its important anatomical position and its common embryologic origin with a cranial base [2,7,8]. Studies found that the ligament between the anterior and posterior clinoid processes may initiate ossification, also known as sella turcica bridging (STB), which was regarded as a developmental abnormality [9]. Previous studies have proven the possible association between STB and multiple diseases such as intrasellar adenoma [10], Down syndrome [11], and so on. Some scholars have proposed that malocclusion may be related to changes in the sella turcica. Tepedino et al. [ 7,12] concluded that there were various sella turcica dimensions in different sagittal growth patterns. Studies have shown that the sella turcica in Class II subjects was smaller than in Class III subjects [13]. In addition, patients with STB were more likely to have a greater distal position in the mandible [14]. Malocclusion can be explained by the developmental changes in the maxilla, mandible, or both. According to embryological studies, they had significant similarities with the sella turcica [12]. The growth and location of the maxilla and mandible affect skeletal patterns, including sagittal and vertical skeletal patterns. Moreover, different vertical growth patterns affect orthodontic treatment decisions. Therefore, the correlation between the sella turcica and the vertical growth patterns needs further research and analysis. Currently, there are a few studies on the different vertical growth patterns. According to the study by Perović, differences were found only for sella depth [15], which provides limited information.
Recently, some scholars have advanced the idea that different imaging methods and judgment methods of STB may affect the research results [16]. There were statistical differences in the measurements of the sella turcica between lateral cephalograms (LCRs) and cone-beam computed tomography (CBCT) [17,18], and the superposition of the overlapping structure of the sella turcica in two-dimensional imaging increased the false-positive rate of STB [19,20]. Tassoker et al. argued that only three-dimensional imaging such as CBCT could produce a more exact characterization of the sella area [21]. Previous studies, based on two-dimensional imaging, were filled with uncertainty. Thus, different skeletal growth patterns of the sella turcica are yet to be evaluated. We need more quantitative and objective research, using standard and highly sensitive methods.
Above all, this study aimed to [1] measure sella turcica shapes in subjects after the pubertal period (including different genders and different vertical patterns) and [2] to analyze the relationship between the sella turcica and vertical growth patterns by comparing differences within examined groups. The null hypothesis was that no relation could be found between sella turcica shapes and different vertical growth patterns.
## 2.1. Sample Selection
Our study was conducted in the Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University. The subjects included 120 individuals (60 women and 60 men) who underwent CBCT for joint discomfort, as well as the extraction of complexes impacted by teeth and other pathologies. This study was ratified by Shandong University Medical Research Ethics Committee (No: 20220910). Consent forms were signed by all subjects and the appropriate person with parental authority.
According to the following criteria, subjects were selected randomly if they: (a) were older than 16 years and if their sella turcica was basically developed, (b) were systemically healthy and had no craniofacial syndrome, (c) had no history of orthodontic or orthognathic surgical treatment, and (d) had Class I sagittal growth patterns evaluated by an ANB angle between 0.7 and 4.7°. They were excluded if they had (a) a history of trauma and treatment in the craniofacial region or (b) a history of long-term drug use affecting bone development.
The CBCT of the selected subjects were collected, and then LCRs were obtained from CBCT as reconstructed LCRs by dolphin software. Depending on the SN-MP, FH-MP, Y axis angle, and FHI [22,23], subjects were allocated to low, normal, or high angles. Thus, three clear-cut groups were generated, and each group had 40 subjects and an equal proportion of genders.
Low: SN-MP < 27.3°, FH-MP < 25.5°, Y axis angle > 68.9°, FHI > 0.68.
Normal: 27.3° ≤ SN-MP ≤ 37.7°, 25.5° ≤ FH-MP36.7°, 62.7° ≤ Y axis angle ≤ 68.9°, 0.62 ≤ FHI ≤ 0.68.
High: SN-MP > 37.7°, FH-MP > 36.7°, Y axis angle < 62.7°, FHI < 0.62.
## 2.2. Imaging Acquisition and Evaluation
The subjects all held the natural head position. All scans were performed by the same technician and were set at the following scanning parameters: FSV, 110 kV; 3.12 mA; voxel size, 0.3 mm; exposure time, 3.6 s; field of view, 18 × 16 cm. CBCT scans were exported in the digital imaging and communications in medicine (DICOM) format. The same device (Orthoceph OP300, Instrumentarium, Tuusula, Finland) was used to determine the vertical growth patterns of subjects performed on LCRs.
## 2.3. Landmarks and Definition of Sella Turcica and STB
Reference planes and the definitions of linear dimensions were identified to perform the analysis (Table 1).
We reoriented the three-dimensional images using reference planes and carried out reconstruction in Mimics (version 21.0, Materialise, Leuven, Belgium). According to methods proposed by Ortiz [18] and Ugurlu [24], the sella turcica was measured, as shown in Figure 1, Figure 2 and Figure 3.
Referring to the criteria given by Ortiz [18], in this study, STB was classified into two groups according to the ratio of interclinoid distance (ACP-PCP) to length (TS-DS), i.e., no bridging (ratio ≥ $60\%$) and bridging (ratio < $60\%$).
## 2.4. Examiner Reliability
To minimize any method errors that may occur in the study, 60 samples were randomly re-evaluated by the same researcher two weeks apart. Intraclass correlation coefficients (ICCs) were calculated, and the method errors were evaluated by Dahlberg’s formula: d$\frac{2}{2}$n (d represents the deviations between the two measurements; n represents the number of paired objects).
## 2.5. Statistical Analysis
All the variables are described by the mean, standard deviation, and percentages. The Kolmogorov–Smirnov test and Levene’s test were performed to determine the distribution of normality and homogeneity of variances, respectively. In three groups, any difference in the ANB, SN-MP, FH-MP, Y axis angle, FHI, and ages of subjects was obtained using one-way analysis of variance. Student’s t test and the Mann–Whitney U test were used to assess the possible diversity in genders. To investigate the relationship between the sella turcica and different vertical growth patterns, one-way analysis of variance and the least significant difference test were applied. The prevalence of STB among different vertical growth patterns was compared using the chi-square test. Moreover, Pearson and Spearman correlations were used to evaluate the correlativity between the sella turcica and the cephalometric measurements that reflect vertical growth patterns for all subjects. The confidence interval was set as $95\%$ and statistical significance was accepted at $p \leq 0.05.$ Statistical analyses were conducted using SPSS (version 26.0, mac OS; IBM, Armonk, NY, USA).
## 3. Results
According to ICC (0.942–0.998), intra-observer reliability was excellent, and Dahlberg’s formula showed that the method errors ranged from 0.00 to 0.13 mm. The mean age, cephalometric measurements, and sex component ratio are shown in Table 2. The sex constituent ratio was consistent. The ANB angle showed that all subjects were skeletal Class I, and a strong agreement was found in the groups. Differences in the SN-MP, FH-MP, Y axis angle, and FHI were highly significant, confirming that they belong to different vertical growth patterns.
## 3.1. Different Vertical Growth Patterns with Different Sella Turcica Shapes
One hundred and twenty subjects were evaluated in this study. Table 3 shows the mean and standard deviation of sella turcica dimensions.
In terms of the dimensions of the sella turcica, among different vertical growth patterns (Table 4), statistical differences were recorded ($p \leq 0.05$). In the low-angle group, the posterior clinoid distance was larger, and the posterior left clinoid height, posterior right clinoid height, tuberculum sellae height, and dorsum sellae height were significantly smaller in the high-angle group, but we could not detect any difference between the normal-angle group and the high-angle group. Further correlation analysis shows that the posterior clinoid distance and the left and right anterior clinoid height were significantly correlated with all four vertical indicators (Table 5).
## 3.2. There Is Nearly No Difference in Sella Turcica Dimensions between Genders
Table 6 shows the differences in dimensions between genders and shows that the mean values were similar. Only slight increases in the anterior and posterior clinoid distance were found in males ($p \leq 0.05$). The mean differences and significance levels between the genders within three groups (Table 7 and Table 8) reported the same results.
## 3.3. Different Vertical Growth Patterns with Different Incidence of Sella Turcica Bridging
Table 9 shows that significant differences (χ2 = 10.00, $p \leq 0.05$) in the percentage of STB among different vertical growth patterns were found by the chi-square test, but no correlation existed in genders (χ2 = 0.556, $p \leq 0.05$). Compared with normal-angle ($30\%$) and high-angle subjects ($30\%$), STB frequency was found to be significantly higher in low-angle subjects ($60\%$). However, no statistical differences were observed between normal-angle and high-angle subjects.
## 4. Discussion
The sella turcica was derived from the neural crest cells and the notochord mesodermal cells. The variation in shapes is affected by the development of the pituitary gland, which has an impact on the growth and development of individuals [5].
Many scholars have studied lengths, depths, and diameters as important indexes of sella turcica size. In our study, the mean length, depth, and diameter of the sella turcica were 10.01 ± 1.47 mm, 7.71 ± 1.30 mm, and 11.36 ± 1.43 mm, respectively. These results are consistent with studies by Sathyanarayana [25], who studied the mean length, depth, and diameter of the sella turcica in the South Indian race (9.6 ± 1.57 mm, 7.5 ± 1.36 mm, and 11.3 ± 1.25 mm, respectively). Compared with our study, the mean Saudi size was larger in the studies conducted by Alkofide EA (11.3 ± 2.58 mm, 9.3 ± 1.31 mm, and 14.5 ± 2.01 mm) [2], and the mean sella turcica size of Nepali citizens was smaller in the studies carried out by Shrestha (8.13 ± 2.03 mm, 9.60 ± 1.43 mm, and 6.40 ± 1.21 mm) [13]. This may be attributed to the difference in ethnicities, genes, and environmental factors.
When sella turcica shapes were compared in genders, Muhammed [26] and Sathyanarayana et al. [ 25] found that a significant difference was recorded in length using the lateral cephalogram. Contrary to these results, our study found a major agreement between genders, and the same results were also found by Islam [27] and Hasan [28], whose studies were based on CBCT. These differences can be attributed to the samples’ age, different imaging uses, or measurement errors, indicating that these influences cannot be ignored.
Axelsson et al. pointed out that no obvious change occurred in the dimensions of the sella turcica after the pubertal period [8,19]. Compared to Muhammed (8–28 years) [26] and Sathyanarayana (6–17 years) [25], our study enrolled subjects older than 16 years whose development had nearly stopped and whose sella turcica dimensions were stable. Hence, the confounding factor of age was avoided. Previous studies found that the overall prevalence of STB in the population ranged from $1\%$ to $81\%$. In addition, a recent systematic review revealed that the large differences in prevalence might be due to measurement errors [16], leading to publication bias. The effect of the superimposition of anatomical structures and images was also confirmed, which made it difficult to discriminate between true bridging and pseudo bridging on lateral cephalograms [20,21]. Three-dimensional imaging, similar to CBCT, can accurately assess sella turcica shapes and is largely free of false-positive results [18]. The method judgment of STB also resulted in various differences. Becktor et al. [ 29] divided STB into two types: obvious band fusion and front–back or thin middle fusion. Leonardi [30] defined the radiographically visible diaphragm sella as STB, according to the calcification degree of the intercostal ligament. To overcome subjectivity in evaluating STB-related calcification, an objective quantitative method was performed in our study. We quantified the calcification degree present in the right and left clinoid processes and used the ratio [18] of interclinoid distance (ACP-PCP) to length (TS-DS) to differentiate whether there was STB or not. Moreover, more than ten CBCT measurement indexes, such as the anterior clinoid height, posterior clinoid height, tuberculum sellae height, dorsum sellae height, and so on, were added to measure the sella turcica with greater accuracy.
In our study, only the anterior clinoid distance and posterior clinoid distance showed slight gender differences, and no gender difference was found in the other indexes. Moreover, it is the first study that focuses on evaluating sella turcica shapes in different vertical growth patterns and genders.
Measuring the sella point as the marker could reflect the development and deformity of the mandibular [2]. In the early stage of orthodontic development, Bjork and Jarabak introduced the saddle angle (N-S-Ar) and articular angle (S-Ar-Go’) concepts to describe the position between the cranial base and facial bones in sagittal and vertical directions [31]. Recent studies correlated the sella turcica with different sagittal growth patterns using the lateral cephalogram to assess whether there is a relationship [12,32]. Investigations between the sella size and both Class II and Class III malocclusions were performed by Sathyanarayana [25] and Shrestha [13], and they found that Class II subjects had smaller sella turcica sizes than Class III subjects. In terms of diameter, Alkofide EA [2] noted that there were more Class III subjects than Class II subjects. Furthermore, in the study by Abdel Kaber [20], patients treated with combined orthodontics and orthognathic treatment had a $10.7\%$ incidence of STB, compared to $7.1\%$ for patients treated with orthodontics alone. Hence, he thought that STB could reflect the degree of jaw deformity. Dasgupta [33] showed that the incidence of STB was significantly higher in Class II patients. Some studies have also pointed out a higher incidence of STB in skeletal Class III subjects [7,34], and the study by Alkofide [2] revealed a strong link between STB and sagittal malocclusion. Based on these findings, scholars believe that sella turcica shapes can be used to assess different sagittal growth patterns [33]. The sagittal growth pattern was reflected in the developmental differences in the jaw.
Several studies have pointed out that the anterior wall of the sella turcica and the frontonasal region are developed from the neural crest cells in embryology, while the posterior wall appears to be associated with the posterior cranial base, which is developed from the para-axial mesoderm [5,12]. The anterior and posterior cranial bases were attached to the maxilla and mandible through growth sutures and temporomandibular joints, respectively. Therefore, any change in the cranial base can have an impact on the development of (and changes in) facial bones [35], not only including sagittal changes but also vertical growth changes. Driven by the vertical growth of the cranial base, vertical growth patterns will change. Consequently, further research on different vertical growth patterns will help to inform better orthodontic treatment decisions. The length, diameter, depth, and cross-sectional area of the sella turcica in different vertical growth patterns in Caucasian subjects have been evaluated via lateral cephalograms [15], but the link between them and STB has not been clarified. With all of this in mind, the relationship between the sella turcica and different vertical growth patterns was analyzed in this study. Statistical differences were observed in some indexes. A higher posterior clinoid distance and smaller posterior clinoid height were determined in the low-angle group, and the occurrence of STB was correlated with vertical growth patterns. A significantly higher incidence of STB was observed in the low-angle subjects.
These results indicate that differences in the sella turcica influenced vertical growth patterns, mostly concerning the posterior clinoid process and the tuberculum sellae. This could be explained from two aspects, as studies on genes have demonstrated that the anterior wall, the low wall, and the posterior wall are located in different maxillofacial developmental fields, respectively, such as the frontonasal boundary area, the palatal boundary area, and the mandibular boundary area. They migrated from diverse sources of cells and had different genetic backgrounds. The posterior wall shared a significantly similar origin with the posterior cranial base from the para-axial mesoderm. Some scholars, especially Kjaer, hold the view that changes in the posterior cranial base could affect the mandible [36] and the vertical growth. On the other hand, sella turcica shapes are closely related to the pituitary gland, which is located at the center of the sella turcica and secretes the important growth hormone. Some scholars have given growth hormone therapy to patients with growth retardation and found that growth hormone can significantly stimulate the growth of the mandible [37], which may be expressed as different vertical growth patterns in the craniofacial region. As a result, it is reasonable to assume that it might be possible to assess vertical trends by measuring the sella turcica.
It is important to understand sella turcica shapes. The sella point, as a cephalometric marker, can reflect the relative position of the maxilla and the mandible [2,38]. However, the role of the sella turcica, an indispensable structure in orthodontic diagnosis, is often ignored. Our study indicates that, in terms of the sella turcica, there were differences in vertical growth patterns, involving a smaller posterior clinoid height, a larger posterior clinoid distance, and a higher incidence of STB in low-angle subjects, which can be used as prompt information to assess vertical growth trends.
The equal sex distribution was considered in this study, but only the class I skeletal pattern was investigated. This limitation could not be ignored. As the research expands, these findings could be confirmed by establishing different vertical groups in Class II or Class III sagittal malocclusions.
Since the subjects were from our hospital rather than the general population, some inherent biases may exist. Although the present study avoids the limitations of 2d lateral cephalometric radiographs, as reported in the previous literature, a comparison of 2d and 3d analysis was not performed. Moreover, the small sample size and anthropological variations cannot be ignored. As the research expands, these findings could be confirmed by establishing a detailed 3d investigation using a larger sample size and a more comprehensive assessment of the sella turcica.
## References
1. Sathyanarayana H.P., Kailasam V., Chitharanjan A.B.. **Sella turcica-Its importance in orthodontics and craniofacial morphology**. *Dent. Res. J.* (2013) **10** 571-575
2. Alkofide E.A.. **The shape and size of the sella turcica in skeletal Class I, Class II, and Class III Saudi subjects**. *Eur. J. Orthod.* (2007) **29** 457-463. DOI: 10.1093/ejo/cjm049
3. Russell B.G., Kjaer I.. **Postnatal structure of the sella turcica in Down syndrome**. *Am. J. Med. Genet.* (1999) **87** 183-188. DOI: 10.1002/(SICI)1096-8628(19991119)87:2<183::AID-AJMG11>3.0.CO;2-A
4. Lieberman D.E., Ross C.F., Ravosa M.J.. **The primate cranial base: Ontogeny, function, and integration**. *Am. J. Phys. Anthropol* (2000) **113** 117-169. DOI: 10.1002/1096-8644(2000)43:31+<117::AID-AJPA5>3.0.CO;2-I
5. Kjaer I.. **Sella turcica morphology and the pituitary gland-a new contribution to craniofacial diagnostics based on histology and neuroradiology**. *Eur. J. Orthod.* (2015) **37** 28-36. DOI: 10.1093/ejo/cjs091
6. Canigur Bavbek N., Akay G., Kar I., Tuncer C., Gungor K., Balos Tuncer B.. **Relationship between palatally impacted canines and dimensions and morphology of sella turcica: A cone-beam computed tomography study**. *Am. J. Orthod. Dentofac. Orthop.* (2022) **162** 626-635. DOI: 10.1016/j.ajodo.2021.05.017
7. Meyer-Marcotty P., Reuther T., Stellzig-Eisenhauer A.. **Bridging of the sella turcica in skeletal Class III subjects**. *Eur. J. Orthod.* (2010) **32** 148-153. DOI: 10.1093/ejo/cjp081
8. Canigur Bavbek N., Dincer M.. **Dimensions and morphologic variations of sella turcica in type 1 diabetic patients**. *Am. J. Orthod. Dentofac. Orthop.* (2014) **145** 179-187. DOI: 10.1016/j.ajodo.2013.10.011
9. Cederberg R.A., Benson B.W., Nunn M., English J.D.. **Calcification of the interclinoid and petroclinoid ligaments of sella turcica: A radiographic study of the prevalence**. *Orthod. Craniofac. Res.* (2003) **6** 227-232. DOI: 10.1034/j.1600-0544.2003.00243.x
10. Alkofide E.. **Pituitary adenoma: A cephalometric finding**. *Am. J. Orthod. Dentofac. Orthop.* (2001) **120** 559-562. DOI: 10.1067/mod.2001.118781
11. Korayem M., AlKofide E.. **Size and shape of the sella turcica in subjects with Down syndrome**. *Orthod. Craniofac. Res.* (2015) **18** 43-50. DOI: 10.1111/ocr.12059
12. Tepedino M., Laurenziello M., Guida L., Montaruli G., Troiano G., Chimenti C., Colonna M., Ciavarella D.. **Morphometric analysis of sella turcica in growing patients: An observational study on shape and dimensions in different sagittal craniofacial patterns**. *Sci. Rep.* (2019) **9** 19309. DOI: 10.1038/s41598-019-55916-y
13. Shrestha G.K., Pokharel P.R., Gyawali R., Bhattarai B., Giri J.. **The morphology and bridging of the sella turcica in adult orthodontic patients**. *BMC Oral Health* (2018) **18**. DOI: 10.1186/s12903-018-0499-1
14. Kucia A., Jankowski T., Siewniak M., Janiszewska-Olszowska J., Grocholewicz K., Szych Z., Wilk G.. **Sella turcica anomalies on lateral cephalometric radiographs of Polish children**. *Dentomaxillofac. Radiol.* (2014) **43** 20140165. DOI: 10.1259/dmfr.20140165
15. Perovic T., Blazej Z., Jovanovic I., Cvetkovic M.. **Morphometric analysis of the sella turcica in subjects with different vertical growth patterns—A cephalometric study**. *Homo* (2021) **72** 215-227. DOI: 10.1127/homo/2021/1413
16. Cuschieri A., Cuschieri S., Zammit C.. **Sella turcica bridging: A systematic review**. *Surg. Radiol. Anat.* (2022) **44** 381-389. DOI: 10.1007/s00276-021-02873-9
17. Arcos-Palomino I., Ustrell-Torrent J.M.. **Association between sella turcica bridging and altered direction of dental eruption: A case-control study**. *J. Clin. Exp. Dent.* (2019) **11** e913-e920. DOI: 10.4317/jced.56165
18. Ortiz P.M., Tabbaa S., Flores-Mir C., Al-Jewair T.. **A CBCT Investigation of the Association between Sella-Turcica Bridging and Maxillary Palatal Canine Impaction**. *Biomed. Res. Int.* (2018) **2018** 4329050. DOI: 10.1155/2018/4329050
19. Axelsson S., Storhaug K., Kjaer I.. **Post-natal size and morphology of the sella turcica. Longitudinal cephalometric standards for Norwegians between 6 and 21 years of age**. *Eur. J. Orthod.* (2004) **26** 597-604. DOI: 10.1093/ejo/26.6.597
20. Abdel-Kader H.M.. **Sella turcica bridges in orthodontic and orthognathic surgery patients. A retrospective cephalometric study**. *Aust. Orthod. J.* (2007) **23** 30-35. PMID: 17679532
21. Tassoker M., Kok H., Ozcan S.. **Investigation of the relationship between “Sella Turcica Bridge” and “Ponticulus Posticus”: A Lateral Cephalometric Study**. *Int. J. Morphol.* (2017) **35** 337-344. DOI: 10.4067/S0717-95022017000100053
22. Cao L., Li J., Yang C., Hu B., Zhang X., Sun J.. **High-efficiency treatment with the use of traditional anchorage control for a patient with Class II malocclusion and severe overjet**. *Am. J. Orthod. Dentofac. Orthop.* (2019) **155** 411-420. DOI: 10.1016/j.ajodo.2017.08.030
23. Wang X.D., Zhang J.N., Liu D.W., Lei F.F., Liu W.T., Song Y., Zhou Y.H.. **Nonsurgical correction using miniscrew-assisted vertical control of a severe high angle with mandibular retrusion and gummy smile in an adult**. *Am. J. Orthod. Dentofac. Orthop.* (2017) **151** 978-988. DOI: 10.1016/j.ajodo.2016.04.034
24. Ugurlu M., Bayrakdar I.S., Kahraman F., Oksayan R., Dagsuyu I.M.. **Evaluation of the relationship between impacted canines and three-dimensional sella morphology**. *Surg. Radiol. Anat.* (2020) **42** 23-29. DOI: 10.1007/s00276-019-02328-2
25. Sathyanarayana H.P., Kailasam V., Chitharanjan A.B.. **The size and morphology of sella turcica in different skeletal patterns among South Indian population: A lateral cephalometric study**. *J. Indian Orthod. Soc.* (2013) **47** 266-271. DOI: 10.1177/0974909820130507S
26. Muhammed F.K., Abdullah A.O., Rashid Z.J., Pusic T., Shbair M.F., Liu Y.. **Morphology, incidence of bridging, and dimensions of sella turcica in different racial groups**. *Oral Radiol.* (2019) **35** 127-134. DOI: 10.1007/s11282-018-0328-x
27. Islam M., Alam M.K., Yusof A., Kato I., Honda Y., Kubo K., Maeda H.. **3D CT Study of Morphological Shape and Size of Sella Turcica in Bangladeshi Population**. *J. Hard Tissue Biol.* (2017) **26** 1-6. DOI: 10.2485/jhtb.26.1
28. Hasan H.A., Alam M.K., Yusof A., Mizushima H., Kida A., Osuga N.. **Size and Morphology of Sella Turcica in Malay populations: A 3D CT Study**. *J. Hard Tissue Biol.* (2016) **25** 313-320. DOI: 10.2485/jhtb.25.313
29. Becktor J.P., Einersen S., Kjaer I.. **A sella turcica bridge in subjects with severe craniofacial deviations**. *Eur. J. Orthod.* (2000) **22** 69-74. DOI: 10.1093/ejo/22.1.69
30. Leonardi R., Barbato E., Vichi M., Caltabiano M.. **A sella turcica bridge in subjects with dental anomalies**. *Eur. J. Orthod.* (2006) **28** 580-585. DOI: 10.1093/ejo/cjl032
31. Bjork A.. **Prediction of mandibular growth rotation**. *Am. J. Orthod.* (1969) **55** 585-599. DOI: 10.1016/0002-9416(69)90036-0
32. Valizadeh S., Shahbeig S., Mohseni S., Azimi F., Bakhshandeh H.. **Correlation of Shape and Size of Sella Turcica with the Type of Facial Skeletal Class in an Iranian Group**. *Iran. J. Radiol.* (2015) **12** e16059. DOI: 10.5812/iranjradiol.12(3)2015.16059
33. Dasgupta P., Sen S., Srikanth H.S., Kamath G.. **Sella Turcica Bridging as A Predictor of Class II Malocclusion-An Investigative Study**. *J. Stomatol. Oral Maxillofac. Surg.* (2018) **119** 482-485. DOI: 10.1016/j.jormas.2018.05.005
34. Magat G., Ozcan Sener S.. **Morphometric analysis of the sella turcica in Turkish individuals with different dentofacial skeletal patterns**. *Folia Morphol.* (2018) **77** 543-550. DOI: 10.5603/FM.a2018.0022
35. Bjork A., Skieller V.. **Normal and abnormal growth of the mandible. A synthesis of longitudinal cephalometric implant studies over a period of 25 years**. *Eur. J. Orthod.* (1983) **5** 1-46. DOI: 10.1093/ejo/5.1.1
36. Kjaer I.. **Orthodontics and foetal pathology: A personal view on craniofacial patterning**. *Eur. J. Orthod.* (2010) **32** 140-147. DOI: 10.1093/ejo/cjp059
37. Forsberg C.M., Krekmanova L., Dahllof G.. **The effect of growth hormone therapy on mandibular and cranial base development in children treated with total body irradiation**. *Eur. J. Orthod.* (2002) **24** 285-292. DOI: 10.1093/ejo/24.3.285
38. Roomaney I.A., Chetty M.. **Sella turcica morphology in patients with genetic syndromes: A systematic review**. *Orthod. Craniofac. Res.* (2021) **24** 194-205. DOI: 10.1111/ocr.12426
|
---
title: Metabolic Health, Obesity, and Intraocular Pressure
authors:
- Younhea Jung
- Gyoung Nyun Kim
- Eun Byeol Oh
- Kyoung Ohn
- Jung Il Moon
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10003993
doi: 10.3390/jcm12052066
license: CC BY 4.0
---
# Metabolic Health, Obesity, and Intraocular Pressure
## Abstract
Obesity has been associated with increased intraocular pressure (IOP), but the results are inconsistent. Recently, a subgroup of obese individuals with good metabolic profiles were suggested to have better clinical outcomes than normal-weight individuals with metabolic diseases. The relationships between IOP and different combinations of obesity and metabolic health status have not been investigated. Therefore, we investigated the IOP among groups with different combinations of obesity status and metabolic health status. We examined 20,385 adults aged 19 to 85 years at the Health Promotion Center of Seoul St. Mary’s Hospital between May 2015 and April 2016. Individuals were categorized into four groups according to obesity (body mass index (BMI) ≥ 25 kg/m2) and metabolic health status (defined based on prior medical history or abdominal obesity, dyslipidemia, low high-density lipoprotein cholesterol, high blood pressure, or high fasting blood glucose levels upon medical examination). ANOVA and ANCOVA were performed to compare the IOP among the subgroups. The IOP of the metabolically unhealthy obese group (14.38 ± 0.06 mmHg) was the highest, followed by that of the metabolically unhealthy normal-weight group (MUNW, 14.22 ± 0.08 mmHg), then, the metabolically healthy groups ($p \leq 0.001$; 13.50 ± 0.05 mmHg and 13.06 ± 0.03 mmHg in the metabolically healthy obese (MHO) and metabolically healthy normal-weight groups, respectively). Subjects who were metabolically unhealthy showed higher IOP compared to their counterparts who were metabolically healthy at all BMI levels, and there was a linear increase in IOP as the number of metabolic disease components increased, but no difference between normal-weight vs. obese individuals. While obesity, metabolic health status, and each component of metabolic disease were associated with higher IOP, those who were MUNW showed higher IOP than those who were MHO, which indicates that metabolic status has a greater impact than obesity on IOP.
## 1. Introduction
Glaucoma is a progressive optic neuropathy that causes irreversible blindness, and the number of people with glaucoma is estimated to reach over 111 million in 2040 [1]. Among many risk factors, intraocular pressure (IOP) is the most important and the only modifiable risk factor for glaucoma; therefore, it is important to identify factors that affect IOP [2,3,4].
Obesity has been reported to be an independent risk factor for elevated IOP and is shown to have a positive correlation with elevated IOP in many studies [5,6,7,8,9,10]. The Beaver Dam Eye Study revealed that body mass index (BMI), the most commonly used indicator of obesity, was positively correlated with IOP [10]. In another large epidemiologic study, IOP was associated with BMI both cross-sectionally and longitudinally [7]. However, higher BMI was only marginally associated with IOP in the Barbados Eye Study [9], and another study reported no association between BMI and IOP [11].
Nevertheless, there is a growing body of literature regarding subgroups of obesity with different metabolic profiles [12,13,14]. A subgroup of obese individuals with good metabolic profiles, known as “metabolically healthy but obese (MHO)” individuals, present a favorable metabolic profile including good insulin sensitivity, a good lipid profile, and no hypertension [12]. However, metabolically unhealthy but normal-weight (MUNW) individuals, characterized by excess visceral adipose tissue deposition and adipose tissue inflammation, have been linked with serious health implications, including a higher risk of mortality, cardiovascular disease, metabolic diseases, elevated markers of systemic inflammation, and cancer [12,13,14,15,16,17].
In this context, obesity, metabolic health, and their interactions could affect the IOP. We have previously shown an association between this interaction and the onset of glaucoma; however, the possible effect of this interaction on IOP has not yet been elucidated [18].
Therefore, we sought to compare the IOP among groups with different combinations of obesity status, defined using BMI, and metabolic health status in a large cohort.
## 2. Experimental Section
This study was approved by the Institutional Review Board of the Seoul St. Mary’s Hospital, Seoul, Korea, which waived informed consent from individual subjects due to its retrospective design (KC17RESI0098). Our research adhered to the tenets of the Declaration of Helsinki.
We reviewed the data of 20,385 individuals who underwent general medical health examinations between May 2015 and April 2016 at the Health Promotion Center in Seoul St. Mary’s Hospital of the Catholic University of Korea, a 1300-bed tertiary university teaching hospital.
The medical health examination included basic laboratory tests (complete blood count and blood chemistry), urine tests, IOP measurement, anthropometric measurements, a self-questionnaire regarding previous medical history, and a chest X-ray. The examinations were performed by trained nurses, doctors, and medical laboratory technologists.
We included subjects between the ages of 19 and 85 and excluded those who were underweight or who had been previously diagnosed with glaucoma. Those who were taking systemic or ocular corticosteroids, which may affect IOP, were also excluded. Participants using eyedrops other than corticosteroids or antiglaucomatous eyedrops or systemic medications other than corticosteroids were allowed to be included in the analyses. If a subject received more than one health examination during the study period, the data from the first visit were included in the analysis.
Intraocular pressure was measured using a noncontact tonometer (Canon TX-F, Tustin, CA, USA). An average of 3 measurements for each eye were used, and the mean IOP of both eyes was used for the analyses. Anthropometric parameters and body composition values including height, weight, BMI, waist circumference, hip circumference, waist–hip ratio, skeletal muscle mass, body fat mass, and body fat percentage, were measured via a bioelectrical impedance method using Inbody 720 (Biospace, Seoul, Republic of Korea). Resting blood pressure (BP) was measured using an automatic sphygmomanometer (TM-2655P; P.M.S., Berkshire, UK) after at least five minutes of rest. Blood samples were obtained after overnight fasting from each individual’s antecubital vein and centrifuged within 30 min. Serum was analyzed for serum fasting blood glucose (FBS), triglycerides, total cholesterol, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol using a Hitachi 7600 autoanalyzer (Hitachi Ltd., Tokyo, Japan). The HbA1c level was analyzed using high-performance liquid chromatography (Tosoh-G8, Tosoh, Tokyo, Japan). Insulin resistance was calculated using the homeostasis model of assessment for insulin resistance (HOMA-IR) formula, as follows: fasting serum insulin (µU/mL) × FBG (mg/dL)/405 [19].
Metabolic health was determined using both the subjects’ answers to the questionnaire regarding previous medical history and health examination results. Those with three or more of the following risk factors were considered metabolically unhealthy [20,21].
Obesity phenotypes were determined based on BMI. Those with 18.5 kg/m2 ≤ BMI < 25 kg/m2 were considered of normal weight, and those with BMI ≥ 25 kg/m2 were considered obese, according to the revised Asia-Pacific obesity criteria [23,24].
Based on the combination of metabolic health and obesity phenotype status, the study subjects were subclassified into the following 4 groups: (i) metabolically healthy normal-weight (MHNW), defined as less than 3 metabolic risk factors and BMI < 25 kg/m2; (ii) metabolically healthy obese (MHO), defined as less than 3 metabolic risk factors and BMI ≥ 25 kg/m2; (iii) metabolically unhealthy normal-weight (MUNW), defined as 3 or more metabolic risk factors and BMI < 25 kg/m2; and (iv) metabolically unhealthy obese (MUO), defined as 3 or more metabolic risk factors and BMI ≥ 25 kg/m2.
For the statistical analyses, continuous variables were analyzed using one-way analysis of variance and categorical variables were analyzed using Pearson’s chi-squared test. In addition, an analysis of covariance was also performed adjusting for age and sex. We used the Bonferroni method to adjust the p values for post hoc analyses. In the sub-analysis, the mean IOP was evaluated according to the number of metabolic syndrome components in the normal-weight and obese groups. Body mass index levels were classified into 4 subgroups (normal weight: 18.5–<23 kg/m2, overweight: 23–<25 kg/m2, obese I: 25–<30 kg/m2, and obese II: ≥30 kg/m2) [24] and mean IOP was analyzed among each BMI subgroup in the metabolically healthy and unhealthy groups. For all analyses, the SPSS computer package (version 24.0; SPSS Inc., Chicago, IL, USA) was used, and a p value < 0.05 was considered statistically significant.
## 3. Results
Figure 1 shows the selection of study subjects. A total of 20,385 subjects underwent medical health examination during the study period. In total, 18,366 subjects were included in this study. Table 1 shows the baseline characteristics of the study subjects. The number of subjects classified into the MHNW group was the largest (11,220, $61.1\%$), followed by MHO (3283, $17.8\%$), MUO (2736, $14.9\%$), and MUNW (1127, $6.1\%$). The mean age was older in the metabolically unhealthy groups (56.65 and 50.64 years in the MUNW and MUO groups, respectively) compared to metabolically health groups (46.47 and 46.57 years in the MHNW and MHO groups, respectively). The distribution of sex was different among the four groups. The mean anthropometric parameters, BMI, systolic and diastolic BP, FBG, total cholesterol, HDL, and LDL were significantly higher in the metabolically unhealthy groups compared with the metabolically healthy groups. The mean BMI values of the normal-weight groups were 22.01 kg/m2 and 23.23 kg/m2 in the MHNW and MUNW groups, respectively, whereas in the obese groups, the mean BMI values were 26.83 kg/m2 and 28.12 kg/m2 in the MHO and MAO groups, respectively, which was statistically significantly different. The metabolically unhealthy groups had a significantly higher percentage of individuals with diabetes, hypertension, and dyslipidemia.
The mean IOP among the four groups showed significant differences (Table 2 and Figure 2). In the post hoc analyses, those in the MUO groups showed the highest mean IOP (14.38 mmHg), followed by those in the MUNW (14.22 mmHg) and metabolically healthy groups. The mean IOP values of the MUNW group before and after adjusting for age and sex (14.22 mmHg and 14.20 mmHg) were statistically significantly higher than the mean IOP of the MHO group (13.50 mmHg and 13.47 mmHg). The mean IOP values of the MHO (13.50 mmHg and 13.47 mmHg) and MHNW groups (13.06 mmHg and 13.08 mmHg) were not statistically significant before and after adjustment. This trend was similar in both men and women (Figure 2).
Table 2 shows the differences in mean IOP in each subgroup. Those with obesity, metabolic syndrome, or each component of metabolic syndrome showed higher mean IOP than those without. A similar pattern was observed in both men and women.
In the sub-analysis, the mean IOP values were compared among the subgroups according to metabolic health and BMI status. The mean IOP showed a stepwise increase as the number of metabolic syndrome components increased in both the obese and normal-weight groups in both men and women (Figure 3). There was no significant difference in subjects who were of normal weight vs. those who were obese in each group. In addition, mean IOP generally increased as the BMI level increased in both metabolically healthy and unhealthy groups in both sexes (Figure 4). Subjects who were metabolically unhealthy showed significantly higher mean IOP compared to their counterparts who were metabolically healthy in each BMI subgroup, although it was not statistically significant in the obese II group. Of note, compared to the metabolically healthy overweight and metabolically healthy obese I groups, those who were MUNW showed significantly higher IOP. These data suggest that metabolic health status is more closely associated with increased IOP than obesity.
## 4. Discussion
In this study, we analyzed a large number of subjects undergoing health examinations and compared the mean IOP after categorizing them into four groups according to obesity and metabolic status. To the best of our knowledge, this is the first study to compare IOP among groups categorized by obesity and metabolic health status. First, we found that while obesity and metabolic status are both linked with increased IOP, the MUNW group showed significantly higher IOP compared with the MHO group, indicating that metabolic health is more associated with increased IOP than obesity. This trend remained significant even after adjusting for age and sex. In addition, individuals who were metabolically unhealthy showed higher IOP compared to their counterparts who were metabolically healthy, regardless of BMI level. Additionally, there was a stepwise increase in IOP as the number of metabolic disease components increased; however, the difference was not significant between those who were of normal weight and those who were obese.
Regarding the association between obesity and elevated IOP, many previous studies have suggested a positive association. The Beaver Dam Eye Study, a population-based study performed in the USA, found that IOP was positively associated with BMI [10]. Another study examined this relationship both cross-sectionally and longitudinally in a large Japanese population and reported a positive association [7]. This positive relationship between obesity and IOP may be mediated by increased corticosteroid secretion in obese subjects and elevated episcleral venous pressure from excess orbital fat, and an increase in blood viscosity. Meanwhile, in the Barbados Eye Study, higher BMI was positively associated with IOP, but only marginally ($$p \leq 0.052$$), and another study reported similar IOP in subjects with different BMI [9,11]. Different characteristics of the study population and different study designs may explain the discrepancy between these studies. It may also be accounted for by different phenotypes of obesity. There is a large body of literature on subgroups of obesity with different metabolic profiles. Subgroups of obese individuals who are MHO have fewer components of metabolic derangements and are reported to have lower cardiometabolic risk and mortality [12,17,25,26]. On the other hand, individuals who are MUNW are characterized by excess deposition of visceral adipose tissue, inflammation of the adipose tissue, altered inflammatory profiles, and higher cardiometabolic risks [27].
In the present study, we studied the association between IOP and different combinations of obesity and metabolic health status. An important finding of our study is that the MUNW group had higher IOP than the MHO group. There could be several postulations for this finding. The MHO group had fewer components of metabolic diseases. It is well known that metabolic derangements such as hypertension, diabetes, and dyslipidemia contribute to IOP elevation [28,29,30,31]. Increased blood pressure may cause IOP elevation by increasing filtration fractions of the aqueous humor through elevated ciliary artery pressure [32]. Increased sympathetic tone and corticosteroids may also have a role in this relationship [33]. High FBG may cause IOP elevation by increasing the osmotic gradient, which can increase aqueous humor production [34]. It can also interrupt the aqueous outflow facility through fibronectin accumulation in the trabecular meshwork [35]. Autonomic dysfunction has also been proposed to be a link between diabetes and IOP [36]. Additionally, being metabolically unhealthy is related with insulin resistance, which has been reported to be associated with increased IOP, possibly through the stimulation of ocular sympathetic activity [34,37]. In our study, insulin resistance was highest in the MUO and MUNW groups, followed by the MHO and MHNW groups. Systemic inflammation may also play a role. Elevated levels of proinflammatory cytokines, including interleukin-6 and tumor necrosis factor-alpha from visceral adipose tissue, have been reported in MUNW individuals [14,38,39].
We also found that those who were metabolically unhealthy showed higher IOP compared to their counterparts who were metabolically healthy, regardless of BMI level. In addition, a stepwise increase in IOP was observed as the number of metabolic diseases increased, with no significant difference between the normal-weight vs. obese groups. These findings suggest that metabolic health status is more closely associated with IOP than obesity. The results of the present study agree with those of previous studies that have reported that each of the metabolic syndrome components was linearly and independently associated with IOP [28,29,30,31].
The strengths of our study include its large-scale design. We also used combinations of disease history as stated by each subject, as well as the results of the laboratory data, to improve the diagnostic validity of metabolic diseases. However, there are also some limitations. First, this study was conducted at a single hospital in subjects undergoing health examinations; therefore, our dataset may involve selection bias, as well as healthy user bias. In addition, information regarding underlying diseases and medications was collected from self-reported questionnaires, so our data may have included some participants using drugs than can affect IOP (i.e., corticosteroids). We tried to mitigate these potential biases by including a large number of subjects. Furthermore, this is a cross-sectional study, so we cannot determine a causal relationship. Such drawbacks warrant future clinical studies to determine the exact causal relationship between metabolic status, obesity, and IOP.
As the prevalence of obesity and metabolic syndrome is increasing worldwide, there is growing research that aims to understand the risk of diseases in subjects with MHO compared to those with MUO or MUNW. This study showed that IOP was highest in the MUO group, followed by the MUNW and MHO groups. Clinicians should be aware of the metabolic status in seemingly lean subjects, as metabolic status has a higher impact on IOP than obesity. Future studies should investigate the underlying mechanisms between metabolic health, obesity, and IOP and the therapeutic effects of lifestyle modifications and the treatment of metabolic diseases on lowering IOP.
## 5. Conclusions
In conclusion, we have shown that the MUNW group showed higher IOP compared to the MHO group. Individuals who were metabolically unhealthy showed higher IOP compared to their counterparts who were metabolically healthy, regardless of BMI level, and there was a linear increase in IOP as the number of metabolic disease components increased, but no difference between normal-weight vs. obese individuals. The present findings have potential clinical and public health implications and highlight the greater role of metabolic health status than obesity in IOP elevation.
## References
1. Tham Y.C., Li X., Wong T.Y., Quigley H.A., Aung T., Cheng C.Y.. **Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis**. *Ophthalmology* (2014) **121** 2081-2090. DOI: 10.1016/j.ophtha.2014.05.013
2. Quigley H.A.. **Glaucoma**. *Lancet* (2011) **377** 1367-1377. DOI: 10.1016/S0140-6736(10)61423-7
3. Weinreb R.N., Aung T., Medeiros F.A.. **The pathophysiology and treatment of glaucoma: A review**. *JAMA* (2014) **311** 1901-1911. DOI: 10.1001/jama.2014.3192
4. Weinreb R.N., Khaw P.T.. **Primary open-angle glaucoma**. *Lancet* (2004) **363** 1711-1720. DOI: 10.1016/S0140-6736(04)16257-0
5. Akinci A., Cetinkaya E., Aycan Z., Oner O.. **Relationship between intraocular pressure and obesity in children**. *J. Glaucoma* (2007) **16** 627-630. DOI: 10.1097/IJG.0b013e318057528a
6. Jang H.D., Kim D.H., Han K., Ha S.G., Kim Y.H., Kim J.W., Park J.Y., Yoon S.J., Jung D.W., Park S.W.. **Relationship between Intraocular Pressure and Parameters of Obesity in Korean Adults: The 2008–2010 Korea National Health and Nutrition Examination Survey**. *Curr. Eye Res.* (2015) **40** 1008-1017. DOI: 10.3109/02713683.2014.975367
7. Mori K., Ando F., Nomura H., Sato Y., Shimokata H.. **Relationship between intraocular pressure and obesity in Japan**. *Int. J. Epidemiol.* (2000) **29** 661-666. DOI: 10.1093/ije/29.4.661
8. Reddy A., Halenda K., Cromer P., Chen L., Butler J., Raed A., Bhagatwala J., Sponseller T., Bollinger K., Zhu H.. **The Association of Intraocular Pressure with Obesity and Cardiometabolic Risk in a Young Farmworker Population**. *J. Glaucoma* (2021) **30** 24-31. DOI: 10.1097/IJG.0000000000001673
9. Wu S.Y., Leske M.C.. **Associations with intraocular pressure in the Barbados Eye Study**. *Arch. Ophthalmol.* (1997) **115** 1572-1576. DOI: 10.1001/archopht.1997.01100160742012
10. Klein B.E., Klein R., Linton K.L.. **Intraocular pressure in an American community. The Beaver Dam Eye Study**. *Investig. Ophthalmol. Vis. Sci.* (1992) **33** 2224-2228. PMID: 1607232
11. Karadag R., Arslanyilmaz Z., Aydin B., Hepsen I.F.. **Effects of body mass index on intraocular pressure and ocular pulse amplitude**. *Int. J. Ophthalmol.* (2012) **5** 605-608. PMID: 23166873
12. Karelis A.D., Faraj M., Bastard J.P., St-Pierre D.H., Brochu M., Prud’homme D., Rabasa-Lhoret R.. **The metabolically healthy but obese individual presents a favorable inflammation profile**. *J. Clin. Endocrinol. Metab.* (2005) **90** 4145-4150. DOI: 10.1210/jc.2005-0482
13. Iacobini C., Pugliese G., Blasetti Fantauzzi C., Federici M., Menini S.. **Metabolically healthy versus metabolically unhealthy obesity**. *Metabolism* (2019) **92** 51-60. DOI: 10.1016/j.metabol.2018.11.009
14. Teixeira T.F., Alves R.D., Moreira A.P., Peluzio Mdo C.. **Main characteristics of metabolically obese normal weight and metabolically healthy obese phenotypes**. *Nutr. Rev.* (2015) **73** 175-190. DOI: 10.1093/nutrit/nuu007
15. Dvorak R.V., DeNino W.F., Ades P.A., Poehlman E.T.. **Phenotypic characteristics associated with insulin resistance in metabolically obese but normal-weight young women**. *Diabetes* (1999) **48** 2210-2214. DOI: 10.2337/diabetes.48.11.2210
16. Liu B., Giffney H.E., Arthur R.S., Rohan T.E., Dannenberg A.J.. **Cancer Risk in Normal Weight Individuals with Metabolic Obesity: A Narrative Review**. *Cancer Prev. Res.* (2021) **14** 509-520. DOI: 10.1158/1940-6207.CAPR-20-0633
17. Romero-Corral A., Somers V.K., Sierra-Johnson J., Korenfeld Y., Boarin S., Korinek J., Jensen M.D., Parati G., Lopez-Jimenez F.. **Normal weight obesity: A risk factor for cardiometabolic dysregulation and cardiovascular mortality**. *Eur. Heart J.* (2010) **31** 737-746. DOI: 10.1093/eurheartj/ehp487
18. Jung Y., Han K., Park H.Y.L., Lee S.H., Park C.K.. **Metabolic Health, Obesity, and the Risk of Developing Open-Angle Glaucoma: Metabolically Healthy Obese Patients versus Metabolically Unhealthy but Normal Weight Patients**. *Diabetes Metab. J.* (2020) **44** 414-425. DOI: 10.4093/dmj.2019.0048
19. Matthews D.R., Hosker J.P., Rudenski A.S., Naylor B.A., Treacher D.F., Turner R.C.. **Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man**. *Diabetologia* (1985) **28** 412-419. DOI: 10.1007/BF00280883
20. Alberti K.G., Eckel R.H., Grundy S.M., Zimmet P.Z., Cleeman J.I., Donato K.A., Fruchart J.C., James W.P., Loria C.M., Smith S.C.. **Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity**. *Circulation* (2009) **120** 1640-1645. PMID: 19805654
21. Lee H.Y., Yang H.K., Song H.J., Chang H.J., Kang J.Y., Lee S.H., Han S., Kim Y.K.. **Metabolic health is more closely associated with decrease in lung function than obesity**. *PLoS ONE* (2019) **14**. DOI: 10.1371/journal.pone.0209575
22. Lee S.Y., Park H.S., Kim D.J., Han J.H., Kim S.M., Cho G.J., Kim D.Y., Kwon H.S., Kim S.R., Lee C.B.. **Appropriate waist circumference cutoff points for central obesity in Korean adults**. *Diabetes Res. Clin. Pract.* (2007) **75** 72-80. DOI: 10.1016/j.diabres.2006.04.013
23. **Regional Office for the Western Pacific**. *The Asia-Pacific Perspective: Redefining Obesity and Its Treatment* (2000)
24. Yang H.K., Han K., Kwon H.S., Park Y.M., Cho J.H., Yoon K.H., Kang M.I., Cha B.Y., Lee S.H.. **Obesity, metabolic health, and mortality in adults: A nationwide population-based study in Korea**. *Sci. Rep.* (2016) **6** 30329. DOI: 10.1038/srep30329
25. Vecchie A., Dallegri F., Carbone F., Bonaventura A., Liberale L., Portincasa P., Fruhbeck G., Montecucco F.. **Obesity phenotypes and their paradoxical association with cardiovascular diseases**. *Eur. J. Intern. Med.* (2018) **48** 6-17. DOI: 10.1016/j.ejim.2017.10.020
26. Yoo H.J., Hwang S.Y., Hong H.C., Choi H.Y., Seo J.A., Kim S.G., Kim N.H., Choi D.S., Baik S.H., Choi K.M.. **Association of metabolically abnormal but normal weight (MANW) and metabolically healthy but obese (MHO) individuals with arterial stiffness and carotid atherosclerosis**. *Atherosclerosis* (2014) **234** 218-223. DOI: 10.1016/j.atherosclerosis.2014.02.033
27. Ding C., Chan Z., Magkos F.. **Lean, but not healthy: The ‘metabolically obese, normal-weight’ phenotype**. *Curr. Opin. Clin. Nutr. Metab. Care* (2016) **19** 408-417. DOI: 10.1097/MCO.0000000000000317
28. Chang Y.C., Lin J.W., Wang L.C., Chen H.M., Hwang J.J., Chuang L.M.. **Association of intraocular pressure with the metabolic syndrome and novel cardiometabolic risk factors**. *Eye* (2010) **24** 1037-1043. DOI: 10.1038/eye.2009.247
29. Park B.J., Park J.O., Kang H.T., Lee Y.J.. **Elevated intraocular pressure is associated with metabolic syndrome in postmenopausal women: The Korean National Health and Nutrition Examination Survey**. *Menopause* (2013) **20** 742-746. DOI: 10.1097/GME.0b013e31827ce3c6
30. Yi Y.H., Cho Y.H., Kim Y.J., Lee S.Y., Lee J.G., Kong E.H., Cho B.M., Tak Y.J., Hwang H.R., Lee S.H.. **Metabolic syndrome as a risk factor for high intraocular pressure: The Korea National Health and Nutrition Examination Survey 2008–2010**. *Diabetes Metab. Syndr. Obes.* (2019) **12** 131-137. DOI: 10.2147/DMSO.S185604
31. Yokomichi H., Kashiwagi K., Kitamura K., Yoda Y., Tsuji M., Mochizuki M., Sato M., Shinohara R., Mizorogi S., Suzuki K.. **Evaluation of the associations between changes in intraocular pressure and metabolic syndrome parameters: A retrospective cohort study in Japan**. *BMJ Open* (2016) **6** e010360. DOI: 10.1136/bmjopen-2015-010360
32. Hennis A., Wu S.Y., Nemesure B., Leske M.C., Barbados Eye Studies G.. **Hypertension, diabetes, and longitudinal changes in intraocular pressure**. *Ophthalmology* (2003) **110** 908-914. DOI: 10.1016/S0161-6420(03)00075-7
33. Carel R.S., Korczyn A.D., Rock M., Goya I.. **Association between ocular pressure and certain health parameters**. *Ophthalmology* (1984) **91** 311-314. DOI: 10.1016/S0161-6420(84)34282-8
34. Oh S.W., Lee S., Park C., Kim D.J.. **Elevated intraocular pressure is associated with insulin resistance and metabolic syndrome**. *Diabetes Metab. Res. Rev.* (2005) **21** 434-440. DOI: 10.1002/dmrr.529
35. Sato T., Roy S.. **Effect of high glucose on fibronectin expression and cell proliferation in trabecular meshwork cells**. *Investig. Ophthalmol. Vis. Sci.* (2002) **43** 170-175. PMID: 11773028
36. Mapstone R., Clark C.V.. **Prevalence of diabetes in glaucoma**. *Br. Med. J.* (1985) **291** 93-95. DOI: 10.1136/bmj.291.6488.93
37. Chun Y.H., Han K., Park S.H., Park K.M., Yim H.W., Lee W.C., Park Y.G., Park Y.M.. **Insulin resistance is associated with intraocular pressure elevation in a non-obese Korean population**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0112929
38. Hainer V., Aldhoon-Hainerova I.. **Obesity paradox does exist**. *Diabetes Care* (2013) **36** S276-S281. DOI: 10.2337/dcS13-2023
39. De Lorenzo A., Del Gobbo V., Premrov M.G., Bigioni M., Galvano F., Di Renzo L.. **Normal-weight obese syndrome: Early inflammation?**. *Am. J. Clin. Nutr.* (2007) **85** 40-45. DOI: 10.1093/ajcn/85.1.40
|
---
title: Polyphenol Profile, Antioxidant Activity, and Hypolipidemic Effect of Longan
Byproducts
authors:
- Si Tan
- Zunli Ke
- Chongbing Zhou
- Yuping Luo
- Xiaobo Ding
- Gangjun Luo
- Wenfeng Li
- Shengyou Shi
journal: Molecules
year: 2023
pmcid: PMC10004001
doi: 10.3390/molecules28052083
license: CC BY 4.0
---
# Polyphenol Profile, Antioxidant Activity, and Hypolipidemic Effect of Longan Byproducts
## Abstract
Longan, a popular fruit in Asia, has been used in traditional Chinese medicine to treat several diseases for centuries. Recent studies have indicated that longan byproducts are rich in polyphenols. The aim of this study was to analyze the phenolic composition of longan byproduct polyphenol extracts (LPPE), evaluate their antioxidant activity in vitro, and investigate their regulating effect on lipid metabolism in vivo. The results indicated that the antioxidant activity of LPPE was 231.350 ± 21.640, 252.380 ± 31.150, and 558.220 ± 59.810 (mg Vc/g) as determined by DPPH, ABTS, and FRAP, respectively. UPLC-QqQ-MS/MS analysis indicated that the main compounds in LPPE were gallic acid, proanthocyanidin, epicatechin, and phlorizin. LPPE supplementation prevented the body weight gain and decreased serum and liver lipids in high-fat diet-induced-obese mice. Furthermore, RT-PCR and Western blot analysis indicated that LPPE upregulated the expression of PPARα and LXRα and then regulated their target genes, including FAS, CYP7A1, and CYP27A1, which are involved in lipid homeostasis. Taken together, this study supports the concept that LPPE can be used as a dietary supplement in regulating lipid metabolism.
## 1. Introduction
Longan (Dimocarpus longan Lour.), also known as guiyuan, belongs to the family of Sapindaceae. It is widely distributed in Southeast Asia and Southern China, especially in Guangdong, Guangxi, and Fujian, and China accounts for $60\%$ of the world’s longan production [1]. Longan has become one of the most popular fruits worldwide due to its delicious flavor and nutritional values. Longan fruits are rich in carbohydrates, fiber, vitamin C, amino acids, and so on. In China, longan is considered to be a “Medical Food Homology” and has been used in traditional Chinese medicine to cure insomnia and neural pain and to stop sweating and bleeding [2]. Longan can be consumed in both fresh and processed products such as ointment, wine, and canned longan. Normally, the pericarps and seeds, accounting for about $30\%$ of the whole fresh fruit, are usually discarded as waste or burned after processing, which not only pollutes the environment but also wastes exploitable resources [3]. Recently, numerous studies have indicated that longan fruits are abundant in phenolic compounds and have strong antioxidant activity [4]. In particular, the types and contents of phenolics in the pericarp and seeds of longan are much richer than those in the pulps [5]. Additionally, polyphenolic extracts from longan pericarps or seeds have been reported to possess a variety of biological activities such as the promotion of healing of deep second-degree burns in mice [6], anti-hyperglycemic activity [2], anti-tyrosinase activity [7], and so on. However, to our knowledge, there is limited information regarding the phenolic profile of longan byproducts, and their effect on lipid metabolism has not been investigated.
Abnormal lipid metabolism is an important risk factor of obesity, hyperlipidemia, fatty liver, and type 2 diabetes, as well as other chronic metabolic diseases. Several mechanisms, including regulation of cholesterol absorption, inhibition of synthesis and secretion of triglyceride, and reduction in low-density lipoprotein oxidation in plasma, contribute to maintaining or improving lipid metabolism [8]. Various factors could influence lipid metabolism, such as eating habits, exercise, and gut microbiota. Growing evidence suggests that many polyphenols are involved in lipid metabolism and, therefore, may prevent obesity [9]. For example, *Goishi tea* polyphenols might reduce cardiovascular disease risk by lowering triglyceride levels, according to a randomized, double-blind, placebo-controlled clinical study [10]. In addition, citrus polyphenols have been reported to regulate lipid metabolism and prevent metabolic diseases, according to numerous animal and human clinical trials [11]. In all, polyphenol-rich dietary supplementation may improve lipid metabolism and prevent metabolic syndrome.
Multiple pathways are involved in lipid metabolism, and peroxisome proliferator-activated receptors (PPARs) play an important regulatory role. In particular, PPARα is highly expressed in the liver and controls a variety of metabolic processes in the liver, including mitochondrial fatty acid oxidation, fatty acid binding, degradation of triglyceride, lipid synthesis, and so on [12]. In addition, liver X receptor α (LXRα) is an important nuclear receptor transcription factor, the expression of which is highly correlated with hepatic adipose deposition and plays a key role in the transcriptional regulation of cholesterol homeostasis [13]. PPARα and LXRα interact to influence the regulation of fatty acid and cholesterol metabolism. For example, by regulating PPARα and LXRα pathways, sesamin ameliorates hepatic steatosis induced in rats fed a high-fat diet [14]. Moreover, Ge et al. also reported that, by regulating PPARα and LXRα pathways, betaine prevented nonalcoholic fatty liver diseases induced by fructose in rats [15]. The important role of PPARα and LXRα in lipid metabolism renders them important targets for pharmacological and dietary approaches to improve lipid metabolism, obesity, and metabolic syndrome.
The aim of this study was to analyze the phenolic composition of longan byproduct polyphenol extracts (LPPE), evaluate its antioxidant activity in vitro, and investigate its regulating effect on lipid metabolism in vivo. First, the polyphenols of longan byproducts were extracted, and the chemical profile was analyzed via high-pressure liquid chromatography, and triple quadrupole mass spectrometry (UPLC-QqQ-MS/MS). Then, the ability of LPPE to lower blood and liver lipids, thereby improving hepatic steatoses, was evaluated. Furthermore, quantitative real-time PCR (RT-PCR) and Western blot were carried out to reveal the molecular mechanisms.
## 2.1. Polyphenol Contents and Antioxidant Activity of LPPE
The UPLC fingerprint of LPPE is shown in Figure 1. Eleven compounds including gallic acid, proanthocyanidin B2, epicatechin, proanthocyanidin A2, syringic acid, p-hydroxybenzoic acid, poncirin, ferulic acid, rutin, phlorizin, and methyl hesperidin were unambiguously identified. As shown in Table 1, the quantitative analyses of LPPE indicated that the principal component in LPPE was phlorizin (38.894 ± 3.765 mg/g), followed by proanthocyanidin A2 (24.382 ± 2.859 mg/g), gallic acid (24.080 ± 2.791 mg/g), and epicatechin (7.592 ± 0.231 mg/g).
The extraction rate of LPPE was $10.69\%$, and the total phenolic content of LPPE measured via the Folin–Ciocalteu colorimetric method was 285.350 ± 36.430 mg GAE/g. To understand the antioxidant activity of LPPE, its scavenging DPPH and ABTS radical abilities and ferric-ion-reducing antioxidant power (FRAP) were evaluated. As shown in Table 1, LPPE exhibited a good capacity for scavenging DPPH (231.350 ± 21.640 mg Vc/g) and ABTS (252.380 ± 31.150 mg Vc/g) radicals and FRAP (558.220 ± 59.810 mg Vc/g).
## 2.2. Effects of LPPE on Body Weight and Cell Size of Epididymal Adipose Tissues in High-Fat Diet-Induced Obese Mice
In this study, C57BL/6J mice fed with an HF diet were used as the lipid metabolism disorder model. As shown in Figure 2A, an HF diet led to a more than $60\%$ higher body weight compared with the mean body weight in the Chow group after 12 consecutive weeks of feeding, which is a significant increase. Moreover, LPPE supplementation significantly decreased the high-fat-diet-induced body-weight gain and caused a mean $11.8\%$ reduction in total body weight compared with the HF group, without affecting the food intake. An HF diet also led to a significant increase in the cell size of epididymal white adipose tissues, and $0.2\%$ LPPE exerted an antagonizing effect (Figure 2B). Those results indicate that LPPE alleviated the mice obesity that was induced by an HF diet.
## 2.3. Effects of LPPE on Serum and Liver Lipids, and Hepatic Steatosis in High-Fat Diet-Induced Obese Mice
Long-term high-fat diet can easily lead to hyperlipidemia. As shown in Figure 3A, the mice in the HFD group exhibited significantly higher levels of serum TC, TG, HDL-c, and LDL-c when compared to those in the Chow group. Interestingly, significant reductions in serum TC, TG, HDL-c, and LDL-c levels were observed in $0.2\%$ LPPE-treated mice. The results of the total liver lipid levels indicated that the highest contents of TG (Figure 3B) and TC (Figure 3C) were found in the HFD group, which were greatly alleviated by $0.2\%$ LPPE supplementation. The histological analysis showed that excessive ballooning degeneration (Figure 3D) and lipid droplets (Figure 3E) occurred in the mice livers from the HFD group compared to those in the Chow group, suggesting that HFD induced hepatic steatosis. Expectedly, liver tissues in the LPPE group showed much less ballooning degeneration and lipid droplets, suggesting that hepatic fat accumulation and hepatic steatosis induced by a high-fat diet were strongly ameliorated by LPPE treatment.
## 2.4. Effects of LPPE on the Gene Expression Involved in Lipid Metabolism
To understand the underlying molecular mechanisms by which LPPE improve lipid metabolism, the expressions of genes associated with lipid metabolism were determined. In this study, two nuclear receptors, peroxisome-proliferation-activated receptor alpha (PPARα) and liver X receptor alpha (LXRα), which play important roles in lipid homeostasis, were analyzed [12,13]. A high-fat diet led to significantly decreased PPARα (Figure 4A) and LXRα (Figure 4B) gene expression. As expected, LPPE significantly enhanced the expressions of PPARα and LXRα. Additionally, fatty acid synthase (FAS) gene expression was significantly increased in the HFD group when compared to that in the Chow group, and LPPE inhibited the expression of FAS (Figure 4C). On the other hand, there were significant decreases in the expressions of the cholesterol 7-alpha hydroxylase (CYP7A1) (Figure 4D) and cholesterol 27-hydroxylase (CYP27A1) (Figure 4E) genes in the HFD group, which were then effectively up-regulated by LPPE supplementation.
## 2.5. Effects of LPPE on the Protein Expressions Involved in Lipid Metabolism
Western blot analysis (Figure 5 and Figure S1, Table S1) revealed that the protein expression levels of PPARα, LXRα, and CYP7A1 were also inhibited by a high-fat diet, and $0.2\%$ LPPE supplementation significantly elevated the expression of PPARα, LXRα, and CYP7A1. Similar to the gene expression result, LPPE also suppressed the expression of FAS, which was activated by a high-fat diet. In summary, the gene and protein analysis results indicated that PPARα/LXRα signaling is likely to contribute to the improvement of lipid metabolism mediated with LPPE.
## 3. Discussion
Lipid metabolism disorder is currently considered to be a hallmark characteristic of many chronic metabolic diseases, such as obesity and cardiovascular disease [16]. As is well known, dietary ingredients play key role in the development, progression, and prevention of lipid accumulation. For example, a high-fat diet induces obesity, whereas large quantities of phytochemicals such as polyphenols have shown potential regulating effects on lipid metabolism, including decreasing lipid accumulation in both the liver and the blood [8]. Therefore, it is promising to regulate lipid metabolism by dietary intervention.
Recently, phytotherapy, which is defined as the therapeutic use of whole or minimally modified plant components, has been focused on preventing diseases and improving health conditions [17]. In particular, polyphenols, which have shown strong antioxidant activity, have been reported to be effective in improving lipid metabolism in the literature [18]. For example, curcumin, a natural polyphenol from turmeric, has been reported to improve features of fatty liver according to a randomized, placebo-controlled trial [19]. In addition, blueberry polyphenols have been shown to inhibit body weight gain and return lipid metabolism to normal in high-fat diet-induced-obese mice [20]. Moreover, polyphenols in pomegranate peel alleviated inflammation and hypercholesterolaemia in high-fat diet-induced-obese mice [21]. In all, polyphenols and polyphenol-rich dietary supplementation are important approaches in improving lipid metabolism.
In this study, the polyphenol profile and antioxidant activities of longan pericarp extracts (LPPE) were analyzed. In total, 11 phenolic compounds were identified in this study, and most of those compounds in longan fruits have already been reported by several studies [1,4,22]. Our results indicated that LPPE were rich in phlorizin, proanthocyanins, gallic acid, and epicatechin. Phlorizin, a natural dihydrochalcone possessing several pharmacological activities such as antioxidant, anti-inflammatory, antidiabetic, and hepatoprotective activities [23], has been reported as one of the main flavonoid glycosides in the seeds of lychee [24]. Furthermore, Li et al. also reported that the longan pericarp extracts are rich in proanthocyanidin A, gallic acid, and epicatechin [2]. However, corilagin, which has been reported as one of the main phenolic compounds in longan by several studies [4,25], was not identified in this study. Similarly, this compound was also not detected in other studies [1,26,27]. This difference may result from the different cultivar, cultivation environment, purification method, and so on.
The total polyphenol content of LPPE was much higher than that reported by Prasad et al. [ 28], who indicated that the total polyphenol content of longan fruit pericarp determined via ultra-high-pressure-assisted extraction was about 100 mg GAE/g, but it was lower than the longan pericarp extracts purified via a Sephadex LH-20 column [2]. Those difference may be caused by the different cultivar, degrees of ripeness, and extraction and purification methods. In addition, the result of antioxidant activity of LPPE was similar to that reported by He et al. [ 27] when the extraction rate was considered, and it was much higher than most of medical plant samples such as ginger, ginkgo, and *Psidium guajava* [29,30]. All of those results indicated that LPPE is rich in the variety of phenolic compounds and has good antioxidant activity.
Furthermore, the effect of LPPE on lipid metabolism was firstly evaluated, and the results revealed that LPPE could decrease body weight and serum lipids and inhibit hepatic lipid accumulation induced by a high-fat diet. According to our component analysis, the main phenolic compounds in LPPE include gallic acid, epicatechin, proanthocyanidins, and phlorizin. Gallic acid showed a hypolipidemic effect in mice fed a high-fat diet [31] and protected the liver against nonalcoholic fatty liver disease by inhibiting inflammatory signaling pathways in Wistar rats [32]. Epicatechin, a natural flavanol which is found in green tea and cocoa, has also been reported to decrease blood lipids and attenuate hepatic steatosis in high-fat diet-induced-obese rats [33]. Furthermore, proanthocyanidins and proanthocyanidin-rich extracts have been observed to decrease serum lipids [34], and regulate lipid metabolism in rats at the molecular level [35]. Another dihydrochalcone phlorizin, which is mainly distributed in the plants of the Malus genus, has been reported to improve lipid metabolism in streptozotocin-induced diabetic rats [36] and to ameliorate lipid deposition in mice fed a high-fat diet [37]. Therefore, these phenolic constituents may contribute to the preventive effect of LPPE on lipid metabolism disorder. In addition, it is necessary to consider the synergistic effects of all the components of LPPE.
Earlier studies have shown that longan pericarps and seeds are rich in polyphenols and have strong antioxidant, anti-inflammatory, and anti-cancer activities [38]. In this study, our results firstly indicated that LPPE might be beneficial for improving lipid metabolism, partly through the PPARα and LXRα pathways. PPARα inhibits liver triglyceride synthesis by inhibiting the expression of FAS and other de novo fatty acid synthesis genes [12]. On the other side, PPARα agonists enhance fatty acid oxidation, increase lipid decomposition, and reverse cholesterol transportation [39]. Activation of LXRα can promote the expression of CYP7A1, which is an important rate-limiting enzyme in the process of bile acid synthesis by cholesterol, and thus plays a positive regulatory role in cholesterol metabolism [40]. Moreover, a loss of LXRα can lead to peripheral cholesterol accumulation and liver lipid deposition, promoting the progression of nonalcoholic fatty liver in mice fed a high-fat diet, whereas a high expression of LXRα can reduce liver inflammation and fibrosis induced by a high-fat diet [41]. In the present study, the transcriptional levels of PPARα and LXRα were increased in the LPPE group when compared to the HFD group, and their target genes FAS, CYP7A1, and CYP27A1, which are related to fatty acid and cholesterol metabolisms, were obviously regulated following supplementation with LPPE. Western blot results were also consistent with the gene expression results. Those results suggested that LPPE may regulate aspects of hepatic lipid metabolism, including lipid synthesis, lipid oxidation, and lipid transportation, thereby improving lipid metabolism disorders induced by a high-fat diet. Similarly, several dietary polyphenols or phenolic-rich extracts were reported to have hepatoprotective effects by regulating PPARα and LXRα pathways [42]. Interestingly, a previous study also showed that polyphenol-rich longan flower water extract had anti-obesity and hypolipidemic effects in rats fed a hypercaloric diet, upregulated PPARα gene expression, and decreased FAS gene expression. The main polyphenols in longan flower extract were gentisic acid, epicatechin, ferulic acid, and gallic acid, which had similar components to those in LPPE [43]. However, Liu et al. reported that longan flower water extracts attenuated nonalcoholic fatty liver by decreasing lipid peroxidation and downregulating matrix metalloproteinases-2 and -9 in rats [44]. The differences in the mechanisms may originate from the different animal models, chemical profile of extracts, and additive dose of polyphenols. Nevertheless, the above studies indicated that the mechanism by which polyphenolic compounds regulate lipid metabolism is complex. Taken together, we speculated that LPPE improved lipid metabolism partly through the PPARα/LXRα/FAS/CYP7A1 pathway.
## 4.1. Reagents and Standards
Acetonitrile (HPLC-grade) was purchased from Adamas reagent, Ltd. (Shanghai, China). All the polyphenol standards (gallic acid, proanthocyanidin B2, epicatechin, proanthocyanidin A2, syringic acid, p-hydroxybenzoic acid, poncirin, ferulic acid, rutin, phlorizin, and methyl hesperidin), purity ≥$99\%$, were purchased from Shanghai yuanye biotechnology Co. Ltd. (Shanghai, China). Vitamin C; 1, 1-diphenyl-2-picrylhydrazyl (DPPH); 2, 2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS); 2, 3, 5-triphenyltetrazolium chloride (TPTZ); triton; tris-HCl; paraformaldehyde; and urethane were purchased from the Beijing Solaibao Technology Co. Ltd. (Beijing, China). Trizol reagent was purchased from TaKaRa (Beijing, China). Hematoxylin and eosin (H&E) kit, Oil Red O (ORO) kit, and a protein extraction kit were obtained from Beyotime Biotechnology (Beijing, China). Antibodies were purchased from Abcam (Shanghai, China).
## 4.2. Polyphenol Extracting
Fresh longan fruits (Chuliang) at the commercial maturity stage were collected from the Institute of China Southern Subtropical Crop Research (Zhanjiang, China). The pericarps and the seeds were manually collected. After washing, those samples were frozen at −60 °C for 12 h. Then, the samples were dried in an experimental vacuum lyophilizer (LGJ-10, Shengchao kechuang biotechnology Co. Ltd., Beijing, China) for 72 h. The dried samples were ground with a pulverizer (FW-100, Beijing Ever Bright Medical Treatment Instrument Co., Ltd., Beijing, China) rapidly and sieved through a 60-mesh screen (screen size of 250 mm). The powder (1 kg) was ultrasonically extracted with an ultrasonic bath (KQ-5200B, Kunshan Ultrasonic Instrument Co., Ltd., Shanghai, China) at 40 °C for 30 min using a solvent of $80\%$ aqueous ethanol (w/$v = 1$:5) [30]. The solid–liquid mixture was vacuum filtered, and the ethanol solution was concentrated under reduced pressure at 35 °C. Then, to remove the impurities and improve the purity of the polyphenols, the crude polyphenol extract was subsequently re-extracted using ethyl acetate (1:5, v/v). The ethyl acetate fraction was concentrated and then lyophilized. The longan byproduct polyphenol extract (LPPE) was stored hermetically at −20 °C for further analysis and animal studies.
## 4.3. Identification and Quantification of Polyphenols via UPLC-QqQ-MS
The polyphenol profile of LPPE was analyzed with an ultra-high-pressure liquid chromatograph (UPLC, Agilent, Santa Clara, CA, USA) and a triple quadrupole mass spectrometer (6460QqQ-MS, Agilent, Santa Clara, CA, USA) equipped with an electrospray ionization source (ESI). A ZORBAX Eclipse Plus C18 column (100 mm × 2.1 mm i.d. 1.8 µm, Agilent, Waldbronn, Germany) was used. The mobile phase consisted of $0.1\%$ aqueous formic acid (A) and methanol with $0.1\%$ formic acid (B) at a flow rate of 0.2 mL min−1. The gradient elution was set as follows: 0–10 min, $100\%$ A–$20\%$ A; 10–11 min, $20\%$ A; 11–12 min, $20\%$–$100\%$ A. The sample injection volume was 5 μL, and the column temperature was 35 °C. All the compounds were identified by comparing the dynamic multiple reaction monitoring (MRM) information with reference standards.
## 4.4. Total Polyphenols and Antioxidant Activities In Vitro
The total polyphenol content was analyzed via a modified Folin–Ciocalteu colorimetric method. Briefly, a 50 μL solution of extracts was mixed with 50 μL Folin–Ciocalteu Phenol reagent. After incubation in the dark for 6 min, 100 μL $7\%$ Na2CO3 and 100 μL distilled water were added, and the mixture was incubated at room temperature for 90 min. The absorbance was measured at 760 nm via a microplate reader. The results were expressed in milligrams of gallic acid equivalent per gram of extracts (mg GAE/g). The antioxidant activities were determined with DPPH, ABTS, and FRAP assays. For the assay of DPPH, 20 μL LPPE solution was added into 100 μL of DPPH solution. After incubation at room temperature for 30 min, the absorbance was measured at 517 nm. For the assay of ABTS, 20 μL LPPE was added into 100 μL of ABTS solution (absorbance of 0.70 ± 0.02 at 734 nm). After incubation at room temperature for 10 min, the absorbance was measured at 734 nm. For the assay of FRAP, 20 μL LPPE solution was mixed with 100 μL FRAP solution (2.5 mL 20 mmol L−1 TPTZ, 2.5 mL 20 mmol/L FeCl3, and 25 mL 0.2 mol L−1 acetate buffer). The solution was incubated at 37 °C for 10 min, and absorbance was measured at 593 nm. Vc was used as the standard, and the antioxidant activity was expressed in mg Vc equivalents per gram of extracts (equivalent mg Vc/g) [30].
## 4.5. Animal Experiment
All the animal experiment protocols were approved by the Institutional Animal Care and Ethical Committee of Guizhou University of Traditional Chinese Medicine. Male wild-type C57BL/6J mice (6–8 weeks old, 22 ± 1 g) were obtained from Changsha Tianqin Biotechnology Co., Ltd. (Changsha, China). Mice were kept in a standard breeding room with temperature 22 ± 1 °C, humidity 60 ± $10\%$, and 12 h dark/light cycle. The mice could obtain food and water ad libitum and were allowed to acclimatize for 1 week before the experiment. Then, the mice were randomly divided into 3 groups ($$n = 8$$), fed with a Chow diet ($10\%$ of calories derived from fat, Research Diets, D12450B), or a high-fat diet (HFD) ($60\%$ of calories from fat, Research Diets, D12492), or a high-fat diet plus $0.2\%$ LPPE for 12 weeks. The food intake and body weight were recorded once per week.
After 12 weeks of feeding, mice were fasted overnight (12 h) and then anesthetized with $20\%$ urethane. Blood was collected via cardiac puncture, and the supernatant serum samples were separated via centrifuging at 1000× g for 20 min at 4 °C (centrifuge 5425, Eppendorf, Hamburg, Germany). Serum samples were stored at −80 °C for further analysis.
The liver and epididymal adipose tissues were collected individually. A small piece of liver or epididymal adipose tissue was fixed in $4\%$ buffered formalin for histological analysis. The remaining livers were rapidly frozen in liquid nitrogen and then stored in −80 °C for hepatic lipid content, RT-PCR, and Western blot analysis.
## 4.6. Histological Analysis
The fixed sections of liver or epididymal adipose tissues were stained with hematoxylin and eosin (H&E). Another portion of the liver samples was frozen and embedded in OCT, and then sliced. Subsequently, the slices were stained with Oil Red O. The images were captured with a Zeiss Axio Imager microscope (Axio Imager M1, Zeiss, Oberkochen, Germany).
## 4.7. Serum Lipids
The serum triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c) levels were detected via biochemistry kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China).
## 4.8. Liver TG and TC Analysis
Liver lipids were determined as described before [45]. Briefly, about 50 mg of frozen liver tissues were homogenized in 0.5 mL lysis buffer and then mixed with an equal volume of chloroform. The chloroform layer was collected, dried overnight at room temperature, and resuspended in isopropyl alcohol. The hepatic TG and TC were detected according to the protocol of assay kits (Dongou Diagnostic Product Ltd. Wenzhou, China).
## 4.9. RT-PCR
Total RNA was extracted from liver tissues with the Trizol reagent. cDNA was synthesized using the cDNA synthesis kit (Thermo Scientific, Waltham, MA, USA). *The* gene expression levels were quantified with the ABI StepOne Plus real-time.
PCR system (Applied Biosystems, Foster City, CA, USA) with SYBR green.
Supermix (Bio-Rad, Hercules, CA, USA). The sequences of the primers used were as follows: FAS (forward primer, 5′CTGAGATCCCAGCACTTCTTGA3′; reverse primer, 5′GCCTCCGAAGCCAAATGAG3′); PPARα (forward primer, 5′AGGCTGTAAGGGCTTCTTTCG3′; reverse primer, 5′GGCATTTGTTCCGGTTCTTC3′); LXRα (forward primer, 5′TCAGAAGAACAGATCCGCTTG3′; reverse primer, 5′CGCCTGTTACACTGTTGCT3′); CYP7A1 (forward primer, 5′AACAACCTGCCAGTACTAGATAGC3′; reverse primer, 5′GTGTAGAGTGAAGTCCTCCTTAGC′); CYP27A1 (forward primer, 5′GCCTCACCTATGGGATCTTCA3′; reverse primer, 5′TCAAAGCCTGACGCAGATG3′). The mRNA expression in liver was calculated after normalization to β-actin (forward primer, 5′TGTCCACCTTCCAGCAGATGT3′; reverse primer, 5′AGCTCAGTAACAGTCCGCCTAGA3′).
## 4.10. Western Blot Assay
Hepatic protein expressions of FAS, PPARα, LXRα, and CYP7A1 were analyzed via the Western blot (WB) assay, as previously described [45]. Briefly, total proteins from liver cells were extracted and separated in SDS-PAGE and then transferred onto a PVDF membrane and blocked with $5\%$ BSA for 2 h at room temperature. The membranes were incubated with antibodies (1:2500 dilution) overnight at 4 °C. Then, they were washed with TBST (10 min × 3) and incubated with horseradish peroxidase conjugated secondary antibody (1:2500 dilution) for 2 h at room temperature. The immunoreactive bands were shown, and the densitometry was quantified via the Gel Image Analysis System (Advansta, San Jose, CA, USA). The β-actin was used as the control protein.
## 4.11. Statistics Analysis
All the values are expressed as mean ± SEM. Statistically significant differences among the groups were analyzed via one-way analysis of variance (ANOVA), followed by Tukey’s post hoc test with SPSS (Version 15.0, SPSS Inc., Chicago, IL, USA). The significant difference level was set at $p \leq 0.05.$
## 5. Conclusions
In conclusion, LPPE was shown to be rich in phenolic compounds and high in antioxidant activity. Dietary supplementation of LPPE decreased body weight and serum lipids and improved hepatic steatosis in high-fat diet-induced-obese mice, suggesting it has a preventive effect on lipid accumulation. LPPE-improved lipid metabolism may occur partly through the PPARα/LXRα/FAS/CYP7A1 pathway. However, the main contributors need to be further investigated. This study provides an effective option in terms of a dietary supplement to improve lipid metabolism.
## References
1. Zhang R., Khan S.A., Lin Y., Guo D., Pan X., Liu L., Wei Z., Zhang Y., Deng Y., Zhang M.. **Phenolic profiles and cellular antioxidant activity of longan pulp of 24 representative Chinese cultivars**. *Int. J. Food Prop.* (2018) **21** 746-759. DOI: 10.1080/10942912.2018.1425705
2. Li L., Xu J., Mu Y., Han L., Liu R., Cai Y., Huang X.. **Chemical characterization and anti-hyperglycaemic effects of polyphenol enriched longan (**. *J. Funct. Foods* (2015) **13** 314-322. DOI: 10.1016/j.jff.2015.01.006
3. Zheng G., Xu L., Wu P., Xie H., Jiang Y., Chen F., Wei X.. **Polyphenols from longan seeds and their radical-scavenging activity**. *Food Chem.* (2009) **116** 433-436. DOI: 10.1016/j.foodchem.2009.02.059
4. Zhang X., Guo S., Ho C.-T., Bai N.. **Phytochemical constituents and biological activities of longan (**. *Food Sci. Hum. Wellness* (2020) **9** 95-102. DOI: 10.1016/j.fshw.2020.03.001
5. Rakariyatham K., Zhou D., Rakariyatham N., Shahidi F.. **Sapindaceae (**. *J. Funct. Foods* (2020) **67** 103846. DOI: 10.1016/j.jff.2020.103846
6. Zhao L., Wang K., Li W., Soteyome T., Xiao H., Hu Z.. **Protective effects of polyphenolic extracts from longan seeds promote healing of deep second-degree burn in mice**. *Food Funct.* (2019) **10** 1433-1443. DOI: 10.1039/C8FO02330A
7. Rangkadilok N., Sitthimonchai S., Worasuttayangkurn L., Mahidol C., Ruchirawat M., Satayavivad J.. **Evaluation of free radical scavenging and antityrosinase activities of standardized longan fruit extract**. *Food Chem. Toxicol.* (2007) **45** 328-336. DOI: 10.1016/j.fct.2006.08.022
8. Morigny P., Boucher J., Arner P., Langin D.. **Lipid and glucose metabolism in white adipocytes: Pathways, dysfunction and therapeutics**. *Nat. Rev. Endocrinol.* (2021) **17** 276-295. DOI: 10.1038/s41574-021-00471-8
9. Ma J., Zheng Y., Tang W., Yan W., Nie H., Fang J., Liu G.. **Dietary polyphenols in lipid metabolism: A role of gut microbiome**. *Anim. Nutr.* (2020) **6** 404-409. DOI: 10.1016/j.aninu.2020.08.002
10. Ishida N., Iizuka M., Kataoka K., Okazaki M., Shiraishi K., Yagi Y., Jobu K., Yokota J., Oishi M., Moriyama H.. **Improvement of blood lipid profiles by Goishi tea polyphenols in a randomised, double-blind, placebo-controlled clinical study**. *Int. J. Food Sci. Nutr.* (2018) **69** 598-607. DOI: 10.1080/09637486.2017.1386629
11. Mulvihill E.E., Burke A.C., Huff M.W.. **Citrus Flavonoids as Regulators of Lipoprotein Metabolism and Atherosclerosis**. *Annu. Rev. Nutr.* (2016) **36** 275-299. DOI: 10.1146/annurev-nutr-071715-050718
12. Wahli W., Michalik L.. **PPARs at the crossroads of lipid signaling and inflammation**. *Trends Endocrinol. Metab.* (2012) **23** 351-363. DOI: 10.1016/j.tem.2012.05.001
13. Ducheix S., Montagner A., Theodorou V., Ferrier L., Guillou H.. **The liver X receptor: A master regulator of the gut-liver axis and a target for non alcoholic fatty liver disease**. *Biochem. Pharmacol.* (2013) **86** 96-105. DOI: 10.1016/j.bcp.2013.03.016
14. Zhang R.J., Yu Y., Hu S.K., Zhang J.H., Yang H.X., Han B., Cheng Y., Luo X.Q.. **Sesamin ameliorates hepatic steatosis and inflammation in rats on a high-fat diet via LXR alpha and PPAR alpha**. *Nutr. Res.* (2016) **36** 1022-1030. DOI: 10.1016/j.nutres.2016.06.015
15. Ge C.X., Yu R., Xu M.X., Li P.Q., Fan C.Y., Li J.M., Kong L.D.. **Betaine prevented fructose-induced NAFLD by regulating LXRa/PPARcic pathway and alleviating ER stress in rats**. *Eur. J. Pharmacol.* (2016) **770** 154-164. DOI: 10.1016/j.ejphar.2015.11.043
16. Ebbert J.O., Jensen M.D.. **Fat Depots, Free Fatty Acids, and Dyslipidemia**. *Nutrients* (2013) **5** 498-508. DOI: 10.3390/nu5020498
17. Chavez-Castillo M., Ortega A., Durant P., Pirela D., Marquina M., Cano C., Salazar J., Gonzalez M.C., Bermudez V., Rojas-Quintero J.. **Phytotherapy for Cardiovascular Disease: A Bench-to-Bedside Approach**. *Curr. Pharm. Des.* (2020) **26** 4410-4429. DOI: 10.2174/1381612826666200420160422
18. Amiot M.J., Riva C., Vinet A.. **Effects of dietary polyphenols on metabolic syndrome features in humans: A systematic review**. *Obes. Rev.* (2016) **17** 573-586. DOI: 10.1111/obr.12409
19. Rahmani S., Asgary S., Askari G., Keshvari M., Hatamipour M., Feizi A., Sahebkar A.. **Treatment of Non-alcoholic Fatty Liver Disease with Curcumin: A Randomized Placebo-controlled Trial**. *Phytother. Res.* (2016) **30** 1540-1548. DOI: 10.1002/ptr.5659
20. Jiao X.Y., Wang Y.H., Lin Y., Lang Y.X., Li E.H., Zhang X.Y., Zhang Q., Feng Y., Meng X.J., Li B.. **Blueberry polyphenols extract as a potential prebiotic with anti-obesity effects on C57BL/6 J mice by modulating the gut microbiota**. *J. Nutr. Biochem.* (2019) **64** 88-100. DOI: 10.1016/j.jnutbio.2018.07.008
21. Neyrinck A.M., Van Hee V.F., Bindels L.B., De Backer F., Cani P.D., Delzenne N.M.. **Polyphenol-rich extract of pomegranate peel alleviates tissue inflammation and hypercholesterolaemia in high-fat diet-induced obese mice: Potential implication of the gut microbiota**. *Br. J. Nutr.* (2013) **109** 802-809. DOI: 10.1017/S0007114512002206
22. Bai X., Pan R., Li M., Li X., Zhang H.. **HPLC Profile of Longan (cv. Shixia) Pericarp-Sourced Phenolics and Their Antioxidant and Cytotoxic Effects**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24030619
23. Tian L., Cao J.X., Zhao T.R., Liu Y.P., Khan A., Cheng G.G.. **The Bioavailability, Extraction, Biosynthesis and Distribution of Natural Dihydrochalcone: Phloridzin**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22020962
24. Xu X.Y., Xie H.H., Hao J., Jiang Y.M., Wei X.Y.. **Flavonoid Glycosides from the Seeds of Litchi chinensis**. *J. Agric. Food Chem.* (2011) **59** 1205-1209. DOI: 10.1021/jf104387y
25. Yang B., Jiang Y., Shi J., Chen F., Ashraf M.. **Extraction and pharmacological properties of bioactive compounds from longan (**. *Food Res. Int.* (2011) **44** 1837-1842. DOI: 10.1016/j.foodres.2010.10.019
26. Sun J., Shi J., Jiang Y., Xue S.J., Wei X.. **Identification of two polyphenolic compounds with antioxidant activities in longan pericarp tissues**. *J. Agric. Food Chem.* (2007) **55** 5864-5868. DOI: 10.1021/jf070839z
27. He N., Wang Z., Yang C., Lu Y., Sun D., Wang Y., Shao W., Li Q.. **Isolation and identification of polyphenolic compounds in longan pericarp**. *Sep. Purif. Technol.* (2009) **70** 219-224. DOI: 10.1016/j.seppur.2009.09.019
28. Prasad K.N., Yang B., Shi J., Yu C., Zhao M., Xue S., Jiang Y.. **Enhanced antioxidant and antityrosinase activities of longan fruit pericarp by ultra-high-pressure-assisted extraction**. *J. Pharm. Biomed. Anal.* (2010) **51** 471-477. DOI: 10.1016/j.jpba.2009.02.033
29. Škrovánková S., Mišurcová L., Machů L.. **Antioxidant activity and protecting health effects of common medicinal plants**. *Adv. Food Nutr. Res.* (2012) **67** 75-139. PMID: 23034115
30. Tan S., Wang Z.W., Xiang Y.Y., Deng T.W., Zhao X., Shi S.Y., Zheng Q.R., Gao X.X., Li W.F.. **The effects of drying methods on chemical profiles and antioxidant activities of two cultivars of Psidium guajava fruits**. *LWT-Food Sci. Technol.* (2020) **118** 108723. DOI: 10.1016/j.lwt.2019.108723
31. Jang A., Srinivasan P., Lee N.Y., Song H.P., Lee J.W., Lee M., Jo C.. **Comparison of hypolipidemic activity of synthetic gallic acid-linoleic acid ester with mixture of gallic acid and linoleic acid, gallic acid, and linoleic acid on high-fat diet induced obesity in C57BL/6 Cr Slc mice**. *Chem.-Biol. Interact.* (2008) **174** 109-117. DOI: 10.1016/j.cbi.2008.05.018
32. Fanaei H., Mard S.A., Sarkaki A., Goudarzi G., Khorsandi L.. **Gallic acid protects the liver against NAFLD induced by dust exposure and high-fat diet through inhibiting oxidative stress and repressing the inflammatory signaling pathways NF-kbeta/TNF-alpha/IL-6 in Wistar rats**. *Avicenna J. Phytomed.* (2021) **11** 527-540. PMID: 34745924
33. Cheng H., Xu N., Zhao W., Su J., Liang M., Xie Z., Wu X., Li Q.. **(-)-Epicatechin regulates blood lipids and attenuates hepatic steatosis in rats fed high-fat diet**. *Mol. Nutr. Food Res.* (2017) **61** 1700303. DOI: 10.1002/mnfr.201700303
34. Salvadó M.J., Casanova E., Fernández-Iglesias A., Arola L., Bladé C.. **Roles of proanthocyanidin rich extracts in obesity**. *Food Funct.* (2015) **6** 1053-1071. DOI: 10.1039/C4FO01035C
35. Baselga-Escudero L., Pascual-Serrano A., Ribas-Latre A., Casanova E., Salvado M.J., Arola L., Arola-Arnal A., Blade C.. **Long-term supplementation with a low dose of proanthocyanidins normalized liver miR-33a and miR-122 levels in high-fat diet-induced obese rats**. *Nutr. Res.* (2015) **35** 337-345. DOI: 10.1016/j.nutres.2015.02.008
36. Najafian M., Jahromi M.Z., Nowroznejhad M.J., Khajeaian P., Kargar M.M., Sadeghi M., Arasteh A.. **Phloridzin reduces blood glucose levels and improves lipids metabolism in streptozotocin-induced diabetic rats**. *Mol. Biol. Rep.* (2012) **39** 5299-5306. DOI: 10.1007/s11033-011-1328-7
37. Liu H.Z., Chen Y.E., Wen Y.F., Zhu S.M., Huang S., He L., Hou S.Z., Lai X.P., Chen S.X., Dai Z.H.. **Phloridzin Ameliorates Lipid Deposition in High-Fat-Diet-Fed Mice with Nonalcoholic Fatty Liver Disease via Inhibiting the mTORC1/SREBP-1c Pathway**. *J. Agric. Food Chem.* (2021) **69** 8671-8683. DOI: 10.1021/acs.jafc.1c01645
38. Tang Y.-Y., He X.-M., Sun J., Li C.-B., Li L., Sheng J.-F., Xin M., Li Z.-C., Zheng F.-J., Liu G.-M.. **Polyphenols and Alkaloids in Byproducts of Longan Fruits (**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24061186
39. Sinha R.A., Rajak S., Singh B.K., Yen P.M.. **Hepatic Lipid Catabolism via PPARα-Lysosomal Crosstalk**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21072391
40. Chiang J.Y.L., Kimmel R., Stroup D.. **Regulation of cholesterol 7 alpha-hydroxylase gene (CYP7A1) transcription by the liver orphan receptor (LXR alpha)**. *Gene* (2001) **262** 257-265. DOI: 10.1016/S0378-1119(00)00518-7
41. Zhao L., Lei W., Deng C., Wu Z., Sun M., Jin Z., Song Y., Yang Z., Jiang S., Shen M.. **The roles of liver X receptor α in inflammation and inflammation-associated diseases**. *J. Cell. Physiol.* (2021) **236** 4807-4828. DOI: 10.1002/jcp.30204
42. Abenavoli L., Larussa T., Corea A., Procopio A.C., Boccuto L., Dallio M., Federico A., Luzza F.. **Dietary Polyphenols and Non-Alcoholic Fatty Liver Disease**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13020494
43. Yang D.-J., Chang Y.-Y., Hsu C.-L., Liu C.-W., Lin Y.-L., Lin Y.-H., Liu K.-C., Chen Y.-C.. **Antiobesity and Hypolipidemic Effects of Polyphenol-Rich Longan (**. *J. Agric. Food Chem.* (2010) **58** 2020-2027. DOI: 10.1021/jf903355q
44. Liu C.-W., Yang D.-J., Chang Y.-Y., Hsu C.-L., Tseng J.-K., Chang M.-H., Wang M., Chen Y.-C.. **Polyphenol-rich longan (**. *Food Res. Int.* (2012) **45** 444-449. DOI: 10.1016/j.foodres.2011.11.007
45. Ke Z.L., Tan S., Li H.Y., Jiang S.S., Li Y.P., Chen R., Li M.X.. **Tangeretin improves hepatic steatosis and oxidative stress through the Nrf2 pathway in high fat diet-induced nonalcoholic fatty liver disease mice**. *Food Funct.* (2022) **13** 2782-2790. DOI: 10.1039/D1FO02989D
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---
title: Synthetic Glabridin Derivatives Inhibit LPS-Induced Inflammation via MAPKs
and NF-κB Pathways in RAW264.7 Macrophages
authors:
- Jaejin Shin
- Leo Sungwong Choi
- Hyun Ju Jeon
- Hyeong Min Lee
- Sang Hyo Kim
- Kwan-Woo Kim
- Wonmin Ko
- Hyuncheol Oh
- Hyung Soon Park
journal: Molecules
year: 2023
pmcid: PMC10004008
doi: 10.3390/molecules28052135
license: CC BY 4.0
---
# Synthetic Glabridin Derivatives Inhibit LPS-Induced Inflammation via MAPKs and NF-κB Pathways in RAW264.7 Macrophages
## Abstract
Glabridin is a polyphenolic compound with reported anti-inflammatory and anti-oxidative effects. In the previous study, we synthesized glabridin derivatives—HSG4112, (S)-HSG4112, and HGR4113—based on the structure–activity relationship study of glabridin to improve its biological efficacy and chemical stability. In the present study, we investigated the anti-inflammatory effects of the glabridin derivatives in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. We found that the synthetic glabridin derivatives significantly and dose-dependently suppressed the production of nitric oxide (NO) and prostaglandin E2 (PGE2), and decreased the level of inducible nitric oxygen synthase (iNOS) and cyclooxygenase-2 (COX-2) and the expression of pro-inflammatory cytokines interleukin-1β (IL-1β), IL-6, and tumor necrosis factor alpha (TNF-α). The synthetic glabridin derivatives inhibited the nuclear translocation of the NF-κB by inhibiting phosphorylation of the inhibitor of κB alpha (IκB-α), and distinctively inhibited the phosphorylation of ERK, JNK, and p38 MAPKs. In addition, the compounds increased the expression of antioxidant protein heme oxygenase (HO-1) by inducing nuclear translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) through ERK and p38 MAPKs. Taken together, these results indicate that the synthetic glabridin derivatives exert strong anti-inflammatory effects in LPS-stimulated macrophages through MAPKs and NF-κB pathways, and support their development as potential therapeutics against inflammatory diseases.
## 1. Introduction
Inflammation is a critical process to protect the host from bacteria, viruses, and toxins, and plays a key role in removing the cause of such inflammation and restoring the damaged tissue. *In* general, when the cause is removed, the inflammation disappears [1]. However, if inflammation is maintained within the body for a long time due to environmental or physiological factors, the body continues to be in a state of chronic inflammation [1,2]. Chronic inflammation disrupts tissue homeostasis by inducing organelle dysfunction and cellular apoptosis within various tissues by the constant release of cytokines and/or chemokines [3,4]. Continuous exposure to these cytokines and/or chemokines is implicated in the pathogenesis of various inflammatory and inflammation-related diseases, such as autoimmune diseases including psoriasis, rheumatoid arthritis, and inflammatory bowel disease, metabolic diseases including non-alcoholic steatohepatitis (NASH) and type 2 diabetes, and even neurodegenerative disease [5,6]. Murine macrophage-like cell lines, such as RAW264.7 cells, are commonly used and are appropriate models for evaluating inflammatory responses under stimulation with lipopolysaccharide (LPS), which is the predominant outer component of gram-negative bacteria. The inflammatory responses are characterized by increased production of nitric oxide (NO), prostaglandin E2 (PGE2), tumor necrosis factor-α (TNF-α), and interleukin (IL)s [7,8]. Evaluating anti-inflammatory effects of potential therapeutics in such a system has been shown to be useful in the search for effective compounds against diverse inflammatory and inflammation-related diseases [9].
Glabridin is an isoflavan isolated from the roots extract of licorice (Glycyrrhiza glabra) [10]. Glabridin has been extensively studied as a natural compound with known anti-oxidative and anti-inflammatory activities, as well as with effects on the improvement of metabolic dysregulation [11]. However, glabridin has low stability and bioavailability, rendering it difficult to develop as a clinical therapeutic agent [12]. Previously, we performed a structure–activity relationship (SAR) study of glabridin and synthesized various glabridin derivatives with improved chemical stability and in vivo efficacy [13,14]. Among them, HSG4112 is currently at clinical phase 2 stage (NCT05197556) and HGR4113 is at clinical phase 1 stage (NCT05642377), and the (S)-enantiomer of HSG4112 (S)-HSG4112) is at a preclinical stage of development. Therefore, detailed characterization and understanding of the mechanism of the anti-inflammatory effects of these synthetic glabridin derivatives are needed.
Mitogen-activated protein kinase (MAPK) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathways both play pivotal roles in the mediation of the LPS-stimulated inflammatory response [15,16]. Three major MAPKs, which are extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and phosphorylated 38 (p38), are activated by phosphorylation and regulate inflammatory cytokine secretion in an LPS-stimulated RAW264.7 model [17,18,19,20,21]. All three MAPKs have been well studied to regulate inflammatory responses, reducing NO, IL-6, and TNF-α levels in LPS-induced models, respectively [22,23]. NF-κB is a transcription factor that is activated by the phosphorylation of nuclear factor kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (IκB-α), and is localized to the nucleus and expresses various inflammatory response genes, including proinflammatory cytokines such as IL-1β, IL-6, and TNF-α, and proinflammatory factors such as inducible nitric oxygen synthase (iNOS) and cyclooxygenase-2 (COX-2) [15,24]. Suppressing the activation of MAPKs and NF-κB is thus an important step in mediating the anti-inflammatory effects of potential therapeutic compounds.
In contrast to pro-inflammatory responses, nuclear transcription factor E2-related factor 2 (Nrf2) is a major transcription factor that regulates anti-inflammatory and anti-oxidative response [25]. Nrf2 is localized to the nucleus under an oxidative stress condition and binds to the antioxidant response element (ARE) to induce gene expression of phase Ⅱ conjugation enzymes including heme oxygenase-1 (HO-1), a major antioxidant enzyme that regulates heme catabolism and cleaves heme to form biliverdin, carbon monoxide, and ferrous iron [25,26,27]. HO-1 and its product carbon monoxide can suppress the production of pro-inflammatory cytokines such as TNF-α, IL-1β and IL-6 [28,29]. The up-stream regulators of Nrf2 pathway include MAPKs (ERK, JNK, and p38) [30]. This pathway has been extensively researched for the search of therapeutic compounds as well.
In this study, we investigated the anti-inflammatory and anti-oxidative effects of the synthetic glabridin derivatives in LPS-induced RAW264.7 cells and examined their mechanisms on the major pro-inflammatory MAPK and NF-κB pathways and anti-inflammatory Nrf2 pathway.
## 2.1. Effects of Compounds on LPS-Induced NO and PGE2 Production
We investigated whether glabridin and the synthetic glabridin derivatives—HSG4112, (S)-HSG4112, and HGR4113 (Figure 1)—exert overall anti-inflammatory effects in the LPS-stimulated RAW264.7 macrophage. RAW264.7 cells were pre-treated with glabridin and the synthetic glabridin derivatives at the indicated concentrations for 3 h before stimulation with LPS for 24 h, and the supernatant was collected. The indicated concentrations were used as the maximum concentration that did not visibly affect cell viability. Butein, a chalcone polyphenol first isolated from *Rhus verniciflua* Stokes, was used as a positive control, since it has various biological properties including anti-oxidative and anti-inflammatory effects, and has previously demonstrated such efficacy in RAW264.7 cell [31,32,33].
The level of NO and PGE2 was determined by the Griess reaction and ELISA, respectively. While LPS-stimulation significantly increased NO and PGE2 production, as shown in the control group; all compounds dose-dependently and significantly reduced such NO and PGE2 production (Figure 2A,B). All synthetic glabridin derivatives had superior inhibitory effects to glabridin and comparable inhibitory effects to butein at 20 µM concentration. As shown in Table 1, for the suppression of NO production, the IC50 values for glabridin, HSG4112, (S)-HSG4112, and HGR4113 were 9.36, 6.79, 3.85, and 11.32 μM, respectively. For the suppression of PGE2 production, the IC50 values for glabridin, HSG4112, (S)-HSG4112, and HGR4113 were 7.09, 3.55, 2.37, and 1.64 μM, respectively.
Next, the protein expressions of iNOS and COX-2, which produce NO and PGE2, respectively, were investigated. Cell lysate was harvested under the same experimental conditions as above, and the protein expression levels of iNOS and COX-2 were measured by Western blot. The expression levels of both iNOS and COX-2 were markedly increased by LPS-stimulation and were decreased in a dose-dependent manner by glabridin and the synthetic glabridin derivatives (Figure 2C–F).
## 2.2. Effects of Compounds on LPS-Induced Cytokines
The effects of glabridin and the synthetic glabridin derivates on the LPS-induced pro-inflammatory cytokines, including IL-1β, IL-6 and TNF-α, were examined. RAW264.7 cells were pre-treated with or without the indicated concentrations of compounds and then were stimulated with LPS. The mRNA expression of all three pro-inflammatory cytokines was markedly increased by LPS stimulation and was significantly decreased in a dose-dependent manner by glabridin and the synthetic glabridin derivatives: HSG4112, (S)-HSG4112, and HGR4113 (Figure 3A–C). All synthetic glabridin derivatives had superior inhibitory effects to glabridin and comparable inhibitory effects to butein at 20 µM concentration.
## 2.3. Effects of Compounds on NF-κB Signaling Pathway
To investigate the potential mechanism of the anti-inflammatory effects of the compounds, we examined the effects of glabridin and the synthetic glabridin derivates on NF-κB activation and DNA binding. NF-κB consists of two subunits (p50, p65) which are localized to the nucleus when activated. We performed Western blot to examine the translocation of NF-κB subunits into the nucleus. While both subunits p50 and p65 were localized into the nucleus by LPS-stimulation, glabridin and the synthetic glabridin derivatives —HSG4112, (S)-HSG4112, HGR4113—dose-dependently inhibited the nuclear translocation of NF-κB. Next, to identify which upstream pathway is affected in the NF-κB signaling, the activity of IκB-α was examined. Upon phosphorylation of IκB, IκB is degraded and separated from NF-κB, allowing NF-κB to translocate into the nucleus [24]. While IκB-α phosphorylation markedly increased with LPS-stimulation, glabridin and the synthetic glabridin derivatives decreased IκB-α phosphorylation in a dose-dependent manner (Figure 4A–D). In addition, nuclear DNA binding assay was performed to confirm the downstream effect of NF-κB. After NF-κB is translocated into the nucleus, it binds to DNA as a transcription factor. While nuclear NF-κB DNA binding markedly increased LPS-stimulation, glabridin and the synthetic glabridin derivatives significantly and dose-dependently decreased such binding (Figure 4E). These results indicate that glabridin and the synthetic glabridin derivatives inhibit the LPS-induced activation of NF-κB signaling by inhibiting phosphorylation of IκB-α.
## 2.4. Effects of Compounds on LPS-Induced MAPK Signaling
To further investigate the mechanism of the anti-inflammatory effects of the compounds, we examined the effects of glabridin and the synthetic glabridin derivates on MAPK signaling. We examined the activation of the three major MAPK (ERK, JNK, and p38) in RAW264.7 cells by quantifying phosphorylation through Western blot [34]. For the negative control, PD98059, SP600125, and SB203580 were used as inhibitors of ERK, JNK, and p38 MAPK, respectively. LPS stimulation markedly induced the phosphorylation of ERK, JNK, and p38 (Figure 5A–D). Under LPS-stimulation, the tested compounds showed a distinct effect on each of the MAPKS: glabridin suppressed phosphorylation of JNK and p38 (Figure 5A), HSG4112 suppressed phosphorylation of ERK (Figure 5B), (S)-HSG4112 suppressed phosphorylation of JNK and p38 (Figure 5C), and HGR4113 suppressed phosphorylation of JNK (Figure 5D). The total unphosphorylated forms of all MAPKs were unaffected by LPS and test compounds (Figure 5A–D). These results show that while glabridin and the synthetic glabridin derivatives inhibit at least one MAPK signaling under LPS-stimulation, the specific MAPK (ERK, JNK, and p38) involved is distinct for each compound.
## 2.5. Effects of Compounds on HO-1 Induction and Nrf2 Signaling
Intracellular inflammation can be caused by exogenous pathogens such as LPS but can also be caused by intracellular oxidative stress [35]. Nrf2 is a well-known anti-oxidative transcription factor that localizes into the nucleus to suppress cellular inflammatory conditions in an oxidative stress environment and induces transcription of antioxidant proteins such as HO-1 [36]. To investigate the potential anti-oxidative effects of the compounds, we examined whether glabridin and the synthetic glabridin derivatives induce HO-1 protein expression in RAW264.7 cells by Western blot. RAW264.7 cells were pretreated with or without the indicated concentrations of compounds or copper (CoPP) for 12 h. CoPP was used as a positive control to create an extreme oxidative stress environment and thus induce HO-1. We found that glabridin and the synthetic glabridin derivatives increase HO-1 protein expression in a dose-dependent manner in RAW264.7 cells (Figure 6A–D). Next, we investigated the mechanism of HO-1 induction by examining the effects of compounds on nuclear translocation of Nrf2 in RAW264.7 cells by Western blot. RAW264.7 cells were treated for 0.5, 1, and 1.5 h with each compound’s respective highest non-toxic concentrations. Butein was used as a positive control to induce nuclear translocation of Nrf2, which was previously reported in murine microglial cells [31]. Glabridin and the synthetic glabridin derivatives all increased nuclear Nrf2 expression and concomitantly decreased cytosolic Nrf2 expression (Figure 6E–H). These results demonstrate the anti-oxidative effects of glabridin and the synthetic glabridin derivatives through the Nrf2 signaling pathway.
## 2.6. Effects of Compounds on MAPK Signaling Involved in HO-1 Induction
To further investigate the mechanism of the anti-oxidative effects of the compounds, we examined the MAPKs upstream of Nrf2/HO-1 pathways, which are ERK, JNK, and p38 [30]. MAPK inhibitor assays were conducted by Western blot to determine which MAPK is involved in the expression of HO-1 in RAW264.7. Cells were pre-treated with respective MAPK inhibitor for 3 h, and then treated with each compound’s respective highest non-toxic concentration for 12 h. We found that for all compounds, p38 inhibitor (SB203580) and ERK inhibitor (PD98059) reduced the induction of HO-1, while the JNK inhibitor (SP600125) had no effect (Figure 7A–D). These results show that glabridin and the synthetic glabridin derivatives all induce the expression of HO-1 through p38 and ERK MAPKs.
## 3. Discussion
This study investigated the anti-inflammatory effects and mechanisms of the synthetic glabridin derivatives—HSG4112, (S)-HSG4112, and HGR4113—in comparison to glabridin in LPS-stimulated RAW264.7 murine macrophages. We found that glabridin and the synthetic glabridin derivates clearly and markedly suppress LPS-activated pro-inflammatory markers and cytokines, and inhibit LPS-induced activation of NF-κB and MAPK signaling pathways. In addition, all compounds induced the anti-inflammatory Nrf2 signaling pathway, increasing antioxidant HO-1 protein expression through distinct MAPKs.
Glabridin has an isoflavan structure and has various reported pharmacological activities, including anti-oxidative, anti-inflammatory, anti-atherogenic, energy-regulating, and neuroprotective effects [37]. In the previous study, we considered obesity as a chronic inflammatory condition and created biochemically stable synthetic glabridin derivatives (HSG4112, (S)-HSG4112, HGR4113) from the structure of glabridin and evaluated their efficacies through an in vivo SAR study [14]. Even though the backbone structure is similar, it is worthwhile to note that glabridin is an (R) enantiomer, while HSG4112 is a racemate and (S)-HSG4112 is an (S) enantiomer; for most cases of small molecular compounds, only one enantiomer is pharmacologically active [38]. In addition, HGR4113 has notable differences to glabridin, which are hydroxy-to-propoxy modification at the resorcinol ring at C-4 and the double bond hydrogenation at the pyranobenzene structure. Therefore, the anti-inflammatory effects and mechanisms of the synthetic glabridin derivatives could not be surmised and needed to be investigated.
The inflammatory response involves a number of key mediators—including NO, PGE2, IL-1β, IL-6, and TNF-α—which can also be used as clinical markers of diagnosis. While LPS induction dramatically increased the level of these markers as expected, the treatment of synthetic glabridin derivatives significantly reduced them in a degree greater than glabridin and comparable to Butein. This is indicative of how the reported functions of glabridin on these pro-inflammatory markers can be enhanced through chemical modifications [11,39].
LPS induction leads to the activation of pro-inflammatory pathways. In this study, we investigated two major signaling pathways—NF-κB and MAPK—to examine whether the tested compounds exhibit anti-inflammatory effects through them. NF-κB is a well-known protein that plays a pivotal role in the inflammatory response, and lies at the center of the pro-inflammatory cytokine response and NLR family pyrin domain containing 3 (NLRP3) inflammasome formation [18,40]. We found that the synthetic glabridin derivatives inhibit nuclear translocation of NF-κB by suppressing phosphorylation of IκB-α. However, it remains unknown whether the compounds directly engage IκB-α or any upstream signaling protein. Moreover, in MAPK phosphorylation, glabridin and (S)-HSG4112 inhibited JNK and p38, while HSG4112 inhibited ERK and HGR4113 inhibited JNK. This finding suggests that each compound has a notably distinctive mechanism of inhibiting the inflammatory response. There are reports of distinctive inhibitions of each of the MAPKs for the anti-inflammatory responses, while such inhibitions ultimately converge to the downstream effect of reducing the production of pro-inflammatory cytokines [41]. Whether each compound’s distinctive inhibition of MAPKs leads to differences in the inhibitory efficacy or mechanism of the inflammatory response is unknown, and the directly binding mechanistic target protein of each compound is also unknown [42,43,44]. The directly binding mechanistic target protein of each compound is yet unknown. Nevertheless, the synthetic glabridin derivatives showed overall superior anti-inflammatory effects compared to glabridin, which suggest that their efficacies would likely increase as therapeutic agents as well. There are pro-inflammatory mediates other than NF-κB. Cyclic AMP-responsive element-binding protein (CREB) and activator protein 1 (AP1) are also transcription factors that induce pro-inflammatory cytokines. These factors are activated by p38 and JNK MAPK pathway, respectively, by LPS-stimulation [45]. Signal transducer and activator of transcription 3 (STAT3) is another transcription factor activated by LPS-stimulation, and it can be activated by several cytokines mediating the expression of several acute-phase response genes [46,47]. Thus, further studies can investigate the compounds’ effects on CREB, AP-1, and STAT3-mediated induction of pro-inflammatory responses.
As opposed to pro-inflammatory signaling pathways, we evaluated the anti-inflammatory Nrf2 pathway to determine whether the synthetic glabridin derivatives exhibit anti-inflammatory and anti-oxidative effects. The Nrf2 pathway is a well-known pathway that induces the transcription of various anti-oxidative proteins including HO-1 [25,30,36,48]. Glabridin has been reported to regulate mitochondrial function and reduce ROS generation through the Nrf2/HO-1 signaling pathway [48,49]. The synthetic glabridin derivatives also induced nuclear translocation of Nrf2 and HO-1 expression. Of note, the MAPKs that are upstream of Nrf2 signaling were identified for each compound and were found to be ERK and p38 for all compounds, which suggests a common mechanism of action in mediating this pathway. However, further study is needed to investigate whether the synthetic glabridin derivatives induce the Nrf2/HO-1 pathway under oxidative stress condition by measuring ROS levels in cells or mitochondria.
In conclusion, our study demonstrated that the three prominent glabridin derivatives —HSG4112, (S)-HSG4112, and HGR4113—exhibit both anti-inflammatory and anti-oxidative effects in macrophages through canonical pathways involving MAPK, NF-κB, and Nrf2 signaling, as shown in Figure 8. These results provide support for their development as therapeutic agents against inflammatory and inflammation-related diseases.
## 4.1. Materials
Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), and various other tissue culture reagents were purchased from Thermo Fisher Scientific (Waltham, MA, USA). All other chemicals were obtained from Sigma-Aldrich Co. (St. Louis, MO, USA). Primary antibodies are anti-iNOS, sc-650; anti-COX-2, sc-1745; anti-IκB-α, sc-371; anti-p-IκB-α, sc-8404; anti-p50, sc-7178; anti-p65, sc-8008, from Santa Cruz Biotechnology (Dallas, TX, USA) anti-p-ERK, #9101; anti-ERK, #9102; anti-p-JNK, #9251; anti-JNK, #9252S; anti-p-p38, #9211; anti-p38, #9212S, from Cell Signaling Technology (Danvers, MA, USA) Secondary antibodies: anti-mouse, ap124p; anti-goat, ap106p; anti-rabbit, ap132p, Millipore. The enzyme-linked immunosorbent assay (ELISA) kit for PGE2 was purchased from R&D Systems (Minneapolis, MN, USA). The compounds glabridin, HSG4112, (S)-HSG4112, HGR4113 were provided by Glaceum Inc (Suwon, Republic of Korea).
## 4.2. Cell Culture and Viability Assay
RAW264.7 was purchased from American Type Culture Collection (ATCC, Manassas, VA, USA). RAW264.7 cells were cultured in 5 × 105 cell/mL in DMEM medium, supplemented with $10\%$ heat-inactivated FBS, penicillin G (100 units/mL), streptomycin (100 mg/mL), and L-glutamine (2 mM), and incubated at 37 °C in a humidified atmosphere containing $5\%$ CO2. For all supernatant collections, the compounds were not washed out and the medium was not changed. Cell viability was measured with 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assays, where 0.5 mg/mL of MTT was added to 200 μL of each cell suspension (1 × 105 cell/mL in 96-well plates) for 4 h. The viability of compound-treated cells was measured through visual estimation to be similar to the control cells. Quantification was not performed.
## 4.3. NO Production
The nitrite concentration was used as an indicator of NO production and was measured with the Griess reaction. Supernatant (100 μL) was mixed with the Griess reagent at a 1:1 ratio (solution A, 222488; solution B, S438081; Sigma-Aldrich Co. (St. Louis, MO, USA)), and the absorbance at 525 nm was measured with ELISA plate reader.
## 4.4. PGE2 Assay
RAW264.7 cells were cultured in 24-well plates and incubated for 3 h with compounds before LPS (Sigma-Aldrich Co.) stimulation for 24 h. Supernatant (100 μL) PGE2 concentration was measured with an ELISA kit (R & D Systems).
## 4.5. Western Blot Analysis
RAW264.7 cells were harvested and centrifuged (16,000 rpm, 15 min). Cells were washed with PBS and lysed with 20 mM Tris-HCl buffer (pH 7.4) with protease inhibitor mixture (0.1 mM PMSF, 5 mg/mL pepstatin A, 5 mg/mL aprotinin, and 1 mg/mL chymostatin). Protein concentration was measured with Lowry Protein Assay Kit (P5626; Sigma-Aldrich Co.). Samples were placed on $12\%$ sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to an enhanced chemiluminescence (ECL) nitrocellulose membrane (Bio-Rad, Hercules, CA, USA). The membrane was blocked with $5\%$ skimmed milk and then incubated with the respective primary antibodies (Santa Cruz Biotechnology) and horseradish peroxidase-conjugated secondary antibodies, before ECL detection (Amersham Pharmacia Biotech, Amersham, UK).
## 4.6. Preparation of Cytosolic and Nuclear Fractions
RAW264.7 cells were homogenized in M-PER-Mammalian Protein Extraction Reagent (Thermo Fisher Scientific). The cytosolic fraction was acquired at 4 °C with centrifugation at 14,000× g for 5 min. Nuclear and cytoplasmic extracts were acquired with NE-PER Nuclear and Cytoplasmic Extraction Reagents (Pierce Biotechnology, Rockford, IL, USA). Cell lysis was performed at 4 °C by shaking for 15 min in RIPA Lysis and Extraction Buffer (Thermo Fisher Scientific). The final supernatant was collected by centrifugation at 16,000× g for 15 min.
## 4.7. DNA-Binding Activity of NF-κB
RAW264.7 cells were treated for 3 h with the compounds before LPS (1 μg/mL) stimulation for 30 min. The DNA-binding activity of NF-κB in the acquired nuclear extracts was measured with TransAM kit (Active Motif, Carlsbad, CA, USA).
## 4.8. Quantitative Real-Time Reverse Transcriptase PCR (qRT-PCR) Assay
Total RNA from RAW264.7 cells was isolated with Trizol (Invitrogen, Carlsbad, CA, USA) and quantified at 260 nm. Total RNA (1 μg) was reverse-transcribed with High-Capacity RNA-to-cDNA Kit (Applied Biosystems, Carlsbad, CA, USA) and cDNA was amplified with SYBR Premix Ex Taq Kit (TaKaRa Bio Inc., Shiga, Japan) and StepOnePlus Real-Time PCR (Applied Biosystems). qRT-PCR (20 μL) sample contained 10 μL SYBR Green PCR Master Mix, 0.8 μM primer, and the remaining diethyl pyrocarbonate (DEPC)-treated water. The primer sequences were designed with Primer Quest (Integrated DNA Technologies, Cambridge, MA, USA): IL-1β, forward 5′-A ATTGGTCATAGCCCGCACT-3′, reverse 5′-AAGCAATGTGCTGGTGCTTC-3′, IL-6, forward 5′-ACTTCACAAGTCGGAGGCTT-3′, reverse 5′- TGCAAGTGCATCATCGTTGT-3′, TNF-α, forward 5′-CCAGACCCTCACACTCACAA-3′, reverse 5′-A CAAGGTACAACCCATCGGC-3′, GAPDH, forward 5′-ACTTTGGTATCGTGGAAGGACT-3′, reverse 5′- GTAGAGGCAGGGATGATGTTCT-3′. Data were analyzed with Thermal Cycler and StepOne software (Applied Biosystems) and the comparative CT method was used to measure the relative gene expression using GAPDH as the endogenous control.
## 4.9. Statistical Analysis
Data are expressed as the mean ± SD. Three independent experiments were performed per assay. One-way analysis of variance (ANOVA) with Dunnett’s multiple comparison tests was used to compare the groups. Statistical analysis was performed with GraphPad Prism software, version 9.40 (GraphPad Software Inc., San Diego, CA, USA).
## References
1. Chen L., Deng H., Cui H., Fang J., Zuo Z., Deng J., Li Y., Wang X., Zhao L., Chen L.. **Inflammatory Responses and Inflammation-Associated Diseases in Organs**. *Oncotarget* (2017) **9** 7204-7218. DOI: 10.18632/oncotarget.23208
2. Furman D., Campisi J., Verdin E., Carrera-Bastos P., Targ S., Franceschi C., Ferrucci L., Gilroy D.W., Fasano A., Miller G.W.. **Chronic Inflammation in the Etiology of Disease across the Life Span**. *Nat. Med.* (2019) **25** 1822-1832. DOI: 10.1038/s41591-019-0675-0
3. Yang Y., Jiang G., Zhang P., Fan J.. **Programmed Cell Death and Its Role in Inflammation**. *Mil. Med. Res.* (2015) **2** 12. DOI: 10.1186/s40779-015-0039-0
4. Anderton H., Wicks I.P., Silke J.. **Cell Death in Chronic Inflammation: Breaking the Cycle to Treat Rheumatic Disease**. *Nat. Rev. Rheumatol.* (2020) **16** 496-513. DOI: 10.1038/s41584-020-0455-8
5. Duan L., Rao X., Sigdel K.R.. **Regulation of Inflammation in Autoimmune Disease**. *J. Immunol. Res.* (2019) **2019** 7403796. DOI: 10.1155/2019/7403796
6. Kanterman J., Sade-Feldman M., Baniyash M.. **New Insights into Chronic Inflammation-Induced Immunosuppression**. *Semin. Cancer Biol.* (2012) **22** 307-318. DOI: 10.1016/j.semcancer.2012.02.008
7. Yang Y.-Z., Tang Y.-Z., Liu Y.-H.. **Wogonoside Displays Anti-Inflammatory Effects through Modulating Inflammatory Mediator Expression Using RAW264. 7 Cells**. *J. Ethnopharmacol.* (2013) **148** 271-276. DOI: 10.1016/j.jep.2013.04.025
8. Yang Y.-H., Li D.-L., Bi X.-Y., Sun L., Yu X.-J., Fang H.-L., Miao Y., Zhao M., He X., Liu J.-J.. **Acetylcholine Inhibits LPS-Induced MMP-9 Production and Cell Migration via the A7 NAChR-JAK2/STAT3 Pathway in RAW264. 7 Cells**. *Cell. Physiol. Biochem.* (2015) **36** 2025-2038. DOI: 10.1159/000430170
9. Dinarello C.A.. **Anti-Inflammatory Agents: Present and Future**. *Cell* (2010) **140** 935-950. DOI: 10.1016/j.cell.2010.02.043
10. Li C., Li T., Zhu M., Lai J., Wu Z.. **Pharmacological Properties of Glabridin (a Flavonoid Extracted from Licorice): A Comprehensive Review**. *J. Funct. Foods* (2021) **85** 104638. DOI: 10.1016/j.jff.2021.104638
11. Hosseinzadeh H., Nassiri-Asl M.. **Pharmacological Effects of Glycyrrhiza Spp. and Its Bioactive Constituents: Update and Review**. *Phytother. Res.* (2015) **29** 1868-1886. DOI: 10.1002/ptr.5487
12. Ao M., Shi Y., Cui Y., Guo W., Wang J., Yu L.. **Factors Influencing Glabridin Stability**. *Nat. Prod. Commun.* (2010) **5** 1907-1912. DOI: 10.1177/1934578X1000501214
13. Bae I.Y., Choi M.S., Ji Y.S., Yoo S.-K., Kim K., Yoo H.H.. **Species Differences in Stereoselective Pharmacokinetics of HSG4112, A New Anti-Obesity Agent**. *Pharmaceutics* (2020) **12**. DOI: 10.3390/pharmaceutics12020127
14. Choi L.S., Jo I.G., Kang K.S., Im J.H., Kim J., Kim J., Chung J.W., Yoo S.-K.. **Discovery and Preclinical Efficacy of HSG4112, a Synthetic Structural Analog of Glabridin, for the Treatment of Obesity**. *Int. J. Obes.* (2021) **45** 130-142. DOI: 10.1038/s41366-020-00686-1
15. Liu T., Zhang L., Joo D., Sun S.-C.. **NF-ΚB Signaling in Inflammation**. *Signal Transduct Target Ther.* (2017) **2** 17023. DOI: 10.1038/sigtrans.2017.23
16. Kaminska B.. **MAPK Signalling Pathways as Molecular Targets for Anti-Inflammatory Therapy—From Molecular Mechanisms to Therapeutic Benefits**. *Biochim. Biophys. Acta (BBA) Proteins Proteom.* (2005) **1754** 253-262. DOI: 10.1016/j.bbapap.2005.08.017
17. Gottschalk R.A., Martins A.J., Angermann B.R., Dutta B., Ng C.E., Uderhardt S., Tsang J.S., Fraser I.D.C., Meier-Schellersheim M., Germain R.N.. **Distinct NF-ΚB and MAPK Activation Thresholds Uncouple Steady-State Microbe Sensing from Anti-Pathogen Inflammatory Responses**. *Cell Syst.* (2016) **2** 378-390. DOI: 10.1016/j.cels.2016.04.016
18. Yu H., Lin L., Zhang Z., Zhang H., Hu H.. **Targeting NF-ΚB Pathway for the Therapy of Diseases: Mechanism and Clinical Study**. *Signal Transduct Target Ther.* (2020) **5** 209. DOI: 10.1038/s41392-020-00312-6
19. Lee T.H., Shank J., Cusson N., Kelliher M.A.. **The Kinase Activity of Rip1 Is Not Required for Tumor Necrosis Factor-α-Induced IκB Kinase or P38 MAP Kinase Activation or for the Ubiquitination of Rip1 by Traf2**. *J. Biol. Chem.* (2004) **279** 33185-33191. DOI: 10.1074/jbc.M404206200
20. Karin M.. **Mitogen Activated Protein Kinases as Targets for Development of Novel Anti-Inflammatory Drugs**. *Ann. Rheum Dis.* (2004) **63** ii62-ii64. DOI: 10.1136/ard.2004.028274
21. Dong J., Li J., Cui L., Wang Y., Lin J., Qu Y., Wang H.. **Cortisol Modulates Inflammatory Responses in LPS-Stimulated RAW264. 7 Cells via the NF-ΚB and MAPK Pathways**. *BMC Vet. Res.* (2018) **14**. DOI: 10.1186/s12917-018-1360-0
22. Kim N.Y., Cheong S.H., Lee K.J., Sok D.E., Kim M.R.. **Anti-Inflammatory Effects of Ribes Diacanthum Pall Mediated via Regulation of Nrf2/HO-1 and NF-ΚB Signaling Pathways in LPS-Stimulated RAW 264.7 Macrophages and a TPA-Induced Dermatitis Animal Model**. *Antioxidants* (2020) **9**. DOI: 10.3390/antiox9070622
23. Park J.W., Kwon O.K., Ryu H.W., Paik J.H., Paryanto I., Yuniato P., Choi S.H., Oh S.R., Ahn K.S.. **Anti-Inflammatory Effects of**. *Int. J. Mol. Med.* (2018) **41** 3709-3716. DOI: 10.3892/ijmm.2018.3559
24. Solt L.A., May M.J.. **The IκB Kinase Complex: Master Regulator of NF-ΚB Signaling**. *Immunol. Res.* (2008) **42** 3-18. DOI: 10.1007/s12026-008-8025-1
25. Loboda A., Damulewicz M., Pyza E., Jozkowicz A., Dulak J.. **Role of Nrf2/HO-1 System in Development, Oxidative Stress Response and Diseases: An Evolutionarily Conserved Mechanism**. *Cell. Mol. Life Sci.* (2016) **73** 3221-3247. DOI: 10.1007/s00018-016-2223-0
26. Ryter S.W., Alam J., Choi A.M.K.. **Heme Oxygenase-1/Carbon Monoxide: From Basic Science to Therapeutic Applications**. *Physiol. Rev.* (2006) **86** 583-650. DOI: 10.1152/physrev.00011.2005
27. Itoh K., Chiba T., Takahashi S., Ishii T., Igarashi K., Katoh Y., Oyake T., Hayashi N., Satoh K., Hatayama I.. **An Nrf2/Small Maf Heterodimer Mediates the Induction of Phase II Detoxifying Enzyme Genes through Antioxidant Response Elements**. *Biochem. Biophys. Res. Commun.* (1997) **236** 313-322. DOI: 10.1006/bbrc.1997.6943
28. Motterlini R., Otterbein L.E.. **Delivering Carbon Monoxide from a Prorous Material with an Entrapped Photoactive Manganese Carbonyl**. *Nat. Rev. Drug Discov.* (2010) **9** 728-743. DOI: 10.1038/nrd3228
29. Shih R.-H., Yang C.-M.. **Induction of Heme Oxygenase-1 Attenuates Lipopolysaccharide-Induced Cyclooxygenase-2 Expression in Mouse Brain Endothelial Cells**. *J. Neuroinflammation* (2010) **7** 86. DOI: 10.1186/1742-2094-7-86
30. Sun Z., Huang Z., Zhang D.D.. **Phosphorylation of Nrf2 at Multiple Sites by MAP Kinases Has a Limited Contribution in Modulating the Nrf2-Dependent Antioxidant Response**. *PLoS ONE* (2009) **4**. DOI: 10.1371/journal.pone.0006588
31. Lee D.-S., Jeong G.-S.. **Butein Provides Neuroprotective and Anti-Neuroinflammatory Effects through Nrf2/ARE-Dependent Haem Oxygenase 1 Expression by Activating the PI3K/Akt Pathway**. *Br. J. Pharmacol.* (2016) **173** 2894-2909. DOI: 10.1111/bph.13569
32. Sung J., Lee J.. **Anti-Inflammatory Activity of Butein and Luteolin Through Suppression of NFκB Activation and Induction of Heme Oxygenase-1**. *J. Med. Food* (2015) **18** 557-564. DOI: 10.1089/jmf.2014.3262
33. Padmavathi G., Roy N.K., Bordoloi D., Arfuso F., Mishra S., Sethi G., Bishayee A., Kunnumakkara A.B.. **Butein in Health and Disease: A Comprehensive Review**. *Phytomedicine* (2017) **25** 118-127. DOI: 10.1016/j.phymed.2016.12.002
34. Zhang L., Zhao Y., Liu G., Yang D., Dong Y., Zhou L.. **Glabridin Attenuates Lipopolysaccharide-Induced Acute Lung Injury by Inhibiting P38MAPK/ERK Signaling Pathway**. *Oncotarget* (2017) **8** 18935. DOI: 10.18632/oncotarget.14277
35. Hussain T., Tan B., Yin Y., Blachier F., Tossou M.C.B., Rahu N.. **Oxidative Stress and Inflammation: What Polyphenols Can Do for Us?**. *Oxid. Med. Cell Longev.* (2016) **2016** 7432797. DOI: 10.1155/2016/7432797
36. Li L., Dong H., Song E., Xu X., Liu L., Song Y.. **Nrf2/ARE Pathway Activation, HO-1 and NQO1 Induction by Polychlorinated Biphenyl Quinone Is Associated with Reactive Oxygen Species and PI3K/AKT Signaling**. *Chem. Biol. Interact.* (2014) **209** 56-67. DOI: 10.1016/j.cbi.2013.12.005
37. Simmler C., Pauli G.F., Chen S.N.. **Phytochemistry and Biological Properties of Glabridin**. *Fitoterapia* (2013) **90** 160-184. DOI: 10.1016/j.fitote.2013.07.003
38. Kasprzyk-Hordern B.. **Pharmacologically Active Compounds in the Environment and Their Chirality**. *Chem. Soc. Rev.* (2010) **39** 4466-4503. DOI: 10.1039/c000408c
39. Kang J.S., Yoon Y.D., Cho I.J., Han M.H., Lee C.W., Park S.-K., Kim H.M.. **Glabridin, an Isoflavan from Licorice Root, Inhibits Inducible Nitric-Oxide Synthase Expression and Improves Survival of Mice in Experimental Model of Septic Shock**. *J. Pharmacol. Exp. Ther.* (2005) **312** 1187-1194. DOI: 10.1124/jpet.104.077107
40. Boaru S.G., Borkham-Kamphorst E., van de Leur E., Lehnen E., Liedtke C., Weiskirchen R.. **NLRP3 Inflammasome Expression Is Driven by NF-ΚB in Cultured Hepatocytes**. *Biochem. Biophys. Res. Commun.* (2015) **458** 700-706. DOI: 10.1016/j.bbrc.2015.02.029
41. Manzoor Z., Koh Y.S.. **Mitogen-Activated Protein Kinases in Inflammation**. *J. Bacteriol. Virol.* (2012) **42** 189-195. DOI: 10.4167/jbv.2012.42.3.189
42. Yang Y., Kim S.C., Yu T., Yi Y.S., Rhee M.H., Sung G.H., Yoo B.C., Cho J.Y.. **Functional Roles of P38 Mitogen-Activated Protein Kinase in Macrophage-Mediated Inflammatory Responses**. *Mediat. Inflamm* (2014) **2014** 352371. DOI: 10.1155/2014/352371
43. Park H.J., Lee H.J., Choi M.S., Son D.J., Song H.S., Song M.J., Lee J.M., Han S.B., Kim Y., Hong J.T.. **JNK Pathway Is Involved in the Inhibition of Inflammatory Target Gene Expression and NF-KappaB Activation by Melittin**. *J. Inflamm.* (2008) **5** 7. DOI: 10.1186/1476-9255-5-7
44. Kim H.K.. **Role of ERK/MAPK Signalling Pathway in Anti-Inflammatory Effects of Ecklonia Cava in Activated Human Mast Cell Line-1 Cells**. *Asian Pac. J. Trop. Med.* (2014) **7** 703-708. DOI: 10.1016/S1995-7645(14)60120-6
45. Koga Y., Tsurumaki H., Aoki-Saito H., Sato M., Yatomi M., Takehara K., Hisada T.. **Roles of Cyclic AMP Response Element Binding Activation in the ERK1/2 and P38 MAPK Signalling Pathway in Central Nervous System, Cardiovascular System, Osteoclast Differentiation and Mucin and Cytokine Production**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20061346
46. Tedgui A., Mallat Z.. **Cytokines in Atherosclerosis: Pathogenic and Regulatory Pathways**. *Physiol. Rev.* (2006) **86** 515-581. DOI: 10.1152/physrev.00024.2005
47. Liu X., Yin S., Chen Y., Wu Y., Zheng W., Dong H., Bai Y., Qin Y., Li J., Feng S.. **LPS-induced Proinflammatory Cytokine Expression in Human Airway Epithelial Cells and Macrophages via NF-κB, STAT3 or AP-1 Activation**. *Mol. Med. Rep.* (2018) **17** 5484-5491. DOI: 10.3892/mmr.2018.8542
48. Choi E.M., Suh K.S., Kim Y.J., Hong S.M., Park S.Y., Chon S.. **Glabridin Alleviates the Toxic Effects of Methylglyoxal on Osteoblastic MC3T3-E1 Cells by Increasing Expression of the Glyoxalase System and Nrf2/HO-1 Signaling and Protecting Mitochondrial Function**. *J. Agric. Food Chem.* (2016) **64** 226-235. DOI: 10.1021/acs.jafc.5b05157
49. Belinky P.A., Aviram M., Fuhrman B., Rosenblat M., Vaya J.. **The Antioxidative Effects of the Isoflavan Glabridin on Endogenous Constituents of LDL during Its Oxidation**. *Atherosclerosis* (1998) **137** 49-61. DOI: 10.1016/S0021-9150(97)00251-7
|
---
title: 'An Image-Based Framework for Measuring the Prestress Level in CFRP Laminates:
Experimental Validation'
authors:
- Jónatas Valença
- Cláudia Ferreira
- André G. Araújo
- Eduardo Júlio
journal: Materials
year: 2023
pmcid: PMC10004035
doi: 10.3390/ma16051813
license: CC BY 4.0
---
# An Image-Based Framework for Measuring the Prestress Level in CFRP Laminates: Experimental Validation
## Abstract
Image-based methods have been applied to support structural monitoring, product and material testing, and quality control. Lately, deep learning for compute vision is the trend, requiring large and labelled datasets for training and validation, which is often difficult to obtain. The use of synthetic datasets is often applying for data augmentation in different fields. An architecture based on computer vision was proposed to measure strain during prestressing in CFRP laminates. The contact-free architecture was fed by synthetic image datasets and benchmarked for machine learning and deep learning algorithms. The use of these data for monitoring real applications will contribute towards spreading the new monitoring approach, increasing the quality control of the material and application procedure, as well as structural safety. In this paper, the best architecture was validated during experimental tests, to evaluate the performance in real applications from pre-trained synthetic data. The results demonstrate that the architecture implemented enables estimating intermediate strain values, i.e., within the range of training dataset values, but it does not allow for estimating strain values outside those range. The architecture allowed for estimating the strain in real images with an error ∼$0.5\%$, higher than that obtained with synthetic images. Finally, it was not possible to estimate the strain in real cases from the training performed with the synthetic dataset.
## 1. Introduction
Image-based methods for civil engineering applications have been developed in the last 20 years. Several methods were developed in the scope of Structural Health Monitoring (SHM), product and material testing, and quality control. Applications based on photogrammetry and image processing are used to support inspection and monitoring of infrastructures, allowing for computing displacement and deformation fields [1,2,3,4], curvatures and rotations [5], and mapping and characterizing anomalies [6,7]. Lately, machine learning and deep learning for compute vision is the trend followed, taking advantage of all the technology available [8,9,10]. Its application to damage analysis and reliability assessment is promising and has several advantages [11,12,13].
The deep learning applications require large and labelled datasets for training and validation. Furthermore, it is often not possible to generalize and apply outside the limits of validation of the training dataset. The data augmentation using synthetic datasets is a possible solution to add knowledge to the networks developed. One of the most applied and successful artificial neural networks (ANNs) for structured regression problems is the ResNet, a deep neural network with hundreds of layers and skip connections between layers [14]. This Convolutional Neural Network (CNN) is broadly applied and trained with synthetic data in several areas of knowledge. However, there is no consensus about the size of the dataset as well as the reliability of using synthetic images in training as data augmentation. Ward et al. [ 15] use ResNet34 for ships classification, by training the neural network with synthetic and real images, and compare the performance with classical object recognition methods. The dataset of real images is composed of 200 images while the synthetic dataset is composed of 200k images. To understand the effects of data dispersion on different object recognition approaches, these authors tested five ratios for data splitting, with $20\%$ of the training dataset used for validation. Many developments have taken place in the field of medicine. Lei et al. [ 16] use ResNet34 to diagnose congenital heart disease in a fetus through the analysis of computed tomography images. The original dataset is composed of 1729 images, in which 1371 images of normal hearts and 358 of hearts with anomalies. To balance the dataset, the last group was duplicated twice, and the final dataset is composed of 2445 images: 1371 images of normal hearts and 1074 of hearts with anomalies. The test dataset is composed of 200 images of normal hearts and 200 of hearts with anomalies. An accuracy of $80.7\%$ was reached at the test stage.
Al-Moosawi and Khudeyer [17], among other methodologies, implement ResNet34 for the diagnosis of diabetic retinopathy. The dataset consists of 4075 images, and the distribution of images is not uniform across the different stages of the disease. The percentages of the training, validation, and test dataset are, respectively, $67.5\%$, $22.5\%$ and $10\%$, and an accuracy of $94.9\%$ was calculated. Yadav et al. [ 18] use ResNet34 and ResNet50 for the detection of patients infected with COVID-19 pneumonia from chest X-rays. The dataset consists of 2481 images, with $80\%$ of the images being used for training and $20\%$ for testing. The results reveal an accuracy of $94.4\%$ for ResNet34, and $96.4\%$ for ResNet50. Other fields, such as Biology, are also using these type of approaches. Pavel et al. [ 19] use ResNet34 to identify diseases in plants from images of their leaves. The dataset is composed of 7600 images (200 images for each category). In this case, 6080 images ($80\%$) were used for training and 1520 images ($20\%$) for validation. The model was trained in 15 epochs and reach $97.0\%$ of accuracy. Gao et al. [ 20] use ResNet34 combined with transfer learning to detect defects in wood. Before data augmentation, the dataset consisted of 448 spruce defects, split in a ratio of 6:2:2 for the training, validation and test datasets, respectively. After increasing the data, the dataset stays with 3136 images, 1885 images for training, 636 images for validation and 615 images for testing. The model uses 300 epochs and hit $98.7\%$ accuracy at the test stage.
The use of Carbon Fibre Reinforced Polymers (CFRP) has been successfully applied in several areas including repair and rehabilitation of reinforced concrete (RC) structures [21,22]. The technique allows a significant improvement in the flexural and shear strength of concrete members. One of the most used methods consists of externally bonded reinforcement (EBR) of concrete members with CFRP laminates [23]. For large span elements, the application of prestressed CFRP laminates is an advantageous solution for both ultimate and service limit states [24]. In these cases, the level of prestress applied can be evaluated directly by measuring the strain in the laminates. This can be achieved using strain gauges [25,26,27,28,29,30] or fiber optic sensors [31,32]. Both cases require instrumentation of the structures, becoming time-consuming and laborious, and thus just applied in special cases. A contact-free architecture for a vision-based system was proposed and benchmarked by the authors [33]. The architecture was analysed with a dataset of synthetic images and testing machine learning and deep learning algorithms. A data augmentation based on the application of filters to mimic real scenarios was also performed. ResNet34 provided the most accurate results, reaching a root mean square error (RMSE) of lower than $0.1\%$ for strain prediction.
In this paper, the developed architecture is validated through the application in an experimental test. The main goal is to evaluate the application of the architecture in real images of CFRP laminates during prestress application. Specifically, the aim is to assess whether the architecture developed allows for estimating:Intermediate strain values within the range of training dataset values;Strain values greater than the range of training dataset values;Strain in real images with an accuracy identical to that obtained with synthetic images;Strain in real images from training synthetic images.
The analysis and responses to these specific objectives will allow for defining the limits of validity of the proposed architecture for real applications. This represents an important contribution to the dissemination of the new monitoring approach, which will increase the number of conveniently monitored reinforcement applications, promoting quality of execution and greater structural safety in the construction sector.
## 2. Methodology
The architecture based on computer vision for strain monitoring of CFRP laminates was tested for machine learning and deep learning. The results indicate deep learning with regression as the better solution for the problem [33]. The solution was implemented with ResNet34 as a backbone network and tested with the synthetic images dataset (Figure 1). ResNet is a deep neural network that considers over one hundred layers without vanishing gradient problems for training [34,35] and uses the skip connection technique. The last layer’s activation function is replaced by a linear activation function, taking into account the mean square error loss and, for ResNet34, 34 layers. These characteristics lead to a more flexible Convolutional Neural Network (CNN) structure. The main block of ResNet34, presented in the center of Figure 1, is composed by:Convolutional layers, to extract features from the images;Batch normalizations (BNs), to accelerate training and provide regularization;Rectified linear unit (ReLU) activation function, to control the exponential growth in computation; andShortcut, for skip layers in the input of the next step.
The deep learning algorithms are integrated using open source platforms, namely TensorFlow and Scikit-Learn [33]. Figure 1Deep learning architecture implemented.
A pattern of three strips, each with 10 mm × 40 mm spaced 50 mm apart, was considered on the surface of the laminates for monitoring proposes. The architecture is fed by images with different levels of strain, for training, validation and testing (Figure 2a). In the case of synthetic images, aiming to mimic real case scenarios, a set of filters were applied, and the dataset was built following the recommendation of [33]: (i) Gaussian noise, to simulate the effect of thermal noise on the sensor [36]; (ii) salt noise, to reproduce overexposed bright pixels [37]; (iii) pepper noise, to underexposed dark pixels [37]; (iv) salt and pepper noise, combining the last two [37]; (v) speckle noise, to mimic the interference phenomenon due to surfaces roughness [38]; and (vi) Poisson noise, representing the electromagnetic waves at infrared waves [39]. The pattern defined was laser printed in CFRP laminates for measuring real cases (Figure 2b).
## 3.1. Set-Up and Material
This section presents the experimental test conducted to validate the architecture implemented. Figure 3 shows an overview of the entire set-up mounted to apply and monitor the application of prestress in the CFRP laminates that comprises the following:CFRP laminate;Anchor plates system;Hydraulic jacks system;Pressure manometer;RealSense D435 Camera;Control computer;Millimeter ruler. Figure 3Experimental set-up overview.
The CFRP laminates are produced with unidirectional fiber reinforcement in the direction of the laminate and embedded in a polymer resin. The laminate tested is available in 150 m rolls, and is 50 mm wide and 1.4 mm thick. In terms of the mechanical characteristics, the laminate has a modulus of elasticity of approximately 170 kN/mm2, and the prestress usually applied leads to strains of between $6\%$ and $8\%$. The laminates are anchored in a steel reinforced table, and a hydraulic jack system is used to apply a unidirectional deformation along the laminate axis, by a manual applied pressure (Figure 4). A millimeter ruler placed in the center of the laminate also enables measuring the displacements during the application of the prestress. For image acquisition, a RealSense D435 Camera, mounted in a specific support box to capture images during prestress application at a predefined distance and with the same light conditions, is used. All of the data acquisition, from the camera to the manometer, is synchronized with the control computer hour (Figure 4c).
## 3.2. Data Acquisition and Preparation
The strain on the laminate can be estimated from the pressure in the manometer by: [1]ε=Am×PAl×El where Am, in mm2, is the area of the hydraulic jack piston (3882 mm2 for this jack); P, in MPa, the pressure observed on the manometer; Al, in mm2, the area of the laminate (for the present case study, 70 mm2); and El, in GPa, the modulus of elasticity of the laminate (170 GPa for this laminate).
The camera has a sensor size of 1751 px × 1493 px and a focal length of 1.93 mm. This leads to acquiring images with 330 mm of laminate, identical to the synthetic images produced. Furthermore, the camera was programmed for an acquisition frequency of 2 Hz, in order to create the dataset for offline testing.
The synthetic images were generated to have the same resolution as the real images and be in accordance with Section 2. Figure 5a shows an acquired real image, with a field of view (FOV) that leads to an image length of 24.5 cm or 1920 px. Then, the real images are cropped to select only the regions of interest (Figure 5b), and the synthetic images are computed to match this image (Figure 5c). This can be confirmed by overlapping both images at stage 0, i.e, with no strain applied, as in Figure 5d. To optimize the computational cost, the final images used in the datasets are cropped, taking into account that the central stripe matches the image centre (Figure 5e,f).
## 3.3. Training, Validation and Testing
The datasets were built to answer the specific goals set in Section 1. Thus, three training datasets and five testing datasets were defined, as in Figure 6. The level of strain imposed is within the limits of the material for real case tests ($6\%$), and above this limit in the case of simulation with synthetic images ($10\%$). In the following sections, all the details of each of those datasets are presented and described.
## 3.3.1. Training and Validation Datasets
The three training datasets referred to in Figure 6 are described below.
Furthermore, it should be mentioned that $20\%$ of the images from each training dataset were used to build the validation dataset. This division of the datasets was performed randomly.
## 3.3.2. Test Datasets
The five test datasets (Figure 6) were built as below.
## 4. Analysis of Results
For training validation, the loss function, in terms of RMSE and MAE (Mean Absolute Error), evolution was evaluated in relation to the number of epochs performed for both the training dataset and validation dataset (Figure 7). The RMSE values for the last 50 epochs and for the last epoch are also computed and presented in Table 1 for all three of the training datasets. The average value of the loss in the last 50 epochs was considered as a stopping criterion of the training. For these case studies, this value was $0.1\%$. All training was computed on Google Colab.
For Training 1, 500 epochs were performed, which took approximately 1 h 15. Figure 7a,b show the loss for the training and validation dataset, and the variation of MAE metrics with the number of epochs, respectively. The average value for the last 50 epochs was $0.0760\%$, with $0.0526\%$ for the last epoch (Table 1). In the case of Training 2, 150 epochs were considered, and the training time was approximately 3 h 30 min. Figure 7c,d show the loss and the MAE over the epochs. The average of the last 50 epochs is $0.1092\%$, with $0.0872\%$ being the last epoch value. Training 3 requires 250 epochs, and the training time was approximately 9 h. Figure 7e,f show the loss and the MAE over the epochs, with an average of $0.0877\%$ for the last 50 epochs, and $0.0600\%$ in the last epoch. The results show that all three of the training datasets reached metrics that allowed for concluding that the training was carried out successfully.
Table 2 presents the metrics of the analysis performed as defined in Figure 6, namely the RMSE and the MAE values for each case.
To analyse if the model developed is able to estimate strains with values between the values trained, Training 1 (synthetic images between $0\%$ and $10\%$ with an increment of $1\%$) was tested with Test D (synthetic images between $0\%$ and $10\%$ with an increment of $0.1\%$). The metrics of Table 2 and Figure 8 clearly demonstrate that the model can estimate intermediate strain values within the range of training dataset values, with an RMSE and MAE of $0.3496\%$ and $0.3190\%$, respectively.
The analysis of the model ability to estimate strains with higher values than the dataset trained was performed by testing Training 1 and Training 2 (synthetic images between $0\%$ and $10\%$) with Test C (synthetic images between $0\%$ and $40\%$). The metrics presented in Table 2 and Figure 9 clearly demonstrate that the model can not estimate strains higher for which the model was trained. All the values estimated from $10\%$ are completely meaningless, as clearly perceptible in the graphics plotted in Figure 9.
The model reaches higher accuracy values for synthetic images. For example, Training 1 and Training 2 have an accuracy lower than $0.35\%$ for Test A, Test B, and Test D (Table 2). However, the best values are always obtained with identical training and test images, namely, Training 1 and Test A, with $0.2941\%$ and $0.2901\%$ for RMSE and MAE, respectively, and for Training 2 and Test D, with $0.0554\%$ and $0.0492\%$ for RMSE and MAE, respectively. For real scenario analysis, Training 3 was tested in Test E, and the values obtained were $0.5702\%$ for RMSE and $0.3597\%$ for MAE, i.e., error values circa 8 to 10 times higher than the results with synthetic images with discretization of $0.1\%$. On the other hand, the order of magnitude is the same when compared with the analysis with synthetic images with discretization of $1\%$.
Finally, the capacity of the model trained with synthetic images to estimate the values of the strain in real cases was tested. For these purposes, the Training 1 and Training 2 were tested with Test E. The results reveal errors between $2\%$ and $6.5\%$, demonstrating that it is not reliable to use a training dataset only composed of synthetic images to estimate strains in real images. However, increasing the discretization level of the trained range values substantially improved the results. This tendency may reveal that the step reduction in the deformation of the training synthetic images may be important for using these training datasets for measuring real cases. However, this requires a significant increase in image resolution.
## 5. Conclusions
The methodology presented in this paper aims for an experimental validation of an image-based architecture for monitoring prestress application in CFRP laminate. The architecture was previously evaluated using synthetic data to benchmark different computer vision algorithms. The best solution, based on the ResNet34 deep learning algorithm with regression, was experimentally tested in a laboratory environment, and the following main conclusions about the model were drawn:It allows for measuring intermediate strain levels within the training range. In that sense, the model is able to measure values divisible by 10 between the training values;*It is* not capable of extrapolating for strain levels outside the training range. Thus, it is essential to check the maximum strain to be imposed in real cases, and training the models for higher strain levels;For real case scenarios, the error can reach values 10 times higher than using synthetic datasets, i.e, for synthetic datasets, the RMSE value was $0.06\%$ while, for real images, the RMSE value was $0.6\%$;The pre-training with synthetic datasets performed is not able to correctly estimate the strain in real application.
## References
1. Valença J., Júlio E.. **MCrack-Dam: The scale-up of a method to assess cracks on concrete dams by image processing. The case study of Itaipu Dam, at the Brazil–Paraguay border**. *J. Civ. Struct. Health Monit.* (2018.0) **8** 857-866. DOI: 10.1007/s13349-018-0309-0
2. Rahman M.M., Zhao X., D’Antino T., Focacci F., Carloni C.. **Fracture Behavior and Digital Image Analysis of GFRP Reinforced Concrete Notched Beams**. *Materials* (2022.0) **15**. DOI: 10.3390/ma15175981
3. Carmo R.N.F., Valença J., Silva D., Dias-Da-Costa D.. **Assessing steel strains on reinforced concrete members from surface cracking patterns**. *Constr. Build. Mater.* (2015.0) **98** 265-275. DOI: 10.1016/j.conbuildmat.2015.08.079
4. Lee J., Lee K.-C., Cho S., Sim S.-H.. **Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges**. *Sensors* (2017.0) **17**. DOI: 10.3390/s17102317
5. Valença J., Carmo R.N.F.. **Method for assessing beam column joints in RC structures using photogrammetric computer vision**. *Struct. Control Health Monit.* (2017.0) **24** e2013. DOI: 10.1002/stc.2013
6. Mendes M.P., Torres-González M., Valença J., Silva A.. **The Maintenance of Monuments as the Main Trigger to Negative Feelings in Tourists**. *Buildings* (2022.0) **12**. DOI: 10.3390/buildings12122153
7. Dai M., Ward W.O., Meyers G., Tingley D.D., Mayfield M.. **Residential building facade segmentation in the urban environment**. *Build. Environ.* (2021.0) **199** 107921. DOI: 10.1016/j.buildenv.2021.107921
8. Salehi H., Burgueno R.. **Emerging artificial intelligence methods in structural engineering**. *Eng. Struct.* (2018.0) **171** 170-189. DOI: 10.1016/j.engstruct.2018.05.084
9. Santo B.-O., Valença J., Costeira J.P., Júlio E.. **Domain adversarial training for classification of cracking in images of concrete surfaces**. *AI Civ. Eng.* (2022.0) **1** 8. DOI: 10.1007/s43503-022-00008-6
10. Russell S.J.. *Artificial Intelligence: A Modern Approach* (2010.0)
11. Jiang F., Guan Z., Wang X., Li Z., Tan R., Cheng Q.. **Study on Prediction of Compression Performance of Composite Laminates After Impact Based on Convolutional Neural Networks**. *Appl. Compos. Mater.* (2021.0) **28** 1153-1173. DOI: 10.1007/s10443-021-09904-z
12. Khan A., Kim N., Shin J., Kim H.-S., Youn B.-D.. **Damage assessment of smart composite structures via machine learning: A review**. *JMST Adv.* (2019.0) **1** 107-124. DOI: 10.1007/s42791-019-0012-2
13. Ghosh Mondal T., Jahanshahi M., Wu R.-T., Wu Z.. **Deep learning-based multi-class damage detection for autonomous post-disaster reconnaissance**. *Struct. Control Health Monit.* (2020.0) **27** e2507. DOI: 10.1002/stc.2507
14. He K., Zhang X., Ren S., Su J.. **Deep Residual Learning for Image Recognition**. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* 770-778. DOI: 10.1109/CVPR.2016.90
15. Ward C.M., Harguess J., Hilton C.. **Ship classification from overhead imagery using synthetic data and domain adaptation**. *Proceedings of the OCEANS 2018 MTS/IEEE Charleston* 1-5
16. Lei L., Zhu H., Gong Y., Cheng Q.. **A deep residual networks classification algorithm of fetal heart CT images**. *Proceedings of the 2018 IEEE international conference on imaging systems and techniques (IST)* 1-4
17. Al-Moosawi N., Khudeyer R.S.. **ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy**. *Informatica* (2021.0) **45** 115-124. DOI: 10.31449/inf.v45i7.3774
18. Yadav A., Verma V.K., Pal V., Singh S.. **Automatic detection of COVID 19 infection using deep learning models from X-ray images**. *IOP Conf. Ser. Mater. Sci. Eng.* (2021.0) **1099** 012050. DOI: 10.1088/1757-899X/1099/1/012050
19. Pavel M.I., Rumi R.I., Fairooz F., Jahan S., Hossain M.A.. **Deep Residual Learning Approach forPlant Disease Recognition**. *Proceedings of the International Conference on Mobile Computing and Sustainable Informatics* (2020.0) 511-521
20. Gao M., Qi D., Mu H., Chen J.. **A transfer residual neural network based on ResNet-34 for detection of wood knot defects**. *Forests* (2021.0) **12**. DOI: 10.3390/f12020212
21. Derkowski W., Walczak R.. **Possibilities of Increasing Effectiveness of RC Structure Strengthening with FRP Materials**. *Materials* (2021.0) **14**. DOI: 10.3390/ma14061387
22. Wysmulski P.. **Load Eccentricity of Compressed Composite Z-Columns in Non-Linear State**. *Materials* (2022.0) **15**. DOI: 10.3390/ma15217631
23. Du Béton F.I.. **Externally bonded FRP reinforcement for RC structures**. *Bulletin* (2001.0) **14** 138
24. Slaitas J., Valivonis J.. **Full moment-deflection response and bond stiffness reduction of RC elements strengthened with prestressed FRP materials**. *Compos. Struct.* (2021.0) **260** 113265. DOI: 10.1016/j.compstruct.2020.113265
25. Li X., Deng J., Wang Y., Xie Y., Liu T., Rashid K.. **RC beams strengthened by prestressed CFRP plate subjected to sustained loading and continuous wetting condition: Time-dependent prestress loss**. *Constr. Build. Mater.* (2021.0) **275** 122187. DOI: 10.1016/j.conbuildmat.2020.122187
26. Al-Mosawe A., Al-Mahaidi R., Alwash D., Zhao X.L., Hosseini A., Motavalli M., Ghafoori E.. **FE analysis of a steel bridge strengthened with CFRP laminates**. *Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations* (2021.0) 1424-1430
27. Yang J., Johansson M., Al-Emrani M., Haghani R.. **Innovative flexural strengthening of RC beams using self-anchored prestressed CFRP plates: Experimental and numerical investigations**. *Eng. Struct.* (2021.0) **243** 112687. DOI: 10.1016/j.engstruct.2021.112687
28. Hu L., Feng P., Gao W., Wang Y.. **Flexural behavior of light steel purlins reinforced by prestressed CFRP laminates**. *Thin-Walled Struct.* (2021.0) **174** 109125. DOI: 10.1016/j.tws.2022.109125
29. Doroudi Y., Fern D., Hosseini A., Ghafoori E.. **Behavior of cracked steel plates strengthened with adhesively bonded CFRP laminates under fatigue Loading: Experimental and analytical study**. *Compos. Struct.* (2021.0) **266** 113816. DOI: 10.1016/j.compstruct.2021.113816
30. Lu Z., Li J., Xie J., Huang P., Xue L.. **Durability of flexurally strengthened RC beams with prestressed CFRP sheet under wet–dry cycling in a chloride-containing environment**. *Compos. Struct.* (2021.0) **255** 112869. DOI: 10.1016/j.compstruct.2020.112869
31. Xian G., Guo R., Li C., Wang Y.. **Mechanical performance evolution and life prediction of prestressed CFRP plate exposed to hygrothermal and freeze-thaw environments**. *Compos. Struct.* (2022.0) **293** 115719. DOI: 10.1016/j.compstruct.2022.115719
32. Deng J., Li J., Zhu M.. **Fatigue behavior of notched steel beams strengthened by a prestressed CFRP plate subjected to wetting/drying cycles**. *Compos. Part B Eng.* (2022.0) **230** 109491. DOI: 10.1016/j.compositesb.2021.109491
33. Valença J., Habibu H., Araújo A.G., Couceiro M.S., Júlio E.. **Benchmarking for Strain Evaluation in CFRP Laminates Using Computer Vision: Machine Learning versus Deep Learning**. *Materials* (2022.0) **15**. DOI: 10.3390/ma15186310
34. Szegedy C., Ioffe S., Vanhoucke V., Alemi A.. **Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning**. *Proceedings of the AAAI’17: Thirty-First AAAI Conference on Artificial Intelligence* 278-4284
35. Hochreiter S.. **The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions**. *Int. J. Uncertain. Fuzziness Knowl.-Based Syst.* (1998.0) **6** 107-116. DOI: 10.1142/S0218488598000094
36. Slepian D.. **The One-Sided Barrier Problem for Gaussian Noise**. *Bell Syst. Tech. J.* (1962.0) **41** 463-501. DOI: 10.1002/j.1538-7305.1962.tb02419.x
37. Chan R., Ho C.-W., Nikolova M.. **Salt-and-Pepper Noise Removal by Median-Type Noise Detectors and Detail-Preserving Regularization**. *IEEE Trans. Image Process.* (2005.0) **14** 1479-1485. DOI: 10.1109/TIP.2005.852196
38. Racine R., Walker G., Nadeau D., Doyon R., Marois C.. **Speckle Noise and the Detection of Faint Companions**. *Publ. Astron. Soc. Pac.* (1999.0) **111** 587-594. DOI: 10.1086/316367
39. Le T., Chartrand R., Asaki T.. **A Variational Approach to Reconstructing Images Corrupted by Poisson Noise**. *J. Math. Imaging Vis.* (2007.0) **27** 257-263. DOI: 10.1007/s10851-007-0652-y
|
---
title: OGT Binding Peptide-Tagged Strategy Increases Protein O-GlcNAcylation Level
in E. coli
authors:
- Yang Li
- Zelan Yang
- Jia Chen
- Yihao Chen
- Chengji Jiang
- Tao Zhong
- Yanting Su
- Yi Liang
- Hui Sun
journal: Molecules
year: 2023
pmcid: PMC10004047
doi: 10.3390/molecules28052129
license: CC BY 4.0
---
# OGT Binding Peptide-Tagged Strategy Increases Protein O-GlcNAcylation Level in E. coli
## Abstract
O-GlcNAcylation is a single glycosylation of GlcNAc mediated by OGT, which regulates the function of substrate proteins and is closely related to many diseases. However, a large number of O-GlcNAc-modified target proteins are costly, inefficient, and complicated to prepare. In this study, an OGT binding peptide (OBP)-tagged strategy for improving the proportion of O-GlcNAc modification was established successfully in E. coli. OBP (P1, P2, or P3) was fused with target protein Tau as tagged Tau. Tau or tagged Tau was co-constructed with OGT into a vector expressed in E. coli. Compared with Tau, the O-GlcNAc level of P1Tau and TauP1 increased 4~6-fold. Moreover, the P1Tau and TauP1 increased the O-GlcNAc-modified homogeneity. The high O-GlcNAcylation on P1Tau resulted in a significantly slower aggregation rate than Tau in vitro. This strategy was also used successfully to increase the O-GlcNAc level of c-Myc and H2B. These results indicated that the OBP-tagged strategy was a successful approach to improve the O-GlcNAcylation of a target protein for further functional research.
## 1. Introduction
O-GlcNAcylation is a highly dynamic and reversible PTM in the serine/threonine residues of nuclear, cytosolic, and mitochondrial proteins [1,2]. Only one O-GlcNAc transferase (OGT) catalyzes the addition of O-GlcNAc and one O-GlcNAcase (OGA) removes the modification [3,4,5]. Mounting evidence has shown that O-GlcNAcylation regulates essential cellular processes such as transcription, metabolic regulation, signal transduction, cell cycle, nutrient sensing, protein stabilization, and cellular responses to diverse stress conditions [6,7,8]. Aberrant O-GlcNAcylation has been implicated in the progression of a wide range of diseases, such as diabetes [9], cancer [10], and cardiovascular [11] and neurodegenerative diseases [12]. In humans, OGT is encoded by the OGT gene on the X chromosome (Xq13.1) [13], and consists of an N-terminal tetratricopeptide repeat (TPR) and a C-terminal catalytic domain [14]. TPR repeats are mainly involved in the binding of OGT to substrate proteins [15]. OGT is conserved throughout eukaryotes, from Caenorhabditis elegans to mammals [8]. Unlike other cell hosts such as mammalian or insect cells, however, OGT has not been found in E. coli [16].
Although more than 4000 proteins (including many disease-related proteins) have been found to be O-GlcNAcylated, the function of O-GlcNAcylation in most proteins has not been clarified [17,18]. It is mainly attributed to the low abundance and small molecular weight of O-GlcNAcylation, and unstable O-glycosidic linkage. Preparing O-GlcNAc peptide/protein is essential to elucidate the role of specific GlcNAc-based pathways. Chemical and biochemical methods have been developed to produce O-GlcNAc peptide/protein [19].
At present, the synthesis methods of O-GlcNAc-modified peptide/protein mainly include OGT co-expression modified protein in E. coli and the chemical synthesis of modified peptide. The chemical synthesis of O-GlcNAc peptide/protein, selectively adding GlcNAc to specific sites of target peptides/proteins using solid phase or chemoenzymatic synthesis, is widely used to generate a specific antigen and desired antibody by immunizing animals [20], such as c-Myc (Thr58) [21], histone H2A (Ser40, Thr101) [22], Tau (Ser400) [23,24], histone H2B (Ser112) [25], SIRT1 (Ser549) [26], insulin receptor substrate 1 (Ser1011) [27], TAB1 (Ser395) [28], and TAB3 (Ser408) [29].
The co-expression of human OGT with a substrate protein in E. coli is a strategy to produce milligram-scale quantities of O-GlcNAc protein, including Nup62, CaMKIV, CARM1, CKII, p53, TAB1, H2B, Tau, and others. Shen et al. used O-GlcNAc proteins prepared in E. coli to measure the activity of human OGA in diverse protein substrates TAB1, CaMKIV, CARM1, Tau, and Nup62 [30]. Han et al. established an adjustable compatible dual plasmid system with varying O-GlcNAc levels by altering the inducer concentration, which could effectively yield O-GlcNAc CKII and p53 proteins in E. coli [31]. Gao et al. further optimized by co-expressing GlmM/GlmU in E. coli, which promoted the synthesis of UDP-GlcNAc to increase OGT activity, and effectively improved the O-GlcNAc level of target proteins TAB1, H2B, and p53 [32]. Yuzwa et al. prepared a Tau S400-O-GlcNAc site-specific antibody by immunizing animals with glycopeptides as the antigen and verified this specific antibody by co-expressing the Tau protein with OGT in E. coli [24]. Obtaining proteins with a high O-GlcNAc modification level is the key to the preparation of monoclonal antibodies and further functional research. However, the chemical synthesis of O-GlcNAc peptide/protein is difficult and costly, and biosynthesis needs to increase the proportion of O-GlcNAcylation [19].
In this study, an OGT binding peptide (OBP)-tagged strategy was developed to increase the O-GlcNAc level of a protein through the co-expression of OGT and tagged protein. This strategy successfully improved the O-GlcNAc level of Tau, H2B, and c-Myc proteins, which was far more efficient than control proteins without P1 and could conveniently and efficiently produce milligram-scale quantities of O-GlcNAc protein with a high O-GlcNAc level. Compared with Tau, O-GlcNAc of P1Tau had obvious interference with P1Tau aggregation in vitro. This strategy provides convenience for further preparation of a large number of O-GlcNAc-modified proteins and functional research.
## 2.1. OBPs Increased O-GlcNAc Level of Tau
It is speculated that OGT has a higher catalytic efficiency for proteins with higher affinity. To improve the affinity between OGT and its substrate, a fusion strategy of OBP and target protein was designed to improve the O-GlcNAc level of the target protein. According to the research of Pathak et al., six OBPs were identified by using a substrate library of synthetic peptides [33]. From the above six peptides, the peptide KKVPVSRA with the highest binding activity to OGT, named P1, and the other two peptides with lower binding activity, KKVPVTRA and KKVGVSRA, named P2 and P3, were selected. Tau was selected as the target protein, and its O-GlcNAc site and function have been clearly studied in previous reports [34,35,36,37]. Then, P1, P2, and P3 were used as tags to fuse with the substrate Tau, named tagged Tau (P1Tau, P2Tau, P3Tau) (Figure 1A). OGT and the target protein (Tau, P1Tau, P2Tau, P3Tau) were cloned into vector pET-4CDS, which could express Tau or tagged Tau and OGT simultaneously in E. coli (Figure 1B). As shown in Figure 1C, the O-GlcNAc level of P1Tau was 4~6-fold higher than Tau, and the O-GlcNAc level of P2Tau and P3Tau was 2~3-fold higher than Tau, indicating that the fusion OBP (P1, P2, or P3) could increase the O-GlcNAc level of Tau. The O-GlcNAcylation of P1Tau was the highest among the tagged Tau, which was consistent with the highest affinity activity of P1 with OGT among the three OBPs (Figure 1D) [33].
To investigate the positional effect of tag, P1 was constructed at the N-Terminal (P1Tau) or C-terminal (TauP1) of Tau, respectively. The protein expression level of P1Tau (or TauP1) and O-GlcNAc level of P1Tau (or TauP1) were detected in the lysis of E. coli using anti-His antibody and RL2 antibody, respectively. The normalized relative ratio of Tau O-GlcNAcylation showed that the O-GlcNAc levels of both P1Tau and TauP1 were significantly enhanced 4~6-fold compared with Tau. Its level was slightly higher in TauP1 than in P1Tau, without a significant difference. ( Figure 1E,F).
## 2.2. O-GlcNAc Sites Identified in Tau and Tagged Tau
The O-GlcNAc Tau protein was purified under different induction temperatures. It was found that P1Tau and TauP1 were all expressed well under 16 °C, 25 °C, or 37 °C (Figure S1A, Supplementary Materials). Then, the recombinant O-GlcNAc-modified Tau, P1Tau, and TauP1 were purified using Ni-NTA columns (Figure S1B).
Tau, P1Tau, and TauP1 were purified using Ni-NTA, and the O-GlcNAc sites were detected using HCD/ETD. The array of low-mass oxocarbenium ions (m/z 126.06, 138.06, 144.07, 168.07, 186.08, and 204.09) generated upon HCD were characteristic of N-acetylglucosamine (GlcNAc). GlcNAc oxonium ion provided evidence of an O-GlcNAc-modified peptide. Based on the mass difference of 203.08 Da in the HCD/ETD spectra, we identified nineteen O-GlcNAcylated sites on Tau, seven sites on P1Tau, and eight sites on TauP1. The locations of these O-GlcNAc sites in the Tau, P1Tau, and TauP1 proteins are shown in Figure 2A. Interestingly, P1Tau displayed a similar modification pattern to TauP1 in the Proline-rich and C-terminal domain. These results indicated that P1 had little effect on O-GlcNAc-modified sites at either the N-terminal or C-terminal.
As shown in Table 1, a total of twenty-two O-GlcNAc modification sites were identified using mass spectrometry (MS), including nine previously reported ones and thirteen new ones. The new O-GlcNAc site S184 (Figure 2B) was identified in both Tau and tagged Tau. S400/T403 indicates O-GlcNAc at one of the sites. T386 (Figure 2C) and S400/T403 (Figure S2A) were identified in Tau and tagged Tau, and T76 (Figure S2B) and S305 (Figure 2D) were identified in tagged TauP1. Another eight new sites, T181 (Figure 2E), S195 (Figure S2C), S198, S241/T245 (Figure S2D), S316 (Figure S2E), T414, S416 (Figure 2F), and T427 (Figure S2F), were only identified in Tau. Of the nine previously reported sites identified, S185, S191, S396, and S400 were identified in both Tau and tagged Tau (Figure S3A, Figure S3B, Figure S3C, Figure S3D), and T205, S208, S412, S413, and S422 were only identified in Tau (Figure S3E,F, partial spectrum). These results indicated that nineteen out of the twenty-two O-GlcNAc sites were identified in Tau. However, far fewer O-GlcNAc sites were identified in tagged Tau than Tau, most of which were common sites in all samples. Combined with the results of Western blotting (Figure 1E), the P1-tagged strategy could promote the interaction between OGT and specific target proteins, improve the O-GlcNAc level of protein, and reduce the diversity of modified sites, which provides materials for subsequent antibody preparation and functional research.
## 2.3. P1Tau Aggregated More Slowly than Tau
As shown above, the O-GlcNAc modification level of the Tau protein was significantly enhanced in the presence of P1. The positive control Tau protein (no OGT) without OGT co-expression, Tau, and P1Tau protein with OGT co-expression were purified using SP cation exchange chromatography for aggregation experiment (Figure 3A). Another anti-O-GlcNAc antibody, CTD110.6, was used to detect the O-GlcNAcylation of Tau and P1Tau. The results showed that the O-GlcNAc level of P1Tau was higher than that of Tau (Figure 3B). To test the effect of increased O-GlcNAcylation on the aggregation rate, we conducted a kinetic analysis of Tau (no OGT), Tau, and P1Tau aggregation in vitro with Congo red and ThT binding assays.
The absorbance at 550 nm represents the amount of Tau fibrils to monitor the kinetic effect on the formation of Tau protein amyloid fibers induced by Congo red. Tau (no OGT) was used as a positive control and HEPES buffer as a negative control. The empirical Hill equation was fitted to the kinetic data in Figure 3C, and the solid lines represent the best fit. The normalized platform value of Tau (no OGT) fiber growth curve showed that Tau aggregated more slowly than Tau (no OGT) and its equilibrium (plateau) value decreased by ~$82\%$, while the equilibrium value of P1Tau decreased by ~$50\%$. To assess the potential effect of P1 on the Tau aggregation, Tau (no OGT) was incubated in HEPES buffer with or without peptide P1. As shown in Figure 3D, P1 had no obvious effect on Tau fibers’ aggregation and fluorescence reaction.
The heparin induced Tau protein aggregation, which was monitored through fluorescence spectroscopy using Thioflavin T (ThT) [38]. Tau protein aggregation occurs in the sigmoid shape curve with a readily apparent lag phase. Three kinetic parameters were obtained from the fitted sigmoid equation, as summarized in Table 2. As shown in Figure 3E, the aggregation of both Tau and P1Tau have an obvious lag phase compared with Tau (no OGT), and P1Tau exhibited a significantly longer lag time of (233.1 ± 4.8) min than that Tau of (138.9 ± 2.2) min.
Overall, these results confirmed that increasing the O-GlcNAc level of P1Tau significantly slowed the formation of amyloid fibrils during Tau protein aggregation.
## 2.4. P1 Tag also Improved the O-GlcNAc Level of c-Myc and H2B
To further verify the feasibility of P1-tagged strategy in improving the O-GlcNAc level of substrate protein, the other two proteins Myc and H2B were each fused with P1. P1 was constructed into the N-terminal or C-terminal of the target protein, respectively. The protein expression of tagged Myc (P1Myc or MycP1) and the O-GlcNAcylation of tagged Myc in the lysis of E. coli were detected using anti-His antibody and RL2 antibody, respectively (Figure 4A). Compared with the control group Myc, the O-GlcNAc level of tagged Myc showed a 17~21-fold increase (Figure 4B). Similarly, the protein expression of tagged H2B (P1H2B or H2BP1) and the O-GlcNAcylation of tagged H2B were detected using anti-His antibody and anti-H2B S112 O-GlcNAc antibody, respectively (Figure 4C). Compared with the control group H2B, the O-GlcNAc level of tagged H2B increased ~3-fold (Figure 4D). P1 showed similar results at the N-terminal or C-terminal of the protein without positional effect. Taken together, these results demonstrated that this strategy successfully expressed O-GlcNAc proteins with a far higher O-GlcNAc level than the control group Myc (H2B) and OGT co-expression.
## 3. Discussion
In this paper, the OBP-tagged strategy was used to successfully improve the O-GlcNAc level of Tau, H2B, and c-Myc proteins. It has been reported that the co-expression of OGT with its target substrates in E. coli could produce O-GlcNAc recombinant proteins using the dual-plasmid system [24,31,32]. However, the transfection efficiency of two plasmids in one system is different, and there is a possibility of plasmid loss. In this study, a new OBP-tagged strategy was developed to address the above problems successfully. The advantage of this strategy is that the target gene and OGT gene were cloned into a plasmid (pET-4CDS), which ensures the consistency of transfection efficiency and avoids the inconvenience and the loss of plasmids in a dual-plasmid system.
Although P1 tag is the substrate of OGT, the O-GlcNAc modification of P1 depends on the fusion proteins. The Ser on P1 in P1p53 was O-GlcNAcylation, but not in P1Tau. These data indicated that the function of the P1 tag is to promote the binding of OGT and the substrate protein, rather than compete for O-GlcNAc sites of the substrate. The OBP-tagged strategy with a high affinity of OGT is a successful approach. We will attempt to find more peptides with higher affinity for OGT to further optimize this OBP-tagged strategy. Ramirez et al. reported that OGT fused with tag-bound nanobody could enhance the O-GlcNAc level of the target protein in HEK293T cells [39], but this was not attempted in E. coli. Therefore, the effectiveness of this tag-bound nanobody in producing O-GlcNAc-modified protein is worthy of further study and verification in E. coli. Previous studies have implied that the asparagines in the N-terminal superhelical tetratricopeptide repeat (TPR) lumen proximal to the catalytic domain of OGT play a critical role in the recognition of most substrates [40,41,42]. However, this recognition has a preference for the amino acid composition of the substrate [43]. The amino acid preferences play an important role in improving binding to TPR, which has a reference significance for designing the TPR recognition tag. The tag P1 selected in this study is an OGT catalytic region binding peptide, while other peptides with a high affinity to OGT-TPR used as tags may be more worthy in an OBP-tagged strategy. Additionally, the O-GlcNAc-modified sites resulting from the OGT-TPR binding tags will be further compared with the catalytic region binding P1.
The results of Tau and tagged Tau O-GlcNAc sites showed that this strategy not only improved the O-GlcNAc modification level, but also reduced the diversity and microheterogeneity of O-GlcNAc sites, which may be more similar to the intracellular modification status in the microenvironment. S400 is an important O-GlcNAcylated site in the Tau protein that plays a major role in regulating Tau aggregation because the S400A mutation in recombinant O-GlcNAcylated Tau eliminates the inhibition of wild-type O-GlcNAcylation on Tau aggregation in vitro [34,35]. The S400 O-GlcNAc site was identified in all three samples of Tau and tagged Tau. S184 is a new site with high reliability, which deserves further study. Except for the S400 site-specific antibody [24], no antibodies for other specific modified sites of Tau were prepared. P1Tau and TauP1 may be used as antigens for preparing the S184-O-GlcNAc site-specific antibody and functional research. These results lay a foundation for the preparation of other site-specific antibodies against H2B and c-Myc proteins and the further study of their functions.
Many O-GlcNAc-modified proteins have been identified with multiple O-GlcNAc sites, some of which have important functions [2,37]. Mutations in functional O-GlcNAc sites are always accompanied by a significant reduction in the O-GlcNAc level of these proteins [44,45,46]. The large-scale preparation of proteins with functional O-GlcNAc sites in vitro is of great significance for functional research. In this study, the results of the Tau protein indicated that large amounts of proteins with functional O-GlcNAc sites could be expressed and purified in vitro. However, the effectiveness of this strategy still needs to be verified using more proteins.
In conclusion, we report an in vitro method for enhancing protein O-GlcNAcylation using the OBP-tagged strategy, which provides highly modified and homogeneous samples for studying the biological functions of protein O-GlcNAcylation.
## 4.1. Preparing Gene Constructs in pET-4CDS
The OGT target proteins used in this study were human Tau441, H2B, and c-Myc. Taking the P1Tau fusion gene as an example, we constructed a fusion gene of P1 and Tau. The flexible and non-helical linker (GSSSS)2 was inserted between P1 and Tau. Using the *Tau* gene as the template, a pair of primers, P1Tau-Fa/Tau-R (P1Tau-Fa: CTC GTG CGG GCG GCG GCG GCA GCG GCG GCG GCG GCA GCG CTG AGC CCC GCC AGG AG and Tau-R: GGT GGT GGT GGT GCT CGA GCT GCA GCA AAC CCT GCT TGG CCA G), were designed for amplification. Then, using the above PCR product as a template, P1Tau-Fb/Tau-R (P1Tau-Fb: AAG GAG ATA TAC CAT GGT CCA TAT GAA AAA GGT GCC GGT CTC TCG TGC GGG CGG CGG CG and Tau-R: GGT GGT GGT GGT GCT CGA GCT GCA GCA AAC CCT GCT TGG CCA G) were designed for the second amplification. This second PCR product was inserted into the pET-4CDS vector between Nde1 and Pst1. A pair of PCR primers, OGT-F/R (OGT-F: GCG GAT GGG ATC CGA ATT CGT CGA CAT GAA AAT CGA AGA AGG TAA AC and OGT-R: TGC CCA CGG AAG ACG CCA TGT CGA CGA TAT CGC GGC CGC CCA TGG), were designed to amplify the OGT gene. The PCR product was inserted into pET-4CDS at the Sal1 site (restriction enzymes were purchased from Takara). The pET-4CDS was given by Dr. Yanting Su. Amplification of the genes was carried out using high-fidelity polymerase enzyme 2×Phanta Flash Master Mix (Vazyme). 2×MultiF Seamless Assembly Mix (Abclonal) was used to link genes with vectors. The final constructs were verified through DNA sequence analysis.
## 4.2. Production of O-GlcNAcylated Protein
The above plasmids were transformed into E. coli BL21 (DE3) prepared in our laboratory. The bacterial strains were grown in LB containing kanamycin (50 μg/mL) at 37 °C until OD 600 reached 0.6~0.8. Then, the culture was induced with 1 mM IPTG and further incubated at 25 °C for 12 h. The bacterial solution after induction was centrifuged at 3500 rpm for 25 min, and the supernatant was discarded.
## 4.2.1. Ni-NTA Column
The remaining sediment was resuspended in binding buffer (50 mM sodium phosphate, 500 mM NaCl, 20 mM imidazole, pH 7.4) and broken ultrasonically. After centrifugation (9000 rpm, 25 min), the supernatant was purified as recombinant protein using a Ni-NTA affinity column (TransGen Biotech). The target proteins adsorbed on the Ni-NTA affinity column were eluted with an elution buffer (50 mM sodium phosphate, 500 mM NaCl, 200 mM imidazole, pH 7.4), and the protein sample was added to 5× loading buffer, boiled at 100 °C for 10 min, and separated in SDS-PAGE for LC-MS/MS analysis.
## 4.2.2. SP Sepharose Column
The above bacteria were harvested and resuspended in SP buffer (20 mM Na2HPO4·12H2O, 20 mM NaH2PO4·2H2O, 1 mM EDTA, $0.5\%$ β-Mercaptoethanol, 1 mM PMSF, pH 6.8) and were then broken by sonication on ice. The supernatants were filtered, loaded onto a cation exchanger HiTrap SP FF (GE Healthcare), and washed with SP buffer. A linear gradient of salt (0–400 mM NaCl) in the same buffer as used to elute the Tau protein. BCA Protein Assay (Yeasen) and Coomassie brilliant blue staining of $10\%$ SDS-PAGE were used to estimate protein concentration and purity, respectively. The purified proteins were dialyzed three times with 10 mM HEPES buffer (10 mM HEPES, 100 mM NaCI, $0.5\%$ β-ME, pH7.4), concentrated with a 10K Amicon centrifugal filter (Millipore) for kinetic analysis, and transferred to −80 °C for long-term storage.
## 4.3. Congo Red Binding Assay
Congo red, widely employed as an inducer for Tau aggregation in vitro, can react specifically with β-sheet-rich amyloid fibrils, and the bound form displays a characteristic red shift in its maximum absorbance from 490 to 550 nm [47,48]. A fresh 5 mM CR (Sigma-Aldrich, USA) stock solution was prepared in HEPES buffer containing 100 mM NaCl (pH 7.4) and filtered (0.22 µm pore size) before use to remove insoluble particles. The polymerization induced by Congo red for full-length human Tau (no OGT) and its O-GlcNAcylated Tau in 96-well plates (Corning) was set up with a mixture of 10 μM proteins and 10 μM P1 peptide (synthesized by GenScript) incubated with 50 μM Congo red in 10 mM HEPES buffer containing 1 mM DTT (pH 7.4). To block the formation of an intramolecular disulfide bond in the Tau protein, 1 mM DTT was added into the HEPES buffer. The reaction components were mixed quickly and immediately read at 37 °C for 6 h in a Cytation 3 Microporous Plate Reader (BioTek, USA) using absorbance at 550 nm. All kinetic experiments were repeated three times. Kinetic parameters were determined by fitting the absorbance at 550 nm versus time to the empirical Hill equation [47]:[1]A=A∞t/t50n1+t/t50n where A is the absorbance at 550 nm, A(∞) is the absorbance at 550 nm in the long time limit, t50 is the elapsed time at which A is equal to one-half of A(∞), and n is a cooperativity parameter.
## 4.4. ThT Binding Assays
The aggregation of Tau protein normally occurs through the nucleation-dependent fibril polymerization in three phases: activation and nucleation (Lag phase), elongation (growth phase), and steady phase (paired helical filaments, PHFs) [49]. A small fluorescent molecule, Thioflavin T (ThT), can be bound preferentially to amyloid fibrils when free in solution, which has been frequently used for monitoring the kinetics of amyloid fibril formation [50]. A stock solution of 2.5 mM Thioflavin T (ThT) (Sigma-Aldrich, St. Louis, MO, USA) was freshly prepared in 10 mM HEPES buffer. Recombinant Tau proteins (10 μM) were incubated in 10 mM HEPES buffer containing 1 mM DTT and 25 μM ThT at 200 rpm and 37 °C for 10 h in the presence of 2.5 μM heparin. The resulting 200 µL mixtures were added to a 96-well fluorescence microplate (Corning) in a Cytation 3 with an excitation wavelength of 440 nm and emission wavelength of 480 nm. Measurements were made at 37 °C with agitation. All kinetic experiments were repeated three times. Kinetic parameters were determined by fitting ThT fluorescence intensity versus time to a sigmoidal equation [51]:[2]F=F0+A+ct/1+ektm−t where F is the fluorescence intensity, k the rate constant for the growth of fibrils, and tm is the time to $50\%$ of maximal fluorescence. F0 describes the initial baseline during the lag time. A + ct describes the final baseline after the growth phase has ended. The lag time is determined to be tm − 2/k.
## 4.5. SDS-PAGE and Western Blot
The target proteins were fractionated with SDS-PAGE (Tau and Myc, $10\%$; H2B, $15\%$; OGT, $8\%$) and visualized by staining with Coomassie blue. For Western blot analysis, the proteins were transferred to PVDF membrane (Millipore, Burlington, MA, USA), and blocked with $5\%$ non-fat milk (or bovine serum albumin) for 1 h at room temperature or overnight at 4 °C, after separating on SDS-PAGE. Target proteins Tau, H2B, and Myc were identified by using anti-His antibody conjugated to HRP (1:10000) (Abclonal, Woburn, MA, USA). Likewise, the OGT was detected by using mouse anti-Flag (1:1000) (Abclonal) as the primary antibody and goat anti-mouse antibody conjugated to HRP (Abclonal) as the secondary antibody. The O-GlcNAcylation of the target was identified by using anti-O-GlcNAc antibody RL2 (Abcam, Cambridge, UK), CTD110.6 (Cell Signaling Technology, Danvers, MA, USA) and anti-H2B S112GlcNAc (Abcam). Super ECL Detection Reagent (Yeasen, Shanghai, China) was used for developing and capturing proteins. Blots were visualized using an automatic chemiluminescence image analysis system (Tanon 5200). The expression of proteins was analyzed using ImageJ and Graphpad prism 7.
## 4.6. Mass Spectrometry Analysis
SDS-PAGE-separated O-GlcNAcylated proteins Tau, P1Tau, and TauP1 were reduced using 10 mM dithiothreitol and alkylated in 55 mM iodoacetamide (Sigma-Aldrich, St. Louis, MO, USA) prior to in-gel proteolytic digestion with trypsin (1:50, enzyme: protein ratio) for 12 h. The digested peptides were extracted from the gel pieces by sequentially adding $0.1\%$ formic acid in $50\%$ acetonitrile, $0.1\%$ formic acid in $80\%$ acetonitrile, and $100\%$ acetonitrile. The extraction products were combined and lyophilized for the following step. The MS approach employed in this study for sequencing O-GlcNAcylated peptides and assigning the modification sites is a combination of HCD and ETD fragmentation. When analyzing O-GlcNAcylated peptides, the unique advantage of HCD fragmentation is the generation of distinct HexNAc oxonium ions (m/z 204.09) and a series of HexNAc fragments (m/z 186.08, m/z168.07, m/z144.07, m/z 138.06, and m/z126.06), which are significant to the diagnosis of O-GlcNAcylated peptides. The samples were analyzed using Fourier transform ion cyclotron resonance (FT-ICR/Orbitrap, Thermo Fisher Scientific, San Jose, CA, USA). The FT-ICR/Orbitrap mass spectrometer was equipped with an online nano-electrospray ionization (ESI) source. The database search results were imported into Peaks Studio 10.6. Search parameters included a precursor tolerance of 10 ppm and a fragment tolerance of 0.02 Da. Enzyme specificity was set to trypsin, and up to two missed cleavage sites were allowed. Carbamidomethylation was set as a fixed modification. For the main GlcNAc search, HexNAc (S/T) was set as a variable modification. GlcNAc was considered positively localized to serine or threonine residues with FDR less than $1\%$. Putative O-GlcNAcylated peptides were manually confirmed by checking for the presence of the expected HexNAc neutral loss from the precursor ion and/or fragmented HexNAc oxonium ions.
## References
1. Torres C.R., Hart G.W.. **Topography and Polypeptide Distribution of Terminal N-Acetylglucosamine Residues on the Surfaces of Intact Lymphocytes**. *J. Biol. Chem.* (1984) **259** 3308-3317. DOI: 10.1016/S0021-9258(17)43295-9
2. Zhao L., Shah J.A., Cai Y., Jin J.. **‘O-GlcNAc Code’ Mediated Biological Functions of Downstream Proteins**. *Molecules* (2018) **23**. DOI: 10.3390/molecules23081967
3. Kreppel L.K., Blomberg M.A., Hart G.W.. **Dynamic glycosylation of nuclear and cytosolic proteins**. *J. Biol. Chem.* (1997) **272** 9308-9315. DOI: 10.1074/jbc.272.14.9308
4. Gao Y., Wells L., Comer F.I., Parker G.J., Hart G.W.. **Dynamic O-glycosylation of nuclear and cytosolic proteins**. *J. Biol. Chem.* (2001) **276** 9838-9845. DOI: 10.1074/jbc.M010420200
5. Ciraku L., Esquea E.M., Reginato M.J.. **O-GlcNAcylation regulation of cellular signaling in cancer**. *Cell. Signal.* (2022) **90** 110201. DOI: 10.1016/j.cellsig.2021.110201
6. Martinez M.R., Dias T.B., Natov P.S., Zachara N.E.. **Stress-induced O-GlcNAcylation: An adaptive process of injured cells**. *Biochem. Soc. Trans.* (2017) **45** 237-249. DOI: 10.1042/BST20160153
7. Bacigalupa Z.A., Bhadiadra C.H., Reginato M.J.. **O-GlcNAcylation: Key regulator of glycolytic pathways**. *J. Bioenerg. Biomembr.* (2018) **50** 189-198. DOI: 10.1007/s10863-018-9742-3
8. Hart G.W.. **Nutrient regulation of signaling and transcription**. *J. Biol. Chem.* (2019) **294** 2211-2231. DOI: 10.1074/jbc.AW119.003226
9. Chatham J.C., Young M.E., Zhang J.H.. **Role of O-linked N-acetylglucosamine (O-GlcNAc) modification of proteins in diabetic cardiovascular complications**. *Curr. Opin. Pharm.* (2021) **57** 1-12. DOI: 10.1016/j.coph.2020.08.005
10. Akella N.M., Ciraku L., Reginato M.J.. **Fueling the fire: Emerging role of the hexosamine biosynthetic pathway in cancer**. *BMC Biol.* (2019) **17** 1-14. DOI: 10.1186/s12915-019-0671-3
11. Dassanayaka S., Jones S.P.. **O-GlcNAc and the cardiovascular system**. *Pharm. Ther.* (2014) **142** 62-71. DOI: 10.1016/j.pharmthera.2013.11.005
12. Park J., Lai M.K.P., Arumugam T.V., Jo D.G.. **O-GlcNAcylation as a Therapeutic Target for Alzheimer’s Disease**. *Neuromolecular Med.* (2020) **22** 171-193. DOI: 10.1007/s12017-019-08584-0
13. Graeber M.B., Kupke K.G., Muller U.. **Delineation of the dystonia-parkinsonism syndrome locus in Xq13**. *Proc. Natl. Acad. Sci. USA* (1992) **89** 8245-8248. DOI: 10.1073/pnas.89.17.8245
14. Stephen H.M., Adams T.M., Wells L.. **Regulating the Regulators: Mechanisms of Substrate Selection of the O-GlcNAc Cycling Enzymes OGT and OGA**. *Glycobiology* (2021) **31** 724-733. DOI: 10.1093/glycob/cwab005
15. Estevez A., Zhu D., Blankenship C., Jiang J.. **Molecular Interrogation to Crack the Case of O-GlcNAc**. *Chemistry* (2020) **26** 12086-12100. DOI: 10.1002/chem.202000155
16. Riu I.H., Shin I.S., Do S.I.. **Sp1 modulates ncOGT activity to alter target recognition and enhanced thermotolerance in**. *Biochem. Biophys. Res. Commun.* (2008) **372** 203-209. DOI: 10.1016/j.bbrc.2008.05.034
17. Ma J., Hart G.W.. **O-GlcNAc profiling: From proteins to proteomes**. *Clin. Proteom.* (2014) **11** 8. DOI: 10.1186/1559-0275-11-8
18. Zhang N., Jiang H.L., Zhang K.X., Zhu J.X., Wang Z.Z., Long Y.T., He Y.J., Feng F., Liu W.Y., Ye F.. **OGT as potential novel target: Structure, function and inhibitors**. *Chem.-Biol. Interact.* (2022) **357** 109886. DOI: 10.1016/j.cbi.2022.109886
19. Groenevelt J.M., Corey D.J., Fehl C.. **Chemical Synthesis and Biological Applications of O-GlcNAcylated Peptides and Proteins**. *ChemBioChem* (2021) **22** 1854-1870. DOI: 10.1002/cbic.202000843
20. Gorelik A., van Aalten D.M.F.. **Tools for functional dissection of site-specific O-GlcNAcylation**. *RSC Chem. Biol.* (2020) **1** 98-109. DOI: 10.1039/D0CB00052C
21. Kamemura K., Hayes B.K., Comer F.I., Hart G.W.. **Dynamic interplay between O-glycosylation and O-phosphorylation of nucleocytoplasmic proteins: Alternative glycosylation/phosphorylation of THR-58, a known mutational hot spot of c-Myc in lymphomas, is regulated by mitogens**. *J. Biol. Chem.* (2002) **277** 19229-19235. DOI: 10.1074/jbc.M201729200
22. Hirosawa M., Hayakawa K., Yoneda C., Arai D., Shiota H., Suzuki T., Tanaka S., Dohmae N., Shiota K.. **Novel O-GlcNAcylation on Ser(40) of canonical H2A isoforms specific to viviparity**. *Sci. Rep.* (2016) **6** 31785. DOI: 10.1038/srep31785
23. Cameron A., Giacomozzi B., Joyce J., Gray A., Graham D., Ousson S., Neny M., Beher D., Carlson G., O’Moore J.. **Generation and characterization of a rabbit monoclonal antibody site-specific for tau O-GlcNAcylated at serine 400**. *FEBS Lett.* (2013) **587** 3722-3728. DOI: 10.1016/j.febslet.2013.09.042
24. Yuzwa S.A., Yadav A.K., Skorobogatko Y., Clark T., Vosseller K., Vocadlo D.J.. **Mapping O-GlcNAc modification sites on tau and generation of a site-specific O-GlcNAc tau antibody**. *Amino Acids* (2011) **40** 857-868. DOI: 10.1007/s00726-010-0705-1
25. Fujiki R., Hashiba W., Sekine H., Yokoyama A., Chikanishi T., Ito S., Imai Y., Kim J., He H.H., Igarashi K.. **GlcNAcylation of histone H2B facilitates its monoubiquitination**. *Nature* (2011) **480** 557-560. DOI: 10.1038/nature10656
26. Shan H., Sun J., Shi M., Liu X., Shi Z., Yu W., Gu Y.. **Generation and characterization of a site-specific antibody for SIRT1 O-GlcNAcylated at serine 549**. *Glycobiology* (2018) **28** 482-487. DOI: 10.1093/glycob/cwy040
27. Klein A.L., Berkaw M.N., Buse M.G., Ball L.E.. **O-linked N-acetylglucosamine modification of insulin receptor substrate-1 occurs in close proximity to multiple SH2 domain binding motifs**. *Mol. Cell. Proteom.* (2009) **8** 2733-2745. DOI: 10.1074/mcp.M900207-MCP200
28. Pathak S., Borodkin V.S., Albarbarawi O., Campbell D.G., Ibrahim A., van Aalten D.M.. **O-GlcNAcylation of TAB1 modulates TAK1-mediated cytokine release**. *EMBO J.* (2012) **31** 1394-1404. DOI: 10.1038/emboj.2012.8
29. Tao T., He Z., Shao Z., Lu H.. **TAB3 O-GlcNAcylation promotes metastasis of triple negative breast cancer**. *Oncotarget* (2016) **7** 22807-22818. DOI: 10.18632/oncotarget.8182
30. Shen D.L., Gloster T.M., Yuzwa S.A., Vocadlo D.J.. **Insights into O-linked N-acetylglucosamine ([0-9]O-GlcNAc) processing and dynamics through kinetic analysis of O-GlcNAc transferase and O-GlcNAcase activity on protein substrates**. *J. Biol. Chem.* (2012) **287** 15395-15408. DOI: 10.1074/jbc.M111.310664
31. Han C., Shan H., Bi C., Zhang X., Qi J., Zhang B., Gu Y., Yu W.. **A highly effective and adjustable dual plasmid system for O-GlcNAcylated recombinant protein production in**. *J. Biochem.* (2015) **157** 477-484. DOI: 10.1093/jb/mvv006
32. Gao H., Shi M., Wang R., Wang C., Shao C., Gu Y., Yu W.. **A widely compatible expression system for the production of highly O-GlcNAcylated recombinant protein in**. *Glycobiology* (2018) **28** 949-957. DOI: 10.1093/glycob/cwy077
33. Pathak S., Alonso J., Schimpl M., Rafie K., Blair D.E., Borodkin V.S., Albarbarawi O., van Aalten D.M.F.. **The active site of O-GlcNAc transferase imposes constraints on substrate sequence**. *Nat. Struct. Mol. Biol.* (2015) **22** 744-750. DOI: 10.1038/nsmb.3063
34. Yuzwa S.A., Shan X., Macauley M.S., Clark T., Skorobogatko Y., Vosseller K., Vocadlo D.J.. **Increasing O-GlcNAc slows neurodegeneration and stabilizes tau against aggregation**. *Nat. Chem. Biol.* (2012) **8** 393-399. DOI: 10.1038/nchembio.797
35. Yuzwa S.A., Cheung A.H., Okon M., McIntosh L.P., Vocadlo D.J.. **O-GlcNAc modification of tau directly inhibits its aggregation without perturbing the conformational properties of tau monomers**. *J. Mol. Biol.* (2014) **426** 1736-1752. DOI: 10.1016/j.jmb.2014.01.004
36. Yuzwa S.A., Vocadlo D.J.. **Production of O-GlcNAc Modified Recombinant Tau in**. *Tau Protein Methods Protoc.* (2017) **1523** 237-248
37. Cantrelle F.X., Loyens A., Trivelli X., Reimann O., Despres C., Gandhi N.S., Hackenberger C.P.R., Landrieu I., Smet-Nocca C.. **Phosphorylation and O-GlcNAcylation of the PHF-1 Epitope of Tau Protein Induce Local Conformational Changes of the C-Terminus and Modulate Tau Self-Assembly Into Fibrillar Aggregates**. *Front. Mol. Neurosci.* (2021) **14** 661368. DOI: 10.3389/fnmol.2021.661368
38. Yuan X., Wang Z., Zhang L., Sui R., Khan S.. **Exploring the inhibitory effects of liquiritigenin against tau fibrillation and related neurotoxicity as a model of preventive care in Alzheimer’s disease**. *Int. J. Biol. Macromol.* (2021) **183** 1184-1190. DOI: 10.1016/j.ijbiomac.2021.05.041
39. Ramirez D.H., Aonbangkhen C., Wu H.Y., Naftaly J.A., Tang S., O’Meara T.R., Woo C.M.. **Engineering a Proximity-Directed O-GlcNAc Transferase for Selective Protein O-GlcNAcylation in Cells**. *ACS Chem. Biol.* (2020) **15** 1059-1066. DOI: 10.1021/acschembio.0c00074
40. Levine Z.G., Fan C., Melicher M.S., Orman M., Benjamin T., Walker S.. **O-GlcNAc Transferase Recognizes Protein Substrates Using an Asparagine Ladder in the Tetratricopeptide Repeat (TPR) Superhelix**. *J. Am. Chem. Soc.* (2018) **140** 3510-3513. DOI: 10.1021/jacs.7b13546
41. Joiner C.M., Levine Z.G., Aonbangkhen C., Woo C.M., Walker S.. **Aspartate Residues Far from the Active Site Drive O-GlcNAc Transferase Substrate Selection**. *J. Am. Chem. Soc.* (2019) **141** 12974-12978. DOI: 10.1021/jacs.9b06061
42. Ramirez D.H., Yang B., D’Souza A.K., Shen D., Woo C.M.. **Truncation of the TPR domain of OGT alters substrate and glycosite selection**. *Anal. Bioanal. Chem.* (2021) **413** 7385-7399. DOI: 10.1007/s00216-021-03731-8
43. Chong P.A., Nosella M., Vanama M., Ruiz-Arduengo R., Forman-Kay J.D.. **Exploration of O-GlcNAc-transferase (OGT) glycosylation sites reveals a target sequence compositional bias**. *bioRxiv* (2022). DOI: 10.1101/2022.09.12.507593
44. Aulak K.S., Barnes J.W., Tian L., Mellor N.E., Haque M.M., Willard B., Li L., Comhair S.C., Stuehr D.J., Dweik R.A.. **Specific O-GlcNAc modification at Ser-615 modulates eNOS function**. *Redox Biol.* (2020) **36** 101625. DOI: 10.1016/j.redox.2020.101625
45. Liu Y.Y., Liu H.Y., Yu T.J., Lu Q., Zhang F.L., Liu G.Y., Shao Z.M., Li D.Q.. **O-GlcNAcylation of MORC2 at threonine 556 by OGT couples TGF-beta signaling to breast cancer progression**. *Cell Death Differ.* (2022) **29** 861-873. DOI: 10.1038/s41418-021-00901-0
46. Zhu G., Qian M., Lu L., Chen Y., Zhang X., Wu Q., Liu Y., Bian Z., Yang Y., Guo S.. **O-GlcNAcylation of YY1 stimulates tumorigenesis in colorectal cancer cells by targeting SLC22A15 and AANAT**. *Carcinogenesis* (2019) **40** 1121-1131. DOI: 10.1093/carcin/bgz010
47. Hu J.Y., Zhang D.L., Liu X.L., Li X.S., Cheng X.Q., Chen J., Du H.N., Liang Y.. **Pathological concentration of zinc dramatically accelerates abnormal aggregation of full-length human Tau and thereby significantly increases Tau toxicity in neuronal cells**. *Biochim. Biophys. Acta (BBA)-Mol. Basis Dis.* (2017) **1863** 414-427. DOI: 10.1016/j.bbadis.2016.11.022
48. Liu X.L., Hu J.Y., Hu M.Y., Zhang Y., Hong Z.Y., Cheng X.Q., Chen J., Pang D.W., Liang Y.. **Sequence-dependent abnormal aggregation of human Tau fragment in an inducible cell model**. *Biochim. Biophys. Acta* (2015) **1852** 1561-1573. DOI: 10.1016/j.bbadis.2015.04.015
49. Chatani E., Yamamoto N.. **Recent progress on understanding the mechanisms of amyloid nucleation**. *Biophys. Rev.* (2018) **10** 527-534. DOI: 10.1007/s12551-017-0353-8
50. Sidhu A., Vaneyck J., Blum C., Segers-Nolten I., Subramaniam V.. **Polymorph-specific distribution of binding sites determines thioflavin-T fluorescence intensity in alpha-synuclein fibrils**. *Amyloid* (2018) **25** 189-196. DOI: 10.1080/13506129.2018.1517736
51. Pan K., Yi C.W., Chen J., Liang Y.. **Zinc significantly changes the aggregation pathway and the conformation of aggregates of human prion protein**. *Biochim. Biophys. Acta* (2015) **1854** 907-918. DOI: 10.1016/j.bbapap.2015.04.020
|
---
title: Identification of Differential Compositions of Aqueous Extracts of Cinnamomi
Ramulus and Cinnamomi Cortex
authors:
- Pei Wang
- Jun Chi
- Hui Guo
- Shun-Xiang Wang
- Jing Wang
- Er-Ping Xu
- Li-Ping Dai
- Zhi-Min Wang
journal: Molecules
year: 2023
pmcid: PMC10004064
doi: 10.3390/molecules28052015
license: CC BY 4.0
---
# Identification of Differential Compositions of Aqueous Extracts of Cinnamomi Ramulus and Cinnamomi Cortex
## Abstract
Cinnamomi ramulus (CR) and Cinnamomi cortex (CC), both sourced from *Cinnamomum cassia* Presl, are commonly used Chinese medicines in the Chinese Pharmacopeia. However, while CR functions to dissipate cold and to resolve external problems of the body, CC functions to warm the internal organs. To clarify the material basis of these different functions and clinical effects, a simple and reliable UPLC-Orbitrap-Exploris-120-MS/MS method combined with multivariate statistical analyses was established in this study with the aim of exploring the difference in chemical compositions of aqueous extracts of CR and CC. As the results indicated, a total of 58 compounds was identified, including nine flavonoids, 23 phenylpropanoids and phenolic acids, two coumarins, four lignans, four terpenoids, 11 organic acids and five other components. Of these compounds, 26 significant differential compounds were identified statistically including six unique components in CR and four unique components in CC. Additionally, a robust HPLC method combined with hierarchical clustering analysis (HCA) was developed to simultaneously determine the concentrations and differentiating capacities of five major active ingredients in CR and CC: coumarin, cinnamyl alcohol, cinnamic acid, 2-methoxycinnamic acid and cinnamaldehyde. The HCA results showed that these five components could be used as markers for successfully distinguishing CR and CC. Finally, molecular docking analyses were conducted to obtain the affinities between each of the abovementioned 26 differential components, focusing on targets involved in diabetes peripheral neuropathy (DPN). The results indicated that the special and high-concentration components in CR showed high docking scores of affinities with targets such as HbA1c and proteins in the AMPK–PGC1–SIRT3 signaling pathway, suggesting that CR has greater potential than CC for treating DPN.
## 1. Introduction
Cinnamomi ramulus (CR, Guizhi in Chinese) and Cinnamomi cortex (CC, Rougui in Chinese), are, respectively, the dried twigs and bark of *Cinnamomum cassia* Presl. They are documented in the Chinese Pharmacopoeia, which has a long history and comprises all well-known Chinese medicines with a high clinical value [1]. CR functions to dissipate wind-cold and resolve external problems of the body; it traverses the arms and warms the meridians. It is known as the first medicine of classic prescription and is widely used in the Treatise on Cold Pathogenic and Miscellaneous Diseases [2]. In contrast, CC functions to warm the internal organs, nourish and warm kidney-yang, dispel cold, and relieve pain. Different pharmacological effects result from variant chemical ingredients. It can be assumed that several significant components differ in CR and CC, leading to differences in their respective efficacies. Previous chemical investigations demonstrated that CR and CC contained various compositions, including phenylpropanoids, phenolic acids, flavonoids and terpenoids and so on [3,4,5]. A previous study showed that the contents of volatile oils in CR and CC, were $0.15\%$ and $0.31\%$, respectively, with the main component being cinnamaldehyde [6]. The traditional usage of decocting with water requires that it is necessary to continue to analyze the different components of the aqueous extracts of CR and CC.
In this study, a simple, rapid, valid, and reliable UPLC-Orbitrap-Exploris-120- MS/MS [7,8,9,10] method was developed to assess the difference in the chemical compositions of CR and CC. By comparing the MS/MS information of the detected compounds with the mass spectrometry database, records in the literature, and standard references, 58 compounds were preliminary identified. Eight batches of CR and CC samples were comparatively analyzed using this method coupled with multivariate statistical analysis [11,12,13]. As a result, 26 statistically significant ($p \leq 0.05$) discrepant compounds were characterized. Additionally, a robust HPLC method combined with hierarchical clustering analysis (HCA) was developed to simultaneously determine the concentrations and differentiating capacities of five major active ingredients in CR and CC: coumarin, cinnamyl alcohol, cinnamic acid, 2-methoxycinnamic acid and cinnamaldehyde. The HCA results showed that trans-cinnamaldehyde and cinnamyl alcohol could be used as markers to successfully distinguish CR and CC.
Diabetes peripheral neuropathy (DPN), one of the most common complications of diabetes mellitus, is commonly categorized as Xiaoke (diabetes) complicated by arthralgia syndrome in TCM [14]. As a diaphoretic, CR functions to dissipate wind-cold and is usually used for treating arthralgia syndrome caused by rheumatoid arthritis and diabetes [15,16]. CR is always included in TCM formulars for treating DPN, e.g., Huangqi Guizhi Wuwu Decoction [17,18]. In clinics, HbA1c is used as an essential indicator to evaluate the severity of DPN [19,20]. As a vital signaling pathway, AMPK–PGC1–SIRT3 was widely reported and involved in the occurrence and development of DPN [21]. Using virtual molecular docking technology, we, therefore, explored whether any ingredients occurring in CR are different from those in CC and, if so, whether they contribute to anti-DNP activities. The findings of this study will provide comparative information on the chemical profiles of CR and CC and lay the groundwork for exploring effective CR substances as anti-DNP agents.
## 2.1. Optimization of the LC-MS Conditions
In order to improve the sensitivity and resolution of the analysis but reduce analytical time, the LC-MS conditions for aqueous extract of CR, which included mobile phase, flow rate, column temperature, ion mode and ion source parameters, were optimized. First, methanol/$0.1\%$ formic acid water and acetonitrile/$0.1\%$ formic acid water were investigated to achieve better separation. It was found that acetonitrile/$0.1\%$ formic acid water was the most suitable to obtain more peak and better peak shape. Second, we compared two types of columns (Waters Acquity UPLC BEH C18 and HYPERSILGOLD Vanquish C18) and found that the latter column had better resolution and more peaks. Different column temperatures (25 and 35 °C) were tested and a good separation effect and higher peak intensity was obtained for the constituents at 35 °C. In addition, we also compared the vaporizer temperature (320 and 350 °C) and the positive ion spray voltage (3.8 and 3.5 kV), and finally we found that the TIC had high sensitivity and good resolution when the vaporizer temperature was 350 °C and the positive ion spray voltage was 3.5 kV. The above data are shown in Table S1.
## 2.2. Identification of the Constituents of CR and CC
Qualitative analysis of the chemical constituents of the aqueous extracts of CR and CC was performed using UPLC-Oribtrap-Exploris-120-MS/MS, in both in the positive and negative modes. In the qualitative analysis, in order to evaluate the stability of the instrument, mixed standards (QC) were repeated for 8 times. The RSDS of the retention times and intensities in QC samples were all less than $5\%$. Typical total ion chromatograms for CR and CC, both in negative ion mode are shown in Figure S1. A comparison of retention times, accurate mass, fragmentation patterns, and a comparison with an online database, as well as referencing to the related literature to validate the data, preliminarily identified 58 compounds (Table 1, Figures S2 and S3); these included nine flavonoids, 23 phenylpropanoids and phenolic acids, two coumarins, four lignans, four terpenoids, 11 organic acids and five other components. Among these compounds, eight compounds were identified by comparing their retention times, characteristic molecular ions, and fragment ions to those of the standards (Figure S4). Significantly, among these compounds, 26 significant differential compounds were identified statistically: the results showed that the peak areas of flavonoids and flavonoid glycosides, and phenylpropanoids except cinnamaldehyde and cinnamyl alcohol in CR were higher than in CC, and the peak areas of terpenoids and organic acids in CC were higher than in CR.
## 2.2.1. Identification of Phenylpropanoids and Phenolic Acids
Phenylpropanoids and phenolic acids are major bioactive constituents in CR and CC [22]. Trans-cinnamic acid, trans-cinnamaldehyde, cinnamyl alcohol and 2-methoxycinnamic acid were shown to be the main phenylpropanoids. In the literature, the diagnostic ions at m/z $\frac{163}{165}$, $\frac{151}{153}$ and $\frac{135}{137}$ $\frac{105}{107}$ were indicators of phenylpropanoids and phenolic acids [23]. However, in addition to the above diagnostic ions reported in the literature and standards, our statistical analysis found that fragment ions at m/z $\frac{105}{107}$ and $\frac{123}{125}$ can also be diagnostic ions [23].
With the transfer of electrons, the carbonyl group was also prone to cleavage and the loss of CO and CH2 to form fragment ion peaks [24,25]. As a result, when compared to the diagnostic ions described above, the mass spectra of the standards, fragmentation information in the database and previously reported fragmentation spectra, a total of 23 phenylpropanoids and phenolic acids were identified in CR and CC. Compounds 45 and 48 were chosen as typical examples to illustrate the fragmentation pathways.
The molecular ion peak m/z 149.0231 [M + H]+ was obtained in the ESI (+) mode for compound 45, which had the molecular formula C9H8O2 and a relative molecular mass of 148.0525. The matching fragments were m/z 131.0493 [M − H2O + H]+ and m/z 105.0539 [M − CO + H]+ (Table 1), which were in general agreement with the literature and data available for cinnamic acid; accordingly, the compound was presumed to be trans-cinnamic acid [26]. The MS2 spectrum of compound 45 is shown in Figure S5 and the possible cleavage process of the positive ions is shown in Figure 1.
**Table 1**
| NO. | Name | RT | Formula | Calc. MW | Error(ppm) | Theoretical Mass (m/z) | Experimental Mass (m/z) | MS2 (m/z) | Total Score (%) a | Ref. | Source b |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Gentisic acid-5-O-glucoside | 3.15 | C13H16O9 | 316.0781 | −0.50 | 315.0711 [M − H]− | 315.0709 [M − H]− | 270.8695 [M − COOH + H]+ 165.0183, 153.0185 [M − C6H10O5 + H]+, 152.0108, 113.0240, 109.0293 [M − C6H10O5 − COOH + H]+, 108.0211 | 91.2 | [27] | |
| 2 | Isovanillic acid | 3.22 | C8H8O4 | 168.0415 | 1.88 | 167.0339 [M − H]− | 167.0342 [M − H]− | 152.0116 [M − CH3 − H]−, 151.0226 [M − OH]−, 123.0449 [M − COOH − H]−, 108.0217 [M − COOH − CH − H]− | | [28,29,30] | CR, C. cassia leaves |
| 3 | Gentisic acid | 3.3 | C7H6O4 | 154.0257 | 3.04 | 153.0182 [M − H]− | 153.0187 [M − H]− | 153.0187, 135.0182 [M − H2O + H]+, 109.0288 [M − COOH + H]+, 85.0289, 81.0343 [M − COOH − CO + H]+, 68.9978 | 96.6 | [31,32] | C. cassia |
| 4 | Syringic acid | 3.65 | C9H10O5 | 198.0527 | −4.53 | 199.0601 [M + H]+ | 199.0588 [M + H]+ | 181.0495 [M − OH + H]+, 163.1478 [M − 2H2O + H]+, 155.0166, 153.0764 [M − OH − OCH3 + H]+, 95.0492 | 75.3 | [31] | CR, CC, C. cassia leaves |
| 5 | Catechol | 3.8 | C6H6O2 | 110.0363 | 4.05 | 109.0284 [M − H]− | 109.0291 [M − H]− | 108.0215 [M − 2H]−, 93.7792 [M − OH − H]−, 81.6772 | 97.4 | [33] | CR |
| 6 | Neochlorogenic acid | 4.4 | C16H18O9 | 354.0936 | −4.21 | 353.0892 [M − H]− | 353.0886 [M − H]− | 191.0550 [M − C9H6O3 − H]−, 179.0343 [M − C7H10O5 − H]−, 161.0237 [M − C7H10O5 − H2O − H]−, 135.0445 [M − C7H10O5 − CO2 − H]−, 111.0446 | | [34] | CR, CC |
| 7 | Salicylic acid | 5.38 | C7H6O3 | 138.0311 | 3.50 | 137.0233 [M − H]− | 137.0238 [M − H]− | 119.0132 [M − H2O − H]−, 108.8992 [M − CO − H]−, 93.0343 [M − COO − H]− | 62.9 | [31,35] | CR, CC |
| 8 | Citrinin | 5.4 | C13H14O5 | 250.084 | −0.46 | 251.0916 [M + H]+ | 251.0915 [M + H]+ | 233.0804 [M − H2O + H]+, 221.0807 [M − 2CH3 + H]+, 205.0857 [M − H2O − CO + H]+, 204.0785, 191.0701 | 75.5 | | |
| 9 | 4-Methoxy benzaldehyde | 6.3 | C8H8O2 | 136.0523 | −0.77 | 137.0597 [M + H]+ | 137.0596 [M + H]+ | 122.0362 [M − CH3 + H]+, 107.0490 [M − OCH2 + H]+, 93.0590 [M − CO − CH3 + H]+, 91.0543, 81.0698, 79.0543 [M − CO − OCH3 + H]+ | 81.9 | [28,36] | CR, CC,C. cassia leaves |
| 10 | Darendoside A | 6.32 | C19H28O11 | 432.1613 | −2.29 | 431.1547 [M − H]− | 431.1538 [M − H]− | 191.0547, 161.0446, 149.0447,113.0245, 99.0081, 89.0244 | 67.5 | | |
| 11 | Epicatechin | 8.41 | C15H14O6 | 290.0788 | −1.08 | 291.0863 [M + H]+ | 291.0860 [M + H]+ | 273.0768 [M − H2O + H]+, 249.0766, 179.0340 [M−C6H6O2+H] +, 165.0544 [M − C6H6O2 − CH2 + H]+, 151.0394, 139.0388 [M − C9H11O2 + H]+,125.0239 [M − C9H8O3 + H]+, 123.0439, 119.0491, 109.0290 [M − C9H10O4 + H]+, 95.0490 | 99.4 | Standard, [37] | CR |
| 12 | 2,4,6-Trihydroxy-2-(4-hydroxybenzyl)-1-benzofuran-3(2H)-one | 8.68 | C15H12O6 | 288.0621 | −4.34 | 287.0521 [M − H]− | 287.0511 [M − H]− | 161.0233 [M − C6H6O3 − H]−, 131.2500, 125.0239 [M − C8H2O4 − H]− | 88.7 | | |
| 13 | Dihydrophaseic acid | 8.75 | C15H22O5 | 282.1454 | −0.53 | 281.1383 [M − H]− | 281.1382 [M − H]− | 237.1486 [M − CHO − H]−, 201.1273 [M − CHO − H2O − OH − H]−, 189.1278, 171.1171, 139.0758 | 76.8 | | |
| 14 | Catechin | 8.83 | C15H14O6 | 290.0777 | −1.26 | 289.0707 [M − H]− | 289.0703 [M − H]− | 289.0703 [M − H2O − H]−, 271.0603, 245.08081, 203.07036, 151.0393, 125.0239, 109.0290 | | [35] | CC |
| 15 | Vanillin | 9.28 | C8H8O3 | 152.0472 | −0.82 | 153.0548 [M + H]+ | 153.0545 [M + H]+ | 153.0545, 125.0595 [M − CO + H]+, 111.0441, 93.0333, 65.0387 | 90.7 | [28,31,38] | C. cassia leaves |
| 16 | 4-Acetyl-3-hydroxy-5-methylphenyl-β-D-glucopyranoside | 10.08 | C15H20O8 | 328.1144 | −1.05 | 327.1074 [M − H]− | 327.1071 [M − H]− | 165.0547 [M − C6H10O5 − H]−, 147.0446 [M − C6H10O5 − H2O − H]−, 121.0653 [M − C6H10O5 − OH − CH3 − CH2 − OH]−, 106.0416 | 91.7 | [27] | |
| 17 | Picconioside B | 10.95 | C26H40O12 | 544.25 | −3.66 | 543.2438 [M − H]− | 543.2421 [M − H]− | 525.2305 [M − H2O − H]−, 363.1800 [M − H2O − C6H10O5 − H]−, 381.1912, 167.1070, 165.0922, 101.0240, 89.0240, 59.0136 | 91.2 | | |
| 18 | 2 Methoxybenzoic acid | 12.68 | C8H8O3 | 152.0472 | −0.92 | 153.0546 [M + H]+ | 153.0545 [M + H]+ | 153.0545, 135.0439, 111.0440, 105.0441, 95.0491 [M − CO2 − CH2 + H]+, 93.0699, 79.0541 | 96.3 | [39,40] | CR |
| 19 | Taxifolin | 13.79 | C15H12O7 | 304.0581 | 2.03 | 305.0656 [M + H]+ | 305.0662 [M + H]+ | 287.0573 [M − H2O + H]+, 259.0591 [M − CO − H2O + H]+, 231.0652, 153.0188 [M − CO − C7H8O2 + H]+, 149.0230 | 91.1 | Standard,[12,41] | CR |
| 20 | Lyoniresinol-3a-O-β-D-glucopyranosid | 13.89 | C28H38O13 | 582.2289 | −4.09 | 581.2230 [M − H]− | 581.2209 [M − H]− | 566.1975, 535.1785, 419.1691 [M − C6H10O5 − H]−, 404.1459, 373.1275 [M − C6H10O5 − 3CH3 − H]−, 359.1110 [M − C6H10O5 − 4CH3 − H]−, 233.0812, 202.0624, 153.0549, 138.0316 [M − C6H10O5 − OH − C14H18O6 − H]−, 101.0238 | 92.3 | [33,42,43] | CC |
| 21 | 4-Ethylphenol | 14.12 | C8H10O | 122.0726 | 4.32 | 121.0649 [M − H]− | 121.0654 [M − H]− | 106.0419 [M − CH3 − H]−, 90.9232 [M − CH3 − O − H]−, 61.9880 | 87.9 | [44,45] | C. cassia |
| 22 | (−)-Lyoniresinol | 14.19 | C22H28O8 | 420.1766 | −1.53 | 419.1700 [M − H]− | 419.1694 [M − H]− | 373.1277 [M − 3CH3 − H]−,359.1119 [M −4 CH3 − H]−, 313.0712, 221.0801, 180.0404, 139.0396, 134.0383 | 96.0 | [33] | |
| 23 | Lyoniside | 14.33 | C27H36O12 | 552.2186 | −3.79 | 551.2123 [M − H]− | 551.2105 [M − H]− | 536.1875, 419.1650, 389.1591, 374.1359, 373.1275 [M − C6H10O5 − 3CH3 − H]−, 359.1105 [M−C6H10O5−4CH3−H]−, 341.1013, 325.1092, 233.0823, 119.0345, 113.0239 | 91.8 | [33] | |
| 24 | 3-Oxoindane-1-carboxylic acid | 14.41 | C10H8O3 | 176.0472 | −0.69 | 177.0546 [M + H]+ | 177.0545 [M + H]+ | 153.9367, 149.0596 [M−CO+H] +, 133.0646 [M−COO+H] +, 131.0490, 121.1010, 107.0490, 105.0693 [M − CO − COO + H]+, 93.0098 [M − CO − COO − C + H]+, 81.0700 [M − CO − COO − 2C + H]+ | 72.2 | | |
| 25 | 3-Methoxy phenylacetic acid | 14.52 | C9H10O3 | 166.0622 | 1.69 | 165.0546 [M − H]− | 165.0549 [M − H]− | 147.0443, 136.9315 [M − OCH3 − H]−, 121.0654, 106.0419, 96.9597 [M − C2H3O2 − H]− | | [39] | |
| 26 | 2-(4-Hydroxyphenyl)-7-((3,4,5-trihydroxy-6-(hydroxymethyl) tetrahydro-2H-pyran-2-yl) oxy) chroman-4-one | 14.53 | C21H22O9 | 418.1248 | −0.74 | 417.1180 [M − H]− | 417.1177 [M − H]− | 301.0338 [M − C6H10O5 − H]−, 255.0651, 153.0187 [M − C6H10O5 − CO − C7H6O2 − H]−, 135.0082 [M − C6H10O5 − CO − C7H6O2 − H2O − H]−, 119.0497, 91.0184 | 67.5 | [12] | |
| 27 | Quercetin-3β-D-glucoside | 14.55 | C21H20O12 | 464.0936 | 0.21 | 463.0871 [M − H]− | 463.0872 [M − H]− | 301.0338 [M − H − C6H10O5]−, 300.0270, 271.0247 | | [34] | |
| 28 | 2-[1-(2H-1,3-Benzodiox ol-5-yl) propan-2-yl]-6-metho xy-4-(prop-2-en-1-yl) phenol | 14.74 | C20H22O4 | 326.1516 | 0.63 | 327.1590 [M + H]+ | 327.1593 [M + H]+ | 312.1348, 295.1328 [M − OCH2 + H]+, 280.1095, 263.1071, 251.0001, 235.1122, 175.0758, 163.0753 [M − C10H12O2 + H]+, 151.075, 137.0596, 133.0647, 103.0540, 98.9841 | 71.6 | | |
| 29 | Cinnamylalcohol-6′-O-α-furanara-binose-O-β-glucopyranoside | 14.81 | C20H28O10 | 428.1664 | −4.38 | 427.1598 [M − H]− | 427.1582 [M − H]− | 293.0861, 233.0650, 191.0549, 161.0451, 149.0448, 125.0239, 89.0240, 81.0344, 59.0136 | 85.9 | [46] | CR |
| 30 | 6-Methoxymellein | 14.99 | C11H12O4 | 208.0734 | −0.55 | 209.0804 [M + H]+ | 209.0802 [M + H]+ | 191.0701, 181.0847, 177.0544, 163.0765, 149.0596 [M − COOCH + H]+, 131.0486, 121.0647 [M − 2OH − OCH3 − CO + H]+, 109.0647, 103.0540, 93.0698, 91.0540, 55.0177 | 85.8 | | . |
| 31 | Coumarin | 15.18 | C9H6O2 | 146.0366 | −1.33 | 147.0441 [M + H]+ | 147.0439 [M + H]+ | 127.0543, 103.0541 [M − CO2 + H]+, 91.0540 [M − 2CO + H]+, 43.0242 | 96.8 | Standard,[46] | CR, CC, C. cassia leaves |
| 32 | Quercetin | 15.36 | C15H10O7 | 302.0424 | −0.98 | 303.0499 [M + H]+ | 303.0496 [M + H]+ | 303.0496, 275.0399 [M − CO + H]+, 257.0446 [M − CO − H2O + H]+, 247.0590, 229.0491 [M − 2CO − H2O + H]+, 199.0434 [M − 3CO − H2O + H]+, 165.0178, 163.0389, 153.0183 [M − CO − C7H6O2 + H]+, 133.0231 [M − CO − C7H4O2 − H2O + H]+, 121.0297, 111.0075 | 99.8 | [29,35] | CR |
| 33 | Quercitrin | 15.36 | C21H20O11 | 448.1003 | −0.58 | 449.1089 [M + H]+ | 449.1087 [M + H]+ | 431.0983, 369.0594, 345.0606, 315.0494, 303.0497 [M + H − C6H9O4]+, 257.0439 [M − C6H9O4 − CO − H2O + H] +, 229.0492 [M − C6H9O4 − 2CO − H2O + H]+, 129.0548, 85.0283, 71.0490 | 82.8 | Standard | |
| 34 | Graveobioside A | 15.36 | C26H28O15 | 580.1402 | −4.46 | 579.1344 [M − H]− | 579.1325 [M − H]− | 476.1080, 417.1531 [M − C6H10O5 − H]−, 300.0259 [M − C6H8O4 − C5H10O4 − H]−, 271.0235 [M − C11H16O10 − H]−, 178.9979 [M − C17H20O11 − H]− | 85.3 | | |
| 35 | Libertellenone B | 15.74 | C20H26O4 | 330.183 | −0.32 | 331.1903 [M + H]+ | 331.1902 [M + H]+ | 313.1796 [M − H2O + H]+, 295.1676, 271.1686, 243.1763, 165.0911, 125.0565 [M − C12H14O3 + H]+ | 75.2 | | |
| 36 | Yucalexin P-17 | 16.06 | C17H20O3 | 272.1412 | 0.27 | 273.1485 [M + H]+ | 273.1488 [M + H]+ | 255.1391 [M − H2O + H]+, 245.1534, 227.1441 [M − H2O − CO + H]+, 203.1070, 149.0964 [M − CH3 − C6H6O2 + H] +, 82.8045 [M − C11H10O3 + H]+ | 86.6 | | |
| 37 | Azelaic acid | 16.24 | C9H16O4 | 188.104 | 0.12 | 187.0965 [M − H]− | 187.0966 [M − H]− | 169.0861, 143.1072, 125.0966 [M − COOH − OH − H]−, 123.0811, 97.0654 [M − 2COOH − H]−, 57.0343 | 95.4 | [15,47] | CR |
| 38 | Kaempferol | 16.27 | C15H10O6 | 286.0475 | −0.89 | 287.0550 [M + H]+ | 287.0548 [M + H]+ | 287.0548, 258.0511 [M − CO + H]+, 183.0288, 165.0183, 153.0189 [M − CO − C7H6O + H]+, 133.0292,121.0281 | 99.2 | [35] | CR, CC, C. cassia leaves |
| 39 | 1-(Carboxymethyl) cyclohexane carboxylic acid | 16.36 | C9H14O4 | 186.0884 | 1.06 | 185.0808 [M − H]− | 185.0819 [M − H]− | 141.0916 [M − COO − H]−, 104.0775 [M − C6H9 − H]− | 87.2 | | |
| 40 | Kaempferol-3-O-α-L-arabinopyranosyl-7-O-α-L-rhamnopyranoside | 16.64 | C26H28O14 | 564.1456 | −4.12 | 563.1393 [M − H]− | 563.1384 [M − H]− | 435.2045, 285.0416 [M − C6H8O4 − C5H10O4 − H]−, 284.0316, 255.0286, 147.5166, 70.7867 | | [48] | |
| 41 | 2-Methoxy benzaldehyde | 16.67 | C8H8O2 | 136.0522 | −0.16 | 137.0597 [M + H]+ | 137.0596 [M + H]+ | 109.0647, 107.0490 [M − OCH2 + H]+, 93.0698 [M − CO − CH3 + H]+, 81.0697, 79.0512 [M − CO − OCH3 + H]+ | 90.9 | [28] | CR, C. cassia leaves |
| 42 | Cinnamyl alcohol | 17.22 | C9H10O | 134.0726 | −0.89 | 117.0698[M + H − H2O]+ | 117.0696 [M + H − H2O]+ | 117.0696 [M − H2O + H]+, 91.0540 [M − C2H4O + H] 78.2648 [M − C3H5O + H]+, 63.4672, 49.4958 | | standard | CR, CC, C. cassia leaves |
| 43 | 4-Methylumbelliferyl-α-D-glucopyranoside | 17.37 | C16H18O8 | 338.1002 | 0.21 | 339.1074 [M + H]+ | 339.1075 [M + H]+ | 321.0970 [M − OH + H]+, 177.0546 [M − C6H10O5 + H]+, 145.0284 [M − C6H10O5 − CH3 − OH + H]+, 127.0389 [M − C6H10O 5− CH3 − OH + H]+, 97.0280 | 94.6 | | |
| 44 | (±)-Abscisic acid | 17.55 | C15H20O4 | 264.1359 | −0.85 | 265.1484 [M + H]+ | 265.1481 [M + H]+ | 247.1332 [M − H2O + H]+, 229.1216 [M − 2H2O + H]+, 187.1108 [M − O − CH2 − COO + H]+ | 90.2 | | |
| 45 | trans-Cinnamic acid | 18.16 | C9H8O2 | 148.0518 | −0.60 | 149.0232 [M + H]+ | 149.0231 [M + H]+ | 144.9817, 131.0493 [M − H2O + H]+, 121.0282, 116.9669, 107.0491, 105.0539 [M − CO + H]+, 93.0698, 79.0545 | | Standard[28,30] | CR, CC, C. cassia leaves |
| 46 | 4-Phenyl-3-buten-2-one | 18.31 | C10H10O | 146.073 | −1.03 | 147.0803 [M + H]+ | 147.0803 [M + H]+ | 132.0567 [M − CH3 + H]+, 129.0699, 119.0854, 117.0698, 107.0489 [M − CH − CO + H]+, 91.0541 [M − C3H4O + H]+, 79.0542 | 95.3 | [32,49] | C. verum |
| 47 | 3-Tert-butyladipic acid | 18.32 | C10H18O4 | 202.1196 | −1.36 | 201.1121 [M − H]− | 201.1120 [M − H]− | 183.1021 [M − OH − H]−, 156.8982 [M − COO − H]−, 139.1124 | 70.0 | [30] | |
| 48 | trans-Cinnamaldehyde | 18.84 | C9H8O | 132.0573 | −0.19 | 133.0647 [M + H]+ | 133.0646 [M + H]+ | 115.0540 [M − H2O + H]+, 105.0697 [M − CO + H]+, 103.0542, 91.0541 [M − CO − CH2 + H]+, 79.0542 [M − CO − C2H2 + H]+, 55.0178 [M − C6H6 + H]+ | 97.9 | Standard,[30] | CR, CC |
| 49 | 2-Methoxycinnamic acid | 19.54 | C10H10O3 | 178.0629 | −0.51 | 161.0597[M + H − H2O]+ | 161.0596 [M + H − H2O]+ | 146.0366, 133.1011 [M − H2O − CO + H]+, 119.0855 [M − CHO − OCH3 + H]+, 105.0698 [M − COOH − CH + H] +, 91.0544 [M − CO − CH2 − OCH3 + H]+ | | Standard,[30] | CR, CC, C. cassia leaves |
| 50 | 9S,13R-12-Oxophytodienoic acid | 20.08 | C18H28O3 | 292.2037 | −0.68 | 293.2111 [M + H]+ | 293.2104 [M + H]+ | 275.2003 [M − H2O + H]+, 257.1893, 239.1799 [M − C4H6 + H]+, 229.1953, 163.1117, 159.1167, 147.1163 [M − C7H14 + H]+, 133.1012, 107.0855, 95.0853, 81.0698 [M − C12H20O3 + H]+, 69.0699 | 91.0 | [50] | |
| 51 | Corchorifatty acid F | 20.43 | C18H32O5 | 328.2237 | −0.10 | 327.2166 [M − H]− | 327.2165 [M − H]− | 309.2062, 291.1955, 242.9845 [M − C5H4 − OH − H]−, 239.1283, 229.1435, 221.1171, 211.1313, 185.1173, 183.1374, 171.101 [M − C9H16O2 − H]−, 137.0968, 97.0655, 85.0290 [M − C13H22O4 − H]−, 57.0343 | | [34,51] | |
| 52 | Deoxyphomalone | 20.47 | C13H18O4 | 238.1204 | −0.29 | 239.1277 [M + H]+ | 239.1275 [M + H]+ | 221.1171, 205.1192 [M − 2OH + H]+, 179.0705 [M − C2H5 − OCH3 + H]+, 174.0678, 163.0750, 151.0753 [M − C2H5 − C3H7O + H]+, 137.0598 [M − 2OH − 2OCH3 − C2H4 − C3H3+H]+, 135.0799, 107.0481, 95.0861 [M − OH − 2OCH3 − C2H5 − C4H3O + H]+, 59.0490 | 74.4 | | |
| 53 | 4-Ethylbenzaldehyde | 20.73 | C9H10O | 134.073 | −0.14 | 135.0804 [M + H]+ | 135.0803 [M + H]+ | 120.0567, 107.0490 [M − CO + H]+, 105.0697 [M − C2H6 + H]+, 103.0542, 79.0542 [M − C2H6 − CO + H]+ | 92.0 | [52] | CR |
| 54 | 1-Naphthol | 21.0 | C10H8O | 144.0573 | −0.09 | 145.0648 [M + H]+ | 145.0647 [M + H]+ | 116.0575 [M − C − OH + H]+, 115.0541, 102.0468 [M − C2H2 − OH + H]+, 91.0539 [M − C3H2 − OH + H]+, 84.9598 | 89.0 | [53,54] | CR |
| 55 | 4-Methoxy cinnamaldehyde | 21.02 | C10H10O2 | 162.0679 | 0.04 | 163.0753 [M + H]+ | 163.0754 [M + H]+ | 145.0650, 135.0805 [M − CO + H]+, 133.0648, 110.0203 [M − C3H3O + H]+, 107.0491, 105.0699 [M − CO − OCH3 + H]+, 91.0542, 79.0542 [M − C3H3O − OCH3 + H]+, 55.0178 | 88.4 | [46] | CR, CC |
| 56 | 9,12,13-Trihydroxy-15-octadecenoic acid | 21.72 | C18H34O5 | 330.2393 | −0.05 | 329.2322 [M − H]− | 329.2322 [M − H]− | 311.2227 [M − H2O −H]−, 293.2102 [M − 2H2O − H]−, 229.1433, 211.1331, 183.1383, 171.1018, 139.1123, 127.1120, 125.0975, 99.0812, 57.0342 | 90.0 | | |
| 57 | (−)-Caryophyllene oxide | 22.32 | C15H24O | 220.1826 | −0.53 | 221.1899 [M + H]+ | 221.1900 [M + H]+ | 203.1795, 175.1483 [M − O − 2CH2 −C + H]+, 161.1323 [M − 2CH3 − CO − CH + H]+, 147.1169 [M − 2CH3 − CO − CH − CH2 + H]+, 133.1010, 119.0855, 95.0855 | 92.9 | [55] | CR, CC, C. cassia leaves |
| 58 | 4-Methoxychalcone | 28.61 | C16H14O2 | 238.0992 | 0.66 | 239.1066 [M + H]+ | 239.1073 [M + H]+ | 221.0961, 193.1012, 178.0875, 161.0595 [M − C6H6 + H]+,133.0647 [M − C7H6O + H]+, 115.054, 105.0333 [M − C6H6 − C2H − OCH3 + H]+ | 86.7 | [56] | C. cassia |
Compound 48 obtained a precursor ionic peak at m/z 133.0645 in the ESI (+) mode; its molecular formula of the compound was C9H8O and it had a molecular weight of 132.0572 based on the elemental composition analysis. The matching fragments were mainly m/z 115.0540 [M − H2O + H]+, m/z 105.0696 [M − CO + H]+, m/z 91.0540 [M − CO − CH2 + H]+, m/z 79.0542 [M − CO − C2H2 + H]+ and m/z 55.0177 [M − C6H6 + H]+ (Table 1), which was consistent with the cleavage fragment of trans-cinnamaldehyde in the literature and database: the compound was, therefore, presumed to be trans-cinnamaldehyde [57]. The MS2 spectrum of compound 48 is shown in Figure S6, and the possible cleavage process of the positive ions was shown in Figure 2.
## 2.2.2. Identification of Flavonoids
The RDA cleavage of the C-ring was the main cleavage of flavonoids, resulting in fragment ions and the loss of a series of neutral small molecules such as H2O, CO2, and CO by energy collisions under certain mass spectrometric conditions [25,58]. Flavonoid oxyglycosides generally lost sugar first, and then cleaved according to the cracking law of flavonoid skeleton structure [26,59]. Combined with the mass fragments information in the literature and standards, the ionic fragments at m/z $\frac{291}{289}$, $\frac{303}{301}$, $\frac{287}{285}$ were selected, respectively, as the diagnostic ion for epicatechin-type, quercetin-type, and kaempferol-type [23]. The cracking of flavonoids and dihydroflavones was very similar, for example, epicatechin [11] and quercetin [32] demonstrated the cracking laws of epicatechin-type and quercetin-type, as shown in Figure 3 and Figure 4, the MS2 spectrum of epicatechin [11] is shown in Figure S7.
Compound 11 had the molecular formula of C15H14O6 which resulted from a precursor ion at m/z 291.0860 [M + H]+, and preeminently from an H2O loss at the fragment peak m/z 273.0768 [M − H2O + H]+ [26]. This was caused, first, by the loss of C6H6O2 obtaining m/z 179.0340 [M − C6H6O2 + H]+, then by the loss of CH2 and CO, obtaining m/z 165.0544 [M − C6H6O2 − CH2 + H]+ and m/z 153.0376 [M − C6H6O2 − CO + H]+. Moreover, losses of C9H8O3, C8H9O4, C9H11O2 and C9H10O4 formed m/z 125.0239 [M − C9H8O3 + H]+, m/z 119.0491 [M − C8H9O4 + H]+, m/z 139.0388 [M − C9H11O2 + H]+ and m/z 109.0290 [M − C9H10O4 + H]+ (Table 1, Figure 3), This was consistent with the cleavage fragment of epicatechin in the literature and standards; accordingly, the compound was presumed to be epicatechin [59].
Compound 32 had the molecular formula C15H10O7 which resulted from a precursor ion at m/z 303.0496 [M + H]+ in the ESI (+) mode; this was primarily caused by losses of H2O and CO and formed m/z 257.0446 [M − CO − H2O + H]+; 2CO loss then occurred and m/z 229.0491 [M − 2CO − H2O + H]+, and m/z 199.0434 [M − 3CO − H2O + H]+ were obtained. In contrast, a loss of C7H4O2 formed m/z 153.0183 [M − CO − C7H6O2 + H]+, followed by a loss of H2O to form m/z 133.0292 [M − CO − C7H4O2 − H2O + H]+ (Table 1, Figure 4). This was consistent with the cleavage fragment of quercetin in the literature [59] and standards, so the compound was presumed to be quercetin.
## 2.2.3. Identification of Coumarins
The main cleavage of coumarins included the losses of OH, CH3 and CO2 from characteristic substituents and the continuous neutral loss of CO from the pyran ring [58,60]. The fragmentation rule for the coumarin glycosides was similar to that for coumarins, except for the initial losses of sugar moieties.
As an example, compound 31 obtained a quasi-ionic peak at m/z 147.0439 [M + H]+ in ESI (+) mode, and the molecular formula for this compound was C9H6O2. The matching fragments were mainly m/z 103.0541 [M + H − CO2]+ and m/z 91.0540 [M + H − 2CO]+ (Table 1). This was consistent with the cleavage fragment of coumarin in the literature [61,62] and standard, so 31 was presumed to be coumarin. The MS2 spectrum of compound 31 was shown in Figure S8, and the possible cleavage process of the positive ions is shown in Figure 5.
Identification information of other compounds were detailed in Sections S1–S7 of Supplementary Materials.
## 2.3. Statistical Analysis
The MS data from different sources of CR and CC were imported into Compound Discoverer 3.2 qualitative analysis software for normalization and export, with a total of 1460 positive and negative ion fragments. The processed data were imported into SICMA 14.1 software for statistical analysis, resulting in the designation of 37 discrepant compounds. Analysis of variance was performed on the discrepant compounds using GraphPad Prism 7.0 software, resulting in 26 statistically significant ($p \leq 0.05$) discrepant compounds.
## 2.3.1. Principal Component Analysis (PCA)
PCA analysis can clearly explain the correlation between a large number of variables and a small number of principal components with less data loss; this can effectively reduce the dimensionality of the multidimensional raw data. The results showed that the CR and CC samples could be clearly separated and classified into one category, indicating that there were differences in the chemical compositions of CR and CC. When statistical analysis was used, the QC samples should be included to evaluate the instrument stability. The results of the PCA analysis are shown in Figure S9.
## 2.3.2. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA)
The difference in the chemical compositions of CR and CC was further studied using supervised OPLS-DA; the score diagram was shown in Figure S10. The results indicated that CR and CC can be clearly separated, which was consistent with the PCA results. The model fitting was also validated by setting the number of tests to 200. The R2 and Q2 points on the left of the data were lower than the original values on the right, and the regression line at the Q2 intersected the vertical axis below the original point with a negative nodal increment. The results of the model validation were R2Y = 0. 999 and Q2 = 0.952 (R2 in the validation results represented the matching of the model. The closer R2 to 1, the more complete the model was in describing the data; the closer Q2 to 1, the higher the predictive ability of the model) (Figure S11). Model evaluation results indicated that the model had robustness and no over-fitting. The commonly used variable importance for the project (VIP) was used to estimate for quality difference markers between groups. Variables with a VIP > 1 was generally considered to be meaningful to the model, the greater the VIP value was, the greater the contribution of the variable. Therefore, a VIP > 1 was selected to analyze the overall variability of CR and CC to find differential markers (Figure S12). A total of 627 characteristic peaks was determined for the differential components (VIP > 1) that contributed significantly to the classification of CR and CC (Table S2). A total of 37 differential compounds with VIP > 1 was identified based on the analysis of compound retention times, accurate relative molecular masses, and cleavage information. Information on compounds with VIP > 1 were shown in Table S2. The peak areas of these 37 compounds were subjected to t-tests using GraphPad Prism7.0 software, resulting in 26 chemical components with VIP > 1 and $p \leq 0.$ 05, including compounds 1–3, 5, 8, 16, 17, 19, 24, 26, 27, 31–36, 38, 40–42, 45, 46, 48, 53 and 58, These comprised two organic acids, six phenolic acids, seven phenylpropanoids, eight flavonoids, two terpenoids and one coumarin. According to the peak areas and secondary fragment information, of the 26 significantly different compounds, 6 (19, 32, 33, 38, 41 and 53) were specific to CR, and 3 (8, 26, and 36) were specific to CC.
## 2.3.3. Semi-Quantitative Analysis of CR and CC
A semi-quantitative analysis was further carried out to compare the intensity trends of CR and CC by calculating the relative peak areas of 26 compounds in 16 samples. The box-lots could visually observe the content changes between chemical compositions. The peak areas data for the 26 compounds in CR and CC were exported, and the boxplots produced by GraphPad Prism 7.0 software were further used to compare the relative content of the compounds in CR and CC, as shown in Figure 6.
The results (Figure 6) showed that the peak areas of compounds 1, 16, 19, 24, 27, 31, 32, 33, 34, 38, 40, 41, 42, 45, 46, 53 and 58 in CR were higher than those in CC. Among these compounds, compounds 19, 27, 32, 33, 38, 40 and 41 were flavonoids and flavonoid glycosides; 16, 41, 42, 45, 46, 53 and 58 were phenylpropanoids; 1 and 24 were organic acids; 31 was coumarin and 34 was lignan glycoside. The peak areas of compounds 2, 3, 5, 8, 17, 26, 35, 36 and 48 in CC were higher than those in CR. Among them, trans-cinnamaldehyde [48] was the major active components in both CR and CC. Compound 26 was flavonoid glycoside; 2, 3, 8 and 17 were organic acids; 35 and 36 were terpenoids and 5 was catechol.
Accordingly, the peak areas of flavonoids and flavonoid glycosides, and phenylpropanoids except trans-cinnamaldehyde in CR were higher than in CC, and the peak areas of terpenoids and organic acids in CC were higher than in CR.
## 2.4. Method Validation
The relative standard deviations (RSDs) for the precision, stability, and repeatability investigations into of coumarin, cinnamyl alcohol, cinnamic acid, dimethoxy cinnamic acid and cinnamaldehyde were all <$5\%$ (Table 2), indicating that the method had good precision, repeatability, and recovery. Additionally, a recovery range of 97−$101\%$ (RSD < $4\%$) indicated its high recovery and reliability (Table S3).
## 2.5. Quantitative Determination of the Major Constituents in CR and CC Using HPLC
The established HPLC analysis method was subsequently used to determine the representative components in eight batches of CR and CC products. In the case of quantitative analysis, all the samples (eight batches of CR and CC) were extracted three times and analyzed by HPLC. The RSD value of the concentrations of these five standards were less than $5\%$. The HPLC chromatograms were shown in the Figure S13. The contents of the five compounds are summarized in Table 3, based on their respective calibration curves. The results showed that there were significant differences in the composition content of CR and CC. Among them, the content of trans-cinnamaldehyde in CC was about twice that in CR, and the content of trans-cinnamic acid in CC was similar to that in CR; the contents of coumarin, cinnamyl alcohol and 2-methoxycinnamic acid in CC were significantly higher than those in CC. We, therefore, believe that these five components play a key role in the different efficacies of CR and CC.
## 2.6. Cluster Analysis
The original data of our compound content was imported into Lianchuan Biotechnology’s advanced heat map statistics software, and the original data was normalized by Z-score to obtain the result (Figure S14). As our statistics result indicated, CR and CC were each clustered into one group. Five compounds were screened out and could be used as chemical markers for distinguishing CR and CC; Among the five, two markers including trans-cinnamaldehyde and cinnamyl alcohol (VIP value is greater than the other three compounds) made greater contributions to sample grouping than the other three ones.
## 2.7. Molecular Docking
The different screened constituents were docked to PGC1α (PDB ID: 1XB7), SIRT3 (PDB ID: 4BN4) and AMPK (PDB ID: 4CFF). CDOCKER_INTERACTION_ENERGY ≤ −5.0 kJ/mol was used to produce a better binding ability for the compounds and proteins. The results were shown in Table S4 and Figure 7.
The results showed that compounds 40, 34, 17, 26 and 27 had the highest binding energy with Glycosylated Hemoglobin, Type A1C (HbA1c); compounds 1, 32, 16 and 19 had the highest binding energy with peroxisome proliferator-activated receptor-gamma coactivator-1alpha (PGC1α); compounds 24, 2, 45, 42 and 3 had the highest binding energy with silent information regulator protein 3 (SIRT3); and compounds 40, 33, 34, 27 and 32 had the highest binding energy with AMP-activated protein kinase (AMPK). Among these compounds, only 2, 3, 17, 26 had a higher content in CC. These results demonstrated that the special and high-concentration components in CR showed high docking scores of affinities with targets such as HbA1c and the proteins in the AMPK–PGC1–SIRT3 signaling pathway, suggesting the greater potentials for CR in treating DPN than for CC. The compounds with high protein binding energy were phenylpropanoid [1, 16, 42, 45] and flavonoids [19, 26, 27, 40].
## 3.1. Materials and Reagents
The following were used: the UPLC-Oribtrap-Exploris-120-MS liquid chromatography-mass spectrometry system (Thermo Scientific, Waltham, MA, USA); a KQ-500B CNC Ultrasonic Cleaner (Kunshan Ultrasonic Instrument Co., Ltd., Kunshan, China); methanol, acetonitrile, formic acid (chromatographic grade, Fisher, Waltham, MA, USA); ultra-pure water was freshly prepared using a Milli-Q system (Millipore, Milford, MA, USA); a high speed refrigerated centrifuge (Thermo Fisher, Karlsruhe, Germany); Watsons purified water. trans-Cinnamaldehyde [104-55-2], trans-cinnamic acid [621-82-9], cinnamyl alcohol [104-54-1], coumarin [91-64-5] and dimethoxy cinnamic acid [6099-03-2] were purchased from Chengdu purechem-standard co. LTD; epicatechin [490-46-0], taxifolin [480-18-3] and quercetin [522-12-3] were purchased from Shanghai yuanye Bio-Technology Co., Ltd.; and the purities of all the standards were greater than $98\%$. The eight batches of *Cinnamomi ramulus* (CR) and Cinnamomi cortex (CC) that were respectively the dried twigs and bark of *Cinnamomum cassia* Presl, were collected from different locations (Guangxi, Guangdong and Sichuan) at different harvest times (2019, 2020 and 2021), and stored in engineering technology research center for the comprehensive development and utilization of authentic medicinal materials in Henan province, as shown in Table S5.
## 3.2. Preparation of Sample Solutions
Each CR and CC sample was weighed (1 g) accurately and extracted under reflux with 50 mL ultrapure water for 2 h. They were left to reach room temperature; water was then added to compensate for the weight loss of the extraction solution. A volume of 1 mL of the extraction solution was diluted with methanol to 2 mL; this was filtered through a 0.22 µm nylon filter membrane and centrifuged at 12,000 rpm for 15 min for UPLC-MS analysis. A volume of 1 mL of the extraction solution was directly filtered and centrifuged for HPLC quantitative analysis. Mixed standard solutions (trans-cinnamaldehyde, trans-cinnamic acid, epicatechin, 2-methoxycinnamic acid and quercitrin) were used as QC samples.
## 3.3. Preparation of Reference Solutions
An appropriate amount of the reference materials of coumarin, cinnamyl alcohol, cinnamic acid, 2-methoxycinnamic acid and cinnamaldehyde were measured and weighed precisely; methanol was added to dissolve them, and 1.0331, 1.8667, 0.6667, 1.1333 and 3.4 mg/L, respectively, were prepared for standby.
## 3.4.1. UPLC Method for Qualitative Analysis
Qualitative analysis was performed on UPLC-Orbitrap-Exploris-120-MS and the preferred column was achieved on A HYPERSILGOLD Vanquish C18 (2.1 mm × 100 mm, 1.9 μm) for chromatographic separation. The mobile phase consisted of acetonitrile (A) and $0.1\%$ aqueous formic acid (B), with a flow rate of 0.3 mL/min, and the gradient elution condition was set as follows: 0~4 min, 5~$8\%$ A; 4~10 min, 8~ $16\%$ A; 10~15 min, $16\%$ A; 15~22 min, 16~$30\%$A; 22~25 min, $30\%$ A; 25~32 min, 30~$40\%$ A; 32~35 min, $50\%$ A; 35~40 min, 50~$90\%$ A. The injection volume was 1 µL and the column temperature was 25 °C.
## 3.4.2. UPLC-MS Method for Qualitative Analysis
MS data was acquired in fast chromatography MS2 mode, the mass spectrometer parameters were set as follows: The ESI was used in negative ion mode (ESI−) and in positive ion mode (ESI+). The following settings were used: the spray voltage was 2.5 kV(−) and 3.5 kV(+); the UHPLC-MS/MS mode was applied with an Orbitrap resolution of 120,000 for full-MS and 15,000 for dd-MS2; the isolation window (m/z) was 2; the RF Lens% was 70; the sheath gas pressure was 45 Arb; the auxilliary gas pressure was 15 Arb; the sweep gas pressure was 0 Arb; the capillary temperature was 320 °C; vaporizer temperature was 350 °C; the scanning range was m/z 80~800; the stepped normalized collision energies (NCE) were 15, 30 and 45 eV.
## 3.4.3. HPLC Method for Quantitative Analysis
The major active components including coumarin, cinnamyl alcohol, trans-cinnamic acid, 2-methoxycinnamic acid and trans-cinnamaldehyde were used for quantitative analysis.
Quantitative analysis was performed on a Waters HPLC E2695 and the preferred column was achieved on a Waters C18 (4.6 mm × 250 mm, 5 μm) for the chromatographic separation. The mobile phase was consisted of acetonitrile (A) and $0.1\%$ aqueous formic acid (B), with a flow rate of 1 mL/min, and the gradient elution condition was set as follows: 0~13 min, 5~$15\%$ A; 13~20 min, 15~ $26\%$ A; 20~25 min, $26\%$ A; 25~28 min, 26~$30\%$A; 28~38 min, $30\%$~$40\%$ A; 38~48 min, 40~$60\%$ A; 48~52 min, $60\%$~$95\%$ A; 52~57 min, $95\%$ A. The injection volume was 20 µL, and column temperature was 30 °C.
To evaluate linearity, a series of standard solutions with appropriate concentrations were obtained by diluting standard compounds with methanol. The calibration curves were drawn with the quality of the reference substance as the abscissa (X) and the peak area as the ordinate (Y), they showed a good linear relationship (r ≥ 0.999) within the test ranges. The same sample solutions of CR and CC were continuously injected to verify the precision of the instrument. The same sample solutions were injected, separately, 0, 2, 4, 8, 12, and 24 h separately to check the stability of the test solution. Six sample solutions were prepared independently to verify the repeatability of the method. The accuracy of the method was evaluated by a recovery test. Recovery (%) = {[Found − (Original sample + Add)]/Add} ∗ 100.
## 3.4.4. LC-MS Data Processing and Statistics
The UPLC-Orbitrap-Exploris-120-MS data were acquired by the Trancefinder 5.1 software; the UHPLC-MS/MS mode was applied with an Orbitrap resolution of 120,000 for full-MS and 15,000 for dd-MS2. The date was then analyzed using the Xcalibur3.0 software. Each raw data processed using Compound Discoverer 3.2 qualitative analysis software following a specific workflow (Figure S15). The screening steps of the target compound were: All ions presenting a signal over 5 times the background noise and a peak intensity over 105 were taken into account to create the extracted ion chromatogram (EIC). MS and MS2 spectra were then used to identify ions by searching the mzCloud, mzValut, Chemspider and mass Lists databases. Then select the matching compounds with a tolerance of 5 ppm and more than $60\%$ of the database matching degree as our preliminary identified compound. Finally, the compounds were reconfirmed by combined with the parent ion and the MS2 fragment ions extracted from their original data with those in the relevant literature. Additionally, the compounds were all rechecked by searching literature materials to exclude the non-natural products. The peak area of fragment ions was statistically analyzed to determine the statistically significant difference components, and then the marker compounds were clustered for the purpose of distinguishing CR and CC.
To evaluate relationships on the basis of similarities or differences between groups of multivariate data, multivariate analyses (PCA and OPLS-DA) were performed using SICMA 14.1. PCA results were displayed in the form of score plots. OPLS-DA was conducted using class information as the Y-variable; the results were shown in the form of score plots. The contribution of variables to the analysis was explained using variable importance in projection (VIP) scores. VIP scores are a weighted sum of squares of PLS weights, with scores larger than 1 indicating variables are important to the mode. T-tests were performed for compounds with VIP > 1: compounds with p value of <0.05 were considered significantly differentiated compounds.
## 3.4.5. Molecular Docking
We used Discovery Studio to predict the docking of small molecule compounds with key proteins. The 3D structure of the compound constructed by ChemOffice software was saved in *mol2 format, and its energy was minimized. The 3D structure of the target protein was downloaded from the PDB data (https://www.rcsb.org/), accessed on 24 November 2022, and Discovery Studio 2020 software was used to perform operations such as water removal and hydrogenation on the protein and generate an effective single 3D conformation by minimizing the energy.
## 4. Conclusions
In this study, chemical compounds in CR and CC were analyzed and identified using UPLC-Oribtrap-Exploris-120-MS/MS: a total of 58 chemical components were identified. Unsupervised PCA and supervised OPLS-DA were used to assess the differences between CR and CC, and GraphPad Prism 7.0 was used to perform t-tests on the differential components. A total of 26 statistically significant differential compounds were obtained, in which the peak areas of flavonoids (19, 27, 32, 33, 38, 40 and 41) and phenylpropanoids (16, 41, 42, 45, 46, 53 and 58) except cinnamaldehyde in CR were higher than in CC, and the peak areas of terpenoids (35 and 36) and organic acids (2, 3, 8 and 17) in CC were higher than in CR. Additionally, HPLC was used to determine the concentrations and differentiating capacities of coumarin, cinnamyl alcohol, cinnamic acid, 2-methoxycinnamic acid and cinnamaldehyde, which were the major active ingredients in both CR and CC. The results showed that the content of cinnamaldehyde in CC was about twice that in CR, the content of cinnamic acid in CC was similar to that in CR, and the content of coumarin, cinnamyl alcohol and 2-methoxycinnamic acid in CR were significantly higher than that in CC. These five major components could be used as markers for successfully distinguishing CR and CC [63]. Compared to the reported method in the literature, the quantitative method was simpler, and demonstrated good stability and reproducibility. In addition, the different screened constituents were docked to HbA1c, PGC1α, SIRT3 and AMPK, and the results showed that the special and high-concentration components in CR showed high docking scores of affinities with targets such as HbA1c or proteins in the AMPK–PGC1–SIRT3 signaling pathway, suggesting the greater potentials of CR in treating DPN than of CC. Furthermore, the compounds with higher protein binding energy were phenylpropanoid [1, 16, 42, 45] and flavonoids [19, 26, 27, 40], from which it be inferred that flavonoids and phenylpropanoids might be an important material basis for the differential efficacies of CR and CC. The above results provide comparative information on the chemical profiles of CR and CC, as well as the groundwork for exploring the effective substances in each.
This is the first time that the compositional differences in the aqueous extracts of CR and CC by LC-MS have been analyzed, which directly reflects the material basis for their different functions under the usage of decoction in clinical practices. The high-content flavonoids and phenylpropanoids in CR may be the key material basis for dispersing wind and cold medicines, and terpenoids and organic acids may be the main active constituents for interior-warming medicines. In previous studies, flavonoids such as quercetin decreased blood glucose levels [64], phenylpropanoids inhibited platelet aggregation [65], and coumarin had suppressive effects on neuropathic cold allodynia in rats [66]. A further investigation of these CR differential substances may make it possible to find effective drugs for treating DPN. The previous studies were mostly focused on trans-cinnamaldehyde, while the CR and CC differential components would be easily acquired, combined with modern separation technology, and are worthy of further modern pharmacological research.
## References
1. Liu J., Zhang Q., Li R.L., Wei S.J., Huang C.Y., Gao Y.X., Pu X.F.. **The traditional uses, phytochemistry, pharmacology and toxicology of Cinnamomi ramulus: A review**. *J. Pharm. Pharmacol.* (2020) **72** 319-342. DOI: 10.1111/jphp.13189
2. Xiang H.J., Zhang L.S., Song J.N., Fan B., Nie Y.L., Bai D., Lei H.M.. **The profiling and identification of the absorbed constituents and metabolites of Guizhi decoction in rat plasma and urine by rapid resolution liquid chromatography combined with quadrupole-time-of-flight mass spectrometry**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17091409
3. Liu L., Chu X.Q., Tian C.L., Xia M.Q., Zhang L., Jiang J.Q., Gui S.Y.. **Chemo proling and simultaneous analysis of different combinations of**. *J. Chromatogr. Sci.* (2021) **59** 606-661. DOI: 10.1093/chromsci/bmab048
4. Wang D., Wu X.M., Zhang D.D., Zhu B.R., Wang S.C., Wang C.X., Jia Q., Li Y.M.. **Study on chemical constituents of Cinnamomi ramulus**. *J. Chin. Mat. Med.* (2020) **45** 124-132
5. Wu X.X., He J., Xu H.R., Bi K.S., Li Q.. **Quality assessment of Cinnamomi ramulus by the simultaneous analysis of multiple active components using high-performance thin-layer chromatography and high-performance liquid chromatography**. *J. Sep. Sci.* (2014) **37** 2490-2498. DOI: 10.1002/jssc.201400494
6. Gong Z., Gong L., Hu L.L., Han J., Gong Y.Y., Xing Y.. **Comparative Study on volatile oil components of Rimulus cinnamon and Cinnamon**. *J. Liaoning Tradit. Chin. Med. Mag.* (2014) **41** 2199-2201
7. Chen P.Y., Yu J.W., Lu F.L., Lin M.C., Cheng H.F.. **Differentiating parts of**. *Biomed. Chromatogr.* (2016) **30** 1449-1457. DOI: 10.1002/bmc.3703
8. Guo Z.L., Zhu P.X., He X.A., Yan T.H., Liang X.R.. **Components identification and isomers differentiation in pigeon pea (**. *J. Sep. Sci.* (2021) **44** 2510-2523. DOI: 10.1002/jssc.202001194
9. Gao X., Sun W.J., Fu Q., Niu X.F.. **Ultra-performance liquid chromatography coupled with electrospray ionization/quadrupole time-of-flight mass spectrometry for the rapid analysis of constituents in the traditional Chinese medical formula Danggui San**. *J. Sep. Sci.* (2014) **37** 53-60. DOI: 10.1002/jssc.201300969
10. Li J.F., Chen W.J., Wang Y.H., Yin H.. **An LC-MS/MS method for simultaneous quantification of 11 components of Xian-Xiong-Gu-Kang in the plasma of osteoarthritic rats and pharmacokinetic analysis**. *J. Sep. Sci.* (2021) **44** 3386-3397. DOI: 10.1002/jssc.202100132
11. Lei H.B., Zhang Y.H., Ye J., Cheng T.F., Liang Y.L., Zu X.P., Zhang W.D.. **A comprehensive quality evaluation of Fuzi and its processed product through integration of UPLC-QTOF/MS combined MS/MS-based mass spectral molecular networking with multivariate statistical analysis and HPLC-MS/MS**. *J. Ethnopharmacol.* (2021) **266** 113455. DOI: 10.1016/j.jep.2020.113455
12. Zheng Y.Y., Zeng X., Peng W., Wu Z., Su W.W.. **Characterisation and classification of Citri Reticulatae Pericarpium varieties based on UHPLC-Q-TOF-MS/MS combined with multivariate statistical analyses**. *Phytochem. Anal.* (2019) **30** 278-291. DOI: 10.1002/pca.2812
13. Wang G.Y., Shang J., Wu Y., Ding G., Xiao W.. **Rapid characterization of the major chemical constituents from Polygoni multiflori caulis by liquid chromatography tandem mass spectrometry and comparative analysis with Polygoni multiflori radix**. *J. Sep. Sci.* (2017) **40** 2107-2116. DOI: 10.1002/jssc.201601255
14. Zeng L.S., OuYang C.X.. **Effect of integrated traditional Chinese and western medicine on diabetic peripheral neuropathy and its influence on serum levels of homocysteine and cystatin-C**. *Chin. Mod. Med.* (2019) **26** 151-156
15. Liu J., Zhang Q., Li R.L., Wei S.J., Gao Y.X., Ai L., Wu C.J., Pu X.F.. **Anti-proliferation and anti-migration effects of an aqueous extract of Cinnamomi ramulus on MH7A rheumatoid arthritis-derived fibroblast-like synoviocytes through induction of apoptosis, cell arrest and suppression of matrix metalloproteinase**. *Pharm. Biol.* (2020) **58** 863-877. DOI: 10.1080/13880209.2020.1810287
16. Han Y., Jung H.W., Bae H.S., Kang J.S., Park Y.K.. **The extract of Cinnamomum cassia twigs inhibits adipocyte differentiation via activation of the insulin signaling pathway in 3T3-L1 preadipocytes**. *Pharm. Biol.* (2013) **51** 961-967. DOI: 10.3109/13880209.2013.772211
17. Zhao N., Li J., Li L., Niu X.Y., Jiang M., He X.J., Bian Z.X., Zhang G., Lu A.P.. **Molecular network-based analysis of guizhi-shaoyao-zhimu decoction, a TCM herbal formula, for treatment of diabetic peripheral neuropathy**. *Acta Pharmacol. Sin.* (2015) **36** 716-723. DOI: 10.1038/aps.2015.15
18. Shen F.H., Yang W.L., Zhang M.J.. **Clinical Effect of Huangqi Guizhi Wuwu Decoction Modified Combined with Western Medicine in the Treatment of Early Diabetic Foot**. *Chin. Foreign Med. Res.* (2021) **19** 146-149
19. Price D.E., Alani S.M., Carrington A., Stickland M.H., Wales J.K.. **The relationship between peripheral nerve resistance to ischaemia and diabetic control**. *J. Neurol. Neurosurg. Psychiatry.* (1987) **50** 1671-1673. DOI: 10.1136/jnnp.50.12.1671
20. Fen N.. **Correlation between glycosylated hemoglobin, C-peptide levels and type 2 diabetes peripheral neuropathy**. *Med. J. Chin. People’s Health* (2020) **32** 134-135
21. Yu X.Y., Zhang L., Yang X.Y., Huang H.K., Huang Z.G., Zhang H.A., Du G.H.. **Salvianolic acid A protects the peripheral nerve function in diabetic rats through regulation of the AMPK-PGC1**. *Molecules* (2012) **17** 11216-11228. DOI: 10.3390/molecules170911216
22. Liang K., Cui S.J., Zhang Q., Bi K.S., Qian Z.Z., Jia Y.. **UPLC simultaneous determination of five active components in Cinnamomi ramulus**. *Chin. J. Chin. Mater. Med.* (2011) **36** 3298-3301
23. Jiang Y.P., Liu R., Chen J.J., Liu M.H., Liu M., Liu B., Yi L.Z., Liu S.. **Application of multifold characteristic ion filtering combined with statistical analysis for comprehensive profiling of chemical constituents in anti-renal interstitial fibrosis I decoction by ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap high resolution mass spectrometry**. *J. Chromatogr. A* (2019) **1600** 197-208. PMID: 31030951
24. Ye X.Y., Wu J.M., Yang J., Kantawong F., Kumsaiyai W., Zeng J.. **Research progress on chemical constituents of Gynura divaricate and mass spectrometry-based fragmentation rules of representative components**. *Chin. Tradit. Herb. Drugs* (2021) **52** 6687-6700
25. Zhou W.W., Liang Z.T., Li P., Zhao Z.Z., Chen J.. **Tissue-specific chemical profiling and quantitative analysis of bioactive components of Cinnamomum cassia by combining laser-microdissection with UPLC-Q/TOF-MS**. *Chem. Cent J.* (2018) **12** 71-80. DOI: 10.1186/s13065-018-0438-x
26. Cheng X., Bi L.W., Zeng W.X., Zhai Z.D., Chen Y.X., Zhang Z.H.. **Chemical constituents of distillation residues from Cinnamon twigs and leaves by using UHPLC-QTOF-MS**. *Chem. Ind. Forest Prod.* (2020) **40** 50-60
27. Kacem N., Hay A.E., Marston A., Zellagui A., Rhouati S., Hostettmann K.. **Antioxidant compounds from Algerian**. *Nat Prod. Commun.* (2012) **7** 873-874. DOI: 10.1177/1934578X1200700716
28. Wang K.H., Tian J.Y., Li Y.S., Liu M.S., Chao Y.X., Cai Y., Zeng G.D., Fang Y.. **Identification of components in**. *ACS Omega.* (2021) **6** 17045-17057. DOI: 10.1021/acsomega.1c02124
29. Weng Q.Q., Yang B., Li B., Deng A.P., Zhao J.C., Lin W.M., Zhan Z.L., Huang L.Q.. **Analysis of chemical constituents in**. *Chin. J. Exp. Tradit. Med. Form.* (2021) **27** 68-75
30. Zeng W.X., Cheng X., Bi L.W., Li S.N., Chen Y.X., Zhao Z.D.. **Effect of biological pretreatment on chemical components from cinnamon cwigs and ceaves**. *Chem. Indus Forest Produ.* (2021) **41** 101-110
31. Ma L.Y., Wang Y.M., Chu K., Chen C., Wu S.. **Determination of phenolic acids and phenol aldehyde compounds in wine by Ultra Performance Liquid Chromatography Tandem Mass Spectrometry**. *Chem. Reag.* (2023) **45** 141-147
32. Farag M.A., Kabbash E.M., Mediani A., Döll S., Esatbeyoglu T., Afifi S.M.. **Comparative metabolite fingerprinting of four different cinnamon species analyzed via UPLC-MS and GC-MS and chemometric tools**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27092935
33. He M.Z., Jia J., Li J.M., Wu B., Huang W.P., Liu M., Li Y., Yang S.L., Ouyang H., Feng Y.. **Application of characteristic ion filtering with ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry for rapid detection and identification of chemical profiling in**. *J. Chromatogr. A* (2018) **1554** 81-91. DOI: 10.1016/j.chroma.2018.04.036
34. Abu Bakar F.I., Abu Bakar M.F., Abdullah N., Endrini S., Fatmawati S.. **Optimization of extraction conditions of phytochemical compounds and anti-Gout activity of**. *Evid. Based Complement. Altern. Med.* (2020) **2020** 501261-501283. DOI: 10.1155/2020/4501261
35. Jia Q.Q., Zhang S.D., Zhang H.Y., Yang X.J., Cui X.L., Su Z.H., Hu P.. **A Comparative study on polyphenolic composition of berries from the Tibetan Plateau by UPLC-Q-Orbitrap MS system**. *Chem. Biodivers.* (2020) **17** 2000033-2000053. DOI: 10.1002/cbdv.202000033
36. Liu H.X., Lin S., Huang C.S., Sun C.H., Huang K.L.. **Study on chemical component about cinnamomum cassia oil from the different sections of cinnamomum cassia by gas chromatography-mass spectrometry**. *Chincondiment* (2011) **36** 102-110
37. Chen P., Ruan A.M., Zhou J., Zhang X.Z., Wang Q.F.. **Exploration on action mechanism of combination of Ramulus Cinnamoni and Radix Paeoniae Alba in treatment of osteoarthritis based on network pharmacology**. *Global Trad. Chin. Med.* (2021) **14** 403-410
38. Yang Y.L., Al-Mahdy D.A., Wu M.L., Zheng X.T., Piao X.H., Chen A.L., Wang S.M., Yang Q., Ge Y.W.. **LC-MS-based identification and antioxidant evaluation of small molecules from the cinnamon oil extraction waste**. *Food Chem.* (2022) **366** 130576. DOI: 10.1016/j.foodchem.2021.130576
39. Zhang F.H., Wang Z.M., Liu Y.T., Huang J.S., Liang S., Wu H.H., Xu Y.T.. **Bioactivities of serotonin transporter mediate antidepressant effects of Acorus**. *J. Ethnopharmacol.* (2019) **241** 111967-111975. DOI: 10.1016/j.jep.2019.111967
40. Liu J.Y., Yang X.D., Xu L.Z., Yang S.L.. **Studies on chemical constituents in dried tender stem of**. *Chin. Trad. Herb. Drugs* (2002) **33** 681-683
41. Fu B.H., Ji Y., Li J., Pei M., Yang H.T.. **Network pharmacology-based mechanism of**. *Trad. Chin. Drug Res. Clinl. Pharm.* (2021) **32** 1675-1684
42. Yoshimura M., Ochi K., Sekiya H., Tamai E., Maki J., Tada A., Sugimoto N., Akiyama H., Amakura Y.. **Identification of characteristic phenolic constituents in Mousouchiku extract used as food additives**. *Chem. Pharm. Bull.* (2017) **65** 878-882. DOI: 10.1248/cpb.c17-00401
43. Chang S.W., Lee J.S., Lee J.H., Kim J.Y., Hong J.K., Kim S.K., Lee D.H., Jang D.S.. **Aromatic and Aliphatic Apiuronides from the Bark of Cinnamomum cassia**. *J. Nat. Prod.* (2021) **84** 553-561. DOI: 10.1021/acs.jnatprod.0c01062
44. Caboni P., Sarais G., Cabras M., Angioni A.. **Determination of 4-ethylphenol and 4-ethylguaiacol in wines by LC-MS-MS and HPLC-DAD-fluorescence**. *J. Agr. Food Chem.* (2007) **55** 7288-7293. DOI: 10.1021/jf071156m
45. Lam R.Y.Y., Lin Z.X., Sviderskaya E., Cheng C.H.K.. **Application of a combined sulphorhodamine B and melanin assay to the evaluation of Chinese medicines and their constituent compounds for hyperpigmentation treatment**. *J. Ethnopharmacol.* (2010) **132** 274-279. DOI: 10.1016/j.jep.2010.08.027
46. Wang Y.F., Harrington P.B., Chen P.. **Metabolomic profiling and comparison of major cinnamon species using UHPLC-HRMS**. *Anal. Bioanal. Chem.* (2020) **412** 7669-7681. DOI: 10.1007/s00216-020-02904-1
47. Han S., Karłowicz-Bodalska K., Potaczek P., Wójcik A., Ozimek L., Szura D., Musiał W.. **Identification of unknown impurity of azelaic acid in liposomal formulation assessed by HPLC-ELSD, GC-FID, and GC-MS**. *AAPS Pharm. SciTech.* (2014) **15** 111-120. DOI: 10.1208/s12249-013-0038-y
48. Shi D.H., Dai Y.P., Wang L.F., Zhou Q., Zhang X.L., Zhang J.. **Chemical composition analysis of platycladi cacumen before and after being carbonized based on identification by UHPLC-QTOF-MS/MS**. *Chin. J. Exp. Tradit. Med. Form.* (2021) **8** 107-116
49. Rius Solé M.A., García Regueiro J.A.. **Role of 4-phenyl-3-buten-2-one in boar taint: Identification of new compounds related to sensorial descriptors in pig fat**. *J. Agr. Food Chem.* (2001) **49** 5303-5309. DOI: 10.1021/jf010482d
50. Razgonova M., Zakharenko A., Pikula K., Manakov Y., Ercisli S., Derbush I., Kislin E., Seryodkin I., Sabitov A., Kalenik T.. **LC-MS/MS screening of phenolic compounds in wild and cultivated grapes Vitis amurensis Rupr**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26123650
51. Oliw E.H., Garscha U., Nilsson T., Cristea M.. **Payne rearrangement during analysis of epoxyalcohols of linoleic and alpha-linolenic acids by normal phase liquid chromatography with tandem mass spectrometry**. *Anal. Biochem.* (2006) **354** 111-126. DOI: 10.1016/j.ab.2006.04.010
52. Caviglioli G., Valeria P., Brunella P., Sergio C., Attilia A., Gaetano B.. **Identification of degradation products of ibuprofen arising from oxidative and thermal treatments**. *J. Pharmaceut. Biomed.* (2002) **30** 499-509. DOI: 10.1016/S0731-7085(02)00400-4
53. Xu F., Koch D.E., Kong I.C., Hunter R.P., Bhandari A.. **Peroxidase-mediated oxidative coupling of 1-naphthol: Characterization of polymerization products**. *Water Res.* (2005) **39** 2358-2368. DOI: 10.1016/j.watres.2005.04.010
54. Xu C.J., Liang Y.Z., Song Y.Q., Li J.S.. **Resolution of the essential constituents of Ramulus cinnamomi by an evolving chemometric approach**. *Fresen J. Anal. Chem.* (2001) **371** 331-336. DOI: 10.1007/s002160100977
55. Yongram C., Sungthong B., Puthongking P., Weerapreeyakul N.. **Chemical composition, antioxidant and cytotoxicity activities of leaves, bark, twigs and oleo-resin of dipterocarpus alatus**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24173083
56. Mueller M., Beck V., Jungbauer A.. **PPARα activation by culinary herbs and spices**. *Planta Med.* (2011) **77** 497-504. DOI: 10.1055/s-0030-1250435
57. Guo Y.P., Yang H., Wang Y.L., Chen X.X., Zhang K., Wang Y.L., Sun Y.F., Huang J., Yang L., Wang J.H.. **Determination of Flavonoids Compounds of Three Species and Different Harvesting Periods in Crataegi folium Based on LC-MS/MS**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26061602
58. Liang H.B., Jiang Y.J., Yuan X.M., Yao J.C., Qiu R.Y., Yang M., Zhang G.M., Li F.. **Chemical constituents of Jingfang granules based on GC-MS and UPLC-Q Exactive MS**. *Chin. Tradit. Herb. Drugs* (2022) **53** 1697-1708
59. Qiao X., Li R., Song W., Miao W.J., Liu J., Chen H.B., Guo D.A., Ye M.. **A targeted strategy to analyze untargeted mass spectral data: Rapid chemical profiling of**. *J. Chromatogr. A* (2016) **1441** 83-95. DOI: 10.1016/j.chroma.2016.02.079
60. Sang Q.N., Jia Q.Q., Zhang H.Y., Lin C.H., Zhao X.D., Zhang M., Wang Y.R., Hu P.. **Chemical profiling and quality evaluation of**. *Pharm. Biomed. Anal.* (2021) **194** 113771. DOI: 10.1016/j.jpba.2020.113771
61. Yang W., Ye M., Liu M., Kong D.Z., Shi R.A., Shi X.W., Zhang K.R., Wang Q., Zhang L.T.. **Practical strategy for the characterization of coumarins in**. *J. Chromatogr. A* (2010) **1217** 4587-4600. DOI: 10.1016/j.chroma.2010.04.076
62. Yang H., Jiang B., Reynertson K.A., Basile M.J., Kennelly E.J.. **Comparative analyses of bioactive Mammea coumarins from seven parts of**. *J. Agric. Food Chem.* (2006) **54** 4114-4412. DOI: 10.1021/jf0532462
63. Liu C., Long H.L., Wu X.D., Hou J.J., Gao L., Yao S.A., Lei M., Zhang Z.J., Gao D.A., Wu W.Y.. **Quantitative and fingerprint analysis of proanthocyanidins and phenylpropanoids in Cinnamomum verum bark, Cinnamomum cassia bark, and Cassia twig by UPLC combined with chemometrics**. *Eur. Food Res. Technol.* (2021) **247** 2687-2698. DOI: 10.1007/s00217-021-03795-x
64. Wang S.H., Huang W.L., Chen Q.S., Zeng L., Zeng F.J.. **Inhibition of rutin and quercetin on α-glycosidase**. *Chin. Brew Maga* (2012) **31** 133-135
65. Li Y.H., Wei J.C., Liang J.L., Huang Q.X., Huang R.S., Lei P.L.. **Smiultaneous determination of coumarin, cinnamic acid and cinnamaldehyde in folium cinnamonic by HPLC**. *Chin. J. Trad. Chin. Med.* (2020) **38** 54-57
66. Kim C., Lee J.H., Kim W.J., Li D.X., Kim Y.S., Lee K.J., Kim S.K.. **The suppressive effects of cinnamomi cortex and its phytocompound coumarin on oxaliplatin-induced neuropathic cold allodynia in rats**. *Molecules* (2016) **21**. DOI: 10.3390/molecules21091253
67. Chen K.K., Liu J., Ma Z.C., Duan F.P., Guo Z.H., Xiao H.B.. **Rapid identification of chemical constituents of Rhodiola crenulata using liquid chromatography-mass spectrometry pseudotargeted analysis**. *J. Sep. Sci.* (2021) **44** 3747-3776. DOI: 10.1002/jssc.202100342
68. Goodner K.L., Rouseff R.L.. **Using an ion-trap MS sensor to differentiate and identify individual components in grapefruit juice headspace volatiles**. *J. Agric. Food Chem.* (2001) **49** 250-253. DOI: 10.1021/jf000867g
69. Chandra P., Rathore A.S., Kay K.L., Everhart J.L., Curtis P., Burton-Freeman B., Cassidy A., Kay C.D.. **Contribution of berry polyphenols to the human metabolome**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24234220
70. Zhang W.D., Wang X.J., Zhou S.Y., Gu Y., Wang R., Zhang T.L., Gan H.Q.. **Determination of free and glucuronidated kaempferol in rat plasma by LC-MS/MS: Application to pharmacokinetic study**. *J. Chromatogr. B* (2010) **878** 2137-2140. DOI: 10.1016/j.jchromb.2010.06.002
71. Hu J., Zhao J.P., Khan S.I., Liu Q., Liu Y., Ali Z., Li X.C., Zhang S.H., Cai X., Huang H.Y.. **Antioxidant neolignan and phenolic glucosides from the fruit of Euterpe oleracea**. *Fitoterapia* (2014) **99** 178-183. DOI: 10.1016/j.fitote.2014.09.020
72. Kim S.E., Lee J., An J.U., Kim T.H., Oh C.W., Ko Y.J., Krishnan M., Choi J., Yoon D.Y., Kim Y.. **Regioselectivity of an arachidonate 9S-lipoxygenase from Sphingopyxis macrogoltabida that biosynthesizes 9S,15S- and 11S,17S-dihydroxy fatty acids from C20 and C22 polyunsaturated fatty acids**. *BBA-Mol. Cell Biol. Lipids* (2022) **1867** 159091. DOI: 10.1016/j.bbalip.2021.159091
73. Kenari F., Molnár S., Perjési P.. **Reaction of Chalcones with Cellular Thiols. The Effect of the 4-Substitution of Chalcones and Protonation State of the Thiols on the Addition Process. Diastereoselective Thiol Addition**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26144332
|
---
title: Characterization of AZ31/HA Biodegradable Metal Matrix Composites Manufactured
by Rapid Microwave Sintering
authors:
- Shivani Gupta
- Apurbba Kumar Sharma
- Dinesh Agrawal
- Michael T. Lanagan
- Elzbieta Sikora
- Inderdeep Singh
journal: Materials
year: 2023
pmcid: PMC10004123
doi: 10.3390/ma16051905
license: CC BY 4.0
---
# Characterization of AZ31/HA Biodegradable Metal Matrix Composites Manufactured by Rapid Microwave Sintering
## Abstract
This study reports the development of magnesium alloy/hydroxyapatite-based biodegradable metal matrix composites (BMMCs) through rapid microwave sintering. Magnesium alloy (AZ31) and hydroxyapatite powder were used in four compositions 0, 10, 15 and $20\%$ by weight. Developed BMMCs were characterized to evaluate physical, microstructural, mechanical and biodegradation characteristics. XRD results show Mg and HA as major phases and MgO as a minor phase. SEM results correlate with the XRD findings by identifying the presence of Mg, HA and MgO. The addition of HA powder particles reduced density and increased the microhardness of BMMCs. The compressive strength and Young’s modulus increased with increasing HA up to 15 wt$.\%$. AZ31-15HA exhibited the highest corrosion resistance and lowest relative weight loss in the immersion test for 24 h and weight gain after 72 and 168 h due to the deposition of Mg(OH)2 and Ca(OH)2 layers at the sample surface. XRD analysis of the AZ31-15HA sintered sample after an immersion test was carried out and these results revealed the presence of new phases Mg(OH)2 and Ca(OH)2 that could be the reason for enhancing the corrosion resistance. SEM elemental mapping result also confirmed the formation of Mg(OH)2 and Ca(OH)2 at the sample surface, which acted as protective layers and prevented the sample from further corrosion. It showed that the elements were uniformly distributed over the sample surface. In addition, these microwave-sintered BMMCs showed similar properties to the human cortical bone and help bone growth by depositing apatite layers at the surface of the sample. Furthermore, this apatite layer can enhance osteoblast formation due to the porous structure type, which was observed in the BMMCs. Therefore, it is indicative that developed BMMCs can be an artificial biodegradable composite for orthopedic applications.
## 1. Introduction
Demand for magnesium alloys and their composites is increasing in medical science due to their lightweight, osteoconductive, nontoxic, and biodegradable attributes. However, the high corrosion rate of pure magnesium in physiological conditions makes it inappropriate for permanent orthopedic implants. On the other hand, magnesium-based artificial materials are the most suitable for biodegradable orthopedic fixation aids, such as screws, bone plates, stents, rods, and scaffolds, in which degradability is the most desired attribute. Biodegradable materials assist tissue growth surrounding the broken joint and get degraded after therapeutic action. Therefore, such materials reduce the risk of secondary trauma and stress shielding effects that are generally witnessed in the case of non-degradable biomaterials. Consequently, surgeons have started to use biodegradable materials in orthopedic implantations to minimize patient pain and inconvenience.
However, limited metals, ceramics, and polymers with biodegradable characteristics are available and suitable for implantation in the human body. Magnesium and its alloys are widely used as biodegradable metals for medical applications since it is an essential mineral in the human body and has excellent biodegradability [1]. The total magnesium content in a healthy human body is approximately 25 g, most of which is in bone and soft tissues. Magnesium deficiency can cause insulin resistance, metabolic syndrome, heart disease, and osteoporosis; indirectly, magnesium deficiency can reduce calcium and potassium levels in the blood [2,3,4,5]. Similarly, hydroxyapatite (Ca10(PO4)6(OH)2) is a bio-ceramic characteristically similar to human bone. Therefore, Mg and HA composites were explored as potential biodegradable materials for orthopedic implants [6]. Due to its potential, there has been significant growth in syntheses, economic processing of these materials, and understanding of characteristics [7]. However, the fabrication of composites with the necessary characteristics is challenging.
It was reported that pure Mg and its alloys could not be directly applicable in the human body environment due to their high degradation rate. Therefore, studies were carried out on improving the corrosion resistance of Mg-based biomaterials coated with HA, Ti alloys, and ZrO2; better corrosion resistance and enhanced biocompatibility were reported [8,9,10]. Magnesium-based biocomposites with HA, PLA, bioglass, etc., were also found as potential materials for orthopedic fixation aids. Therefore, many studies have focused on processing these composites using powder metallurgy, casting, extrusion, and friction stir processing [11,12,13,14,15,16,17]. Composites like Mg/HA, Mg-Zn-Zr/HA, AZ91/HA, and ZK60A/CPP were also fabricated with improved hardness, compressive strength, and bio-corrosion resistance with in vitro biocompatibility [18,19,20,21,22,23,24,25]. Jaiswal et al. fabricated Mg-3Zn-HA BMMC using conventional sintering (CS) with different HA contents and reported that 5 wt$.\%$ HA composition showed the highest corrosion resistance in simulated body fluid (SBF) and adequate compressive strength along with improved osteogenic cell adhesion properties [26]. Later, Mg/HA BMMCs were developed from a high-frequency induction sintering technique and an increase in relative density, compressive strength, microhardness, and crystal size was observed when the sintering temperature was increased [27]. Mg/HA BMMCs of 10, 20, and 30 wt$.\%$ of HA were also developed using hot pressing and extrusion. It was reported that 10 wt$.\%$ HA sample exhibited higher strength. In contrast, the 30 wt$.\%$ HA sample obtained higher resistance in corrosion than 10 wt$.\%$ HA with better cytocompatibility [28]. In addition, magnesium alloy AZ91/fluorapatite composites were also developed using powder metallurgy with improved corrosion resistance, mechanical properties, and osteoconductivity [29]. In addition, AZ31/nano HA composites were fabricated from friction stir processing (FSP) and reported that fine grain structure was obtained with the integration of nano HA. Reinforcement of nano HA particles enhanced the cell corrosion resistance and adhesion properties of composites in vitro tests [30]. Yee-Hsien Ho et al. fabricated AZ31/HA composites using a friction stir additive manufacturing technique and observed that the addition of HA particles refined grain structure and improved biomineralization during the immersion test. It also reported good biocompatibility in higher HA content composition [31]. Other than FSP, conventional powder metallurgy processes like spark plasma sintering (SPS) are widely explored in composites processing. The microwave sintering (MS) technique is used in limited studies to process pure HA bio-ceramic in comparative studies with conventional sintering. It is found that microwave-sintered composites exhibited higher compressive strength, reduced porosity, and smaller grain sizes (33–$50\%$) than conventionally sintered composites [32,33].
Therefore, there is ample scope to explore microwave-assisted powder metallurgy routes in the fabrication of magnesium alloy-based BMMCs. This study used magnesium alloy (AZ31) and hydroxyapatite for metal matrix composites since this alloy has exhibited a nontoxic nature due to its low aluminum presence [34]. Further, microwave-sintered AZ31-HA BMMCs were characterized for density, phase analysis, microstructural evaluation, mechanical properties, and in vitro biodegradable behavior.
## 2. Materials and Experimental Procedure
The details of the materials used, the experimental procedure followed for developing the microwave-sintered magnesium alloy/HA-based metal matrix biodegradable composites, and their characterizations are presented in the following sections.
## 2.1. Material Details
AZ31 powder (Nextgen Steel & Alloys, Mumbai, India) of average particle size of 40 µm, and hydroxyapatite (Aldrich Chemical Company Inc., Milwaukee, WI, USA) of agglomerated particles of submicron size of 44 µm were used as precursors. Table 1 lists the chemical composition of AZ31 alloy. Typical XRD spectra of AZ31 and HA powders are shown in Figure 1. The XRD results individually confirmed the presence of α-Mg phase in AZ31 and Ca10(PO4)6(OH)2 phase in HA powder of JSP. Earlier studies revealed good results from 10 to 20 wt$.\%$ of HA [35,36]. Therefore, this study was focused on four different compositions, such as 0, 10, 15 and 20 weight percent of HA with AZ31. Further, the powders were mixed in polyethylene bottles using MgZr cylindrical balls, rotating at 100 rpm for 12 h. The powder mixtures were compacted into green pellets (ϕ13 mm × 3 mm) using a cold static hydraulic pressure of 450 MPa.
## 2.2. Development of AZ31/HA Composites
The compacted green pellets of different compositions were sintered in a multimode microwave tube furnace operating at 2.45 GHz frequency and 3 kW maximum power. All samples were sintered in a working environment of forming gas. Table 2 represents the processing conditions used during the development of BMMCs.
## 3. Characterization Details
Microwave-sintered BMMCs were characterized by their physical, metallurgical, mechanical and corrosion behavior; details are discussed in the following sections.
## 3.1. Evaluation of Physical and Metallurgical Properties
The density of the developed BMMCs was calculated from the mixing rule followed by Archimedes’ principle. Four readings were taken to calculate the experimental density of the BMMCs. X-ray diffractometry (Make: Malvern Panalytical, Model: Empyrean 3rd generation) was then used to estimate the phases formed in the sintered BMMCs and after corrosion testing. The used 2θ range was 20° to 90° at a scanning speed of 1°/min. The XRD results were confirmed and correlated with the microstructural results. Samples for microstructural analysis were prepared using waterproof sandpapers from 1000 to 2500 mesh size, followed by cloth polishing using colloidal silica of 0.4 µm. The samples were polished on a mechanical polishing machine (Made: Allied High Tech Products Inc.,Rancho Dominguez, CA, USA). Further, scanning electron microscopy (Make: Thermo Scientific, Model: Verios) was used for microstructural analysis at 15 kV and 500X. Additionally, elemental mapping was also extracted from the energy-dispersive X-ray spectroscopy (EDS) analysis that confirmed the presence of existing elements (Mg, Al, Zn, Ca, P, and O) as a result of various major phases such as Mg, MgO and Ca10(PO4)6(OH)2.
## 3.2. Evaluation of Mechanical Properties
The present study measured microhardness, compressive strength and Young’s modulus to assess the mechanical properties. The BMMCs samples were prepared using waterproof polishing since magnesium is highly reactive with water. Ten indentations were carried out for each composition using (ASTM standard C 1327-99) Vickers’s microhardness tester (Make: Chennai Metco Pvt. Ltd., Chennai, India). These indentations were performed radially from edge to edge. The average value was considered and reported. Further, a Universal Testing Machine (Model: Instron 5982, Make: Instron Corporation, Norwood, MA, USA) was used to measure the compressive strength and Young’s modulus of the sintered BMMCs; the associated scanning rate was 0.2 mm min−1, and ASTM E9 standard was used.
## 3.3. Evaluation of Corrosion Behavior of BMMCs
Assessment of corrosion resistance is essential for any biodegradable material as it can decide the degradation rate of the developed material in actual working conditions. Therefore, the following measurements were carried out on three samples of each composition.
## 3.3.1. Immersion Test for Measurement of Weight Loss
The immersion test on microwave-sintered BMMCs was carried out to quantify the weight loss at 37 ± 2 °C in Hanks’ Balanced Salt Solution (HBSS) as per the standard ASTM-G31-72. The composition of HBSS ions (in mmol/L) is listed in Table 3. For comparison, the composition of the blood plasma is also included in the table. The ratio of immersion solution volume to sample area used was 20 mL/cm2. A sample of each composition was immersed in separate flasks filled with HBSS at 37 ± 2 °C with 7.2 ± 0.2 pH in open-air conditions. The weight loss samples were placed on the marble balls to maximize the exposed area in the HBSS electrolyte. The exposed surface area of the cylindrical sample and relative weight loss was calculated using Equations [1] and [2], respectively. [ 1]As=2πr2+2πrh [2]W=(wb−wa)wb×100 where *As is* the exposed surface area of the cylindrical sample, r is the radius (0.65 cm) and h is the thickness (0.2 cm). The calculated exposed surface area is 3.47 cm2. W is the relative weight loss of immersed sample after 24 h, wb and wa are the weights of samples before and after the immersion test. The corrosion rate was calculated from Equation [3]. [ 3]CR=87.6×ΔWAs×ρ×t where CR is corrosion rate (mm/year), ∆w is weight loss (milligrams), *As is* the exposed surface area (cm2), ρ is the density of sample (g/cm3) and t is the exposure time (h).
## 3.3.2. Measurement of pH and Mg2+ Ion Concentration in HBSS
The pH value of HBSS bulk solution after 24 h of the immersion test was measured by a pH meter (Make: Thermo Scientific, Waltham, MA, USA), with the pH and temperature measurements ranging from 0 to 14 and 0 °C to 50 °C, respectively. Inductively coupled plasma mass spectroscopy (Make: Thermo Scientific, Model: iCAP TQe Quadrupole, USA) was used to measure Mg2+ ions concentration in an immersion solution. After 24 h, the samples of each composition were removed from the HBSS, gently rinsed with DI water, cleaned with chromium trioxide, and then dried to measure the relative weight loss of BMMCs immersed in HBSS.
## 3.3.3. Electrochemical Analysis
Electrochemical analysis of various BMMCs was carried out using a potentiostat (Made: Gamry Instruments, Philadelphia, PA, USA, Model: Reference 600). Samples of each composition were prepared by welding copper wire to the surface and mounted in epoxy. Samples were then polished using silicon carbide paper of grades 1000 to 2500, and finally polished with colloidal silica of 0.04 µm particle size. The exposed sample surface area was 1.32 cm2. The prepared samples were set as a working electrode to measure the corrosion behavior. The graphite was employed as a counter and saturated calomel electrode (SCE) as reference electrodes to complete the 3-electrode electrochemical cell setup. The cell was filled with HBSS solution and coupled with a heating system containing a heating plate, thermocouple and temperature controller that was used to maintain the solution temperature at 37 ± 2 °C. Before electrochemical measurements, the samples were immersed in the HBSS solution for 30 min for open circuit potential stabilization. Potentiodynamic polarization was carried out at a scan rate of 1 mV/s within the potential range of –1.8 V to 0 V vs. SCE. Corrosion current (Icorr) and corrosion potential (Ecorr) were calculated from the Tafel curve.
## 3.3.4. Observation of Surface Morphology
Following the corrosion test, a sample of BMMCs was kept out of the solution for phase analysis through XRD. SEM and EDS analyses were carried out to identify the surface morphology and the possible elements at the sample surface after the immersion test.
## 4. Results and Discussion
The AZ31-HA biocomposites (AZ31-0HA, AZ31-10HA, AZ31-15HA, and AZ31-20HA) were successfully developed using microwave sintering at a microwave power of 650 W in forming gas with the direct microwave heating method. Forming gas helped to reduce MgO formation by providing a reduced atmosphere. Hydrogen suppresses the formation of oxides that could form because of the presence of OH− in HA. The time-temperature curve of the AZ31-15HA BMMC sintered at 500 °C with 10 min sintering time is shown in Figure 2. AZ31-15HA composition showed the best properties among the other compositions. AZ31-15HA (Figure 2) revealed the characteristics of different heating zones. The sample temperature was recorded using an infrared pyrometer, which records temperatures above 250 °C. The slope (θ1) of the heating curve at point A in zone-I is considerably high. However, in zone II, the heating rate is very slow (θ2 << θ1 << θ3) in comparison to zones I and III. The profile thus indicates that the material increases in temperature during zone-I due to less microwave absorption by the hydroxyapatite particles and more reflection from the metal alloy particles. In zone II, however, the temperature of the material increases very slowly due to less microwave power exposure (min 330 W) and the slope of the heating curve 2.2 was calculated at zone midpoint B. While increasing microwave power slowly (min 350 W to max 650 W), the temperature increases, the sample achieves sufficient temperature for coupling with the microwaves, and rapid heating starts resulting in the observed higher rate of temperature rise (θ3 = 25.6). The sintered material gets softened during this period, followed by the onset of necking and bonding in zone IV for 10 min sintering time. In addition, Figure 3 shows the mechanism of microwave sintering and the interaction of microwave radiations and AZ31 metal alloy and HA ceramic powder particles. The HA ceramic particles absorb more microwave energy than the AZ31 alloy. This is attributed to the differences in the dielectric properties of the alloy and the ceramic. The HA has high dielectric loss because of the presence of hydroxyl ions which leads to volumetric heating; while the AZ31 is metal-based and behaves as a reflective material during microwave exposure. In case (a) in Figure 3, the material with less HA content experiences non-uniform heating. The HA particles, located fairly apart, behave like tiny localized heat sources by absorbing microwave energy. Further, these heat sources transfer heat to metal particles and help in raising the temperature of metal alloy particles primarily by a conductive mode of heat transfer. Conversely, the addition of more HA content per unit volume of the material makes the density of the localized heat sources higher resulting in a fairly uniform distribution of the sintering temperature in a relatively short time as observed during sintering and illustrated in Figure 3 (case (b). This results in more uniform necking and sintering. Excessive addition of HA, however, might result in localized hotspots or overheating in the material, causing eventual deterioration of the sintered material as evidenced and discussed in the subsequent sections. The developed BMMCs were characterized to measure physical, mechanical, and metallurgical properties and corrosion behavior.
## 4.1. Density and Metallurgical Properties
Figure 4 shows the relative density of AZ31-HA biocomposites. It is observed that the AZ31-0HA sample exhibits the highest relative density of $99.43\%$ in a sintering time of 10 min, while AZ31-20HA shows the lowest relative density. The reduction in density is due to the integration of HA particles, which hinders the densification of biocomposites [38]. HA particles are harder than AZ31 particles, which are difficult to compress during compaction. In addition, the formation of MgO after sintering may also reduce the relative density of final sintered biocomposites. The difficulty in achieving higher densification of HA at higher temperatures has been reported by other researchers who could not achieve more than $95\%$ relative density [39]. Therefore, the relative density of microwave-sintered AZ31-HA composites depends on HA content. The relative porosity percentage in BMMCs is $0.57\%$, $1.63\%$, $2.12\%$ and $5.16\%$ for AZ31-0HA, AZ31-10HA, AZ31-15HA and AZ31-20HA, respectively.
Figure 5 presents the XRD results of microwave-sintered biocomposites. Major diffraction peaks belong to Mg and hydroxyapatite; a minor phase of MgO also exists. Magnesium is highly reactive with atmospheric oxygen and needs extra care to prevent oxidation at room temperature. OH− group present in HA evaporates at around 100 °C and may react with Mg to form MgO [38,39]. Moreover, no other peaks are found that indicate the absence of a chemical reaction between AZ31 and HA at 500 °C sintering temperature. It helps to maintain the bioactivity of AZ31-HA BMMCs.
Figure 6 shows the backscattered electron microscope images of microwave-sintered BMMCs, which also exhibit the same results and correlate with the optical results. These results illustrate that the HA clusters occupied space in the matrix around the metal alloy particles and contributed to improving the properties of the BMMCs. Figure 6a AZ31-0HA sample indicates the presence of magnesium and pores at the surface. In contrast, Figure 6b–d shows SEM micrographs of AZ31-10HA, AZ31-15HA and AZ31-20HA BMMCs samples. These samples were analyzed on the backscattered detector and observed magnesium grains and the distribution of nano-HA particles at the grain boundaries of Mg grains. Black spots were observed in the microstructural images that resemble similar pores. Therefore, these black spots were analyzed at higher magnification (400 nm) and we found that they are not pores. They are agglomerated nano-HA particles that settled in between Mg grains and are distributed in the matrix. Moreover, Al and Ca particles are also observed at the sample surface, which is identified by EDX results shown in Figure 7.
Figure 7 represents the elemental mapping of microwave-sintered AZ31-HA composites with the percentage of elements present in the EDX samples. These results confirm the Mg, O, Ca, P and Al distribution. Zn is present in a very small weight percentage, less than the detectable limit in elemental mapping, while it is found in the elemental percentage data. EDX results depict Mg as green in color and uniformly distributed. It also has proper grain size. Conversely, Ca, P and O are available as HA at the grain boundaries of magnesium due to the nano size of HA particles. Al and Ca are observed as free elements.
## 4.2. Mechanical Properties
Figure 8 shows the microhardness data of AZ31-HA BMMCs with their respective optical images of indentation. The average microhardness values of 49.23 ± 1.95 HV, 57.96 ± 1.56 HV, 61.42 ± 1.65 HV and 65.86 ± 1.89 HV are obtained for AZ31-0HA, AZ31-10HA, AZ31-15HA and AZ31-20HA, respectively. The data reveal that the microhardness of BMMCs increases with increased HA content. HA is a ceramic material that imparts additional microhardness to composites [31,40]. Figure 6 also represents that HA is present at the grain boundaries. The ceramic phases of HA and MgO were observed in the XRD analysis (Figure 5), which can be the primary reason for the increase in microhardness values. Nano-HA particles are present at the grain boundary of Mg. HA percentage at the grain boundary increases with its content in the matrix, which can be an obstacle in dislocation movement during the indentation of BMMCs. Subsequently, the microhardness of the composite with the addition of HA is relatively higher than that of AZ31-0HA.
Figure 9 shows the compressive strength and Young’s modulus of the BMMCs. Noticeably, compressive strength and Young’s modulus improved in BMMCs because of the presence of the HA as reinforcement. Moreover, the highest compressive strength was observed with AZ31-15HA of 198.08 ± 10.07 MPa and Young’s modulus of 39.86 ± 2.12 GPa. The lower compressive strength and Young’s modulus of AZ31-20HA compared to AZ31-15HA are due to the HA particle agglomeration in composites resulting in reduced relative density. The agglomeration of nano-HA particles was observed in SEM micrographs at higher magnifications up to 400 nm (Figure 6). The mechanical properties of AZ31-15HA were observed in the range of the human cortical bone [41].
## 4.3. Biodegradation Behavior
The degradation behavior of developed BMMCs was measured using an immersion test in the HBSS solution at 37 ± 2 °C with 7.2 ± 0.2 pH for 24 h. The degradation of the samples occurs through electrochemical reactions resulting in Mg2+, Ca2+ and PO43− ions leaching out into the surrounding solution. The relative weight loss was measured using Equation [1] after 24, 72 and 168 h, and is shown in Figure 10a. AZ31-0HA sample shows the highest weight loss in the HBSS solution after 24, 72 and 168 h. It happened because of the leaching out of Mg2+ ions from the AZ31-0HA sample surface in the anodic oxidation region. Mg2+ ions combined with OH− (present at the cathode) and formed magnesium hydroxide (Mg(OH)2), which is called brucite [42]. Further, Mg(OH)2 reacted with Cl− ions present in HBSS and formed more soluble MgCl2 [43]. The formation of soluble MgCl2 accelerated the degradation rate and resulted in the maximum weight loss in AZ31-0HA. The chemical reactions at the anode and cathode are given by Equations [4] to [7]. [ 4]Anodic region: Mg→Mg2++2e− [5]Cathodic region: 2H2O+2e−→H2↑+2OH− [6]Mg2++2OH−→MgOH2s [7]Mg2++2Cl−→MgCl2s Conversely, relative weight loss was lower in BMMCs than in the pure AZ31-0HA sample. AZ31-15HA BMMC showed the lowest value of weight loss after 24 h. This means it has good integrity in HBSS and leaches out fewer ions from the sample surface during the immersion period. In addition, after 72 and 168 h, it shows weight gain that is also confirmed by the XRD and EDX mapping of the AZ31-15HA sample after 168 h of immersion test. The new phases of Mg(OH)2 and Ca(OH)2 were observed that protect against further corrosion and weight loss. Moreover, in BMMCs, the formation of the HA layer at the surface may be the reason for low weight loss. Ca2+ and PO43− ions released from the composite samples increased the degree of supersaturation of HBSS during the immersion test. The HA layer can easily grow by consuming Ca2+ and PO43− ions and OH− ions in the surrounding solution. This apatite layer can form in a short time and be the reason for reducing the corrosion rate and increasing the bioactivity of biocomposites [44]. AZ31-20HA showed an almost constant weight loss during 24 and 72 h, but after 168 h, it showed weight gain. Lower densification of AZ31-20HA could be the reason for such weight loss and weight gain, which facilitated the leaching out of Mg2+ ions from the sample along with Ca2+ and PO43−. However, after the formation of (Mg(OH)2) and apatite layers, the weight loss stopped and these layers could be the reason for weight gain.
Figure 10b shows the pH of HBSS after 24, 72 and 168 h of immersion test. The bulk solution of the HBSS was used for measuring pH value. When it was compared to other BMMCs, in AZ31-15HA BMMC, a lower pH value was recorded. It could be because there was a sufficient amount of HA content to retard the release of Mg2+ ions compared to AZ31-0HA, along with the formation of an apatite layer at the sample surface. AZ31-10HA exhibited the maximum pH value, i.e., due to diffusion of Mg2+, Ca2+ and PO43− ions from the sample. The formation of MgCl2 in the AZ31-0HA can increase the pH value after the immersion test. In the case of AZ31-20HA, the pH value could be increased due to the release of magnesium, calcium, or phosphate ions since the presence of Ca2+ and HPO42− ions also enhanced the solution’s pH value due to the addition of more bases [44].
ICPMS was used to determine how many Mg2+ ions were released during immersion in HBSS for 24 h. Figure 10c depicts the concentration of the released Mg2+ ions present in HBSS after 24, 72 and 168 h of immersion test. This test is intended to mimic their performance in an actual working environment. Results from the immersion test indicate that AZ31-15HA performs better in the in vitro test than other compositions.
It could be realized due to the formation of Mg(OH)2 and hydroxyapatite corrosion product layers at the sample surface. These corrosion products are deposited over the sample and behave like protective layers against further corrosion [45,46,47]. These protective layers make the degradation rate slow. Consequently, it is summarized that AZ31-15HA could be the most suitable composition of developed BMMCs that may facilitate the healing process during implantation in the human body with a growing apatite layer that helps in integration with the human bone.
Figure 11 illustrates the XRD spectra of AZ31-15HA BMMC after 168 h of immersion test in HBSS. The XRD results show the presence of new phases, such as Mg(OH)2 and Ca(OH)2, after the immersion test. These results also confirm the formation of brucite and calcium hydroxide layers at the surface, preventing the sample from further corrosion. Both layers are insoluble and act like corrosion-protective layers. These layers can also be observed from the EDX elemental mapping, as shown in Figure 12. It is clearly seen from EDX elemental mapping that after 168 h, Mg, O, P, Ca and Zn are detected, along with Na, Cl and K. All these elements were deposited on the sample surface due to the surrounding solution that contained these elements. These results also confirm the presence of protective layers at the surface that controlled the degradation rate of AZ31-15HA and fewer Mg2+ ions in the bulk HBSS solution after 168 h.
Electrochemical corrosion tests were also carried out to measure the polarization resistance and electrochemical behavior of developed BMMCs. Figure 13 shows the potentiodynamic polarization curves of BMMCs recorded in HBSS at 37 ± 2 °C and 7.2 ± 0.2 pH in open-to-air conditions. These plots display two different portions: anodic polarization and cathodic polarization. The anodic polarization curve represents the dissolution of Mg2+ ions from the sample and the formation of the protective layers. In contrast, the cathodic polarization curve represents the rate of cathodic reaction (in this case, oxygen reduction reaction) [46]. The intersection point of anodic and cathodic Tafel lines in the potentiodynamic polarization curve is the primary point for the determination of corrosion current (Icorr) and corrosion potential (Ecorr). Further, the corrosion rate concerning weight loss/gain analysis was calculated from Equation [3], which depicts a high corrosion rate (representing low corrosion resistance), as shown in Table 4. It was observed that the BMMCs exhibit a negative corrosion rate. The XRD, SEM, and EDS analyses revealed that the formation of passive layers of corrosion products caused a net weight gain in all BMMCs except AZ31-0HA. The potentiodynamic curve of AZ31-0HA shows the breakdown potential, which indicates the existence of localized corrosion and the curve shifted in the anodic direction, which means more Mg2+ ions were released from the sample. The electrochemical corrosion test confirms that the pure AZ31-0HA sample exhibits pitting corrosion at the exposed surface.
Similarly, AZ31-10HA BMMC also shows a more anodic region in the potentiodynamic curve that confirms the high Mg2+ ions concentration in immersing solution after 24, 72, and 168 h. In contrast, AZ31-15HA and AZ31-20HA BMMCs have more cathodic region than anodic region, indicating more cathodic reactions and hydrogen generation. These results show a good correlation with immersion test results. It was seen that these samples (AZ31-15HA and AZ31-20HA) discharged Mg2+ ions as well as Ca2+, which formed layers of Mg(OH)2 and Ca(OH)2 as corrosion products [46]. This is due to changes in the value of open circuit potential (OCP); since the test parameters were set with the position of the OCP, it consequently allowed for more dissolution of the material. Hence the samples with the more anodic OCP were not subjected to prolonged anodic polarization. The AZ31-15HA has the most noble corrosion potential value (Ecorr) amongst the samples tested, while the AZ31-15HA and AZ31-20HA samples show high corrosion current densities. The reason for showing high corrosion current density could be the higher percentage of the HA on the surface available for corrosion. Figure 6 shows the distribution of HA nanopowder particles surrounding the Mg grains at the surface. Table 4 represents the Ecorr, Icorr and corrosion rate of BMMCs (from weight loss/gain analysis), which indicate that AZ31-15HA is the optimum composition among the compositions considered.
## 5. Conclusions
In this study, AZ31-HA BMMCs were fabricated through microwave sintering using a multimode microwave applicator at 2.45 GHz and 650 W maximum power. The mechanism of microwave sintering and time-temperature profile with targeted materials has shown that microwave material processing is a material-dependent technique and interacts differently with different materials. Moreover, the sintered BMMCs were characterized for their phase composition, sintered density, microhardness, compressive strength, Young’s modulus, and corrosion behavior in an in vitro environment. The major conclusions are:AZ31-0HA showed the highest relative density. It decreased with increased HA content. The addition of hard HA particles hindered the densification of the BMMCs as it was difficult to press during compaction and with a large difference in the sintering temperature of AZ31 metal alloy and HA.The microhardness of the BMMCs increased with increasing HA content owing to the presence of HA hard phase in the composite. It was uniformly distributed in the composite volume that was observed in microstructures. The highest compressive strength and Young’s modulus were recorded with the AZ31-15HA. Further HA addition decreased the compressive strength and Young’s modulus. It is attributed to a decrease in the density and agglomeration of HA particles. The AZ31-15HA exhibited the least weight loss for 24, 72 and 168 h immersion tests, and lower pH change of HBSS and discharge of Mg2+ ions after immersion tests in HBSS.The corrosion resistance of AZ31-15HA is the best among the four compositions. It exhibited weight gain after 72 h, resulting in a negative (−3.25 mm/year) corrosion rate. A negative corrosion rate indicates passive corrosion because of the formation of protective layers of Mg(OH)2 and Ca(OH)2 layers at the surface. It was also observed in electrochemical impedance spectroscopy (EIS) results and these results will be reported in a future study. The developed biodegradable composites in this study can be considered for human body fixation aids as they show favorable properties for orthopedic application. The BMMCs in this study did not show any toxicity during the in vitro cytotoxicity test.
## References
1. Agarwal S., Curtin J., Duffy B., Jaiswal S.. **Biodegradable magnesium alloys for orthopaedic applications: A review on corrosion, biocompatibility and surface modifications**. *Mater. Sci. Eng. C* (2016) **68** 948-963. DOI: 10.1016/j.msec.2016.06.020
2. Mammoli F., Castiglioni S., Parenti S., Cappadone C., Farruggia F., Iotti S., Frassineti C.. **Magnesium is a key regulator of the balance between osteoclast and osteoblast differentiation in the presence of vitamin D3**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20020385
3. Chan C.Y., Subramaniam S., Mohamed N., Ima-Nirwana S., Muhammad N., Fairus A., Ng P.Y., Jamil N.A., Aziz N.A., Chin K.Y.. **Determinants of bone health status in a multi-ethnic population in Klang Valley, Malaysia**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17020384
4. Mendia S., Luis E., Sahebkar A., Moran M.R., Romero F.G.. **A systematic review and meta-analysis of randomized controlled trials on the effects of magnesium supplementation on insulin sensitivity and glucose control**. *Pharm. Res.* (2016) **111** 272-282. DOI: 10.1016/j.phrs.2016.06.019
5. Christian D., Cardot J.M., Joanny F., Trzeciakiewicz A., Martin E., Pickering G., Dubray C.. **An advanced formulation of a magnesium dietary supplement adapted for a long-term use supplementation improves magnesium bioavailability: In vitro and clinical comparative studies**. *Bio. Trace. Elem. Res.* (2018) **186** 1-8. PMID: 29524192
6. Rachel N., Howard J.M., Henein M.Y.. **Cardiovascular calcification and bone: A comparison of the effects of dietary and serum calcium, phosphorous, magnesium and vitamin D**. *Int. Cardio. Forum J.* (2014) **1** 209-218
7. Senthilkumar A., Gupta M.. **Current and Emerging Bioresorbable Metallic Scaffolds: An Insight into Their Development, Processing and Characterisation**. *J. Indian Inst. Sci.* (2022) **102** 585-598. DOI: 10.1007/s41745-021-00276-8
8. Singh S., Gupta D., Jain V., Sharma A.K.. **Microwave processing of materials and applications in manufacturing industries: A review**. *Mater. Manufac. Process.* (2015) **30** 1-29. DOI: 10.1080/10426914.2014.952028
9. Fang Y., Agrawal D.K., Roy D.M., Roy R.. **Fabrication of porous hydroxyapatite ceramics by microwave processing**. *J. Mater. Res.* (1992) **7** 490-494. DOI: 10.1557/JMR.1992.0490
10. Asl S.K.F., Nemeth S., Tan M.J.. **Improved corrosion protection of Magnesium by hydrothermally deposited biodegradable calcium phosphate coating**. *Mater. Chem. Phys.* (2015) **161** 185-193
11. Zhang M., Cai S., Shen S., Xu G., Li Y., Ling R., Wu X.. **In-situ defect repairing in hydroxyapatite/phytic acid hybrid coatings on AZ31 magnesium alloy by hydrothermal treatment**. *J. Alloy. Comp.* (2016) **658** 649-656. DOI: 10.1016/j.jallcom.2015.10.282
12. Li L., Zhang M., Li Y., Zhao J., Qin L., Lai Y.. **Corrosion and biocompatibility improvement of magnesium-based alloys as bone implant materials: A review**. *Reg. Biomater.* (2017) **4** 129-137. DOI: 10.1093/rb/rbx004
13. Khalajabada S.Z., Ahmad N., Izman S., Abu A.B.H., Haider W., Kadir M.R.A.. **In vitro biodegradation, electrochemical corrosion evaluations and mechanical properties of an Mg/HA/TiO**. *J. Alloy. Compd.* (2017) **696** 768-781. DOI: 10.1016/j.jallcom.2016.11.106
14. Khalajabada S.Z., Kadir M.R.A., Izman S., Bakhsheshi H.R., Farahany S.. **Effect of mechanical alloying on the phase evolution, microstructure and bio-corrosion properties of a Mg/HA/TiO**. *Ceram. Int.* (2014) **40** 16743-16759. DOI: 10.1016/j.ceramint.2014.08.039
15. Khalajabadi S.Z., Kadir M.R.A., Izman S., Marvibaigi M.. **The effect of MgO on the biodegradation, physical properties and biocompatibility of a Mg/HA/MgO nanocomposite manufactured by powder metallurgy method**. *J. Alloy. Comp.* (2016) **655** 266-280. DOI: 10.1016/j.jallcom.2015.09.107
16. Onoki T., Yamamoto S., Onodera H., Nakahira A.. **New technique for bonding hydroxyapatite ceramics and magnesium alloy by hydrothermal hot-pressing method**. *Mater. Sci. Eng. C* (2011) **31** 499-502. DOI: 10.1016/j.msec.2010.09.002
17. Nazirah R., Zuhailawati H., Hazwani M.R.S.N., Abdullah T., Azzura I., Dhindaw B.. **The Influence of Hydroxyapatite and Alumina Particles on the Mechanical Properties and Corrosion Behavior of Mg-Zn Hybrid Composites for Implants**. *Materials* (2021) **14**. DOI: 10.3390/ma14216246
18. Liu Y., Liu D., Liu L.. **Corrosion properties of com- posite materials HA (beta-TCP)/Mg-Zn-Ca**. *Rare Metal Mater. Eng.* (2014) **43** 205-209
19. Liu D., Zuo Y., Meng W., Chen M., Fan Z.. **Fabrication of biodegradable nano-sized b-TCP/Mg composite by a novel melt shearing technology**. *Mater. Sci. Eng. C* (2012) **32** 1253-1258. DOI: 10.1016/j.msec.2012.03.017
20. Witte F., Feyerabend F., Maier P., Fischer J., Stormer M., Blawert C., Hort N.. **Biodegradable magnesium-hydroxyapatite metal matrix composites**. *Biomaterials* (2007) **28** 2163-2174. DOI: 10.1016/j.biomaterials.2006.12.027
21. Khanra A.K., Jung H.C., Yu S.H., Hong K.S., Shin K.S.. **Microstructure and mechanical properties of Mg-HAP composites**. *Bull. Mater. Sci.* (2010) **33** 43-47. DOI: 10.1007/s12034-010-0006-z
22. Ye X., Chen M., Yang M., Wei J., Liu D.. **In vitro corrosion resistance and cytocompatibility of nano-hydroxyapatite reinforced Mg-Zn-Zr composites**. *J. Mater. Sci. Mater. Med.* (2010) **21** 1321-1328. DOI: 10.1007/s10856-009-3954-3
23. Feng A., Han Y.. **The microstructure, mechanical and corrosion properties of calcium polyphosphate reinforced ZK60A magnesium alloy composites**. *J. Alloy. Compd.* (2010) **504** 585-593. DOI: 10.1016/j.jallcom.2010.06.013
24. Del Campo R., Savoini B., Munoz A., Monge M.A., Pareja R.. **Processing and mechanical characteristics of magnesium-hydroxyapatite metal matrix biocomposites**. *J. Mech. Behav. Biomed. Mater.* (2017) **69** 135-143. DOI: 10.1016/j.jmbbm.2016.12.023
25. Sunil B.R., Kumar T.S.S., Chakkingal U., Nandakumar V., Doble M., Prasad V.D., Raghunath M.. **In vitro and in vivo studies of biodegradable fine grained AZ31 magnesium alloy produced by equal channel angular pressing**. *Mater. Sci. Eng. C* (2016) **59** 356-367. DOI: 10.1016/j.msec.2015.10.028
26. Gupta S., Sharma A.K.. **Sintering of biomaterials for arthroplasty: A comparative study of microwave and conventional sintering techniques**. *Appl. Mech. Mater.-Tran. Technol. Pub.* (2019) **895** 83-89. DOI: 10.4028/www.scientific.net/AMM.895.83
27. Jaiswal S., Kumar R.M., Gupta P., Kumaraswamy M., Roy P., Lahiri D.. **Mechanical, corrosion and biocompatibility behaviour of Mg-3Zn-HA biodegradable composites for orthopaedic fixture accessories**. *J. Mech. Behav. Biomed. Mater.* (2018) **78** 442-454. DOI: 10.1016/j.jmbbm.2017.11.030
28. Khalil K.A., Almajid A.A.. **Effect of high-frequency induction heat sintering conditions on the microstructure and mechanical properties of nanostructured magnesium/hydroxyapatite nanocomposites**. *Mater. Des.* (2012) **36** 58-68. DOI: 10.1016/j.matdes.2011.11.008
29. Gu X., Zhou W., Zheng Y., Dong L., Xi Y., Chai D.. **Microstructure, mechanical property, bio-corrosion and cytotoxicity evaluations of Mg/HA composites**. *Mater. Sci. Eng. C* (2010) **30** 827-832. DOI: 10.1016/j.msec.2010.03.016
30. Razavi M., Fathi M.H., Meratian M.. **Microstructure, mechanical properties and bio-corrosion evaluation of biodegradable AZ91-FA nanocomposites for biomedical applications**. *Mat. Sci. Eng.* (2010) **A527** 6938-6944. DOI: 10.1016/j.msea.2010.07.063
31. Sunil B.R., Kumar T.S.S., Chakkingal U., Nandakumar V., Doble M.. **Nano-hydroxyapatite reinforced AZ31 magnesium alloy by friction stir processing: A solid state processing for biodegradable metal matrix composites**. *J. Mat. Sci Mat. Med.* (2014) **25** 975-988. DOI: 10.1007/s10856-013-5127-7
32. Ho Y.H., Man K., Joshi S.S., Pantawane M.V., Wu T.C., Yang Y., Dahotre N.B.. **In-vitro biomineralization and biocompatibility of friction stir additively manufactured AZ31B magnesium alloy-hydroxyapatite composites**. *Bio. Mater.* (2020) **5** 891-901. DOI: 10.1016/j.bioactmat.2020.06.009
33. Shamami D.Z., Rabiee S.M., Shakeri M.. **Characterization of magnesium-hydroxyapatite functionally graded composites prepared by rapid microwave sintering technique**. *Cer. Int.* (2022) **48** 12641-12653. DOI: 10.1016/j.ceramint.2022.01.133
34. Parkhomei O.R., Pinchuk N.D., Sych O.E., Tomila T.V., Tovstonog G.B., Gorban V.F., Yevych Y.I., Kuda O.A.. **Structural and mechanical properties of bioactive glass–ceramic composites**. *Powder Metall. Met. Cer.* (2016) **55** 172-184. DOI: 10.1007/s11106-016-9792-1
35. Xiong G., Nie Y., Ji D., Li J., Li C., Li W., Zhu Y., Luo H., Wan Y.. **Characterization of biomedical hydroxyapatite/magnesium composites prepared by powder metallurgy assisted with microwave sintering**. *Curr. Appl. Phy.* (2016) **16** 830-836. DOI: 10.1016/j.cap.2016.05.004
36. Acheson J.G., McKillop S., Lemoine P., Boyd A.R., Meenan B.J.. **Control of magnesium alloy corrosion by bioactive calcium phosphate coating: Implications for resorbable orthopaedic implants**. *Materialia* (2019) **6** 100291. DOI: 10.1016/j.mtla.2019.100291
37. Hing K.A.. **Bone repair in the twenty–first century: Biology, chemistry or engineering?**. *Phys. Eng. Sci.* (2004) **362** 2821-2850. DOI: 10.1098/rsta.2004.1466
38. Kokubo T., Kushitani H., Sakka S., Kitsugi T., Yamamuro T.. **Solutions able to reproduce in vivo surface-structure changes in bioactive glass-ceramic A-W3**. *J. Biomed. Mater. Res.* (1990) **24** 721-734. DOI: 10.1002/jbm.820240607
39. Shamami D.Z., Rabiee S.M., Shakeri M.. **Use of rapid microwave sintering technique for the processing of magnesium-hydroxyapatite composites**. *Ceram. Int.* (2021) **47** 13023-13034. DOI: 10.1016/j.ceramint.2021.01.166
40. Capanema N.S., Mansur A.A., Carvalho S.M., Silva A.R., Ciminelli V.S., Mansur H.S.. **Niobium-Doped Hydroxyapatite Bioceramics: Synthesis, Characterization and In Vitro Cytocompatibility**. *Materials* (2015) **8** 4191-4209. DOI: 10.3390/ma8074191
41. Sharma A.K., Gupta S.. **Microwave processing of biomaterials for orthopedic implants: Challenges and possibilities**. *JOM* (2020) **72** 1211-1228. DOI: 10.1007/s11837-020-04003-z
42. Hansen A.W., Fuhr L.T., Antonini L.M., Villarinho D.J., Marino C.E.B., Malfatti C.D.F.. **The electrochemical behavior of the NiTi alloy in different simulated body fluids**. *Mater. Res.* (2015) **18** 184-190. DOI: 10.1590/1516-1439.305614
43. Del Campo R., Savoini B., Munoz A., Monge M.A., Garcés G.. **Mechanical properties and corrosion behavior of Mg–HAP composites**. *J. Mech. Behav. Biomed. Mater.* (2014) **39** 238-246. DOI: 10.1016/j.jmbbm.2014.07.014
44. Rodzi S.N.H.M., Zuhailawati H., Dhindaw B.K.. **Mechanical and degradation behaviour of biodegradable magnesium-zinc/hydroxyapatite composite with different powder mixing techniques**. *J. Magnes. Alloy.* (2019) **7** 566-576. DOI: 10.1016/j.jma.2019.11.003
45. Tang H., Han Y., Wu T., Tao W., Jian X., Wu Y., Xu F.. **Synthesis and properties of hydroxyapatite-containing coating on AZ31 magnesium alloy by micro-arc oxidation**. *Appl. Surf. Sci.* (2017) **400** 391-404. DOI: 10.1016/j.apsusc.2016.12.216
46. Morcillo M., Veleva L.. **Degradation of AZ31 and AZ91 magnesium alloys in different physiological media: Effect of surface layer stability on electrochemical behaviour**. *J. Magnes. Alloy.* (2020) **8** 667-675. DOI: 10.1016/j.jma.2020.02.014
47. Hing K.A.. **Bioceramic bone graft substitutes: Influence of porosity and chemistry**. *Int. J. Appl. Ceram. Technol.* (2005) **2** 184-199. DOI: 10.1111/j.1744-7402.2005.02020.x
|
---
title: New Type of Tannins Identified from the Seeds of Cornus officinalis Sieb. et
Zucc. by HPLC-ESI-MS/MS
authors:
- Jun Li
- Lin Chen
- Hua Jiang
- Min Li
- Lu Wang
- Jia-Xing Li
- Yue-Yue Wang
- Qing-Xia Guo
journal: Molecules
year: 2023
pmcid: PMC10004147
doi: 10.3390/molecules28052027
license: CC BY 4.0
---
# New Type of Tannins Identified from the Seeds of Cornus officinalis Sieb. et Zucc. by HPLC-ESI-MS/MS
## Abstract
There is a lack of information on the compound profile of *Cornus officinalis* Sieb. et Zucc. seeds. This greatly affects their optimal utilization. In our preliminary study, we found that the extract of the seeds displayed a strong positive reaction to the FeCl3 solution, indicating the presence of polyphenols. However, to date, only nine polyphenols have been isolated. In this study, HPLC-ESI-MS/MS was employed to fully reveal the polyphenol profile of the seed extracts. A total of 90 polyphenols were identified. They were classified into nine brevifolincarboxyl tannins and their derivatives, 34 ellagitannins, 21 gallotannins, and 26 phenolic acids and their derivatives. Most of these were first identified from the seeds of C. officinalis. More importantly, five new types of tannins were reported for the first time: brevifolincarboxyl-trigalloyl-hexoside, digalloyl-dehydrohexahydroxydiphenoyl (DHHDP)-hexdside, galloyl-DHHDP-hexoside, DHHDP-hexahydroxydiphenoyl(HHDP)-galloyl-gluconic acid, and peroxide product of DHHDP-trigalloylhexoside. Moreover, the total phenolic content was as high as 79,157 ± 563 mg gallic acid equivalent per 100 g in the seeds extract. The results of this study not only enrich the structure database of tannins, but also provide invaluable aid to its further utilization in industries.
## 1. Introduction
Studies have shown that plant-based diets rich in polyphenols can exert health-promoting effects by reducing the risk of many diseases, such as cancer and neurodegenerative, cardiovascular, and inflammatory diseases. Therefore, it is vital to explore new sources of bioactive plant polyphenols and carry out their characterization for promoting human health [1,2,3].
Cornus officinalis, also known as Asiatic dogwood, is a deciduous shrub in the genus Cornus of the family Cornaceae that is mainly distributed in China, Korea, and Japan [4]. The pericarp of its fruit is used as a traditional Chinese herbal medicine that is widely used clinically along with other herbal medicine to treat different symptoms. For example, it has been used in combination with Mantidis Oötheca, Rubi Fructus, and *Rosae laevigatae* Fructus to clinically treat the urinary bladder dysfunction. It is prescribed together with Radix Rehmanniae Praeparata, Dioscoreae Rhizoma, Alismatis rhizoma, Moutan Cortex, and Poria to treat patients with vertigo, tinnitus, and waist and knees weakness [5]. Because of its wide traditional clinical use, many phytochemical and pharmacological studies have been conducted on the fruit pericarp. To date, about 90 compounds have been isolated and identified and are classified as terpenoids, flavonoids, tannins, polysaccharides, phenylpropanoids, sterols, and carboxylic acids, with iridoids, tannins, and flavonoids being the major compounds [6]. They display a wide range of pharmacological activities, such as hypoglycemic [7], antibacterial [8], hypolipidemic [9], antioxidant [10], anticancer [11], neuroprotective [12], and hepatoprotective activities [13].
In contrast, few reports have been published on the seeds because of their minor applications. The seeds account for approximately $50\%$ of the fresh fruit’s weight. It is estimated that approximately 6000 tons of seeds are generated annually, owning to the huge pericarp consumption in the Chinese medicine industry [14]. Moreover, seed use meets the 12th sustainable development goal (SDG 12), sustainable consumption and production of the United Nations (UN) 2030 Agenda for Sustainable Development 2015 [15]. Therefore, there is an urgent need to develop optimal processing methods for the valorization of seeds.
Notably, fruit seeds can be converted into biofuels. Production of bio-oil from the seeds of cherry plum and peach has been reported [16,17,18,19]. However, based on our observations of the seeds of C. officinalis, we found that each seed has a tiny kernel surrounded by a thick wall of the lignified endocarp. Biofuel conversion of the seeds is not feasible, owing to the tiny kernel. The kernel is the main source for biofuel conversion, as it comprises abundant fatty acids; however, it accounts for less than $5\%$ of the seed weight. Nevertheless, we found a water-soluble yellow powder substance in the cavities of the thick endocarp, which accounts for $40\%$ of the endocarp weight. Moreover, it gives a strong positive reaction to FeCl3 solution (6C6H6OH + FeCl3 → H3[Fe(C6H6O)6] (purple color) + 3HCl), indicating the presence of polyphenol.
However, only nine polyphenols have been reported from the seeds of C. officinalis: 1,2,3-tri-O-galloyl-β-D-glucose, 1,2,6-tri-O-galloyl-β-D-glucose, 1,2,3,6-tetra-O-galloyl-β-D-glucose, 1,2,4,6-tetra-O-galloyl-β-D-glucose, 1,2,3,4,6-penta-O-galloyl-β-D-glucose, tellimagrandin II, gallic acid 4-O-β-D-glucoside, and gallic acid 4-O-β-D-(6′-O-galloyl)-glucoside [20]. Moreover, ellagic acid can be detected abundantly in its acid-hydrolyzed sample, which indicates that it is rich in ellagitannins [21].
In our pre-experimental study, we found that the number of reported polyphenols was far less than the number of polyphenols detected by HPLC in the pre-experiment. Therefore, the main objectives of the present study were to characterize and identify polyphenols in the seeds of C. officinalis and to provide valuable information for its use on an industrial scale, such as antioxidant additives in food or drugs.
The HPLC-ESI-MS/MS is a powerful tool used for the separation and identification of polyphenols in plant extracts and can provide an invaluable contribution to polyphenol analysis. It was employed as the main investigation tool to achieve the study objectives [22].
## 2.1. General
In this study, a total of 90 phenolic components were identified using coupled chromatographic and mass spectrometric analysis of the water-soluble extract obtained from the seeds of C. officinalis. They were classified into nine brevifolincarboxyl tannins and their derivatives, as well as 34 ellagitannins, 21 gallotannins, and 26 phenolic acids and their derivatives. Among them, we reported five new types of tannin for the first time: brevifolincarboxyl-trigalloyl-hexoside, digalloyl-dehydrohexahydroxydiphenoyl (DHHDP)-hexdside, galloyl-DHHDP-hexoside, DHHDP-hexahydroxydiphenoyl(HHDP)-galloyl-gluconic acid, and the peroxide product of DHHDP-trigalloylhexoside.
The polyphenols were identified based on their chromatographic profiles, their MS data of [M-H]−, and their MS/MS fragmentation profiles by comparing with published data. Notably, the obtained deprotonated polyphenol molecules and their typical cleavage of precursor ions accelerated their identification. The MS spectrum of the brevifolincarboxyl moiety was first revealed by the specific fragment ions at m/z 247, 273, and 291. The DHHDP moiety in the tannin structure was indicated by the fragment ions of brevifolincarboxyl moiety, together with a fragment ion, indicating a 44-Da mass loss from the [M-H]− resulting from rearrangement and decarboxylation, and this greatly helped in the identification of the DHHDP moiety. The fragment ions at m/z 249.03, 275.02, and 300.99 are typical of the HHDP moiety. The galloyl moiety was revealed by the fragment ions at m/z 169.01 and 125.02. Furthermore, the number of galloyl structures in tannin can be determined by a group fragment ion representing a 152-Da mass difference, indicating that consecutive galloyl moieties are lost. The 44-Da mass loss from the pseudo-molecular ion is characteristic of phenolic acid.
The total ion chromatogram of the seed water extract of C. officinalis in the negative ESI model is illustrated in Figure 1. Compound identification within each class is detailed below and summarized in Table 1.
## 2.2. Brevifolincarboxylic Tannins and Their Derivatives
An [M-H]− ion at m/z 909.1014 with a retention time of 19.62 min was observed for A1-1, producing fragment ions at m/z 757.0958, 604.7536 and 453.0302, indicating the consecutive loss of three galloyl moieties (152 Da) from the [M-H]− ion [3]. Additionally, typical fragment ions for brevifolincarboxyl moiety (274 Da) at m/z 247.0257, 273.0030, and 291.0139 were exhibited (see D8) [23]. Additionally, a hexose core (180 Da) can be determined based on the mass difference between the molecular weight (910 Da) and the total weight of the determined moieties (730 Da). Therefore, A1-1 was putatively assigned as a brevifolincarboxyl-trigalloyl-hexoside. Moreover, we found a fragment ion at m/z 435.0560 that resulted from the loss of H2O from the fragment ion at m/z 453.0302. This shows the presence of brevifolincarboxyl-hexoside moiety, hence supporting the proposed structure. Furthermore, two other compounds A1-2 and A1-3, with retention times of 21.28 min and 23.80 min, respectively, displayed the same pseudomolecular fragment ion and fragment patterns, indicating the occurrence of two brevifolincarboxyl-trigalloyl-hexoside isomers. Regarding the structures of the three isomers, the differences were based on the position of the linkages of the three gallic and brevifolincarboxyl moiety to the hexose core.
To the best of our knowledge, brevifolincarboxyl-trigalloyl-hexoside-type tannins have not been reported. The only two analog compounds reported are decarboxylated geraniin, a product of geraniin treated with sodium benzenesulfinate [24], and repandusinin from the genus Mallotus [25].
Hydrolysable tannins include gallotannin (GT) and ellagitannin (ET). They are the polyol esters, usually of glucose or quinic acid [26], with the moieties of HHDP and gallic acid [22]. A1 features a tannin with a brevifolincarboxyl moiety linked to hexose, which has not been widely described. The MS/MS data of A1-1 revealed that the fragment ions at m/z 247, 273, and 291 could be used as typical indicator ions for identifying a brevifolincarboxyl moiety in a tannin structure [3,27]. The A1-1 MS fragment pattern is shown in Figure 2.
A2 exhibited an [M-H]− ion at m/z 605.0013, with a retention time of 18.36 min, that released a fragment ion at m/z 331.0200, corresponding to a monogalloyl-hexoside moiety (C1-1). This resulted from the loss of a brevifolincarboxyl moiety (274 Da) from the [M-H]− ion. Monogalloyl-brevifolincarboxyl-hexoside was tentatively assigned to A2. Moreover, a fragment ion at m/z 453.0401 attributed to the brevifolincarboxyl-hexoside moiety, resulting from the loss of a galloyl unit from its pseudo-molecular ion. This supported the identification of A2. Typical fragment ions for galloyl moiety were found at m/z 169.0128 and 125.0230. Those for the brevifolincarboxyl moiety were observed at m/z 247.0255, 273.0026, and 291.0116 and supported the identification of A2. The fragment ions at m/z 587.0662 and 435.0555 were obtained from the consecutive loss of H2O (18 Da) and galloyl (152 Da) moieties, respectively, from the pseudomolecular ion. Monogalloyl-brevifolincarboxyl-hexoside, a tannin type with the structure of 1-O-galloyl-4-O-brevifolincarboxyl-β-D-glucoside, has previously been isolated and characterized in the leaves of *Marcaranga tanarious* (L.) MUELL et ARG. [ 28]. However, to the best of our knowledge, this type of tannin has not been reported in the seeds of *Cornus officinalis* Sieb. et Zucc.
An [M-H]− ion at m/z 801.0795 with a retention time of 17.01 min was observed with A3. The fragment ions produced at m/z 247.0251, 273.0023, and 291.0128 indicated the presence of a brevifolincarboxyl moiety, similar to A1-1 and A2. At the fragment ion 435.0555, dehydrated brevifolincarboxyl-hexoside was revealed, as mentioned in A2. This was speculated to be formed by the loss of two consecutive galloyl moieties and H2O from the fragment ion at 757.0929. However, the mass difference of 44 Da indicated that the fragment ion at 757.0929 was formed by the loss of a carboxylic moiety from the pseudo-molecular ion at m/z 801.0795. Thus, it can be inferred that the brevifolincarboxyl group did not constitute the final structure of A3. Based on the reaction of geraniin with sodium benzenesulfinate [24], we suggested that a brevifolincarboxyl group was formed from DHHDP by rearrangement and decarboxylation (Figure 3). Therefore, A3 was tentatively identified as digalloyl-DHHDP-hexoside.
Based on the above, a simple and reliable method to identify a DHHDP moiety in tannin is characterized by typical fragment ions at m/z 247, 273, and 291 for the brevifolincarboxyl moiety and a 44-Da mass difference between the [M-H]− ion and a decarboxylated fragment ion based on A3 identification. A3 with a tannin-type digalloyl-DHHDP-hexoside has not been previously reported, to the best of our knowledge.
A4 displayed a pseudo-molecular ion at m/z 953.0915, with a retention time of 19.62 min, that produced the typical fragment ions for brevifolincarboxyl moiety at m/z 247.0257, 273.0031, and 291.0140. Moreover, a fragment ion at m/z 909.0996, 44-Da mass lower than its [M-H]−, was observed, indicating the presence of a DHHDP moiety. Fragment ions at m/z 757.0971, 605.0547, and 435.0559 resulted from the sequential loss of three galloyl moieties from the fragment ion at 909.0996, corresponding to the decarboxylated [M-H]− ion. Moreover, a fragment ion at 435.0559 was also observed, which is typical of the dehydrated brevifolincarboxyl-hexoside observed in A1-1, A1-2, A1-3, A2, and A3. Therefore, A4 was tentatively identified as a DHHDP-trigalloylhexoside. This study is the first to report this finding from the seeds of C. officinalis. The only type of tannin in DHHDP-trigalloylhexoside is isoterchebin with a structure of 1,2,3-O-galloyl-4,6-O-DHHDP-β-D-glucose, which has been reported in the fruit of *Cornus officinalis* Sieb. et Zucc. by Okuda, 1981 [29].
The chromatogram and MS/MS profile of A5 showed an [M-H]− ion at m/z 649.1073, with a retention time of 18.36 min. It was tentatively identified as a monogalloyl-DHHDP-hexoside based on the fragment ions at m/z 497.0930, corresponding to DHHDP-hexoside. This fragment resulted from the loss of a galloyl moiety from its pseudo-molecular ion. The fragment ions at m/z 169.0128 and 125.0230 were typical of galloyl moiety. To our knowledge, the tannin-type monogalloyl-DHHDP-hexoside has not yet been reported.
A6 exhibited an [M-H]− ion at m/z 967.1068 with a retention time of 22.55 min. The fragment ions at m/z 249.0377, 275.0203, and 300.9964 resulted from the ellagic acid moiety, indicating the occurrence of the HHDP moiety in A6. The mass differences of 152 and 326 Da (=318 Da + 18 Da) between two pairs of fragment ions at m/z 765.0391, 917.0799 581.0547, and 917.0799, respectively, indicating the loss of a galloyl moiety and a DHHDP moiety. Based on these results, the mass difference of 196 Da between its pseudo-molecular weight (967 Da) and the total weight (771 Da) of the identified moieties of HHDP, galloyl, and DHHDP indicated the presence of a gluconic acid moiety [30]. Therefore, A6 was tentatively assigned as a DHHDP-HHDP-galloyl-gluconic acid. The group fragment ions at m/z 247.0253, 273.0068, and 291.0096, corresponding to the brevifolincarboxyl moiety, confirmed the occurrence of the DHHDP moiety.
Tannin-type DHHDP-HHDP-galloyl-gluconic acid of A6 has not been reported to date. A typical mass loss of 318 Da was observed with the DHHDP moiety [3]. This tannin type is characterized by a gluconic acid as the polyol core, that is rarely reported in the tannin structure. Lagerstannin C (galloyl-HHDP-gluconic acid) from *Lagerstroemia speciosa* L. pers, punigluconin (digalloyl-HHDP-gluconic acid) from pomegranate (*Punica granatum* L.) peel, and 12 mixed HHDP-galloylgluconic acids from the jabuticaba species are examples of gluconic acid as the core of tannin [31].
As indicated by the MS/MS spectrum of A7, it had a retention time of 23.39 min and comprised almost all the fragment ions that were liberated by A1, such as ions at m/z 909.1002, 757.0970, 605.0627, 435.0560, 291.0138, and 247.0257. Therefore, it can be inferred that A1 and A7 were structurally similar. The only difference between A1 and A7 is the [M-H]− ion. A7 exhibited an [M-H]− ion at m/z 941.1275, while that of A4 was 12 Da lower. Based on the fragmentation pattern of A4, it was assumed that A7 was a peroxide product of DHHDP-trigalloylhexoside (Figure 4), which should be regarded as an intermediate product in the decarboxylation process from A4 to A1.
Possible structures of the tannin-type A1–A7 are illustrated in Figure 5. Regarding the polyphenol structure of tannin, the moieties attached to the polyol are galloyl group in gallotannin (type I), HHDP group in ellagitannin (type II), DHHDP group in dehydroellagitannin (type III), and transformed DHHDP group in transformed dehydroellagitannin (type IV) [32]. Herein, we first report the occurrence of brevifolincarboxyl moiety as the substituent to the hexose core in A1-1, A1-2, A1-3, and A2 from the seeds of C. officinalis. To date, there are only two reported brevifolincarboxyl tannin: 1-O-galloyl-3,6-HHDP-4-O-brevifolincarboxyl-β-D-glucopyranose, that is the basic hydrolytic product of geraniin and repandusinin from the genus Mallotus [24,25]. Our study promoted the brevifolincarboxyl tannin structure diversity, which can be classified as a new type V tannin. Based on the DHHDP moiety fragment pattern in A3, A4, and A5, the brevifolincarboxyl moiety is thought to be biosynthetically derived from the DHHDP moiety by rearrangement decarboxylation and lactonization [24]. The bio-relationship can be then illustrated in Figure 6.
## 2.3. Ellagitannins
B1-1 showed [M-2H]2– at an m/z of 708.0711 with a retention time of 9.6 min, corresponding to a molecular weight of 1418 Da. The produced mono-charged fragment ion at m/z of 785.0749 corresponded to a valoneoyl-galloyl-hexoside, such as isorugosin B [33], without resulting from the loss of the HHDP-galloyl-hexose moiety (e.g., gemin D, B2). This enabled the tentative identification of B1-1 as a dimer composed of a valoneoyl-galloyl-hexoside and HHDP-galloyl-hexoside, such as camptothin A [34]. The fragment ions at m/z 450.9911, 300.9971, and 633.078 indicated the presence of valoneoic acid trilactone (VTL), HHDP, and HHDP-galloyl-hexoside moieties, respectively, which supported the proposed identification of B1-1. B1-2 and B1-3 had the same [M-2H]2– at m/z 708.07 and showed similar fragmentation patterns at retention times of 10.06 and 10.69 min, respectively. These indicated the other two isomers of B1-1.
Furthermore, the HHDP moiety was observed to be part of B2-1 and B2-2, as they both showed an [M-H]− ion at m/z 633 and fragment ions at m/z 300.99, 275.01, 249.03, 169.01, and 125.02. These were typical of the HHDP and gallic acid moieties. The fragment ion at m/e 331.06 was attributed to monogalloyl-hexoside, due to the loss of an HHDP moiety from the [M-H]− ion. Thus, B2-1 and B2-2 were identified as isomers of HHDP-monogalloyl-hexoside-type tannins, such as gemin D [35]. The fragment ion was observed at m/z 481.05, corresponding to HHDP-hexoside, resulting from the loss of a galloyl moiety (152 Da), thus supporting their identification. B2-1 and B2-2 differed in the linkage of the HHDP and galloyl moieties to the hexoside core.
B3 had an [M-H]− ion at m/z 783.0750 with a retention time of 10.09 min. The fragment ions at m/z 481.0508 corresponded to the HHDP-hexoside moiety. It was the result from the loss of one HHDP moiety from the pseudo-parent ion. B3 was then tentatively identified as a bis-HHDP-hexose-type tannin [22]. The fragment ion at m/z 300.9970 was typical for ellagic acid, indicating the occurrence of the HHDP moiety. Dissociation of the ion at m/z 300.9970 yielded an m/z 257.0208 (loss of 44 Da, free carboxyl unit), which is characteristic of LHHDP produced by the loss of 44 Da, a free carboxyl unit, from ellagic acid.
B4-3 with a retention time of 16.19 min was characterized as a digalloyl-HHDP-hexoside-type tannin, as with tellimagrandin I as an exemple [36]. This identification was possible based on its [M-H]− ion at m/z 785.0776 and the release of typical fragment ions at m/z 483.1278 corresponding to an HHDP-hexoside moiety, resulting from the loss of two galloyl moieties (152 Da) from the [M-H]− ion. Typical fragment ions at m/z 300.9963, 275.0201, and 249.0407 confirmed the appearance of the HHDP moiety in B4-3. B4-3 has other two isomers, B4-2 and B4-1, with retention times at 11.65 and 13.85 min, respectively, that showed a similar fragment pattern as B4-3.
The molecular weight of B5-1 was determined to be 1570 Da based on the doubly deprotonated ion at m/z 784.0739 with a retention time of 12.51 min. The fragment ion at m/z 785.0741 corresponded to an HHDP-digalloyl-hexoside moiety, such as tellimagrandin I (B4), indicating that B5-1 was a dimer composed of two HHDP-digalloyl-hexoside moieties by the elimination of H2. The fragment ion at m/z 450.990, attributed to valoneic acid tridilactone [VTL-1]−, indicated the occurrence of a valoneoyl bridge. Thus, B5-1 was tentatively determined as an HHDP-digalloyl-hexoside dimer type tannin, such as cornusiin A [34]. Moreover, the fragment ions at m/z 633.06 and 300.9970 attributed to an HHDP-digalloyl-hexoside moiety, such as gemin D (B2), and ellagic acid further supported the proposed B5-1 structure. Additionally, there were five other isomers or anomers of B5-1, with retention times of 13.46, 14.38, 15.51, 17.95, and 19.62 min, that showed identical MW and fragmentation patterns.
B6 displayed a molecular weight of 1086 Da, based on the doubly deprotonated ion at m/z 542.03, with a retention time of 14.38 min. The fragment ion at m/z 785.0763 was attributed to the HHDP-digalloyl-hexoside moiety, such as tellimagrandin I (B4), which resulted from the loss of the EA moiety from the pseudo-parent ion. The cornusiin B isomer was tentatively assigned as E13 [37]. The fragment ions at m/z 633.0634, 450.970, and 300.997 indicated the appearance of gemin D, VTL, and EA moieties, respectively, which supported the proposed structure.
B7 exhibited a pseudomolecular ion at m/z 953.0909. The fragment ion m/z at 785.0754 resulted from the loss of a valoneoyl moiety, which was supported by the fragment ions at m/z 909.0972, corresponding to the loss of 44-Da carboxyl unit. The fragment ions at m/z 615.0253 and 462.9903 were attributed to dehydrated galloyl-HHDP-hexoside and dehydrated HHDP-hexoside, respectively. They indicated the consecutive loss of two gallyol moieties from the fragment ion at m/z 785.0754. Thus, B7 was presumed to be a compound of valoneoyl-HHDP-digalloyl-hexoside-type tannin, such as isocoriariin B [15]. The fragment ions at m/z 249.0408, 275.0200, 300.996, 169.0124, and 125.0101 were attributed to the HHDP and galloyl moieties in B7, which further confirmed the supposed structure.
A molecular weight of 2202 Da was assigned to B8, based on the doubly deprotonated ion at m/z 1100, with a retention time of 15.51 min. The fragment ion at m/z 1417.0668 resulted from the loss of valoneoyl-galloyl-hexoside moiety, such as isorugssin F. This indicated the appearance of a B1 moiety, the dimer conjugated by HHDP-galloyl-hexoside and valoneoyl-digalloyl-hexoside, such as gemin D (B2) and isorugssin F. The fragment ion at m/z 633.0761 indicated the occurrence of the HHDP-galloyl-hexoside moiety, such as gemin D (B2), by the 1568-Da mass loss of a dimer conjugated with two valoneoyl-digalloyl-hexoside, such as isorugssin B. Based on these results, B8 was identified as a trimer of HHDP-galloyl-hexoside and two valoneoyl-digalloyl-hexoside, such as cornusiin F [38]. Moreover, the fragment ions at m/z 450.9950, 783.0518, and 1567.1799 indicated the occurrence of the VTL moiety, dehydrated valoneoyl-digalloyl-hexoside moiety, such as isorugssin F, and the moiety resulting from the loss of an HHDP-galloyl-hexoside moiety, such as gemmin D, from the pseudo-parent ion. These findings supported the stipulated structure of B8.
A molecular weight of 2354 Da was assigned to B9-1, based on the doubly deprotonated ion at m/z 1176.0540, with a retention time of 15.51 min. The fragment ion at m/z 1417.1671 resulted from the loss of a valoneoyl-digalloyl-hexoside moiety, such as isorugssin B, indicating the appearance of B1 moiety, a dimer conjugated by HHDP-galloyl-hexoside, and valoneoyl-digalloyl-hexoside, such as gemmin D and isorugssin F. Fragment ion at m/z 633.0614 indicated the occurrence of gemmin D by the 1720-Da mass loss of a dimer conjugated with valoneoyl-digalloyl-hexoside and valoneoyl-trigalloyl-hexoside moieties, such as isorugssin B and isorugssin F, from the pseudo-parent ion. Based on these results, B9-1 was identified as a trimer of HHDP-galloyl-hexoside, valoneoyl-digalloyl-hexoside, and valoneoyl-trigalloyl-hexoside, such as cornusiin C [34]. Moreover, the fragment ions at m/z 450.9904, 783.052, and 935.064 indicated the occurrence of a valoneoyl moiety, dehydrated valoneoyl-digalloyl-hexoside moiety, such as isorugssin F, and dehydrated valoneoyl-trigalloyl-hexoside, such as isorugssin B. These supported the assumed structure of the B9-1. B9-2, B9-3, B9-4, and B9-5 displayed retention times of 18.36, 17.01, 17.95, 19.62, and 22.04 min, respectively. Moreover, they exhibited similar fragment patterns as B9-1. Therefore, they have identified as isomers of B9-1 with a difference in the position of the moieties linked to the hexose core or anomers with a different configuration of the anomeric hydrogen at C-1 of the hexose core.
A molecular weight of 938 Da was assigned to B10-1, based on the doubly deprotonated ion at m/z 468.0396, with a retention time of 17.95 min. The fragment ion at m/z 767.05313 was attributed to the dehydrated HHDP-digalloyl-hexoside (B4), such as tellimagrandin I, resulting from the loss of a galloy moiety and H2O, which enabled the identification of B10-1 as a HHDP-trigalloyl-hexoside-type tannin, such as tellimagrandin II [39]. The other fragment ions at m/z 614.9811 and 300.9920 indicated the occurrence of dehydrated HHDP-digalloyl-hexoside and HHDP moieties, which supported the proposed structure of B10-1. B10-2, with a retention time of 18.36 min, was also identified as a HHDP-trigalloyl-hexoside-type tannin as B10-1 with the difference in the position of the moieties linked to the hexose core base on the similar fragment pattern.
A molecular weight of 1722 Da was assigned to B11-1, based on the doubly deprotonated ion at m/z 860.0783, with a retention time of 17.01 min. The fragment ion at m/z 937.0754 resulted from the loss of HHDP-digalloyl-hexoside, indicating the occurrence of valoneoyl-digalloyl-hexoside, such as isorugosin B moiety, which enabled the tentative identification of B11-1 as a dimer of HHDP-digalloyl-hexoside and valoneoyl-digalloyl-hexoside, such as cornusiin D [39]. The other fragment ions at m/z 1419.8393 and 1087.0227 resulted from the loss of an HHDP moiety and two galloyl moieties, respectively. The fragment ion m/z of 300.9920 and 450.9815 indicated the occurrence of ellagic acid and VTL moieties, which supported the proposed structure identification. Moreover, there were four other types of tannins, such as B11-1, with retentions time of 17.95, 18.36, 19.62, and 21.28 min, displaying similar fragment patterns.
B12-1 gave an [M-H]− at m/z 935.0833 with a retention time of 20.29 min. It released a fragment ion at m/z 632.97, which was attributed to a HHDP-galloyl-hexoside moiety (B2) resulting from the loss of an HHDP (302 Da) from the pseudo-molecular ion. B12-1 was tentatively identified as galloyl-bis-HHDP-hexoside. Moreover, the fragment ion at m/z 783.01 resulted from the loss of gallic acid from [M-H]−. The presence of the HHDP moiety was confirmed by the fragment ion at m/z 300.997. B12-2, with a retention time of 21.64 min, exhibited a galloyl-bis-HHDP-hexoside-type tannin that displayed a fragment pattern similar to that of B12-1. The galloyl-bis-HHDP-hexoside-type tannin has been reported in pomegranate (*Punica granatum* L.) peel [40], but that has not been detected in the seeds of *Cornus officinalis* Sieb. et Zucc.
B13 was assigned as an ellagic acid pentoside which had a pseudo-molecular ion at m/z 433.0394 and MS/MS fragment ions at 299.997 and 300.9634; this dissociation pattern was observed in *Fragaria chiloensis* berries [41] and attributed to an ellagic acid pentoside.
B14, which exhibited a pseudo-molecular ion at m/z 447.0561 and fragmentation ions at m/z 315.0157 (loss of pentoside residue, 132 Da) and m/z 299.9885 (further loss of methyl) in the MS/MS spectrum, could be attributed to methyl ellagic acid pentoside. This hypothesis is in agreement with the result of the fragmentation that yielded m/z of 271 by the loss of CO2 from methyl ellagic acid. Methyl ellagic acid derivatives were also detected in strawberries by Seeram et al. [ 42].
The examples of the structures of the tannin type of B1-B14 are illustrated in Figure 7.
## 2.4. Gallotannins
C1-1, C1-2, and C1-3, with retention times of 4.2, 5.37, and 7.05 min, respectively, were characterized as monogalloyl-hexoside isomers. This identification was based on the [M-H]− ion at m/z 331.069, and the fragment ions at m/z 169.012 indicating the loss of a hexose moiety (162 Da) and m/z 125.023 typical for the galloyl moiety resulting from the loss of the carboxylic function (44 Da) [31]. These compounds differ in the linkage position of the galloyl moiety to the hexose core.
Five compounds C2-1 to C2-5 (tR 6.0, 7.05, 8.69, 10.09, and 10.69 min), with the same precursor ion of m/z 483.07, were identified as digalloyl-hexoside isomers, relying on the product ions at m/z 331.069, corresponding to a monogalloyl-hexoside, and resulting from the loss of galloyl moiety (152 Da) from the parent ion [31]. Moreover, the fragment ion at m/z 169.012 indicating a galloyl moiety resulted from the loss of a hexose moiety (162 Da) from the pseudo-molecular ion.
C3-1, C3-2, C3-3, and C3-4 showed the same [M-H]− ion at m/z 635.09. The fragment ion at m/z 465.065 corresponded to digalloyl-hexoside moiety, resulting from the loss of galloyl moiety (152 Da) and H2O (18 Da). Therefore, these compounds were tentatively identified as trigalloyl-hexoside isomers [43]. Additionally, the fragment ion at m/z 331.056 resulted from the consecutive loss of two galloyl moieties supporting the assignment.
C4-1 and C4-2, with retention times of 18.36 and 19.62 min, respectively, both gave a pseudo-molecular ion [M-H]− at m/z 787.1058, which produced the fragment ions at m/z 635.081, 465.065, and 313.0570, corresponding to the trigalloyl-hexoside, digalloyl-hexoside, and monogalloyl-hexoside moieties, respectively. These moieties resulted from the consecutive loss of three galloyl (152 Da) moieties and H2O (18 Da). Thus, these two compounds were tentatively assigned as tetragalloyl-hexoside isomers [43].
C5-1, C5-2, and C5-3 produced fragment ions at m/z 787.06, indicating the presence of tetragalloyl-hexoside moiety (C4) in their structures. Moreover, C5-1, C5-2, and C5-3 exhibited an [M-H]− ion at m/z 939.111, which was 152 Da higher than that of C4, indicating the structural difference of a galloyl moiety. Then, pentagalloyl-hexoside-type tannins were assigned to C5-1, C5-2, and C5-3 [15].
C6-1 and C6-2 displayed a doubly deprotonated ion at m/z 545.03, indicating a molecular weight of 1092. Fragment ion at m/z 769.05 corresponded to a dehydrated tetragalloyl-hexoside moiety, owing to the loss of two gallic acid units (152 Da) and H2O (18 Da) from the pseudo-molecule ion. Therefore, C6-1 and C6-2 were tentatively identified as hexogalloyl-hexoside isomers [31].
C7 gave a pseudo-molecular ion [M-H]− at m/z 361.0796, which liberated fragment ions at m/z 169.0129, indicating the loss of a heptose moiety (210 Da), and m/z 125.023 typical for galloyl moiety. Therefore, this compound was identified as monogalloyl-heptoside, which was in accordance with previous results [13].
C8 was assigned as a digalloyl heptoside, which displayed an [M-H]− at m/z 513.0904. It produced fragment ions at m/z 361.0781 (C7) and 343.071, which resulted from the loss of a gallic acid moiety (152 Da) and a further loss of water (18 Da).
The examples of the structures of the tannin type of C1–C8 are illustrated in Figure 8.
## 2.5.1. Phenolic Acids
D1 to D6 were identified as salicylic acid, cinnamic acid, caffeic acid, coumaric acid, syringic acid, and gallic acid, respectively. Typical product ions resulted from the decarboxylation of the acidic group with m/z values of 92.9184, 102.9471, 135.0435, 119.0482, 153.0536, and 125.0229. These ions were identified in their MS/MS spectrum. Additionally, all the identified phenolic acids were characterized by comparison of their mass data with those from the reported literatures [44,45,46,47,48]. The ellagic acid was assigned to D7, based on the typical fragment ions m/e at 249.03, 275.02, and 300.9966. D8, with a precursor ion [M-H]− at m/z 291.05, was assigned as brevifolin carboxylic acid, relying on the fragment ion of m/z 247.0256 resulted from the loss of carboxyl moiety. The MS data were in agreement with those previously reported for brevifolin carboxylic acid [23].
## 2.5.2. Hydroxycinnamic Acids and Their Derivatives
D9 and D10 were tentatively identified as the citric acid derivatives based on the typical fragment ions of citric acid at m/z 102.9472, 146.9371, and 190.9266. D13-1 was identified as caftaric acid (m/z 311.0364), which showed the loss of a tartaric acid moiety in the MS/MS experiment (132 Da) and a partial decarboxylation of the caffeic acid moiety resulting in fragments at m/z 179.0555 and 135.0125. This fragmentation pattern was also observed for D13-2, as characterized by the retention times specified in Table 1. This is presumably due to D/L isomers of tartaric acid. D11 (m/z 295.0671) was identified as a caffeoylmalic acid, based on its fragments at m/z 133.0124, 71.0120, and 115.0020, which are characterized to malic acid moiety, as well as the typical fragment of caffeic acid at m/z 179. D12 revealed a [M-H]− ion at m/z 393.03 and a loss of 98 Da in the MS/MS, resulting in a fragment at m/z 295.0671, which, in turn, showed a fragmentation pattern identical to D11. Therefore it was concluded that D12 represented a caffeoylmalic acid derivative [44,45,46,47,48].
## 2.5.3. Hydroxybenzoic Acids and Their Derivatives
D14 with an [M-H]− ion at m/z 321.02 was assigned as digallate, based on the fragment ions at m/z 125.0230, 169.0128, as characteristic for gallic acid. D15 revealed an [M-H]− ion at m/z 333.0613 and fragments at m/z 152.9943, 109.8816 in the MS/MS, indicating a presence of a protocatechuic acid derivative. D17 gave a deprotonated molecular ion at m/z 325.0565 and four product ions at m/z 134.0356, 149.0081, 178.0262, and 193.0488, which indicated the presence of a feruloyl moiety. The loss of 132 Da from [M-H]− indicated tartaric acid substitution. Therefore, D17 was identified as feruloyl tartaric acid. D16 with [M-H]− ion at m/z 373.1 was tentatively identified as feruloyl acid derivative, based on the observation of the typical fragment ions of feruloyl acid as described in D17. D18, D19, and D20 were identified as malic acid derivatives based on the observation of the typical fragments of malic acid at m/z 71.012, 115.0021, 133.0124. Additionally, the MS/MS revealed the presence of the moieties of syringic acid (at m/z 153.0536, 182.0219, 197.0432) and gallic acid (at m/z 169.01263, 125.023) in D18 and D19 respectively, which enabled the tentative identification of syringoylmalic acid to D18 with a [M-H]− ion at m/z 313.05 and galloylmalic acid to D19 with a [M-H]− ion at m/z 285.02. The typical ions (at m/z 163.0381, 119.0410), characteristic for p-coumaric acid, were observed in the MS/MS of D21, D22, and D23, identified tentatively as p-coumaric acid derivatives. For D21, the product ions typical for tartaric acid (m/z 87.0067, 103.0018, 105.1979) were observed that confirmed its structure as p-coumaroyltartaric acid [44,45,46,47,48].
An [M-H]− ion at m/z 468.9910 with a retention time of 15.51 min was observed for D24, producing a fragment ion at m/z 425.0161, thus indicating the loss of a carboxyl group. Additionally, typical fragments of ellagic acid at m/z 300.9971 and 299.9874 were observed. Therefore, D24 was identified as valoneic acid bilactone isomer [3]. To our knowledge, valoneic acid bilactone has not been reported in the seeds of *Cornus officinalis* Sieb. et Zucc.
The structures of the phenolic acids are illustrated in Figure 9.
## 2.6. Non-Phenolic Compounds
Other non-phenolic compounds, such as free malic, citric, tartaric, and quinic acids, were identified. E3-1 and E3-2 exhibited the same fragments at m/z 71.01199, 115.00212, and 133.01247, which are characterized by the fragmentation pattern of malic acids. However, differences in the retention time were observed for E3-1 and E3-2 at 4.2 and 5.37 min, respectively, indicating the two isomers of malic acids. E2-1 and E2-2 exhibited the same [M-H]− ion at m/z 149.0081, which were detected with retention times of 3.91 and 12.84 min, indicating the occurrence of two tartaric acid isomers. This identification was based on the fragments at m/z 105.0180 and 87.0066. E4-1 and E4-2, exhibiting the same [M-H]− ion at m/z 191.0193, were detected at 4.2 and 5.37 min, indicating the occurrence of different isomeric structures, and they were identified as quinic acids, based on the typical fragment of quinic acid at m/z 191.0193,173.0683, and 111.0068 [44,45,46,47,48].
The structures of the non-phenolic compounds are illustrated in Figure 10.
## 2.7. Total Phenolic Content (TPC)
The identified compounds indicated that the aqueous extract of the seeds was rich in tannins. We then investigated the TPC using the Folin–Ciocalteu colorimetric method, which showed a result of 79,157 ± 563 mg gallic acid equivalent (GAE)/100 g in the seed extract. Compared to the tannin-rich fruits, such as raspberries (average 233.50 mg/100 g in fresh weight), pomegranates (>10 g/100 g in dry material) peach kernels (ranging from 12.7 to 3.8 g/100 g), or the kernels of apricot cultivars (ranging from 209.4 to 10.60 mg GAE/100 g), the seeds extract of C. officinalis provides a new source of tannins, indicating its potential as an antioxidant for use in the food industry [49,50,51,52].
## 3.1. Solvents and Reagents
Gallic acid (GA) was obtained from the National Institutes for Food and Drug Control (Beijing, China). Acetonitrile and formic acid were of HPLC grade and purchased from Dikma Scientific (Tianjin, China). Folin–Ciocalteu reagent was obtained from Yuanyie Biotech Co., Ltd. (Shanghai, China). The water was distilled and deionized.
## 3.2. Plant Source
Mature fruits of C. officinalis were harvested in October 2021 from the Muzhi country in Luoyang, Henan, China. The samples were identified by Prof. Ximing Lu, Medical College, Henan University of Science and Technology, Luoyang, China. After separation from fruits, the seeds were air-dried at room temperature and then stored at 4 °C prior to analysis. Voucher specimens are maintained in the college herbarium, with certificate No. 22-7[7].
## 3.3. Sample Preparation
Owing to the structurally unstable nature of polyphenols, we performed the percolation extraction method at room temperature (20 °C). For the polyphenols, being water soluble, we used water as the extracting solvent. Percolation was performed in a stainless-steel percolator with a ball valve at the bottom. The inner diameter and height of the percolator were 5 cm and 30 cm, respectively. First, 20 mL water was poured into the percolator, then 50 g milled seeds were added. Percolation was performed at room temperature, with a flow rate of percolate 0.5 L/h using 600 mL H2O. Thereafter, the seed extract solvent was placed in a freeze dryer (SCIENTZ-30FG, Ningbo, China). After thermal equilibration, the shelf temperature was lowered to −40 °C and maintained for 12 h. Subsequently, the system was evacuated to a pressure of 20 Torr, and the shelf temperature was adjusted to −40 °C and held for 24 h. The shelf temperature was then raised successively to −20 °C (8 h), 0 °C (6 h), and finally, to 20 °C (2 h). The resulting amorphous samples were weighted and sealed at 4 °C for further analysis.
## 3.4. LC-MS Analysis
LC-MS analyses were carried out using a Dinonex Ultimate 3000 UHPLC system (Ultimate 3000—Thermo Scientific, Waltham, MA, USA), coupled with a quadrupole-orbitrap hybrid mass analyzer (Q-Exactive, Thermo Scientific). The chromatographic separation of the polyphenol extract was achieved on an Eclipse Plus C18 analytical column (250 mm × 4.6 m, 2.6 µm, ZORBAX, Agilent, Palo Alto, CA, USA). The column temperature was set at 30 °C. The mobile phase was composed of (A) water with $0.2\%$ formic acid and (B) acetonitrile with $0.2\%$ formic acid. Elution was accomplished with the following solvents gradient: 0-3 min $10\%$ B, $18\%$ B at 13 min and kept unchanged until 16 min, and $30\%$ B at 25 min and kept unchanged until 30 min. Finally, the system returned to $10\%$ B in 2 min. The flow rate and the injection volume were 0.6 mL/min and 10 μL, respectively. The acquisition was carried out in negative ionization mode (ESI-). The ESI temperature was set at 300 °C, the capillary temperature at 320 °C, and the electrospray voltage at 2.8 kV. Sheath and auxiliary gas were 30 and 5 arbitrary units, respectively. The acquisition was performed in full scan/ddMS 2 modes. The parameters were optimized as follows: (i) full scan acquisition: resolution 70,000 FWHM (at m/z 200); (ii) dd-MS 2: resolution 17,500 FWHM (at m/z 200). The normalised collision energy (NCE) was set at 30.
## 3.5. Total Phenolic Content
Diluted seeds extract (5 µL) was placed in each well of a 96-well plate and mixed with 10 µL of Folin–Ciocalteu reagent, 100 µL of H2O, and 50 µL of $10\%$ sodium carbonate, and the mixture was shaken for 30 s. Total polyphenols were determined after 1 h of incubation at room temperature in the dark. The absorbance was then measured at 765 nm on a microplate reader HBS-1096A (DeTie, Nanjing, China). Gallic acid was used as a standard. The standard curve [1] with r as 0.9993 was prepared using different concentrations of gallic acid. The total phenolic contents were calculated as mg of gallic acid equivalent (GAE) per 100 g of the extract. The results were expressed as the mean ± standard deviations of three replications. $Y = 0.1227$ X + 0.0085,[1] where Y is the value of the absorbance; X is the concentration of samples.
## 4. Conclusions
In this study, water-soluble compounds in the seeds of *Cornus officinalis* Sieb. et Zucc. were identified using HPLC-ESI-MS/MS. A total of 97 compounds were characterized and classified as brevifolincarboxyl tannins and their derivatives, ellagitannins, gallotannins, phenolic acids and their derivatives, and non-phenolic acids. Five new types of tannins have been identified. Moreover, the method to effectively recognize the brevifolincarboxyl moiety and DHHDP moiety from the MS/MS data using typical fragment ions was summarized. Furthermore, the study of the inferred structures of tannins with technologies such as NMR and X-ray crystallography is needed in further research. The results of this study not only enrich the structures of tannin-type compounds, but also provide invaluable information for its further utilization in the industry.
## References
1. Bravo L.. **Polyphenols: Chemistry, dietary sources, metabolism, and nutritional significance**. *Nutr. Rev.* (1998) **56** 317-333. DOI: 10.1111/j.1753-4887.1998.tb01670.x
2. Scalbert A., Manach C., Morand C., Rémésy C., Jiménez L.. **Dietary polyphenols and the prevention of diseases**. *Crit. Rev. Food Sci. Nutr.* (2005) **45** 287-306. DOI: 10.1080/1040869059096
3. Fischer U.A., Carle R., Kammerer D.R.. **Identification and quantification of phenolic compounds from pomegranate (**. *Food Chem.* (2011) **127** 807-821. DOI: 10.1016/j.foodchem.2010.12.156
4. Ma W., Wang K.J., Cheng C.S., Yan G.Q., Lu W.L., Ge J.F., Cheng Y.X., Li N.. **Bioactive compounds from Cornus officinalis fruits and their effects on diabetic nephropathy**. *J. Ethnopharmacol.* (2014) **153** 840-845. DOI: 10.1016/j.jep.2014.03.051
5. Dong Y., Feng Z.L., Chen H.B., Wang F.S., Lu J.H.. **Corni Fructus: A review of chemical constituents and pharmacological activities**. *Chin. Med.* (2018) **13** 1-20. DOI: 10.1186/s13020-018-0191-z
6. Czerwinska M.E., Melzig M.F.. **Cornus mas and Cornus Officinalis-Analogies and Differences of Two Medicinal Plants Traditionally Used**. *Front. Pharmacol.* (2018) **9** 894. DOI: 10.3389/fphar.2018.00894
7. He K., Song S., Zou Z., Feng M., Wang D., Wang Y., Li X., Ye X.. **The hypoglycemic and synergistic effect of loganin, morroniside, and ursolic acid isolated from the fruits of Cornus officinalis**. *Phytother. Res.* (2015) **30** 283-291. DOI: 10.1002/ptr.5529
8. Mau J., Chen C., Hsieh P.. **Antimicrobial effect of extracts from Chinese chive, cinnamon, and corni fructus**. *J. Agric. Food Chem.* (2001) **49** 183-188. DOI: 10.1021/jf000263c
9. Park C.H., Cho E.J., Yokozawa T.. **Protection against hypercholesterolemia by Corni fructus extract and its related protective mechanism**. *J. Med. Food* (2009) **12** 973-981. DOI: 10.1089/jmf.2009.0037
10. Cao G., Cai H., Cai B., Tu S.. **Effect of 5-hydroxymethylfurfural derived from processed Cornus officinalis on the prevention of high glucose-induced oxidative stress in human umbilical vein endothelial cells and its mechanism**. *Food Chem.* (2013) **140** 273-279. DOI: 10.1016/j.foodchem.2012.11.143
11. Telang N.T., Li G., Sepkovic D.W., Bradlow H.L., Wong G.Y.. **Anti-proliferative effects of Chinese herb Cornus officinalis in a cell culture model for estrogen receptor-positive clinical breast cancer**. *Mol. Med. Rep.* (2012) **5** 22-28. DOI: 10.3892/mmr.2011.617
12. Bhakta H.K., Park C.H., Yokozawa T., Tanaka T., Jung H.A., Choi J.S.. **Potential anticholinesterase and β-site amyloid precursor protein cleaving enzyme 1 inhibitory activities of cornuside and gallotannins from Cornus officinalis fruits**. *Arch. Pharm. Res.* (2017) **40** 836-853. DOI: 10.1007/s12272-017-0924-z
13. Park C.H., Noh J.S., Tanaka T., Roh S.S., Lee J.C., Yokozawa T.. **Polyphenol isolated from Corni Fructus, 7-O-galloyl-**. *Arch. Pharm. Res.* (2015) **38** 1270-1280. DOI: 10.1007/s12272-014-0457-7
14. Yue X., Li Z., Lam S., Peng W., He Y.. **Reuse of Cornus officinalis nutlet for bioenergy**. *BioResources* (2022) **17** 6411-6444. DOI: 10.15376/biores.17.4.6411-6444
15. Przybylska D., Kucharska A.Z., Cybulska I., Sozánski T., Piórecki N., Fecka I.. *Molecules* (2020) **25**. DOI: 10.3390/molecules25204646
16. Kowalczyk R., Piwnicki K.. **Pestki owoców jako cenny surowiec wtórny przemysłu spożywczego**. *Postep. Tech. Przetwórstwa Spożywczego* (2007) **2** 62-66
17. Mendu V., Harman-Ware A.E., Crocker M., Jae J., Stork J., Morton S., Placido A., Huber G., DeBolt S.. **Identification and thermochemical analysis of high-lignin feed stocks for biofuel and biochemical production**. *Biotechnol. Biofuels* (2011) **4** 43. DOI: 10.1186/1754-6834-4-43
18. Kostíc M.D., Velickovíc A.V., Jokovic N., Stamenkovíc O.S., Veljkovíc V.B.. **Optimization and kinetic modeling of esterification of the oil obtained from waste plum stones as a pretreatment step in biodiesel production**. *Waste Manag.* (2016) **48** 619-629. DOI: 10.1016/j.wasman.2015.11.052
19. Anwar M., Rasul M., Ashwath N.. **Optimization of biodiesel production from stone fruit kernel oil**. *Energy Procedia* (2019) **160** 268-276. DOI: 10.1016/j.egypro.2019.02.146
20. Lee J., Jang D.S., Kim N.H., Lee Y.M., Kim J., Kim J.S.. **Galloyl Glucoses from the Seeds of Cornus officinalis with Inhibitory Activity against Protein Glycation, Aldose Reductase, and Cataractogenesis ex Vivo**. *Biol. Pharm. Bull.* (2011) **343** 443-446. DOI: 10.1248/bpb.34.443
21. Zhang N., Li J., Jiang H.. **Cornus officinalis Sieb. et Zucc. Seeds as a Dietary Source of Ellagic Acid**. *Nat. Prod. Commun.* (2022) **17** 1-5. DOI: 10.1177/1934578X221080342
22. Bubba M.D., Checchini L., Chiuminatto U., Doumett S., Fibbi D., Giordani E.. **Liquid chromatographic/electrospray ionization tandem mass spectrometric study of polyphenolic composition of four cultivars of**. *J. Mass Spectrom.* (2012) **47** 1207-1220. DOI: 10.1002/jms.3030
23. He Z., Xia W.. **Analysis of phenolic compounds in chinese olive (**. *Food Chem.* (2007) **105** 1307-1311. DOI: 10.1016/j.foodchem.2007.04.049
24. Tanaka T., Nonaka G., Nishioka I.. **Tannins and related compounds. C. Reaction of dehydrohexahydroxydiphenic acid esters with bases, and its application to the structure determination of pomegranate tannins, granatins A and B**. *Chem. Pharm. Bull.* (1990) **38** 2424-2428. DOI: 10.1248/cpb.38.2424
25. Saijo R., Nonaka G.I., Nishioka I., Chen I.S., Hwang T.H.. **Tannins and Related Compounds. LXXXVIII.: Isolation and Characterization of Hydrolyzable Tannins from**. *Chem. Pharm. Bull.* (1989) **37** 2940-2947. DOI: 10.1248/cpb.37.2940
26. Quideau S., Feldman K.S.. **Ellagitannin Chemistry**. *Chem. Rev.* (1996) **96** 475-503. DOI: 10.1021/cr940716a
27. Akkawi M., Abu-Lafi S., Abu-Remeleh Q., Qutob M., Lutgen P.. **Phytochemical screening of Pomegranate juice, peels, leaves and membranes water extracts and their effect on β-hematin formation, a comparative study**. *Pharm. Pharmacol. Int. J.* (2019) **7** 193-200
28. Lin J.H., Nonaka G.I., Nishioka I.. **Tannins and related compounds. XCIV. Isolation and characterization of seven new hydrolyzable tannins from the leaves of**. *Chem. Pharm. Bull.* (2008) **38** 1218-1223. DOI: 10.1248/cpb.38.1218
29. Okuda T., Hatano T., Yasui T.. **Revised structure of isoterchebin, isolated from Cornus officinalis**. *Heterocycles* (1981) **16** 1321-1324. DOI: 10.3987/R-1981-08-1321
30. Tanaka T., Tong H., Xu Y., Ishimaru K., Nonaka G., Nishioka I.. **Tannins and related compounds CXVII. Isolation and characterization of three new ellagitannins, lagerstannins A, B, C, having a gluconic acid core, from**. *Chem. Pharm. Bull.* (1992) **40** 2975-2980. DOI: 10.1248/cpb.40.2975
31. Tarone A., Goupy P., Ginies C., Marostica M., Dufour C.. **Advanced characterization of polyphenols from Myrciaria jaboticaba peel and lipid protection in in vitro gastrointestinal digestion**. *Food Chem.* (2021) **359** 129959. DOI: 10.1016/j.foodchem.2021.129959
32. Okuda T., Yoshida T., Hatano T.. **Correlation of oxidative transformations of hydrolyzable tannins and plant evolution**. *Phytochemistry* (2000) **55** 513-529. DOI: 10.1016/S0031-9422(00)00232-6
33. Yisimayili Z., Abdulla R., Tian Q., Wang Y., Chen M., Sun Z., Li Z., Liu F., Aisa H.A., Huang C.. **A comprehensive study of pomegranate flowers polyphenols and metabolites in rat biological samples by high-performance liquid chromatography quadrupole time-of-flight mass spectrometry**. *J. Chromatogr. A* (2019) **1604** 460472. DOI: 10.1016/j.chroma.2019.460472
34. Efenberger-Szmechtyk M., Nowak A., Czyzowska A., Kucharska A.Z., Fecka I.. **Composition and Antibacterial Activity of**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25092011
35. Romani A., Campo M., Pinelli P.. **HPLC/DAD/ESI-MS analyses and anti-radical activity of hydrolysable tannins from different vegetal species**. *Food Chem.* (2012) **130** 214-221. DOI: 10.1016/j.foodchem.2011.07.009
36. Hanhineva K., Rogachev I., Kokko H., Mintz-Oron S., Venger I., Kärenlampi S., Aharoni A.. **Non-targeted analysis of spatial metabolite composition in strawberry (**. *Phytochemistry* (2008) **69** 2463-2481. DOI: 10.1016/j.phytochem.2008.07.009
37. Okuda T., Hatano T., Ogawa N., Kira R., Matsuda M.. **Cornusiin A, a dimeric ellagitannin forming four tautomers, and accompanying new tannins in Cornus officinalis**. *Chem. Pharm. Bull.* (1984) **32** 4662-4665. DOI: 10.1248/cpb.32.4662
38. Hatano T., Ogawa N., Kira R., Yasuhara T., Okuda T.. **Tannins of cornaceous plants. I. Cornusiins A, B and C, dimeric monomeric and trimeric hydrolyzable tannins from Cornus officinalis, and orientation of valoneoyl group in related tannins**. *Chem. Pharm. Bull.* (1989) **37** 2083-2090. DOI: 10.1248/cpb.37.2083
39. Hatano T., Yasuhara T., Okuda T.. **Tannins of cornaceous plants. II. Cornusiins D, E and F, new dimeric and trimeric hydrolyzable tannins from Cornus officinalis**. *Chem. Pharm. Bull.* (1989) **37** 2665-2669. DOI: 10.1248/cpb.37.2665
40. Karvela E., Makris D., Kefalas P., Moutounet M.. **Extraction of phenolics in liquid model matrices containing oak chips: Kinetics, liquid chromatography mass spectroscopy characterisation and association with in vitro antiradical activity**. *Food Chem.* (2008) **110** 263272. DOI: 10.1016/j.foodchem.2008.02.001
41. Simirgiotis M.J., Schmeda-Hirschmann G.. **Determination of phenolic composition and antioxidant activity in fruits, rhizomes and leaves of the white strawberry (**. *J. Food Compos. Anal.* (2010) **23** 545. DOI: 10.1016/j.jfca.2009.08.020
42. Seeram N.P., Lee R., Scheuller H.S., Heber D.. **Identification of phenolic compounds in strawberries by liquid chromatography electrospray ionization mass spectroscopy**. *Food Chem.* (2006) **97** 1-11. DOI: 10.1016/j.foodchem.2005.02.047
43. Regazzoni L., Arlandini E., Garzon D., Santagati N.A., Beretta G., Facino R.M.. **A rapid profiling of gallotannins and flavonoids of the aqueous extract of**. *J. Pharm. Biomed. Anal.* (2013) **72** 202-207. DOI: 10.1016/j.jpba.2012.08.017
44. Perestrelo R., Lu Y., Santos S.A.O., Silvestre A.J.D., Neto C.P., Câmara J.S., Rocha S.M.. **Phenolic profile of Sercial and Tinta Negra**. *Food Chem.* (2012) **135** 94-104. DOI: 10.1016/j.foodchem.2012.04.102
45. Li J., Xu Q., Jiang H.. **Identification and Characterization of Two New Forced Degradation Products of Saikosaponin A under the Stress of Acid Hydrolytic Conditions**. *Molecules* (2016) **21**. DOI: 10.3390/molecules21091232
46. Abu-Reidah I.M., Mohammed S.A., Jamous R.M., Arráez-Román D., Segura-Carretero A.. **HPLC-DAD-ESI-MS/MS screening of bioactive components from**. *Food Chem.* (2015) **166** 179-191. DOI: 10.1016/j.foodchem.2014.06.011
47. Magdalena M.T., Grazyna Z.. **In-depth phytochemical and biological studies on potential AChE inhibitors in red and zigzag clover dry extracts using reversed–phase liquid chromatography (RP-LC) coupled with photodiode array (PDA) and electron spray ionization-quadrupole/time of flight-mass spectrometric (ESI-QToF/MS-MS) detection and thin-layer chromatography-bioautography**. *Food Chem.* (2022) **375** 131846. PMID: 34920306
48. Duckstein S.M., Stintzing F.C.. **Investigation on the phenolic constituents in**. *Anal. Bioanal. Chem.* (2011) **401** 677-688. DOI: 10.1007/s00216-011-5111-3
49. Ancos B.D., González E.M., Cano M.P.. **Ellagic acid, Vitamin C, and total phenolic contents and radical scavenging capacity affected by freezing and frozen storage in raspberry fruit**. *J. Agric. Food Chem.* (2000) **48** 4565-4570. DOI: 10.1021/jf0001684
50. Nuncio-Jáuregui N., Nowicka P., Munera-Picazo S., Hernández F., Carbonell-Barrachina A.A., Wojdyło A.. **Identification and quantification of major derivatives of ellagic acid and antioxidant properties of thinning and ripe Spanish pomegranates**. *J. Funct. Foods* (2015) **12** 354-364. DOI: 10.1016/j.jff.2014.11.007
51. Nowicka P., Wojdyło A.. **Content of bioactive compounds in the peach kernels and their antioxidant, anti-hyperglycemic, anti-aging properties**. *Eur. Food Res. Technol.* (2019) **245** 1123-1136. DOI: 10.1007/s00217-018-3214-1
52. Chen Y., Al-Ghamdi A.A., Elshikh M.S., Shah M.H., Abbasi A.M.. **Phytochemical Profiling, Antioxidant and HepG2 Cancer Cells’ Antiproliferation Potential in the Kernels of Apricot Cultivars**. *Saudi J. Biol. Sci.* (2020) **27** 163-172. DOI: 10.1016/j.sjbs.2019.06.013
|
---
title: Spatio-Temporal Dynamics of Diffusion-Associated Deformations of Biological
Tissues and Polyacrylamide Gels Observed with Optical Coherence Elastography
authors:
- Yulia M. Alexandrovskaya
- Ekaterina M. Kasianenko
- Alexander A. Sovetsky
- Alexander L. Matveyev
- Vladimir Y. Zaitsev
journal: Materials
year: 2023
pmcid: PMC10004177
doi: 10.3390/ma16052036
license: CC BY 4.0
---
# Spatio-Temporal Dynamics of Diffusion-Associated Deformations of Biological Tissues and Polyacrylamide Gels Observed with Optical Coherence Elastography
## Abstract
In this work, we use the method of optical coherence elastography (OCE) to enable quantitative, spatially resolved visualization of diffusion-associated deformations in the areas of maximum concentration gradients during diffusion of hyperosmotic substances in cartilaginous tissue and polyacrylamide gels. At high concentration gradients, alternating sign, near-surface deformations in porous moisture-saturated materials are observed in the first minutes of diffusion. For cartilage, the kinetics of osmotic deformations visualized by OCE, as well as the optical transmittance variations caused by the diffusion, were comparatively analyzed for several substances that are often used as optical clearing agents, i.e., glycerol, polypropylene, PEG-400 and iohexol, for which the effective diffusion coefficients were found to be 7.4 ± 1.8, 5.0 ± 0.8, 4.4 ± 0.8 and 4.6 ± 0.9 × 10−6 cm2/s, respectively. For the osmotically induced shrinkage amplitude, the influence of the organic alcohol concentration appears to be more significant than the influence of its molecular weight. The rate and amplitude of osmotically induced shrinkage and dilatation in polyacrylamide gels is found to clearly depend on the degree of their crosslinking. The obtained results show that observation of osmotic strains with the developed OCE technique can be applied for structural characterization of a wide range of porous materials, including biopolymers. In addition, it may be promising for revealing alterations in the diffusivity/permeability of biological tissues that are potentially associated with various diseases.
## 1. Introduction
Passive diffusion, as a method of matter exchange and establishing equilibria, occurs both in living and inanimate matter, for example, during nutrient delivery within tissues and organs, bioimplant fabrication or moisture saturation of polymers. Optical clearing of biological tissues, intended to improve the conditions of optical diagnostics, also utilizes short-term saturation of tissues with optical clearing agents [1,2]. In the presence of significant concentration gradients that are typical of such situations, diffusion processes in porous media can be accompanied by significant osmotic deformations; the nature, amplitude and duration of which can affect the properties of the whole system. Under non-equilibrium conditions, it is extremely difficult to monitor the rate and sign of deformations using standard approaches based on mechanical measurements. In recent decades, deformations of porous materials associated with the diffusion of osmotically active solutions and/or dehydration have been considered mainly as by-pass phenomena accompanying the intended “useful” effects of such substances in biomedical procedures or industrial processes. Such osmotic deformations are common, in particular, for optical clearing of biological tissues with the application of hyperosmotic solutions [1,2,3,4,5] and for the diffusion of non-isotonic solutions through cell membranes [6,7], as well as for some technological processes of food preparation, especially drying of vegetables and fruits [8,9,10]. The attention on osmotically induced alterations of mechanical properties of the analyzed objects, including the quantification of the observed deformations, has been growing during the last few years [11,12,13,14]. Regarding biological tissues, the diagnostic prospects of diffusion measurements have attracted increasing interest, for example, for diagnosing diabetes mellitus [15,16]. It should be noted that techniques allowing for minimally invasive and even non-contact measurements of diffusion-associated deformations represent a special interest for this area of research.
Until recently, studies of osmotically induced deformations have been rather challenging because of the lack of suitable and practically convenient methods, as well as an insufficient understanding of the useful information that can be provided by observations of such deformations. In particular, in the approach which uses osmotically active substances for optical clearing of biological tissues, diffusion-induced deformations were reasonably considered as a factor violating the safety of the diagnostic procedures that should be reduced [17,18,19].
This article introduces a non-contact study of the spatially resolved dynamics of osmotically induced deformations in biological tissue and acrylamide polymeric gels, which are a good model of a porous material with composition-controlled properties, using optical coherent elastography (OCE). This research technique was proposed recently due to the development of appropriate methods of analysis of signals obtained by phase-sensitive optical coherence tomography [20].
The work continues the pilot studies on this topic, previously carried out by us and other scientific groups [14,21,22,23]. The present study is focused on demonstrating that osmotic deformations are characterized by complex spatio-temporal dynamics during osmotic agent diffusion into the material depth, and observation of these features can provide useful information about the structural properties of the objects under the study. Solutions of organic alcohols, such as glycerol, propylene-glycol (PG) and PEG-400, are used as the osmotically active substances, which are known to be strong osmotic agents and capable of inducing deformations of a sufficiently large amplitude [24,25,26,27,28,29,30,31,32]. For a comparison of their effect, a rather mild osmotic agent, iohexol, is also used [33,34]. All these components have been used for many years in different approaches of optical clearing of biological tissues, and their effect on biopolymers has been studied quite extensively. Therefore, we expect that the results of this work will be of interest both to the specialists in the field of optical diagnostics and to a wider range of specialists interested in diffusion-associated processes in polymers.
## 2.1. Polyacryalmide Hydrogels (Tissue Phantoms)
For tissue mimicking phantoms, polyacrylamide hydrogels were synthesized by previously developed techniques [35,36,37] using the following reagents: acrylamide (Sigma-Aldrich, St. Louis, MO, USA, ≥$99\%$), NN′-methylenebis-acrylamide ($99\%$), ammonium persulfate (Sigma-Aldrich, St. Louis, MO, USA, $98\%$) and TEMED (tetramethylethylenediamine) (Sigma-Aldrich, St. Louis, MO, USA, ≥$99.5\%$) as an initiator of polymerization. In this work, the main variable parameter for hydrogels was their crosslinking degree, determined by the ratio of acrylamide and bis-acrylamide (Table 1). The preparation technique can be briefly described as follows: the quantities of acrylamide and bis-acrylamide powders indicated in Table 1 were mixed and dissolved in distilled water. Then, 0.0005 g of ammonium persulfate was added. After complete dissolution of the reagents, 1 drop (30 µL) of TEMED was added to start the polymerization. Immediately after the addition of TEMED, a part of the mixture was collected with a cylindrical syringe of 1 mm volume. The polymerized gels were obtained as cylinders with $d = 10$ mm and kept hydrated until use.
For complementary OCE measurements, cross-sectional cylindrical cuts of hydrogels with a diameter, d, of ≈10 mm and a thickness of ~2.0 mm were prepared using a scalpel.
## 2.2. Cartilaginous Tissue
Porcine costal cartilages of the 5th–8th ribs were taken from a local butcher immediately after slaughter and stored frozen at −15 °C. Thawing was performed stepwise: first, at 4 °C for at least 8 h, then at room temperature for 1 h. Prior to the measurements, all samples were equilibrated in saline solution containing $0.9\%$ NaCl (~300 mOsm). Cross-sectional cylindrical cuts of cartilages (d ≈ 10 mm and thickness of ~2.0 mm) were prepared using a scalpel and a metal punch tool. Such prepared samples were used for OCT measurements. For spectrophotometry, the parameters of the samples were d ≈ 0.7 mm and various thicknesses (0.6–1.6 mm).
## 2.3. Spectrophotometry
Spectrophotometry was carried out on biological tissue only. The intensity of optical transmission was determined using an Ecroschim PE-5400VI Spectrophotometer (ECROSKHIM Co., Ltd., Saint-Petersburg, Russia) at 700 nm wavelength. The cartilaginous samples were put in quartz cuvettes and oriented so that the normal to the surface of the samples coincided with the axis of the optical beam in the measuring cell of the spectrophotometer. The cuvettes were filled with liquid and the dynamics of the optical transmission increase were recorded. All the chemicals used for the preparation of osmotically active solutions were of analytical grade and purchased from ChemReagent (Ufa, Bashkortostan, Russia). Omnipaque-300 (300 mg/mL iodine, $39.2\%$ mass iohexol solution) was purchased from GE Healthcare Ireland (Cork, Ireland). Distilled water was used to dissolve the reagents. The following water-based solutions were used for the transmission measurements: glycerol ($30\%$ v/v), PG ($30\%$ v/v), PEG-400 ($30\%$ v/v) and iohexol (Omnipaque, $30\%$ v/v). All the measurements were repeated at least 3–6 times using samples with different thicknesses ranging from 0.8 mm to 1.4 mm (and mechanically measured with an accuracy of 0.1 mm). These thicknesses were used for the estimation of the diffusion coefficients, the results of which are presented in Table 2.
## 2.4. Diffusion Kinetics
The diffusion kinetics corresponding to the optical clearing of the samples were estimated according to the analytical solution of Fick’s diffusion problem for the bilateral adsorption by a flat plate slab [38,39]. Diffusion through the ends of the slab was neglected. One-dimensional diffusion can be described by Fick’s second law:[1]∂C(x,t)∂t=D∂C2(x,t)∂x2 where C is the concentration of a diffusant, t is the time of diffusion, x is the coordinate inside the slab and D is the diffusion coefficient.
Within the boundary conditions corresponding to bilateral sorption by the slab with thickness H, namely C(0,t) = C0, C(H,t) = C0 and C(x,0) = 0, where C0 is the initial concentration of the solution, the analytical solution can be derived as [38,39]:[2]C(x,t)=C01−4π∑$k = 0$∞12k+1e(2k+1)2π2DtH2⋅sin(2k+1)πHx In terms of the amount of diffused substance (M) and the short-term diffusion, when the amount of substance absorbed by the slab meets the condition Mt ≤ 0.5 M∞, where *Mt is* the amount of substance absorbed for the certain time t and M∞ = C0HS (S is the surface square of the slab), the solution can be represented as:[3]MtM∞=4DtH$\frac{21}{21}$π+2∑0∞(−1)nierfcnH2Dt where ierfc(x)=1πe−x2−x⋅erfc(x) and erfc(x)=1−2π∫0xe−z2dz.
For an experimental derivation of D, one can plot the dependence of Mt/M∞ as a function of t/H. From the straight sections of the curve, it is possible to calculate the diffusion coefficient. Indeed, when the time of diffusion is small:[4]MtM∞=4πDtH$\frac{21}{2}$ parameter D can be obtained from the slope, tg(α), of Mt/M∞ versus t$\frac{1}{2}$ as [5]D=πH2tg2α16 During the measurement of optical transmission, one can assume that the ratio of the amount of substance, Mt/M∞, is proportional to the ratio of the intensity of the transmitted radiation at the moment (t) to the intensity of “saturation” when the transparency of the samples no longer changes. Therefore, the transmission intensity, measured as described in the Section 2.3, can be used for an estimation of D.
## 2.5. OCE Observation
The osmotically induced strain dynamics were studied using a custom-made common path spectral domain OCT setup designed and produced at the Institute of Applied Physics RAS. It operates at a central wavelength of 1300 nm (~90 nm spectral width), a 20 kHz rate of obtaining spectral fringes and a 20 Hz rate of acquiring B-scans, covering 4 mm laterally with a visualization depth of 2 mm (in air). The common path scheme makes it possible to use a flexible fiber-optic connection between the scanning optical probe and the basic OCT block (see Figure 1a). The OCT setup operation is controlled by a PC. Due to moderate data flow it possible to use a standard USB-2 connection without the need of a special data acquisition card. For obtaining strain maps (spatially resolved in both the axial and lateral directions), the system performs a comparison of B-scans sequentially obtained for the same position. In this study, the minimal interframe time step was 50 ms, although for sufficiently slowly varying strains, the interframe interval could be many times greater (up to tens of seconds), which allows one to perform continuous monitoring of slowly varying deformations on fairly long time intervals (~tens of minutes) without acquiring many gigabytes of data.
Unlike the widely discussed correlation-based methods used for estimating displacements and strains in various elastographic applications (e.g., [40,41,42,43]), we used the phase-resolved approach to strain estimation. It is based on the utilization of the interframe phase variation for a pair of compared OCT scans and allows one to obtain axial interframe displacements and axial strains [20]. During the acquisition of the B-scan sequence, along with the structural OCT scans (Figure 1b), color-coded maps of interframe phase variations, ΔΦ=φ2−φ1, for each pair of subsequent OCT scans were displayed in real time (see Figure 1d).
The elastographic processing basically utilizes the well-known relationship between the axial interframe displacement, ΔU, of scatterers and the resultant variation, ΔΦ=φ2−φ1, in the OCT signal phase:[6]ΔU=λ0ΔΦ4πn where λ0 is the central wavelength of the illuminating OCT signal in a vacuum and n is the refractive index of the examined tissue. The interframe axial strain, Δε, (along the depth gradient) is evidently proportional to the axial gradient of the interframe phase variation: [7]Δε≡∂(ΔU)/∂z=(λ$\frac{0}{4}$πn)∂(ΔΦ)/∂z To find interframe phase gradients, conventionally the least-square fitting of ΔΦ(z) dependence is discussed [44]; however, we used the vector approach proposed in [45,46]. It is termed vector because in this approach, until the very last processing step, the phase is not explicitly singled out and the complex value signals are treated as vectors in the complex plane. The advantages of this approach are its high robustness with respect to various measurement noises and its very high computational efficiency [45,46]. An example of the interframe strain corresponding to the phase-variation map shown in Figure 1d is presented in Figure 1e.
Usually, the maximal measurable interframe strains are on the order of 10−2 (because for even larger strains, the sequentially obtained OCT scans become too strongly decorrelated to be compared). However, much larger cumulative strains, ε, can be measured by performing summation of incremental interframe values [47], ε=∑Δε, for a series of consequently acquired OCT scans of the deformed sample. This allows one to estimate cumulative strains with magnitudes over $10\%$, for which directly compared OCT scans become completely decorrelated. An additional advantage of such a method of finding cumulative strains is the reduction in total measurement error in comparison with the initial level of noise for interframe strain maps [47,48]. Concerning the sensitivity, the developed OCE method allows for evaluation of osmotically induced strains as small as 10−4, as demonstrated in our previous study [23] (and with certain precautions, an order of magnitude smaller strains can be measurable [48]). Besides strains of osmotic origin discussed in this paper, the described method can be applied in studying strains of arbitrary origin, for example, thermally produced deformations [49], mechanical relaxations caused by internal strains [50], shrinkage due to drying [48], etc.
In the discussed studies of osmotic strains, the samples were fixed in water using an isolating plasticine cover with only the upper surface open for application of the solution that should diffuse into the sample bulk (Figure 1b). When starting the OCT recording, a ~1 mm layer of the solution was poured on the sample surface. In these OCE measurements, we used water-based solutions with various concentrations of the following components: glycerol, PG, PEG-400 and iohexol (Omnipaque). All the measurements were repeated at least 3–6 times.
## 3.1. Kinetics of Optical Clearing of Cartilaginous Samples
Figure 2 presents the kinetic curves of radiation transmittance through cartilaginous samples during their immersion in solutions of different clearing agents. The results were averaged over the measurements performed for the samples with various thicknesses: 1.4 ± 0.4 for the ones immersed in glycerol, 1.1 ± 0.3 for PG, 1.1 ± 0.3 for PEG-400 and 1.5 ± 0.3 for Omnipaque.
The maximum rate of optical clearing is observed for immersion in glycerol; the intensity of the transmitted radiation increased five-fold in the first 400 s of immersion. However, the data scattering for this group is also quite high; the relative deviation from the average is as much as 0.5. The curves for PG, PEG-400 and Omnipaque are more uniform and almost overlap taking into account the data scattering. The corresponding effective diffusion coefficients reflecting the rates of optical clearing during immersion in the solutions are presented in Table 2.
It should be noted that the values of the diffusion coefficients (Table 2) were obtained on the basis of optical measurements and reflect numerous processes at once, such as the diffusion of components into the biological tissue and the redistribution of water. The alteration in the geometrical parameters of the samples, which inevitably occurs during immersion, also may affect the measured kinetics. Thus, these coefficients obtained on the basis of Fick’s theory (see Section 2.4) are presented as effective coefficients including the interfering accompanying processes.
## 3.2. Osmotic-Induced Strain Dynamics in Polymeric Tissue Phantoms
The dynamics of osmotic-induced strain in polyacrylamide gels with different crosslinking ratios are given in Figure 3. The figure shows the distribution of strain accumulated in the first minute (Figure 3, the first row), for the first 5 min (Figure 3, the second row) and for the first 10 min (Figure 3, the third row). “ Waterfall” diagrams were built on the basis of strain monitoring with OCE during the osmotic action of a $50\%$ (v/v) water-based solution of glycerol. Blue corresponds to structural shrinkage, while yellow reflects the local dilatation of the polymers (Figure 3). One can see that with by increasing the bis-acrylamide/acrylamide ratio from $\frac{4}{1}$ to $\frac{7}{1}$, the shrinkage rate increases dramatically in the first minute of immersion (Figure 3a-1–d-1). The observed minima of the negative strain for the 1st minute of immersion with the action of $50\%$ glycerol are −0.14, −0.21, −0.24 and −0.30 for $\frac{4}{1}$, $\frac{5}{1}$, $\frac{6}{1}$ and $\frac{7}{1}$ crosslinking ratios, respectively (Figure 3a-1–d-1). Over the next 3 min, the shrinkage continues to increase; however, in the near-surface area, which is about 300 μm deep, there was an increase in the positive strain related to dilatation of the polymer (Figure 3a-2–d-2). This positive-signed strain was most pronounced for the polymer with the lowest crosslinking ratio ($\frac{4}{1}$) (Figure 3a-2), while for $\frac{6}{1}$ and $\frac{7}{1}$ ratios it is strongly overlapped by the shrinkage effect (Figure 3c-2,d-2). For the strain accumulation after about 10 min, the distribution of strain within the observed areas noticeably averages out; the diagrams show both shrinkage and near-surface dilatation (Figure 3a-3–d-3).
The negative strain reaches its minimum values (maximal shrinkage) for all experimental groups of samples within the first 50 to 150 s of immersion (Figure 4), and then it slightly increases, which is most pronounced for the $\frac{4}{1}$ ratio polymer (Figure 4).
From the presented kinetic curves (Figure 4), one can see that the osmotic-induced shrinkage rate and amplitude is simply dependent on the polymer crosslinking ratio; the higher the crosslinking degree, the more pronounced and rapid the osmotic-induced shrinkage. This observation is illustrated in Figure 5.
Thus, the dependence of the polymer osmotic-induced shrinkage rate and amplitude on the crosslinking degree was revealed. Moreover, it was also found that the sub-surface positive strain exhibited quite a different dependence on the degree of polymer crosslinking. The development of an intermediate maximum for the positive-strain was not clearly seen, in contrast to the negative-strain maximum. The waterfall, Figure 3, also shows that the development of the positive strain itself could be most clearly seen for the minimum and maximum degrees of crosslinking (for $\frac{4}{1}$ and $\frac{7}{1}$ ratios).
## 3.3. Osmotic-Induced Strain Dynamics in Cartilaginous Tissue
The osmotic-induced strain distribution accumulated after 10 min of PG and PEG-400 diffusion into cartilaginous tissue is shown in Figure 6. The strain variations according to the different concentrations of the osmotic-active agents are illustrated. A similar analysis for various concentrations of glycerol was carried out and described in our previous work [21].
Note that at concentrations below $25\%$, the negative-signed strain (shrinkage) corresponding to the blue color in the diagrams is weakly manifested and is almost absent for a concentration of $12.5\%$ (Figure 6, first row). For $25\%$ concentration, the degree of osmotic-induced shrinkage is still insignificant, while the accumulation of near-surface dilatation is already clearly seen (Figure 6, second row, yellow and red colors). At $30\%$ concentration, the sign-alternating nature of the osmotic-induced deformation becomes more pronounced; the area of near-surface dilatation exhibits a rather sharp transition to the area of shrinkage (Figure 6, third row). At $50\%$ concentration, there is a rapid pronounced shrinkage while maintaining a thin layer of sub-surface dilatation (Figure 6, fourth row).
A quantitative analysis of the dynamics of the negative-signed strain minima shows that the action of PEG-400 among all the considered agents leads to the most intense shrinkage (Figure 7c-1). It is also worth noting that for all considered agents, after the negative-signed strain curve passes through the minimum, the shrinkage amplitude gradually decreases (Figure 7a-1–c-1). The dependence of the observed minima on the agent concentration is close to linear (Figure 7a-2–c-2). The observed deviations can be associated with inhomogeneities in the tissue structure, as well as with irreversible processes occurring in high concentration solutions.
It is also interesting to consider the kinetics of the maxima of positive-signed sub-surface strain under the action of various concentrations of osmotic active agents (Figure 8). In most cases, this parameter constantly increases with time and tends to reach a certain “saturation value”. The maximum of the observed positive strain also near-linearly depends on the concentration of the osmotic agent (Figure 8a-2–c-2).
Note that for all analyzed osmotic agents, the strain value, both negative and positive, is much more dependent on concentration variations than on the nature of the agent. Thus, in the concentration range of 30–$50\%$, the values of both positive and negative strains for the three considered osmotic agents are quite close (Figure 7a-1–c-1) and Figure 8a-1–c-1). However, this observation seems only to apply to the organic alcohol osmotic agents. Omnipaque, at a comparable concentration of the active component, initiates osmotic strain of a noticeably lower rate and amplitude (Figure 9). The comparative dynamics of strain minima and maxima shown in Figure 9 also reveal that the action of the high molecular weight agent PEG-400 induces the most intense osmotic shrinkage, and thus affects the value of the positive-signed strain, which is lower than for the other two alcohols (Figure 9).
## 4. Discussion
To date, the osmotic effects of various active solutions have attracted significant attention in the context of various biomedical applications. In particular, such effects may be rather pronounced in the applications of optical clearing agents (OCAs) in biological tissues [1,2,3,4,30,51]. Optical clearing makes it possible to expand the scope of optical diagnostics by increasing the depth of optical probing of biological tissues (up to examining entire organs and even small organisms) and improving the accuracy/resolution of optical methods. The majority of solutions used for optical clearing of biological tissues are osmotically active. In order to achieve a more pronounced effect of optical clearing, the concentrations of optical clearing agents (OCAs) are required to be higher than the isotonic concentrations of biological fluids, which are usually around 300 mOsm, for example, in $0.9\%$ NaCl solution. The administration of isotonic solutions usually does not generate gradients of chemical potential and does not induce active redistribution of the solutes. In our previous work, we confirmed the absence of any noticeable osmotically induced deformation after administration of fresh saline into biological tissue [21]. However, it should be taken into account that during long-term storage, the acidity of the saline solution noticeably increases, which can affect both the chemical potential gradient upon contact with biological tissues and the resulting deformation [52].
Meanwhile, the effects induced by OCA administration are quite different. For instance, one of the most popular OCAs, $50\%$ (w/w) water-based solution of glycerol, has an osmolarity around 6000 mOsm, which is capable of generating rather high osmotic gradients when in contact with a physiological environment. In turn, such a high concentration gradient can cause a number of negative side effects, such as severe dehydration of tissues and cells, destruction of cell membranes and cell death. For this reason, there is a continuous search for new formulas of substances and solutions that allow achieving an acceptable effect of optical clearing and avoid these extreme conditions [17,18,53]. One of the methods used is gradually increasing the concentration of the active solution. However, this approach requires a longer time for the development of the OCA administration effect and for this reason is not well suited for in vivo application.
Besides, in recent decades, osmotic effects in biological tissues have also been studied with regard to the effect of concentration gradients on cells and cell membranes, which is of importance for the use of various osmotically active solutions for diagnostics and therapy [3,6,7]. At the same time, it is rather challenging to study the dynamics of osmotically induced deformations of the extracellular matrix at the macroscopic level because of its multicomponent composition, structural complexity, the variability of the level of its hydration and, importantly, due to the lack of convenient methods and techniques for monitoring such deformations. At the same time, numerous works have shown that the structural features of the extracellular matrix of porous water-rich collagenous tissues maintain a certain amount of tissue hydration and govern the osmotic-mechanical regulation of tissue functioning [54,55,56,57]. The quality of the latter, in turn, depends on the tissue condition. Thus, the monitoring of the dynamics and amplitude of osmotically induced deformations of extracellular tissue matrices can represent a separate direction in the diagnosis of biological tissues.
In the present work, we utilize the recently developed method of visualizing spatially resolved strains using phase-resolved optical coherence tomography. Usually, this technique is discussed in the context of quantitative characterization of elastic properties of biological tissues based on the principle of compression elastography [20], mostly for diagnostic of various tumors [58,59,60,61], including characterization of nonlinear elastic properties of biological tissues [62,63]. However, the method of OCT-based phase-resolved visualization of strains can be applied for studying deformations of very different origins, including thermally induced strains [64] and strains related to drying [48] or mechanical relaxations [50], as well as the evolution of osmotically induced deformations (strains) in porous biological tissues and polymeric tissue mimicking gels as in [21,23] and in the present work.
The experimental study of osmotically induced deformations in biopolymers, especially the time-resolved dynamics of their evolution, represents a rather complex non-trivial task. Basically, the description of such deformations relies on the averaged measurements of a sample’s thickness, weight and/or volume parameters [7,8,9,10,19] or on the consideration of elastic characteristics based on the knowledge of the initial conditions and the state of the system at the end of diffusion [3]. However, the time-resolved evolution of strain itself may provide useful information regarding the structure of the analyzed objects and their specific response to diffusion, especially as they are determined in non-equilibrium conditions where concentrations of diffusants and chemical potential gradients are high. Until recently, the direct observation of the dynamics of tissue deformations during diffusion was not possible. The first results were obtained using different modalities of OCE [21,22,23].
As it is shown in the present study, at least for the first 10–15 min of diffusion of an osmotically active agent, strongly non-uniform sign-alternating deformations of the porous media accumulate on the sub-millimeter scale (Figure 2 and Figure 5). The most pronounced effect observed in the first several minutes of diffusion of highly concentrated solutions of osmotically active agents is the subsurface shrinkage related to biopolymer dehydration. Interestingly, this was observed previously as a hindrance for optical clearing of skin during the first 10–20 min of diffusion [65]. The osmotically induced shrinkage is clearly visualized for both the polyacrylamide gels (Figure 3 and Figure 4) and cartilaginous tissue (Figure 6, Figure 7 and Figure 9a). The OCE diagrams in Figure 3 show that the area of shrinkage zones colored in blue pronouncedly increases with increasing time; the amplitude of the dehydration-related negative strain (shrinkage) depends on the agent concentration and the type of material (see Figure 6). In terms of the strain minima kinetics (Figure 4 and Figure 7), cartilaginous tissue is more similar to the polyacrylamide gels with the crosslinking ratios of $\frac{6}{1}$ and $\frac{7}{1}$; for these groups of samples, the strain minima is reached at ~200 s of diffusion when the diffusant concentration is 35–$50\%$ (v/v). Accordingly, its amplitude for gels with crosslinking ratios of $\frac{6}{1}$ and $\frac{7}{1}$ and cartilage is within the range of (−0.30)–(−0.35). For the gels with crosslinking ratios of $\frac{4}{1}$ and $\frac{5}{1}$, the dehydration-related strain is less pronounced, with the minima of −0.15 and −0.25, respectively. Since the initial hydration levels for all synthesized types of polymeric gels are very similar (Table 1), the observed variations in the amplitude of their osmotically induced shrinkage apparently should be determined by the differences in their matrix structure. For the $\frac{4}{1}$, $\frac{5}{1}$, $\frac{6}{1}$ and $\frac{7}{1}$ gels, the percentages of crosslinker bis-acrylamide are 4.00, 3.32, 2.84 and $2.50\%$, respectively. This means that the percentage of crosslink connections between the acrylamide linear chains decreases with the increasing acrylamide/bis-acrylamide ratio (Figure 10).
For polyacrylamide gels, it was reported that the pore radii substantially decrease with the increase in bis-acrylamide content [36,37]. For example, in [36], the pore size was estimated from Ferguson plots of linear DNA fragments. For a total polyacrylamide concentration of $10.5\%$, in the initial solution (acrylamide + bis-acrylamide, w/v), the pore radii increased to 32, 70, 102, 108 and 110 nm for 4.0, 2.0., 1.5 and $1.0\%$ (w/w) of bis-acrylamide, respectively. It is noteworthy that the decrease in bis-acrylamide concentration from 4 to $2\%$ (w/w) resulted in the most dramatic pore size increase (more than two-fold), whereas the further decrease in concentration did not lead to a significant change in the pore size [36]. Additionally, the lower the total polyacrylamide concentration (w/v) in the initial solution, the larger the pore size of the synthesized polymeric gel [36]; for the fixed $4\%$ bis-acrylamide concentration, the pore size dropped from 60 to 32 nm with an initial polyacrylamide concentration increase from 3.5 to $10.5\%$.
In the present study, the polyacrylamide concentration in the initial solution for all prepared samples was ~$20\%$ (the total weight with extraction of water, Table 1). Therefore, the pore size, although not specially studied in this work, is expected to be even less than that described in [36] for a polyacrylamide concentration of $10.5\%$. Additionally, the tendency of the pore size to decrease with the increase in bis-acrylamide concentration is expected to be retained. These structural features in a straightforward way explain the increase in the rate of polymer dehydration with an increase in the ratio of acrylamide to bis-acrylamide (Figure 5); the osmotic water outflow is faster through larger pores. Note that according to Figure 5, the dehydration rate slows down when the total concentration of bis-acrylamide falls below $3\%$, as $\frac{6}{1}$ and $\frac{7}{1}$ gels possess quite close strain minima values (Figure 5). It seems reasonable to expect that a further increase in pore size will have a smaller influence on the rate of liquid outflow. Thus, a quantitative analysis of the rate and amplitude of osmotically induced deformation performed with OCE paves the way to differentiate between similar materials with variable specific characteristics, namely the pore size according to the crosslinking degree. This observation opens up the prospect of structural diagnostics of polymers and biological tissues by the developed OCE technique.
The subsurface dilatation, i.e., the positive strain shown on OCE diagrams as yellow to red (Figure 3), is also dependent on the gel crosslinking ratio, however, in a more complex manner. The depth range with dilatation is wider for $\frac{4}{1}$ and $\frac{5}{1}$ gels than for $\frac{6}{1}$ and $\frac{7}{1}$ ones. Namely, for the first 10 min of strain accumulation, the area where S ≥ 0 goes as deep as ~500 µm and ~400 µm for $\frac{4}{1}$ and $\frac{5}{1}$ gels, respectively (Figure 3). For $\frac{6}{1}$ and $\frac{7}{1}$ gels, the depth where S ≥ 0 does not exceed 160–180 µm. The maximum dilatation amplitude after 10 min of strain accumulation is the highest for the $\frac{7}{1}$ gel at 0.18 compared to 0.10, 0.08 and 0.07 for the $\frac{4}{1}$, $\frac{5}{1}$ and $\frac{6}{1}$ gels, respectively. Apparently, this positive-signed deformation is due to the swelling of the structure caused by the diffusion of the osmotically active agent within the polymer matrix.
The process of swelling during optical clearing of biological tissues has been pointed out in some previous works [4,51,53]. It was shown that swelling requires a longer time than dehydration, which can develop within several minutes. Organic molecules of clearing agents move slower compared to water molecules. On a macroscopic scale, however, tissues do not always swell when exposed to optical clearing. Often, as a result of tissue saturation, a pronounced overall shrinkage was observed [25,26,32]. Evidently, the resultant variations in the macroscopic parameters, such as thickness, volume and other geometric characteristics of the samples, depend on the ratio of dehydration and swelling effects under the action of particular solutions. Using the OCE technique described in this work, these processes can be observed separately by analyzing the spatial and time-resolved strain distributions (Figure 3 and Figure 6).
The kinetics of optical clearing of cartilaginous samples with glycerol, PG, PEG-400 and Omnipaque, measured for the first several minutes of diffusion, show that at this stage the differences among the used substances are not pronounced, which agrees with the comparable values of the effective diffusion coefficients (Table 2). It is reasonable to assume that the increase in the tissue transparency measured by means of the optical transmission method is the result of both partial dehydration and diffusion of the clearing agent. Thus, the measured kinetic parameters reflect the rate of the multistage optical clearing mechanism, which in our case is somewhat more efficient for glycerol. *In* general, according to the data shown in Table 2, with an increase in molecular weight, the rate of the optical clearing process slows down. The more pronounced rate of glycerol-induced optical clearing, clearly seen in Figure 2 for the optical transmission data originates, presumably, from glycerol’s ability to form compact hydrogen bonds with the components of the tissue matrix; glycerol is comparatively small, mobile and contains three OH groups. PG, although it has a lower molecular weight, contains only two OH groups per molecule, which reduces its dehydrating effect. The values of the effective diffusion coefficients measured by the optical transmission method coincide by an order of magnitude with the values measured previously for other types of tissues [27,28,29]. The correlation between diffusion, measured by the optical transmittance method, and deformation does not necessarily reveal itself in a straightforward way, as their interplay depends on numerous factors, including water outflow and redistribution and agent integration into the tissue. For a concentration of $30\%$, which was used for optical transmission measurements, the terminal strain values are observed more quickly (in 100–200 s) (Figure 9a) than the time if takes for the gradually increasing clearing effect to reach its maximum (about 400 s) (Figure 2). This fact testifies in favor of the assumption of different processes responsible for the deformation and optical clearing; water outflow is faster and depends mainly on the concentration gradient, while the agent inflow is dependent on the dehydration rate as well as on the molecular weight of a substance. Direct measurements of diffusion can probably clarify the mechanism of formation of diffusion-coupled deformation.
The evolution of osmotically induced deformations in cartilaginous tissue is qualitatively similar to that obtained for gels (Figure 6), which indicates the presence of a universal mechanism for such deformations in porous materials. With an increase in the solution concentration, the alternating-sign character of the deformation distribution becomes more pronounced, which agrees with previous observations [21]. Interestingly, alternating-sign tissue deformations have been observed previously during tensile tests of aorta impregnated with PG solution [14]. However, in [14], with a fairy long acquisition time (8 min), the equilibrated accumulated deformation was caused by additional tensile loading of the blood vessel wall with a pronounced layered inhomogeneity in the wall thickness. On the contrary, in the present study there are no external stresses applied to the tissue, except of chemical potential gradients. Moreover, the areas of strain with opposite signs were clearly observed from the first several seconds of diffusion in both very homogeneous gel samples (Figure 3) and cartilage samples (Figure 9) that were also fairly uniform. This, at first glance, is “counterintuitive”. The alternating-sign strain distribution even in homogeneous samples can be attributed to the redistribution of water (as the fastest diffusant) in response to a rapid increase in chemical potential due to the diffusion of osmotically active agents.
A comparison of the osmotic effects for equal concentrations of glycerol, PG and PEG-400 on the strain distributions does not reveal a stronger effect of glycerol (Figure 6, Figure 7a, Figure 8a and Figure 9). Among the used organic alcohols, both positive- and negative-signed deformations depend more on concentration than on the type of alcohol in the range of concentrations from 30 to $50\%$ (v/v) (Figure 7a and Figure 8a). A decrease in the concentration of glycerol leads to a stronger drop in its dehydrating effect than in the case of other alcohols (Figure 7a). It is shown that the application of an equal concentration of a substance with a more branched molecular structure, i.e., iohexol (Omnipaque), has a weaker effect in terms of osmotically induced deformations (Figure 9), which do not exceed 0.1 for both positive- and negative-signed strains.
## 5. Conclusions
In this work, we applied optical coherence elastography to the study of the spatio-temporal dynamics of osmotically induced deformations in cartilage and polyacrylamide tissue mimicking gels under the action of organic alcohols and iohexol (Omnipaque). This method allows one to obtain the time and spatially resolved diagrams of deformations under non-equilibrium conditions on a sub-millimeter scale. It is shown that the highest intensity of deformation is observed within the first 10 min of diffusion and appears as a complex alternating sign field in which both the dehydration and swelling of the material occur. For polyacrylamide gels, the rate and amplitude of shrinkage and dilatation depend on the degree of crosslinking. For cartilaginous tissue, the influence of the concentration of organic alcohols on the osmotically induced deformations is more significant than the influence of the molecular weights of the agents. However, the significantly lower intensity of strain observed for the equal concentration of iohexol indicates that the osmotic deformation effect still depends on the nature of the diffusant. The obtained results show that the developed OCE technique is promising for the structural diagnostics of a wide range of porous materials, including biopolymers.
Furthermore, our results indicate the close connection of the characteristics of osmotic strains with the permeability of tissues and their diffusion properties, for which numerous discussions in the literature can be found on how these properties may be significantly affected by various diseases. However, up to now, there was not a sufficiently simple and nondestructive means to study these diffusion properties. In view of this, it may be expected that the described OCE-based technique, allowing observation of osmotic strains developed in tissues in reaction to application of biologically non-destructive/non-toxic osmotically active agents, may be used as a prospective rapid and minimally invasive method of biomedical diagnostics, with the possibility of in vivo usage.
## References
1. Tuchin V.V., Dan Z., Genina E.A.. *Handbook of Tissue Optical Clearing: New Prospects in Optical Imaging* (2022)
2. Genina E.A., Bashkatov A.N., Sinichkin Y.P., Yanina I.Y., Tuchin V.V.. **Optical Clearing of Biological Tissues: Prospects of Application in Medical Diagnostics and Phototherapy**. *J. Biomed. Photonics Eng.* (2015) **1** 22-58. DOI: 10.18287/JBPE-2015-1-1-22
3. Neu C.P., Novak T., Gilliland K.F., Marshall P., Calve S.. **Optical Clearing in Collagen- and Proteoglycan-Rich Osteochondral Tissues**. *Osteoarthr. Cartil.* (2015) **23** 405-413. DOI: 10.1016/j.joca.2014.11.021
4. Costantini I., Cicchi R., Silvestri L., Vanzi F., Pavone F.S.. **In-Vivo and Ex-Vivo Optical Clearing Methods for Biological Tissues: Review**. *Biomed. Opt. Express* (2019) **10** 5251. DOI: 10.1364/BOE.10.005251
5. Bashkatov A.N., Berezin K.V., Dvoretskiy K.N., Chernavina M.L., Genina E.A., Genin V.D., Kochubey V.I.. **Measurement of Tissue Optical Properties in the Context of Tissue Optical Clearing**. *J. Biomed. Opt.* (2018) **23** 1. DOI: 10.1117/1.JBO.23.9.091416
6. Finan J.D., Guilak F.. **The Effects of Osmotic Stress on the Structure and Function of the Cell Nucleus**. *J. Cell. Biochem.* (2010) **109** 460-467. DOI: 10.1002/jcb.22437
7. Van den Abbeel E., Schneider U., Liu J., Agca Y., Critser J.K., Van Steirteghem A.. **Osmotic Responses and Tolerance Limits to Changes in External Osmolalities, and Oolemma Permeability Characteristics, of Human in Vitro Matured MII Oocytes**. *Hum. Reprod.* (2007) **22** 1959-1972. DOI: 10.1093/humrep/dem083
8. Giraldo G., Talens P., Fito P., Chiralt A.. **Influence of Sucrose Solution Concentration on Kinetics and Yield during Osmotic Dehydration of Mango**. *J. Food Eng.* (2003) **58** 33-43. DOI: 10.1016/S0260-8774(02)00331-X
9. Aboltins A., Rubina T., Palabinskis J.. **Shrinkage Effect on Diffusion Coefficient during Carrot Drying**. *Agron. Res.* (2018) **16** 1301-1311. DOI: 10.15159/AR.18.112
10. Lazarides H.N., Mavroudis N.E.. **Kinetics of Osmotic Dehydration of a Highly Shrinking Vegetable Tissue in a Salt-Free Medium**. *J. Food Eng.* (1996) **30** 61-74. DOI: 10.1016/S0260-8774(96)00042-8
11. Fajrial A.K., Liu K., Gao Y., Gu J., Lakerveld R., Ding X.. **Characterization of Single-Cell Osmotic Swelling Dynamics for New Physical Biomarkers**. *Anal. Chem.* (2021) **93** 1317-1325. DOI: 10.1021/acs.analchem.0c02289
12. Natonik-Białoń S., Borowska-Wykręt D., Mosca G., Grelowski M., Wrzalik R., Smith R.S., Kwiatkowska D.. **Deformation of a Cell Monolayer Due to Osmotic Treatment: A Case Study of Onion Scale Epidermis**. *Botany* (2020) **98** 21-36. DOI: 10.1139/cjb-2019-0027
13. Song Z., Song W., Cheng Y., Yang T., Wang T., Wang K.. **Investigation on Strain Characteristics and Fatigue Constitutive Model of Limestone under Osmotic Pressure and Cyclic Disturbance Coupling**. *KSCE J. Civ. Eng.* (2022) **26** 1740-1753. DOI: 10.1007/s12205-022-1416-3
14. Acosta Santamaría V.A., García M.F., Molimard J., Avril S.. **Characterization of Chemoelastic Effects in Arteries Using Digital Volume Correlation and Optical Coherence Tomography**. *Acta Biomater.* (2020) **102** 127-137. DOI: 10.1016/j.actbio.2019.11.049
15. Tuchina D., Bashkatov A., Bucharskaya A., Genina E., Tuchin V.. **Study of Glycerol Diffusion in Skin and Myocardium Ex Vivo under the Conditions of Developing Alloxan-Induced Diabetes**. *JBPE* (2017) **3** 020302. DOI: 10.18287/JBPE17.03.020302
16. Smolyanskaya O.A., Schelkanova I.J., Kulya M.S., Odlyanitskiy E.L., Goryachev I.S., Tcypkin A.N., Grachev Y.V., Toropova Y.G., Tuchin V.V.. **Glycerol Dehydration of Native and Diabetic Animal Tissues Studied by THz-TDS and NMR Methods**. *Biomed. Opt. Express* (2018) **9** 1198. DOI: 10.1364/BOE.9.001198
17. Sdobnov A., Darvin M.E., Lademann J., Tuchin V.. **A Comparative Study of Ex Vivo Skin Optical Clearing Using Two-Photon Microscopy**. *J. Biophoton.* (2017) **10** 1115-1123. DOI: 10.1002/jbio.201600066
18. Kim K., Na M., Oh K., Cho E., Han S.S., Chang S.. **Optimized Single-Step Optical Clearing Solution for 3D Volume Imaging of Biological Structures**. *Commun. Biol.* (2022) **5** 431. DOI: 10.1038/s42003-022-03388-8
19. Varaka M., Vardaki M.Z., Gaitanis G., Bassukas I.D., Kourkoumelis N.. **The Effect of Different Optical Clearing Agents on the Attenuation Coefficient and Epidermal Thickness of Human Skin Assessed by Optical Coherence Tomography**. *Appl. Sci.* (2022) **12**. DOI: 10.3390/app12168277
20. Zaitsev V.Y., Matveyev A.L., Matveev L.A., Sovetsky A.A., Hepburn M.S., Mowla A., Kennedy B.F.. **Strain and Elasticity Imaging in Compression Optical Coherence Elastography: The Two-decade Perspective and Recent Advances**. *J. Biophotonics* (2021) **14** e202000257. DOI: 10.1002/jbio.202000257
21. Lawman S., Madden P.W., Romano V., Dong Y., Mason S., Williams B.M., Kaye S.B., Willoughby C.E., Harding S.P., Shen Y.-C.. **Deformation Velocity Imaging Using Optical Coherence Tomography and Its Applications to the Cornea**. *Biomed. Opt. Express* (2017) **8** 5579. DOI: 10.1364/BOE.8.005579
22. Liu C.-H., Singh M., Li J., Han Z., Wu C., Wang S., Idugboe R., Raghunathan R., Sobol E.N., Tuchin V.V.. **Quantitative Assessment of Hyaline Cartilage Elasticity During Optical Clearing Using Optical Coherence Elastography**. *Sovrem Teh. Med.* (2015) **7** 44-51. DOI: 10.17691/stm2015.7.1.06
23. Alexandrovskaya Y., Baum O., Sovetsky A., Matveyev A., Matveev L., Sobol E., Zaitsev V.. **Optical Coherence Elastography as a Tool for Studying Deformations in Biomaterials: Spatially-Resolved Osmotic Strain Dynamics in Cartilaginous Samples**. *Materials* (2022) **15**. DOI: 10.3390/ma15030904
24. Oliveira L., Lage A., Clemente M.P., Tuchin V.V.. **Optical Clearing of Muscle with Propylene Glycol**. *Proc. SPIE* (2009) **7547** 75470E. DOI: 10.1117/12.853540
25. Tuchina D.K., Genin V.D., Bashkatov A.N., Genina E.A., Tuchin V.V.. **Optical Clearing of Skin Tissue Ex Vivo with Polyethylene Glycol**. *Opt. Spectrosc.* (2016) **120** 28-37. DOI: 10.1134/S0030400X16010215
26. Jaafar A., Darvin M.E., Tuchin V.V., Veres M.. **Confocal Raman Micro-Spectroscopy for Discrimination of Glycerol Diffusivity in Ex Vivo Porcine Dura Mater**. *Life* (2022) **12**. DOI: 10.3390/life12101534
27. Silva H.F., Martins I.S., Bogdanov A.A., Tuchin V.V., Oliveira L.M.. **Characterization of Optical Clearing Mechanisms in Muscle during Treatment with Glycerol and Gadobutrol Solutions**. *J. Biophotonics* (2022). DOI: 10.1002/jbio.202200205
28. Lai J.-H., Liao E.-Y., Liao Y.-H., Sun C.-K.. **Investigating the Optical Clearing Effects of 50% Glycerol in Ex Vivo Human Skin by Harmonic Generation Microscopy**. *Sci. Rep.* (2021) **11** 329. DOI: 10.1038/s41598-020-77889-z
29. Wen X., Mao Z., Han Z., Tuchin V.V., Zhu D.. **In Vivo Skin Optical Clearing by Glycerol Solutions: Mechanism**. *J. Biophoton.* (2009) **3** 44-52. DOI: 10.1002/jbio.200910080
30. Yu T., Zhu J., Li D., Zhu D.. **Physical and Chemical Mechanisms of Tissue Optical Clearing**. *iScience* (2021) **24** 102178. DOI: 10.1016/j.isci.2021.102178
31. Mao Z., Zhu D., Hu Y., Wen X., Han Z.. **Influence of Alcohols on the Optical Clearing Effect of Skin in Vitro**. *J. Biomed. Opt.* (2008) **13** 021104. DOI: 10.1117/1.2892684
32. Alexandrovskaya Y., Sadovnikov K., Sharov A., Sherstneva A., Evtushenko E., Omelchenko A., Obrezkova M., Tuchin V., Lunin V., Sobol E.. **Controlling the Near-Infrared Transparency of Costal Cartilage by Impregnation with Clearing Agents and Magnetite Nanoparticles**. *J. Biophotonics* (2018) **11** e201700105. DOI: 10.1002/jbio.201700105
33. Alexandrovskaya Y.M., Evtushenko E.G., Obrezkova M.V., Tuchin V.V., Sobol E.N.. **Control of Optical Transparency and Infrared Laser Heating of Costal Cartilage via Injection of Iohexol**. *J. Biophotonics* (2018) **11** e201800195. DOI: 10.1002/jbio.201800195
34. Bykov A., Hautala T., Kinnunen M., Popov A., Karhula S., Saarakkala S., Nieminen M.T., Tuchin V., Meglinski I.. **Imaging of Subchondral Bone by Optical Coherence Tomography upon Optical Clearing of Articular Cartilage**. *J. Biophotonics* (2016) **9** 270-275. DOI: 10.1002/jbio.201500130
35. Fedulova V., Yuzhakov A., Baum O.. **Study of Laser Radiation Effect on the Cornea of the Eye by Speckle Interferometry**. *J. Biomed. Photonics Eng.* (2020) **6** 010302. DOI: 10.18287/JBPE20.06.010302
36. Holmes D.L., Stellwagen N.C.. **Estimation of Polyacrylamide Gel Pore Size from Ferguson Plots of Linear DNA Fragments. II. Comparison of Gels with Different Crosslinker Concentrations, Added Agarose and Added Linear Polyacrylamide**. *Electrophoresis* (1991) **12** 612-619. DOI: 10.1002/elps.1150120903
37. Carvalho B.M.A., Da Silva S.L., Da Silva L.H.M., Minim V.P.R., Da Silva M.C.H., Carvalho L.M., Minim L.A.. **Cryogel Poly(Acrylamide): Synthesis, Structure and Applications**. *Sep. Purif. Rev.* (2014) **43** 241-262. DOI: 10.1080/15422119.2013.795902
38. Crank J.. *The Mathematics of Diffusion* (1975)
39. Bagratashvili V.N., Sobol E.N., Sviridov A.P., Popov V.K., Omel’chenko A.I., Howdle S.M.. **Thermal and Diffusion Processes in Laser-Induced Stress Relaxation and Reshaping of Cartilage**. *J. Biomech.* (1997) **30** 813-817. DOI: 10.1016/S0021-9290(97)00028-6
40. Ophir J., Cespedes I., Ponnekanti H., Yazdi Y., Li X.. **Elastography: A Quantitative Method for Imaging the Elasticity of Biological Tissues**. *Ultrason. Imaging* (1991) **13** 111-134. DOI: 10.1177/016173469101300201
41. Schmitt J.M.. **OCT Elastography: Imaging Microscopic Deformation and Strain of Tissue**. *Opt. Express* (1998) **3** 199. DOI: 10.1364/OE.3.000199
42. Zaitsev V.Y., Matveev L.A., Gelikonov G.V., Matveyev A.L., Gelikonov V.M.. **A Correlation-Stability Approach to Elasticity Mapping in Optical Coherence Tomography**. *Laser Phys. Lett.* (2013) **10** 065601. DOI: 10.1088/1612-2011/10/6/065601
43. Fu J., Haghighi-Abayneh M., Pierron F., Ruiz P.D.. **Depth-Resolved Full-Field Measurement of Corneal Deformation by Optical Coherence Tomography and Digital Volume Correlation**. *Exp. Mech.* (2016) **56** 1203-1217. DOI: 10.1007/s11340-016-0165-y
44. Kennedy B.F., Koh S.H., McLaughlin R.A., Kennedy K.M., Munro P.R.T., Sampson D.D.. **Strain Estimation in Phase-Sensitive Optical Coherence Elastography**. *Biomed. Opt. Express* (2012) **3** 1865. DOI: 10.1364/BOE.3.001865
45. Matveyev A.L., Matveev L.A., Sovetsky A.A., Gelikonov G.V., Moiseev A.A., Zaitsev V.Y.. **Vector Method for Strain Estimation in Phase-Sensitive Optical Coherence Elastography**. *Laser Phys. Lett.* (2018) **15** 065603. DOI: 10.1088/1612-202X/aab5e9
46. Zaitsev V.Y., Ksenofontov S.Y., Sovetsky A.A., Matveyev A.L., Matveev L.A., Zykov A.A., Gelikonov G.V.. **Real-Time Strain and Elasticity Imaging in Phase-Sensitive Optical Coherence Elastography Using a Computationally Efficient Realization of the Vector Method**. *Photonics* (2021) **8**. DOI: 10.3390/photonics8120527
47. Sovetsky A.A., Matveyev A.L., Matveev L.A., Shabanov D.V., Zaitsev V.Y.. **Manually-Operated Compressional Optical Coherence Elastography with Effective Aperiodic Averaging: Demonstrations for Corneal and Cartilaginous Tissues**. *Laser Phys. Lett.* (2018) **15** 085602. DOI: 10.1088/1612-202X/aac879
48. Zaitsev V.Y., Matveev L.A., Matveyev A.L., Sovetsky A.A., Shabanov D.V., Ksenofontov S.Y., Gelikonov G.V., Baum O.I., Omelchenko A.I., Yuzhakov A.V.. **Optimization of Phase-Resolved Optical Coherence Elastography for Highly-Sensitive Monitoring of Slow-Rate Strains**. *Laser Phys. Lett.* (2019) **16** 065601. DOI: 10.1088/1612-202X/ab183c
49. Zaitsev V.Y., Matveyev A.L., Matveev L.A., Gelikonov G.V., Baum O.I., Omelchenko A.I., Shabanov D.V., Sovetsky A.A., Yuzhakov A.V., Fedorov A.A.. **Revealing Structural Modifications in Thermomechanical Reshaping of Collagenous Tissues Using Optical Coherence Elastography**. *J. Biophotonics* (2019) **12** e201800250. DOI: 10.1002/jbio.201800250
50. Alexandrovskaya Y.M., Baum O.I., Sovetsky A.A., Matveyev A.L., Matveev L.A., Sobol E.N., Zaitsev V.Y.. **Observation of Internal Stress Relaxation in Laser-Reshaped Cartilaginous Implants Using OCT-Based Strain Mapping**. *Laser Phys. Lett.* (2020) **17** 085603. DOI: 10.1088/1612-202X/ab9446
51. Genina E.A.. **Tissue Optical Clearing: State of the Art and Prospects**. *Diagnostics* (2022) **12**. DOI: 10.3390/diagnostics12071534
52. Reddi B.A.. **Why Is Saline So Acidic (and Does It Really Matter?)**. *Int. J. Med. Sci.* (2013) **10** 747-750. DOI: 10.7150/ijms.5868
53. Berezin K.V., Dvoretsky K.N., Chernavina M.L., Nechaev V.V., Likhter A.M., Shagautdinova I.T., Antonova E.M., Tuchin V.V.. **In Vivo Optical Clearing of Human Skin under the Effect of Aqueous Solutions of Some Monosaccharides**. *J. Phys. Conf. Ser.* (2019) **1400** 033018. DOI: 10.1088/1742-6596/1400/3/033018
54. Wan L.Q., Miller C., Guo X.E., Mow V.C.. **Fixed Electrical Charges and Mobile Ions Affect the Measurable Mechano-Electrochemical Properties of Charged-Hydrated Biological Tissues: The Articular Cartilage Paradigm**. *Mech. Chem. Biosyst.* (2004) **1** 81-99. PMID: 16783948
55. Maroudas A.. **Biophysical Chemistry of Cartilaginous Tissues with Special Reference to Solute and Fluid Transport**. *BIR* (1975) **12** 233-248. DOI: 10.3233/BIR-1975-123-416
56. Maroudas A.. **Balance between Swelling Pressure and Collagen Tension in Normal and Degenerate Cartilage**. *Nature* (1976) **260** 808-809. DOI: 10.1038/260808a0
57. Lanir Y.. **Mechanisms of Residual Stress in Soft Tissues**. *J. Biomech. Eng.* (2009) **131** 044506. DOI: 10.1115/1.3049863
58. Allen W.M., Chin L., Wijesinghe P., Kirk R.W., Latham B., Sampson D.D., Saunders C.M., Kennedy B.F.. **Wide-Field Optical Coherence Micro-Elastography for Intraoperative Assessment of Human Breast Cancer Margins**. *Biomed. Opt. Express* (2016) **7** 4139. DOI: 10.1364/BOE.7.004139
59. Gubarkova E.V., Sovetsky A.A., Zaitsev V.Y., Matveyev A.L., Vorontsov D.A., Sirotkina M.A., Matveev L.A., Plekhanov A.A., Pavlova N.P., Kuznetsov S.S.. **OCT-Elastography-Based Optical Biopsy for Breast Cancer Delineation and Express Assessment of Morphological/Molecular Subtypes**. *Biomed. Opt. Express* (2019) **10** 2244. DOI: 10.1364/BOE.10.002244
60. Sirotkina M.A., Gubarkova E.V., Plekhanov A.A., Sovetsky A.A., Elagin V.V., Matveyev A.L., Matveev L.A., Kuznetsov S.S., Zagaynova E.V., Gladkova N.D.. **In Vivo Assessment of Functional and Morphological Alterations in Tumors under Treatment Using OCT-Angiography Combined with OCT-Elastography**. *Biomed. Opt. Express* (2020) **11** 1365. DOI: 10.1364/BOE.386419
61. Plekhanov A.A., Sirotkina M.A., Sovetsky A.A., Gubarkova E.V., Kuznetsov S.S., Matveyev A.L., Matveev L.A., Zagaynova E.V., Gladkova N.D., Zaitsev V.Y.. **Histological Validation of in Vivo Assessment of Cancer Tissue Inhomogeneity and Automated Morphological Segmentation Enabled by Optical Coherence Elastography**. *Sci. Rep.* (2020) **10** 11781. DOI: 10.1038/s41598-020-68631-w
62. Qiu Y., Zaki F.R., Chandra N., Chester S.A., Liu X.. **Nonlinear Characterization of Elasticity Using Quantitative Optical Coherence Elastography**. *Biomed. Opt. Express* (2016) **7** 4702. DOI: 10.1364/BOE.7.004702
63. Gubarkova E.V., Sovetsky A.A., Matveev L.A., Matveyev A.L., Vorontsov D.A., Plekhanov A.A., Kuznetsov S.S., Gamayunov S.V., Vorontsov A.Y., Sirotkina M.A.. **Nonlinear Elasticity Assessment with Optical Coherence Elastography for High-Selectivity Differentiation of Breast Cancer Tissues**. *Materials* (2022) **15**. DOI: 10.3390/ma15093308
64. Baum O.I., Zaitsev V.Y., Yuzhakov A.V., Sviridov A.P., Novikova M.L., Matveyev A.L., Matveev L.A., Sovetsky A.A., Sobol E.N.. **Interplay of Temperature, Thermal-stresses and Strains in Laser-assisted Modification of Collagenous Tissues: Speckle-contrast and OCT-based Studies**. *J. Biophotonics* (2020) **13**. DOI: 10.1002/jbio.201900199
65. Lin Q., Lazareva E.N., Kochubey V.I., Duan Y., Tuchin V.V.. **Kinetics of Optical Clearing of Human Skin Studied in Vivo Using Portable Raman Spectroscopy**. *Laser Phys. Lett.* (2020) **17** 105601. DOI: 10.1088/1612-202X/abae6d
|
---
title: Induction of Labor in Twins—Double Trouble?
authors:
- Miriam Lopian
- Lior Kashani-Ligumsky
- Ronnie Cohen
- Izaak Wiener
- Bat-Chen Amir
- Yael Gold Zamir
- Ariel Many
- Hadar Rosen
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004183
doi: 10.3390/jcm12052041
license: CC BY 4.0
---
# Induction of Labor in Twins—Double Trouble?
## Abstract
Objective: To determine and compare the safety and efficacy of different methods of induction of labor in twin gestations and their effect on maternal and neonatal outcomes. Methods: A retrospective observational cohort study was conducted at a single university-affiliated medical center. Patients with a twin gestation undergoing induction of labor at >32 + 0 weeks comprised the study group. Outcomes were compared to patients with a twin gestation at >32 + 0 weeks who went into labor spontaneously. The primary outcome was cesarean delivery. Secondary outcomes included operative vaginal delivery, postpartum hemorrhage, uterine rupture, 5 min APGAR < 7, and umbilical artery pH < 7.1. A subgroup analysis comparing outcomes for the induction of labor with oral prostaglandin E1 (PGE1), IV Oxytocin ± artificial rupture of membranes (AROM), and extra-amniotic balloon (EAB)+ IV Oxytocin was performed. Data were analyzed using Fisher’s exact test, ANOVA, and chi-square tests. Results: 268 patients who underwent induction of labor with a twin gestation comprised the study group. 450 patients with a twin gestation who went into labor spontaneously comprised the control group. There were no clinically significant differences between the groups for maternal age, gestational age, neonatal birthweight, birthweight discordancy, and non-vertex second twin. There were significantly more nulliparas in the study group compared to the control group ($23.9\%$ vs. $13.8\%$ $p \leq 0.001$). The study group was significantly more likely to undergo a cesarean delivery of at least one twin ($12.3\%$ vs. $7.5\%$ OR, 1.7 $95\%$ CI 1.04–2.85 $$p \leq 0.03$$). However, there was no significant difference in the rate of operative vaginal delivery ($15.3\%$ vs. $19.6\%$ OR, 0.74, $95\%$ CI 0.5–1.1 $$p \leq 0.16$$), PPH ($5.2\%$ vs. $6.9\%$ OR, 0.75 $95\%$ CI 0.39–1.42 $$p \leq 0.37$$), 5-min APGAR scores < 7 ($0\%$ vs. $0.2\%$ OR, 0.99 $95\%$CI 0.99–1.00 $$p \leq 0.27$$), umbilical artery pH < 7.1 ($1.5\%$ vs. $1.3\%$ OR, 1.12 $95\%$ CI 0.3–4.0), or combined adverse outcome ($7.8\%$ vs. $8.7\%$ OR, 0.93 $95\%$ CI 0.6–1.4 $$p \leq 0.85$$). Furthermore, there were no significant differences in the rates of cesarean delivery or combined adverse outcomes in patients undergoing induction with oral PGE1 compared to IV Oxytocin ± AROM ($13.3\%$ vs. $12.5\%$ OR, 1.1 $95\%$ CI 0.4–2.0 $$p \leq 1.0$$) ($7\%$ vs. $9.3\%$ OR, 0.77 $95\%$ CI 0.5–3.5 $$p \leq 0.63$$) or EAB+ IV Oxytocin ($13.3\%$ vs. $6.9\%$ OR, 2.1 $95\%$ CI 0.1–2.1 $$p \leq 0.53$$) ($7\%$ vs. $6.9\%$ OR, 1.4 $95\%$ CI 0.15–3.5 $$p \leq 0.5$$) or between patients undergoing induction of labor with IV Oxytocin ± AROM and EAB+ IV Oxytocin ($12.5\%$ vs. $6.9\%$ OR, 2.1 $95\%$ CI 0.1–2.4 $$p \leq 0.52$$) ($9.3\%$ vs. $6.9\%$ OR, 0.98 $95\%$ CI 0.2–4.7 $$p \leq 0.54$$). There were no cases of uterine rupture in our study. Conclusions: Induction of labor in twin gestations is associated with a two-fold increased risk of cesarean delivery, although this is not associated with adverse maternal or neonatal outcomes. Furthermore, the method of induction of labor used does not affect the chances of success nor the rate of adverse maternal or neonatal outcomes.
## 1. Introduction
The prevalence of twin pregnancies has increased over the past several decades, largely due to the development and widespread availability of Artificial Reproductive Technology and advanced maternal age [1]. Indeed, in 2020, twin births accounted for over $3\%$ of live births compared to $1.9\%$ in 1980 [2]. Twin pregnancies are at an increased risk of a range of obstetric complications, including hypertensive disorders of pregnancy [3,4], gestational diabetes [5], growth restriction [6,7], and intrauterine fetal demise [8]. These conditions are often indications for the delivery and induction of labor. Furthermore, although twin pregnancies are at an increased risk of spontaneous preterm birth [9], evidence of increased perinatal morbidity and mortality near term [10] has led the American College of Obstetricians and Gynecologists to recommend elective delivery of uncomplicated dichorionic twins between 38 + 0–38 + 6 weeks and monochorionic twins between 34 + 0–37 + 6 weeks [11]. Therefore, another common indication for induction of labor in twin pregnancies is gestational age. Whilst there is much data in the literature regarding the safety and efficacy of various methods of induction of labor in singleton gestations, data in twin gestations is sparse, and as such, the management of induction of labor in twin gestations is largely derived from data derived from singleton gestations. That said, there are distinct differences between a singleton and twin trial of labor. Rates of spontaneous vaginal delivery are lower in twin gestations [12,13], and recent evidence suggests that the rate of labor progress both in the first and second stages of labor differs in twin gestations [14,15]. Furthermore, there are theoretical concerns that an overdistended uterus in twin gestations might be more prone to uterine rupture with induction of labor compared to singleton gestations [16].
The aim of this study is to determine and compare the safety and efficacy of different methods of induction of labor in twin gestations and their effect on maternal and neonatal outcomes.
## 2. Materials and Methods
A retrospective observational cohort study was conducted at a single university-affiliated medical center from 2012–2022. This center has a delivery ward with approximately 11,000 deliveries per year, with a twin delivery rate of $1.2\%$.
Pre-natal and post-natal outcomes were collected from a computerized database for patients carrying twin gestations who underwent a spontaneous or induced trial of labor during the study period. The study group consisted of patients with a twin gestation with a gestational age greater than 32 + 0 weeks of gestation who underwent a medical induction of labor with either oral prostaglandin E1 (Cytotec), IV Oxytocin±artificial rupture of membranes (AROM), or mechanical induction with an extra-amniotic balloon + IV Oxytocin. Maternal and neonatal outcomes were compared to those of twin gestations who went into labor spontaneously.
The primary outcome was the cesarean delivery of either one or both twins. Secondary outcomes included mode of delivery (spontaneous delivery or vacuum extraction) and combined adverse outcome (postpartum hemorrhage (PPH), uterine rupture, umbilical artery pH < 7.1, and APGAR < 7 at 5 min for either twin).
Further analyses were conducted to compare maternal and neonatal outcomes in twin gestations according to the method of induction used. Data was collected regarding indications for cesarean delivery in patients undergoing induction of labor.
Patients with a previous cesarean delivery or contraindications to vaginal delivery were excluded from the study. Contraindications to vaginal delivery of twins in our center include any contraindication to vaginal delivery, presenting twin non-vertex, a sonographic estimated fetal weight of either twin less than 1500 g, gestational age less than 32 + 0 weeks, and twin discordancy of greater than $20\%$ in favor of the second twin in a non-vertex presentation.
Patients with a poor Bishop score (<6) on admission were induced with either oral PGE1 or an extra-amniotic balloon + IV Oxytocin. Patients with a Bishop score ≥6 were induced with IV Oxytocin ± AROM. The protocol for induction of labor with PGE1 at our center involves the administration of an initial dose of 50 micrograms of PGE1 orally. This dose is repeated every four hours until active labor develops, up to a maximal dose of 300 micrograms. Contraindications to the use of PGE1 in our center are the same in singleton and twin gestations and include the presence of any uterine scar, grandmultiparity (parity > 5), or non-reassuring fetal status. The IV Oxytocin induction protocol at our center involves the administration of 2.5 mu/min of IV Oxytocin which is increased at increments of 2.5 mu/min every 20 min until a maximum of 22.5 mu/min. AROM is performed at the attending physician’s discretion. Induction of labor with an extra-amniotic balloon involves the passage of a 22-Gauge Foley catheter through the internal os of the cervix. The catheter balloon is filled with 60 mL of normal saline and remains in place for a maximum of 24 h.
Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA: IBM Corp. Continuous variables were analyzed using the independent samples t-test. Non-continuous variables were analyzed using Fishers’ exact test, ANOVA, and Chi-square test. The local ethical review board approved the study. Approval number MHMC-0025-21.
## 3. Results
268 patients comprised the study group of twin gestations undergoing induction of labor. Of those, 142 ($53.3\%$) underwent induction of labor with prostaglandin E1(Cytotec) according to the local hospital protocol, 96($35.8\%$) patients were induced with IV Oxytocin ± AROM, and 29 ($10.8\%$) patients received an extra-amniotic balloon + IV Oxytocin. Maternal and neonatal outcomes were compared to 450 patients with a twin gestation who went into labor spontaneously. There were no clinically significant differences between the groups in mean maternal age (29.9 ± 5.7 vs. 30.7 ± 5.6 $$p \leq 0.03$$), multiparity ($56\%$ vs. $58\%$ $$p \leq 0.59$$), gestational age at delivery (36.9 ± 1.5 vs. 36. ± 1.5 $$p \leq 0.8$$), birthweight (2562 ± 402 vs. 2565 ± 390 $$p \leq 0.94$$), birthweight discordancy >$20\%$ ($12.7\%$ vs. $9.1\%$ $$p \leq 0.13$$), and the rate of non-vertex second twins ($42.4\%$ vs. $44.2\%$ $$p \leq 0.64$$) (Table 1). There was a significantly higher prevalence of nulliparas ($23.9\%$ vs$.13.8\%$ $p \leq 0.001$) and a lower prevalence of grand multiparas ($20.1\%$ vs. $28.2\%$ $$p \leq 0.01$$) in the group undergoing induction of labor.
Patients undergoing induction of labor with a twin gestation were significantly more likely to undergo a cesarean delivery of at least one twin ($12.3\%$ vs. $7.5\%$ OR, 1.7 $95\%$ CI 1.04–2.85 $$p \leq 0.03$$). There was no significant difference in the rate of spontaneous vaginal delivery of both twins ($72.3\%$ vs. $72.7\%$ OR, 0.99 $95\%$ CI 0.7–1.4 $$p \leq 0.93$$) or operative vaginal delivery of at least one twin ($15.3\%$ vs. $19.6\%$ OR, 0.74, $95\%$ CI 0.5–1.1 $$p \leq 0.16$$). There were no differences between the groups in the rate of PPH ($5.2\%$ vs. $6.9\%$ OR, 0.75 $95\%$ CI 0.39–1.42 $$p \leq 0.37$$), and there were no cases of uterine rupture in this study population.
With regards to neonatal outcomes, there were no significant group differences in 5-min APGAR score < 7 ($0\%$ vs. $0.2\%$ OR, 0.99 $95\%$CI 0.99–1.00 $$p \leq 0.27$$) or umbilical artery pH < 7.1 ($1.5\%$ vs. $1.3\%$ OR, 1.12 $95\%$ CI 0.3–4.0). Overall, there were also no differences in the rate of combined adverse maternal and neonatal outcomes ($7.8\%$ vs. $8.7\%$ OR, 0.93 $95\%$ CI 0.6–1.4 $$p \leq 0.85$$) (Table 2) A subgroup analysis was performed comparing the different methods of labor induction used, oral PGE1 (Cytotec), IV Oxytocin ± AROM, or extra-amniotic balloon + IV Oxytocin. There were no significant between-group differences in mean maternal age, parity, gestational age at delivery, birthweight, birthweight discordancy >$20\%$, or the rate of non-vertex second twins in patients undergoing induction of labor (Table 3). There were no significant differences in the rates of cesarean delivery in patients undergoing induction with PGE1 compared to Oxytocin ± AROM ($13.3\%$ vs. $12.5\%$ OR, 1.1 $95\%$ CI 0.4–2.0 $$p \leq 1.0$$), extra-amniotic balloon + IV Oxytocin ($13.3\%$ vs. $6.9\%$ OR, 2.1 $95\%$ CI 0.1–2.1 $$p \leq 0.53$$), or between patients undergoing induction of labor with Oxytocin ±AROM and extra-amniotic balloon + IV Oxytocin ($12.5\%$ vs. $6.9\%$ OR, 2.1 $95\%$ CI 0.1–2.4 $$p \leq 0.52$$).
There were also no significant differences in the rates of combined adverse outcomes in patients undergoing induction with PGE1 compared to Oxytocin ± AROM ($7\%$ vs. $9.3\%$ OR, 0.77 $95\%$ CI 0.5–3.5 $$p \leq 0.63$$), extra-amniotic balloon + IV Oxytocin ($7\%$ vs. $6.9\%$ OR, 1.4 $95\%$ CI 0.15–3.5 $$p \leq 0.5$$), or between patients undergoing induction of labor with IV Oxytocin ± AROM and extra-amniotic balloon + IV Oxytocin ($9.3\%$ vs. $6.9\%$ OR, 0.98 $95\%$ CI 0.2–4.7 $$p \leq 0.54$$) (Table 4). There were no cases of uterine rupture in twin gestations undergoing induction of labor. Indications for cesarean delivery in patients undergoing induction of labor are listed in Table 5. Cesarean deliveries that were performed for complications of the second twin include four cases of umbilical cord prolapse of the second twin, three cases of placental abruption of the second twin, one case of hand presentation of the second twin, and one case of non-reassuring fetal heart rate tracing of the second twin.
## 4. Discussion
Our findings indicate that patients with twin pregnancies who undergo induction of labor after 32 + 0 weeks are twice as likely to undergo cesarean delivery than those who enter labor spontaneously. However, they are not at increased risk of experiencing adverse maternal outcomes, including unplanned operative vaginal delivery, uterine rupture, and PPH, nor are they at increased risk of having adverse neonatal outcomes, including umbilical artery pH < 7.1 and APGAR scores of <7 at 5 min. Furthermore, despite the increased risk of cesarean delivery in this population, overall, they have a good chance of achieving vaginal delivery ($88.2\%$).
Despite the prevalence of this clinical scenario, little data exists regarding maternal and neonatal outcomes following the induction of labor in twin pregnancies. Results from larger studies that have been conducted regarding the safety and feasibility of induction of labor in twin gestations are summarized in Table 6 and include the data from this study [17,18,19,20,21,22].
The published success rate for induction of labor in twin pregnancies ranges between $59.5\%$ and $81.0\%$ [17,18,19,20,21,22]. We report a success rate of $87.7\%$, the highest reported in the scientific literature thus far. Several factors may account for our higher success rates. One is due to the characteristics of our study population, which has a young mean maternal age of 30.2 years old and low rates of nulliparity ($17.5\%$). Indeed, advanced maternal age and nulliparity are known risk factors for a failed trial of labor in twin gestations [23]. Furthermore, due to a cultural desire for higher parity, our patient population is highly motivated for vaginal delivery. Over $90\%$ of patients with twin gestations who were eligible to undergo a trial of labor in our center chose to do so, and our patients’ desire to avoid cesarean delivery may be responsible for physician bias when managing labor, e.g., opting to perform an internal podalic version or total breech extraction rather than cesarean delivery for a non-vertex second twin.
There is much debate in the literature concerning the impact of induction of labor in singleton gestations on the risk of cesarean delivery. Some studies report that induction of labor is associated with an increased risk of cesarean delivery compared to the spontaneous onset of labor [24], whilst others have found that induction of labor may reduce the risk of cesarean delivery compared to expectant management, even when performed for non-medical indications [25].
The few studies on the induction of labor in twins have traditionally used singleton pregnancies undergoing induction of labor or twin pregnancies in spontaneous labor as the control groups [17,18,19,20,21,22] (Table 6). Most studies concur with the results of this present study and demonstrate a higher risk of cesarean delivery in twin gestations undergoing induction of labor compared to both these control groups [17,18,19,20,21]. Similar to this study, no studies reported an increased risk of maternal or neonatal adverse outcomes or any cases of uterine rupture in twins undergoing induction of labor [17,18,19,20,21,22].
Possible reasons that have been given for the increased rate of cesarean delivery in twins undergoing induction of labor include uterine inertia to uterotonic medications as a result of overdistention [26] and confounding risk factors for cesarean delivery in patients undergoing induced rather than spontaneous labor, e.g., comorbidities and non-reassuring fetal status. In our study, the higher prevalence of nulliparas ($23.9\%$) in the group undergoing induction of labor compared to spontaneous labor ($13\%$) certainly could contribute to this difference, as nulliparity is a known risk factor for cesarean delivery [23].
We selected patients with twin pregnancies undergoing spontaneous labor as our control group due to the inherent distinct differences between twin and singleton gestations in terms of maternal and neonatal outcomes [27], rates of vaginal delivery [12,13], normal progress of labor [14,15], and complications owing from the delivery of a second twin [27]. Furthermore, data from this comparison may be useful for counseling patients with twin pregnancies who are presented with the option of inducing labor for non-urgent indications versus waiting for spontaneous labor.
Patients with twin gestations may be offered induction of labor for several indications. The first is for medical or obstetric complications. Here, the indication for delivery is strong, and the alternative is usually an elective cesarean delivery. Another common indication for delivery in twins is gestational age. Despite the increased risk of spontaneous preterm birth in twins, almost half remain undelivered at 37 + 0 weeks [28], and due to evidence of increased perinatal morbidity and mortality after 38 weeks, ACOG recommends delivery for dichorionic twins between 38 + 0 and 38 + 6 weeks [11]. Lastly, induction of labor is often considered for non-medical indications, including maternal anxiety, discomfort, and the need for proximity to the hospital [29].
In the absence of a strong medical indication for induction, patients and their physicians must balance the potential advantages and disadvantages of induction of labor versus awaiting the spontaneous onset of labor. The results of our study suggest that whilst induction of labor in twin gestations is associated with high chances of success, there is an increased risk ($12\%$) of unplanned intrapartum cesarean delivery. Although this increased risk did not translate into worse maternal and neonatal outcomes, patients should be made aware of this when considering induction of labor.
The second aim of our study was to determine the safety and efficacy of induction of labor with oral PGE1 in twins and compare outcomes to other common methods of labor induction, namely IV Oxytocin and extra-amniotic balloon + IV Oxytocin. Induction with oral PGE1 has advantages in terms of ease of administration (oral or vaginal), obviates the discomfort of EAB balloon insertion, and offers convenient storage (no need for a refrigerator). Furthermore, a recent Cochrane review on the use of low-dose oral PGE1 for the induction of labor in singleton gestations demonstrated reduced rates of cesarean delivery compared to induction of labor with oxytocin, extra-amniotic balloon, and vaginal dinoprostone with no increase in the rates of non-reassuring fetal heart rate status or uterine hyperstimulation [30]. That said, like in many other developed countries, in Israel, the label indications for oral PGE1 do not include induction of labor [31]. Therefore, its administration in these settings requires institutional authorization by the Israeli Ministry of Health for off-label use according to rule 29c of the Israeli pharmaceutical guidelines [32]. Of note, its use for induction of labor is endorsed by the Israeli Society for Maternal and Fetal Medicine guidelines [33].
Indeed, much of the management of the induction of labor in twins is based on data from singleton gestations. However, induction of labor in this population may pose additional theoretical challenges related to uterine overdistention, including both uterine rupture and uterine resistance to oxytocin [26]. Small cohort studies investigating induction of labor with prostaglandins [21,34], extra-amniotic balloons [35], and oxytocin [26] in twins have shown these methods to be safe and effective but have conflicting results regarding their effect on cesarean delivery rates. Whilst some studies show that prostaglandins increase the risk of cesarean delivery compared to Oxytocin [21,34], others have demonstrated increased risks for cesarean delivery with an extra-amniotic balloon [36]. A recent secondary analysis of patients participating in the Twin Birth study compared cesarean delivery rates in twins undergoing induction of labor with prostaglandins (153 women, $42\%$) versus amniotomy +/− Oxytocin (215 women, $58\%$). The rate of cesarean delivery was $59.5\%$ in both groups, and there were no differences in other maternal and neonatal outcomes [20].
We found that the method of induction of labor did not influence the risk of cesarean delivery nor create adverse maternal or neonatal outcomes. Furthermore, when analyzing the indications for cesarean delivery, non-reassuring fetal heart rate status and complications related to the second twin were the most common indications for cesarean delivery (Table 5), and only two cases ($6\%$) were due to unsuccessful labor induction. Therefore, patients can be reassured that if they choose induction of labor, they have a high chance of entering active labor, and with appropriate patient selection, no one method is superior at achieving vaginal delivery.
The risk of uterine rupture during induction of labor in twins was another important outcome we sought to investigate. Induction of labor increases the risk of uterine rupture in patients undergoing a trial of labor after cesarean delivery (TOLAC) [37]. Indeed, for this reason, misoprostol use is contraindicated in patients undergoing TOLAC [38]. We hypothesized that similar concerns might apply in twin gestations due to an overdistention of the uterus, particularly at term. Until now, only one study has investigated the risk of uterine rupture in twin induction of labor, and whilst in this study there were no cases of uterine rupture, the authors did not specify the method of induction of labor [19]. In our cohort, there were no cases of uterine rupture in patients undergoing induction of labor, including 142 patients receiving oral misoprostol. Due to the rarity of uterine rupture, this study was not powered to detect any significant differences in uterine rupture; however, the lack of uterine rupture in this cohort of over 268 twins undergoing induction of labor, including 142 patients with oral PGE1, is reassuring.
## 4.1. Strengths
This is the largest cohort to date investigating both maternal and neonatal outcomes in twins undergoing induction of labor (268 patients) and comparing outcomes to a control group of twins entering labor spontaneously. Using this population as our control group better demonstrates the contribution of induction of labor-to-labor outcomes in twin gestations and addresses a clinical dilemma in patients with twin gestations who are considering elective induction of labor at term. This is also the largest cohort in the literature to report outcomes from the use of prostaglandins for the induction of labor in twin pregnancies (142 patients) and only the second study investigating the impact of induction of labor in twins on uterine rupture. The use of PGE1 for the induction of labor is increasing due to its superiority both in terms of convenience and efficacy compared to other methods of induction. However, until now, there has been limited data regarding its use in twins and its effect on uterine rupture, and our results suggest that this is a safe and effective method of induction for twin gestations This study also compared outcomes of three distinct labor induction techniques, allowing patients and physicians to make an informed decision regarding the optimal mode of induction for twin pregnancies.
## 4.2. Limitations
Aside from its retrospective nature, the current study is limited by a lack of information regarding chorionicity and medical co-morbidities. In addition, as previously stated, our study population comprised a large proportion of multiparous and grandmultiparous women who were highly motivated to achieve vaginal delivery.
Although we believe that the low rates of complications are reassuring and applicable to other patient populations, this should be taken into consideration when counseling patients. Randomized prospective studies comparing methods of induction in twin deliveries are required to investigate these findings further.
## 5. Conclusions
The results of our study demonstrate that induction of labor in twin gestations is safe, feasible, and has a high chance of success. Although associated with an increased risk of cesarean delivery, it does not increase the risk of any other adverse maternal or neonatal outcomes, including uterine rupture. Furthermore, the method of induction of labor used, namely, oral PGE1, IV Oxytocin, or extra-amniotic balloon + IV Oxytocin, has no effect on success rates nor on adverse maternal or neonatal outcomes. This information can be utilized to counsel patients and aid in decision-making when contemplating the option of inducing labor in twin pregnancies.
## References
1. Gleicher N., A Kushnir V., Barad D.. **Fertility treatments and multiple births in the United States**. *N. Engl. J. Med.* (2014.0) **370** 1069-1071. DOI: 10.1056/nejmc1400242
2. Osterman M., Hamilton B., Martin J.A., Driscoll A.K., Valenzuela C.P.. **Births: Final data for 2020**. *Natl. Vital Stat. Rep.* (2021.0) **70** 1-50
3. Sibai B.M., Hauth J., Caritis S., Lindheimer M.D., MacPherson C., Klebanoff M., VanDorsten J., Landon M., Miodovnik M., Paul R.. **Hypertensive disorders in twin versus singleton gestations**. *Am. J. Obstet. Gynecol.* (2000.0) **182** 938-942. DOI: 10.1016/S0002-9378(00)70350-4
4. Francisco C., Wright D., Benkő Z., Syngelaki A., Nicolaides K.H.. **Hidden high rate of pre-eclampsia in twin compared with singleton pregnancy**. *Ultrasound Obstet. Gynecol.* (2017.0) **50** 88-92. DOI: 10.1002/uog.17470
5. Schwartz D.B., Daoud Y., Zazula P., Goyert G., Bronsteen R., Wright D., Copes J.. **Gestational diabetes mellitus: Metabolic and blood glucose parameters in singleton versus twin pregnancies**. *Am. J. Obstet. Gynecol.* (1999.0) **181** 912-914. DOI: 10.1016/S0002-9378(99)70324-8
6. Alexander G.R., Kogan M., Martin J., Papiernik E.. **What are the fetal growth patterns of singletons, twins, and triplets in the United States?**. *Clin. Obstet. Gynecol.* (1998.0) **41** 115-125. DOI: 10.1097/00003081-199803000-00017
7. Grantz K.L., Grewal J., Albert P.S., Wapner R., D’Alton M.E., Sciscione A., Grobman W.A., Wing D.A., Owen J., Newman R.B.. **Dichorionic twin trajectories: The NICHD Fetal Growth Studies**. *Am. J. Obstet. Gynecol.* (2016.0) **215** 221.e1-221.e16. DOI: 10.1016/j.ajog.2016.04.044
8. Mackie F., Rigby A., Morris R., Kilby M.D.. **Prognosis of the co-twin following spontaneous single intrauterine fetal death in twin pregnancies: A systematic review and meta-analysis**. *BJOG Int. J. Obstet. Gynaecol.* (2018.0) **126** 569-578. DOI: 10.1111/1471-0528.15530
9. Martin J.A., Hamilton B.E., Osterman M.J.K., Driscoll A.K.. **Births: Final Data for 2018**. *Natl Vital Stat Rep.* (2019.0) **68** 1-47
10. Cheung Y.B., Yip P., Karlberg J.. **Mortality of twins and singletons by gestational age: A varying-coefficient approach**. *Am. J. Epidemiol.* (2000.0) **152** 1107-1116. DOI: 10.1093/aje/152.12.1107
11. **Multifetal Gestations: Twin, Triplet, and Higher-Order Multifetal Pregnancies**. *Obstet. Gynecol.* (2021.0) **137** e145-e162. DOI: 10.1097/AOG.0000000000004397
12. Cruikshank D.P.. **Intrapartum management of twin gestations**. *Obstet. Gynecol.* (2007.0) **109** 1167-1176. DOI: 10.1097/01.AOG.0000260387.69720.5d
13. Lee Y.M.. **Delivery of Twins**. *Semin. Perinatol.* (2012.0) **36** 195-200. DOI: 10.1053/j.semperi.2012.02.004
14. Schiff E., Cohen S.B., Dulitzky M., Novikov I., Friedman S.A., Mashiach S., Lipitz S.. **Progression of labor in twin versus singleton gestations**. *Am. J. Obstet. Gynecol.* (1998.0) **179** 1181-1185. DOI: 10.1016/S0002-9378(98)70128-0
15. Levin G., Meyer R., Many A., Schwartz A., Tsur A., Yinon Y., Yogev Y., Yagel S., Rosenbloom J.I.. **Length of the Second Stage of Labor in Women Delivering Twins**. *Obstet. Gynecol.* (2021.0) **137** 664-669. DOI: 10.1097/AOG.0000000000004308
16. Turton P., Arrowsmith S., Prescott J., Ballard C., Bricker L., Neilson J., Wray S.. **A comparison of the contractile properties of myometrium from singleton and twin pregnancies**. *PLoS ONE* (2013.0) **8**. DOI: 10.1371/journal.pone.0063800
17. Loscul C., Schmitz T., Blanc-Petitjean P., Goffinet F., Le Ray C.. **Risk of cesarean after induction of labor in twin compared to singleton pregnancies**. *Eur. J. Obstet. Gynecol. Reprod. Biol.* (2019.0) **237** 68-73. DOI: 10.1016/j.ejogrb.2019.04.005
18. Taylor M., Rebarber A., Saltzman D.H., Klauser C., Roman A., Fox N.. **Induction of labor in twin compared with Singleton Pregnancies**. *Obstet. Gynecol.* (2012.0) **120** 297-301. DOI: 10.1097/AOG.0b013e31825f3643
19. Okby R., Shoham-Vardi I., Ruslan S., Sheiner E.. **Is induction of labor risky for twins compare to singleton pregnancies?**. *J. Matern. Neonatal Med.* (2013.0) **26** 1804-1806. DOI: 10.3109/14767058.2013.804047
20. Mei-Dan E., Asztalos E.V., Willan A.R., Barrett J.F.R.. **The effect of induction method in twin pregnancies: A secondary analysis for the twin birth study**. *BMC Pregnancy Childbirth* (2017.0) **17**. DOI: 10.1186/s12884-016-1201-8
21. Jonsson M.. **Induction of twin pregnancy and the risk of caesarean delivery: A cohort study**. *BMC Pregnancy Childbirth* (2015.0) **15**. DOI: 10.1186/s12884-015-0566-4
22. Mikaelsen I., Johansen A.N., Tappert C., Eggebø T.M.. **Induction of labor in twin pregnancies—A retrospective cohort study**. *Sex. Reprod. Healthc.* (2022.0) **32** 100724. DOI: 10.1016/j.srhc.2022.100724
23. Fox N.S., Gupta S., Melka S., Silverstein M., Bender S., Saltzman D.H., Klauser C.K., Rebarber A.. **Risk factors for cesarean delivery in twin pregnancies attempting vaginal delivery**. *Am. J. Obstet. Gynecol.* (2015.0) **212** 106.e1-106.e5. DOI: 10.1016/j.ajog.2014.07.056
24. Davey M.-A., King J.. **Caesarean section following induction of labour in uncomplicated first births- a population-based cross-sectional analysis of 42,950 births**. *BMC Pregnancy Childbirth* (2016.0) **16**. DOI: 10.1186/s12884-016-0869-0
25. Grobman W.A., Caughey A.B.. **Elective induction of labor at 39 weeks compared with expectant management: A meta-analysis of cohort studies**. *Am. J. Obstet. Gynecol.* (2019.0) **221** 304-310. DOI: 10.1016/j.ajog.2019.02.046
26. Bardettfausett M., Barthjr W., A Yoder B., Satin A.J.. **Oxytocin labor stimulation of twin gestations: Effective and efficient**. *Obstet. Gynecol.* (1997.0) **90** 202-204. DOI: 10.1016/S0029-7844(97)00213-5
27. Santana D.S., Silveira C., Costa M.L., Souza R.T., Surita F.G., Souza J.P., Mazhar S.B., Jayaratne K., Qureshi Z.. **Perinatal outcomes in twin pregnancies complicated by maternal morbidity: Evidence from the WHO Multicountry Survey on Maternal and Newborn Health**. *BMC Pregnancy Childbirth* (2018.0) **18**. DOI: 10.1186/s12884-018-2082-9
28. Dudenhausen J.W., Maier R.F.. **Perinatal problems in multiple births**. *Dtsch. Arztebl. Int.* (2010.0) **107** 663-668. DOI: 10.3238/arztebl.2010.0663
29. **ACOG Practice Bulletin No. 107: Induction of Labor**. *Obstet. Gynecol.* (2009.0) **114** 386-397. DOI: 10.1097/AOG.0b013e3181b48ef5
30. Kerr R.S., Kumar N., Williams M.J., Cuthbert A., Aflaifel N., Haas D.M., Weeks A.D.. **Low-dose oral misoprostol for induction of labour**. *Cochrane Database Syst. Rev.* (2021.0) **2021** CD014484. DOI: 10.1002/14651858.cd014484
31. **The Israeli Drug Registry**
32. **Approval of Medicinal Preparations According to Rule 29 of the Israeli Pharama-cology Guidelines—1986**
33. **Guideline Number 11. Induction of Labor and Cervical Ripening**. (2023.0)
34. Bush M.C., Csaba A., Eddleman K.A., Saphier C.J.. **Is misoprostol safe for labor induction in twin gestations?**. *J. Matern. Neonatal Med.* (2006.0) **19** 35-38. DOI: 10.1080/14767050500463582
35. Manor M., Blickstein I., Ben-Arie A., Weissman A., Hagay Z.. **Case series of labor induction in twin gestations with an Intrauterine Balloon catheter**. *Gynecol. Obstet. Investig.* (1999.0) **47** 244-246. DOI: 10.1159/000010114
36. De Castro H., Haas J., Schiff E., Sivan E., Yinon Y., Barzilay E.. **Trial of labour in twin pregnancies: A retrospective cohort study**. *BJOG Int. J. Obstet. Gynaecol.* (2015.0) **123** 940-945. DOI: 10.1111/1471-0528.13521
37. Zelop C.M., Shipp T.D., Repke J.T., Cohen A., Caughey A.B., Lieberman E.. **Uterine rupture during induced or augmented labor in gravid women with one prior cesarean delivery**. *Am. J. Obstet. Gynecol.* (1999.0) **181** 882-886. DOI: 10.1016/S0002-9378(99)70319-4
38. Obstetricians Aco G.. **ACOG Practice Bulletin No. 205: Vaginal Birth after Cesarean Delivery**. *Obstet. Gynecol.* (2019.0) **133** e110-e127. DOI: 10.1097/aog.0000000000003078
|
---
title: Phytomediated Silver Nanoparticles (AgNPs) Embellish Antioxidant Defense System,
Ameliorating HLB-Diseased ‘Kinnow’ Mandarin Plants
authors:
- Muhammad Umair Raza
- Fozia Abasi
- Muhammad Shahbaz
- Maria Ehsan
- Wajiha Seerat
- Abida Akram
- Naveed Iqbal Raja
- Zia ur-Rehman Mashwani
- Hammad Ul Hassan
- Jarosław Proćków
journal: Molecules
year: 2023
pmcid: PMC10004207
doi: 10.3390/molecules28052044
license: CC BY 4.0
---
# Phytomediated Silver Nanoparticles (AgNPs) Embellish Antioxidant Defense System, Ameliorating HLB-Diseased ‘Kinnow’ Mandarin Plants
## Abstract
Citrus production is harmed worldwide by yellow dragon disease, also known as Huanglongbing (HLB), or citrus greening. As a result, it has negative effects and a significant impact on the agro-industrial sector. There is still no viable biocompatible treatment for Huanglongbing, despite enormous efforts to combat this disease and decrease its detrimental effects on citrus production. Nowadays, green-synthesized nanoparticles are gaining attention for their use in controlling various crop diseases. This research is the first scientific approach to examine the potential of phylogenic silver nanoparticles (AgNPs) to restore the health of Huanglongbing-diseased ‘Kinnow’ mandarin plants in a biocompatible manner. AgNPs were synthesized using *Moringa oleifera* as a reducing, capping, and stabilizing agent and characterized using different characterization techniques, i.e., UV–visible spectroscopy with a maximum average peak at 418 nm, scanning electron microscopy (SEM) with a size of 74 nm, and energy-dispersive spectroscopy (EDX), which confirmed the presence of silver ions along with different elements, and Fourier transform infrared spectroscopy served to confirm different functional groups of elements. Exogenously, AgNPs at various concentrations, i.e., 25, 50, 75, and 100 mgL−1, were applied against Huanglongbing-diseased plants to evaluate the physiological, biochemical, and fruit parameters. The findings of the current study revealed that 75 mgL−1 AgNPs were most effective in boosting the plants’ physiological profiles, i.e., chl a, chl b, total chl, carotenoid content, MSI, and RWC up to $92.87\%$, $93.36\%$, $66.72\%$, $80.95\%$, $59.61\%$, and $79.55\%$, respectively; biochemical parameters, i.e., 75 mgL−1 concentration decreased the proline content by up to $40.98\%$, and increased the SSC, SOD, POD, CAT, TPC, and TFC content by $74.75\%$, $72.86\%$, $93.76\%$, $76.41\%$, $73.98\%$, and $92.85\%$, respectively; and fruit parameters, i.e., 75 mgL−1 concentration increased the average fruit weight, peel diameter, peel weight, juice weight, rag weight, juice pH, total soluble solids, and total sugarby up to $90.78\%$, $8.65\%$, $68.06\%$, $84.74\%$, $74.66\%$, $52.58\%$, $72.94\%$, and $69.69\%$, respectively. These findings enable us to develop the AgNP formulation as a potential citrus Huanglongbing disease management method.
## 1. Introduction
Genus Citrus belongs to the Rutaceae family. It is one of the most frequently cultivatedfruits in the world [1], with varieties growing in tropical, subtropical, and other climates, between latitudes of 35° N and 35° S [2]. Approximately 121 million tons of citrus are produced each year [3]. Citrus subsists in almost 150 genera and 1600 species [4], with the most commercially important being mandarin *Citrus reticulata* Blanco [5].Citrus fruits are eaten fresh, but are also processed for use in the cosmetics (e.g., fragrances, scrubs, and masques) and food (e.g., drinks, cakes, and candies) industries [6]. Citrus fruits are rich in vitamin C, a nutrient known to strengthen the immune system and ensure good heart health by effectively controlling cholesterol levels [7]. In Pakistan, citrus is grown mainly in Punjab and is one of the most widely exported fruits inthe country [8]. Almost $96\%$ of the total citrus production is cultivated in Punjab [9]. The ‘Kinnow’ mandarin (*Citrus reticulata* Blanco), which is grown mainly in Sargodha and its adjacent areas, including the districts of Toba Tek Singh, Faisalabad, and Sahiwal, accounts for more than $80\%$ of the citrus planted [10]. Currently, one of the most potent factors to reduce citrus yields includes a wide range of diseases caused by fungi, bacteria, nematodes, and viruses [11]. Huanglongbing (HLB), often known as citrus greening, is one of the deadliest bacterial invasive citrus diseases, having a global presence in more than 40 countries, and has been proven to be the most devastating for the citrus industry around the world [12,13]. HLB is widespread in most citrus-growing regions in Asia, Africa, and America [14]. HLB is caused by a fastidious phloem-limited Gram-negative uncultivable bacterium transmitted by vectors belonging to the α-subdivision of the Proteobacterium ‘Candidatus Liberibacter spp.’ [ 15]. HLB bacteria have three main strains, Asiaticus, Africanus, and Americanus, which have been distinguished based on environmental factors and insect vectors. ‘ Candidatus *Liberibacter asiaticus* (CLas)’ is prevalent in most regions of Asia and America; it reduces the flow of vital nutrients in the phloem, affecting the health of citrus trees and their fruit quality [16,17]. Typical diagnostic symptoms of HLB can include leaf mottling, premature defoliation, severe yellowing of the veins and surrounding tissues, vein corking, small green fruits with aborted seeds, and twig dieback [18,19].
Huanglongbing disease has been identified as the main cause of the decline of citrus in Pakistan, resulting in significant losses to the citrus industry, especially in the Sargodha and Multan districts of Punjab province, together with the Malakand district of Khyber Pakhtunkhwa province [9]. The incidence of HLB disease ranges from $4.6\%$, as the lowest, recorded in Sahiwal, and the highest incidence of the disease among C. sinensis ‘Musambi’ of $26\%$ in kotmomin [20], and for ‘Kinnow’ mandarin (C. reticulata), itwas $51.7\%$ in non-core areas of Pakistan [21]. Currently, there is no cure for HLB infection, and no specific management measures have been developed to control HLB disease [22]. There are no good sources of HLB genetic resistance in the *Citrus genus* or its relatives [23,24]. Various strategies have beenused to hinder the propagation of HLB disease, including insecticides [25], antimicrobial agents [26], and injections of antibiotics into infected trees to reduce HLB symptoms [27]. However, the prospect of microbial resistance, as well as various indirect negative effects on human health, is a growing and pressing concern that restricts antibiotic use in the field [28].
As a result, finding a long-term strategy to cure Huanglongbing disease with minimal side effects is critical. Nanotechnology concerns the formation of nanoparticles with sizes of 1–100 nm and plays a significant role in plant disease management [29]. It is the most advanced technology in thepresentera, having a wide range of applications in agriculture [30]. In this case, green nanotechnology has proven to be the most efficient and environmentally friendly technique against pathogen-based damage to diverse crops and fruits around the world, by reducing the use of agrochemicals, increasing the plant’s defense system against pathogen attack, improving nutrient uptake, and improving plant growth [31]. Green-synthesized silver nanoparticles (AgNPs) have emerged as effective antimicrobial and antioxidant agents to reduce the negative impacts of plant diseases in a variety of crops [32]. AgNPs produced from plant leaf extracts have been demonstrated to have excellent biocompatibility, bioavailability, and lower toxicity, and they are cost-effective, biodegradable, and eco-friendly [33]. Biogenic Ag nanomaterials’ increased methylglyoxal detoxification, antioxidant defense mechanisms, and tolerance to stress-induced ROS injury have been methodically explained in plants over the past ten years [34]. The plant extract is embellished with a variety of alkaloids, phenols, amines, and ketones that help to reduce agents and help to stabilize the capping in the fabrication of AgNPs [35]. AgNPs have different sizes, shapes, and biochemical functional characteristics that endow them with the ability to exerttherapeutic effects via a variety of molecular mechanisms [36]. A previous study revealed that silver nanoparticles have been used for disease management. Khan et al. [ 37] used phytosynthesized silver nanoparticles against plant parasitic nematode Meloidogyne incognita, and plant pathogens *Ralstonia solanacearum* and Fusarium oxysporum. Similarly, biogenic silver nanoparticles were applied against Stromatiniacepivora, which caused white rot disease in onion and garlic [38] (Figure 1).
The present study is confined to the synthesis of AgNPs using the extracts of *Moringa oleifera* (Moringaceae). Moringa or drumstick treeis highly rich in a variety of bioactive components, including phenolic acids, tannin, flavonoids, glucosinolates, alkaloids, terpenoids, saponins, protein, minerals, and vitamins, which contribute to its goodnutritional, nutraceutical, and medicinal profiles [47]. Antimicrobial [48], anticancer, antidiabetic, antioxidant, anti-atherosclerotic, antiproliferation, hepatoprotective, antiperoxidative, anti-inflammatory, and cardioprotective activities have been demonstrated in M. oleifera seeds [49].
To date, there has been no scientific research on the use of plant-mediated AgNPs to control Huanglongbing disease in *Citrus reticulata* ‘Kinnow’ mandarin plants. The presentstudy is the first to reveal that AgNPs affect physiological, biochemical, and antioxidant functions differently against the Huanglongbing-infected ‘Kinnow’ mandarin plant defense system. Exogenously, different concentrations of AgNPs were applied and used to identify the best effective concentration to treatHLB-infected ‘Kinnow’ mandarin plants.
## 2.1. Synthesis and Characterization of AgNPs
The synthesis of AgNPs was validated by using the UV–visible spectrum between 200 and 800 nm. BiofabricatedAgNPs had characterization peaks in the range of 200 to 400 nm, according to the findings. However, the characterizing absorption peak was obtained at 418 nm, showing that plant-mediated AgNPs had surface plasmon resonance properties (Figure 2).
SEM was used to performthe structural quantification of plant-mediated AgNPs. Green-synthesized AgNPs were spherical, cylindrical, or rectangular in shape, with an average size of 74 nm, as demonstrated in the SEM image (Figure 3). Some of these particles, however, were irregular and anisotropic in shape.
Energy-dispersive X-ray analysis was performed to confirm the elemental makeup of the plant-mediated AgNPs. The purity and presence of AgNPs were confirmed by EDX spectroscopic analysis. The highest peaks of silver characterization ranged from 2.7 to 3.7 KeV (Figure 4).
FTIR spectroscopy analysis was used to determine which organic biomolecules in the *Moringa oleifera* leaf extract were responsible for the fabrication of the spherical AgNPs (Figure 5). The following absorption peaks were found in the FTIR spectra of such phytofabricated AgNPs in the range of 450 to 4000 cm−1: 3419, 2922, 2370, 1734, 1637, 1508, 1458, 1366, 1261, 1097, 796, 619, and 453 cm−1. The source of these absorption peaks was determined to be the O–H stretching vibration (3419 cm−1) in alcohol, phenol, and flavonoid compounds [50], C–H stretching vibration (2922 cm−1) of aromatic compounds [51], C=O stretching vibration (2370, 1734 cm−1) for carbonyl compounds [52], C=C stretching vibration (1637 cm−1) of aromatic compounds [53], N–O stretching vibration (1508 cm−1) in aliphatic amines [54], C–H stretching vibration (1458 cm−1) in methylene moieties [55], C–O stretching vibration (1261, 1097 cm−1) in alcohols and phenols [56], C=C stretching vibration (796 cm−1) of aromatic compounds, and, finally [57], C–Br and C–Cl bonding vibration (619, 453 cm−1, respectively) in organic compounds. This confirmed that the organic compounds present in the *Moringa oleifera* leaf extract contributed to the reduction of Ag ions, resulting in the creation of AgNPs and the capping of the resulting nanostructures that provide stability, biocompatibility, and functionality in biological activity [58].
## 2.2. Physiological Parameters
The potential of the green AgNPs against Huanglongbing-diseased citrus mandarins was examined physiologically. In the current findings, chl a, chl b, total chl, and carotenoids were reduced by $64.42\%$, $64.47\%$, $64.61\%$, and $60.58\%$, respectively, due to HLB disease. However, the application of AgNPs significantly increased the chl and carotenoids. AgNPs improved chlorophyll along with carotenoids, with an increase in concentration from 25 mgL−1 up to 75 mgL−1 compared to untreated diseased citrus plants. AgNPs at 75 mgL−1 exhibited, surprisingly, a significant increase in chl a, chl b, total chl, and carotenoid content by $92.87\%$, $93.36\%$, $66.72\%$, and $80.95\%$, respectively. The results demonstrated that AgNPs at 100 mgL−1 enhanced the above values to $67.21\%$, $67.64\%$, $52.54\%$, and $63.26\%$, compared to untreated plants (Figure 6).
In plants, the amount of chlorophyll and carotenoids determines the rate of photosynthesis. Chlorophyll helps to absorb sunlight for plants, asdo carotenoids; along with absorbing light energy, carotenoids provide photoprotection by nonphotochemical quenching. Plants’ photosynthetic capacity can be damaged by phytopathogenic bacteria, which cause necrosis and a drop in chlorophyll. Biotic stress inflicts significant oxidative damage, and ROS generation causes the photosynthetic machinery to be destroyed, resulting in plant mortality [59,60]. When plants are attacked by numerous pathogens, preserving their chlorophyll and carotenoid concentrations is critical, since this allows the plants to continue performing the photosynthetic process. The level of chlorophyll and carotenoid in mandarin plants is reduced due to Huanglongbing, as the current study suggests. Foliar treatments withAgNPs considerably improved the chlorophyll and carotenoid concentrations in HLB-infected plants (Figure 6). Plants that were treated with the dosage of silver nanoparticles set at 75 mgL−1 had the highest chlorophyll and carotenoid content. The amounts of chlorophyll and carotenoids decreased when the concentration of the AgNPs increased to 100 mgL−1.
In plants, the effects of AgNPs under biotic stress are not well understood. The current findings, which show a considerable improvement in the chlorophyll and carotenoid content, are consistent with those of Sadak et al. [ 61] and Gupta et al. [ 62], who found that AgNPs enhanced the chlorophyll content of *Oryza sativa* L. Furthermore, Khalofah et al. [ 63] observed that AgNPs increased the carotenoid and chlorophyll content in the leaves of *Linum usitatissimum* L.
In addition, an earlier study revealed that silver could speed up chlorophyll biosynthesis by easing the electron transport chain and the respiratory process. As a result, a rise in chlorophyll content in HLB-infected plants treated with AgNPs could help to restore the photosynthetic machinery and thus growth qualities. Furthermore, devastating effects on the stroma and grana lamellae are avoided by silver nanoparticles; AgNPs also help chloroplasts to protect chloroplast enzymes and regulate soil bacterial diversity, thus speeding up the photosynthetic machinery’s biosynthesis [64].
Furthermore, the membrane stability index (MSI) and the relative water content (RWC) are important variables in the appropriate physiological functioning of plants. AgNPs had various effects on diseased citrus plants. MSI and RWC levels were reduced by $42.60\%$ and $51.71\%$, compared to healthy mandarins. However, the findings of the current study demonstrated that AgNPs steadily improved both of these respective parameters.
As the concentration of silver nanoparticles increased from 25 to 50 to 75 mgL−1, an improvement was noticed in the diseased plants. In particular, 25 mgL−1 AgNPs boosted the MSI and RWC content to $14\%$ and $26.94\%$, respectively. The best results were notedat 75 mgL−1 of AgNPs, increasing the MSI and RWC content to $59.61\%$ and $79.55\%$, following Huanglongbing-untreated mandarins. However, both of these amounts declined as the concentration of silver nanoparticles increased to 100 mgL−1 (Figure 6).
Plant MSI and RWC are severely reduced by biotic stressors. A low water supply causes a decrease in turgor pressure in plants, limiting cell expansion and causing morphological, physiological, and biochemical problems. The structural integrity of the plasma membrane is distorted due to the production of reactive oxygen species and causes electrolyte leakage inflicted on the cell. The amount of RWC in plants is determined by their water absorption and transpiration rate [65]. The findings demonstrated that, compared to unaffected control plants, biotic stress considerably decreased the relative water and membrane stability content in Huanglongbing-affected plants. On the other hand, foliar applications of plant-based AgNPs increased RWC and MSI (Figure 6).
Silver nanoparticles have been found to increase antioxidant defenses, lower ROS levels, and improve RWC and cell membrane integrity among plants [66]. Further, the results of the current study are in line with Shahbaz et al. [ 67], who found thatsilver nanostructures improved RWC while maintaining plasma membrane integrity under stress conditions [68]. The decrease in chlorophyll and carotenoid content in Huanglongbing-diseased plants applied with AgNPs at 100 mgL−1 could be attributed to a variety of factors, including ROS formation, inflicting severe oxidative damage to the plant.
## 2.3. Biochemical Parameters
Infected ‘Kinnow’ mandarin plants produce more proline as a result of HLB disease. The proline content increased by $132.06\%$ compared to healthy ones. However, our findings revealed that plant-based AgNPs showed various effects on proline content formation. Exogenously applied silver nanoparticles at 25 mgL−1 reduced proline by $23.18\%$ in diseased citrus plants. The application of 50 mgL−1 AgNPs further helped to decrease the respective content by $30.21\%$ as compared to untreated plants. Silver nanoparticles at a75 mgL−1 concentration proved to be the best applied formulationof AgNPs, which decreased the proline content by up to $40.98\%$. However, the content was found to have increased with an AgNP concentration at 100 mgL−1 (Figure 7).
Sugars play a crucial role as osmolytes in helping plants to defend themselves from biotic stress. Our results show that the soluble sugar content of plants is significantly affected by the HLB disease. The findings showed that compared to healthy plants, the level of SSC in diseased plants was reduced by $57.93\%$. The application of AgNPs dramatically improved SSC in diseased plants compared to healthy ones. The concentration of silver nanoparticles at 25, 50, and 75 mgL−1 helped to enhance the amounts of SSC. The application of AgNPs at 75 mgL−1 produced the best results, increasing SSC by $74.75\%$ compared to plants infected with Huanglongbing that were not treated. The best results were obtained withAgNPs at 75 mgL−1, which increased SSC by $74.75\%$. However, the SSC amounts decreased as the AgNP concentration increased to 100 mgL−1 (Figure 7).
In plants, proline production is massively boosted as a result of stress being inflicted upon plants by their surroundings. *Proline* generation is essential to restore normal stress levels. Proline helps to protect plants against oxidative damage and maintains a healthy osmotic environment. Furthermore, it also protects proteins against denaturation when exposed to harsh circumstances [68].
Similarly, soluble sugars play an important role in the defense mechanisms of plants. Sugars are the basic substrate for plant defense responses, providing structural material and energy. Sugar content also affects the plant immune system by acting as a signal molecule that interacts with hormone signaling [69]. The results of the study show that the proline content is higher in plants with HLB disease than in healthy plants, as, under stress, plants produce proline to deal with the detrimental effects of biotic stressors. However, the findings of the current study revealed that AgNPs mediated by plant extracts decreased the proline levels in diseased mandarin plants. The results are consistent with Fimognari et al. [ 70] and Hadwan et al. [ 71], finding that the exogenous application of AgNPs decreased proline levels, demonstrating a stress reduction. HLB-diseased ‘Kinnow’ plants had a reduced amount of total sugars, according to the findings; however, the applied AgNPs raised the level of SSC. The findings corroborate those of Salih et al. [ 72], who found that silver nanoparticles improved the total sugar synthesis in tomato plants affected by stress circumstances.
Varied amounts of plant-mediated AgNPs considerably boosted the performance of enzymatic and non-enzymatic antioxidants such as superoxide dismutase, peroxidase, catalase, total phenolics, and flavonoid content. However, HLB disease reduced the above-mentioned content of SOD, POD, CAT, TPC, and TFC by $46.98\%$, $59.66\%$, $60.26\%$, $69.25\%$, and $54.47\%$, respectively (Figure 7). Plant-mediated AgNPs had various effects on diseased citrus plants, altering antioxidant activity to ameliorate the negative consequences. AgNPs at 50 mgL−1 increased this potential content by $41.61\%$, $69.45\%$, $37.01\%$, $40.11\%$, and $59.82\%$, respectively (Figure 7). AgNPs at 75 mgL−1 were shown to improve the antioxidant defense of Huanglongbing-infected plants by upregulating the respective activities, such as SOD, POD, CAT, TPC, and TFC content. They increased by $72.86\%$, $93.76\%$, $76.41\%$, $73.98\%$, and $92.85\%$, respectively. However, the results demonstrated that these activities decreased as the AgNP concentration increased to 100 mgL−1 (Figure 7).
Living organisms rely on their immune systems to protect them from diseases that can be deadly. Immune-specified disorders are quite common among animals, but are rarely investigated in plants. HLB disease falls into this category, as it attacks the antioxidant defense system of citrus plants [73]. In this respective work, the results demonstrated that HLB drastically reduced the levels of enzymes that help in defense, i.e., CAT and POD [74]. Exogenous foliar treatments with green-synthesized AgNPs increased the levels of defense-related enzymes in diseased plants compared to diseased mandarins (Figure 7). According to the findings, the use of AgNPs at 75 mgL−1 proved ideal in increasing antioxidant enzymes; the respective findings are consistent with Fang et al. [ 75], whofoundthat silver nanoparticles boosted the levels of enzymes. In plants, limited study has beendone to implement AgNPs against biotic stressors. However, it has been observed that the application of AgNPs increased the amount of SOD and CAT enzymes in stressed rice plant seedlings, increasing their capacity to withstand stress [76]. Similar to this, numerous other studies show that the use of silver nanoparticles in stressed tomato plants increased the activity of POD and SOD [77]. Our results are based on those of Mirmoeini et al. [ 78], who found that plants have an antioxidant defense response that is mediated by silver. At the same time, the study findings revealed that 100 mgL−1 silver nanoparticles reduced the amount of antioxidant defense enzymes; this may be due to the toxicological effects of a high concentration of AgNPs, which could have contributed to an increase in silver, because silver at high levels functions as apro-oxidant and has dramatic impacts on plants, according to several published research works, such as Qian et al. [ 79].
In plants, biotic or abiotic stressors disrupt the electron transport pathway, which causes the generation of ROS, being powerful oxidizing agents that harm plant cells [80]. Subsequently, plants respondusing the enzymatic and non-enzymatic chemicals as a defensive mechanism against the harmful effects of ROS that neutralize their effects and protect cells [81]. Plants produce non-enzymatic antioxidants such as TFC and TPC as an instinctive response to stress [82]. Many studies have found that the use of various nanoparticles can cause the creation of antioxidant chemicals, which can help plants to resist pathogens [83]. In infected plants, non-enzymatic chemicals were found to be lower than in healthy control plants, according to our findings). This is because HLB infections inflict a severe, devastating impact on the antioxidant defense systems of plants. However, compared to untreated diseased trees, the application of AgNPs increased the production of TFC and TPC in HLB-diseased plants. The current findings are consistent with those of Chung et al. [ 84], who found that silver nanoparticles altered the metabolic profile in *Cucumis anguria* plants under biotic stress by increasing the defense systems. However, Raigond et al. [ 85] found that ZnNPs increased TPC levels in potato plants, supporting our findings. The excellent antioxidant capacity of AgNPs could be attributed to the numerous phytochemical functional groups, which enable the capping and stabilization of nanoparticles, thus contributing to the enzymatic and non-enzymatic attributes of plants [66].
## 2.4. Fruit Quality Parameters
The findings revealed that the average fruit weight decreased by $52.72\%$ in infected plants. However, compared to untreated ‘Kinnow’ mandarin plants, the treatment of AgNPs significantly increased the average fruit weight in diseased plants. The optimum results were obtained with an AgNPs concentration at 75 mgL−1, which increased the average fruit weight by up to $90.78\%$ compared to Huanglongbing-untreated plants. However, the average weight of the fruit decreased as the AgNPs concentration rose to 100 mgL−1 (Figure 8). Furthermore, according to the research, the average diameter of the fruits in infected plants was $22.75\%$ lower than in healthy plants. However, the average fruit diameter was increased to a considerable value after the application of AgNPs. The study revealed that AgNPs at 25 mgL−1 increased the mean diameter of the fruit by $6.26\%$, an increase of $15.39\%$ was recorded at 50 mgL−1 of AgNPs, and AgNPs at 75 mgL−1 generated the optimal results, increasing by $23.37\%$ compared to diseased plants. However, the mean diameter measurement of the fruit decreased to $14.97\%$ as the AgNP concentration was increased to 100 mgL−1 (Figure 8). Compared to healthy plants, Huanglongbing disease reduced the diameter of the peel, the weight of the peel, the weight of the juice, and the weight of the rag by $3.70\%$, $30.08\%$, $47.08\%$, and $45.23\%$, respectively (Figure 8). AgNPs at 75 mgL−1 were shown to be the ideal formulation, with $8.65\%$, $68.06\%$, $84.74\%$, and $74.66\%$ increases in peel diameter, peel weight, juice weight, and rag weight, respectively. However, a decrease was recorded in the measurements of further AgNP treatments (Figure 8). Fruits are a very important attribute that determines the productivity of fruit crops. Fruits appear on the plant after a certain amount of time of plant growth. Hence, any factor affecting the plant will automatically impact the quality and productivity of the fruit. Fruits are affected by different diseases in unique and diverse ways, ranging from physical to biochemical characteristics [86]. Citrus greening disease, or HLB, has serious impacts on diseased plants, and these impacts are variably observed in the average fruit weight, fruit diameter, peel diameter, peel weight, rag, and juice weight. The investigation’s findings demonstrated that Huanglongbing significantly decreased the average weight compared to healthy citrus plants, thus having variable impacts on the fruit diameter, peel diameter, peel weight, rag weight, and juice weight [87].
Exogenous foliar treatments with AgNPs enhanced the levels of relative attributes described in plants infected with Huanglongbing (Figure 8). According to this respective research, the use of AgNPs at a dose of 75 mgL−1 produced very promising results. The findings of this study are similar to those of Nejatzadeh et al. [ 88], who discovered that silver nanoparticles increased the germination speed, plant height, and stem length under salinity stress.
HLB disease had differential effects on juice pH, total soluble solids, and total sugar in mandarin plants. Juice pH, TSS, and TS were reduced by $36.67\%$, $42.60\%$, and $49.38\%$, respectively, due to HLB disease (Figure 9). AgNPs at 75 mgL−1 were shown to be the most optimal formulation, with improvements in juice pH, total soluble solids, and total sugar of $52.58\%$, $72.94\%$, and $69.69\%$, respectively. The AgNP concentration, when increased above this level, led toa decrease (Figure 9). Infected ‘Kinnow’ mandarin plants have more titratable acidity (TA) as a result of HLB disease. Titratable acidity (TA) increased by $148.17\%$compared to healthy plants. Our findings revealed that plant-based AgNPs showed diverse effects on titratable acidity (TA) production. The exogenous application of AgNPs proved to be effective in decreasing TA. AgNPs at 25, 50, and 75 mgL−1 reduced TA by $16.09\%$, $36.97\%$, and $64.74\%$. Hence, AgNPs at 75 mgL−1 proved to be the optimal concentration. However, the titratable acidity increased when the AgNP concentration also increased (Figure 9).
The biochemical quality parameters of the fruit, also known as internal quality parameters, including acids, soluble solids, and sugars, help us to accurately calculate the maturity index, helping us to determinethe ripe fruit of the ‘Kinnow’ mandarin plant to its maximum potential for these biochemical characteristics [89,90]. Before harvesting, citrus maturity standards, such as the juice content, soluble solids, and acid ratio, are applied in the modern world, with variations according to citrus fruits and exporting market norms [91,92]. HLB disease, or citrus greening disease, has a significant influence on HLB-infected ‘Kinnow’ mandarin plants, with pH, TA, TSS, and TS levels varyingin this study. Two related concepts that address acidity in food analysis are TA and pH. Compared to healthy citrus plants, the pH level of the juice of the HLB-infected ‘Kinnow’ plant was significantly lower [93]. Exogenous foliar sprays of green-synthesized AgNPs improved the pH levels in HLB-infected plants, thus helping the plants to achieve abetter flavor and texture. Using an AgNP dose of 75 mgL−1 yielded very promising results, according to this study (Figure 9). The findings of this work are similar to those of Srivastava et al. [ 94], who discovered that nanostructured silver particles improved the pH levels during the catalytic degradation of azo dyes through the electron relay effect. TA predicts the ways in which organic acids affect the flavor of food. Strong acids are completely dissociated; on the other hand, food acids are only partially ionized. Although the overall acid content affects some dietary characteristics, others are only affected by the ionized fraction of the acid molecules. When intrinsic acids are titrated with a reference base, the TA of the food is evaluated to establish its overall acid content [95]. Compared to healthy citrus plants, the titratable acidity (TA) of HLB-infected ‘Kinnow’ plants was much higher [77]. Exogenous foliar sprays of green-synthesized AgNPs decreased thetitratable acidity (TA) in HLB-infected plants, aiding plants with better flavor. According to this study, the use of 75 mgL−1 AgNPs was the most effective and produced the best results. The findings of this study are similar to those of Alidoust et al. [ 96], who found that nanoparticles decreased the growth of TA levels inrice plants.
Total soluble solids (TSS) are also an essential indicator of fruit quality, accounting for 10–$20\%$ of the fresh weight of the fruit. The TSS value has an impact on the taste of the fruit because it indicates the level of sweetness of the fruit. As the fruit grows, it becomes sweeter and less acidic. Therefore, TSS plays an important role in fruit maturation, contributing to the economic benefits of the trade of fruits [97]. Compared to healthy citrus plants, the TSS level of HLB-infected ‘Kinnow’ mandarin plants was much lower [80]. Exogenous foliar sprays of green-synthesized AgNPs increased the TSS levels in HLB-infected plants, helping plants to taste better (Figure 9). According to this study, the use of an AgNP dose of 75 mgL−1 produced highly promising results. The findings of this study are similar to those of Faghihi et al. [ 98], who found that asilver nanocomposite with grapefruit peel improved the TSS levels and thus the nutritional value of the cucumber after harvest.
All monosaccharides and disaccharides found in food, regardless of their source, constitute total sugars (TS). The term “sugar” normally refers to sucrose (table sugar), although it can also refer to all sugars [99]. Sugars are the main source of energy and structural material for plant defense responses, and may also act as signal molecules that interact with the hormone signaling network of the plant immune system [100]. The TS level of HLB-infected ‘Kinnow’ mandarin plants was considerably lower than that of healthy citrus plants [101]. Exogenous foliar sprays of green-synthesized AgNPs increased the TS levels in ‘Kinnow’ plants infected with HLB, helping them to obtain higher taste quality (Figure 9). Using AgNPs at a dose of 75 mgL−1 yielded highly effective results, according to this study. The findings of this study are comparable to those of Faghihi.
## 3. Materials and Methods
For the experiment, 8-year-old ‘Kinnow’ trees (Citrus reticulata) were selected in Chak no 77 SB Sargodha, Pakistan. Chak no 77 is located in the Sargodha district, Punjab province, Pakistan; its geographical coordinates are latitude, 32°03′40.6″ N and longitude, 72°52′51.5″ E, with climatic conditions comprising summers that are hot, humid, and short, while winters are short, cool, and dry. The ‘Kinnow’ mandarin trees with severe symptoms were distinguished and marked with blue ribbons along with specific digit codes assigned to different branches to analyze the impacts imparted by the application of AgNPs. Leaves showing symptoms of HLB were collected from 25 different trees to confirm the presence of the causative agent ‘Candidatus Liberibacter asiaticus’ (CLas) by adopting the conventional PCR method with accession number (https://www.ncbi.nlm.nih.gov, and accessed on 13 December 2021).
## 3.1.1. Preparation of Plant Extract
Fresh and healthy leaves were collected from the main campus of University Rawalpindi, Punjab, Pakistan; they were washed with distilled water and we added 20 g fresh leaves into a 500 mL beaker containing 200 mL distilled water. Leaves were heated at 80 °C for 25 min using a hot plate. After heating, the solution was filtered two to three times to obtain apure and transparent *Moringa oleifera* leaf extract. The required amount of leaf extract was used to synthesize silver nanoparticles and the remaining was stored at 4 °C for further use [102].
## 3.1.2. Biofabrication of AgNPs
Approximately 50 mL of *Moringa oleifera* aqueous leaf extract was poured dropwise into 450 mL of silver nitrate solution (5 mM) and the mixture was stirred continuously for 4 h at room temperature for complete AgNP biosynthesis. Color changes of reaction mixtures were closely monitored to confirm nanoparticle formation; AgNP production was characterized by a dark brown color of the reaction solution [103]. After the formation of AgNPs, the solution was stored in the dark at room temperature to prevent the agglomeration of AgNPs in the solution. Subsequently, the solution was centrifuged at 10,000 rpm for 15 min at 8 °C to collect pure AgNPs, the supernatant was collected, and purified AgNPs were oven-dried at 80 °C overnight for UV–vis analysis [104,105] (Figure 10).
## 3.2. Characterization of Nanoparticles
Characterization of AgNPs was achieved using UV–visible spectrometry, X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared (FTIR).
## 3.3. Experimental Plan and Application of Biogenic AgNPs
To evaluate the effects of different doses of biofabricated AgNPs, 20 diseased trees were used for exposure to the AgNPs. Six distinct treatments, three replicates, and various amounts of biogenic silver nanoparticles were applied topically to ‘Kinnow’ mandarins that had been exposed to HLB. The foliar treatments were applied using a sprayer (Hand Sprayer AP-20P, Jiaodian Technology Co., Wuhan, China). AgNPs were sprayed at 5:00 and 10:00 in the morning to ensure the gaseous exchange process through the stomata. Before the flowering stage in February 2021, all treatments were applied exogenously twice at 14-day intervals. In Table 1, a complete treatment plan is provided (Figure 11).
## 3.4.1. Chlorophyll and Carotenoid Content
At wavelengths of 480, 645, 652, and 663 nm, absorbance was observed using a spectrophotometer (Model U-2900 Sr. 26E82-018 Hitachi High-Teck Global Jp) against $80\%$ solvent acetone as a blank [106]. The following formula was used to determine the chlorophyll content [107]:*Chl a* (mgL−1) = 12.7 (A663) − 2.69 (A645) Chl b (mgL−1) = 22.9 (A645) − 4.68 (A663) Total chl (mgL−1) = 20.2 (A645) + 8.02 (A663) Carotenoid = [A480 + (0.114 (A663) − (0.638-A645)] × V/1000 × W A is the absorbance measured at the respective wavelength.
## 3.4.2. Relative Water Content
To assess RWC, the method proposed by Hussain et al. [ 108] was followed. RWC = (fresh weight − dry weight)/(saturated weight − dry weight) × 100
## 3.4.3. Membrane Stability Index
The protocol of Karami et al. [ 109] was followed and the following formula was used to compute the MSI: MSI = [1 − (C1/C2)] × 100
## 3.4.4. Peroxidase, Superoxide Dismutase, and Catalase Activity
The peroxidase dismutase (POD) activity was determined using a spectrophotometer (Shimadzu, UV micro-1240) as described by Chandra et al. [ 110]. The ability of superoxide dismutase (SOD) to resist a reduction in NBT was investigated following the method published by Marslin et al. [ 111]. The method described by Kasote et al. [ 112] was used to test the catalase (CAT) activity of treated and untreated citrus plants.
## 3.4.5. Total Phenolic Content (TPC)
The Folin–Ciocalteu reagent method proposed by Vennila et al. [ 113] was followed to determine the total phenolic content.
## 3.4.6. Total Flavonoid Content (TFC)
The aluminum chloride colorimetric procedure was adopted for the determination of TFC as proposed by Mattos et al. [ 114].
## 3.4.7. Proline Content and Sugar Content
The proline content of the treated and untreated leaves of the ‘Kinnow’ mandarin plants was measured at 520 nm following the method published by Kunwar et al. [ 115]. For sugar content, absorbance was observed at a 490 nm wavelength, adopting the procedure of Thompson et al. [ 116]. Soluble Sugar= Sample absorbance×dilution factor×K valWeight of Fresh Plant Tissue
## 3.4.8. Fruit Weight, Fruit Diameter, Peel Diameter, and Peel Weight
For the diameter, 36 ‘Kinnow’ mandarin fruit samples were randomly selected against specific treatments that comprised their respective replicates. Using an electronic weighing balance (Digital Electronic Lab Weighing Balance Scale—5000 g × 0.1 g), the average weight of each fruit was determined. The diameter of the fruit was measured using a manual Vernier caliper in millimeters. The fruits were thoroughly washed, dried, and peeled. The diameter of the peel (thickness) was measured with a Vernier caliper (mm). Subsequently, the peel weight was also measured using an electronic weighing balance [117].
## 3.4.9. Juice Weight, Rag Weight, and Juice pH
The samples of the ‘Kinnow’ mandarin fruits were peeled and the juice was extracted in a beaker and poured into a measuring cylinder with readings. Therefore, the weight of the juice was measured (mL) and the remaining rag was weighed using an electronic weighing balance (Digital Electronic Lab Weighing Balance Scale—5000 g × 0.1 g) [118]. The pH of the juice was measured using a digital pH meter (HI-98127) [65].
## 3.4.10. Total Soluble Solids and Titratable Acidity
The TSS was recorded using a digital spectrometer (AtagoPAL-3 refractometer). Juice samples of ‘Kinnow’ mandarin fruits were placed in the refractometer detector three times and the average value of each sample was recordedafter cleaning the refractometer detector with distilled water. TSS measurements were taken to obtain the percent Brix value, which indicated the sweetness level of the sample [119]. Titratable acidity (TA) was recorded taking into account the protocol used by Tyl et al. [ 120].
## 3.5. Statistical Analysis
In the factorial experimental arrangement of the treatments, all experiments were laid out. Each treatment was repeated three times. Analysis of variance (ANOVA) was applied to the data obtained using Microsoft Excel 2007 and STATISTICS 10 software.
## 4. Conclusions
The present research study demonstrates that treating Huanglongbing-diseased ‘Kinnow’ plants with green silver nanoparticles (AgNPs) is a biocompatible and environmentally benign method. Moringa oleifera plant extract was used as a reducing, capping, and stabilizing agent in the environmentally friendly synthesis of AgNPs in the current study. The AgNPs produced by green synthesis were spherical, cylindrical, or rectangular according to scanning electron microscopy. The increases in photosynthetic capacity, total sugars, and other fruit quality indices were more pronounced in plants infected with Huanglongbing when AgNPs were used at 75 mgL−1. Furthermore, cured plants showed a significant increase in antioxidants, and a significant reduction in titratable acidity (TA) and proline content was detected, highlighting the role of green AgNPs in reducing the devastating impacts of HLB. To summarize, the remarkable antibacterial potential discovered for these AgNPs at a concentration of 75 mgL−1 with a short exposure time may provide a viable solution to a disease that has yet to be eradicated. Overall, the study findings help citrus growers to achieve the better control Huanglongbing through management and treatment with a revolutionary control strategy and the introduction of the first biocompatible drug. The huge potential applications of phyto-nanotechnology in agriculture make it an essential study topic that demands the attention of plant scientists. To fully understand the relationship with silver nanoparticles, other factors that have an impact on citrus trees, and the responses of plants as a result, extensive scientific investigation is required. To explore the precise molecular activity of silver nanoparticles under in vivo circumstances, plant biologists, physiologists, pathologists, and nanotechnologists must work together. This will significantly affect the control of HLB disease in citrus and help to safeguard citrus production around the world.
## References
1. Shakour Z.T.A., Fayek N.M., Farag M.A.. **How do biocatalysis and biotransformation affect Citrus dietary flavonoids chemistry and bioactivity? A review**. *Crit. Rev. Biotechnol.* (2020) **40** 689-714. DOI: 10.1080/07388551.2020.1753648
2. Liu Y.Q., Heying E.. **Tanumihardjo SA History, Global Distribution, and Nutritional Importance of Citrus Fruits**. *Compr. Rev. Food Sci. Food Saf.* (2012) **11** 530-545. DOI: 10.1111/j.1541-4337.2012.00201.x
3. Patsalou M., Menikea K.K., Makri E., Vasquez M.I., Drouza C., Koutinas M.. **Development of a citrus peel-based biorefinery strategy for the production of succinic acid**. *J. Clean. Prod.* (2017) **166** 706-716. DOI: 10.1016/j.jclepro.2017.08.039
4. Karn A., Zhao C., Yang F., Cui J., Gao Z., Wang M., Zheng J.. **In-vivo biotransformation of citrus functional components and their effects on health**. *Crit. Rev. Food Sci. Nutr.* (2021) **61** 756-776. DOI: 10.1080/10408398.2020.1746234
5. Ledesma-Escobar C.A., de Castro M.D.L.. **Towards a comprehensive exploitation of citrus**. *Trends Food Sci. Technol.* (2014) **39** 63-75. DOI: 10.1016/j.tifs.2014.07.002
6. Zema D.A., Calabrò P.S., Folino A., Tamburino V., Zappia G., Zimbone S.M.. **Valorisation of citrus processing waste: A review**. *Waste Manag.* (2018) **80** 252-273. DOI: 10.1016/j.wasman.2018.09.024
7. Lu X., Zhao C., Shi H., Liao Y., Xu F., Du H., Zheng J.. **Nutrients and bioactives in citrus fruits: Different citrus varieties, fruit parts, and growth stages**. *Crit. Rev. Food Sci. Nutr.* (2021) 1-24. DOI: 10.1080/10408398.2021.1969891
8. Nawaz R., Abbasi N.A., Hafiz I.A., Khalid A., Ahmad T., Aftab M.. **Impact of climate change on kinnow fruit industry of Pakistan**. *Agrotechnology* (2019) **8** 2. DOI: 10.35248/2168-9881.19.8.186
9. Razi M.F., Keremane M.L., Ramadugu C., Roose M., Khan I.A., Lee R.F.. **Detection of citrus huanglongbing-associated ‘CandidatusLiberibacter asiaticus’ in citrus and Diaphorinacitri in Pakistan, seasonal variability, and implications for disease management**. *Phytopathology* (2014) **104** 257-268. DOI: 10.1094/PHYTO-08-13-0224-R
10. Khalil S.A., Sattar A., Zamir R.. **Development of sparse-seeded mutant kinnow (**. *Afr. J. Biotechnol.* (2011) **10** 14562-14565
11. Jaouad M., Moinina A., Ezrari S., Lahlali R.. **Key pests and diseases of citrus trees with emphasis on root rot diseases: An overview**. *Moroc. J. Agric. Sci.* (2020) **1**
12. Tipu M.M.H., Rahman M.M., Islam M.M., Elahi F.E., Jahan R., Islam M.R.. **Citrus greening disease (HLB) on**. *Physiol. Mol. Plant Pathol.* (2020) **112** 101558. DOI: 10.1016/j.pmpp.2020.101558
13. Brlansky R.. *Citrus Greening or Huanglonbing Disease* (2007)
14. Zheng Z., Chen J., Deng X.. **Historical perspectives, management, and current research of citrus HLB in Guangdong Province of China, where the disease has been endemic for over a hundred years**. *Phytopathology* (2018) **108** 1224-1236. DOI: 10.1094/PHYTO-07-18-0255-IA
15. Pitino M., Sturgeon K., Dorado C., Cano L.M., Manthey J.A., Shatters R.G., Rossi L.. **Quercus leaf extracts display curative effects against**. *Plant Physiol. Biochem.* (2020) **148** 70-79. DOI: 10.1016/j.plaphy.2020.01.013
16. Devi E.J., Labala R.K., Modak R., Singh N.S., Devi H.S.. **Molecular detection of “**. *Trop. Plant Pathol.* (2020) **45** 658-667. DOI: 10.1007/s40858-020-00392-3
17. Johnson E.G., Wu J., Bright D.B., Graham J.H.. **Early root infection and damage in Huanglongbing disease development**. *J Citrus Pathol.* (2014) **1** 232. DOI: 10.5070/C411025181
18. Gopal K., Gopi V., Kalyani L., Sreelatha M., Sreenivasulu B.. **Symptom-based diagnosis of Huanglongbing (citrus greening) disease by PCR in sweet orange (**. *Arch. Phytopathol. Plant Prot.* (2010) **43** 863-870. DOI: 10.1080/03235400802021116
19. Mira A., Yu S., Mohamed E., El Aiedy A., Elabasy U., Gmitter F.. **Evaluation of Huanglongbing tolerance in citrus breeding populations**. *J. Product. Dev.* (2019) **24** 371-390. DOI: 10.21608/jpd.2019.41443
20. Sajid A., Ghazanfar M.U., Rauf S., Hussain Z., Ahmad S., Iftikhar Y.. **Incidence and molecular detection of greening disease in two citrus cultivars in Sargodha, Pakistan**. *Sarhad J. Agric.* (2021) **37** 296-301. DOI: 10.17582/journal.sja/2021/37.1.296.301
21. Naqvi S.A.H., Atta S., Liu H., Rehman A.U., Khan A.A.. **Serological and molecular based detection of graft-transmissible pathogens associated with citrus from non-core areas of pakistan**. *Pak. J. Agric. Sci.* (2017) **54** 793-799
22. Canales E., Coll Y., Hernández I., Portieles R., Rodríguez García M., López Y., Borrás-Hidalgo O.. **‘CandidatusLiberibacter asiaticus’, causal agent of citrus Huanglongbing, is reduced by treatment with Brassinosteroids**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0146223
23. Albrecht U., Bowman K.D.. **Tolerance of the trifoliate citrus hybrid US-897 (**. *HortScience* (2011) **46** 16-22. DOI: 10.21273/HORTSCI.46.1.16
24. Wang N., Pierson E.A., Setubal J.C., Xu J., Levy J.G., Zhang Y., Li J., Rangel L.T., Martins J.. **The**. *Annu. Rev. Phytopathol.* (2017) **55** 451-482. DOI: 10.1146/annurev-phyto-080516-035513
25. Chen X.D., Gill T.A., Pelz-Stelinski K.S., Stelinski L.L.. **Risk assessment of various insecticides used for management of Asian citrus psyllid, Diaphorinacitri in Florida citrus, against honey bee, Apismellifera**. *Ecotoxicology* (2017) **26** 351-359. DOI: 10.1007/s10646-017-1768-5
26. Zhang M., Guo Y., Powell C.A., Doud M.S., Yang C., Duan Y.. **Effective antibiotics against ‘**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0111032
27. Hu J., Jiang J., Wang N.. **Control of citrus Huanglongbing via trunk injection of plant defense activators and antibiotics**. *Phytopathology* (2018) **108** 186-195. DOI: 10.1094/PHYTO-05-17-0175-R
28. Li B., Webster T.J.. **Bacteria antibiotic resistance: New challenges and opportunities for implant-associated orthopedic infections**. *J. Orthop. Res.* (2018) **36** 22-32. DOI: 10.1002/jor.23656
29. Shahbaz M., Akram A., Raja N.I., Mukhtar T., Mashwani Z.U., Mehak A., Fatima N., Sarwar S., Haq E.U., Yousaf T.. **Green synthesis and characterization of selenium nanoparticles and its application in plant disease management: A review**. *Pak. J. Phytopathol.* (2022) **34** 189-102. DOI: 10.33866/phytopathol.034.01.0739
30. Hassan H.U., Raja N.I., Abasi F., Mehmood A., Qureshi R., Manzoor Z., Shahbaz M., Proćków J.. **Comparative study of antimicrobial and antioxidant potential of olea ferruginea fruit extract and its mediated selenium nanoparticles**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27165194
31. Duhan J.S., Kumar R., Kumar N., Kaur P., Nehra K., Duhan S.. **Nanotechnology: The new perspective in precision agriculture**. *Biotechnol. Rep.* (2017) **15** 11-23. DOI: 10.1016/j.btre.2017.03.002
32. Balciunaitiene A., Viskelis P., Viskelis J., Streimikyte P., Liaudanskas M., Bartkiene E., Lele V.. **Green synthesis of silver nanoparticles using extract of**. *Processes* (2021) **9**. DOI: 10.3390/pr9081304
33. Gopinath V., Kamath S.M., Priyadarshini S., Chik Z., Alarfaj A.A., Hirad A.H.. **Multifunctional applications of natural polysaccharide starch and cellulose: An update on recent advances**. *Biomed. Pharmacother.* (2022) **146** 112492. DOI: 10.1016/j.biopha.2021.112492
34. Abasi F., Raja N.I., Mashwani Z.U.R., Amjad M.S., Ehsan M., Mustafa N., Proćków J.. **Biogenic Silver Nanoparticles as a Stress Alleviator in Plants: A Mechanistic Overview**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27113378
35. Ulaeto S.B., Mathew G.M., Pancrecious J.K., Nair J.B., Rajan T.P.D., Maiti K.K., Pai B.C.. **Biogenic Ag nanoparticles from neem extract: Their structural evaluation and antimicrobial effects against**. *ACS Biomater. Sci. Eng.* (2019) **6** 235-245. DOI: 10.1021/acsbiomaterials.9b01257
36. Javed B., Ikram M., Farooq F., Sultana T., Mashwani Z.U.R., Raja N.I.. **Biogenesis of silver nanoparticles to treat cancer, diabetes, and microbial infections: A mechanistic overview**. *Appl. Microbiol. Biotechnol.* (2021) **105** 2261-2275. DOI: 10.1007/s00253-021-11171-8
37. Khan M., Khan A.U., Bogdanchikova N., Garibo D.. **Antibacterial and antifungal studies of biosynthesized silver nanoparticles against plant parasitic nematode**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26092462
38. Darwesh O.M., Elshahawy I.E.. **Silver nanoparticles inactivate sclerotial formation in controlling white rot disease in onion and garlic caused by the soil borne fungus Stromatinia cepivora**. *Eur. J. Plant Pathol.* (2021) **160** 917-934. DOI: 10.1007/s10658-021-02296-7
39. Popova T.P., Ignatov I., Huether F., Petrova T.. **Antimicrobial activity of colloidal nanosilver 24 ppm in vitro**. *Bulg. Chem. Commun.* (2021) **53** 365-370
40. Bruna T., Maldonado-Bravo F., Jara P., Caro N.. **Silver nanoparticles and their antibacterial applications**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22137202
41. Ghojavand S., Madani M., Karimi J.. **Green synthesis, characterization and antifungal activity of silver nanoparticles using stems and flowers of felty germander**. *J. Inorg. Organomet. Polym. Mater.* (2020) **30** 2987-2997. DOI: 10.1007/s10904-020-01449-1
42. El-Naggar N.E.A., Hussein M.H., Shaaban-Dessuuki S.A., Dalal S.R.. **Production, extraction and characterization of Chlorella vulgaris soluble polysaccharides and their applications in AgNPs biosynthesis and biostimulation of plant growth**. *Sci. Rep.* (2020) **10** 1-19. DOI: 10.1038/s41598-020-59945-w
43. Bao V.V.Q., Vuong L.D., Luan L.V.. **Biomimetic synthesis of silver nanoparticles for preparing preservative solutions for mandarins (**. *Nano LIFE* (2018) **8** 1850003. DOI: 10.1142/S1793984418500034
44. Osonga F.J., Akgul A., Yazgan I., Akgul A., Eshun G.B., Sakhaee L., Sadik O.A.. **Size and shape-dependent antimicrobial activities of silver and gold nanoparticles: A model study as potential fungicides**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25112682
45. Sathiyaseelan A., Saravanakumar K., Mariadoss A.V.A., Wang M.H.. **Biocompatible fungal chitosan encapsulated phytogenic silver nanoparticles enhanced antidiabetic, antioxidant and antibacterial activity**. *Int. J. Biol. Macromol.* (2020) **153** 63-71. DOI: 10.1016/j.ijbiomac.2020.02.291
46. Nurul Aini A., Al Farraj D.A., Endarko E., Rubiyanto A., Nur H., Al Khulaifi M.M., Syafiuddin A.. **A new green method for the synthesis of silver nanoparticles and their antibacterial activities against gram-positive and gram-negative bacteria**. *J. Chin. Chem. Soc.* (2019) **66** 705-712. DOI: 10.1002/jccs.201800412
47. Wang Y., Zhang X., Bai Y., Li W., Li X., Xing X., Fu J.. **Anticancer and Antibacterial Activities of Silver Nanoparticles (AgNPs) Synthesized from**. *J. Nanosci. Nanotechnol.* (2020) **20** 4143-4151. DOI: 10.1166/jnn.2020.17524
48. Jain N., Jain P., Rajput D., Patil U.K.. **Green synthesized plant-based silver nanoparticles: Therapeutic prospective for anticancer and antiviral activity**. *Micro Nano Syst. Lett.* (2021) **9** 1-24. DOI: 10.1186/s40486-021-00131-6
49. Ratan Z.A., Haidere M.F., Nurunnabi M.D., Shahriar S.M., Ahammad A.J., Shim Y.Y., Reaney M.J.T., Cho J.Y.. **Green chemistry synthesis of silver nanoparticles and their potential anticancer effects**. *Cancers* (2020) **12**. DOI: 10.3390/cancers12040855
50. Stephano-Hornedo J.L., Torres-Gutiérrez O., Toledano-Magaña Y., Gradilla-Martínez I., Pestryakov A., Sánchez-González A., Bogdanchikova N.. **Argovit™ silver nanoparticles to fight Huanglongbing disease in Mexican limes (**. *RSC Adv.* (2020) **10** 6146-6155. DOI: 10.1039/C9RA09018E
51. Dzuvor C.K., Pan S., Amanze C., Amuzu P., Asakiya C., Kubi F.. **Bioactive components from**. *Crit. Rev. Biotechnol.* (2022) **42** 271-293. DOI: 10.1080/07388551.2021.1931804
52. Shahbaz M., Akram A., Raja N.I., Mukhtar T., Mehak A., Fatima N., Ajmal M., Ali K., Mustafa N., Abasi F.. **Antifungal activity of green synthesized selenium nanoparticles and their effect on physiological, biochemical, and antioxidant defense system of mango under mango malformation disease**. *PLoS ONE* (2023) **18**. DOI: 10.1371/journal.pone.0274679
53. Huang D., Dang F., Huang Y., Chen N., Zhou D.. **Uptake, translocation, and transformation of silver nanoparticles in plants**. *Environ. Sci. Nano* (2022) **9** 12-39. DOI: 10.1039/D1EN00870F
54. Liao S., Zhang Y., Pan X., Zhu F., Jiang C., Liu Q., Chen L.. **Antibacterial activity and mechanism of silver nanoparticles against multidrug-resistant**. *Int. J. Nanomed.* (2019) **14** 1469. DOI: 10.2147/IJN.S191340
55. Arora S., Arora S.. **Nutritional significance and therapeutic potential of**. *J. Food Biochem.* (2021) **45** e13933. DOI: 10.1111/jfbc.13933
56. Shahbaz M., Fatima N., Mashwani Z.U.R., Akram A., Haq E.U., Mehak A., Abasi F., Ajmal M., Yousaf T., Raja N.I.. **Effect of Phytosynthesized Selenium and Cerium Oxide Nanoparticles on Wheat (**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27238149
57. Moodley J.S., Krishna S.B.N., Pillay K., Govender P.. **Green synthesis of silver nanoparticles from**. *Adv. Nat. Sci. Nanosci. Nanotechnol.* (2018) **9** 015011. DOI: 10.1088/2043-6254/aaabb2
58. Okaiyeto K., Hoppe H., Okoh A.I.. **Plant-based synthesis of silver nanoparticles using aqueous leaf extract of**. *J. Clust. Sci.* (2021) **32** 101-109. DOI: 10.1007/s10876-020-01766-y
59. Avirdi E., Hooshmand S.E., Sepahvand H., Vishwanathan V., Bahadur I., Katata-Seru L.M., Varma R.S.. **Ionic liquids-assisted greener preparation of silver nanoparticles**. *Curr. Opin. Green Sustain. Chem.* (2021) **33** 100581. DOI: 10.1016/j.cogsc.2021.100581
60. Biba R., Košpić K., Komazec B., Markulin D., Cvjetko P., Pavoković D., Balen B.. **Surface coating-modulated phytotoxic responses of silver nanoparticles in plants and freshwater green algae**. *Nanomaterials* (2022) **12**. DOI: 10.3390/nano12010024
61. Sadak M.S.. **Impact of silver nanoparticles on plant growth, some biochemical aspects, and yield of fenugreek plant (**. *Bull. Natl. Res. Cent.* (2019) **43** 38. DOI: 10.1186/s42269-019-0077-y
62. Gupta S.D., Agarwal A., Pradhan S.. **Phytostimulatory effect of silver nanoparticles (AgNPs) on rice seedling growth: An insight from antioxidative enzyme activities and gene expression patterns**. *Ecotoxicol. Environ. Saf.* (2018) **161** 624-633. DOI: 10.1016/j.ecoenv.2018.06.023
63. Khalofah A., Kilany M., Migdadi H.. **Phytostimulatory influence of**. *Plants* (2021) **10**. DOI: 10.3390/plants10040790
64. Kumari R., Ashraf S., Bagri G.K., Khatik S.K., Bagri D.K., Bagdi D.L.. **Extraction and estimation of chlorophyll content of seed treated lentil crop using DMSO and acetone**. *J. Pharmacogn. Phytochem.* (2018) **7** 249-250
65. Amin B., Atif M.J., Meng H., Ghani M.I., Ali M., Wang X., Cheng Z.. **Biochemical and Physiological Responses of Cucumis sativus Cultivars to Different Combinations of Low-Temperature and High Humidity**. *J. Plant Growth Regul.* (2022) **42** 390-406. DOI: 10.1007/s00344-021-10556-3
66. Kusumiyati K., Hadiwijaya Y., Suhandy D., Munawar A.A.. **Prediction of water content and soluble solids content of ‘manalagi’apples using near infrared spectroscopy**. *IOP Conf. Ser. Earth Environ. Sci.* (2021) **922** 012062. DOI: 10.1088/1755-1315/922/1/012062
67. Shahbaz M., Akram A., Mehak A., Haq E.U., Fatima N., Wareen G., Fitriatin B.N., Sayyed R.Z., Ilyas N., Sabullah M.K.. **Evaluation of Selenium Nanoparticles in Inducing Disease Resistance against Spot Blotch Disease and Promoting Growth in Wheat under Biotic Stress**. *Plants* (2023) **12**. DOI: 10.3390/plants12040761
68. Kojonna T., Suttiyut T., Khunpolwattana N., Pongpanich M., Suriya-Arunroj D., Comai L., Chadchawan S.. **Identification of a Negative Regulator for Salt Tolerance at Seedling Stage via a Genome-Wide Association Study of Thai Rice Populations**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23031842
69. Denaxa N.K., Damvakaris T., Roussos P.A.. **Antioxidant defense system in young olive plants against drought stress and mitigation of adverse effects through external application of alleviating products**. *Sci. Hortic.* (2020) **259** 108812. DOI: 10.1016/j.scienta.2019.108812
70. Fimognari L., Dölker R., Kaselyte G., Jensen C.N., Akhtar S.S., Großkinsky D.K., Roitsch T.. **Simple semi-high throughput determination of activity signatures of key antioxidant enzymes for physiological phenotyping**. *Plant Methods* (2020) **16** 1-19. DOI: 10.1186/s13007-020-00583-8
71. Hadwan M.H., Abed H.N.. **Data supporting the spectrophotometric method for the estimation of catalase activity**. *Data Brief* (2016) **6** 194-199. DOI: 10.1016/j.dib.2015.12.012
72. Salih A.M., Qahtan A.A., Al-Qurainy F., Al-Munqedhi B.M.. **Impact of Biogenic Ag-Containing Nanoparticles on Germination Rate, Growth, Physiological, Biochemical Parameters, and Antioxidants System of Tomato (**. *Processes* (2022) **10**. DOI: 10.3390/pr10050825
73. Aktumsek A., Zengin G., Guler G.O., Cakmak Y.S., Duran A.. **Antioxidant potentials and anticholinesterase activities of methanolic and aqueous extracts of three endemic Centaurea L. species**. *Food Chem. Toxicol.* (2013) **55** 290-296. DOI: 10.1016/j.fct.2013.01.018
74. Bhagyawant S.S., Narvekar D.T., Gupta N., Bhadkaria A., Koul K.K., Srivastava N.. **Variations in the antioxidant and free radical scavenging under induced heavy metal stress expressed as proline content in chickpea**. *Physiol. Mol. Biol. Plants* (2019) **25** 683-696. DOI: 10.1007/s12298-019-00667-3
75. Fang W., Chi Z., Li W., Zhang X., Zhang Q.. **Comparative study on the toxic mechanisms of medical nanosilver and silver ions on the antioxidant system of erythrocytes: From the aspects of antioxidant enzyme activities and molecular interaction mechanisms**. *J. Nanobiotechnol.* (2019) **17** 66. DOI: 10.1186/s12951-019-0502-2
76. Gurrieri L., Merico M., Trost P., Forlani G., Sparla F.. **Impact of drought on soluble sugars and free proline content in selected Arabidopsis mutants**. *Biology* (2020) **9**. DOI: 10.3390/biology9110367
77. Sharifi-Rad M., Pohl P., Epifano F., Álvarez-Suarez J.M.. **Green synthesis of silver nanoparticles using Astragalustribuloidesdelile. root extract: Characterization, antioxidant, antibacterial, and anti-inflammatory activities**. *Nanomaterials* (2020) **10**. DOI: 10.3390/nano10122383
78. Mirmoeini T., Pishkar L., Kahrizi D., Barzin G., Karimi N.. **Phytotoxicity of green synthesized silver nanoparticles**. *Physiol. Mol. Biol. Plants* (2021) **27** 417-427. DOI: 10.1007/s12298-021-00946-y
79. Qian H., Peng X., Han X., Ren J., Sun L., Fu Z.. **Comparison of the toxicity of silver nanoparticles and silver ions on the growth of terrestrial plant model Arabidopsis thaliana**. *J. Environ. Sci.* (2013) **25** 1947-1956. DOI: 10.1016/S1001-0742(12)60301-5
80. Quiterio-Gutiérrez T., Ortega-Ortiz H., Cadenas-Pliego G., Hernández-Fuentes A.D., Sandoval-Rangel A., Benavides-Mendoza A., Juárez-Maldonado A.. **The application of selenium and copper nanoparticles modifies the biochemical responses of tomato plants under stress by Alternariasolani**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20081950
81. Qureshi M.A., Jaskani M.J., Khan A.S., Haider M.S., Shafqat W., Asif M., Mehmood A.. **Influence of different rootstocks on physico-chemical quality attributes of Kinnow mandarin**. *Pak. J. Agric. Sci* (2021) **58** 929-935
82. Saleem B.A., Malik A.U., Pervez M.A., Khan A.S., Khan M.N.. **Spring application of growth regulators affects fruit quality of ‘Blood Red’sweet orange**. *Pak. J. Bot* (2008) **40** 1013-1023
83. Adumanya O.C.U., Onyeme A.C., Umensofor G.A., Nwinee S.A.. **Ascorbic acid and pH levels of matured indigenous fruits in south-east Nigeria**. *Transatl. J. Multidiscip. Res.* (2021) **3** 82-100
84. Chung I.M., Rajakumar G., Thiruvengadam M.. **Effect of silver nanoparticles on phenolic compounds production and biological activities in hairy root cultures of**. *Acta Biol. Hung.* (2018) **69** 97-109. DOI: 10.1556/018.68.2018.1.8
85. Raigond P., Raigond B., Kaundal B., Singh B., Joshi A., Dutt S.. **Effect of zinc nanoparticles on antioxidative system of potato plants**. *J. Environ. Biol.* (2017) **38** 435. DOI: 10.22438/jeb/38/3/MS-209
86. Marchan S., Hector T., Bascombe K.. **The pH and Titratable Acidity of Still and Sparkling Flavored Waters: The Effects of Temperature and Storage Time**. *Open J. Stomatol.* (2021) **11** 148. DOI: 10.4236/ojst.2021.113012
87. Dada A.O., Adekola F.A., Odebunmi E.O.. **Liquid phase scavenging of Cd (II) and Cu (II) ions onto novel nanoscale zerovalent manganese (nZVMn): Equilibrium, kinetic and thermodynamic studies**. *Environ. Nanotechnol. Monit. Manag.* (2017) **8** 63-72. DOI: 10.1016/j.enmm.2017.05.001
88. Nejatzadeh F.. **Effect of silver nanoparticles on salt tolerance of**. *Heliyon* (2021) **7** e05981. DOI: 10.1016/j.heliyon.2021.e05981
89. Femi-Adepoju A.G., Dada A.O., Otun K.O., Adepoju A.O., Fatoba O.P.. **Green synthesis of silver nanoparticles using terrestrial fern (**. *Heliyon* (2019) **5** e01543. DOI: 10.1016/j.heliyon.2019.e01543
90. Pujol D., Liu C., Fiol N., Olivella M.À., Gominho J., Villaescusa I., Pereira H.. **Chemical characterization of different granulometric fractions of grape stalks waste**. *Ind. Crops Prod.* (2013) **50** 494-500. DOI: 10.1016/j.indcrop.2013.07.051
91. Khan S.A., Shahid S., Lee C.S.. **Green synthesis of gold and silver nanoparticles using leaf extract of clerodendruminerme; characterization, antimicrobial, and antioxidant activities**. *Biomolecules* (2020) **10**. DOI: 10.3390/biom10060835
92. Kavaz D., Umar H., Shehu S.. **Synthesis, characterization, antimicrobial and antimetastatic activity of silver nanoparticles synthesized from Ficusingens leaf**. *Artif. Cells Nanomed. Biotechnol.* (2018) **46** S1193-S1203. DOI: 10.1080/21691401.2018.1536060
93. Yu C., Tang J., Liu X., Ren X., Zhen M., Wang L.. **Green biosynthesis of silver nanoparticles using Eriobotrya japonica (Thunb.) leaf extract for reductive catalysis**. *Materials* (2019) **12**. DOI: 10.3390/ma12010189
94. Srivastava V., Choubey A.K.. **Synthesis of nanostructured silver particles using Citrus limetta peel extract for catalytic degradation of azo dyes through electron relay effect**. *Adv. Nat. Sci. Nanosci. Nanotechnol.* (2019) **10** 045015. DOI: 10.1088/2043-6254/ab52f3
95. Xu H., Wang L., Su H., Gu L., Han T., Meng F., Liu C.. **Making good use of food wastes: Green synthesis of highly stabilized silver nanoparticles from grape seed extract and their antimicrobial activity**. *Food Biophys.* (2015) **10** 12-18. DOI: 10.1007/s11483-014-9343-6
96. Alidoust D., Isoda A.. **Phytotoxicity assessment of γ-Fe**. *Environ. Earth Sci.* (2014) **71** 5173-5182. DOI: 10.1007/s12665-013-2920-z
97. Khan M., Khan A.U., Hasan M.A., Yadav K.K., Pinto M., Malik N., Sharma G.K.. **Agro-nanotechnology as an emerging field: A novel sustainable approach for improving plant growth by reducing biotic stress**. *Appl. Sci.* (2021) **11**. DOI: 10.3390/app11052282
98. Faghihi R., Larijani K., Abdossi V., Moradi P.. **Evaluation of the effect of plastics bags containing silver nanocomposite of grapefruit’s peel on cucumber postharvest nutritional value and their possible penetration in tissue**. *Food Health J.* (2020) **3** 35-39
99. Mehta C.M., Srivastava R., Arora S., Sharma A.K.. **Impact assessment of silver nanoparticles on plant growth and soil bacterial diversity**. *3 Biotech* (2016) **6** 254
100. Satti S.H., Raja N.I., Javed B., Akram A., Mashwani Z.U.R., Ahmad M.S., Ikram M.. **Titanium dioxide nanoparticles elicited agro-morphological and physicochemical modifications in wheat plants to control Bipolarissorokiniana**. *PLoS ONE* (2021) **16**. DOI: 10.1371/journal.pone.0246880
101. Iqbal M., Raja N.I., Mashwani Z.U.R., Wattoo F.H., Hussain M., Ejaz M., Saira H.. **Assessment of AgNPs exposure on physiological and biochemical changes and antioxidative defence system in wheat (**. *IET Nanobiotechnol.* (2019) **13** 230-236. DOI: 10.1049/iet-nbt.2018.5041
102. Yashveer S., Redhu N.S., Singh V., Sangwan S., Laxman H.V., Tokas J., Sindhu A.. **Nanoparticles in Agriculture: Characterization, Uptake and Role in Mitigating Heat Stress**. *Nat. Resour. Hum. Health* (2022) 1-22. DOI: 10.53365/nrfhh/144175
103. Hajian M.H., Ghorbanpour M., Abtahi F., Hadian J.. **Differential effects of biogenic and chemically synthesized silver-nanoparticles application on physiological traits, antioxidative status and californidine content in California poppy (Eschscholzia californica Cham)**. *Environ. Pollut.* (2022) **292** 118300. DOI: 10.1016/j.envpol.2021.118300
104. Monreal J.A., Jimenez E.T., Remesal E., Morillo-Velarde R., García-Mauriño S., Echevarría C.. **Proline content of sugar beet storage roots: Response to water deficit and nitrogen fertilization at field conditions**. *Environ. Exp. Bot.* (2007) **60** 257-267. DOI: 10.1016/j.envexpbot.2006.11.002
105. Shahri W., Ahmad S.S., Tahir I.. **Sugar signaling in plant growth and development**. *Plant Signaling: Understanding the Molecular Crosstalk* (2014) 93-116
106. Gupta N.K., Agarwal S., Agarwal V.P., Nathawat N.S., Gupta S., Singh G.. **Effect of short-term heat stress on growth, physiology and antioxidative defence system in wheat seedlings**. *Acta Physiol. Plant.* (2013) **35** 1837-1842. DOI: 10.1007/s11738-013-1221-1
107. Ma W., Pang Z., Huang X., Pandey S.S., Li J., Xu J., Wang N.. **Citrus Huanglongbing is an immune-mediated disease that can be treated by mitigating reactive oxygen species triggered cell death of the phloem tissues caused by**. *bioRxiv* (2021). DOI: 10.1101/2021.08.15.456409
108. Hussain S., Rao M.J., Anjum M.A., Ejaz S., Umar U.U.D., Ali M.A., Naqvi S.A.H.. **Effect of different combinations of antibiotics on fruit quality and antioxidant defense system in Huanglongbing infected Kinnow orchards**. *AMB Express* (2019) **9** 147. DOI: 10.1186/s13568-019-0871-9
109. KaramiMehrian S., Heidari R., Rahmani F.. **Effect of silver nanoparticles on free amino acids content and antioxidant defense system of tomato plants**. *Indian J. Plant Physiol.* (2015) **20** 257-263. DOI: 10.1007/s40502-015-0171-6
110. Chandra S., Chakraborty N., Dasgupta A., Sarkar J., Panda K., Acharya K.. **Chitosan nanoparticles: A positive modulator of innate immune responses in plants**. *Sci. Rep.* (2015) **5** 15195. DOI: 10.1038/srep15195
111. Marslin G., Sheeba C.J., Franklin G.. **Nanoparticles alter secondary metabolism in plants via ROS burst**. *Front. Plant Sci.* (2017) **8** 832. DOI: 10.3389/fpls.2017.00832
112. Kasote D.M., Katyare S.S., Hegde M.V., Bae H.. **Significance of antioxidant potential of plants and its relevance to therapeutic applications**. *Int. J. Biol. Sci.* (2015) **11** 982. DOI: 10.7150/ijbs.12096
113. Vennila K., Chitra L., Balagurunathan R., Palvannan T.. **Comparison of biological activities of selenium and silver nanoparticles attached with bioactive phytoconstituents: Green synthesized using Spermacoce hispida extract**. *Adv. Nat. Sci. Nanosci. Nanotechnol.* (2018) **9** 015005. DOI: 10.1088/2043-6254/aa9f4d
114. Mattos D., Kadyampakeni D.M., da Silva J.R., Vashisth T., Boaretto R.M.. **Reciprocal effects of huanglongbing infection and nutritional status of citrus trees: A review**. *Trop. Plant Pathol.* (2020) **45** 586-596. DOI: 10.1007/s40858-020-00389-y
115. Kunwar S., Grosser J., Gmitter F.G., Castle W.S., Albrecht U.. **Field performance of ‘Hamlin’orange trees grown on various rootstocks in huanglongbing-endemic conditions**. *HortScience* (2021) **56** 244-253. DOI: 10.21273/HORTSCI15550-20
116. Thompson A.K.. **Harvest maturity and methods (Chapter 2 and postharvest treatments (Chapter 4)**. *Postharvest Technology of Fruits and Vegetables* (2014) **Volume 95** 35-37
117. Silva S.R.D., Girardi E.A., Santos M.G., Cantuarias-Avilés T.E., Stuchi E.S.. **Plant growth, yield and fruit quality of Clementine mandarin selections under subtropical climate in Brazil**. *Rev. Frutic.* (2018) **40**. DOI: 10.1590/0100-29452018051
118. Lado J., Rodrigo M.J., Zacarías L.. **Maturity indicators and citrus fruit quality**. *Stewart Postharvest Rev.* (2014) **10** 1-6
119. Koh J., Morales-Contreras B.E., Guerra-Rosas M.I., Osorio-Hernández E., Culver C.A., Morales-Castro J., Wicker L.. **Huanglongbing disease and quality of pectin and fruit juice extracted from Valencia oranges**. *LWT* (2020) **131** 109692. DOI: 10.1016/j.lwt.2020.109692
120. Tyl C., Sadler G.D.. **pH and titratable acidity**. *Food Analysis* (2017) 389-406
|
---
title: Hyperglycemia and Glycemic Variability Associated with Glucocorticoids in Women
without Pre-Existing Diabetes Undergoing Neoadjuvant or Adjuvant Taxane Chemotherapy
for Early-Stage Breast Cancer
authors:
- Dana Mahin
- Sayeh Moazami Lavasani
- Leon Cristobal
- Niki Tank Patel
- Mina Sedrak
- Daphne Stewart
- James Waisman
- Yuan Yuan
- Wai Yu
- Raynald Samoa
- Nora Ruel
- Susan E. Yost
- Hayley Lee
- Sung Hee Kil
- Joanne E. Mortimer
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004215
doi: 10.3390/jcm12051906
license: CC BY 4.0
---
# Hyperglycemia and Glycemic Variability Associated with Glucocorticoids in Women without Pre-Existing Diabetes Undergoing Neoadjuvant or Adjuvant Taxane Chemotherapy for Early-Stage Breast Cancer
## Abstract
Glucocorticoids, which are administered with chemotherapy, cause hyperglycemia. Glycemic variability among breast cancer patients without diabetes is not well known. A retrospective cohort study was conducted involving early-stage breast cancer patients without diabetes who received dexamethasone prior to neoadjuvant or adjuvant taxane chemotherapy between August 2017–December 2019. Random blood glucose levels were analyzed, and steroid-induced hyperglycemia (SIH) was defined as a random glucose level of >140 mg/dL. A multivariate proportional hazards model was used to identify the risk factors of SIH. Out of 100 patients, the median age was 53 years (IQR: 45–63.5). A total of $45\%$ of patients were non-Hispanic White, $28\%$ Hispanic, $19\%$ Asian, and $5\%$ African American. The incidence of SIH was $67\%$, and glycemic fluctuations were highest in those with glucose levels of >200 mg/dL. Non-Hispanic White patients represented a significant predictor for time to SIH, with a hazard ratio of 2.5 ($95\%$ CI: 1.04, 5.95, $$p \leq 0.039$$). SIH was transient in over $90\%$ of the patients, and only seven patients remained hyperglycemic after glucocorticoid and chemotherapy completion. Pretaxane dexamethasone-induced hyperglycemia was observed in $67\%$ of the patients, with the greatest glycemic lability in those patients with blood glucose levels of >200 mg/dL. The non-Hispanic White patients had a higher risk of developing SIH.
## 1. Introduction
Glucocorticoid treatment is ubiquitous across many diseases for curative treatment as well as supportive care. Glucocorticoids are frequently used prior to the administration of chemotherapy as antiemetics to minimize allergic reactions to the cremophor diluent with paclitaxel and to decrease third space fluid accumulation that is associated with docetaxel [1,2]. Notwithstanding their efficacy, glucocorticoids induce a wide range of side effects. Hyperglycemia is considered to have high clinical significance because it directly affects cancer treatment and has been reported to increase infection, organ dysfunction, and chemotherapy-associated toxicities [3,4,5,6]. It can also lead to chemotherapy dose delays or reductions [3,7], and decrease chemotherapy response [8].
Steroid-induced hyperglycemia (SIH) is an abnormal increase in blood glucose levels with the use of glucocorticoids [9] by reducing insulin sensitivity, increasing hepatic gluconeogenesis, and impairing beta cells and insulin secretion [9,10,11,12]. SIH is characterized by postprandial hyperglycemia, which is primarily mediated by insulin resistance. SIH is difficult to detect, challenging to prevent and treat, and vastly understudied. The clinical principle and practice of early detection, risk factor identification, and treatment algorithms do not exist for SIH. In addition, the prevalence of SIH is not known as studies use different criteria. Some studies use the steroid-induced diabetes diagnosis criteria set by the American Diabetes Association (ADA). although patients with random glucose levels > 200 mg/dL are asymptomatic and do not meet these criteria [10,13,14]. Other studies define SIH as any elevated glucose level outside of the normal range using fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and/or random blood glucose levels [9,10,14,15]. Given the transient nature of SIH with its resolution upon steroid discontinuation [13], SIH should be identified during glucocorticoid treatment as any abnormal elevation in glucose. Diabetes diagnosis should be made according to the ADA criteria and should be applied rigidly during, and if possible, after glucocorticoid treatment ends.
A meta-analysis in both nondiabetic and noncancer populations suggests that $32.3\%$ of glucocorticoid-treated patients experience SIH [16]. It is a pervasive condition that can lead to complications, longer hospitalization, and mortality [17,18]. Since most studies are retrospective and no conclusive data are available from prospective randomized controlled clinical studies, the treatment targets and guidelines for asymptomatic and transient hyperglycemia have not been established [19]. In noncancer settings, SIH is associated with subsequent diabetes development, with the odds ratio ranging from 1.36 to 2.31 [20]. The risk factors for diabetes associated with SIH are not conclusive, but glucocorticoid-related parameters, such as dose, type, and duration of therapy, contribute more to diabetes development than traditional patient characteristics such as age, BMI, and family history [10].
Breast cancer is the most common cancer in the US, except for skin cancers [21]. Studies report widely different incidences of hyperglycemia associated with treatment ranging from $1\%$ to $43\%$, but these data are not just limited to SIH and include clinical trials with novel therapies [22,23,24,25]. There is a paucity of research on the incidence, risk factors, prognosis, and outcomes of SIH in early-stage breast cancer patients undergoing adjuvant chemotherapy. The incidence of transient SIH during chemotherapy is virtually unknown in receptor subtypes of breast cancer, treatment type, and duration of treatment. Two recent studies investigated SIH prevalence in homogenous patient populations of Koreans; however, the applicability of disease course and risk factors may not be generalizable in other patient populations [4,14].
Owing to the uncertainties surrounding the incidence of SIH in early-stage breast cancer patients, we aim to determine the incidence of SIH and the degree of glycemic lability in women without pre-existing diabetes who received dexamethasone premedication for taxane-containing (neo) adjuvant chemotherapy. In this study, we sought to assess random blood glucose levels for each cycle of dexamethasone and taxane-containing chemotherapy and determine SIH prevalence and the degree of glycemic variability. We also aim to investigate the predictors of elevated glucose levels in early-stage breast cancer women.
## 2. Materials and Methods
A retrospective chart review was conducted at the City of Hope Comprehensive Cancer Center from 1 August 2017 to 31 December 2019. Women with breast cancer were considered eligible if they were >18 years of age, treated with paclitaxel or docetaxel in the neoadjuvant or adjuvant setting, received dexamethasone premedication, had no prior diagnosis of diabetes or prediabetes, and completed chemotherapy by 31 December 2019. Patients were excluded from the study if they had a diagnosis of diabetes before taxane chemotherapy or if they received chemotherapy outside the adjuvant or neoadjuvant setting ($$n = 100$$). The data collection included age, race/ethnicity, hypertension as a comorbidity, BMI, patient’s random blood glucose level throughout each cycle of chemotherapy, and treatment for hyperglycemia after completion of therapy.
Patients who had hyperglycemia defined by random blood glucose > 140 mg/dL after receiving steroid treatment were recorded. Glycemic thresholds do not exist for SIH other than traditional glucose concentrations used for prediabetes and diabetes diagnosis [13]. In this study, we defined the higher-level SIH group as >200 mg/dL, the lower-level SIH group as 140–199 mg/dL, and the euglycemia group as <140 mg/dL. When accompanied by hyperglycemic symptoms, these thresholds for random glucose levels indicate a definitive diagnosis of prediabetes and diabetes per ADA. In this study, random rather than fasting glucose was used because glucocorticoids generally cause exaggerated postprandial hyperglycemia and have a lesser effect on fasting glucose. HbA1C is typically not evaluated for breast cancer patients undergoing therapy. Thus, no other metrics from blood tests were used.
The baseline random blood glucose was reported 1–4 weeks prior to the start of chemotherapy. The random blood glucose readings after initiation of chemotherapy were taken on the first day of each chemotherapy cycle after the required dose of dexamethasone premedication was given. The follow-up random blood glucose was reported 3–4 weeks after all cycles of chemotherapy were concluded. In addition, follow-up glucose levels were monitored after 2–3 months, 6 months, 1 year, and 2 years.
Data were summarized using median and interquartile range (IQR) for continuous variables and frequency/percentage for categorical fields. Data distribution was compared across categories of patients for euglycemia (<140 mg/dL), lower-level SIH (140–199 mg/dL), and higher-level SIH (>200 mg/dL) using the Wilcoxon rank sum test or t-test for continuous and chi-square or fisher’s exact test for categorical data, as deemed appropriate. Time to first elevated glucose level at or above 200 mg/dL was calculated, and Kaplan-Meier was used to estimate the probability of event-free survival. Univariate and multivariate proportional hazards models were used to test age, race/ethnicity, BMI, baseline glucose level, hypertension, and type of taxane as predictors for time to first elevated blood glucose level at or over 200 mg/dL. Graphs were generated to depict changes in blood glucose from baseline, using the direct ratio of values after initiation of treatment to the baseline value for each patient. The threshold for significance was set at 0.05. SAS® 9.4 was used to conduct data analysis and generate figures.
## 3. Results
A total of 131 female patients with breast cancer were identified. Thirty-one patients were excluded due to metastatic cancer ($$n = 16$$), type 2 diabetes ($$n = 13$$), and blood glucose levels at or higher than 200 mg/dL prior to the initiation of chemotherapy ($$n = 2$$). Table 1 summarizes the baseline patient characteristics for the 100 patients enrolled in the study. The random glucose assessment identified 45 ($45\%$) patients with lower-level SIH, 22 ($22\%$) with higher-level SIH, and 33 ($33\%$) with euglycemia. The median age was 53 years with an interquartile range (IQR) of 45–63.5 years, with $45\%$ non-Hispanic White, $28\%$ Hispanic, $19\%$ Asian, $5\%$ African American, and $3\%$ unknown. The median BMI was 27.1 kg/m2 (IQR, 23.7–30.8). According to BMI classification [26], $1\%$ was underweight (BMI < 18.5), $32\%$ were normal weight (BMI 18.5 to <25), $40\%$ were overweight (BMI 25 to <30), $16\%$ were obese (BMI > 30), and $11\%$ were morbidly obese (BMI > 40). Twenty-four percent of patients were hypertensive, and $63\%$ gained weight during treatment which consisted of docetaxel ($51\%$) and paclitaxel ($49\%$). A total of $67\%$ of patients were estrogen receptor (ER) positive, $48\%$ were progesterone receptor (PR) positive, and $38\%$ were human epidermal growth factor receptor 2 (HER2) positive.
The baseline random blood glucose range was 68–186 mg/dL. Patients with a blood glucose level of >200 mg/dL were excluded from the study, and three patients with glucose levels of 140–199 mg/dL were included in the analysis. The pretreatment glucose levels were available for 84 patients with a median glucose level of 99 mg/dL (IQR, 91.5–109.5). Out of the 16 patients without pretreatment glucose levels, 11 had elevated glucose levels (140–199 mg/dL range), and two had >200 mg/dL post dexamethasone treatment (Table 2).
The median age for the 22 patients with at least one blood glucose level of > 200 mg/dL was 59.5 years (range 50–68). Out of these patients, 14 are non-Hispanic White ($63.6\%$), 7 are Hispanic ($31.8\%$), and 1 is Asian ($4.5\%$) (Table 1). Seven ($31.8\%$) patients were obese or morbidly obese, and 8 ($36.4\%$) were overweight. Eight ($36.4\%$) patients were on hypertensive therapy. Patients received a median of 4 cycles of chemotherapy, and 11 ($50\%$) patients had glucose levels of >200 mg/dL during the first cycle, while 5 ($22.7\%$) patients reached >200 mg/dL during the second cycle and 6 ($27.3\%$) patients between the 3rd and 6th cycles.
The median age of the 45 patients in the lower-level SIH group was 53 years (range 45–64), 19 ($42.2\%$) were overweight, and 11 ($24.5\%$) were obese or morbidly obese. Eighteen patients ($40\%$) are non-Hispanic White, 11 ($24.4\%$) are Hispanic, 11 ($24.4\%$) are Asian, 2 ($4.4\%$) are African American, and 3 ($6.7\%$) are unknown. Nine ($20.0\%$) patients were on antihypertensive therapy. These patients received a median of four cycles of chemotherapy.
Out of the 33 patients who consistently maintained a blood glucose level less than 140 mg/dL, their median age was 51 years (range 43–59). Thirteen ($39.4\%$) were overweight, and nine ($27.3\%$) were obese or morbidly obese. Thirteen ($39.4\%$) are non-Hispanic White, 10 ($30.3\%$) are Hispanic, 7 ($21.2\%$) are Asian, and 3 ($9.1\%$) are African American. Seven ($21.2\%$) patients were being treated for hypertension. A median of five cycles of chemotherapy was administered among the euglycemia patients.
Figure 1 shows the median blood glucose level from dexamethasone initiation at week 0 to treatment completion at week 16, which corresponds to the duration of chemotherapy cycle 1 through to cycle 4. The elevations in glucose levels were seen as early as the first week and remained high until week 6. Most reverted to the normal range around week 7 and remained normoglycemic until the end of chemotherapy.
In Figure 2, the fluctuations in glucose levels over time are plotted for the euglycemic patients, lower-level SIH patients, and higher-level SIH patients. The greatest fluctuations were observed in the higher-level SIH patients.
Univariate proportional hazards models were used to test whether age (<50 vs. ≥50), race/ethnicity (non-Hispanic White vs. non-White), BMI (≤normal BMI vs. obese/morbidly obese), baseline glucose level (continuous), hypertension (yes vs. no), and taxane type (docetaxel vs. paclitaxel) were significant predictors of time to first elevated blood glucose level > 200 mg/dL. Race/ethnicity was the only significant predictor, with a hazard ratio (HR) of 2.5 ($95\%$ CI: 1.04, 5.95, $$p \leq 0.039$$) in the White vs. non-White patients. Figure 3 shows a Kaplan–Meier curve for time to elevated blood glucose > 200 mg/dL in the White vs. non-White patients. Race/ethnicity showed a statistical significance in the univariate model; however, the multivariate model did not maintain this significance.
Postchemotherapy random blood glucose levels were available for 96 patients, and of these, 89/out of 96 ($93\%$) patients had glucose levels within the normal range. Five had glucose levels between 140 and 199 mg/dL, and two had glucose levels at or over 200 mg/dL. None of the patients received treatment for hyperglycemia or were referred for an endocrinology consultation. More extensive follow-up revealed that after 2–3 months, four patients had hyperglycemia. At 6 months follow-up, seven patients had hyperglycemia (one with >200). At 1-year follow-up, 16 patients had hyperglycemia (one with >200), and at 2-year follow-up, 11 patients had hyperglycemia (two with >200).
## 4. Discussion
This study sought to identify the incidence of steroid-induced hyperglycemia and the degree of glycemic variability among early-stage breast cancer patients undergoing chemotherapy. A total of 67 women ($67\%$) without a known diagnosis of diabetes or prediabetes had hyperglycemia during (neo) adjuvant chemotherapy using dexamethasone and either docetaxel or paclitaxel. Given the intermittent use of glucocorticoids prior to chemotherapy and no continuous or extended administration, the incidence of hyperglycemia was high. Recent studies in a similar clinical context in breast cancer patients undergoing adjuvant chemotherapy indicate a much lower incidence of SIH, with one study reporting $3.3\%$ ($\frac{5}{152}$) [4] and another reporting $19\%$ ($\frac{82}{423}$) [14]. The threshold of SIH was defined differently, where the former included patients whose random glucose was >200 mg/dL, whereas the latter included random glucose > 140 mg/dL. In previous studies involving breast cancer patients that investigated hyperglycemia across all cancer stages with various treatments, including new pharmacological agents and glucocorticoids, the incidence of hyperglycemic events was reported to be 1–$43\%$ [22,23,24,25,27].
Our observed high incidence of SIH ($67\%$) was even higher than other cancer patient types whose treatment included a high glucocorticoid dose and extended treatment duration. Hyperglycemia among cancer patients with brain tumor/metastasis, lymphoma, or bone marrow transplant (BMT) is reported to be $58.9\%$ [28]. Other cancer types with reported hyperglycemia include acute lymphocytic leukemia ($58.1\%$) [29], non-*Hodgkin lymphoma* ($43.2\%$), and prostate cancer ($49.2\%$) [3]. Of note, these studies are of largely hospitalized cancer patients who either underwent bone marrow transplants and/or metastatic cancer patients on continuous high steroid use for a longer duration. The current study included early-stage breast cancer patients who received intermittent glucocorticoids as a pretreatment regimen for chemotherapy in an outpatient setting.
After completion of dexamethasone and chemotherapy, $93\%$ of patients were found to have random glucose levels that were within the normal range. In previous studies involving breast cancer patients, 6 out of 17 ($35.3\%$) patients with SIH remained in a hyperglycemic state after chemotherapy [4]. In another study, 10 out of 82 ($12.2\%$) progressed to newly diagnosed diabetes [14]. We did not measure or investigate other diagnostic criteria for diabetes, such as HbA1c, oral glucose tolerance test (OGTT), or fasting plasma glucose (FPG).
Although SIH is transient and many patients revert to a euglycemic state, the data indicate that even transient hyperglycemic episodes are associated with decreased mortality and complications among the noncancer patient population [30,31,32]. The relationship between the observed high prevalence of SIH in our patient population and clinical outcomes has not been analyzed in this study. More analyses are needed to fully elucidate the impact of hyperglycemia during chemotherapy on subsequent diabetes occurrence, cancer prognosis, and cancer outcomes, including mortality. A recent study suggests that SIH in breast cancer patients may be associated with lower relapse-free survival, but more studies are warranted [14].
We found a significantly higher incidence of glucose dysregulation in non-Hispanic White patients compared with other races/ethnicities. To date, this is the first study to evaluate the prevalence of SIH across different race/ethnicities among early-stage breast cancer patients. Diabetes and diabetes-related complications disproportionately affect racial and ethnic minority groups. Hispanic and African American patients have one of the highest rates, and non-Hispanic White patients have the lowest rate [33]. Interestingly, the frequency of hyperglycemia that we observed in this study suggests a different racial/ethnicity distribution, with non-Hispanic White patients having the highest SIH rate.
In terms of glucose lability in our patient population, the degree of glucose excursions was greatest in the higher-level SIH group compared to lower-level SIH and euglycemic groups. Glycemic excursions are dependent on the pharmacokinetic and pharmacodynamic properties of glucocorticoids as well as the severity of decreased insulin sensitivity. *In* general, blood glucose levels are elevated postprandially in the latter part of the day, with stabilized levels observed in the morning [10,11,19]. The duration of glucose variability can be short-term or long-term, with fluctuations both within-day and day-to-day. Both short and long-term glucose variability is associated with tissue damage, microvascular complications, cardiovascular diseases, and mortality in the noncancer patient population [34,35]. Studies of cancer patients are needed to assess the relationship between glucose fluctuations and clinical outcomes. The deleterious effects of glucose variability are driven by oxidative stress, endothelial dysfunction, and inflammation [34]. Hyperglycemia increases the reactive oxygen species production and inactivates nitric oxide, which results in endothelial dysfunction and vascular complications [36,37]. In addition to hyperglycemia, glucose lability is associated with changes in endothelial nitric oxide synthase, increased oxidate stress, and tissue damage via intracellular signal transduction pathways, including protein kinase B (AKT) [34,38]. Of note, glucose variability is associated with microvascular complications, cardiovascular outcomes, and mortality independent of HbA1c [39,40].
For patients without diabetes, SIH is linked to the subsequent development of diabetes, with the odds ratio for diabetes reported to be 1.36–2.31 [20]. Diabetes increases chemotherapy toxicities and complications [41], poor breast cancer prognosis [42,43], diabetes-related mortality [44], cardiovascular mortality [44], breast cancer-specific mortality [44], and overall mortality [41,42,43,45] in breast cancer patients. However, SIH is difficult to monitor in cancer patients because patients are either asymptomatic or have symptoms such as dry mouth, fatigue, and polyuria, which may be attributed to cancer therapy. In addition, the diagnosis of diabetes after chemotherapy and surgery may be delayed or go undetected due to a lack of continuity of care in post-treatment survivors.
Our study demonstrated the high prevalence of SIH and glycemic variability among early breast cancer patients undergoing (neo) adjuvant chemotherapy, specifically for non-Hispanic White patients. Though most patients revert to euglycemia, more definitive diagnostic tests such as HbA1c, OGTT, and FPG are needed to accurately diagnose patients who progress to newly developed diabetes post-SIH. This is of high clinical significance as diabetes in breast cancer patients is associated with worse prognosis, complications, and mortality. SIH patients, specifically those whose blood glucose level is > 200 mg/dL and patients with a high degree of glucose excursions, should be monitored closely and should be considered for an endocrinology consultation for further workup. Moreover, these patients may be considered for continuous glucose monitoring (CGM), which can automatically and serially track glucose levels, or self-monitoring of blood glucose (SMBG) using glucometers to monitor glucose variability during and after chemotherapy. CGM and SMBG might be especially important in this patient population as the dose, frequency, and duration of steroid use vary across patients, and the increases and fluctuations in glucose levels may not be detected during routine laboratory assessments. Given that steroids primarily increase postprandial blood glucose levels, the diagnostic sensitivity of SIH is greatest in the afternoon or in the evening, but most patients get tested in the morning during their clinic visits. CGM and SMBG may capture SIH that are otherwise underdiagnosed and underreported due to this limitation.
The study has several limitations, including a small sample size and limiting the study to patients without prediabetes or diabetes. The incidence and glucose lability among patients with diabetes was not investigated. Information on the history of gestational diabetes or family history of diabetes was not collected. Although random glucose levels were extracted at the end of chemotherapy to determine the persistence of SIH, these were not definitive diagnostic tests for diabetes, and the follow-up data after chemotherapy were not assessed. Notwithstanding these limitations, our study, to our knowledge, is the first study to examine the incidence of SIH across different races/ethnicities in early breast cancer patients undergoing (neo) adjuvant chemotherapy prior to surgery. Though additional follow-up is needed to investigate the incidence of subsequent diabetes development, the study revealed that SIH is prevalent, with a high degree of glycemic variability, particularly in non-Hispanic White patients. Close monitoring with CGM and SMBG may be needed for patients at risk of hyperglycemia. Diagnostic tests for diabetes, such as HbA1c, OGTT, FPG, and endocrinology consultation, should also be recommended. Moreover, further research is warranted to examine whether transient SIH is associated with cancer prognosis, complications, and survival.
## References
1. Gelderblom H., Verweij J., Nooter K., Sparreboom A.. **Cremophor EL: The drawbacks and advantages of vehicle selection for drug formulation**. *Eur. J. Cancer* (2001.0) **37** 1590-1598. DOI: 10.1016/S0959-8049(01)00171-X
2. Della Cuna G.R., Pellegrini A., Piazzi M.. **Effect of methylprednisolone sodium succinate on quality of life in preterminal cancer patients: A placebo-controlled, multicenter study. The Methylprednisolone Preterminal Cancer Study Group**. *Eur. J. Cancer Clin. Oncol.* (1989.0) **25** 1817-1821. PMID: 2698804
3. Brunello A., Kapoor R., Extermann M.. **Hyperglycemia during chemotherapy for hematologic and solid tumors is correlated with increased toxicity**. *Am. J. Clin. Oncol.* (2011.0) **34** 292-296. DOI: 10.1097/COC.0b013e3181e1d0c0
4. Lee E.K., Koo B., Hwangbo Y., Lee Y.J., Baek J.Y., Cha Y.J., Kim S.Y., Sim S.H., Lee K.S., Park I.H.. **Incidence and disease course of new-onset diabetes mellitus in breast and colorectal cancer patients undergoing chemotherapy: A prospective multicenter cohort study**. *Diabetes Res. Clin. Pract.* (2021.0) **174** 108751. DOI: 10.1016/j.diabres.2021.108751
5. Healy S.J., Nagaraja H.N., Alwan D., Dungan K.M.. **Prevalence, predictors, and outcomes of steroid-induced hyperglycemia in hospitalized patients with hematologic malignancies**. *Endocrine* (2017.0) **56** 90-97. DOI: 10.1007/s12020-016-1220-2
6. Liu H., Liu Z., Jiang B., Ding X., Huo L., Wan X., Liu J., Xia Z.. **Prognostic Significance of Hyperglycemia in Patients with Brain Tumors: A Meta-Analysis**. *Mol. Neurobiol.* (2016.0) **53** 1654-1660. DOI: 10.1007/s12035-015-9115-4
7. Richardson L.C., Pollack L.A.. **Therapy insight: Influence of type 2 diabetes on the development, treatment and outcomes of cancer**. *Nat. Clin. Pract. Oncol.* (2005.0) **2** 48-53. DOI: 10.1038/ncponc0062
8. Zeng L., Biernacka K.M., Holly J.M., Jarrett C., Morrison A.A., Morgan A., Winters Z., Foulstone E.J., Shield J.P., Perks C.M.. **Hyperglycaemia confers resistance to chemotherapy on breast cancer cells: The role of fatty acid synthase**. *Endocr.-Relat. Cancer* (2010.0) **17** 539-551. DOI: 10.1677/ERC-09-0221
9. Suh S., Park M.K.. **Glucocorticoid-Induced Diabetes Mellitus: An Important but Overlooked Problem**. *Endocrinol. Metab.* (2017.0) **32** 180-189. DOI: 10.3803/EnM.2017.32.2.180
10. Bonaventura A., Montecucco F.. **Steroid-induced hyperglycemia: An underdiagnosed problem or clinical inertia? A narrative review**. *Diabetes Res. Clin. Pract.* (2018.0) **139** 203-220. DOI: 10.1016/j.diabres.2018.03.006
11. Clore J.N., Thurby-Hay L.. **Glucocorticoid-induced hyperglycemia**. *Endocr. Pract. Off. J. Am. Coll. Endocrinol. Am. Assoc. Clin. Endocrinol.* (2009.0) **15** 469-474. DOI: 10.4158/EP08331.RAR
12. Gornik I., Vujaklija-Brajkovic A., Renar I.P., Gasparovic V.. **A prospective observational study of the relationship of critical illness associated hyperglycaemia in medical ICU patients and subsequent development of type 2 diabetes**. *Crit. Care* (2010.0) **14** R130. DOI: 10.1186/cc9101
13. **2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021**. *Diabetes Care* (2021.0) **44** S15-S33. DOI: 10.2337/dc21-S002
14. Ahn H.R., Kang S.Y., Youn H.J., Jung S.H.. **Hyperglycemia during Adjuvant Chemotherapy as a Prognostic Factor in Breast Cancer Patients without Diabetes**. *J. Breast Cancer* (2020.0) **23** 398-409. DOI: 10.4048/jbc.2020.23.e44
15. Jeong Y., Han H.S., Lee H.D., Yang J., Jeong J., Choi M.K., Kwon J., Jeon H.-J., Oh T.-K., Lee K.H.. **A Pilot Study Evaluating Steroid-Induced Diabetes after Antiemetic Dexamethasone Therapy in Chemotherapy-Treated Cancer Patients**. *Cancer Res. Treat.* (2016.0) **48** 1429-1437. DOI: 10.4143/crt.2015.464
16. Liu X.X., Zhu X.M., Miao Q., Ye H.Y., Zhang Z.Y., Li Y.M.. **Hyperglycemia induced by glucocorticoids in nondiabetic patients: A meta-analysis**. *Ann. Nutr. Metab.* (2014.0) **65** 324-332. DOI: 10.1159/000365892
17. Umpierrez G.E., Hellman R., Korytkowski M.T., Kosiborod M., Maynard G.A., Montori V.M., Seley J.J., Van den Berghe G.. **Management of hyperglycemia in hospitalized patients in non-critical care setting: An endocrine society clinical practice guideline**. *J. Clin. Endocrinol. Metab.* (2012.0) **97** 16-38. DOI: 10.1210/jc.2011-2098
18. Baldwin D., Apel J.. **Management of hyperglycemia in hospitalized patients with renal insufficiency or steroid-induced diabetes**. *Curr. Diabetes Rep.* (2013.0) **13** 114-120. DOI: 10.1007/s11892-012-0339-7
19. Aberer F., Hochfellner D.A., Sourij H., Mader J.K.. **A Practical Guide for the Management of Steroid Induced Hyperglycaemia in the Hospital**. *J. Clin. Med.* (2021.0) **10**. DOI: 10.3390/jcm10102154
20. Gulliford M.C., Charlton J., Latinovic R.. **Risk of Diabetes Associated with Prescribed Glucocorticoids in a Large Population**. *Diabetes Care* (2006.0) **29** 2728-2729. DOI: 10.2337/dc06-1499
21. Chu Z., Lin H., Liang X., Huang R., Tang J., Bao Y., Jiang J., Zhan Q., Zhou X.. **Association between axillary lymph node status and Ki67 labeling index in triple-negative medullary breast carcinoma**. *Jpn. J. Clin. Oncol.* (2015.0) **45** 637-641. DOI: 10.1093/jjco/hyv052
22. Baselga J., Campone M., Piccart M., Burris H.A., Rugo H.S., Sahmoud T., Noguchi S., Gnant M., Pritchard K.I., Lebrun F.. **Everolimus in Postmenopausal Hormone-Receptor–Positive Advanced Breast Cancer**. *N. Engl. J. Med.* (2011.0) **366** 520-529. DOI: 10.1056/NEJMoa1109653
23. Naing A., Kurzrock R., Burger A., Gupta S., Lei X., Busaidy N., Hong D., Chen H.X., Doyle L.A., Heilbrun L.K.. **Phase I trial of cixutumumab combined with temsirolimus in patients with advanced cancer**. *Clin. Cancer Res.* (2011.0) **17** 6052-6060. DOI: 10.1158/1078-0432.CCR-10-2979
24. Infante J.R., Yardley D.A., Burris H.A., Greco F.A., Farley C.P., Webb C., Spigel D., Hainsworth J.. **Phase II trial of weekly docetaxel, vinorelbine, and trastuzumab in the first-line treatment of patients with HER2-positive metastatic breast cancer**. *Clin. Breast Cancer* (2009.0) **9** 23-28. DOI: 10.3816/CBC.2009.n.004
25. Lee Y.J., Doliny P., Gomez-Fernandez C., Powell J., Reis I., Hurley J.. **Docetaxel and Cisplatin as Primary Chemotherapy for Treatment of Locally Advanced Breast Cancers**. *Clin. Breast Cancer* (2004.0) **5** 371-376. DOI: 10.3816/CBC.2004.n.044
26. James P.T., Leach R., Kalamara E., Shayeghi M.. **The Worldwide Obesity Epidemic**. *Obes. Res.* (2001.0) **9** 228S-233S. DOI: 10.1038/oby.2001.123
27. Hershey D.S., Bryant A.L., Olausson J., Davis E.D., Brady V.J., Hammer M.. **Hyperglycemic-inducing neoadjuvant agents used in treatment of solid tumors: A review of the literature**. *Oncol. Nurs. Forum* (2014.0) **41** E343-E354. DOI: 10.1188/14.ONF.E343-E354
28. Harris D., Barts A., Connors J., Dahl M., Elliott T., Kong J., Keane T., Thompson D., Stafford S., Ur E.. **Glucocorticoid-induced hyperglycemia is prevalent and unpredictable for patients undergoing cancer therapy: An observational cohort study**. *Curr. Oncol.* (2013.0) **20** e532-e538. DOI: 10.3747/co.20.1499
29. Sonabend R.Y., McKay S.V., Okcu M.F., Yan J., Haymond M.W., Margolin J.F.. **Hyperglycemia during induction therapy is associated with poorer survival in children with acute lymphocytic leukemia**. *J. Pediatr.* (2009.0) **155** 73-78. DOI: 10.1016/j.jpeds.2009.01.072
30. Leite S.A., Locatelli S.B., Niece S.P., Oliveira A.R., Tockus D., Tosin T.. **Impact of hyperglycemia on morbidity and mortality, length of hospitalization and rates of re-hospitalization in a general hospital setting in Brazil**. *Diabetol. Metab. Syndr.* (2010.0) **2** 49. DOI: 10.1186/1758-5996-2-49
31. Hsu C.-W.. **Glycemic control in critically ill patients**. *World J. Crit. Care Med.* (2012.0) **1** 31-39. DOI: 10.5492/wjccm.v1.i1.31
32. Evans N.R., Dhatariya K.K.. **Assessing the relationship between admission glucose levels, subsequent length of hospital stay, readmission and mortality**. *Clin. Med.* (2012.0) **12** 137-139. DOI: 10.7861/clinmedicine.12-2-137
33. **National Diabetes Statistics Report Website**
34. Martinez M., Santamarina J., Pavesi A., Musso C., Umpierrez G.E.. **Glycemic variability and cardiovascular disease in patients with type 2 diabetes**. *BMJ Open Diabetes Res. Care* (2021.0) **9** e002032. DOI: 10.1136/bmjdrc-2020-002032
35. Siegelaar S.E., Holleman F., Hoekstra J.B.L., DeVries J.H.. **Glucose Variability; Does It Matter?**. *Endocr. Rev.* (2010.0) **31** 171-182. DOI: 10.1210/er.2009-0021
36. Ceriello A., Monnier L., Owens D.. **Glycaemic variability in diabetes: Clinical and therapeutic implications**. *Lancet Diabetes Endocrinol.* (2019.0) **7** 221-230. DOI: 10.1016/S2213-8587(18)30136-0
37. Paneni F., Beckman J.A., Creager M.A., Cosentino F.. **Diabetes and vascular disease: Pathophysiology, clinical consequences, and medical therapy: Part I**. *Eur. Heart J.* (2013.0) **34** 2436-2443. DOI: 10.1093/eurheartj/eht149
38. Ying C., Liu T., Ling H., Cheng M., Zhou X., Wang S., Mao Y., Chen L., Zhang R., Li W.. **Glucose variability aggravates cardiac fibrosis by altering AKT signalling path**. *Diabetes Vasc. Dis. Res.* (2017.0) **14** 327-335. DOI: 10.1177/1479164117698917
39. Hirakawa Y., Arima H., Zoungas S., Ninomiya T., Cooper M., Hamet P., Mancia G., Poulter N., Harrap S., Woodward M.. **Impact of visit-to-visit glycemic variability on the risks of macrovascular and microvascular events and all-cause mortality in type 2 diabetes: The ADVANCE trial**. *Diabetes Care* (2014.0) **37** 2359-2365. DOI: 10.2337/dc14-0199
40. Nusca A., Tuccinardi D., Albano M., Cavallaro C., Ricottini E., Manfrini S., Pozzilli P., Di Sciascio G.. **Glycemic variability in the development of cardiovascular complications in diabetes**. *Diabetes Metab. Res. Rev.* (2018.0) **34** e3047. DOI: 10.1002/dmrr.3047
41. Srokowski T.P., Fang S., Hortobagyi G.N., Giordano S.H.. **Impact of diabetes mellitus on complications and outcomes of adjuvant chemotherapy in older patients with breast cancer**. *J. Clin. Oncol.* (2009.0) **27** 2170-2176. DOI: 10.1200/JCO.2008.17.5935
42. Lega I.C., Austin P.C., Fischer H.D., Fung K., Krzyzanowska M.K., Amir E., Lipscombe L.L.. **The Impact of Diabetes on Breast Cancer Treatments and Outcomes: A Population-Based Study**. *Diabetes Care* (2018.0) **41** 755-761. DOI: 10.2337/dc17-2012
43. Luo J., Hendryx M., Virnig B., Wen S., Chlebowski R., Chen C., Rohan T., Tinker L.F., Wactawski-Wende J., Lessin L.S.. **Pre-existing diabetes and breast cancer prognosis among elderly women**. *Br. J. Cancer* (2015.0) **113** 827-832. DOI: 10.1038/bjc.2015.249
44. Tao H., O’Neil A., Choi Y., Wang W., Wang J., Wang Y., Jia Y., Chen X.. **Pre- and Post-diagnosis Diabetes as a Risk Factor for All-Cause and Cancer-Specific Mortality in Breast, Prostate, and Colorectal Cancer Survivors: A Prospective Cohort Study**. *Front. Endocrinol.* (2020.0) **11** 60. DOI: 10.3389/fendo.2020.00060
45. Barone B.B., Yeh H.C., Snyder C.F., Peairs K.S., Stein K.B., Derr R.L., Wolff A., Brancati F.. **Long-term all-cause mortality in cancer patients with preexisting diabetes mellitus: A systematic review and meta-analysis**. *JAMA* (2008.0) **300** 2754-2764. DOI: 10.1001/jama.2008.824
|
---
title: GC/MS Profiling of the Essential Oil and Lipophilic Extract of Moricandia sinaica
Boiss. and Evaluation of Their Cytotoxic and Antioxidant Activities
authors:
- Shaza H. Aly
- Nariman H. Kandil
- Roqaya M. Hemdan
- Sara S. Kotb
- Sara S. Zaki
- Omnia M. Abdelaziz
- Mohamed M. M. AbdelRazek
- Hadia Almahli
- Mahmoud A. El Hassab
- Sara T. Al-Rashood
- Faizah A. Binjubair
- Wagdy M. Eldehna
journal: Molecules
year: 2023
pmcid: PMC10004251
doi: 10.3390/molecules28052193
license: CC BY 4.0
---
# GC/MS Profiling of the Essential Oil and Lipophilic Extract of Moricandia sinaica Boiss. and Evaluation of Their Cytotoxic and Antioxidant Activities
## Abstract
The genus Moricandia (Brassicaceae) comprises about eight species that were used in traditional medicine. Moricandia sinaica is used to alleviate certain disorders such as syphilis and exhibits analgesic, anti-inflammatory, antipyretic, antioxidant, and antigenotoxic properties. Throughout this study, we aimed to figure out the chemical composition of lipophilic extract and essential oil obtained from M. sinaica aerial parts using GC/MS analysis, as well as their cytotoxic and antioxidant activities correlated with the major detected compounds’ molecular docking. The results revealed that both the lipophilic extract and the oil were found to be rich in aliphatic hydrocarbons, accounting for $72.00\%$ and $79.85\%$, respectively. Furthermore, the lipophilic extract’s major constituents are octacosanol, γ-sitosterol, α-amyrin, β-amyrin acetate, and α-tocopherol. Contrarily, monoterpenes and sesquiterpenes accounted for the majority of the essential oil. The essential oil and the lipophilic extract of M. sinaica showed cytotoxic properties towards human liver cancer cells (HepG2) with IC50 values of 126.65 and 220.21 µg/mL, respectively. The lipophilic extract revealed antioxidant activity in the DPPH assay with an IC50 value of 2679 ± 128.13 µg/mL and in the FRAP assay, moderate antioxidant potential was expressed as 44.30 ± 3.73 µM Trolox equivalent/mg sample. The molecular docking studies revealed that ꞵ-amyrin acetate, α -tocopherol, γ-sitosterol, and n-pentacosaneachieved the best docking scores for NADPH oxidase, phosphoinositide-3 kinase, and protein kinase B. Consequently, M. sinaica essential oil and lipophilic extract can be employed as a viable management strategy for oxidative stress conditions and the formulation of improved cytotoxic treatment regimens.
## 1. Introduction
For millennia, the spotlight has been directed toward medicinal plants as a plentiful supply of bioactive compounds, and many of the therapeutic medications currently in use are natural products or compounds derived from plants [1,2,3,4,5]. According to the World Health Organization (WHO), about $80\%$ of the world’s population lives in developing and underdeveloped countries and relies on medicines of natural origin as a remedy for medical ailments [6]. Plants are considered a potential source for the finding of new candidate compounds. Natural plants and their isolated chemicals provide us with a great source of biologically active leads for the development of drugs with benefits both in terms of cost and fewer side effects [1,7,8,9,10,11].
The mustard family, Brassicaceae, comprises approximately 338 genera and 3709 species, which are cultivated presently worldwide with high economic importance since they are widely consumed in human diets all over the world. Moreover, they are used to produce food and oilseed crops, while a number of other varieties are used as ornamental plants (with violet, purple, and white flowers) and noxious weeds [12,13]. The genus Moricandia (family: Brassicaceae) has eight species, namely arvensis, foetida, foleyii, oricandioides, nitens, sinaica, spinosa, and suffruticosa, some of which are used in traditional medicine [14]. These species are distributed all over North Africa, the Mediterranean basin, West Asia, and Southeast Asia [15]. Moricandia plants have revealed some significant health properties. For instance, M. arvensis leaves, which are frequently used in Tunisian traditional dishes, exhibit beneficial effects in the management of syphilis [16,17]. Additionally, some M. arvensis extracts have been shown to have antioxidant and antigenotoxic properties, and they effectively slow the spread of human cancer cells [18,19].
One of these species, Moricandia sinaica, is native to Saudi Arabia and mainly seen in desert areas such as Western Asia, the Middle East, and Egypt [13,20]. Moricandia sinaica is a suffrutescent to suffruticose annual to perennial plant; the leaves are thick, pointed, and vary in shape from oval to oblong-ovate, with pink or white corolla flowers [13]. It is indigenous to the Mediterranean area, Europe, and America, and has therapeutic uses in traditional medicine [20]. To date, there are few investigations that focus on the therapeutic potential or phytoconstituents of M. sinaica. Crude extracts from the roots, stem, leaves, and shoots of M. sinaica were investigated and evaluated for potential antiangiogenic effects in zebrafish embryos [13]. Besides, in vivo, various fractions of M. sinaica aerial parts exhibit analgesic, anti-inflammatory, and antipyretic properties [21].
Herein, the current research is primarily focused on the phytochemical investigations of the essential oil and lipophilic extract of M.sinaica through GC/MS analysis, besides the cytotoxic and antioxidant biological investigations of M. sinaica extracts.
## 2.1. GC/MS Analysis of the Essential Oil and Lipophilic Extract
The GC/MS analysis investigation of the lipophilic extract and essential oil of M. sinaica is shown in Table 1 (Supplementary Figures S1 and S2). The evaluation of the lipophilic extract and essential oil contents revealed the identification of 15 and 22 chemicals, representing $97.80\%$ and $99.90\%$, respectively. Both the lipophilic extract and the essential oil were found to be rich in aliphatic hydrocarbons, accounting for $72.00\%$ and $79.85\%$, respectively, where n-pentacosane ($78.01\%$) is the major hydrocarbon in the essential oil and n-nonacosane ($65.66\%$) is the major hydrocarbon in the lipophilic extract. In the lipophilic extract, octacosanol ($12.67\%$), γ-sitosterol ($6.60\%$), α-amyrin ($3.51\%$), β-amyrin acetate ($0.81\%$), and α-tocopherol ($0.54\%$) are the major constituents. In contrast, monoterpenes in the essential oil ($8.10\%$) are represented as tricyclene, camphene, ꞵ-citronellene, octanal, linalool, and α-terpineol, in addition to sesquiterpenes ($2.82\%$) such as α-cadinene, caryophylla-4[12],8[13]-dien-5α-ol, and ꞵ-eudesmol. The chemical structures of the major compounds as well as the percentage distribution of volatile components of the lipophilic extract and essential oil of M. sinaica are displayed in Figure 1 and Figure 2.
Prior studies on the chemical characteristics of M. arvensis aerial parts essential oil obtained from two areas in Algeria revealed the richness of the oil of the Southern Setif population with palmitic acid (13.2–$12.9\%$) and phytol (7.9–$10.5\%$). In comparison, the oil of the Northern population is rich with 3-butenylisothiocyanate and octadecanoic acid, 2-hydroxy-1,3-p [27]. Another report by Marrelli et al. revealed the n-hexane extract’s chemical composition of M. arvensis using GC/MS analysis. The findings revealed the existence of different fatty acids such as palmitic, stearic, and myristic acids, together with phytosterols such as ꞵ-Sitosterol, 22,24-dimethylcholesterol, and stigmasta-3,5-dien-7-one [28]. Comparing the current findings to the previously published findings, both the essential oil and the lipophilic extract revealed variations in the components and their relative quantities that might be utilized as a chemical fingerprint to assess the validity and to discriminate between the given oils or extracts.
## 2.2. Cytotoxic Activity Using SRB Assay
Previous studies on essential oils and lipophilic extracts of M. sinaica revealed their richness with potent secondary metabolites that have an effect on numerous cancer cells [23,29]. The cytotoxicity results of the essential oil and the lipophilic extract of M. sinaica aerial parts revealed their inhibitory activities toward human liver cancer cells (HepG2) with IC50 values of 126.65 and 220.21 µg/mL, respectively (Figure 3). In agreement with our results, GC/MS evaluation demonstrated the abundance of n-pentacosane in the essential oil of M. sinaica aerial parts ($78.01\%$); it is a 25-carbon unbranched chain that was discovered in several essential oils with antimicrobial properties [30]. Additionally, it was reported to contain anticancer, antifungal, anti-inflammatory, antioxidant, and antiviral agents [31,32]. α-amyrin ($3.51\%$) and β-amyrin acetate ($0.81\%$) are also found to have antioxidant, antimicrobial, anti-inflammatory, and anticancer properties [23]. Octacosanol and 1-Octacosanol were also found to account for $12.67\%$ and $0.50\%$, respectively. Studies have shown that octacosanol is an antiangiogenic substance that inhibits angiogenesis. Octacosanol prevents neovascularization and the proliferation of endothelial cells [33].
## 2.3. Antioxidant Activity
Natural polyphenols from the Brassicaceae family are abundant, provide a wide range of health implications, and are known for their antioxidant effects [34]. That gives us an interest in exploring the essential oils’ and the lipophilic extract’s potential as antioxidants using DPPH and FRAP assays. The lipophilic extract in the DPPH assay showed an IC50 value of 2679 ± 128.13 µg/mL as compared to the Trolox reference drug (IC50 = 4.94 ± 0.263 µg/mL). On the other hand, the lipophilic extract showed considerable antioxidant activity in the FRAP assay, expressed as 44.30 ± 3.73 µM Trolox equivalent/mg sample. The essential oil of M. sinaica aerial parts did not show any significant results in both assays. Previous reports regarding the biological activity of different Moricandia species revealed that M. nitens green synthesized GNPs showed substantial anti-*Helicobacter pylori* and anticancer activity against HepG2 and HCT-116 and were reported to be an effective α-glucosidase inhibitor [35]. Moreover, the methanolic extract of M. arvensis exhibited potent lipase-inhibitory activity and antioxidant activity [28].
## 2.4. Molecular Docking
The essential oil and lipophilic extract of M. sinaica, which has potential cytotoxic and antioxidant properties, led us to perform a docking investigation of the major components against the enzymes NADPH oxidase, phosphoinositide-3 kinase (PI3K), and Akt, also known as protein kinase B (PKB). This research aimed to determine the probable binding mechanisms by which the major investigated metabolites may function. Therefore, the major components were docked into the NADPH oxidase, phosphoinositide-3 kinase (PI3K), and Akt 3D coordinates that were downloaded from the protein data bank using the following PDB IDs: 2cdu, 1E90, and 3cwq, respectively. Re-docking each co-crystallized ligand into its associated active site enabled us to confirm the docking parameters applied. The estimated RMSD values between the co-crystallized pose and the docked pose were 1.02, 0.97, and 1.03 Å NADPH oxidase, phosphoinositide-3 kinase (PI3K), and Akt, respectively, helping to ensure the docking technique is valid. The co-crystallized ligand’s re-docking resulted in docking scores of −11.3, −12.1, and −13.4 Kcal/mole for NADPH oxidase, phosphoinositide-3 kinase (PI3K), and Akt, respectively. The docking of the main metabolites to the three enzymes showed acceptable results corresponding to those of the reference compounds. Interestingly, in the docking with NADPH oxidase, ꞵ-amyrin acetate, α-tocopherol and γ-sitosterol were the best compounds, achieving docking scores of −14.19, −13.12, and −12.65 Kcal/Mol, respectively (Figure 4). Similarly, in the docking with PI3K, n-pentacosane, α-tocopherol and ꞵ-amyrin acetate were the best compounds, achieving docking scores of −13. 91, −14.15, and −14.05 Kcal/Mol, respectively (Figure 5). Worth noting, in the docking with AKT, n-pentacosane, α-tocopherol, and γ-sitosterol were the best compounds, achieving docking scores of −11. 9, −13.19, and −12.05 Kcal/Mol, respectively (Figure 6). The major compounds’ docking scores against the three proposed target enzymes are represented in Table 2. The major compounds’ interactions with the three enzymes are represented in Table 3, Table 4 and Table 5. The results of docking correlated with the results of the cytotoxic and antioxidant activities of the lipophilic extract of M. sinaica.
## 3.1. Plant Material
Leaves of *Moricandia sinaica* Boiss. ( Bra) were obtained in February 2022 from South Sinai, Egypt 27°57′43.2″ N and 34°16′16.7″ E. The plant was thankfully recognized and verified by Dr. Mohammed El-Gebaly (Department of Botany, National Research Centre), Giza, Egypt. Voucher specimens were provided in the Pharmacognosy Department, Faculty of Pharmacy, Badr University in Cairo (Voucher specimen number: BUC-PHG-MS-10).
## 3.2. The Essential Oil Isolation
The leaves were finely chopped and hydro-distilled for 5 h with a Clevenger apparatus. The produced oil is yellowish orange in color with a fragrant odor; the yield was $0.21\%$ (21 mg/100 g). It was collected and maintained at −4 °C in a tight, dark glass vial for GC/MS analysis.
## 3.3. Preparation of the Lipophilic Extract
Dry leaves (100 g) of *Moricandia sinaica* Boiss. were extracted three times with n-hexane. To obtain the dried residue of the lipophilic extract, the filtrate was fully evaporated in vacuo at 40 °C until dryness, and (2.41 g) of the lipophilic extract was obtained. The lipophilic extract was kept in a tightly sealed container for subsequent examination.
## 3.4. Gas Chromatography–Mass Spectrometry (GC/MS)
A Shimadzu GCMS-QP 2010 chromatograph (Kyoto, Japan) with a DB-5 capillary column (30 m × 0.25 mm i.d. × 0.25 μm film thickness; Restek, Bellefonte, PA, USA) was used for gas chromatography/mass spectrometry (GC/MS) analysis. The oven temperature was set at 50 °C for 3 min (isothermal), programmed to 30 °C at a rate of 5 °C/min, and kept constant at 300 °C for 10 min (isothermal); the temperature of the injector was 280 °C. Helium was employed as the carrier gas, with a flow rate of 1.40 mL/min. Diluted samples ($1\%$ v/v) were injected at a split ratio of 15:1 in a volume of 1 μL. The following were the MS running specifications: 280 °C for the interface, 220 °C for the ion source, 70 eV for the EI mode, and 35–500 amu for the scan range.
## 3.5. Characterization of the Essential Oil and Lipophilic Extract Components
The volatile constituents were characterized based on their retention indices and fragmentation patterns matching with NIST Mass Spectral Library, Wiley library database and published in the literature [23,26,29,36,37,38,39]. Retention indices (RI) were estimated in comparison to homologous series of n-alkanes (C8-C30) injected under the same conditions.
## 3.6. Assessment of Cytotoxic Activity Using SRB Assay
Nawah Scientific Inc., (Mokatam, Cairo, Egypt) provided the *Hepatocellular carcinoma* (HepG2). Cells were cultured in DMEM media treated with 100 mg/mL of streptomycin, 100 units/mL of penicillin, and $10\%$ heat-inactivated fetal bovine serum in a humidified, $5\%$ (v/v) CO2 atmosphere at 37 °C. The SRB assay was employed to assess cell viability. In 96-well plates, aliquots of 100 μL cell suspension (5 × 103 cells) were incubated in complete media for 24 h. Another aliquot of 100 μL of media containing different doses of drugs was used to treat the cells. Cells were fixed by replacing media with 150 μL of $10\%$ TCA and incubated at 4 °C for 1 h, after 72 h of drug exposure. After removing the TCA solution, the cells were washed 5 times with distilled water. Aliquots of 70 μL SRB solution ($0.4\%$ w/v) were added and incubated in a dark place at room temperature for 10 min. Plates were washed with $1\%$ acetic acid for 3 times before being let to air dry overnight. Then, 150 μL of TRIS (10 mM) was added to dissolve protein-bound SRB stain; with the use of a BMGLABTECH®- FLUO star Omega microplate reader (Ortenberg, Germany), the absorbance was determined at 540 nm [40].
## 3.7.1. DPPH Assay
Samples were prepared using DMSO at a concentration of 50 mg/mL. Then, from this stock, the oil was prepared at a concentration of 2 mg/mL in methanol, and the lipophilic extract was prepared at a concentration of 10 mg/mL in methanol. A stock solution of 20 μg/mL of Trolox was prepared in methanol, from which 5 concentrations were prepared, including 12.5, 7.5, 6.25, 2.5, and 1.25 μg/mL. DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) free radical assay was carried out according to the method of [41]. Briefly, at room temperature, 100 μL of freshly prepared DPPH reagent ($0.1\%$ in methanol) was added to 100 μL of the sample in 96-well plates ($$n = 6$$), which was incubated for 30 min in the dark. The resulting reduction in DPPH color intensity was measured at 540 nm after the incubation time. The data are displayed as means ± SD according to the following equation: Percentage inhibition=Average absorbance of blank−average absorbance of the testAverage absorbance of blank×100 The microplate reader FluoStar Omega was used for recording the results. The data were processed using Microsoft Excel® and the IC50 value calculation was performed using Graph pad Prism 6® by converting the concentrations to their logarithmic value and selecting a non-linear inhibitor regression equation (log (inhibitor) vs. normalized response—variable slope equation).
## 3.7.2. FRAP Assay
Trolox Standard for FRAP assay Trolox stock solution of 3 mM in methanol was prepared, and the following dilutions were prepared at the concentrations of 1200, 1000, 800, 500, 400, 300, 200, and 100 μM. Samples were prepared at a concentration of 50 mg/mL in DMSO. Then, the oil was prepared at a concentration of 2 mg/mL in methanol, and the lipophilic extract was prepared at a concentration of 10 mg/mL in methanol. The assay was carried out in accordance with the method of Benzie et al. [ 42] with some modifications to be carried out in microplates. A freshly prepared TPTZ reagent (300 mM Acetate Buffer (PH = 3.6), 10 mM TPTZ in 40 mM HCl, and 20 mM FeCl3, in a ratio of 10:1:1 v/v/v, respectively). About 190 uL of the freshly prepared TPTZ reagent were mixed with 10 μL of the sample in 96-well plates ($$n = 3$$), the reaction was incubated at room temperature for 30 min in the dark. The resulting blue color was measured at 593 nm at the end of the incubation period. The data are represented as means ± SD. The microplate reader FluoStar Omega was used to record the results. The ferric-reducing ability of the samples is presented as μM TE/ mg sample using the linear regression equation obtained from the following calibration curve (linear dose-response curve of Trolox) (Figure 7).
## 3.8. Molecular Docking
Applying the software Molecular Operating Environment (MOE 2019.02), docking investigations were carried out [43,44]. The X-ray crystal structures of NADPH oxidase, phosphoinositide-3 kinase (PI3K), and Akt, known as protein kinase B (PKB) were obtained from the protein data bank www.pdb.org (accessed on 10 December 2022) using the following PDB IDs: 2CDU, 1E90, and 3CWQ, respectively [45,46,47]. Hydrogens and charges of the receptors were adjusted through AMBER10: EHT implanted in MOE software. The relevant co-crystallized ligand is bound at the established binding sites of three enzymes. Five major compounds identified in the essential oil (n-pentacosane, camphene, ꞵ-citronellene, linalool, and α-terpineol) and six compounds from the lipophilic extract (n-nonacosane ($65.66\%$), octacosanol, γ-sitosterol, α-amyrin and β-amyrin acetate, and α-tocopherol) were produced using the 2D builder of MOE2019 and transformed into 3D structures with the same software. Utilizing the triangular matcher and London dg, the compounds were docked onto the three enzyme binding sites as placement and scoring methods, respectively. Finally, MOE produced 2D interaction diagrams to analyze the docking observations.
## 4. Conclusions
In the present research, the chemical investigation of *Moricandia sinaica* lipophilic extract and essential oil was analyzed using the GC/MS technique. The results revealed that both are rich in aliphatic hydrocarbons, where n-pentacosane ($78.01\%$), and n-nonacosane ($65.66\%$) are the major hydrocarbons in the essential oil and the lipophilic extract. It is worth noting that γ-sitosterol, α-amyrin, β-amyrin acetate, and α-tocopherol are the main important constituents in the lipophilic extract that correlate with its antioxidant activity using DPPH and FRAP assays. In addition, monoterpenes and sesquiterpenes are present in the essential oil, which correlates with its cytotoxic activity against HEPG-2 cells. The biological investigations were supported by the molecular docking study and interactions of the major constituents with NADPH oxidase, phosphoinositide-3 kinase (PI3K), and Akt, known as protein kinase B (PKB) enzymes. Accordingly, the lipophilic extract and essential oil of M. sinaica would be a promising alternative for the production of cytotoxic and antioxidant agents, supported by further in vivo investigations as well as pharmacodynamic and pharmacokinetic studies.
## References
1. Kinghorn A.D., Pan L., Fletcher J.N., Chai H.. **The Relevance of Higher Plants in Lead Compound Discovery Programs**. *J. Nat. Prod.* (2011.0) **74** 1539-1555. DOI: 10.1021/np200391c
2. Aly S.H., Elissawy A.M., Fayez A.M., Eldahshan O.A., Elshanawany M.A., Singab A.N.B.. **Neuroprotective Effects of**. *Nat. Prod. Res.* (2020.0) **35** 5848-5852. DOI: 10.1080/14786419.2020.1795853
3. El-Nashar H.A.S., Aly S.H., Ahmadi A., El-Shazly M.. **The Impact of Polyphenolics in the Management of Breast Cancer: Mechanistic Aspects and Recent Patents**. *Recent Pat. Anticancer. Drug Discov.* (2021.0) **17** 358-379. DOI: 10.2174/1574892816666211213090623
4. Ads E.N., Hassan S.I., Rajendrasozhan S., Hetta M.H., Aly S.H., Ali M.A.. **Isolation, Structure Elucidation and Antimicrobial Evaluation of Natural Pentacyclic Triterpenoids and Phytochemical Investigation of Different Fractions of**. *Molecules* (2022.0) **27**. DOI: 10.3390/molecules27061805
5. Aly S.H., Elissawy A.M., Allam A.E., Farag S.M., Eldahshan O.A., Elshanawany M.A., Singab A.N.B.. **New Quinolizidine Alkaloid and Insecticidal Activity of**. *Nat. Prod. Res.* (2021.0) **36** 2722-2734. DOI: 10.1080/14786419.2021.1919108
6. Farnsworth N.R., Akerele O., Bingel A.S.. **Medicinal Plants in Therapy**. *J. Ethnopharmacol.* (1987.0) **19** 336. DOI: 10.1016/0378-8741(87)90016-X
7. Cragg G.M., Newman D.J.. **Natural Products: A Continuing Source of Novel Drug Leads**. *Biochim. Biophys. Acta (BBA)-Gen. Subj.* (2013.0) **1830** 3670-3695. DOI: 10.1016/j.bbagen.2013.02.008
8. Atanasov A.G., Waltenberger B., Pferschy-Wenzig E.-M., Linder T., Wawrosch C., Uhrin P., Temml V., Wang L., Schwaiger S., Heiss E.H.. **Discovery and Resupply of Pharmacologically Active Plant-Derived Natural Products: A Review**. *Biotechnol. Adv.* (2015.0) **33** 1582-1614. DOI: 10.1016/j.biotechadv.2015.08.001
9. Saber F.R., Munekata P.E.S., Rizwan K., El-nashar H.A.S., Fahmy N.M., Aly S.H., El-shazly M., Bouyahya A., Lorenzo J.M.. **Family Myrtaceae: The Treasure Hidden in the Complex/Diverse Composition**. *Crit. Rev. Food Sci. Nutr.* (2023.0) 1-19. DOI: 10.1080/10408398.2023.2173720
10. Aly S.H., Elissawy A.M., Salah D., Alfuhaid N.A., Zyaan O.H., Mohamed H.I., Singab A.N.B., Farag S.M.. **Phytochemical Investigation of Three Cystoseira Species and Their Larvicidal Activity Supported with In Silico Studies**. *Mar. Drugs* (2023.0) **21**. DOI: 10.3390/md21020117
11. Abdel Razek M.M.M., Moussa A.Y., El-Shanawany M.A., Singab A.N.B.. **A New Phenolic Alkaloid from**. *Chem. Biodivers.* (2020.0) **17** e2000496. DOI: 10.1002/cbdv.202000496
12. Vaughn S.F., Berhow M.A.. **Glucosinolate Hydrolysis Products from Various Plant Sources: PH Effects, Isolation, and Purification**. *Ind. Crop. Prod.* (2005.0) **21** 193-202. DOI: 10.1016/j.indcrop.2004.03.004
13. Warwick S.I., Francis A., Gugel R.K.. **Guide to Wild Germplasm of Brassica and Allied Crops (Tribe Brassiceae, Brassicaceae)**. *Canada Agric. Agri-Food Canada* (2009.0) **1** 19-36
14. Perfectti F., Gómez J.M., González-Megías A., Abdelaziz M., Lorite J.. **Molecular Phylogeny and Evolutionary History of Moricandia DC (Brassicaceae)**. *PeerJ* (2017.0) **5** e3964. DOI: 10.7717/peerj.3964
15. Kilian B., Mammen K., Millet E., Sharma R., Graner A., Salamini F., Hammer K., Ozkan H., Kole C.. **Aegilops**. *Wild Crop Relatives, Genomic and Breeding Resources Cereals* (2011.0) 1-76. DOI: 10.1007/978-3-642-14871-2_12
16. Skandrani I., Bouhlel I., Limem I., Boubaker J., Bhouri W., Neffati A., Sghaier M.B., Kilani S., Ghedira K., Ghedira-Chekir L.. **Moricandia Arvensis Extracts Protect against DNA Damage, Mutagenesis in Bacteria System and Scavenge the Superoxide Anion**. *Toxicol. Vitr.* (2009.0) **23** 166-175. DOI: 10.1016/j.tiv.2008.10.010
17. Le Floc’h E.. *Contribution à Une Étude Ethnobotanique de La Flore Tunisienne* (1983.0)
18. Skandrani I., Ben Sghaier M., Neffati A., Boubaker J., Bouhlel I., Kilani S., Mahmoud A., Ghedira K., Chekir-Ghedira L.. **Antigenotoxic and Free Radical Scavenging Activities of Extracts from Moricandia Arvensis**. *Drug Chem. Toxicol.* (2007.0) **30** 361-382. DOI: 10.1080/01480540701522494
19. Skandrani I., Limem I., Neffati A., Boubaker J., Sghaier M.B., Bhouri W., Bouhlel I., Kilani S., Ghedira K., Chekir-Ghedira L.. **Assessment of Phenolic Content, Free-Radical-Scavenging Capacity Genotoxic and Anti-Genotoxic Effect of Aqueous Extract Prepared from**. *Food Chem. Toxicol.* (2010.0) **48** 710-715. DOI: 10.1016/j.fct.2009.11.053
20. Arif I.A., Bakir M.A., Khan H.A., Al Farhan A.H., Al Homaidan A.A., Bahkali A.H., Al Sadoon M., Shobrak M.. **Application of RAPD for Molecular Characterization of Plant Species of Medicinal Value from an Arid Environment**. *Genet. Mol. Res.* (2010.0) **9** 2191-2198. DOI: 10.4238/vol9-4gmr848
21. El-Mekkawy S., Shahat A.A., Alqahtani A.S., Alsaid M.S., Abdelfattah M.A.O., Ullah R., Emam M., Yasri A., Sobeh M.. **A Polyphenols-Rich Extract from**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25215049
22. Radulović N.S., Dordević N.D.. **Steroids from Poison Hemlock (**. *J. Serbian Chem. Soc.* (2011.0) **76** 1471-1483. DOI: 10.2298/JSC110206128R
23. Aly S.H., Elissawy A.M., Eldahshan O.A., Elshanawany M.A., Singab A.N.B.. **Phytochemical Investigation Using GC/MS Analysis and Evaluation of Antimicrobial and Cytotoxic Activities of the Lipoidal Matter of Leaves of**. *Arch. Pharm. Sci. Ain Shams Univ.* (2020.0) **4** 207-214. DOI: 10.21608/aps.2020.38371.1039
24. Jamalova D.N., Gad H.A., Akramov D.K., Tojibaev K.S., Al Musayeib N.M., Ashour M.L., Mamadalieva N.Z.. **Discrimination of the Essential Oils Obtained from Four Apiaceae Species Using Multivariate Analysis Based on the Chemical Compositions and Their Biological Activity**. *Plants* (2021.0) **10**. DOI: 10.3390/plants10081529
25. Al-Sayed E., Gad H.A., El-Kersh D.M.. **Characterization of Four Piper Essential Oils (GC/MS and ATR-IR) Coupled to Chemometrics and Their Anti- Helicobacter Pylori Activity**. *ACS Omega* (2021.0) **6** 25652-25663. DOI: 10.1021/acsomega.1c03777
26. Aly S.H., Eldahshan O.A., Al-rashood S.T., Binjubair F.A., El Hassab M.A., Eldehna W.M., Acqua S.D., Zengin G.. **Chemical Constituents, Antioxidant, and Enzyme Inhibitory Activities Supported by In-Silico Study of n-Hexane Extract and Essential Oil of Guava Leaves**. *Molecules* (2022.0) **27**. DOI: 10.3390/molecules27248979
27. Zeraib A., Ramdani M., Lograda T., Chalard P., Figueredo G.. **Chemical Composition and Antimicrobial Activity of Essential Oil of**. *Asian J. Plant Sci.* (2011.0) **10** 342-346. DOI: 10.3923/ajps.2011.342.346
28. Marrelli M., Morrone F., Gambacorta L., Argentieri M.P., Conforti F., Avato P.. **Phytochemical and Biological Profile of**. *Molecules* (2018.0) **23**. DOI: 10.3390/molecules23112829
29. El-Nashar H.A.S., Eldehna W.M., Al-Rashood S.T., Alharbi A., Eskandrani R.O., Aly S.H.. **GC/MS Analysis of Essential Oil and Enzyme Inhibitory Activities of**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26226984
30. Turgumbayeva A., Ustenova G., Datkhayev U., Rahimov K., Abramavicius S., Tunaityte A., Zhakipbekov K., Kozhanova K., Tulemissov S., Ustenova O.. **Safflower (**. *Phyton* (2020.0) **89** 137-146. DOI: 10.32604/phyton.2020.07665
31. Alamery S.F., Algaraawi N.L.. **Phytochemical Profile and Antifungal Activity of Stems and Leaves Methanol Extract from the**. *AIP Conf. Proc.* (2020.0) **2290** 020034. DOI: 10.1063/5.0027554
32. Gad H.A., Mukhammadiev E.A., Zengen G., Musayeib N.M.A., Hussain H., Ware I.B., Ashour M.L., Mamadalieva N.Z.. **Chemometric Analysis Based on GC-MS Chemical Profiles of Three Stachys Species from Uzbekistan and Their Biological Activity**. *Plants* (2022.0) **11**. DOI: 10.3390/plants11091215
33. Thippeswamy G., Sheela M.L., Salimath B.P.. **Octacosanol Isolated from Tinospora Cordifolia Downregulates VEGF Gene Expression by Inhibiting Nuclear Translocation of NF-<kappa>B and Its DNA Binding Activity**. *Eur. J. Pharmacol.* (2008.0) **588** 141-150. DOI: 10.1016/j.ejphar.2008.04.027
34. Braham H., Mighri Z., Jannet H.B., Matthew S., Abreu P.M.. **Antioxidant Phenolic Glycosides from Moricandia Arvensis**. *J. Nat. Prod.* (2005.0) **68** 517-522. DOI: 10.1021/np049581m
35. Soliman N.A., Ismail E.H., Abd El-Moaty H.I., Sabry D.Y., Khalil M.M.H.. **Anti-Helicobacter Pylori, Anti-Diabetic and Cytotoxicity Activity of Biosynthesized Gold Nanoparticles Using**. *Egypt. J. Chem.* (2018.0) **61** 691-703. DOI: 10.21608/EJCHEM.2018.3744.1318
36. Adams R.P.. *Identification of Essential Oil Components by Gas Chromatography/Mass Spectroscopy* (2007.0)
37. Aly S.H., Elissawy A.M., Eldahshan O.A., Elshanawany M.A., Singab A.N.B.. **Variability of the Chemical Composition of the Essential Oils of Flowers and the Alkaloid Contents of Leaves of**. *J. Essent. Oil-Bearing Plants* (2020.0) **23** 442-452. DOI: 10.1080/0972060X.2020.1750489
38. **NIST Standard Reference Database Number 69**
39. Aly S.H., El-hassab M.A., Elhady S.S., Gad H.A.. **Comparative Metabolic Study of**. *Plants* (2022.0) **12**. DOI: 10.3390/plants12010087
40. Skehan P., Storeng R., Scudiero D., Monks A., McMahon J., Vistica D., Warren J.T., Bokesch H., Kenney S., Boyd M.R.. **New Colorimetric Cytotoxicity Assay for Anticancer-Drug Screening**. *J. Natl. Cancer Inst.* (1990.0) **82** 1107-1112. DOI: 10.1093/jnci/82.13.1107
41. Boly R., Lamkami T., Lompo M., Dubois J., Guissou I.P.. **DPPH Free Radical Scavenging Activity of Two Extracts From**. *Int. J. Toxicol. Pharmacol. Res.* (2016.0) **8** 29-34
42. Benzie I.F.F., Strain J.J.. **The Ferric Reducing Ability of Plasma (FRAP) as a Measure of “Antioxidant Power”: The FRAP Assay**. *Anal. Biochem.* (1996.0) **239** 70-76. DOI: 10.1006/abio.1996.0292
43. El Hassab M.A., Fares M., Amin M.K.A., Al-rashood S.T., Alharbi A., Eskandrani R.O., Alkahtani H.M., Eldehna W.M.. **Toward the Identification of Potential α-Ketoamide Covalent Inhibitors for SARS-CoV-2 Main Protease: Fragment-Based Drug Design and MM-PBSA Calculations**. *Processes* (2021.0) **9**. DOI: 10.3390/pr9061004
44. Vilar S., Cozza G., Moro S.. **Medicinal Chemistry and the Molecular Operating Environment (MOE): Application of QSAR and Molecular Docking to Drug Discovery**. *Curr. Top. Med. Chem.* (2008.0) **8** 1555-1572. DOI: 10.2174/156802608786786624
45. Lountos G.T., Jiang R., Wellborn W.B., Thaler T.L., Bommarius A.S., Orville A.M.. **The Crystal Structure of NAD(P)H Oxidase from**. *Biochemistry* (2006.0) **45** 9648-9659. DOI: 10.1021/bi060692p
46. Walker E.H., Pacold M.E., Perisic O., Stephens L., Hawkins P.T., Wymann M.P., Williams R.L.. **Structural Determinants of Phosphoinositide 3-Kinase Inhibition by Wortmannin, LY294002, Quercetin, Myricetin, and Staurosporine**. *Mol. Cell* (2000.0) **6** 909-919. DOI: 10.1016/S1097-2765(05)00089-4
47. Forouhar F., Neely H., Seetharaman J., Mao L., Xiao R., Janjua H., Maglaqui M., Foote E.L., Lee D., Everett J.K.. **Crystal Structure of Chromosome Partitioning Protein (ParA) in Complex with ADP from**. *Northeast Struct. Genom. Consort.* (2008.0). DOI: 10.2210/pdb3cwq/pdb
|
---
title: Acenocoumarol Exerts Anti-Inflammatory Activity via the Suppression of NF-κB
and MAPK Pathways in RAW 264.7 Cells
authors:
- Hyun-Ju Han
- Chang-Gu Hyun
journal: Molecules
year: 2023
pmcid: PMC10004255
doi: 10.3390/molecules28052075
license: CC BY 4.0
---
# Acenocoumarol Exerts Anti-Inflammatory Activity via the Suppression of NF-κB and MAPK Pathways in RAW 264.7 Cells
## Abstract
The repurposing of already-approved drugs has emerged as an alternative strategy to rapidly identify effective, safe, and conveniently available new therapeutic indications against human diseases. The current study aimed to assess the repurposing of the anticoagulant drug acenocoumarol for the treatment of chronic inflammatory diseases (e.g., atopic dermatitis and psoriasis) and investigate the potential underlying mechanisms. For this purpose, we used murine macrophage RAW 264.7 as a model in experiments aimed at investigating the anti-inflammatory effects of acenocoumarol in inhibiting the production of pro-inflammatory mediators and cytokines. We demonstrate that acenocoumarol significantly decreases nitric oxide (NO), prostaglandin (PG)E2, tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β levels in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Acenocoumarol also inhibits the expression of NO synthase (iNOS) and cyclooxygenase (COX)-2, potentially explaining the acenocoumarol-induced decrease in NO and PGE2 production. In addition, acenocoumarol inhibits the phosphorylation of mitogen-activated protein kinases (MAPKs), c-Jun N terminal kinase (JNK), p38 MAPK, and extracellular signal-regulated kinase (ERK), in addition to decreasing the subsequent nuclear translocation of nuclear factor κB (NF-κB). This indicates that acenocoumarol attenuates the macrophage secretion of TNF-α, IL-6, IL-1β, and NO, inducing iNOS and COX-2 expression via the inhibition of the NF-κB and MAPK signaling pathways. In conclusion, our results demonstrate that acenocoumarol can effectively attenuate the activation of macrophages, suggesting that acenocoumarol is a potential candidate for drug repurposing as an anti-inflammatory agent.
## 1. Introduction
Inflammation is the complex biological response of the immune system and can be triggered as a part of a defensive mechanism by a variety of factors such as cell damage, irritants, pathogens, toxins, and other compounds; this is characterized by redness, swelling, fever, pain, and impaired function at the tissue level [1,2]. Although the inflammatory response is an important mechanism for host defense, chronic inflammation is the underlying cause of several diseases including asthma, endometriosis, obesity, atherosclerosis, rheumatoid arthritis (RA), and psoriasis [3,4,5]. Therefore, controlling abnormal inflammatory responses is a vital tool for the prevention and treatment of inflammatory diseases [6].
Macrophages are central players in systemic inflammation, associated with meta-inflammation and inflammaging. Over the years, several experimental models have been designed to facilitate the development of novel anti-inflammatory drugs, although in vitro models of RAW 264.7 cells are now the most widely used [7]. Mouse macrophage RAW264.7 is a functional macrophage line, transformed by the *Abelson leukemia* virus, that requires LPS for full activation. There is a consensus that inflammatory mediators and pro-inflammatory cytokines should initially be measured to screen for possible anti-inflammatory effects. More specifically, pro-inflammatory cytokines such as tumor necrosis factor (TNF)-α and interleukins (IL)-1β and IL6 as well as inflammatory mediators, together with NO and PGE2, should be measured. Therefore, the LPS stimulation of RAW 264.7 macrophages is commonly used as a classical inflammatory cell model to evaluate the anti-inflammatory activity and the mechanisms underlying the action of drugs [7,8,9]. There are four main categories of signaling pathways, whose activation during inflammation leads to the secretion of inflammatory mediators and pro-inflammatory cytokines: [1] I kappa B kinase (IκB)/nuclear factor kappa B (NF-κB); [2] mitogen-activated protein kinase (MAPK); [3] phosphoinositide 3-kinase; and [4] Janus kinase (JAK) signal transducer and activator of transcription (STAT) signaling pathways [10,11,12].
Drug repurposing, or repositioning, is an attractive and practical approach to drug discovery that has demonstrated success in health care for years. The use of existing drugs in new indications is also a promising strategy for treating chronic inflammatory diseases as it can significantly reduce the development costs and time compared with de novo drug discovery [13]. Drug repurposing studies can be divided into two main categories, according to whether their approach is screening-based or mechanism-based. In the screening-based approach, the approved drugs are explored to discover new actions in the treatment or prevention of chronic inflammation whereas in the mechanism-based approach, drugs are screened for the confirmation of action against specific molecular targets. However, the search for novel therapies in the management of chronic inflammation continues, as the current treatments with these repositioned drugs are associated with one or more side effects [13,14].
Herein, we examine the applicability of the anticoagulant acenocoumarol in drug repositioning as an anti-inflammatory agent. As shown in Figure 1, 4-hydroxycoumarin is a structural fragment of various natural and synthetic compounds, exhibiting various pharmacological activities. Its derivatives are attracting attention because of their properties as oral anticoagulants or rodenticides, photosensitizers, anti-HIV agents, and antibiotics. There has been a constant interest in the synthesis of these compounds [15,16,17]. In our efforts of drug repurposing with potent and safe skin health effects, we focused on the anticoagulants acenocoumarol to elucidate the anti-inflammatory properties of acenocoumarol using a RAW 264.7 macrophage model.
## 2.1. Effects of Acenocoumarol on Viability, Pro-Inflammatory Mediators, and Cytokines of RAW 264.7 Cells
To investigate whether acenocoumarol exerts cytotoxicity on mouse macrophage RAW 264.7 cells, the cells were treated with various concentrations (31.3. 62.5, 125, 250, and 500 μM) of acenocoumarol and lipopolysaccharide (LPS) (1 μg/mL) for 24 h. The results show that there were no significant differences up to a 250 μM concentration of acenocoumarol in the RAW 264.7 cells (Figure 2a). Compared with those in the normal cells, the survival rates in the above samples were $98.06\%$, $93.92\%$, $94.14\%$, $90.58\%$, and $87.34\%$, respectively. Therefore, we used acenocoumarol concentrations of 31.3, 62.5, 125, and 250 μM in the subsequent experiments [18]. Nitric oxide (NO) is considered as an important mediator of increased production in cases of inflammation. Therefore, the suppression of excessive NO production is commonly used to evaluate the anti-inflammatory effects of compounds. NO production levels in the supernatants of RAW 264.7 macrophages activated by LPS are generally investigated using the Griess reagent. Figure 2b shows that in comparison with the control group, NO production was higher in the LPS-treated group. Acenocoumarol inhibits NO synthesis in LPS-activated RAW264.7 macrophages in a concentration-dependent manner, and the IC50 value was 191.62 ± 9.21 μM. L-N6-(1-iminoethyl)lysine (L-NIL), known to represent a potent inhibitor of inducible nitric oxide synthase, was used as a positive control. We next investigated whether acenocoumarol inhibits prostaglandin (PG)E2 and inflammatory cytokines in the LPS-stimulated RAW 264.7 cells. Our results show that acenocoumarol inhibits PGE2, interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α production in a concentration-dependent manner (Figure 3).
## 2.2. Effect of Acenocoumarol on NO Synthase (iNOS) and Cyclooxygenase (COX)-2 Production
Inducible NO synthase and COX-2 are strongly linked to the regulation of NO and PGE2 production during inflammatory responses. Hence, many researchers have tried to find new compounds that can inhibit the production of these enzymes [19]. To understand the mechanism by which acenocoumarol inhibits NO and PGE2 production, the abundance of iNOS and COX-2 proteins was evaluated using Western blotting. As shown in Figure 4, iNOS production was significantly decreased in a concentration-dependent manner by acenocoumarol ($p \leq 0.05$), decreasing by 67.00, 75.13, and $97.60\%$ at 62.5, 125, and 250 μM of acenocoumarol, respectively. Furthermore, the COX-2 levels were significantly reduced after the exposure of LPS-induced RAW264.7 to acenocoumarol, also in a concentration-dependent manner (62.5, 125, and 250 μM), resulting in decreases of 0.54, 44.16, and $67.09\%$, respectively, compared with the activated macrophages. The expression of iNOS and COX-2 was not affected when unstimulated RAW 264.7 cells were treated only with acenocoumarol. After stimulation, the macrophages produced increased amounts of iNOS and COX-2, indicating that the pro-inflammatory response is directly related to the production of NO and PGE2.
## 2.3. Effect of Acenocoumarol on the Mitogen-Activated Protein Kinase (MAPK) Signaling Pathway
It has been reported that the phosphorylation of MAPK, consisting of three pathways involving extracellular-signal-regulated kinase 1 and 2 (ERK$\frac{1}{2}$), c-Jun N-terminal kinase 1, 2, and 3 (JNK$\frac{1}{2}$/3), and p38 MAPK activates signaling pathways and increases the production of various inflammatory cytokines. This pathway is reportedly involved in inflammatory cytokine expression, cell growth, cell proliferation, cell differentiation, stress response, migration, and apoptosis and is activated in response to various extracellular stimuli (KK). Overall, the MAPK signaling pathway plays a crucial role in regulating inflammatory cytokines in an inflammatory response. To investigate the relationship between acenocoumarol function and MAPK pathways, the phosphorylation levels of ERK, JNK, and p38 were evaluated in the LPS-stimulated macrophages exposed to acenocoumarol. As presented in Figure 5, the phosphorylation levels of ERK, JNK, and p38 increased significantly after LPS treatment in the vehicle + LPS group compared with the untreated group. However, the administration of acenocoumarol to the LPS-stimulated RAW264.7 cells significantly inhibited the phosphorylation of ERK, JNK, and p38 compared with the vehicle + LPS group. Even acenocoumarol treatment remarkably decreased the level of phosphorylated ERK and p38 compared with the LPS treatment at 125 μM and 250 μM concentrations. Taken together, our results suggest that acenocoumarol suppresses the LPS-induced inflammatory response through the regulation of the iNOS-mediated COX-2-induced pathway, inflammatory cytokine transcription, and the MAPK signaling pathway.
## 2.4. Effect of Acenocoumarol on the Nuclear Factor κB (NF-κB) Signaling Pathway
It has been reported that when macrophages are stimulated with LPS, IκBα is phosphorylated and ubiquitinated. Accordingly, phosphorylated NF-κB is translocated from the cytoplasm to the nucleus to increase the levels of inflammatory cytokines. As above-mentioned, aspirin and sodium salicylate block the degradation of IκBα, thus inhibiting the migration of NF-κB into the nucleus, thereby showing anti-inflammatory effects [20]. Therefore, Western blotting experiments were performed to investigate whether acenocoumarol inhibits the production of inflammatory cytokines through the NF-κB signaling pathway in LPS-stimulated RAW 264.7 cells. As shown in Figure 6, the levels of p-p65/β-actin and p-IκBα/β-actin phosphorylated proteins were markedly upregulated by LPS stimulation. Conversely, the IκBα/β-actin protein levels were downregulated in acenocoumarol-treated RAW 264.7 cells. We further investigated whether acenocoumarol could dampen the NF-κB p65 nuclear translocation in the LPS-stimulated RAW264.7 cells. As shown in Figure 7, the nuclear translocation of p65 was increased by LPS stimulation. However, following pre-treatment with acenocoumarol, p65 nuclear translocation was profoundly decreased compared with the model group. The purity of the nuclear and cytoplasmic extracts was verified by probing the blots with cytoplasmic protein β-actin and nuclear protein lamin B1. Minimal cross-contamination of the fractions was seen. APDTC also blocked NF-κB translocation as a positive control. In the presence of APDTC (10 μM), the levels of the nuclear NF-KB induced by LPS were decreased in the RAW 264.7 cells, confirming the involvement of NF-KB in the activation of RAW264.7 cells by acenocoumarol. Overall, this experimental work documents that acenocoumarol inhibits the LPS-stimulated p65 nuclear translocation and the NF-κB signaling pathway.
## 3. Discussion
Developing a new drug takes more than 10 years because it goes through various processes such as target discovery, the screening and optimization of candidates, evaluation of pharmacological and pharmacokinetics, and formulation. However, drug repurposing is the process of registering a new use for an approved drug that can be achieved within three years, which is much faster than the drug development period. In the course of the drug-repurposing strategy for the development of functional components, we have reported new applications of many FDA-approved existing drugs such as tobramycin, lincomycin, and fosfomycin to enhance the melanogenic effects [21,22,23], and spiramycin and D-cycloserine for exerting anti-inflammatory effects [24,25].
Acenocoumarol is not approved for marketing in the United States by the U.S. Food and Drug Administration, but is available in Canada and other countries. Acenocoumarol is a 4-hydroxycoumarin derivative used as an anticoagulant in the prevention of thromboembolic diseases in infarction and transient ischemic attacks as well as in the management of deep vein thrombosis and myocardial infarction. In addition, acenocoumarol also dose-dependently inhibits tryptophan breakdown in IFN-γ-stimulated Caco-2 cells [26,27]. Warzecha et al. found that low doses of acenocoumarol, given before the induction of acute pancreatitis by cerulein, inhibited the development of that inflammation [28]. Furthermore, treatment with acenocoumarol accelerates the recovery of ischemia/reperfusion-induced acute pancreatitis in rats [29]. On the other hand, 4-hydroxycoumarin itself is not an anticoagulant, but it has anticoagulant activity when a large aromatic substituent is added to the 3-position (the ring-carbon between the hydroxyl and the carbonyl). Large 3-position substituents are required for anticoagulant activity [30]. As is well-known, aenocoumarol has anticoagulant activity in the form of substituted α-acetonyl-ρ-nitrobenzyl structure at position 3 of 4-hydroxy-coumarin. Other than position 3, simple 4-hydroxycoumarin derivatives have been reported to have some anti-inflammatory effects, but they are difficult to use for drugs or skin damage due to the side effects. Previous studies have shown that among the natural coumarins, several compounds such as psoralen, bergapten, and xanthotoxin evoked a limited number of dermal phototoxic reactions in humans. It also showed that coumarins, especially their 3,4-epoxide intermediates, could induce vacuolar degeneration, necrosis, and hepatocyte apoptosis in rat liver [31]. As an extension of these studies, we therefore investigated the possibility of repurposing acenocoumarol as an anti-inflammatory agent.
RAW 264.7, a murine macrophage cell line, is an excellent model for anti-inflammatory drug screening and for subsequently evaluating the inhibitors of pathways leading to the induction of pro-inflammatory cytokines. The present study was undertaken to elucidate the pharmacological effects and mechanisms of acenocoumarol on the production of inflammatory mediators and pro-inflammatory cytokines in the RAW 264.7 cells stimulated with LPS in vitro.
First, the anti-inflammatory effects of acenocoumarol were investigated on the RAW264.7 cells stimulated with LPS (1 μg/mL), and we determined the nontoxic concentrations of acenocoumarol with or without LPS in the RAW264.7 macrophages using a MTT viability assay. The decrease in RAW264.7 cell viability following LPS treatment due to the release of inflammatory substances, which may act as cytotoxic agents, has been previously demonstrated, but an increase in the cell viability or little change in cell viability has also been reported [32,33,34,35]. However, in our MTT assay, LPS (1 μg/mL) treatment of the RAW264.7 cells was associated with a slight decrease in their viability compared with the nontreated cells or cells treated with acenocoumarol alone. Acenocoumarol did not decrease the cell viability at 500 μM; therefore, acenocoumarol was used at 62.5, 125, and 250 μM in the efficacy study.
Furthermore, we evaluated the production of NO and PGE2 as important inflammatory biomarkers responsible for rubefaction, pain, fever, swelling, and malfunction. Our data indicate that the upregulated secretion of NO and PGE2 by LPS in the RAW 264.7 macrophages was progressively inhibited by acenocoumarol in a concentration-dependent manner, and this is related to the inhibition of iNOS and COX-2 protein expression. These results indicate that the anti-inflammatory effect of acenocoumarol is at least due to the reduced expression of iNOS and COX-2, which are required for NO and PGE2 production.
Pro-inflammatory cytokines are mutually inducible and have a dual role of inhibiting or promoting the progression of inflammation [36]. TNF-α can be upregulated to an appropriate amount and restored to enhance immune effects, whereas excessive amounts of TNF-α can easily induce the release of IL-6 and other related inflammatory cytokines. IL-1β is secreted by monocytes and macrophages, and it should be noted that IL-1β acts cooperatively with IL-6 to generate a cascade effect that exacerbates the inflammatory process. It has been reported that the LPS treatment of RAW264.7 macrophages significantly increases the release of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1β. Therefore, the levels of pro-inflammatory cytokines are applied as an indicator to evaluate the anti-inflammatory efficacy of macrophages [37,38]. In this study, we found that acenocoumarol inhibits the production of TNF-α, IL-1β, and IL-6 in the LPS-induced RAW 264.7 macrophages by suppressing their expression.
To elucidate the underlying mechanism responsible for the anti-inflammatory effects of acenocoumarol, its regulatory effects on the NF-κB inflammatory signaling pathway were investigated in RAW 264.7 cells stimulated by LPS. Numerous studies have reported that NF-κB is a key transcription factor in the pathogenesis of inflammatory diseases, and its activation positively regulates the expression of inflammatory mediators (e.g., iNOS and COX-2) and pro-inflammatory cytokines (e.g., TNF-α, IL-1β, and IL-6). In addition, NF-κB mainly consists of p50 and p65 subunits, the latter responding to pre-inflammatory cytokine stimulation [6,19,20]. NF-κB is a cytosolic inactive form that binds to unphosphorylated IκB in a steady state. Under inflammatory stimuli such as LPS, active IKK phosphorylates IκBα, followed by the phosphorylation and then ubiquitination of IκBα, which is subsequently degraded by the 26S proteasome. After IκBα degradation, cytoplasmic NF-κB migrates to the nucleus and binds to the target gene, resulting in the transcription of inflammatory genes [39]. To establish whether NF-κB mediates anti-inflammatory processes in response to acenocoumarol, IκBα phosphorylation and NF-κB p65 translocation were evaluated by Western blotting. Acenocoumarol was found to inhibit the phosphorylation of the IκBα protein and suppress LPS-induced NF-κB (p65) translocation. Thus, acenocoumarol inhibits the NF-κB signaling pathway to alleviate LPS-induced inflammation.
Mitogen-activated protein kinase (MAPK) is another important signaling pathway activated in LPS-induced macrophages through the TLR4. During the inflammation process, members of the MAPK family, consisting of three main subfamily members (i.e., ERK, p38, and JNK), are phosphorylated to adjust the expression of iNOS, COX-2, and other pro-inflammatory cytokines. Thus, it has been suggested that inhibitors targeting the p38 and JNK MAPK pathways have anti-inflammatory activity [40,41]. To investigate whether the MAPK signaling pathway is also affected by acenocoumarol, the phosphorylation of ERK, p38, and JNK was analyzed. The results indicate that the levels of phosphorylated ERK, p38, and JNK in the RAW 264.7 macrophages are significantly increased in the presence of LPS, while treatment with acenocoumarol results in a concentration-dependent decrease in the phosphorylation of the MAPK family proteins. These results suggest that the effects of acenocoumarol on the production of inflammatory mediators and pro-inflammatory cytokines are likely mediated through the blocking of the ERK, p38, and JNK signaling pathways in the RAW 264.7 cells.
## 4.1. Materials
The acenocoumarol used in this study was purchased from TCI (Tokyo, Japan), and warfarin was purchased from Sigma-Aldrich (St. Louis, MO, USA). For cell culture, Dulbecco’s modified Eagle’s medium (DMEM) and penicillin–streptomycin (P/S) were purchased from Thermo Fisher Scientific (Waltham, MA, USA) and fetal bovine serum (FBS) from Merck Millipore (Burling, MA, USA). Protease/phosphatase inhibitor cocktail, sodium hydroxide (NaOH), lipopolysaccharide (LPS), and Griess reagent used for the cell experiments were purchased from Sigma-Aldrich (St. Louis, MO, USA), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), dimethyl sulfoxide (DMSO), phosphate-buffered saline (PBS), Tris-buffered saline (TBS), sodium dodecyl sulfate (SDS), radioimmunoprecipitation assay (RIPA) buffer, and an enhanced chemiluminescence (ECL) kit were purchased from Biosesang (Seongnam, Gyeonggi-do, Republic of Korea). A BCA protein assay kit, NE-PER nuclear and cytoplasmic extraction reagents, and $0.5\%$ trypsin–ethylenediaminetetraacetic acid (10×) were purchased from Thermo Fisher Scientific (Waltham, MA, USA), and Tween 20 and 2× Laemmli sample buffer were obtained from Bio-Rad (Hercules, CA, USA). Skim milk was purchased from BD Difco (Sparks, MD, USA). Among the enzyme-linked immunosorbent assessment (ELISA) kits, PGE2 was purchased from abcam (Cambridge, EN, UK), and IL-1β, IL-6, and TNF-α from BD Biosciences (Franklin Lakes, NJ, USA). COX-2 was purchased from BD Biosciences (Franklin Lakes, NJ, USA) and p-ERK (9101S), ERK (9102S), p-p38 (9211S), p38 (9212S), p-JNK (9251S), JNK (9252S), iNOS (2982S), p-IκBα (9246S), IκBα (4812S), p65 (4764S), lamin B [12586], β-actin (4967S), and secondary anti-mouse and anti-rabbit antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA).
## 4.2. Cell Culture
The RAW 264.7 murine macrophage cells were purchased from the Korea Cell Line Bank (Seoul, Republic of Korea). Cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) with $10\%$ fetal bovine serum (FBS) and $1\%$ penicillin–streptomycin at 37 °C in a humidified $5\%$ CO2 atmosphere.
## 4.3. MTT Assay
Cytotoxicity was assessed using the MTT assay. Cultured RAW 264.7 cells (1.5 × 105 cells/well) were treated with coumarin derivatives and LPS (1 μg/mL) in 24-well plates and incubated for 24 h. For the MTT assay, the culture medium was replaced with 0.2 mg/mL MTT (500 μL). The cells were incubated at 37 °C for 3 h, the medium was removed, and the formazan product was dissolved in dimethyl sulfoxide. Absorbance was measured at 570 nm using a microplate reader (Biotek; Winooski, VT, USA).
## 4.4. Measurement of NO Production
The NO concentration in the culture supernatants was measured in the form of nitrite in the cell culture medium using the Griess reagent. Cultured RAW 264.7 cells (1.5 × 105 cells/well) were treated with acenocoumarol and LPS (1 μg/mL) in 24-well plates and incubated for 24 h. As a positive control group, iNOS-specific inhibitor L-NIL (40 μM) was used. Cell culture supernatants were mixed with an equal volume (100 μL) of the Griess reagent and incubated in 96-well plates for 10 min at room temperature. Absorbance was measured at 540 nm using a microplate reader (Biotek; Winooski, VT, USA).
## 4.5. Measurement of PGE2 and Cytokines
The levels of PGE2 and cytokines (IL-1β, IL-6, and TNF-α) in the culture supernatants were determined using cytokine detection ELISA kits, as per the manufacturer’s instructions. Cultured RAW 264.7 cells (1.5 × 105 cells/well) were treated with acenocoumarol and LPS (1 μg/mL) in 24-well plates and incubated for 24 h. As a positive control when measuring PGE2 production, the COX-2 specific inhibitor NS 398 (100 nM) was used. Protein levels were determined by measuring absorbance at 405 or 450 nm using a microplate reader (Biotek; Winooski, VT, USA).
## 4.6. Preparation of Nuclear and Cytoplasmic Extraction
Nuclear and cytoplasmic extracts were isolated using an extraction kit. The RAW 264.7 cells (6.0 × 105 cells/dish) were incubated in 60 mm cell culture dishes for 24 h. Acenocoumarol and LPS (1 μg/mL) were treated and cultured to determine the expression of each protein. After incubation, a nuclear extract was obtained according to the protocols provided by the manufacturer of the extraction kit.
## 4.7. Western Blotting
The RAW 264.7 cells (6.0 × 105 cells/dish) were incubated in 60 mm cell culture dishes for 24 h. Acenocoumarol and LPS (1 μg/mL) were treated and cultured for each protein expression time. The cells were washed with 1× PBS, and lysis buffer (RIPA buffer, $1\%$ protease inhibitor cocktail) was added for lysis at 4 °C for 20 min. Supernatants were obtained after centrifugation at 15,000 rpm for 20 min at −8 °C. The protein concentration was quantified using a BCA protein assay kit and adjusted to 30 μg/mL. For loading onto gels, samples of the protein and 2× Laemmli sample buffer were mixed in a 1:1 ratio and heated at 100 °C for 5 m. Samples were electrophoresed on an SDS–polyacrylamide gel for size separation of the proteins. After transferring the proteins to a PVDF membrane, the membrane was blocked in $5\%$ skim milk dissolved in TBS-T (Tris-buffered saline with $1\%$ Tween 20) for 2 h. The membrane was washed with 1× TBS-T and incubated with primary antibody diluted at a ratio of 1:2000 overnight at 4 °C. After washing the antibody, the membrane was incubated with secondary antibody diluted at a ratio of 1:1000 at room temperature for 2 h. After washing the antibody, the signal was developed using an ECL kit and measured using Chemidoc (VILBER LOURMAT, Marne La Vallée, France).
## 4.8. Statistical Analyses
The results of the experiments were expressed as the mean and standard deviation (mean ± SD) through three repeated experiments. Statistical significance was determined based on p-values using the Student’s t-test, # $p \leq 0.001$ vs. the unstimulated control group. * $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$ vs. LPS alone.
## 5. Conclusions
After collecting all of the information regarding the anti-inflammatory effects of acenocoumarol identified in this study, a map of the relevant molecular pathways was constructed (Figure 8). First, acenocoumarol inhibited iNOS and COX-2 expression, indicating that the pro-inflammatory response is directly related to the production of NO and PGE2 in the LPS-induced RAW 264.7 cells. Second, acenocoumarol inhibited interleukin IL-6, IL-1β, and TNF-α production in a concentration-dependent manner. Finally, acenocoumarol exhibited anti-inflammatory activity that depends on its ability to regulate the production of NO, PGE2, and other pro-inflammatory cytokines in the LPS-induced RAW 264.7 cells through the suppression of NF-κB activation and MAPK phosphorylation. Considering these results, acenocoumarol can be considered as a possible candidate for repurposing as an anti-inflammatory agent. However, further studies are needed to fully understand the role of cellular signaling pathways other than the MAPK and NF-κB pathways, which may be involved in the anti-inflammatory activity of acenocoumarol. Furthermore, the mechanisms involved in the anti-inflammatory efficacy of acenocoumarol should be assessed with another cell line and in vivo.
## References
1. Takahashi S., Yoshida M., Watanabe M.M., Isoda H.. **Anti-Inflammatory Effects of**. *Biomed. Res. Int.* (2019) **2019** 3104057. DOI: 10.1155/2019/3104057
2. Chen L., Deng H., Cui H., Fang J., Zuo Z., Deng J., Li Y., Wang X., Zhao L.. **Inflammatory responses and inflammation-associated diseases in organs**. *Oncotarget* (2017) **9** 7204-7218. DOI: 10.18632/oncotarget.23208
3. Henein M.Y., Vancheri S., Longo G., Vancheri F.. **The Role of Inflammation in Cardiovascular Disease**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms232112906
4. Li H., Meng Y., He S., Tan X., Zhang Y., Zhang X., Wang L., Zheng W.. **Macrophages, Chronic Inflammation, and Insulin Resistance**. *Cells* (2022) **11**. DOI: 10.3390/cells11193001
5. Nowowiejska J., Baran A., Flisiak I.. **Psoriasis and neurodegenerative diseases—A review**. *Front. Mol. Neurosci.* (2022) **15** 917751. DOI: 10.3389/fnmol.2022.917751
6. Yang L., He J.. **Anti-inflammatory effects of flavonoids and phenylethanoid glycosides from**. *Med. Ther.* (2022) **22** 55. DOI: 10.1186/s12906-022-03540-1
7. Facchin B.M., Dos Reis G.O., Vieira G.N., Mohr E.T.B., da Rosa J.S., Kretzer I.F., Demarchi I.G., Dalmarco E.M.. **Inflammatory biomarkers on an LPS-induced RAW 264.7 cell model: A systematic review and meta-analysis**. *Inflamm. Res.* (2022) **71** 741-758. DOI: 10.1007/s00011-022-01584-0
8. Qu L., Matz A.J., Karlinsey K., Cao Z., Vella A.T., Zhou B.. **Macrophages at the Crossroad of Meta-Inflammation and Inflammaging**. *Genes* (2022) **13**. DOI: 10.3390/genes13112074
9. Hartley J.W., Evans L.H., Green K.Y., Naghashfar Z., Macias A.R., Zerfas P.M., Ward J.M.. **Expression of infectious murine leukemia viruses by RAW264.7 cells, a potential complication for studies with a widely used mouse macrophage cell line**. *Retrovirology* (2008) **5** 1. DOI: 10.1186/1742-4690-5-1
10. Ghelani H., Khursheed M., Adrian T.E., Jan R.K.. **Anti-Inflammatory Effects of Compounds from Echinoderms**. *Mar. Drugs* (2022) **20**. DOI: 10.3390/md20110693
11. Zhao H., Wu L., Yan G., Chen Y., Zhou M., Wu Y., Li Y.. **Inflammation and tumor progression: Signaling pathways and targeted intervention. Signal Transduct**. *Target. Ther.* (2021) **6** 263. DOI: 10.1038/s41392-021-00658-5
12. Yeung Y.T., Aziz F., Guerrero-Castilla A., Arguelles S.. **Signaling Pathways in Inflammation and Anti-inflammatory Therapies**. *Curr. Pharm. Des.* (2018) **24** 1449-1484. DOI: 10.2174/1381612824666180327165604
13. Chaffey L., Roberti A., Greaves D.R.. **Drug repurposing in cardiovascular inflammation: Successes, failures, and future opportunities**. *Front. Pharmacol.* (2022) **13** 1046406. DOI: 10.3389/fphar.2022.1046406
14. Paul A., Kumar M., Das P., Guha N., Rudrapal M., Zaman M.K.. **Drug repurposing—A search for novel therapy for the treatment of diabetic neuropathy**. *Biomed. Pharmacother.* (2022) **156** 113846. DOI: 10.1016/j.biopha.2022.113846
15. Kuang Y., Xia Y., Wang X., Rao Q., Yang S.. **Magnetic Surface Molecularly Imprinted Polymer for Selective Adsorption of 4-Hydroxycoumarin**. *Front. Chem.* (2022) **10** 862777. DOI: 10.3389/fchem.2022.862777
16. Stanchev S., Jensen F., Hinkov A., Atanasov V., Genova-Kalou P., Argirova R., Manolov I.. **Synthesis and Inhibiting Activity of Some 4-Hydroxycoumarin Derivatives on HIV-1 Protease**. *ISRN Pharm.* (2011) **2011** 137637. DOI: 10.5402/2011/137637
17. Li M.K., Li J., Liu B.H., Zhou Y., Li X., Xue X.Y., Hou Z., Luo X.X.. **Synthesis, crystal structures, and anti-drug-resistant Staphylococcus aureus activities of novel 4-hydroxycoumarin derivatives**. *Eur. J. Pharmacol.* (2013) **721** 151-157. DOI: 10.1016/j.ejphar.2013.09.040
18. Basak V., Bahar T.E., Emine K., Yelda K., Mine K., Figen S., Rustem N.. **Evaluation of Cytotoxicity and Gelatinases Activity in 3T3 Fibroblast Cell by Root Repair Materials**. *Biotechnol. Biotechnol. Equip.* (2016) **30** 984-990. DOI: 10.1080/13102818.2016.1192960
19. Lee Y., Hyun C.G.. **Anti-Inflammatory Effects of Psoralen Derivatives on RAW264.7 Cells via Regulation of the NF-κB and MAPK Signaling Pathways**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23105813
20. Kang J.K., Chung Y.C., Hyun C.G.. **Anti-Inflammatory Effects of 6-Methylcoumarin in LPS-Stimulated RAW 264.7 Macrophages via Regulation of MAPK and NF-κB Signaling Pathways**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26175351
21. Ullah S., Chung Y.C., Hyun C.G.. **Induction of Melanogenesis by Fosfomycin in B16F10 Cells Through the Upregulation of P-JNK and P-p38 Signaling Pathways**. *Antibiotics* (2020) **9**. DOI: 10.3390/antibiotics9040172
22. Moon S.H., Chung Y.C., Hyun C.G.. **Tobramycin Promotes Melanogenesis by Upregulating p38 MAPK Protein Phosphorylation in B16F10 Melanoma Cells**. *Antibiotics* (2019) **8**. DOI: 10.3390/antibiotics8030140
23. Lee M.S., Chung Y.C., Moon S.H., Hyun C.G.. **Lincomycin induces melanogenesis through the activation of MITF via p38 MAPK, AKT, and PKA signaling pathways**. *J. Appl. Biol. Chem.* (2021) **64** 323-331. DOI: 10.3839/jabc.2021.044
24. Kang J.K., Kang H.K., Hyun C.G.. **Anti-Inflammatory Effects of Spiramycin in LPS-Activated RAW 264.7 Macrophages**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27103202
25. Kang H.K., Hyun C.G.. **Anti-inflammatory Effect of d-(+)-Cycloserine Through Inhibition of NF-κB and MAPK Signaling Pathways in LPS-Induced RAW 264.7 Macrophages**. *Nat. Prod. Commun.* (2020) **15** 1934578X20920481. DOI: 10.1177/1934578X20920481
26. Schroecksnadel S., Gostner J., Jenny M., Kurz K., Schennach H., Weiss G., Fuchs D.. **Immunomodulatory effects in vitro of vitamin K antagonist acenocoumarol**. *Thromb. Res.* (2013) **131** e264-e269. DOI: 10.1016/j.thromres.2013.02.012
27. Salvador J.P., Brettschneider T., Dorrer C., Marco M.P.. **Development of a Fluorescent Microfluidic Device Based on Antibody Microarray Read-Out for Therapeutic Drug Monitoring of Acenocoumarol**. *Front. Bioeng. Biotechnol.* (2022) **10** 848501. DOI: 10.3389/fbioe.2022.848501
28. Warzecha Z., Sendur P., Ceranowicz P., Dembiński M., Cieszkowski J., Kuśnierz-Cabala B., Olszanecki R., Tomaszewska R., Ambroży T., Dembiński A.. **Protective Effect of Pretreatment with Acenocoumarol in Cerulein-Induced Acute Pancreatitis**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17101709
29. Warzecha Z., Sendur P., Ceranowicz P., Cieszkowski J., Dembiński M., Sendur R., Bonior J., Jaworek J., Ambroży T., Olszanecki R.. **Therapeutic Effect of Low Doses of Acenocoumarol in the Course of Ischemia/Reperfusion-Induced Acute Pancreatitis in Rats**. *Int. J. Mol. Sci.* (2017) **18**. DOI: 10.3390/ijms18040882
30. Liu B., Raeth T., Beuerle T., Beerhues L.. **A novel 4-hydroxycoumarin biosynthetic pathway**. *Plant Mol. Biol.* (2010) **72** 17-25. DOI: 10.1007/s11103-009-9548-0
31. Nigg H.N., Nordby H.E., Beier R.C., Dillman A., Macias C., Hansen R.C.. **Phototoxic coumarins in limes**. *Food Chem. Toxicol.* (1993) **31** 331-335. DOI: 10.1016/0278-6915(93)90187-4
32. Hwang J.H., Ma J.N., Park J.H., Jung H.W., Park Y.K.. **Anti-inflammatory and antioxidant effects of MOK, a polyherbal extract, on lipopolysaccharide-stimulated RAW 264.7 macrophages**. *Int. J. Mol. Med.* (2019) **43** 26-36. DOI: 10.3892/ijmm.2018.3937
33. Zhang X., Xiong H., Liu L.. **Effects of taraxasterol on inflammatory responses in lipopolysaccharide-induced RAW 264.7 macrophages**. *J. Ethnopharmacol.* (2012) **141** 206-211. DOI: 10.1016/j.jep.2012.02.020
34. Choi H.S., Seo H.S., Kim S.R., Choi Y.K., Shin Y.C., Ko S.G.. **Anti-inflammatory and anti-proliferative effect of herbal medicines (APR) in RAW264.7 cells**. *Mol. Med. Rep.* (2014) **9** 1569-1574. DOI: 10.3892/mmr.2014.2033
35. Li T., Gao D., Du M., Cheng X., Mao X.. **Casein glycomacropeptide hydrolysates inhibit PGE2 production and COX2 expression in LPS-stimulated RAW 264.7 macrophage cells via Akt mediated NF-κB and MAPK pathways**. *Food Funct.* (2018) **9** 2524-2532. DOI: 10.1039/C7FO01989K
36. Lenzlinger P.M., Morganti-Kossmann M.C., Laurer H.L., McIntosh T.K.. **The duality of the inflammatory response to traumatic brain injury**. *Mol. Neurobiol.* (2001) **24** 169-181. DOI: 10.1385/MN:24:1-3:169
37. Li Q., Yang S., Yang S., Xin F., Wang M.. **Anti-inflammatory activity of phlomisoside F isolated from Phlomis younghusbandii Mukerjee**. *Int. Immunopharmacol.* (2015) **28** 724-730. DOI: 10.1016/j.intimp.2015.07.035
38. Peng F., Yin H., Du B., Niu K., Yang Y., Wang S.. **Anti-inflammatory effect of flavonoids from chestnut flowers in lipopolysaccharide-stimulated RAW 264.7 macrophages and acute lung injury in mice**. *J. Ethnopharmacol.* (2022) **290** 115086. DOI: 10.1016/j.jep.2022.115086
39. Duan X., Li J., Cu J., Dong Y., Xin X., Aisa H.A.. **Anti-inflammatory activity of Anchusa italica Retz. in LPS-stimulated RAW264.7 cells mediated by the Nrf2/HO-1, MAPK and NF-κB signaling pathways**. *J. Ethnopharmacol.* (2022) **286** 114899. DOI: 10.1016/j.jep.2021.114899
40. Liu Q., Xiao X.H., Hu L.B., Jie H.Y., Wang Y., Ye W.C., Li M.M., Liu Z.. **Anhuienoside C Ameliorates Collagen-Induced Arthritis through Inhibition of MAPK and NF-κB Signaling Pathways**. *Front. Pharmacol.* (2017) **8** 299. DOI: 10.3389/fphar.2017.00299
41. Zhou X., Ahn D.U., Xia M., Zeng Q., Li X., Cai Z.. **Fab Fragment of Immunoglobulin Y Modulates NF-κB and MAPK Signaling through TLR4 and αVβ3 Integrin and Inhibits the Inflammatory Effect on R264.7 Macrophages**. *J. Agric. Food Chem.* (2021) **69** 8747-8757. DOI: 10.1021/acs.jafc.1c03330
|
---
title: Hippocampal ceRNA networks from chronic intermittent ethanol vapor-exposed
male mice and functional analysis of top-ranked lncRNA genes for ethanol drinking
phenotypes
authors:
- Sonja L. Plasil
- Valerie J. Collins
- Annalisa M. Baratta
- Sean P. Farris
- Gregg E. Homanics
journal: Advances in Drug and Alcohol Research
year: 2022
pmcid: PMC10004261
doi: 10.3389/adar.2022.10831
license: CC BY 4.0
---
# Hippocampal ceRNA networks from chronic intermittent ethanol vapor-exposed male mice and functional analysis of top-ranked lncRNA genes for ethanol drinking phenotypes
## Abstract
The molecular mechanisms regulating the development and progression of alcohol use disorder (AUD) are largely unknown. While noncoding RNAs have previously been implicated as playing key roles in AUD, long-noncoding RNA (lncRNA) remains understudied in relation to AUD. In this study, we first identified ethanol-responsive lncRNAs in the mouse hippocampus that are transcriptional network hub genes. Microarray analysis of lncRNA, miRNA, circular RNA, and protein coding gene expression in the hippocampus from chronic intermittent ethanol vapor- or air- (control) exposed mice was used to identify ethanol-responsive competing endogenous RNA (ceRNA) networks. Highly interconnected lncRNAs (genes that had the strongest overall correlation to all other dysregulated genes identified) were ranked. The top four lncRNAs were novel, previously uncharacterized genes named Gm42575, 4930413E15Rik, Gm15767, and Gm33447, hereafter referred to as Pitt1, Pitt2, Pitt3, and Pitt4, respectively. We subsequently tested the hypothesis that CRISPR/Cas9 mutagenesis of the putative promoter and first exon of these lncRNAs in C57BL/6J mice would alter ethanol drinking behavior. The Drinking in the Dark (DID) assay was used to examine binge-like drinking behavior, and the Every-Other-Day Two-Bottle Choice (EOD-2BC) assay was used to examine intermittent ethanol consumption and preference. No significant differences between control and mutant mice were observed in the DID assay. Female-specific reductions in ethanol consumption were observed in the EOD-2BC assay for Pitt1, Pitt3, and Pitt4 mutant mice compared to controls. Male-specific alterations in ethanol preference were observed for Pitt1 and Pitt2. Female-specific increases in ethanol preference were observed for Pitt3 and Pitt4. Total fluid consumption was reduced in Pitt1 and Pitt2 mutants at $15\%$ v/v ethanol and in Pitt3 and Pitt4 at $20\%$ v/v ethanol in females only. We conclude that all lncRNAs targeted altered ethanol drinking behavior, and that lncRNAs Pitt1, Pitt3, and Pitt4 influenced ethanol consumption in a sex-specific manner. Further research is necessary to elucidate the biological mechanisms for these effects. These findings add to the literature implicating noncoding RNAs in AUD and suggest lncRNAs also play an important regulatory role in the disease.
## Introduction
Alcohol use disorder (AUD) is a chronic and debilitating neurological disorder that has extensive global, social, and economic burdens. In the United States AUD is one of the leading risk factors for premature death and disability [1] and has an annual estimated socioeconomic cost of ∼$250 billion [2]. Many consequences of chronic alcohol misuse are attributed to alcohol’s effect on the brain [3, 4], and alcohol acts in part by altering neural gene expression (4–8). Deciphering alcohol’s impact on gene expression within discrete brain regions and subsequent downstream effects offers an opportunity to identify novel pharmacological targets that could prevent sustained alcohol-induced alterations from occurring in humans.
The hippocampus is an important ethanol-sensitive brain region involved in the transition to AUD (9–11). The hippocampus is susceptible to the detrimental impacts of excessive alcohol exposure (12–14), and binge-like ethanol consumption has been shown to significantly impact neuroimmune functions within the hippocampus in mice [15]. Neuroimmune, transcriptional, and epigenetic cell signaling changes are shown to underly the loss of hippocampal neurogenesis (15, 17–20) and plasticity [9, 19, 21] following both exposure to ethanol and other drugs of abuse [17, 19, 22, 23]. This supports the concept that hippocampal neuroadaptations are critical targets to understand ethanol withdrawal and consumption.
The noncoding RNA (ncRNA) transcriptome acts as epigenetic regulators controlling cellular homeostasis [24]. Evidence supports important roles for ncRNA in the progression of AUD (7, 8, 25–27). Functional studies targeting specific RNAs in animal models for AUD have shown that the ethanol-responsive RNA transcriptome is involved in ethanol consumption, withdrawal, and the progression of addiction. Transcriptome data gathered from both humans and animals chronically exposed to ethanol has revealed mass dysregulation of multiple RNA subtypes in the brain [7, 8], such as mRNAs and their coded proteins (28–34), miRNAs (7, 35–39), circular RNAs (circRNA) [40], and long noncoding RNAs (lncRNAs) (4, 41–43). LncRNAs are an abundant and diverse subclass of ncRNAs defined as transcripts exceeding 200 nucleotides (nts) that do not encode protein [7, 44]. There are over 100,000 different lncRNA transcripts (45–49), with many showing brain-specific expression [50]. LncRNAs are known for their roles in epigenetic regulation (44, 50–53), such as impacting chromatin modifications, RNA processing events, modulation of miRNAs, gene silencing, regulation of neighboring genes, synaptic plasticity [44] and molecular networks by acting and interacting as central hubs [8, 54]. Those that have been studied largely function by regulating gene expression through cis- and trans-mechanisms [55, 56]. LncRNA expression can be developmentally regulated, can show tissue- and cell-type specific expression, and can be involved in numerous cellular pathways critical to normal development and physiology (50–53, 57–59). The dysregulation of lncRNAs has been linked to the pathophysiology of several disease states (7, 8, 41, 44, 53, 60–66) including AUD [41, 67, 68], drug addiction (63, 69–71), psychiatric disorders [72, 73], and stress responses [74, 75]. Identifying and directly testing lncRNAs that regulate ethanol consumption and related behaviors is important to fully understand the initiation and progression of AUD. Here, we hypothesize that specific ethanol-responsive lncRNAs are critical hubs of molecular networks that act as key determinants of ethanol consumption. Targeting specific ethanol-responsive lncRNAs for genetic modulation that have strong correlations to other ethanol-responsive RNAs may help discern transcriptomic network alterations that can impact ethanol drinking phenotypes.
To shed light on how ncRNAs interact with each other in vivo, competing endogenous RNA (ceRNA) networks can be bioinformatically generated from transcriptome data sets (76–81). LncRNA, circRNA, and miRNA are all known as ncRNA epigenetic regulators, which work in concert to coordinate mRNA expression, protein levels, and homeostasis via such functions as transcription factors, molecular sponges, scaffolds, decoys, and guides (for reviews, see: [7,24, 44, 51, 53, 54, 63]. These networks provide insight into discrete clusters of RNAs that interact and/or compete with each other to maintain the network’s function (76–81). These correlated RNAs can then be intertwined and linked together computationally to either increase or decrease the rank of hub genes based on their relative interconnectivity with other genes. Generating ethanol-responsive ceRNA networks from four prominent RNA subtypes, lncRNA, mRNA, circRNA, and miRNA, allowed for novel networks and hub genes to be identified in the present study. A list of top-ranked putative hub ethanol-responsive lncRNAs was generated and genes were prioritized for functional interrogation via CRISPR/Cas9 mutagenesis.
The acquisition of transcriptome data has greatly outpaced our capacity to functionally study genes in vivo that are hypothesized to contribute to AUD [82]. To circumvent this bottleneck, we recently developed an accelerated CRISPR/Cas9 approach to create a cohort of functional KnockOut (KO) animals in a single generation [83]. Here we applied this CRISPR Turbo Accelerated KO (CRISPy TAKO) methodology to test the hypothesis that mutation of ethanol-responsive lncRNAs identified from hippocampal ceRNA network analyses impact ethanol drinking behavior. We tested the top four lncRNAs that were identified as potential hubs for ethanol-responsive networks via ceRNA analysis. *We* generated four CRISPy TAKO mouse lines targeting the top four lncRNA candidates identified: Gm42575, 4930413E15Rik, Gm15767, and Gm33447, hereafter referred to as Pitt1, Pitt2, Pitt3, and Pitt4, respectively. *All* gene-targeted cohorts were tested for binge-like drinking behavior and intermittent ethanol consumption and preference.
## Animals
All experiments were approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh and conducted in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. C57BL/6J male and female mice used for chronic intermittent ethanol vapor (CIEV) exposure, generation of embryos for electroporation, and purchased control groups were procured from The Jackson Laboratory (Bar Harbor, ME). CD-1 recipient females and vasectomized males were procured from Charles River Laboratories, Inc. (Wilmington, MA). Mice were housed in individually ventilated caging under specific pathogen-free conditions with 12-h light/dark cycles (lights on at 7 AM) and had ad libitum access to food (irradiated 5P76 ProLab IsoProRMH3000; LabDiet, St. Louis, MO) and water.
## Chronic intermittent ethanol vapor exposure
Male mice were exposed to a 16-h CIEV or room-air paradigm as previously reported [84] ($$n = 5$$–6/treatment). Briefly, mice were given a priming intraperitoneal injection of either 1.5 g/kg ethanol (Decon Labs, Inc., #2716GEA) and 68 mg/kg pyrazole (Sigma-Aldrich, P56607-5G) or saline and 68 mg/kg pyrazole, then immediately subjected to vaporized ethanol or room air (respectively) for 16 h/day, 4 days/week, for 7 weeks. Hippocampal tissue was harvested 24 h following the final vapor exposure.
## Total RNA isolation and microarray profiling
Left hippocampi were homogenized in 1 ml TRIzol reagent (Invitrogen, #15596018) and sent to Arraystar Inc. (Rockville, MD) for transcriptome analysis. For circRNA analysis, Arraystar Inc. isolated total RNA, digested with RNase R (Epicentre, Inc.), fluorescently labeled (Arraystar Super RNA Labeling Kit), and subsequently hybridized to Arraystar Mouse circRNA Array V2 (8 × 15K). For lncRNA and mRNA analysis, Arraystar Inc. isolated rRNA depleted RNA (mRNA-ONLY™ Eukaryotic mRNA Isolation Kit, Epicentre) from total RNA. rRNA depleted RNA was amplified, fluorescently labeled (Arraystar Flash RNA Labeling Kit), and hybridized to Agilent Arrays (Mouse LncRNA Array v3.0, 8 × 60K). An Agilent Scanner G2505C was used to scan the arrays. The University of Pittsburgh Genomics Sequencing Core used Applied Biosystems GeneChip miRNA 4.0 Arrays to measure changes in abundance of miRNAs from the total RNA samples isolated from the hippocampal tissue. The median intensity expression values were log2 transformed and quantile normalized across samples. Differential expression were determined using linear models for microarray data (limma) [85] with nominal p-value less than or equal to 0.05 as statistically significant. *Weighted* gene co-expression network (WGCNA) was used to determine all pairwise correlation among RNAs (i.e., lncRNA, mRNA, circRNA, miRNA) across samples. An unsigned network was constructed using minimum module size of 100, a cut height of 0.99, and a power of 6 to approximate a scale-free topology. The expression of unassigned RNAs were labeled as gray. The total connectivity of individual probes was determined from the pairwise adjacency matrix for an unsigned network.
## gRNA design
Guide RNAs (gRNAs) were generated using a commercially available two-piece system termed ALT-R™ CRISPR/Cas9 Genome Editing System (IDT DNA, Coralville, IA). This system combines a custom CRISPR RNA (crRNA) for genomic specificity with an invariant trans-activating RNA (tracrRNA) to produce gRNAs [86]. crRNAs were designed using the computational program CCTop/CRISPRator [87, 88], which gauges candidate gRNAs for efficiency and specificity. Each crRNA was annealed separately with tracrRNA in a 1:2 M ratio then combined into a single solution for each gene.
Four gRNAs were used to target each of the ethanol-responsive lncRNA genes Pitt1, Pitt3, and Pitt4 and six gRNAs for Pitt2 (see Supplementary Table S1 for gRNA target sequences). These specifically designed gRNAs bind within a 598, 796, 341, or 372 base pairs (bp) target region that includes the putative promoter and first exon of Pitt1-Pitt4, respectively. We followed the annotations available at the time on the Ensembl Genome Browser (GRCm38/mm10).
## CRISPR/Cas9 mutagenesis
Female C57BL/6J mice were superovulated with 0.1 ml of CARD HyperOva (CosmoBio, #KYD-010) between 10 and 11 AM, followed by 100 IU of human chorionic gonadotropin (Sigma, #CG10) 46–48 h later. Donor females were caged overnight with C57BL/6J males starting 4–6 h post-gonadotropin injection and allowed to mate. Embryos were harvested from oviducts between 9 and 10 AM the following morning, cumulus cells were removed using hyaluronidase, and embryos were cultured under $5\%$ CO2 in KSOM medium (Cytospring, #K0101) for 1–2 h. Embryos were electroporated in 5 µL total volume of Opti-MEM medium (ThermoFisher, #31985088) containing 100 ng/μL of each gRNA cocktail and 200 ng/μL Alt-R® S.p. HiFi Cas9 Nuclease V3 protein (IDT, #1081060) with a Bio-Rad Gene-Pulser Xcell in a 1 mm-gap slide electrode (Protech International, #501P1-10) using square-wave pulses (five repeats of 3 msec 25V pulses with 100 msec interpulse intervals). Electroporated embryos were placed back into culture under $5\%$ CO2 in KSOM. For in vitro validation of Pitt1-Pitt4 gRNAs, embryos were cultured for 3 days until the morulea/blastocyst stage and subsequently analyzed for mutations. For in vivo cohort generation, one- or two-cell embryos were surgically implanted into the oviducts of plug-positive CD-1 recipients (20–40 embryos per recipient) that had been mated to vasectomized males the previous night.
## Genotyping
DNA was amplified from individual Pitt1-Pitt4 gRNA-electroporated embryos using a Qiagen Repli-G kit (Qiagen, #150025). DNA was isolated from ear snips of Pitt1-Pitt4 TAKO offspring using Quick Extract (Lucigen, #QE09050). DNAs were genotyped by PCR under the following settings: 95°C for 5 min (1x); 95°C for 30 s, 60°C for 30 s, 72°C for 1 min (40x); 72°C for 10 min (1x). Primers for PCR amplification of Pitt1-Pitt4 are listed in Supplementary Table S1. PCR amplicons of Pitt1-Pitt4 [Wild-type (WT): 929, 963, 581 and 583 bp, respectively] were analyzed by agarose gel electrophoresis.
## RNA preparation
Hippocampal brain tissue from Pitt1-Pitt4 mice was used for RT-PCR analysis. All mice were 16–20 weeks of age at time of euthanasia. Total RNA was isolated using TRIzol (Invitrogen, #15596018) according to the manufacturer’s protocol, and DNA contamination was removed with a TURBO DNA-free™ Kit (Invitrogen, #AM1907). Total RNA was analyzed for purity and concentration using a Nanodrop Spectrophotometer (Thermo Scientific, Waltham, MA). One microgram of purified RNA was converted into cDNA using Superscript™ III First-Strand Synthesis System (Invitrogen, #18080051) with random hexamer primers. RT-PCR primers were used that span both the mutation site as well as the downstream probe-binding exonic region for Pitt1-Pitt4 (Supplementary Table S1). A reaction that lacked reverse transcriptase was used as a negative control for each sample tested.
## Behavioral testing
All mice were moved into a reverse light-cycle housing/testing room (lights off at 10 AM) at 5 weeks of age and allowed to acclimate for 2–3 weeks before the start of experiments. Mice were weighed weekly during behavioral experimentation. Ethanol-drinking experiments were performed in the housing room. Mice were singly-housed for all behavioral studies. Mice were sequentially tested on DID and EOD-2BC, with a minimum of 7 days between assays.
Pitt1 and Pitt2 were studied together with a purchased control group (controlled for age, sex, and strain) previously shown to be comparable to mock-treatment controls [83]. Similarly, Pitt3 and Pitt4 were studied together with a separate purchased control group.
## One-bottle drinking in the dark
Mice were given access to ethanol ($20\%$ v/v) in 15 ml drinking bottles with 3.5-inch sipper tubes (Amuza, San Diego) 2 h into the dark-cycle for 2 consecutive days. Fresh ethanol solution was prepared daily. The first day’s training session lasted for 2 h. The second day’s experimental session lasted 4 h. The amount of ethanol consumed by each mouse was recorded. Empty cages with sipper bottles only were used to control for sipper tube leakage, and leakage amount was subtracted from amount of ethanol consumed by the mice. Immediately following the experimental session, blood samples were collected from tail nicks and the plasma isolated. An Analox analyzer was used to measure the blood ethanol concentrations (BECs) of each mouse (mg/dL; 5 μL).
The Pitt1/Pitt2/control cohorts were assayed based on genotype and not sex (i.e., the Pitt1 TAKOs were assayed separately from the Pitt2 TAKOs). The Pitt3/Pitt4/control cohorts were assayed based on sex and not genotype (i.e., the male Pitt3 and Pitt4 TAKOs were assayed separately from the female Pitt3 and Pitt4 TAKOs).
## Every-other-day two-bottle choice drinking
Mice were given access to ethanol (v/v; ramping every-other-day from $3\%$, $6\%$, $9\%$, $12\%$ until $15\%$ was reached then maintained for a total of 12 days at $15\%$) and water for 24-h sessions every other day. If a $20\%$ difference from controls in ethanol consumption was not observed at $15\%$ ethanol, then the concentration was increased to $20\%$ v/v and the experiment extended an additional 12 days. Water alone was offered on off days. The side placement of the ethanol bottles was switched with each drinking session to avoid side preference. Bottles were weighed before placement and after removal from the experimental cages. Empty cages with sipper bottles only were used to control for fluid leakage, and leakage amount was subtracted from the amount consumed by the mice. The quantity of ethanol consumed, and total fluid intake was calculated as g/kg body weight per 24 h. Preference was calculated as amount ethanol consumed divided by total fluid consumed per 24 h. Ethanol drinking results were transformed to reflect the percent change in ethanol consumption compared to control. Ethanol solutions were prepared fresh daily.
Pitt1, Pitt2, and control mice were tested for ethanol drinking using an EOD-2BC ethanol consumption assay over a period of 20 days. Pitt1, Pitt2 and control male analysis of ethanol intake revealed a main effect of day [F (5.103, 199.0) = 159.5; $p \leq 0.0001$], but no effect of genotype or day x genotype (Figure 5A). Analysis of ethanol preference in males revealed a main effect of day [F (4.715, 183.9) = 15.83; $p \leq 0.0001$] and genotype [F [2, 39] = 3.755; $p \leq 0.05$], but no day x genotype significant differences (Figure 5C). Post-hoc analysis revealed that on day 14 Pitt1 males had significantly higher ethanol preference than control males (q < 0.05). Pitt1 male ethanol preference at $15\%$ v/v ranged from $0\%$ to $9\%$ increase, while Pitt2 male ethanol preference ranged from an increase of $6\%$ to a decrease of $17\%$ (Supplementary Figure S3C). For total fluid intake, there was a main effect of day [F (3.508, 136.8) = 4.612; $p \leq 0.01$] but no effect of genotype or day × genotype interaction for the males (Figure 5E). Due to a record-keeping error, data from day 16, at $15\%$ v/v ethanol, was lost.
**FIGURE 5:** *EOD-2BC drinking in Pitt1, Pitt2, and control mice. Left, males; right, females. (A,D) ethanol intake (g/kg/24 h), (B,E) ethanol preference, and (C,F) total fluid intake (g/kg/24 h) in Pitt1 mutant, Pitt2 mutant, and control mice across time and concentration. # or *q < 0.05, ## or **q < 0.01, and ### or ***q < 0.001 between Pitt1 and control, and Pitt2 and control, respectively. N = 11–16/sex/genotype.*
Analysis of Pitt1, Pitt2, and control female cohorts on total ethanol intake revealed a day × genotype interaction [F [16, 304] = 2.679; $p \leq 0.001$] and main effect of day [F (4.409, 167.5) = 286.3; $p \leq 0.0001$], but no effect of genotype (Figure 5B). Post-hoc analysis revealed that on days 14, 16, and 20 Pitt1 females consumed significantly less ethanol than control (q < 0.01), and Pitt2 females consumed significantly more ethanol than control on day 4 (q < 0.05), and significantly less on day 14 (q < 0.05). Pitt1 females consistently consumed $10\%$–$20\%$ less ethanol at $15\%$ v/v. Pitt2 females only consumed up to $10\%$ less ethanol at $15\%$ v/v (Supplementary Figure S3B). Analysis of ethanol preference in females revealed a main effect of day [F (3.743, 142.2) = 13.60; $p \leq 0.0001$], but no effect of genotype or day x genotype (Figure 5D). For total fluid intake, there was a day x genotype [F [16, 304] = 1.938; $p \leq 0.01$] and main effect of day [F (2.272, 86.32) = 31.91; $p \leq 0.0001$], but no effect of genotype (Figure 5F). Post-hoc analysis revealed that on days 14, 18, and 20 Pitt1 females consumed significantly less total fluid than control females (q < 0.0001, q < 0.05, and q < 0.01, respectively) and that on days 14 and 18 Pitt2 females consumed less total fluid than control females (q < 0.0001 and q < 0.05, respectively). The change in ethanol intake coincided with a reduction in total fluid for Pitt1 females at $15\%$ v/v ethanol ranging from a reduction of $8.5\%$–$20.5\%$, and Pitt2 females ranging from a reduction of $5\%$–$18\%$ (Supplementary Figure S3F). Due to a record-keeping error, data from day 8, at $12\%$ v/v ethanol, was lost. Since the decrease in female ethanol intake could be linked to a reduction in overall fluid intake, and the male data was not highly compelling, the experiment was terminated following the completion of $15\%$ v/v EOD-2BC.
Pitt3, Pitt4, and control mice were tested for ethanol drinking using an EOD-2BC ethanol consumption assay. Because this set of TAKO animals did not present a significant difference in total fluid intake following $15\%$ v/v ethanol, the experimental paradigm was expanded to include $20\%$ v/v ethanol. Analysis of male Pitt3, Pitt4, and control ethanol intake revealed a main effect of day [F [15, 625] = 335.2; $p \leq 0.0001$], but no effect of genotype or day x genotype (Figure 8A). Analysis of male ethanol preference revealed a main effect of day [F [15, 624] = 39.54; $p \leq 0.0001$], but no effect of genotype or day x genotype (Figure 8C). Consistently, analysis of male total fluid revealed a significant main effect of day [F [15, 624] = 19.39; $p \leq 0.0001$], but no effect of genotype or day x genotype (Figure 8E).
**FIGURE 8:** *EOD-2BC drinking in Pitt3, Pitt4, and control mice. Left, males; right, females. (A,D) ethanol intake (g/kg/24 h), (B,E) ethanol preference, and (C,F) total fluid intake (g/kg/24 h) in Pitt3 mutant, Pitt4 mutant and control mice across time and concentration. Values represent Mean ± SEM. # or *q < 0.05, ## or **q < 0.01, and ### or ***q < 0.001 between Pitt3 and control, and Pitt4 and control, respectively).*
Analysis of ethanol intake in Pitt3, Pitt4, and control females revealed significant main effects of genotype [F [2, 42] = 3.302; $p \leq 0.05$], day [F [15, 630] = 248.6; $p \leq 0.0001$], and a day x genotype [F [30, 630] = 2.201; $p \leq 0.001$] (Figure 8B). Post-hoc analysis revealed that on day 22, 26, and 32 Pitt3 females consumed significantly less ethanol than controls (q < 0.05). On days 22–32 Pitt4 females consumed significantly less than control females (q < 0.01, q < 0.01, q < 0.01, q < 0.001, q < 0.01, and q < 0.01, respectively). Pitt3 females at both $15\%$ and $20\%$ v/v ethanol consumed up to $10\%$ less ethanol compared to control. Pitt4 females consumed up to $12\%$ less at $15\%$ v/v and reached a reduction of up to $18.5\%$ at $20\%$ v/v ethanol. Interestingly, both Pitt3 and Pitt4 females consumed ∼$50\%$ more ethanol at $3\%$ v/v (Supplementary Figure S6B). Analysis of female ethanol preference revealed a significant main effect of day [F [15, 630] = 19.28; $p \leq 0.0001$] and day x genotype [F [30, 630] = 1.596; $p \leq 0.05$], but no effect of genotype (Figure 8D). Post-hoc analysis revealed a significant increase in ethanol preference compared to control on day 2 for both Pitt3 and Pitt4 (q < 0.001). Both Pitt3 and Pitt4 females had a preference ranging from 0–$10\%$ difference from control at $15\%$ and $20\%$ v/v ethanol, with ∼$35\%$ increase at $3\%$ v/v (Supplementary Figure S6D). Considering total fluid intake in females, there was a significant main effect of day [F [15, 630] = 43.97; $p \leq 0.0001$] and day x genotype [F [30, 630] = 1.542; $p \leq 0.05$], but no effect of genotype (Figure 8F). Post-hoc analysis revealed that on day 4 Pitt3 females consumed significantly less total fluid than control females (q < 0.01) and on day 22 both Pitt3 and Pitt4 females consumed significantly less total fluid than control females (q < 0.01). Both Pitt3 and Pitt4 females had reductions in total fluid intake by up to $19\%$ in Pitt3 and $16\%$ in Pitt4 females at $20\%$ v/v ethanol (Supplementary Figure S6F).
## Preference for non-ethanol tastants
When a significant difference in ethanol consumption was observed between genotypes, mice were subsequently tested for saccharin (sweet tastant; Sigma-Aldrich, 240931) and quinine (bitter tastant; Sigma-Aldrich, 145912) preference using a 24-h Two-Bottle Choice (2BC) paradigm. One sipper bottle contained the tastant solution and the other contained water. Mice were offered two concentrations of saccharin ($0.03\%$ and $0.06\%$) and quinine (0.03 and 0.06 mM). For each tastant, the lower concentration was presented first followed by the higher concentration. Each concentration was presented for 2 days (4 days total) with at least 7 days of water-only between tastants. Empty cages with sipper bottles only were used to control for leakage, and leakage amount was subtracted from the amount consumed by the mice. Fresh tastant solution was prepared daily.
Changes in taste perception can alter ethanol consumption in mice (94–96). Because female Pitt1 and Pitt2 displayed altered EOD-2BC ethanol consumption compared to controls, females were subjected to both sweet (i.e., saccharin) and bitter (i.e., quinine) tastants. A 24-h 2BC assay was used to determine whether an alteration in taste perception could account for the observed changes in ethanol consumption in the mutant lines tested. No significant difference was observed between genotypes for either saccharin (Supplementary Figure S4A) or quinine preference (Supplementary Figure S4B).
Since Pitt3 and Pitt4 females had altered EOD-2BC ethanol consumption when compared to controls, females were subject to both sweet (i.e., saccharin) and bitter (i.e., quinine) tastant preference analysis. No differences were observed between genotypes for saccharin preference (Supplementary Figure S7A). For quinine preference, there was a significant main effect of day [F [3, 126] = 3.444; $p \leq 0.05$], but no main effect of genotype or day x genotype (Supplementary Figure S7B).
## Statistical analysis
Statistical analysis was performed using GraphPad Prism (GraphPad Software, Inc., La Jolla, CA). Two-way ANOVA with multiple comparisons was used for Pitt1, Pitt2, and control DID and BEC data, and one-way ANOVA with multiple comparisons was used for Pitt3, Pitt4, and control DID and BEC data. Two-way mixed-effects ANOVA with multiple comparisons and repeated measures was used for Pitt1, Pitt2, and control weight over time, and two-way ANOVA with multiple comparisons and repeated measures was used for EOD-2BC data and Pitt3, Pitt4, and control weight over time. Significant main effects were subsequently analyzed with Benjamini, Krieger, and Yekutieli two-stage linear step up procedure post-hoc analysis [89]. Technical failures were appropriately removed from analysis.
Because of well-known sex differences of C57BL/6J on ethanol consumption in the DID and EOD-2BC assays (90–93), male and female mice were tested on separate days (except for Pitt1/Pitt2/control DID and BEC), and each sex was analyzed separately. Statistical significance was defined as p ≤ 0.05 and q ≤ 0.05. All data are presented as mean ± S.E.M.
## Perturbation of the transcriptome following CIEV exposure
Hippocampi were dissected from male mice chronically exposed to ethanol vapor (CIEV) or room air control for 16 h/day, 4 days/week, for 7 weeks, 24 h after the final vapor exposure. The first 24 h of withdrawal from alcohol is a critical window of time associated with relapse, which can be highly detrimental to the long-term goal of reduced drinking [16]. This hippocampal tissue originated from the sires previously described in [84] wherein males maintained BECs ranging from 100 to 250 mg/dl throughout the experiment. Total RNA was isolated from hippocampi for transcriptome analysis to identify biological systems affected by chronic ethanol exposure (Figure 1). We detected a total of 18,283 mRNA probes, 27,177 lncRNA probes, 14,182 circRNA probes, and 23,386 miRNA probes on the microarray. To identify RNAs differentially expressed due to CIEV, our analysis separately examined statistically significant changes ($p \leq 0.05$) in expression for mRNA, lncRNA, circRNA, and miRNA. Among these four classes of RNAs we found that lncRNAs showed the largest number of changes in expression due to chronic ethanol exposure ($$n = 1$$,923 up-regulated, $$n = 2$$,694 down-regulated). This was followed by mRNA ($$n = 1$$,948 up-regulated, $$n = 2$$,121 down-regulated), circRNA ($$n = 750$$ up-regulated, $$n = 729$$ down-regulated), and miRNA ($$n = 481$$ up-regulated, $$n = 723$$ down-regulated) (Figure 2). This data may suggest that a large number of different RNA within the hippocampus are susceptible to chronic ethanol exposure; however, each of these RNA biotypes do not exist in isolation and must work in concert for homeostatic function of cellular systems.
**FIGURE 1:** *Schematic diagram detailing the experimental pipeline utilized to generate the list of top novel ethanol-responsive hub lncRNA candidates to target for ethanol-related functional interrogation. Male mice were given a priming injection of either ethanol and pyrazole or saline and pyrazole and placed in either an ethanol- or room-air vapor champers for 16 h/day, 4 days/week, for 7 weeks, respectively. Hippocampi were dissected 24 h after the final vapor exposure and then subject to mRNA, lncRNA, circRNA, and miRNA microarray analysis. These data sets were then used to generate ceRNA networks of ethanol-responsive RNA genes.* **FIGURE 2:** *Volcano plots showing differential RNA expression based on log2 fold-change in expression (x-axis) and log10
p-value (y-axis) for (A) protein-coding RNA (mRNA), (B) long non-coding RNA (lncRNA), (C) circular RNA (circRNA), and (D) microRNA (miRNA). Each point indicates an individual non-duplicated probe on the microarray with blue = significantly down-regulated, red = significantly up-regulated, and black = non-significant. Significance is defined by p < 0.05.*
The expression of different RNA subtypes creates tightly coordinated ceRNA networks to mediate the biological function of molecular circuits (76–81) (Figure 1). We used WGCNA to determine the pairwise correlation of RNA expression across samples and assess the total connectivity of lncRNA, mRNA, circRNA, and miRNA. Due to the known biological roles in the regulation of gene expression and their perturbation by chronic ethanol exposure, our analysis focused on identifying ethanol-responsive lncRNAs for in vivo characterization. Our unbiased transcriptome analysis determined that there were multiple ethanol-responsive lncRNAs that are present in the GRCm38/mm10 mouse genome assembly but have yet to be characterized for molecular or behavioral function. To determine suitable lncRNAs for follow-up in vivo studies, we used a summed rank of lncRNAs based on their statistical significance ($p \leq 0.05$), fold-change in up-regulation of expression, overall level of expression to focus on the most abundant lncRNAs, and lncRNAs with the highest total connectivity within the correlation networks to concentrate on hubs of coordinatedly regulated RNA expression. Additionally, lncRNAs were screened for the capacity to easily create CRISPy TAKO mice by identifying candidates within intergenic regions that did not overlap any other known genes or regulatory regions in the GRCm38/mm10 mouse genome. Based on this selection criteria the top 4 candidate lncRNA selected for testing were Gm42575, 4930413E15Rik, Gm15767, and Gm33447 (Table 1).
**TABLE 1**
| Name | Probe | Gene symbol | Chromosome | Strand | Start | End | log fold-change | Mean expression | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Pitt1 | ASMM10P031898 | Gm42575 | chr5 | + | 74754373 | 74754432 | 0.35 | 9.71 | 0.03 |
| Pitt2 | ASMM10P032341 | 4930413E15Rik | chr5 | + | 118961191 | 118961250 | 0.28 | 8.82 | 0.02 |
| Pitt3 | ASMM10P034032 | Gm15767 | chr6 | − | 147242527 | 147242586 | 0.27 | 9.27 | 0.03 |
| Pitt4 | ASMM10P010493 | Gm33447 | chr13 | + | 97380367 | 97380426 | 0.35 | 8.25 | 0.02 |
## CRISPR/Cas9-mediated mutagenesis
To enhance CRISPR mutagenesis frequency as previously described [83], all lncRNA genes were targeted simultaneously with 4–6 gRNAs tiled 50–200 bp apart from each other, spanning the putative promoter and first exon of each gene. Four gRNAs were designed to span a 598 bp range within the *Pitt1* gene (Figure 3A). Six gRNAs were designed to span a 796 bp range within the *Pitt2* gene (Figure 3D).
**FIGURE 3:** *CRISPy TAKO schematics and genotypes for Pitt1 and Pitt2. (A) Pitt1 gene symbol and structure. The gRNAs, PCR primers, RT-PCR primers, and probe binding site are shown as yellow, green, orange, and red arrows, respectively. (B) Agarose gel electrophoresis of PCR amplicons of Pitt1 DNA in a random representative subset of Pitt1 TAKOs demonstrating abnormal amplicons in TAKO mice compared to WT control. Individual mouse numbers are presented above the gel. (C) Random representative subset RT-PCR results from Pitt1 hippocampal brain tissue showing abnormal RNA transcripts. (Top) RT-PCR of Pitt1 exon 1 amplicons using the F2/R2 primers demonstrating abnormal RNA transcripts in TAKO mice compared to WT control. (Middle) RT-PCR amplicons using the F3/R3 primers spanning downstream Pitt1 exons, demonstrating abnormal RNA products in Pitt1 mutant TAKOs that are not present in WT. (Bottom) RT-PCR of MyD88 amplicons used as an internal control. (D) Pitt2 gene symbol and structure. The gRNAs, PCR primers, and probe binding site are shown as yellow, green, and red arrows, respectively. (E) Agarose gel electrophoresis of PCR amplicons of Pitt2 DNA in a random representative subset of Pitt2 TAKOs demonstrating abnormal amplicons in TAKO mice compared to WT control. Individual mouse numbers are presented above the gel.*
Pitt1 and Pitt2 gRNAs were validated for efficient mutagenesis by analyzing in vitro cultured embryos following electroporation. Agarose gel electrophoresis of PCR amplicons that span the targeted locus of Pitt1 and Pitt2 indicated that $100\%$ of embryos harbored indels of various sizes (Supplementary Figures S1A,B, respectively).
A cohort of 35 Pitt1 offspring and 42 Pitt2 offspring, all on the C57BL/6J genetic background, were generated using the CRISPy TAKO approach. All mice born from electroporated embryos were genotyped for gross indels using PCR. The Pitt1 929 bp WT PCR amplicon was readily apparent in control WT DNA but only 2 out of 35 Pitt1 animals (data not shown). The remaining 33 displayed gross indels encompassing the targeted region of interest. PCR bands from a random representative subset of Pitt1 mice selected for behavioral experimentation is shown in Figure 3B. The Pitt2 963 bp WT PCR amplicon was readily apparent in the WT control and 2 out of 42 Pitt2 animals (data not shown). The remaining 40 displayed gross indels encompassing the targeted region of interest. PCR bands from a random representative subset of Pitt2 mice selected for behavioral experimentation is shown in Figure 3E.
The indels varied from animal to animal and most appeared to be deletions, as evidenced by the PCR products being ∼50–400 bp smaller than the 929 bp WT amplicons for Pitt1, and ∼50–600 bp smaller than the 963 bp WT amplicons for Pitt2 (Figures 3B,E, respectively). Out of the 35 Pitt1 mice and 42 Pitt2 mice, only a subset ($$n = 11$$M/14F Pitt1; 16M/12F Pitt2) harboring a large mutation(s) spanning the putative promoter and exon 1 of Pitt1 or Pitt2 were selected for behavioral phenotyping. It should be noted that the mice used for phenotyping presented variable deletions mainly ranging in 230–730 bp (Figures 3B,E, respectively). Despite all Pitt1 and Pitt2 mice showing variability in mutation site and size, all mice within a genotype were expected to manifest the same effect on gene expression and behavioral phenotypes [as previously shown [83]].
We have previously demonstrated that control C57BL/6J mice purchased from Jackson Laboratories are not significantly different from in-house generated Mock-treatment control mice [83]. Therefore, Pitt1 and Pitt2 TAKO mice were compared to age and sex-matched C57BL/6J controls. Mice were weighed once per week during behavioral experimentation. Both TAKO cohorts for both sexes had significantly increased weight compared to controls. Males and females had an effect of genotype [F (1.715, 7.717) = 87.22; $p \leq 0.0001$] and [F (1.626, 9.758) = 89.44; $p \leq 0.0001$], respectively (Supplementary Figure S2). Post-hoc analysis revealed an effect of genotype for both Pitt1 and Pitt2 males (q < 0.001), and Pitt1 and Pitt2 females (q < 0.0001). These results are consistent with previously observed differences in our laboratory in purchased versus in-house produced offspring (data not shown).
A second cohort of mice targeting Pitt3 and Pitt4 (Figures 6A,D, respectively) were subsequently characterized and tested for behavior. Initial validation of gRNAs designed to target Pitt3 and Pitt4 occurred in vitro using electroporated embryos (Supplementary Figures S1C,D, respectively) and demonstrated that both genes were mutated at a high frequency.
**FIGURE 6:** *CRISPy TAKO schematics and genotypes for Pitt3 and Pitt4. (A) Pitt3 gene symbol and structure. The gRNAs, PCR primers, RT-PCR primers, and probe binding site are shown as yellow, green, orange, and red arrows, respectively. (B) Agarose gel electrophoresis of PCR amplicons of DNA from a random representative subset of Pitt3 TAKOs. Individual mouse numbers are presented above the gel. (C) Random representative subset of RT-PCR results from Pitt3 hippocampal brain tissue showing abnormal RNA transcripts in TAKO mice compared to WT control. (Top) RT-PCR of Pitt3 exon 1 using the F2/R2 primers demonstrating the absence of the WT amplicon in most mice, although two animals (5304 and 5306) express a WT sized transcript at an apparently reduced level. (Middle) RT-PCR amplicons using F3/R3 primers spanning downstream Pitt3 exons demonstrating abnormal RNA products in Pitt3 mutant TAKOs compared to controls. (Bottom) RT-PCR of MyD88 used as an internal control. (D) Pitt4 gene symbol and structure. The gRNAs, PCR primers, RT-PCR primers, and probe binding site are shown as yellow, green, orange, and red arrows, respectively. (E) Agarose gel electrophoresis of PCR amplicons of DNA from a random representative subset of Pitt4 TAKOs. Individual mouse numbers are presented above the gel. (F) Random representative subset of RT-PCR results from Pitt4 hippocampal brain tissue showing abnormal RNA transcripts. (Top) RT-PCR of Pitt4 exon 1 amplicons using the F2/R2 primers demonstrating that the mutations eliminate expression of the WT transcript in 7 of 8 Pitt4 TAKOs analyzed. (Middle) RT-PCR amplicons of downstream Pitt4 exons amplified with the F3/R3 primers demonstrating expression of normal sized transcripts in TAKOs compared to WT control. (Bottom) RT-PCR of MyD88 amplicons used as an internal control.*
A total of 70 offspring for Pitt3 and 62 offspring for Pitt4 were generated on the C57BL/6J background using the CRISPy TAKO approach. All mice born from electroporated embryos were genotyped for gross indels using PCR and agarose gel electrophoresis. The Pitt3 581 bp WT PCR amplicon was readily apparent in WT control and 9 out of 70 Pitt3 animals (data not shown). The remaining 61 mutants displayed gross indels encompassing the targeted region of interest. The indels from a random representative subset of Pitt3 TAKOs used for behavioral phenotyping varied from animal to animal and most appeared to be deletions, as evidenced by the PCR products being ∼50–350 bp smaller than the 581 bp WT amplicons (Figure 6B). The Pitt4 583 bp WT PCR amplicon was readily apparent in WT control and 4 out of 62 Pitt4 animals (data not shown). The remaining 58 mutants displayed gross indels encompassing the targeted region of interest. The indels from a random representative subset of Pitt4 TAKOs used for behavioral phenotyping demonstrated deletions ranging from ∼50–350 bp smaller than the 583 bp WT amplicon (Figure 6E). Of the Pitt3 and Pitt4 mutant mice produced, a subset ($$n = 15$$/sex/genotype) harboring large deletions spanning the putative promoter and first exon of Pitt3 or Pitt4 were selected for behavioral phenotyping.
As noted for Pitt1 and Pitt2 cohorts, Pitt3 and Pitt4 males and females consistently weighed significantly more than controls (Supplementary Figure S5). Analysis of male Pitt3, Pitt4, and control weight over time revealed a main effect of day [F (2.477, 104) = 412.1; $p \leq 0.0001$], a main effect of genotype [F [2, 42] = 19.48; $p \leq 0.0001$], and day x genotype [F [12, 252] = 3.599; $p \leq 0.0001$]. Post-hoc analysis for both males and females, for all weeks, had a significant increase in weight compared to control (q < 0.0001).
## RNA analysis
Hippocampal RNA from a subset of mutant mice used for phenotyping was analyzed by RT-PCR to validate that the DNA mutations surrounding the putative promoter and first exon of Pitt1 and Pitt2 disrupted expression of the targeted genes. Two RT-PCR primer sets were used for each genotype to characterize the RNA transcript in TAKO versus WT hippocampal RNA. F2/R2 RT-PCR primers were used to validate KO of RNA at the mutation site. F3/R3 RT-PCR primers were used to characterize the downstream exon containing the microarray probe-binding site to investigate expression of downstream lncRNA sequences (Figures 3A,D, respectively).
Pitt1—The top panel of Figure 3C demonstrates that the targeted exon 1 region is not transcribed in Pitt1 TAKOs. The middle panel highlights that the mutation(s) modulate the downstream lncRNA transcript, resulting in expression of a novel transcript that is not observed in the WT control. The bottom panel targeting MyD88 was used as an internal control.
Pitt2—Despite extensive efforts to produce reliable RT-PCR amplicons for the Pitt2 RNA transcript(s), it was not achievable. RT-PCR amplicons for both the mutation site and probe-binding site of the Pitt2 transcript were inconsistent and variable even in WT control samples (data not shown).
Hippocampal RNA was isolated from a subset of mutant mice used for behavioral phenotyping and analyzed by RT-PCR to validate that the DNA mutations surrounding the putative promoter and first exon of Pitt3 and Pitt4 disrupted expression. Two RT-PCR primer sets were used for each genotype to characterize the RNA transcript in TAKO versus control hippocampal RNA. F2/R2 RT-PCR primers were used to examine RNA at the site of mutation, and F3/R3 RT-PCR primers were used to characterize expression of the downstream exon containing the microarray probe-binding site (Figures 6A,D, respectively).
Pitt3—The top panel of Figure 6C demonstrates that the exon 1 region in the control sample expressed both the expected 303 bp amplicon as well as an unexpected, slightly larger amplicon. These transcripts were not transcribed in $75\%$ of the Pitt3 TAKOs tested. Two of eight mice ($25\%$; 5304 and 5306) still expressed the slightly larger RNA transcript from exon 1, but at an apparently reduced level. The middle panel highlights variability in expression between animals. Some TAKO mice expressed two downstream transcripts (5306 and 5307), some only one transcript (5295, 5304, 5229, 5309, and 5339), and one had no downstream transcripts [5320]. This is likely due to variability in deletions of poorly characterized regulatory sequences surrounding the mutation site. The bottom panel targeting MyD88 was used as an internal control.
Pitt4—The top panel of Figure 6F demonstrates that the targeted exon 1 region was not transcribed in $75\%$ of Pitt4 TAKOs tested. One sample, 5365, still expressed the control-sized transcript, and one sample, 5409, expressed a slightly smaller RNA transcript. This ∼10–20 nt smaller RNA transcript likely reflects an internal mutation that was within the boundaries of the RT-PCR primers. The middle panel revealed that all Pitt4 TAKO mice still produced the downstream Pitt4 transcript, albeit at variable levels of expression. The bottom panel targeting MyD88 was used as an internal control.
Hippocampal RNA was analyzed by RT-PCR to confirm that mutation of the putative promoter and first exon of each lncRNA gene disrupted gene expression from each targeted locus. Using primers that bind to the putative first exon (Pitt1 and Pitt3) or exon 1 and exon 2 (Pitt4) we established that the CRISPy TAKO mutagenesis approach successfully disrupted gene expression of the targeted loci. Nearly all animals failed to amplify with these primer sets. It should be noted that Pitt4 5365 was the only mouse to express transcripts that appeared like WT, but likely at a reduced level of expression (Figure 6F; top panel). The other Pitt4 mouse, 5409, expressed a slightly smaller transcript than WT, suggesting that an internal mutation within the boundaries of the RT-PCR primers may have been retained, or an alternate splice variant was expressed.
Each hippocampal RNA sample was also analyzed with RT-PCR using primers targeting the probe-binding exon used for the initial microarray analyses that identified these lncRNAs, downstream from the mutation site. This was conducted to determine if the full transcript had been knocked out, or if downstream sequences were still transcribed following mutagenesis of the putative promoter and first exonic region. Regions downstream of the Pitt1, Pitt3, and Pitt4 mutations were expressed in the majority of animals. Surprisingly, the Pitt1 downstream amplicon was not detectable in control samples but was consistently expressed in all Pitt1 TAKO mice (Figure 3C; middle panel). These results are likely due to mutation of the putative promoter activating a normally silent promoter, or by altering downstream splicing events. Pitt3 RT-PCR results revealed variable downstream RNA products; of the eight TAKOs used for RT-PCR, two TAKOs express two downstream transcripts (5306 and 5307), five TAKOs express only a single downstream transcript (5295, 5304, 5229, 5309, and 5339), and one TAKO does not express either downstream transcript [5320]. Interestingly, none of the Pitt3 TAKOs had similar RT-PCR results compared to WT (Figure 6C; middle panel). As detailed previously, CRISPy TAKO mutants harbor variable mutations [83] and at some loci such as Pitt3, this can lead to expression of novel transcripts from the targeted locus. This could be the result of the mutations impacting the 5’ splice site(s), or mutating splicer enhancer/repressor binding sites and therefore shifting splicing dynamics (97–101). Analysis of downstream sequences in Pitt4 mutants revealed that the downstream cDNA amplicon was readily detected in control and all TAKOs analyzed (Figure 6F; middle panel). The most parsimonious explanation for these results is that an alternate promoter is present that is driving this downstream transcript (102–104).
Unexpectedly, following extensive experimentation, the Pitt2 transcript at the mutation site and probe-binding site were unable to be reliably amplified from either control or Pitt2 TAKO cDNA. This could have occurred due to Pitt2 RNA being expressed at very low levels, or the *Pitt2* gene structure could have been inaccurately annotated. These results highlight an important limitation of working with previously unstudied genes including the majority of lncRNAs. *Current* gene structure annotations may not accurately predict function and unexpected changes in gene expression may be observed when putative regulatory sequences are deleted form the genome.
The RT-PCR data provided a representative look into the potential transcriptome differences between the TAKO mice within a genotype, such as the three different variants of the downstream Pitt3 amplicon(s). Whereas all Pitt1 TAKOs tested produced identical amplicons for both the mutation site and downstream probe-binding region, it is possible that the Pitt3 TAKO mice could be further divided into sub-genotypes based on their retained RNA transcripts and their expression levels. The observed Pitt3 phenotype could be dampened by the variability of transcripts expressed in each TAKO. Variation in behaviors within a mutant line could be the result of small versus large mutations, novel transcripts being produced, altered expression levels of unmutated transcripts, altered or ablated lncRNA functionality, ethanol-responsive versus ethanol-unresponsive variations, or a combination of such molecular events. However, the spread of data points from all genotypes were similar to control and each other; they were well clustered together, suggesting that independent sub-genotypes did not differ in behavior significantly from each other. To discern these intricacies however, Sanger Sequencing, subcloning, and rigorous molecular testing and statistical analysis of the individual animals would be required.
## Drinking in the dark
Pitt1 and Pitt2 DID data were analyzed separately based on genotype (i.e., Pitt1 males and females were analyzed together with half of the controls, and Pitt2 males and females were analyzed together with the other half of the controls). No statistically significant difference was observed between Pitt1 versus control or Pitt2 versus control for either the 2-h training day (data not shown) or the 4-h experimental day (Figures 4A,B, respectively). Consistently, there was no significant difference between the BECs of Pitt1 and control or Pitt2 and control following the 4-h experimental day for both males and females (Figures 4C,D, respectively). We observed a significant main effect of sex for Pitt1 DID [F [1, 39] = 8.300; $p \leq 0.01$] where females consumed more ethanol than males. Interestingly, a significant main effect of sex was also observed in Pitt2 DID [F [1, 37] = 5.545; $p \leq 0.05$], however females unexpectedly consumed less ethanol than the males.
**FIGURE 4:** *Effect of Pitt1 and Pitt2 mutation on ethanol consumption in the Drinking in the Dark assay. (A) Total ethanol consumption of Pitt1 and control mice over a 4-h experimental period (g/kg/4h). N = 13–14 Pitt1 TAKOs; n = 8 controls. (B) Total ethanol consumption of Pitt2 and control mice over a 4-h experimental period (g/kg/4h). N = 12–14 Pitt2 TAKOs; n = 7–8 controls. (C) Blood ethanol concentrations (mg/dL; 5 μL) from plasma collected from all Pitt1 mice immediately following removal of ethanol-filled bottles. N = 12–14 Pitt1 TAKOs; n = 8 controls. (D) Blood ethanol concentrations (mg/dL; 5 μL) from plasma collected from all Pitt2 mice immediately following removal of ethanol-filled bottles. N = 12–14 Pitt2 TAKOs; n = 7–8 controls.*
Mice were tested for binge-like drinking behavior using the DID ethanol consumption paradigm. Cohorts were separated and analyzed based on sex. No significant difference was observed between Pitt3, Pitt4, and control males (Figure 7A) or females (Figure 7B) for either the 2-h training day (data not shown) or the 4-h experimental day. Consistently, there were also no significant differences between Pitt3, Pitt4, and control male (Figure 7C) or female (Figure 7D) BECs following the 4-h experimental day.
**FIGURE 7:** *Effect of Pitt3 and Pitt4 mutation on ethanol consumption in the Drinking in the Dark assay. Total ethanol consumption of Pitt3, Pitt4, and control male (A) and female (B) mice over a 4-h experimental period (g/kg/4h). Blood ethanol concentrations (mg/dL; 5 μL) from plasma collected from all male (C) and female (D) mice immediately following the removal of ethanol-filled bottles.*
## Discussion
Identification of phenotypically relevant ethanol-responsive regulatory genes that control brain transcriptional networks offer valuable insight into the chronic effects of ethanol exposure and AUD. Microarray analysis of hippocampal RNA from male mice exposed to CIEV was used to discern ceRNA expression networks that included four prominent RNA subtypes: lncRNA, mRNA, circRNA, and miRNA (Figure 1). The top four ethanol-responsive hub lncRNAs were identified and selected for functional interrogation. These novel lncRNAs, named Pitt1-Pitt4, interact and compete with a myriad of transcripts to modulate specific ceRNA networks. We hypothesized that directly altering the expression of these lncRNAs would change downstream biological processes and change ethanol-related drinking behavior. Cohorts of Pitt1-*Pitt4* gene KO mice were created using the CRISPy TAKO method [83] and subsequently screened for changes in ethanol drinking using the DID and EOD-2BC drinking assays. We observed female-specific reductions in ethanol consumption ranging from $10\%$–$20\%$ in the EOD-2BC paradigm compared to control in three of the tested Pitt mutant lines; Pitt1, Pitt3, and Pitt4. Some of the observed changes were associated with reductions in total fluid consumption but they were not influenced by a change in taste perception. No changes in binge-like drinking in the DID paradigm were observed in either the male or female mutants for any Pitt TAKO genotype (Table 2).
**TABLE 2**
| Behavior | M | M.1 | M.2 | M.3 | F | F.1 | F.2 | F.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Behavior | Pitt1 | Pitt2 | Pitt3 | Pitt4 | Pitt1 | Pitt2 | Pitt3 | Pitt4 |
| DID and BEC | No | No | No | No | No | No | No | No |
| Ethanol Intake | No | No | No | No | Yes (−20%–6%) | Yes (−10%–26%) | Yes (−18%–49%) | Yes (−19%–48%) |
| Ethanol Preference | Yes (−6%–9%) | Yes (−28%–6%) | No | No | No | No | Yes (−10%–33%) | Yes (−10%–33%) |
| Total Fluid | No | No | No | No | Yes (−21%–6%) | Yes (−18%–6%) | Yes (−19%–11%) | Yes (−16%–6%) |
The CRISPy TAKO approach was utilized to rapidly generate a cohort of mutant animals in a single generation [83]. This offers a quick approach to functionally screen novel lncRNAs of interest so the genes can be quickly tested for the ability to alter behavior, saving both time and resources. This is important when screening large numbers of genes with unknown function for ethanol-related behaviors and avoids the bottleneck of standard reverse-genetic approaches. Electroporating embryos with 4–6 gRNAs targeting a >1 kb region led to unique mutations from the various combinations of gRNAs in each animal produced [83]. Those harboring desirable large mutations in their DNA were selected for behavioral experimentation, producing a cohort of uniquely mutated mice in one generation, all hypothesized to interfere with gene function [83].
## Behavioral results
Pitt1-Pitt4 female TAKO mice all demonstrated at least a $10\%$ difference from control in ethanol drinking behavior when tested with the EOD-2BC paradigm (Table 2). This includes ∼$20\%$ decrease in ethanol consumption in Pitt1 females at $15\%$ v/v ethanol and in Pitt4 females at $20\%$ v/v ethanol. However, the associated reduction in total fluid intake at their respective concentrations could suggest an alternate reason for the ethanol consumption reduction beyond genotype and sex alone. It should be noted, however, that there was no difference found in total fluid intake under the non-ethanol 2BC tastant paradigms for females of all genotypes (data not shown). Large changes in ethanol consumption and/or preference were also observed between mutant lines and controls during the initial ethanol ramping stage (Figures 5, 8). Pitt2, Pitt3, and Pitt4 female mutants all showed increased ethanol consumption ranging from ∼$25\%$–$50\%$ on ramping days with $3\%$ and $6\%$ v/v ethanol (Supplementary Figures S3, S6, respectively). While these results at lower ethanol concentrations are intriguing, our primary focus was the impact on the higher-level concentrations of $15\%$ and $20\%$ v/v ethanol. All four of the lncRNAs targeted are capable of modulating ethanol drinking behavior, with Pitt1, Pitt3, and Pitt4 influencing ethanol consumption in a sex-specific manner.
While differences in ethanol intake were readily apparent throughout the EOD-2BC paradigm in all mutant lines, no differences were observed in DID ethanol consumption or the BECs of the animals immediately following DID (Table 2). This could be due to the obvious differences between the short-term binge-like paradigm and the long-term escalation-of-drinking paradigm and suggestive of specific behavioral patterns being altered by mutation of these lncRNAs that only present in one manner of ethanol consumption. The impacted ceRNA networks may function alternatively from control dependent on the paradigm employed, leading to the deviation in drinking behavior over time.
## Sexual dimorphism
Our data supports the identification and partial characterization of four novel ethanol-responsive lncRNAs that can alter ethanol drinking behavior, specifically in females. Sexually dimorphic behavioral responses to ethanol have been previously reported in the literature for alcohol (30, 105–109). LncRNA genes have shown sex-specific expression in reward pathways, cell signaling, structural plasticity, complex decision making, and behaviors (110–112). Sexually dimorphic biology is present in many stages of drug addiction, including acute reinforcement, the transition to compulsive drug use, withdrawal-associated states of negative affect, craving, and relapse [113]. Further, there are known differences in neural systems related to addiction and reward behavior such as epigenetic organization, expression, and contingency that are sex-dependent [113]. This suggests that lncRNAs may be important in sexually dimorphic biology and behaviors associated with substance misuse.
The female-specific behavioral changes observed in ethanol drinking were somewhat unexpected as the ethanol-regulated lncRNAs studied were identified from microarray data that originated from a male-only cohort. Male samples were used because of tissue availability [hippocampal tissue originated from the sires described in [84]]. The sex differences observed are likely either qualitative and/or based on underlying differences in mechanism(s) of action [113]. For example, there may be differences between the sexes in baseline or ethanol-induced expression levels of Pitt1-Pitt4 lncRNAs. To investigate possible expression differences, analogous female tissue would need to be collected, analyzed, and compared to the male microarray data. This would shed light on not only potential differences in Pitt1-Pitt4 expression between sexes and insight into the observed behavior presented, but also would allow for the identification of sex-independent and additional sex-specific genes.
## LncRNAs and conclusion
A handful of studies has already begun to research lncRNAs in relation to the neurobiology of AUD (4, 41, 42, 114–116). The biological functions of these novel ethanol-linked lncRNAs have been associated with altered gene networks and RNA co-expression [114], alternative splicing [4], and neural function [116]. The lncRNA brain-derived neurotrophic factor antisense has previously been described as a regulator of epigenetic events in the amygdala of humans with AUD [41]. Additionally, the lncRNA named long non-coding RNA for alcohol preference was identified as a hub gene whose mutation increased alcohol consumption and preference in Wistar rats compared to controls [42]. While the field is growing, there are still over 100,000 lncRNA transcripts (45–49) that remain uncharacterized for their relevance to AUD and other human disorders but hold the potential to regulate multiple cellular mechanisms and behaviors.
Mutating these novel uncharacterized Pitt1-Pitt4 lncRNA genes may impact a number of molecular functions, such as subcellular localization, sequestration, scaffolding, and epigenetic regulation of gene expression (44, 50–53). Our study was specifically designed to test genes with no known molecular or behavioral functions related to models for AUD. We conducted these studies with the hypothesis that several, if not all, of the top-ranked genes would have the ability to alter ethanol drinking and provide an ideal candidate gene for more in-depth molecular characterization. By removing a large exonic region of these genes, many different mechanisms of action could have been altered that manifest as a change in ethanol drinking behavior. Future studies should delve into further ethanol-related behaviors and the mechanism(s) of action of these ethanol-responsive lncRNAs.
Here, we demonstrated that mutating and screening top-ranked ethanol-responsive hub lncRNA genes from chronic ethanol exposed mouse hippocampus led to altered ethanol drinking behavior in all of the generated TAKO cohorts. Among the mutant lines tested, Pitt4 appears to be the ideal target to generate a true breeding line for further studies. This would permit studying additional ethanol-related behaviors as well as an in-depth molecular analysis to discern the potential function(s) and mechanism of action(s) for this novel lncRNA. The data presented here add to the growing body of literature supporting the hypothesis that expression of specific lncRNAs is important for mediating addiction-related behaviors relevant to human health (63, 69–71).
## 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
The animal study was reviewed and approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh.
## Author contributions
Project conception and gRNA design devised by GH and SP. Bioinformatics and ceRNA network generation completed by SF. In vitro analysis, in vivo project design, organization, and analysis conducted by SP. SP and VC managed the behavioral experimentation. All authors contributed to writing and editing 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.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontierspartnerships.org/articles/10.3389/adar.2022.10831/full#supplementary-material
## References
1. Jones C, Paulozzi L, Mack K,. **Alcohol involvement in opioid pain reliever and benzodiazepine drug abuse-related emergency department visits and drug-related deaths-United States, 2010**. *MMWR Morb Mortal Wkly Rep* (2014) **63** 881-5. PMID: 25299603
2. Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, Brewer RD. **2010 national and state costs of excessive alcohol consumption**. *Am J Prev Med* (2015) **49** e73-e79. DOI: 10.1016/j.amepre.2015.05.031
3. Sullivan EV, Harris RA, Pfefferbaum A. **Alcohol’s effects on brain and behavior**. *Alcohol Res Health* (2010) **33** 127-143. PMID: 23579943
4. Van Booven D, Li M, Sunil Rao J, Blokhin IO, Dayne Mayfield R, Barbier E. **Alcohol use disorder causes global changes in splicing in the human brain**. *Transl Psychiatry* (2021) **11** 2-9. DOI: 10.1038/s41398-020-01163-z
5. Pignataro L, Varodayan FP, Tannenholz LE, Harrison NL. **The regulation of neuronal gene expression by alcohol**. *Pharmacol Ther* (2009) **124** 324-35. DOI: 10.1016/j.pharmthera.2009.09.002
6. Grantham EK, Farris SP. **Bioinformatic and biological avenues for understanding alcohol use disorder**. *Alcohol* (2019) **74** 65-71. DOI: 10.1016/j.alcohol.2018.05.004
7. Mayfield RD. **Emerging roles for ncRNAs in alcohol use disorders**. *Alcohol* (2017) **60** 31-9. DOI: 10.1016/j.alcohol.2017.01.004
8. Farris SP, Arasappan D, Hunicke-Smith S, Harris RA, Mayfield RD. **Transcriptome organization for chronic alcohol abuse in human brain**. *Mol Psychiatry* (2015) **20** 1438-47. DOI: 10.1038/mp.2014.159
9. Mira RG, Lira M, Tapia-Rojas C, Rebolledo DL, Quintanilla RA, Cerpa W. **Effect of alcohol on hippocampal-dependent plasticity and behavior: Role of glutamatergic synaptic transmission**. *Front Behav Neurosci* (2020) **13** 288. DOI: 10.3389/fnbeh.2019.00288
10. Sun W, Li X, Tang C, An L. **Acute low alcohol disrupts hippocampus-striatum neural correlate of learning strategy by inhibition of PKA/CREB pathway in rats**. *Front Pharmacol* (2018) **9** 1439. DOI: 10.3389/fphar.2018.01439
11. White AM, Matthews DB, Best PJ. **Ethanol, memory, and hippocampal function: A review of recent findings**. *Hippocampus* (2000) **10** 88-93. DOI: 10.1002/(SICI)1098-1063(2000)10:1<88:AID-HIPO10>3.0.CO;2-L
12. Montesinos J, Alfonso‐Loeches S, Guerri C. **Impact of the innate immune response in the actions of ethanol on the central nervous system**. *Alcohol Clin Exp Res* (2016) **40** 2260-70. DOI: 10.1111/acer.13208
13. Geil CR, Hayes DM, McClain JA, Liput DJ, Marshall SA, Chen KY. **Alcohol and adult hippocampal neurogenesis: Promiscuous drug, wanton effects**. *Prog Neuropsychopharmacol Biol Psychiatry* (2014) **54** 103-13. DOI: 10.1016/j.pnpbp.2014.05.003
14. Zahr NM, Mayer D, Rohlfing T, Hasak MP, Hsu O, Vinco S. **Brain injury and recovery following binge ethanol: Evidence from**. *Biol Psychiatry* (2010) **67** 846-54. DOI: 10.1016/j.biopsych.2009.10.028
15. Grifasi IR, McIntosh SE, Thomas RD, Lysle DT, Thiele TE, Marshall SA. **Characterization of the hippocampal neuroimmune response to binge-like ethanol consumption in the drinking in the dark model**. *Neuroimmunomodulation* (2019) **26** 19-32. DOI: 10.1159/000495210
16. Glover EJ, Starr EM, Chao Y, Jhou TC, Chandler LJ. **Inhibition of the rostromedial tegmental nucleus reverses alcohol withdrawal-induced anxiety-like behavior**. *Neuropsychopharmacology* (2019) **44** 1896-905. DOI: 10.1038/s41386-019-0406-8
17. Macht V, Crews FT, Vetreno RP. **Neuroimmune and epigenetic mechanisms underlying persistent loss of hippocampal neurogenesis following adolescent intermittent ethanol exposure**. *Curr Opin Pharmacol* (2020) **50** 9-16. DOI: 10.1016/j.coph.2019.10.007
18. Edenberg HJ, Strother WN, McClintick JN, Tian H, Stephens M., Jerome RE. **Gene expression in the hippocampus of inbred alcohol-preferring and -nonpreferring rats**. *Genes Brain Behav* (2005) **4** 20-30. DOI: 10.1111/j.1601-183X.2004.00091.x
19. Kutlu MG, Gould TJ. **Effects of drugs of abuse on hippocampal plasticity and hippocampus-dependent learning and memory: Contributions to development and maintenance of addiction**. *Learn Mem* (2016) **23** 515-33. DOI: 10.1101/lm.042192.116
20. Taffe MA, Kotzebue RW, Crean RD, Crawford EF, Edwards S, Mandyam CD. **Long-lasting reduction in hippocampal neurogenesis by alcohol consumption in adolescent nonhuman primates**. *Proc Natl Acad Sci U S A* (2010) **107** 11104-9. DOI: 10.1073/pnas.0912810107
21. Basu S, Suh H. **Role of hippocampal neurogenesis in alcohol withdrawal seizures**. *Brain Plast* (2020) **6** 27-39. DOI: 10.3233/BPL-200114
22. Zhou Z, Yuan Q, Mash DC, Goldman D. **Substance-specific and shared transcription and epigenetic changes in the human hippocampus chronically exposed to cocaine and alcohol**. *Proc Natl Acad Sci U S A* (2011) **108** 6626-31. DOI: 10.1073/pnas.1018514108
23. Han H, Dong Z, Jia Y, Mao R, Zhou Q, Yang Y. **Opioid addiction and withdrawal differentially drive long-term depression of inhibitory synaptic transmission in the hippocampus**. *Sci Rep* (2015) **5** 9666. DOI: 10.1038/srep09666
24. Zhang P, Wu W, Chen Q, Chen M. **Non-coding RNAs and their integrated networks**. *J Integr Bioinform* (2019) **16** 20190027. DOI: 10.1515/jib-2019-0027
25. Saba L, Vanderlinden L, Homanics GE, Flink S, Hoffman PL, Tabakoff B.. **Control of alcohol consumption by a long non-coding RNA**. *Alcoholism-Clinical and experimental research* (2016)
26. Kapoor M, Wang JC, Farris SP, Liu Y, McClintick J, Gupta I. **Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism**. *Transl Psychiatry* (2019) **9** 89. DOI: 10.1038/s41398-019-0384-y
27. Osterndorff-Kahanek EA, Becker HC, Lopez MF, Farris SP, Tiwari GR, Nunez YO. **Chronic ethanol exposure produces time-and brain region-dependent changes in gene coexpression networks**. *PloS one* (2015) **10** e0121522. DOI: 10.1371/journal.pone.0121522
28. Bach H, Arango V, Kassir SA, Tsaava T, Dwork AJ, Mann JJ. **Alcoholics have more tryptophan hydroxylase 2 mRNA and protein in the dorsal and median raphe nuclei**. *Alcohol Clin Exp Res* (2014) **38** 1894-901. DOI: 10.1111/acer.12414
29. Even-Chen O, Herburg L, Kefalakes E, Urshansky N, Grothe C, Barak S. **FGF2 is an endogenous regulator of alcohol reward and consumption**. *Addict Biol* (2022) **27** e13115. DOI: 10.1111/adb.13115
30. Blednov YA, Bergeson SE, Walker D, Ferreira VMM, Kuziel WA, Harris RA. **Perturbation of chemokine networks by gene deletion alters the reinforcing actions of ethanol**. *Behav Brain Res* (2005) **165** 110-25. DOI: 10.1016/j.bbr.2005.06.026
31. Mayfield J, Arends MA, Harris RA, Blednov YA. **Genes and alcohol consumption: Studies with mutant mice**. *Int Rev Neurobiol* (2016) **126** 293-355. DOI: 10.1016/bs.irn.2016.02.014
32. Mitsuyama H, Little KY, Sieghart W, Devaud LL, Morrow AL. **GABA(A) receptor alpha1, alpha4, and beta3 subunit mRNA and protein expression in the frontal cortex of human alcoholics**. *Alcohol Clin Exp Res* (1998) **22** 815-22. DOI: 10.1097/00000374-199806000-00007
33. Bhandage AK, Jin Z, Bazov I, Kononenko O, Bakalkin G, Korpi ER. **GABA-A and NMDA receptor subunit mRNA expression is altered in the caudate but not the putamen of the postmortem brains of alcoholics**. *Front Cel Neurosci.* (2014) **8** 415. DOI: 10.3389/fncel.2014.00415
34. Jin Z, Bhandage AK, Bazov I, Kononenko O, Bakalkin G, Korpi ER. **Selective increases of AMPA, NMDA, and kainate receptor subunit mRNAs in the hippocampus and orbitofrontal cortex but not in prefrontal cortex of human alcoholics**. *Front Cel Neurosci.* (2014) **8** 11. DOI: 10.3389/fncel.2014.00011
35. Osterndorff-Kahanek EA, Tiwari GR, Lopez MF, Becker HC, Harris RA, Mayfield RD. **Long-term ethanol exposure: Temporal pattern of microRNA expression and associated mRNA gene networks in mouse brain**. *PloS One* (2018) **13** e0190841. DOI: 10.1371/journal.pone.0190841
36. Kyzar EJ, Bohnsack JP, Zhang H, Pandey SC. **MicroRNA-137 drives epigenetic reprogramming in the adult amygdala and behavioral changes after adolescent alcohol exposure**. *Eneuro* (2019) **6** ENEURO.0401-19.2019. DOI: 10.1523/ENEURO.0401-19.2019
37. Ignacio C, Hicks SD, Burke P, Lewis L, Szombathyne-Meszaros Z, Middleton FA. **Alterations in serum microRNA in humans with alcohol use disorders impact cell proliferation and cell death pathways and predict structural and functional changes in brain**. *BMC Neurosci* (2015) **16** 55. DOI: 10.1186/s12868-015-0195-x
38. Lim Y, Beane-Ebel JE, Tanaka Y, Ning B, Husted CR, Henderson DC. **Exploration of alcohol use disorder-associated brain miRNA–mRNA regulatory networks**. *Transl Psychiatry* (2021) **11** 504-10. DOI: 10.1038/s41398-021-01635-w
39. Wyczechowska D, Lin HY, LaPlante A, Jeansonne D, Lassak A, Parsons CH. **A miRNA signature for cognitive deficits and alcohol use disorder in persons living with HIV/AIDS**. *Front Mol Neurosci* (2017) **10** 385. DOI: 10.3389/fnmol.2017.00385
40. Vornholt E, Drake J, Mamdani M, McMichael G, Taylor ZN, Bacanu SA. **Identifying a novel biological mechanism for alcohol addiction associated with circRNA networks acting as potential miRNA sponges**. *Addict Biol* (2021) **26** e13071. DOI: 10.1111/adb.13071
41. Bohnsack JP, Teppen T, Kyzar EJ, Dzitoyeva S, Pandey SC. **The lncRNA BDNF-AS is an epigenetic regulator in the human amygdala in early onset alcohol use disorders**. *Transl Psychiatry* (2019) **9** 34. DOI: 10.1038/s41398-019-0367-z
42. Saba LM, Hoffman PL, Homanics GE, Mahaffey S, Daulatabad SV, Janga SC. **A long non‐coding RNA (Lrap) modulates brain gene expression and levels of alcohol consumption in rats**. *Genes Brain Behav* (2021) **20** e12698. DOI: 10.1111/gbb.12698
43. Kryger R, Fan L, Wilce PA, Jaquet V. **MALAT-1, a non protein-coding RNA is upregulated in the cerebellum, hippocampus and brain stem of human alcoholics**. *Alcohol* (2012) **46** 629-34. DOI: 10.1016/j.alcohol.2012.04.002
44. Quinn JJ, Chang HY. **Unique features of long non-coding RNA biogenesis and function**. *Nat Rev Genet* (2016) **17** 47-62. DOI: 10.1038/nrg.2015.10
45. Benetatos L, Benetatou A, Vartholomatos G. **Long non-coding RNAs and MYC association in hematological malignancies**. *Ann Hematol* (2020) **99** 2231-42. DOI: 10.1007/s00277-020-04166-4
46. Salviano-Silva A, Lobo-Alves SC, Almeida RCd, Malheiros D, Petzl-Erler ML. **Besides pathology: Long non-coding RNA in cell and tissue homeostasis**. *Noncoding RNA* (2018) **4** 3. DOI: 10.3390/ncrna4010003
47. Spurlock CF, Shaginurova G, Tossberg JT, Hester JD, Chapman N, Guo Y. **Profiles of long noncoding RNAs in human naive and memory T cells**. *J Immunol* (2017) **199** 547-58. DOI: 10.4049/jimmunol.1700232
48. Marchese FP, Huarte M. **Long non-coding RNAs and chromatin modifiers: Their place in the epigenetic code**. *Epigenetics* (2014) **9** 21-6. DOI: 10.4161/epi.27472
49. Volders P-J, Anckaert J, Verheggen K, Nuytens J, Martens L, Mestdagh P. **LNCipedia 5: Towards a reference set of human long non-coding RNAs**. *Nucleic Acids Res* (2019) **47** D135-D139. DOI: 10.1093/nar/gky1031
50. Mercer TR, Dinger ME, Sunkin SM, Mehler MF, Mattick JS. **Specific expression of long noncoding RNAs in the mouse brain**. *Proc Natl Acad Sci U S A* (2008) **105** 716-21. DOI: 10.1073/pnas.0706729105
51. Jarroux J, Morillon A, Pinskaya M. **History, discovery, and classification of lncRNAs**. *Adv Exp Med Biol* (2017) **1008** 1-46. DOI: 10.1007/978-981-10-5203-3_1
52. Kopp F, Mendell JT. **Functional classification and experimental dissection of long noncoding RNAs**. *Cell* (2018) **172** 393-407. DOI: 10.1016/j.cell.2018.01.011
53. Beermann J, Piccoli MT, Viereck J, Thum T. **Non-coding RNAs in development and disease: Background, mechanisms, and therapeutic approaches**. *Physiol Rev* (2016) **96** 1297-325. DOI: 10.1152/physrev.00041.2015
54. Statello L, Guo C-J, Chen L-L, Huarte M. **Gene regulation by long non-coding RNAs and its biological functions**. *Nat Rev Mol Cel Biol.* (2021) **22** 96-118. DOI: 10.1038/s41580-020-00315-9
55. Guttman M, Rinn JL. **Modular regulatory principles of large non-coding RNAs**. *Nature* (2012) **482** 339-46. DOI: 10.1038/nature10887
56. Khalil AM, Guttman M, Huarte M, Garber M, Raj A, Rivea Morales D. **Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression**. *Proc Natl Acad Sci U S A* (2009) **106** 11667-72. DOI: 10.1073/pnas.0904715106
57. Sarropoulos I, Marin R, Cardoso-Moreira M, Kaessmann H. **Developmental dynamics of lncRNAs across mammalian organs and species**. *Nature* (2019) **571** 510-4. DOI: 10.1038/s41586-019-1341-x
58. Chen L-L. **Linking long noncoding RNA localization and function**. *Trends Biochem Sci* (2016) **41** 761-72. DOI: 10.1016/j.tibs.2016.07.003
59. Raveendra BL, Swarnkar S, Avchalumov Y, Liu XA, Grinman E, Badal K. **Long noncoding RNA GM12371 acts as a transcriptional regulator of synapse function**. *Proc Natl Acad Sci U S A* (2018) **115** E10197-E10205. DOI: 10.1073/pnas.1722587115
60. Liu J, Zhang KS, Hu B, Li SG, Li Q, Luo YP. **Systematic analysis of RNA regulatory network in rat brain after ischemic stroke**. *Biomed Res Int* (2018) **2018** 8354350. DOI: 10.1155/2018/8354350
61. Elkouris M, Kouroupi G, Vourvoukelis A, Papagiannakis N, Kaltezioti V, Matsas R. **Long non-coding rnas associated with neurodegeneration-linked genes are reduced in Parkinson’s disease patients**. *Front Cel Neurosci.* (2019) **13** 58. DOI: 10.3389/fncel.2019.00058
62. Huo X, Han S, Wu G, Latchoumanin O, Zhou G, Hebbard L. **Dysregulated long noncoding RNAs (lncRNAs) in hepatocellular carcinoma: Implications for tumorigenesis, disease progression, and liver cancer stem cells**. *Mol Cancer* (2017) **16** 165. DOI: 10.1186/s12943-017-0734-4
63. Sartor GC, St Laurent G, Wahlestedt C. **The emerging role of non-coding RNAs in drug addiction**. *Front Genet* (2012) **3** 106. DOI: 10.3389/fgene.2012.00106
64. Yu V, Singh P, Rahimy E, Zheng H, Kuo SZ, Kim E. **RNA-seq analysis identifies key long non-coding RNAs connected to the pathogenesis of alcohol-associated head and neck squamous cell carcinoma**. *Oncol Lett* (2016) **12** 2846-53. DOI: 10.3892/ol.2016.4972
65. Liu Y, Chang X, Hahn CG, Gur RE, Sleiman PAM, Hakonarson H. **Non-coding RNA dysregulation in the amygdala region of schizophrenia patients contributes to the pathogenesis of the disease**. *Transl Psychiatry* (2018) **8** 44. DOI: 10.1038/s41398-017-0030-5
66. Dallner OS, Marinis JM, Lu YH, Birsoy K, Werner E, Fayzikhodjaeva G. **Dysregulation of a long noncoding RNA reduces leptin leading to a leptin-responsive form of obesity**. *Nat Med* (2019) **25** 507-16. DOI: 10.1038/s41591-019-0370-1
67. Plasil SL, Blednov Y, Harris RA, Messing R, Aziz HC, Lambeth PS. **Long noncoding RNA and AUD; molecular pharmacology, behavior, and electrophysiology of TX2 knockout mice**. *Alcohol Clin Exp Res* (2020) **44**. DOI: 10.1111/acer.14358
68. Carrizales DG, Aziz HC, Borghese CM, Osterndorff-Kahanek EA, Homanics GE, Farris SP. **Deletion of an alcohol-responsive long non-coding RNA, “TX1”, has sexually divergent effects on NMDA receptor-mediated transmission in hippocampal area CA1 of mice**. *Alcoholism-Clinical Exp Res* (2019) **43** 93A. DOI: 10.1111/acer.14058
69. Zhu L, Zhu J, Liu Y, Chen Y, Li Y, Huang L. **Methamphetamine induces alterations in the long non-coding RNAs expression profile in the nucleus accumbens of the mouse**. *BMC Neurosci* (2015) **16** 18-3. DOI: 10.1186/s12868-015-0157-3
70. Bannon MJ, Savonen CL, Jia H, Dachet F, Halter SD, Schmidt CJ. **Identification of long noncoding RNA s dysregulated in the midbrain of human cocaine abusers**. *J Neurochem* (2015) **135** 50-9. DOI: 10.1111/jnc.13255
71. Xu H, Brown AN, Waddell NJ, Liu X, Kaplan GJ, Chitaman JM. **Role of long noncoding RNA Gas5 in cocaine action**. *Biol Psychiatry* (2020) **88** 758-66. DOI: 10.1016/j.biopsych.2020.05.004
72. Rusconi F, Battaglioli E, Venturin M. **Psychiatric disorders and lncRNAs: A synaptic match**. *Int J Mol Sci* (2020) **21** 3030. DOI: 10.3390/ijms21093030
73. Zhou Y, Lutz P-E, Wang YC, Ragoussis J, Turecki G. **Global long non-coding RNA expression in the rostral anterior cingulate cortex of depressed suicides**. *Transl Psychiatry* (2018) **8** 224-13. DOI: 10.1038/s41398-018-0267-7
74. Valadkhan S, Valencia-Hipólito A. **Long non-coding RNAs**. *Human disease* (2015) 203-36
75. Pirogov SA, Gvozdev VA, Klenov MS. **Long noncoding RNAs and stress response in the nucleolus**. *Cells* (2019) **8** 668. DOI: 10.3390/cells8070668
76. Kartha RV, Subramanian S. **Competing endogenous RNAs (ceRNAs): New entrants to the intricacies of gene regulation**. *Front Genet* (2014) **5** 8. DOI: 10.3389/fgene.2014.00008
77. Ala U. **Competing endogenous RNAs, non-coding RNAs and diseases: An intertwined story**. *Cells* (2020) **9** 1574. DOI: 10.3390/cells9071574
78. Moreno-García L, Lopez-Royo T, Calvo AC, Toivonen JM, de la Torre M, Moreno-Martinez L. **Competing endogenous RNA networks as biomarkers in neurodegenerative diseases**. *Int J Mol Sci* (2020) **21** 9582. DOI: 10.3390/ijms21249582
79. Lan C, Peng H, Hutvagner G, Li J. **Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information**. *BMC genomics* (2019) **20** 943-10. DOI: 10.1186/s12864-019-6321-x
80. Ala U, Karreth FA, Bosia C, Pagnani A, Taulli R, Leopold V. **Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments**. *Proc Natl Acad Sci U S A* (2013) **110** 7154-9. DOI: 10.1073/pnas.1222509110
81. Mitra A, Pfeifer K, Park K-S. **Circular RNAs and competing endogenous RNA (ceRNA) networks**. *Transl Cancer Res* (2018) **7** S624-S628. DOI: 10.21037/tcr.2018.05.12
82. Pandey AK, Lu L, Wang X, Homayouni R, Williams RW. **Functionally enigmatic genes: A case study of the brain ignorome**. *PloS one* (2014) **9** e88889. DOI: 10.1371/journal.pone.0088889
83. Plasil SL, Seth A, Homanics GE. **CRISPR Turbo Accelerated KnockOut (CRISPy TAKO) for rapid**. *Front Genome Ed* (2020) **2** 598522. DOI: 10.3389/fgeed.2020.598522
84. Rathod RS, Ferguson C, Seth A, Baratta AM, Plasil SL, Homanics GE. **Effects of paternal preconception vapor alcohol exposure paradigms on behavioral responses in offspring**. *Brain Sci* (2020) **10** E658. DOI: 10.3390/brainsci10090658
85. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W. **Limma powers differential expression analyses for RNA-sequencing and microarray studies**. *Nucleic Acids Res* (2015) **43** e47. DOI: 10.1093/nar/gkv007
86. Homanics GE. **Gene-edited CRISPy Critters for alcohol research**. *Alcohol* (2019) **74** 11-9. DOI: 10.1016/j.alcohol.2018.03.001
87. Stemmer M, Thumberger T, del Sol Keyer M, Wittbrodt J, Mateo JL. **CCTop: An intuitive, flexible and reliable CRISPR/Cas9 target prediction tool**. *PloS one* (2015) **10** e0124633. DOI: 10.1371/journal.pone.0124633
88. Labuhn M, Adams FF, Ng M, Knoess S, Schambach A, Charpentier EM. **Refined sgRNA efficacy prediction improves large-and small-scale CRISPR–Cas9 applications**. *Nucleic Acids Res* (2018) **46** 1375-85. DOI: 10.1093/nar/gkx1268
89. Benjamini Y, Krieger A, Yekutieli D. **Adaptive linear step-up procedures that control the false discovery rate**. *Biometrika* (2006) **93** 491-507. DOI: 10.1093/biomet/93.3.491
90. Sneddon EA, White RD, Radke AK. **Sex differences in binge‐like and aversion‐resistant alcohol drinking in C57BL/6J mice**. *Alcohol Clin Exp Res* (2019) **43** 243-9. DOI: 10.1111/acer.13923
91. Becker JB, Koob GF. **Sex differences in animal models: Focus on addiction**. *Pharmacol Rev* (2016) **68** 242-63. DOI: 10.1124/pr.115.011163
92. Finn DA, Hashimoto JG, Cozzoli DK, Helms ML, Nipper MA, Kaufman MN. **Binge ethanol drinking produces sexually divergent and distinct changes in nucleus accumbens signaling cascades and pathways in adult C57BL/6J mice**. *Front Genet* (2018) **9** 325. DOI: 10.3389/fgene.2018.00325
93. Gelineau RR, Arruda NL, Hicks JA, Monteiro De Pina I, Hatzidis A, Seggio JA. **The behavioral and physiological effects of high‐fat diet and alcohol consumption: Sex differences in C57BL/6J mice**. *Brain Behav* (2017) **7** e00708. DOI: 10.1002/brb3.708
94. Belknap JK, Crabbe JC, Young E. **Voluntary consumption of ethanol in 15 inbred mouse strains**. *Psychopharmacology* (1993) **112** 503-10. DOI: 10.1007/BF02244901
95. Blednov Y, Walker D, Martinez M., Levine M., Damak S, Margolskee RF. **Perception of sweet taste is important for voluntary alcohol consumption in mice**. *Genes Brain Behav* (2008) **7** 1-13. DOI: 10.1111/j.1601-183X.2007.00309.x
96. Blednov Y, Ozburn AR, Walker D, Ahmed S, Belknap JK, Harris RA. **Hybrid mice as genetic models of high alcohol consumption**. *Behav Genet* (2010) **40** 93-110. DOI: 10.1007/s10519-009-9298-4
97. Koren E, Lev-Maor G, Ast G. **The emergence of alternative 3′ and 5′ splice site exons from constitutive exons**. *Plos Comput Biol* (2007) **3** e95. DOI: 10.1371/journal.pcbi.0030095
98. Nolte C, Staiger D. **RNA around the clock–regulation at the RNA level in biological timing**. *Front Plant Sci* (2015) **6** 311. DOI: 10.3389/fpls.2015.00311
99. Roca X, Krainer AR, Eperon IC. **Pick one, but be quick: 5′ splice sites and the problems of too many choices**. *Genes Dev* (2013) **27** 129-44. DOI: 10.1101/gad.209759.112
100. Anna A, Monika G. **Splicing mutations in human genetic disorders: Examples, detection, and confirmation**. *J Appl Genet* (2018) **59** 253-68. DOI: 10.1007/s13353-018-0444-7
101. Fredericks AM, Cygan KJ, Brown BA, Fairbrother WG. **RNA-Binding proteins: Splicing factors and disease**. *Biomolecules* (2015) **5** 893-909. DOI: 10.3390/biom5020893
102. Landry J-R, Mager DL, Wilhelm BT. **Complex controls: The role of alternative promoters in mammalian genomes**. *Trends Genet* (2003) **19** 640-8. DOI: 10.1016/j.tig.2003.09.014
103. Wang J, Zhang S, Lu H, Xu H. **Differential regulation of alternative promoters emerges from unified kinetics of enhancer-promoter interaction**. *Nat Commun* (2022) **13** 2714-4. DOI: 10.1038/s41467-022-30315-6
104. Singer GA, Wu J, Yan P, Plass C, Huang THM, Davuluri RV. **Genome-wide analysis of alternative promoters of human genes using a custom promoter tiling array**. *BMC genomics* (2008) **9** 349-15. DOI: 10.1186/1471-2164-9-349
105. Finn DA, Snelling C, Fretwell AM, Tanchuck MA, Underwood L, Cole M. **Increased drinking during withdrawal from intermittent ethanol exposure is blocked by the CRF receptor antagonist D‐Phe‐CRF (12–41)**. *Alcohol Clin Exp Res* (2007) **31** 939-49. DOI: 10.1111/j.1530-0277.2007.00379.x
106. Souza TP, Franscescon F, Stefanello FV, Muller TE, Santos LW, Rosemberg DB. **Acute effects of ethanol on behavioral responses of male and female zebrafish in the open field test with the influence of a non-familiar object**. *Behav Process.* (2021) **191** 104474. DOI: 10.1016/j.beproc.2021.104474
107. Jury NJ, DiBerto JF, Kash TL, Holmes A. **Sex differences in the behavioral sequelae of chronic ethanol exposure**. *Alcohol* (2017) **58** 53-60. DOI: 10.1016/j.alcohol.2016.07.007
108. Barker JM, Bryant KG, Osborne JI, Chandler L. **Age and sex interact to mediate the effects of intermittent, high-dose ethanol exposure on behavioral flexibility**. *Front Pharmacol* (2017) **8** 450. DOI: 10.3389/fphar.2017.00450
109. Darnieder L, Melon LC, Do T, Walton NL, Miczek KA, Maguire JL. **Female-specific decreases in alcohol binge-like drinking resulting from GABAA receptor delta-subunit knockdown in the VTA**. *Sci Rep* (2019) **9** 8102-11. DOI: 10.1038/s41598-019-44286-0
110. Shansky RM, Murphy AZ. **Considering sex as a biological variable will require a global shift in science culture**. *Nat Neurosci* (2021) **24** 457-64. DOI: 10.1038/s41593-021-00806-8
111. Shansky RM. **Estrogen, stress and the brain: Progress toward unraveling gender discrepancies in major depressive disorder**. *Expert Rev Neurother* (2009) **9** 967-73. DOI: 10.1586/ern.09.46
112. Farrell MR, Gruene TM, Shansky RM. **The influence of stress and gonadal hormones on neuronal structure and function**. *Horm Behav* (2015) **76** 118-24. DOI: 10.1016/j.yhbeh.2015.03.003
113. Becker JB, Chartoff E. **Sex differences in neural mechanisms mediating reward and addiction**. *Neuropsychopharmacology* (2019) **44** 166-83. DOI: 10.1038/s41386-018-0125-6
114. Drake J, McMichael GO, Vornholt ES, Cresswell K, Williamson V, Chatzinakos C. **Assessing the role of long noncoding RNA in nucleus accumbens in subjects with alcohol dependence**. *Alcohol Clin Exp Res* (2020) **44** 2468-80. DOI: 10.1111/acer.14479
115. Zheng H, Li P, Kwok JG, Korrapati A, Li WT, Qu Y. **Alcohol and hepatitis virus-dysregulated lncRNAs as potential biomarkers for hepatocellular carcinoma**. *Oncotarget* (2018) **9** 224-35. DOI: 10.18632/oncotarget.22921
116. Zhou A, Wang Y, Liu Y, Feng W, Edenberg HJ. (2016) 167-73
|
---
title: Types of Physical Activity in Nonalcoholic Fatty Liver Disease and All-Cause
and Cardiovascular Mortality
authors:
- Donghee Kim
- Karn Wijarnpreecha
- Brittany B. Dennis
- George Cholankeril
- Aijaz Ahmed
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004264
doi: 10.3390/jcm12051923
license: CC BY 4.0
---
# Types of Physical Activity in Nonalcoholic Fatty Liver Disease and All-Cause and Cardiovascular Mortality
## Abstract
The impact of different types of physical activity (PA) on mortality in the context of nonalcoholic fatty liver disease (NAFLD) is not clearly defined and was investigated. This prospective study was performed using the 2007–2014 US National Health and Nutrition Examination Survey with mortality follow-up through 2019. Over a median follow-up of 8.6 years, leisure-time and transportation-related PA that fulfilled the criteria outlined in the PA guidelines (≥150 min/week) in NAFLD were associated with a risk reduction in all-cause mortality (hazard ratio [HR]: 0.76, $95\%$ confidence interval [CI]: 0.59–0.98 for leisure-time PA; HR: 0.62, $95\%$ CI: 0.45–0.86 for transportation-related PA). Leisure-time and transportation-related PA in NAFLD were inversely associated with all-cause mortality in a dose-dependent manner (p for trends <0.01). Furthermore, the risk for cardiovascular mortality was lower in those meeting the PA guidelines for leisure-time PA (HR: 0.63, $95\%$ CI: 0.44–0.91) and transportation-related PA (HR: 0.38, $95\%$ CI: 0.23–0.65). Increasing sedentary behavior was linked to an increased risk of all-cause and cardiovascular mortality (p for trend <0.01). Meeting PA guidelines (≥150 min/week) for leisure-time and transportation-related PA has beneficial health effects on all-cause and cardiovascular mortality among individuals with NAFLD. Sedentary behavior in NAFLD showed harmful effects on all-cause and cardiovascular mortality.
## 1. Introduction
Nonalcoholic fatty liver disease (NAFLD), the world’s most prevalent chronic liver disease, affects a third of the United States (US) population [1]. NAFLD consists of a clinicopathologic spectrum ranging from nonalcoholic fatty liver and nonalcoholic steatohepatitis to end-stage liver disease, including cirrhosis and hepatocellular carcinoma [2,3]. On top of its enormous existing disease burden, NAFLD is projected to demonstrate an exponential growth in its prevalence in the next decade. In addition, the risk of progression to end-stage liver disease, combined with an increased risk of co-morbid cardiovascular disease [4] and an unapproved pharmacological treatment, makes clinical management extremely difficult. Individuals with NAFLD are treated with lifestyle modifications, which are challenging to implement. NAFLD is closely associated with individual components of the metabolic syndrome, including diabetes, dyslipidemia, and abdominal obesity, which also benefit from physical activity (PA) [5]. PA is a pivotal determinant of lifestyle modifications and is commonly recommended for individuals with NAFLD, usually alongside dietary control and weight loss. Several studies have demonstrated an inverse association between PA and NAFLD [6,7,8]. However, most studies have focused on leisure-time PA. Additionally, guidelines for the management of NAFLD mainly focused on weight loss and diet, while the types, intensity, and amount of PA needed for optimal therapeutic effects in NAFLD were unclear [2,3]. Sedentary behavior was also associated with diabetes and cardiovascular disease, which were closely associated with NAFLD [9]. Therefore, education on reversing sedentary behavior PA among individuals with NAFLD may provide an additional therapeutic option. Although there is robust observational evidence for the beneficial effects of PA on the risk of NAFLD [6,8,10,11], the impact of PA on mortality in individuals with NAFLD is not well determined. A recent study using accelerometer-assessed PA reported that increasing PA is associated with lower all-cause and cardiovascular mortality in individuals with NAFLD [12]. Although a recent study showed that leisure-time PA and transportation-related PA have a significant dose-dependent protective effect on NAFLD [10], there is little evidence determining whether the types and amounts of PA beneficially affect all-cause mortality and cardiovascular mortality among individuals with NAFLD. The 2018 Physical Activity Guidelines for Americans recently outlined the amounts and types of PA that provide significant health benefits [13]. For substantial health benefits, these guidelines recommend adults be physically active for at least 150 to 300 min per week of moderate intensity [13]. This recommendation emphasizes that reversing sedentary behavior will benefit nearly everyone [13]. Therefore, we studied whether the types of PA meeting guidelines and the amount of PA among individuals with NAFLD are associated with all-cause and cardiovascular mortality in a nationally representative sample of the United States.
## 2.1. Subjects and Study Design
We performed analyses of the recent four 2-year waves of the National Health and Nutrition Examination Survey (NHANES) 2007–2014, with follow-up for at least 5 years through 31 December 2019, to estimate mortality in the study cohort. A stratified, multi-staged, and clustered probability sampling design was employed to provide a nationally representative sample of non-institutionalized civilians in the US.
A total of 22,673 adults (≥20 years of age) were examined for laboratory tests at a mobile examination center. We excluded 3173 individuals that had hepatitis B virus infection (determined by the presence of the hepatitis B surface antigen), hepatitis C virus infection (determined by the presence of hepatitis C antibody), significant alcohol consumption (>30 g/day in men and >20 g/day in women), were pregnant, those with incomplete or missing data on PA questionnaire and/or serum aminotransferase, body mass index (BMI), and mortality status, and those that had been exposed to a medication with a known association to fatty infiltration of the liver (i.e., amiodarone, corticosteroid, methotrexate, tamoxifen, and valproate) for more than 6 months. The first study sample consisted of 10,853 individuals with NAFLD, as defined by the Hepatic Steatosis Index (HSI) [14]. The second study sample included 3263 individuals with NAFLD as defined by the US Fatty Liver Index (USFLI) [15], and was created from the individuals that underwent laboratory tests after a fast of at least 8 hours from the first study sample.
The National Center for Health Statistics’ Research Ethics Review Board has reviewed and approved this original NHANES, and all individuals signed a full informed consent. Since de-identified data was used in the study, this analysis was exempted by the Institutional Review Board.
## 2.2. Clinical and Laboratory Evaluations
Methods used for clinical and laboratory evaluations have been described in detail elsewhere [10]. Briefly, the NHANES 2007–2014 consisted of sociodemographic information, anthropometric measures, comprehensive questionnaires, and laboratory tests. We categorized race/ethnicity as non-Hispanic white, non-Hispanic black, Hispanic (Mexican-American or Other Hispanic), or others. Marital status was dichotomized as being married or cohabitating with a partner versus others. Educational status was defined as lack of high school graduation versus high school graduation. The individuals’ family income-to-poverty ratio classified the economic situation as either ≤0.99 or ≥1.00 (at or above poverty). We defined hypertension as having a systolic blood pressure of ≥140 mmHg or a diastolic blood pressure of ≥90 mmHg and/or being on anti-hypertensive medication. We defined diabetes mellitus as fasting plasma glucose levels of ≥126 mg/dL and/or the use of a hypoglycemic agent or insulin.
## 2.3. Physical Activity and Sedentary Behavior Questionnaire
Methods used for PA and sedentary behavior have been described in detail elsewhere [10]. In brief, all participants answered a questionnaire modeled after the Global Physical Activity Questionnaire. The types of PA assessed were leisure-time, occupation, and transportation-related PA. Each type of PA included questions detailing intensity (vigorous vs. moderate), frequency (per week), and duration (minutes) in a typical week. Individuals provided details and a breakdown of vigorous versus moderate-intensity PA during occupation and leisure time. As previously validated, minutes of vigorous PA were doubled and added to moderate PA minutes for occupation-related and leisure-time PA [16]. The amount of total PA was estimated by summing up leisure-time PA, occupation-related PA, and transportation-related PA. According to the 2018 Physical Activity Guidelines for Americans (adults engage in ≥150 min/week of moderate-intensity PA, 75 min/week of vigorous-intensity PA, or an equivalent combination) [13]. We categorized PA as physically inactive (“not meeting PA guidelines”) in those who did not meet the criteria for the 2018 Physical Activity Guidelines for Americans and physically active (“meeting PA guidelines”) in those who met the 2018 Physical Activity Guidelines for Americans. We investigated sedentary behavior as total sitting time, which was recorded in hours per day in a typical week.
## 2.4. Definition of NAFLD
To define NAFLD, we used two previously well-validated non-invasive panels for fatty liver [14,15,17,18,19]. In the absence of other causes of chronic liver disease, significant alcohol consumption, and use of steatogenic medication, NAFLD was defined using the HSI and the USFLI. We calculated HSI by using the following equation: HSI = 8 × (alanine aminotransferase/aspartate aminotransferase ratio) + BMI (+2, if diabetes; +2, if female) [14]. We used the published cut-off of 36 to define the presence of NAFLD [14]. As the USFLI equation requires fasting glucose and insulin, analyses using the USFLI included a subgroup of individuals examined after a minimum fast of 8 h. We calculated the USFLI using the following equation: USFLI = (e−0.8073 × non-Hispanic black + 0.3458 × Mexican American + 0.0093 × age + 0.6151 × loge (gamma-glutamyl transferase) + 0.0249 × waist circumference + 1.1792 × loge (insulin) + 0.8242 × loge (glucose) − 14.7812)/(1+ e−0.8073 × non-Hispanic black + 0.3458 × Mexican American + 0.0093 × age + 0.6151 × loge (gamma-glutamyl transferase) + 0.0249 × waist circumference + 1.1792 × loge (insulin) + 0.8242 × loge (glucose) − 14.7812) × 100 [15]. Advanced fibrosis was defined as having at least one of the high probabilities for advanced fibrosis calculated using three non-invasive fibrosis panels (the NAFLD fibrosis score, the fibrosis-4 score, and the aspartate aminotransferase-to-platelet ratio index) [17].
## 2.5. Mortality
All individuals over 20 years in the NHANES 2007–2014 had passive mortality follow-up through 31 December 2019 [20]. For decedents, an enhanced linkage algorithm was designed to assess mortality status by date of death and cause of death based on the National Death Index. The leading cause of death was coded as the Underlying Cause of Death 113 (UCOD_113) code. All-cause mortality and the following two cause-specific mortalities were defined as cardiovascular disease (UCOD_113: 55–68, 70) and cancer (UCOD_113: 19–43).
## 2.6. Statistical Analysis
Due to the complex survey design employed by the NHANES, we used appropriate sample weights, stratification, and clustering to obtain representative US population-level data. The weighted frequencies ($95\%$ confidence intervals [CI]) and the weighted means ± standard errors were presented appropriately. The chi-square test for categorical variables or linear regression for continuous variables was used. We used the weighted Cox proportional hazards regression analysis for survival, including all-cause and cause-specific mortality. Multivariable weighted Cox proportional models were performed to assess the independent association of types of PA and sedentary behavior with all-cause and cause-specific mortalities after considering other potential demographic and clinical confounders. We performed all analyses using STATA 17.0 (Stata Corp., College Station, TX, USA) using Taylor series linearization.
## 3. Results
We performed analyses using 10,853 individuals with NAFLD (mean age, 47.9 years; $48.3\%$ males). As shown in Table 1, there were noticeable differences in the clinical characteristics of individuals with NAFLD based on their PA status. When compared to physically inactive individuals with NAFLD, physically active individuals with NAFLD were more likely to be younger, educated, above the poverty level, men, and less likely to have diabetes and/or hypertension. Physically active individuals with NAFLD also had a lower BMI and waist circumference, fasting glucose levels, hemoglobin A1c, high-density lipoprotein-cholesterol, and higher aminotransferase levels than physically inactive individuals with NAFLD.
During a median follow-up period of 8.6 years (interquartile range: 6.5–10.8 years), 1111 deaths (369 from cardiovascular disease and 272 from cancer) were reported among individuals with NAFLD. Results of Cox-regression analyses are summarized in Table 2. Total PA that met the criteria outlined by PA guidelines (≥150 min/week) was independently associated with a $26\%$ lower risk of all-cause mortality (hazard ratio [HR]: 0.74, $95\%$ CI: 0.60–0.90). When we sub-analyzed each type of PA, leisure-time PA fulfilling the PA guidelines (≥150 min/week) was associated with a reduction in the risk of all-cause mortality in the age- and sex-adjusted model (HR: 0.66, $95\%$ CI: 0.52–0.84). In the multivariable model adjusted for known demographic variables and traditional risk factors, leisure-time PA meeting the criteria of PA guidelines demonstrated a $24\%$ lower hazard for all-cause mortality. Transportation-related PA that met PA guidelines (≥150 min/week) was associated with a lower risk for all-cause mortality in the age- and sex-adjusted model (HR: 0.62, $95\%$ CI: 0.46–0.85) and in the multivariable model (HR: 0.62, $95\%$ CI: 0.45–0.86). The addition of waist circumference to the model did not change the HRs significantly for leisure-time and transportation-related PA. Occupation-related PA showed no association with all-cause mortality in age- and sex-adjusted models or in multivariable models.
To assess whether there is a dose-response relationship between different types of PA and all-cause mortality and to further assess the impact of various levels of PA above and below the recommended PA guidelines, we categorized PA into four levels of intensity based on the duration: 0, <150, 150–299, ≥300 min/week (Table 3). Similar beneficial effects on survival were also observed across different total PA categories. When adjusted for multiple confounders, individuals performing <150 min/week of leisure-time PA had a $29\%$ lower risk of all-cause mortality (HR: 0.71, $95\%$ CI: 0.55–0.90) compared to physically inactive individuals. Those who reported 1–2 times (150–299 min/week) and over 2 times (≥300 min/week) the recommended level of leisure-time PA had a $32\%$ (HR: 0.68, $95\%$ CI: 0.48–0.97) and a $26\%$ (HR: 0.74, $95\%$ CI: 0.56–0.98) lower risk of all-cause mortality, respectively. Transportation-related PA was also inversely associated with all-cause mortality in dose-dependent manners (p for trend = 0.002) in the multivariable model. When we adjusted for waist circumference, the inverse dose-response association between total, leisure-time, or transportation-related PA and all-cause mortality was attenuated but maintained (p for trend < 0.01). However, there was no significant dose-response relationship between occupation-related PA and all-cause mortality except for individuals performing 1–300 min/week. When we performed sensitivity analyses using the USFLI (Supplementary Tables S1 and S2), adjusting for age and sex, and in multivariable models, we observed a similar and significant association of total and leisure-time PA with all-cause mortality. We performed sensitivity analyses to determine the impact of diabetes-related NAFLD or obesity-related NAFLD on all-cause mortality compared to their counterparts, respectively (Supplementary Table S3). We found that NAFLD with diabetes- or obesity-related NAFLD was associated with an increased risk for all-cause mortality. Similar and significant associations were shown between leisure-time or transportation-related PA and all-cause mortality. After adjusting for advanced fibrosis (Supplementary Table S4), sensitivity analysis revealed comparable significant associations between leisure-time or transportation-related PA and all-cause mortality. We found advanced fibrosis to be an independent risk factor for all-cause mortality. When the analysis was stratified by advanced fibrosis in NAFLD (Supplementary Table S5), results were largely identical except for the significant association between occupation-related PA and all-cause mortality.
When the analysis was restricted to cardiovascular mortality (Table 4 and Table 5), there was an association with a lower risk for cardiovascular mortality in those meeting PA guidelines for total PA (HR: 0.65, $95\%$ CI: 0.49–0.87), leisure-time PA (HR: 0.63, $95\%$ CI: 0.44–0.91), and transportation-related PA (HR: 0.38, $95\%$ CI: 0.23–0.65), and this association remained significant after adjusting for waist circumference. As shown in Table 5, multivariable analyses showed a dose-dependent relationship between the degree of leisure-time or transportation-related PA and cardiovascular mortality (p for trend = 0.001). In particular, individuals with over two times (≥300 min/week) the recommended level of activity per PA guidelines for leisure-time PA or transportation-related PA demonstrated a $66\%$ lower risk of cardiovascular mortality (HR: 0.34, $95\%$ CI: 0.21–0.55) and $72\%$ (HR: 0.28, $95\%$ CI: 0.14–0.55). Our results were largely identical when we performed sensitivity analyses using USFLI (Supplementary Tables S6 and S7) in age- and sex-adjusted and multivariable models. When we performed the same analyses among individuals without NAFLD (Supplementary Tables S8–S10), the overall results remained similar: the protective effect of leisure-time PA on all-cause and cardiovascular mortality was slightly higher than estimates in those with NAFLD, but the protective effect of transportation-related PA on all-cause and cardiovascular mortality was statistically insignificant. Instead, increasing occupation-related PA significantly influenced all-cause mortality (HR 0.79, $95\%$ CI 0.66–0.95) among individuals without NAFLD. In terms of cancer-related mortality, there was no association between various types of PA in NAFLD and cancer-related mortality in age- and sex-adjusted and multivariable models (Supplementary Table S11). Notably, we found that total PA meeting the criteria of PA guidelines demonstrated a $46\%$ lower hazard for cancer-related mortality among individuals without NAFLD (HR: 0.54, $95\%$ CI: 0.38–0.78, Supplementary Table S12).
Increasing sedentary behavior was dose-dependently associated with increased risk for all-cause mortality and cardiovascular mortality in the age- and sex-adjusted and multivariable model (p for trend < 0.001, Table 6). When we considered total PA and sitting time simultaneously, the dose-response association of sitting time with all-cause and cardiovascular mortality was slightly attenuated but maintained (p for trend < 0.01). Over 8 h of sitting time, irrespective of different total PA categories, had a $27\%$ (HR: 1.27, $95\%$ CI: 1.06–1.53) and $62\%$ (HR: 1.62, $95\%$ CI: 1.10–2.39) higher risk of all-cause and cardiovascular mortality, respectively. When the analysis was repeated using the USFLI formula to define NAFLD, the results were largely identical but changed insignificantly with wide CIs (Supplementary Table S13). Similarly, increasing sedentary behavior was associated with increased risk for all-cause mortality and cardiovascular mortality among individuals without NAFLD (Supplementary Table S14).
## 4. Discussion
While strong evidence supports the beneficial effects of specific types of PA on NAFLD [10,11], the longitudinal association of different types of PA with all-cause and cardiovascular mortality has not been sufficiently investigated among individuals with NAFLD. In this prospective population-based study, we noted that fulfilling PA guidelines for leisure-time and transportation-related PA in individuals with NAFLD was associated with lower all-cause and cardiovascular mortality. Leisure-time and transportation-related PA had a significant dose-dependent protective effect on all-cause and cardiovascular mortality, independent of known coexisting risk factors. In addition, we noted a harmful effect of sedentary behavior on survival independent of total PA among individuals with NAFLD.
There is a scarcity of literature on the association between different types of PA and mortality in individuals with NAFLD. Previous studies on the effects of PA on NAFLD have investigated the severity or development of NAFLD rather than all-cause and cause-specific mortality [6,7,21]. A Norwegian study showed the survival benefit of an estimated high cardiorespiratory fitness by a prediction model in NAFLD [22]. A recent study determined that increasing total cumulative PA in NAFLD benefits all-cause mortality, which may be partly derived from a reduction in cardiovascular mortality [12]. Consistent with these studies, our study found that increasing total PA among individuals with NAFLD was associated with a lower risk for all-cause and cardiovascular mortality in a nationally representative sample. In comparison to other above-mentioned studies, our study has the advantage of demonstrating an association of specific types of PA and meeting the criteria for PA guidelines with all-cause and cardiovascular mortality. We observed significant benefits for leisure-time and transportation-related PA for recommended PA on all-cause mortality and an additional benefit of more than twofold for the minimum recommended PA on cardiovascular mortality. The likelihood of meeting PA guidelines was lower in individuals with NAFLD than in those without NAFLD [10]. In the absence of an approved pharmacologic treatment for NAFLD, our study found that meeting PA guidelines (>150 min/week) for leisure time and transportation-related PA improved survival in individuals with NAFLD. While we observed that advanced fibrosis and NAFLD co-existing with diabetes or obesity were independent risk factors for all-cause mortality, the independent association of leisure-time and transportation-related PA with all-cause mortality was also noted. Therefore, we suggest that specific types of PA for >150 min/week be integrated as lifestyle modifications into the clinical management plan of patients with NAFLD to provide a survival benefit.
This study did not show the beneficial effect of any type of PA on cancer-related mortality among individuals with NAFLD, although there was a beneficial effect of total PA on cancer-related mortality among individuals without NAFLD, consistent with a previous study [12]. The putative mechanistic pathways may include improvements in insulin sensitivity, reduction of visceral fat, lowering the level of carcinogenic adipocytokines, etc. [ 23]. This discrepancy may be explained by the small sample size due to the relatively short follow-up period. In addition, it has been observed that the association of PA with cancer-related mortality differs depending on the type of cancer [24]. While significant inverse associations were observed for colorectal and breast cancer-related mortality, which may be more prevalent among individuals without NAFLD [24], a few studies have reported an inverse association for liver cancer-related mortality, which was more prevalent among individuals with NAFLD [25]. Moreover, it may be more challenging to achieve decreased visceral fat, improved insulin sensitivity, and decreased adipokines among individuals with NAFLD than those without NAFLD [12]. Future studies are needed to determine these observations.
A previous study showed a significant decline in the frequency of meeting the criteria for transportation-related PA during 2007–2016 for individuals with NAFLD compared to those without NAFLD in the United States [10]. In this study, transportation-related PA significantly impacted all-cause and cardiovascular mortality only among individuals with NAFLD. Therefore, clinicians may recommend individuals with NAFLD to engage in transportation-related PA, beyond leisure-time PA, to improve health outcomes.
The 2018 Physical Activity Guidelines for Americans recently addressed sedentary behavior and its harmful effects on health [13]. Given its high prevalence among individuals with NAFLD, sedentary behavior can be considered an important therapeutic target in treating individuals with NAFLD. Our study is the first to report that increasing sedentary behavior among individuals with NAFLD was dose-dependently associated with increased risk for all-cause mortality and cardiovascular mortality. In the two previous studies assessing the association between sedentary behavior and mortality in the setting of NAFLD [12,22], sedentary behavior was not associated with an increased risk of all-cause and cardiovascular mortality compared with those with non-sedentary behavior. The current 2018 Physical Activity Guidelines for Americans do not specify a time threshold for sedentary behavior [13]. Our study has clearly demonstrated that over 8 h of sitting time has a higher risk of all-cause and cardiovascular mortality, irrespective of total PA categories. Therefore, clinicians may need to educate physically inactive individuals with NAFLD about the health benefits of avoiding sedentary behavior to improve survival.
The strengths of this study are the utilization of various types and amounts of PA and sedentary behavior using the validated questionnaire collected by trained personnel with a systematic protocol, the prospective cohort design with a follow-up over 8 years for all-cause and cause-specific mortality, a variety of clinical and metabolic variables, and a large number of recent multiethnic cohorts (2007–2014) that represent the current US population. Therefore, we believe our findings are generalizable to the US population. However, several limitations must be noted. First, we defined NAFLD using non-invasive panels (in the absence of any steatogenic medication(s) and other known causes of chronic liver disease), which may misclassify, overestimate, and/or underestimate the true prevalence of NAFLD and may limit the accuracy of identifying NAFLD in the general population. The NHANES 2007–2014 lacks radiological and histological data, considered the gold standard for diagnosing NAFLD. The HSI and USFLI, on the other hand, have been validated as robust non-invasive panels for the detecting NAFLD and independent predictors of all-cause and liver-related mortality [14,15,18,19,26]. Second, the NHANES data does not provide the longitudinal PA questionnaire, clinical, or laboratory data. Therefore, we are unable to assess serial changes in NAFLD status, PA status, weight, or metabolic changes related to PA in this study. Third, we assessed the types of PA status from a self-reported questionnaire rather than objectively measured PA, although this PA questionnaire was based on the well-validated Global Physical Activity Questionnaire. Fourth, we were unable to analyze and report liver-related mortality, which was not publicly available due to the small number of deaths. In addition, the data describing the severity of liver disease and the occurrence of liver outcomes were not included in the NHANES study design. Finally, unmeasured residual confounders must be considered as a possible explanation for at least part of the association, although we tried to adjust known risk factors to determine the independent association between the types of PA and mortality.
In conclusion, this population-based study suggests that meeting the PA guidelines of >150 min/week for leisure-time and transportation-related PA may provide a survival benefit for all-cause and cardiovascular mortality among individuals with NAFLD. Sedentary behavior in the setting of NAFLD demonstrated a survival disadvantage in all-cause and cardiovascular mortality. Therefore, we suggest the implementation of increasing PA and decreasing sedentary behavior in the management plan for the rapidly rising population of individuals with NAFLD. In the future, these lifestyle modifications can be easily integrated with approved pharmacologic therapy.
## References
1. Henry L., Paik J., Younossi Z.M.. **Review article: The epidemiologic burden of non-alcoholic fatty liver disease across the world**. *Aliment. Pharmacol. Ther.* (2022) **56** 942-956. DOI: 10.1111/apt.17158
2. **EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease**. *J. Hepatol.* (2016) **64** 1388-1402. DOI: 10.1016/j.jhep.2015.11.004
3. Chalasani N., Younossi Z., Lavine J.E., Charlton M., Cusi K., Rinella M., Harrison S.A., Brunt E.M., Sanyal A.J.. **The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases**. *Hepatology* (2018) **67** 328-357. DOI: 10.1002/hep.29367
4. Romero-Gomez M., Zelber-Sagi S., Trenell M.. **Treatment of NAFLD with diet, physical activity and exercise**. *J. Hepatol.* (2017) **67** 829-846. DOI: 10.1016/j.jhep.2017.05.016
5. Pagano G., Pacini G., Musso G., Gambino R., Mecca F., Depetris N., Cassader M., David E., Cavallo-Perin P., Rizzetto M.. **Nonalcoholic steatohepatitis, insulin resistance, and metabolic syndrome: Further evidence for an etiologic association**. *Hepatology* (2002) **35** 367-372. DOI: 10.1053/jhep.2002.30690
6. Kwak M.S., Kim D., Chung G.E., Kim W., Kim Y.J., Yoon J.H.. **Role of physical activity in nonalcoholic fatty liver disease in terms of visceral obesity and insulin resistance**. *Liver Int.* (2015) **35** 944-952. DOI: 10.1111/liv.12552
7. Zelber-Sagi S., Nitzan-Kaluski D., Goldsmith R., Webb M., Zvibel I., Goldiner I., Blendis L., Halpern Z., Oren R.. **Role of leisure-time physical activity in nonalcoholic fatty liver disease: A population-based study**. *Hepatology* (2008) **48** 1791-1798. DOI: 10.1002/hep.22525
8. Kwak M.S., Kim D., Chung G.E., Kim W., Kim J.S.. **The preventive effect of sustained physical activity on incident nonalcoholic fatty liver disease**. *Liver Int.* (2017) **37** 919-926. DOI: 10.1111/liv.13332
9. Grontved A., Hu F.B.. **Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: A meta-analysis**. *JAMA* (2011) **305** 2448-2455. DOI: 10.1001/jama.2011.812
10. Kim D., Vazquez-Montesino L.M., Li A.A., Cholankeril G., Ahmed A.. **Inadequate Physical Activity and Sedentary Behavior Are Independent Predictors of Nonalcoholic Fatty Liver Disease**. *Hepatology* (2020) **72** 1556-1568. DOI: 10.1002/hep.31158
11. Kim D., Konyn P., Cholankeril G., Ahmed A.. **Physical Activity Is Associated with Nonalcoholic Fatty Liver Disease and Significant Fibrosis Measured by FibroScan**. *Clin. Gastroenterol. Hepatol.* (2022) **20** e1438-e1455. DOI: 10.1016/j.cgh.2021.06.029
12. Kim D., Murag S., Cholankeril G., Cheung A., Harrison S.A., Younossi Z.M., Ahmed A.. **Physical Activity, Measured Objectively, Is Associated with Lower Mortality in Patients with Nonalcoholic Fatty Liver Disease**. *Clin. Gastroenterol. Hepatol.* (2021) **19** 1240-1247.e5. DOI: 10.1016/j.cgh.2020.07.023
13. Piercy K.L., Troiano R.P., Ballard R.M., Carlson S.A., Fulton J.E., Galuska D.A., George S.M., Olson R.D.. **The Physical Activity Guidelines for Americans**. *JAMA* (2018) **320** 2020-2028. DOI: 10.1001/jama.2018.14854
14. Lee J.H., Kim D., Kim H.J., Lee C.H., Yang J.I., Kim W., Kim Y.J., Yoon J.H., Cho S.H., Sung M.W.. **Hepatic steatosis index: A simple screening tool reflecting nonalcoholic fatty liver disease**. *Dig. Liver Dis.* (2010) **42** 503-508. DOI: 10.1016/j.dld.2009.08.002
15. Ruhl C.E., Everhart J.E.. **Fatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey**. *Aliment. Pharmacol. Ther.* (2015) **41** 65-76. DOI: 10.1111/apt.13012
16. Divney A.A., Murillo R., Rodriguez F., Mirzayi C.A., Tsui E.K., Echeverria S.E.. **Diabetes Prevalence by Leisure-, Transportation-, and Occupation-Based Physical Activity Among Racially/Ethnically Diverse U.S. Adults**. *Diabetes Care* (2019) **42** 1241-1247. DOI: 10.2337/dc18-2432
17. Kim D., Kim W., Adejumo A.C., Cholankeril G., Tighe S.P., Wong R.J., Gonzalez S.A., Harrison S.A., Younossi Z.M., Ahmed A.. **Race/ethnicity-based temporal changes in prevalence of NAFLD-related advanced fibrosis in the United States, 2005–2016**. *Hepatol. Int.* (2019) **13** 205-213. DOI: 10.1007/s12072-018-09926-z
18. Meffert P.J., Baumeister S.E., Lerch M.M., Mayerle J., Kratzer W., Volzke H.. **Development, external validation, and comparative assessment of a new diagnostic score for hepatic steatosis**. *Am. J. Gastroenterol.* (2014) **109** 1404-1414. DOI: 10.1038/ajg.2014.155
19. Hajifathalian K., Torabi Sagvand B., McCullough A.J.. **Effect of Alcohol Consumption on Survival in Nonalcoholic Fatty Liver Disease: A National Prospective Cohort Study**. *Hepatology* (2019) **70** 511-521. DOI: 10.1002/hep.30226
20. **National Center for Health Statistics, Public-Use Linked Mortality File**. (2019)
21. Kistler K.D., Brunt E.M., Clark J.M., Diehl A.M., Sallis J.F., Schwimmer J.B., Group N.C.R.. **Physical activity recommendations, exercise intensity, and histological severity of nonalcoholic fatty liver disease**. *Am. J. Gastroenterol.* (2011) **106** 460-468. DOI: 10.1038/ajg.2010.488
22. Croci I., Coombes J.S., Bucher Sandbakk S., Keating S.E., Nauman J., Macdonald G.A., Wisloff U.. **Non-alcoholic fatty liver disease: Prevalence and all-cause mortality according to sedentary behaviour and cardiorespiratory fitness. The HUNT Study**. *Prog. Cardiovasc. Dis.* (2019) **62** 127-134. DOI: 10.1016/j.pcad.2019.01.005
23. Li Y., Gu M., Jing F., Cai S., Bao C., Wang J., Jin M., Chen K.. **Association between physical activity and all cancer mortality: Dose-response meta-analysis of cohort studies**. *Int. J. Cancer* (2016) **138** 818-832. DOI: 10.1002/ijc.29828
24. Lugo D., Pulido A.L., Mihos C.G., Issa O., Cusnir M., Horvath S.A., Lin J., Santana O.. **The effects of physical activity on cancer prevention, treatment and prognosis: A review of the literature**. *Complement. Ther. Med.* (2019) **44** 9-13. DOI: 10.1016/j.ctim.2019.03.013
25. Baumeister S.E., Leitzmann M.F., Linseisen J., Schlesinger S.. **Physical Activity and the Risk of Liver Cancer: A Systematic Review and Meta-Analysis of Prospective Studies and a Bias Analysis**. *J. Natl. Cancer Inst.* (2019) **111** 1142-1151. DOI: 10.1093/jnci/djz111
26. Unalp-Arida A., Ruhl C.E.. **Liver fat scores predict liver disease mortality in the United States population**. *Aliment. Pharmacol. Ther.* (2018) **48** 1003-1016. DOI: 10.1111/apt.14967
|
---
title: Effect of Hypercholesterolemia, Systemic Arterial Hypertension and Diabetes
Mellitus on Peripapillary and Macular Vessel Density on Superficial Vascular Plexus
in Glaucoma
authors:
- María Sanz Gomez
- Ni Zeng
- Gloria Estefania Catagna Catagna
- Paula Arribas-Pardo
- Julian Garcia-Feijoo
- Carmen Mendez-Hernandez
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004387
doi: 10.3390/jcm12052071
license: CC BY 4.0
---
# Effect of Hypercholesterolemia, Systemic Arterial Hypertension and Diabetes Mellitus on Peripapillary and Macular Vessel Density on Superficial Vascular Plexus in Glaucoma
## Abstract
Background/Aims: Vascular factors are involved in the development of glaucoma, including diseases such as hypercholesterolemia (HC), systemic arterial hypertension (SAH), and diabetes mellitus (DM). The aim of this study was to determine the effect of glaucoma disease on peripapillary vessel density (sPVD) and macular vessel density (sMVD) on the superficial vascular plexus, controlling differences on comorbidities such as SAH, DM and HC between glaucoma patients and normal subjects. Methods: *In this* prospective, unicenter, observational cross-sectional study, sPVD and sMVD were measured in 155 glaucoma patients and 162 normal subjects. Differences between normal subjects and glaucoma patients’ groups were analyzed. A linear regression model with $95\%$ confidence and $80\%$ statistical power was performed. Results: Parameters with greater effect on sPVD were glaucoma diagnosis, gender, pseudophakia and DM. Glaucoma patients had a sPVD $1.2\%$ lower than healthy subjects (Beta slope 1.228; $95\%$CI 0.798–1.659, $p \leq 0.0001$). Women presented $1.19\%$ more sPVD than men (Beta slope 1.190; $95\%$CI 0.750–1.631, $p \leq 0.0001$), and phakic patients presented $1.7\%$ more sPVD than men (Beta slope 1.795; $95\%$CI 1.311–2.280, $p \leq 0.0001$). Furthermore, DM patients had $0.9\%$ lower sPVD than non-diabetic patients (Beta slope 0.925; $95\%$CI 0.293–1.558, $$p \leq 0.004$$). SAH and HC did not affect most of the sPVD parameters. Patients with SAH and HC showed $1.5\%$ lower sMVD in the outer circle than subjects without those comorbidities (Beta slope 1.513; $95\%$CI 0.216–2.858, $$p \leq 0.021$$ and 1.549; $95\%$CI 0.240–2.858, $$p \leq 0.022$$ respectively. Conclusions: Glaucoma diagnosis, previous cataract surgery, age and gender seem to have greater influence than the presence of SAH, DM and HC on sPVD and sMVD, particularly sPVD.
## 1. Introduction
Primary open-angle glaucoma (POAG) is a progressive optic neuropathy that leads to retinal ganglion cell loss and is the leading cause of irreversible blindness worldwide [1]. The physiopathology of glaucoma is not well known. Intraocular pressure (IOP) increase is the main risk factor for both the development and progression of glaucoma and the only one on which successful therapeutic action can be conducted [2], but there are other known risk factors [3,4,5,6]. Retinal microvasculature and vascular disfunction plays an important role in the development of glaucoma, and one of the leading proposed mechanisms of retinal ganglion cell injury is based on the vascular hypothesis [7,8]. The mechanisms of glaucomatous damage to the optic nerve head (ONH) remain controversial. The mechanical hypothesis, centered on the effect of high IOP on the retinal ganglion cell axons within the lamina cribosa, and the vascular hypothesis focused on vascular dysregulation and its association with ONH blood supply, can be closely related [9]. Multiple vascular factors can be involved, including diseases which are highly prevalent such as atherosclerosis, hypercholesterolemia (HC), systemic arterial hypertension (SAH), and diabetes mellitus (DM) [10,11,12,13].
The retina is considered as a window to the whole body. It is a unique site where the in vivo microvasculature can be directly visualized and monitored over time, which shares similar physiological features with vital organs such the brain, the heart, or the kidney [14,15,16]. Some studies have suggested that the retinal microvasculature could reflect the systemic circulation in vivo [17].
Retinal microvasculature visible in retinography can also provide information on the status of the systemic circulation in vivo [18]. On the other hand, optical coherence tomography angiography (OCTA) is a novel, non-contact, non-invasive imaging method that provides a three-dimensional assessment of ocular microcirculation. *It* generates data on the microvascular structure of the eye by measuring and processing the notion contrast of intravascular erythrocytes by sequential optical coherence tomography (OCT) scans of a particular area of the retina [19]. With this non-invasive tool, circumpapillary, papillary, and macular vessel density have been shown to be lower in glaucoma patients [20].
The main objective of this prospective, single center, observational study was to determine the effect of glaucoma disease on peripapillary vessel density (sPVD) and macular vessel density (sMVD) on superficial vascular plexus measured with OCTA controlling differences on comorbidities such as SAH, DM and HC between patients diagnosed with glaucoma, glaucoma suspects, and normal subjects.
A secondary objective of the study was to compare the diagnostic ability of OCTA in glaucoma patients.
## 2.1. Study Design
This was a prospective, unicenter, observational cross-sectional study, approved by the Institutional Review Board of our university hospital, the Hospital Clínico San Carlos of Madrid, and carried out in accordance with the principles of the Declaration of Helsinki. Patients with glaucoma, glaucoma suspects, and healthy control subjects were prospectively enrolled. Written informed consent was obtained from each participant who fulfilled the eligibility requirements for inclusion in the study.
## 2.2. Study Participants
All glaucoma patients who attended glaucoma consultation for a routine exam, who presented minimal best-corrected visual acuity (BCVA) of $\frac{20}{40}$, clinically determined glaucomatous optic disc changes, repeatable glaucomatous visual field loss, or both, and open-angle on gonioscopy were eligible for inclusion.
An abnormal visual field was defined as a mean defect (MD) greater than 2 dB or depressed points in the defect curve and variance loss higher than 7 dB2 using the Octopus TOP G1 program, Octopus 600, Haag Streit AG, Bern, Switzerland.
Glaucomatous optic disc changes were defined as focal (localized notching) or diffuse neuroretinal rim narrowing, or concentric enlargement of the optic disc cup.
Glaucoma suspects were considered those with a family history of glaucoma, or a former IOP increase either with or without hypotensive treatment.
Exclusion criteria were non-glaucomatous ocular disease, systemic disease or treatment affecting the visual field, refraction exceeding 5 D equivalent sphere or 3 D astigmatism, angle closure, or uveitic and neovascular glaucoma.
Control subjects were recruited with the following inclusion criteria: a minimal BCVA of $\frac{20}{40}$, normal eye examination in both eyes, IOP < 20 mmHg, normal aspect of the optic disc, and no personal or familiar history of glaucoma.
## 2.3. Examination Protocol
For the assessment of the primary and secondary endpoints, once the informed consent had been signed, all participants were explored the same day upon the performing of a complete ophthalmologic examination, which included refraction and keratometry, BCVA, slit-lamp biomicroscopy examination, IOP measurement using the handheld Goldmann applanation tonometer (GAT) Perkins (Clement-Clarke, Haag-Streit, Harlow, United Kingdom) and the iCare 200 rebound tonometer (iCare, Tiolat Oy, Helsinki, Finland), pachymetry using the anterior segment module spectral domain optical coherence tomography (SD-OCT, Cirrus, HD-OCT 5000, software version 5.2; Carl Zeiss Meditec, Inc., 2020. AG, Jena, German) and funduscopy using a 90D lens.
Peripapillary retinal nerve fiber layer (RNFL) thickness, retinal ganglion cell complex analysis, average ganglion cell layer, inner plexiform layer (GCIPL), and superficial peripapillary and macular vessel parameters were automatically measured using the Cirrus HD-OCT 5000, software version 11.5.2.54532 (Carl Zeiss Meditec, Inc., 2020. AG, Jena, Germany), which automatically provides all parameters in the SD-OCT and OCT-A.
A visual field examination was conducted and analyzed for glaucoma patients, but not for all healthy control subjects, as per the protocol.
The independent variables analyzed were peripapillary vessel density and whole macular, outer and inner macular vessel density measured using OCTA.
The parameters of both SD-OCT and OCTA were independently, blindly, and prospective measured, since the values are automatically provided by the Cirrus, HD-OCT 5000 device.
Study variables such as demographics and medical/ophthalmological history were assessed by questionnaire and extracted from the patients’ medical file at the visit. The focus was on ophthalmological history (age-related macular degeneration (AMD), cataract, glaucoma, retinopathies, ocular surgeries and trauma), and history of SAH, HC and DM. In addition, in the clinical history, the reason for the prescription of medication and the medical background of every subject included in the study were checked.
Only one randomly selected eye per subject was included in the final statistical analysis.
## 2.4. Optical Coherence Tomography Angiography
The Cirrus HD-OCT 5000, software version 11.5.2.54532 (Carl Zeiss Meditec, Inc., 2020. AG, Jena, German) not only automatically calculates the peripapillary RNFL, the segmented ganglion cell complex, and papillary cup parameters, but also allows for the measuring of vessel density on the superficial vascular plexus in an area previously determined by the device. It has a 68-kHz axial scan repetition rate per second. This provides a transverse and axial resolution of 15 and 5 microns, respectively. The Cirrus 5000 software AngioplexTM automictically quantifies the superficial plexus, which is defined from the inner limiting membrane to the inner plexiform layer. Blood flow information is generated by the algorithm OMAG (optical microangiopathy) with 245 A-scans in each B-scan.
All scans were performed by the same operators, who were blind to the diagnosis (MS, NZ, and EC), with pupil dilation in a dark room on the same day as the other tests. Poor-quality scans with a signal strength index (SSI) < 8 were excluded from the analysis.
Peripapillary 4.5 × 4.5 mm scans centered on the ONH were obtained. Two peripapillary parameters were measured in the area located between an inner circle with a radius of 2 mm and an outer circle with a 4.5 mm radius, both centered on the ONH, divided into four quadrants: superior, inferior, temporal, and nasal, the peripapillary perfusion density and the flux index for the whole peripapillary circle, and the four sectors. Perfusion density is the total area of perfused radial peripapillary capillary vasculature per unit area in a specific region (%), and flux index is a dimensionless parameter between 0 and 1 representing the average decorrelation signal (Figure 1).
Macular 6 × 6 mm scans centered on the fovea were performed. The OCTA Cirrus 5000 software subdivides the macular scan into the Early Treatment of Diabetic Retinopathy Study (ETDRS) map: a central circle within the 1-mm central circle on the ETDRS grid, an inner circle from 1 to 3 mm, an outer or external circle from 3 to 6 mm, and the whole circle.
Macular vessel density is defined as the total length of blood vessels from the skeletonized image to the total area in mm ratio and macular perfusion density, defined as the total area of perfused vasculature per unit area (%), and foveal avascular zone (FAZ) were automatically measured. The FAZ area, defined as the macular foveal area without blood flow signal (mm2), FAZ perimeter (mm), and the acicularity index (AI) i.e., the measured perimeter of FAZ-to-the perimeter of the projected circle ratio with the same area as the FAZ, was collected (Figure 1).
## 2.5. Statistical Analysis
The aim of this study was to evaluate the effect of suffering glaucoma on peripapillary and macular vessel density measured with OCTA controlling differences as to SAH, HC and DM between glaucoma diagnosed patients, glaucoma suspects, and normal subjects.
For this purpose, a linear regression model with $95\%$ confidence and $80\%$ statistical power was performed.
The Kolmogorov-Smirnov test was used to confirm the normal distribution of quantitative data.
A descriptive analysis of the variables included in the study was performed. The calculation of frequency distributions for qualitative variables and measures of central tendency and dispersion, mean, standard deviation [SD] in variables with normal distribution, and median and interquartile range in those variables with non-normal distribution for quantitative variables were performed. The $95\%$ confidence intervals ($95\%$CI) were shown for results associated with the primary and secondary objectives.
Sex, the percentage of patients who had undergone cataract surgery, patients with SAH, DM and HC, differences between the normal subjects and the glaucoma patients’ groups were compared using the Pearson χ2 test. The Student’s t-test was used to compare measurements between the two groups in parameters with normal distribution. Parameters not following normal distribution were evaluated with non-parametric tests. Relationships between parameters were analyzed using the Pearson and Spearman correlation coefficients.
Receiver operating characteristic (ROC) curves were constructed and areas under the ROC curves (AUROCs) were used to assess the capacity of each OCTA and SD-OCT variable to distinguish between glaucomatous and healthy eyes. AUROCs were compared for the different parameters using the DeLong method [21].
All statistical tests were performed using the software package IBM SPSS (version 26.0; IBM Corp., Armonk, NY, USA). Significance was set at $p \leq 0.05.$
## 3. Results
A total of 412 subjects scheduled for ophthalmology consultation were eligible for screening: 213 glaucoma patients and 199 control subjects. Out of these 412, 38 did not have enough quality macular and ONH OCTA scans in at least one eye; therefore, images were not available for analysis. The primary reason for poor OCTA image quality was the high quota of motion artifact, making analysis unfeasible. Twenty-two subjects were excluded from the analysis because ONH or macular images with SD-OCT could not be obtained. Finally, a further 35 were excluded from the statistical analysis because the inclusion/exclusion criteria were not satisfied, mainly because of refraction exceeding the 5 D equivalent sphere or 3 D astigmatism and visual acuity <$\frac{20}{40.}$ *As a* result, only 317 out of the 412 subjects that were initially eligible for screening were included in the final analysis. ( Figure 2).
## 3.1. Analysis of Demographics
The data of 317 subjects were included in the final analysis: 155 glaucoma patients or glaucoma suspects, and 162 normal subjects, $55.8\%$ of which male. The mean age was 58.27 years.
Eighty-eight subjects were pseudophakic ($27.8\%$). A total of $38.8\%$ of patients suffered from SAH, and $13.9\%$ were diabetic, none of whom presented clinically significant diabetic macular edema or proliferative diabetic retinopathy, and $28.4\%$ had a history of HC. There were significant differences in age, gender, patients having undergone cataract surgery, DM, HC, and SAH between glaucoma patients and control subjects. There were no differences in BCVA, pachymetry, and the disc area between groups. Detailed baseline demographics are presented in Table 1.
## 3.2. Qualitative Analysis
Out of 155 glaucoma patients, eighteen had undergone glaucoma surgery ($11.6\%$), of which 15 had undergone surgery once and three presented refractory glaucoma, two of them undergoing reintervention once, and a single patient undergoing reintervention twice.
Fifty-two glaucoma patients ($32.2\%$) were on antiglaucomatous treatment. Twenty-four ($15.5\%$) were taking a single drug, twenty-five were taking ($16.1\%$) two drugs, and three patients ($1.9\%$) were under three antiglaucoma medications, while forty-three patients ($27.7\%$) remained without ocular hypertensive treatment (Table 1).
## 3.3. Quantitative Analysis
IOP values measured with iCare 200 and GAT, RNFL, as well as ganglion cell complex results evaluated using SD-OCT are shown in Table 1. Statistical differences were found between healthy and glaucomatous subjects in all parameters evaluated with SD-OCT except disc area and GCIPL, which was similar in both groups, 1.84 vs. 1.85 mm2 and 264.88 ± 29.55 vs. 258.25 ± 30.49 µ in glaucoma and healthy subjects, respectively. The thinnest RNFL and greatest cup volume and cup-to-disc ratio were found in patients included in the glaucoma and glaucoma suspects group.
In Table 2, the results of the peripapillary and macular vessel density analysis performed with OCTA are shown.
Significant differences between glaucoma patients and normal subjects in whole peripapillary and macular vessel density were found (44.92 ± 1.65 vs. 43.22 ± $2.64\%$; <0.0001 and 43.72 ± 4.55 vs. 40.88 ± 5.75; $p \leq 0.001$). In the foveal area, however, AI showed significantly lower values in glaucoma subjects (0.68 ± 0.11) than in controls (0.71 ± 0.09), $p \leq 0.005$; both the FAZ area and the perimeter did not show differences between the groups, finding slightly inferior values in glaucoma patients in FAZ area, 0.21 ± 0.13 mm2 in glaucomatous subjects vs. 0.23 ± 0.11 mm2 in controls, $$p \leq 0.191$$, and similar values in FAZ perimeter, 1.96 ± 0.75 mm in glaucoma subjects vs. 1.96 ± 0.50 mm in control group, $$p \leq 0.929.$$ Macular perfusion density in the central circle did not show differences between the groups; however, vessel density in this macular sector showed slightly lower values in glaucoma patients: 20.18 ± $8.35\%$ vs. 21.38 ± $7.44\%$, $$p \leq 0.177.$$ Significantly lower vessel density values were found in the two concentric macular external rings or circles in glaucomatous patients (Table 2).
Both peripapillary vessel density and flux index showed significatively lower values in glaucoma patients and glaucoma suspects, not only globally, but also in all peripapillary sectors analyzed with OCTA.
In Table 3, most relevant correlations between peripapillary and macular vessel density parameters analyzed with OCTA and demographic data, RNFL thickness, papillary and macular parameters obtained with SD-OCT are shown.
Significant negative moderate correlation between age and number of antiglaucomatous medications and peripapillary vessel density in all peripapillary sectors as well as most parameters that quantify macular vessel density were found, the greatest being the correlation between the number of antiglaucomatous medications and the peripapillary perfusion density in the inferior quadrant (Pearson’s correlation coefficient −0.443, $p \leq 0.001$).
Regarding the RNFL, ONH and ganglion cell complex parameters analyzed with SD-OCT, a positive correlation between RNFL thickness and peripapillary vessel density parameters was observed, with the greatest the correlation found between RNFL thickness and peripapillary perfusion density in the superior quadrant (Pearson’s correlation coefficient 0.429, $p \leq 0.0001$). Parameters related to size of the ONH cup, i.e., cup-to-disc ratio, vertical cup-to-disc ratio, and cup volume showed a low negative correlation with peripapillary vessel density; that is, the greatest cup-to-disc ratio and cup volume, and the least peripapillary vessel density. On the other hand, significant positive moderate correlation between peripapillary vessel density indices and rim area were found, the greatest being the correlation between rim area and peripapillary perfusion density in the inferior quadrant (Pearson’s correlation coefficient 0.502, $p \leq 0.0001$). The greater the rim area, the higher the peripapillary vessel density.
A significant but low negative correlation between ganglion cell complex measurement and most of peripapillary and macular vessel density parameters was found (Table 3).
Table 4 shows the results of the multiple regression model.
Whole peripapillary and macular vessel density, peripapillary and macular vessel density in each peripapillary and macular sector, foveal avascularity indices, FAZ area, FAZ perimeter, and AI were considered dependent variables. Independent variables included in the regression model were glaucoma diagnosis, SAH, DM and HC, and all demographic data that showed differences between both diagnostic groups, including age, gender, and previous cataract surgery.
Parameters with greater effect on peripapillary vessel density were glaucoma diagnosis, gender, pseudophakia, and DM. These parameters showed an effect on both whole peripapillary vessel density and vessel density in the superior and inferior peripapillary quadrants. Glaucoma patients had a peripapillary vessel density $1.2\%$ lower than healthy subjects (β slope 1.228; $95\%$CI 0.798–1.659, $p \leq 0.0001$). Women presented $1.19\%$ more peripapillary vessel density than men (β slope 1.190; $95\%$CI 0.750–1.631, $p \leq 0.0001$), and phakic patients presented $1.7\%$ more peripapillary vessel density (β slope 1.795; $95\%$CI 1.311–2.280, $p \leq 0.0001$). Furthermore, DM patients had $0.9\%$ lower vessel density than non-diabetic patients (β slope 0.925; $95\%$CI 0.293–1.558, $$p \leq 0.004$$). SAH and HC did not affect all the peripapillary vessel density analyzed except the peripapillary perfusion density in the nasal quadrant (β slope 1.205; $95\%$CI 0.389–2.020, $$p \leq 0.004$$).
With regard to the factors that can influence macular vessel density, their behavior was different. Glaucoma diagnosis does not seem the parameter with the greatest effect on macular vessel density. Again, phakic patients showed a macular vessel density higher than pseudophakic patients, $1.9\%$ higher in the outer circle (β slope 1.921; $95\%$CI 0.568–3.274, $$p \leq 0.006$$), while patients with SAH and HC showed $1.5\%$ lower macular vessel density in the outer circle than subjects without those comorbidities (β slope 1.513; $95\%$CI 0.216–2.858, $$p \leq 0.021$$ and 1.549; $95\%$CI 0.240–2.858, $$p \leq 0.022$$, respectively.
Given the significant age difference between normal subjects and glaucoma patients, to examine the effect of age in the analyses, a comparison of a reduced age-matched cohort of healthy subjects versus the glaucoma group was performed. The results are shown in Tables S1–S4 in Supplementary Materials. No relevant differences were found in the results of this sub-analysis and the results obtained in the analysis performed including all the subjects enrolled in the study.
Table 5 shows AUROCs for each OCTA and ONH SD-OCT parameter evaluated in this study and the cut-off providing sensitivity for $95\%$ and $80\%$ specificity determined in each case.
Overall, the AUROCs for discriminating between healthy and glaucomatous eyes were higher for the OCTA peripapillary blood flux index in the nasal quadrant, whole peripapillary blood flux index, and peripapillary perfusion density in the inferior quadrant: 0.79; $95\%$CI 0.73 to 0.85, $p \leq 0.001$ vs. 0.78; $95\%$CI 0.0.72 to 0.85, $p \leq 0.001$ vs. 0.78; $95\%$CI 0.72 to 0.84, $p \leq 0.001.$ The OCTA macular vessel density indices and ONH SD-OCT parameters showed lower AUROCs. Macular vessel density in the outer circle and SD-OCT rim area showed the highest AUROCs for OCTA macular and SD-OCT indices: 0.75; $95\%$CI 0.68 to 0.81; $p \leq 0.001$ vs. 0.74; $95\%$CI 0.67 to 0.81, $p \leq 0.001$, respectively.
The SD-OCT rim area had the greatest sensitivity, with a specificity of $95\%$ (Whole peripapillary blood flux index had a sensitivity of $29\%$, a positive likelihood ratio of 5.85, and a negative likelihood ratio of 0.74 and SD-OCT rim area had a sensitive of $44\%$, a positive likelihood ratio of 8.8 and a negative likelihood ratio of 0.58) and a specificity of $80\%$ (whole peripapillary blood flux index had a sensitivity of $65\%$, a positive likelihood ratio of 3.26, and a negative likelihood ratio of 0.43, and the SD-OCT rim area had a sensitivity of $60\%$, a positive likelihood ratio of 3.02, and a negative likelihood ratio of 0.49).
Pairwise comparisons showed that the AUROC of the whole peripapillary blood flux index (0.78) was not significantly different than the SD-OCT rim area (0.74) ($$p \leq 0.628$$) or macular vessel density in the outer circle (0.75) ($$p \leq 0.332$$).
## 4. Discussion
Although the main risk factor in glaucoma development is IOP increase [22], vascular theory is included among theories of this pathology development. Vascular theory explains vascularization importance because it is responsible for the nutrient supply to the retinal ganglion cells. It is likely that both risk factors come together, having the greatest weight on the mechanical effect of IOP increase on the papilla [9].
OCTA quantitatively measures vascular density the in retina using an indirect method based on erythrocyte movement inside the blood vessels [19].
The clinical utility of OCTA has been demonstrated in different pathologies with retina vascular damage such as diabetic retinopathy [23]. It has been found that patients with SAH present macular vessel density reduction in their central area, and concrete foveal and parafoveal levels [24]. A recent study found microcirculatory disturbances in patients with familiar HC [25]. The prospective follow up of these patients with SAH, DM and HC using OCTA could help to identify vascular changes that might help with better metabolic and therapeutic disease control.
Despite the importance of vascular factors in glaucoma development, the role of vascular risk diseases such as SAH, DM or HC has not been significantly studied in glaucoma patients.
In this study, the effect of those diseases on superficial retinal vessel density has been analyzed, evaluating the effect of glaucoma on peripapillary and macular vessel density, controlling for differences in comorbidities such as SAH, DM or HC. Our results show that some clinical factors in patients have greater weight on macular and peripapillary density than the presence of SAH, DM and HC themselves. Phakic patients present higher macular and peripapillary density, its most marked effect being at some peripapillary quadrants, obtaining values $2.22\%$ above in vessel density in some peripapillary sectors such as the peripapillary perfusion density in the inferior quadrant in phakic patients, which is in concordance with what has been formerly published [26].
Macular vessel density is influenced by age and gender, but peripapillary density is not. For each year-old increase, macular vessel density decreases $0.1\%$ in the central and inner macular circle.
With respect to the effect of age on vascular density, a relationship with central macular vessel density has been found. Our study agrees with those previously performed on healthy subjects. Abay et al. evaluated macular and papillary and peripapillary vessel density on healthy subjects in different age groups, finding a decrease in total parafoveal and perifoveal macular density with no differences in the foveal avascular zone [27]. Changes in peripapillary vessel density found by other authors in different age groups are in concordance with our results, as we have not found an age effect on peripapillary vessel density [28,29]. On the contrary, macular vessel density was lower as age increased. For each year the age increases, macular vessel density decreases $0.081\%$, and is more accentuated in macular perfusion density in the inner circle and macular perfusion density in the central circle, which is in line with previous results [27]. The reason for these discrepancies might be the methods used for measuring vessel density. The software we used was different from those used in these studies.
With regard to vascular density differences determined with OCTA between gender, women present a $1.1\%$ increase in peripapillary vessel density compared to men, this association being more evident in some peripapillary sectors such as the inferior and superior, and not finding a gender effect on macular vessel density. Comparative retinal vascular density studies examining gender are scarce. A former study performed by Wang et al. in 111 healthy subjects and 130 glaucomatous subjects found that women presented a $1.2\%$ and $0.7\%$ significant increase in circumpapillary vessel density and macular vessel density, respectively, compared to men [30]. This trend was greater in glaucomatous patients in an early stage. In our study, the mean defect of glaucoma patients was 2.5 dB, that is, there is a predominance of early glaucoma, which is in line with Wang et al. ’s findings [30].
In this study, glaucoma diagnosis seems to have more effect on peripapillary vessel density than the presence of vascular risk factors such as SAH and HC. In our study, suffering DM supposes a peripapillary vessel density that is $0.9\%$ lower. Suffering SAH or HC does not show an effect on peripapillary vessel density. However, having undergone cataract surgery, or being a woman, seems to have more relevance on peripapillary vessel density, but not greater than whether one suffers from glaucoma.
Except for this effect of DM on peripapillary vessel density, the influence of these three diseases on vascular density determined by OCTA has not been shown in our results. Furthermore, glaucoma diagnosis, the presence of pseudophakia, sex, and age seem to have more of an effect on the evaluated parameters than the fact of having high blood pressure, DM or HC.
With regard to the diagnostic capacity of the measurement of vessel density in glaucoma, when evaluating differences on macular vessel density between glaucoma patients or glaucoma suspects and healthy subjects, differences were found in whole macular perfusion density, macular perfusion density in the outer circle, and macular perfusion density in the inner circle, but macular vascular indices did not show statistically significant differences between both groups in macular perfusion density in the central circle, FAZ area (mm2), foveal avascular perimeter (mm), and AI. However, peripapillary vessel density did show differences between both groups, both globally and in sectors. AUROCs results also show a higher diagnostic performance in peripapillary vessel density than macular vessel density. Our results are in line with other studies that found that the diagnostic capacity of peripapillary vessel density showed higher diagnostic performance [31,32].
In addition, a correlation exists between peripapillary vessel density and RNFL thickness, so that patients with lower RNFL thickness, and therefore advanced structural damage, would present lower vascular density. This does not happen with retinal ganglion cell complex indices. Glaucoma studies performed by other authors have demonstrated diagnostic superiority on RNFL over retinal ganglion cell complex in patients with early glaucoma, ganglion cell complex analysis being considered to be useful for detecting glaucoma in myopic eyes or advanced glaucoma, in which structural features such as peripapillary atrophy or a potential floor effect may reduce the diagnostic ability of peripapillary RNFL analysis [33,34]. In our study, AUROCs are inferior by 0.8 and slightly superior in peripapillary vascular density indices. The fact that glaucoma patients, whose mean defect is 2.5 dB, have been selected and glaucoma suspects without ocular hypotensive treatment have been included in the glaucoma group, could explain those results.
Our study has several limitations. A visual field examination was done for glaucoma patients, but not for all healthy control subjects, so the analysis of mean deviation in glaucoma patients was not assessed.
Axial length could influence OCT imaging. In this study, axial length was not measured, but the refractometry was. Patients with a refraction exceeding a 5 D equivalent sphere or 3 D astigmatism were excluded, and therefore the effect of axial length on OCT imaging should be irrelevant.
Glaucoma patients and healthy subjects present differences in SAH, DM and HC distribution, as well as age, sex or pseudophakia differences. However, primary objectives such as the determination of the effect of suffering glaucoma on peripapillary and on macular vessel density measured with OCTA, controlling comorbidities such as SAH, DM and HC identification of patient ophthalmologic exam factors that influence AOCT parameters, were adjusted for those confusion factors. Thus, we believe that those differences did not affect the results.
On the other hand, the regression model used to evaluate the presence of glaucoma, clinical examination parameters, and SAH, DM and HC comorbidities reached sufficient statistical power, above $80\%$ and $95\%$ confidence levels.
All analyses performed on DM or HC have a lower statistical power. A great number of diabetic or HC patients would be needed to definite conclusions; however, due to our hospital casuistry this sample size cannot be reached. No matter its low statistical power, our results are relevant as a trend is detected.
Prior to the start of this study, a sample size estimation was performed by comparing the mean differences in OCTA whole peripapillary vessel density between groups.
To evaluate the effect of SAH, DM and DL on peripapillary vessel density, 65 SAH and no-SAH patients needed to be included: 34 HC and non-HC patients and 24 diabetic and non- diabetic patients.
Our research has focused on vessel density quantitative metrics derived from OCTA images, but skeletonized vessel density was not evaluated. Since skeletonization could narrow vessels to a width of a single pixel, it does not create a vascular density, but rather becomes a length-based measurement.
The Cirrus HD-OCT 5000 Angioplex has built-in software to automatically calculate vessel density. The results shown in this study have been derived from the quantitative data automatically provided by this device, and no additional quantification has been performed on the images obtained for each subject. The underlying algorithms used have not been made publicly available, and calculated OCTA quantitative metrics cannot be generalized beyond this device.
There are no studies that evaluate the relevance of these risk factors in glaucoma patients. More studies are needed to evaluate the effect on different glaucoma types and possible differences between primary open angle and normotensive glaucoma. Time since diagnosis, hemoglobin A1C value in diabetic patients, or blood pressure values should also be assessed in future studies.
In conclusion, glaucoma diagnosis has more of an effect on superficial peripapillary and macular vessel density than comorbidities such as SAH, DM and HC in glaucoma patients, glaucoma suspects and normal subjects. Other factors such as cataract surgery, age and gender seem to present a greater influence than the presence of SAH, DM and HC in peripapillary vessel density. This effect is more evident on peripapillary than macular superficial vascular density.
Peripapillary vessel density provides a greater diagnostic ability than macular vessel density in glaucoma patients, and in addition to being more clinically relevant in glaucoma, it may be more affected by these factors than macular vascular density, which should be considered in long-term studies in glaucoma patients.
## References
1. Bourne R.R.A., Taylor H.R., Flaxman S.R., Keeffe J., Leasher J., Naidoo K., Pesudovs K., White R.A., Wong T.Y., Resnikoff S.. **Number of People Blind or Visually Impaired by Glaucoma Worldwide and in World Regions 1990–2010: A Meta-Analysis**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0162229
2. **Validated Prediction Model for the Development of Primary Open-Angle Glaucoma in Individuals with Ocular Hypertension**. *Ophthalmology* (2007) **114** 10-19.e2. DOI: 10.1016/j.ophtha.2006.08.031
3. Gordon M.O., Beiser J.A., Brandt J.D., Heuer D.K., Higginbotham E.J., Johnson C.A., Keltner J.L., Miller J.P., Parrish R.K., Wilson M.R.. **The Ocular Hypertension Treatment Study: Baseline Factors That Predict the Onset of Primary Open-Angle Glaucoma**. *Arch. Ophthalmol.* (2002) **120** 714-720. DOI: 10.1001/archopht.120.6.714
4. Dueker D.K., Singh K., Lin S.C., Fechtner R.D., Minckler D.S., Samples J.R., Schuman J.S.. **Corneal Thickness Measurement in the Management of Primary Open-Angle Glaucoma**. *Ophthalmology* (2007) **114** 1779-1787. DOI: 10.1016/j.ophtha.2007.04.068
5. **The Advanced Glaucoma Intervention Study (AGIS): 12. Baseline Risk Factors for Sustained Loss of Visual Field and Visual Acuity in Patients with Advanced Glaucoma**. *Am. J. Ophthalmol.* (2002) **134** 499-512. DOI: 10.1016/S0002-9394(02)01659-8
6. Gillespie B.W., Niziol L.M., Ehrlich J.R., Johnson C.A., Caprioli J., VanVeldhuisen P.C., Lichter P.R., Musch D.C.. **Demographic, Comorbid, and Clinical Variables Associated with Pointwise Visual Field Damage in Glaucoma: Data From the AGIS and CIGTS Clinical Trials**. *Transl. Vis. Sci. Technol.* (2021) **10** 28. DOI: 10.1167/tvst.10.12.28
7. Yarmohammadi A., Zangwill L.M., Diniz-Filho A., Suh M.H., Yousefi S., Saunders L.J., Belghith A., Manalastas P.I.C., Medeiros F.A., Weinreb R.N.. **Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma**. *Ophthalmology* (2016) **123** 2498-2508. DOI: 10.1016/j.ophtha.2016.08.041
8. Shin J.D., Wolf A.T., Harris A., Verticchio Vercellin A., Siesky B., Rowe L.W., Packles M., Oddone F.. **Vascular Biomarkers from Optical Coherence Tomography Angiography and Glaucoma: Where Do We Stand in 2021?**. *Acta Ophthalmol.* (2022) **100** e377-e385. DOI: 10.1111/aos.14982
9. Burgoyne C.F., Crawford Downs J., Bellezza A.J., Francis Suh J.-K., Hart R.T.. **The Optic Nerve Head as a Biomechanical Structure: A New Paradigm for Understanding the Role of IOP-Related Stress and Strain in the Pathophysiology of Glaucomatous Optic Nerve Head Damage**. *Prog. Retin. Eye Res.* (2005) **24** 39-73. DOI: 10.1016/j.preteyeres.2004.06.001
10. Mursch-Edlmayr A.-S., Bolz M., Strohmaier C.. **Vascular Aspects in Glaucoma: From Pathogenesis to Therapeutic Approaches**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22094662
11. Wang S., Bao X.. **Hyperlipidemia, Blood Lipid Level, and the Risk of Glaucoma: A Meta-Analysis**. *Investig. Ophthalmol. Vis. Sci.* (2019) **60** 1028. DOI: 10.1167/iovs.18-25845
12. Kim K.E., Oh S., Baek S.U., Ahn S.J., Park K.H., Jeoung J.W.. **Ocular Perfusion Pressure and the Risk of Open-Angle Glaucoma: Systematic Review and Meta-Analysis**. *Sci. Rep.* (2020) **10** 10056. DOI: 10.1038/s41598-020-66914-w
13. Costa L., Cunha J.P., Amado D., Pinto L.A., Ferreira J.. **Diabetes Mellitus as a Risk Factor in Glaucoma’s Physiopathology and Surgical Survival Time: A Literature Review**. *J. Curr. Glaucoma Pract.* (2015) **9** 81-85. PMID: 26997842
14. Salobrar-Garcia E., Méndez-Hernández C., de Hoz R., Ramírez A.I., López-Cuenca I., Fernández-Albarral J.A., Rojas P., Wang S., García-Feijoo J., Gil P.. **Ocular Vascular Changes in Mild Alzheimer’s Disease Patients: Foveal Avascular Zone, Choroidal Thickness, and ONH Hemoglobin Analysis**. *J. Pers. Med.* (2020) **10**. DOI: 10.3390/jpm10040231
15. Madala S., Adabifirouzjaei F., Lando L., Yarmohammadi A., Long C.P., Bakhoum C.Y., Goldbaum M.H., Sarraf D., DeMaria A.N., Bakhoum M.F.. **Retinal Ischemic Perivascular Lesions, a Biomarker of Cardiovascular Disease**. *Ophthalmol. Retin.* (2022) **6** 865-867. DOI: 10.1016/j.oret.2022.05.005
16. Kasumovic A., Matoc I., Rebic D., Avdagic N., Halimic T.. **Assessment of Retinal Microangiopathy in Chronic Kidney Disease Patients**. *Med. Arch.* (2020) **74** 191. DOI: 10.5455/medarh.2020.74.191-194
17. Wagner S.K., Fu D.J., Faes L., Liu X., Huemer J., Khalid H., Ferraz D., Korot E., Kelly C., Balaskas K.. **Insights into Systemic Disease through Retinal Imaging-Based Oculomics**. *Transl. Vis. Sci. Technol.* (2020) **9** 6. DOI: 10.1167/tvst.9.2.6
18. Ptito M., Bleau M., Bouskila J.. **The Retina: A Window into the Brain**. *Cells* (2021) **10**. DOI: 10.3390/cells10123269
19. Kashani A.H., Chen C.-L., Gahm J.K., Zheng F., Richter G.M., Rosenfeld P.J., Shi Y., Wang R.K.. **Optical Coherence Tomography Angiography: A Comprehensive Review of Current Methods and Clinical Applications**. *Prog. Retin. Eye Res.* (2017) **60** 66-100. DOI: 10.1016/j.preteyeres.2017.07.002
20. Rao H.L., Pradhan Z.S., Suh M.H., Moghimi S., Mansouri K., Weinreb R.N.. **Optical Coherence Tomography Angiography in Glaucoma**. *J. Glaucoma* (2020) **29** 312-321. DOI: 10.1097/IJG.0000000000001463
21. DeLong E.R., DeLong D.M., Clarke-Pearson D.L.. **Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach**. *Biometrics* (1988) **44** 837-845. DOI: 10.2307/2531595
22. Weinreb R.N., Aung T., Medeiros F.A.. **The Pathophysiology and Treatment of Glaucoma**. *JAMA* (2014) **311** 1901. DOI: 10.1001/jama.2014.3192
23. Boned-Murillo A., Albertos-Arranz H., Diaz-Barreda M.D., Orduna-Hospital E., Sánchez-Cano A., Ferreras A., Cuenca N., Pinilla I.. **Optical Coherence Tomography Angiography in Diabetic Patients: A Systematic Review**. *Biomedicines* (2021) **10**. DOI: 10.3390/biomedicines10010088
24. Anjos R., Ferreira A., Barkoudah E., Claggett B., Abegão Pinto L., Miguel A.. **Application of Optical Coherence Tomography Angiography Macular Analysis for Systemic Hypertension. A Systematic Review and Meta-Analysis**. *Am. J. Hypertens.* (2022) **35** 356-364. DOI: 10.1093/ajh/hpab172
25. Eid P., Arnould L., Gabrielle P.-H., Aho L.S., Farnier M., Creuzot-Garcher C., Cottin Y.. **Retinal Microvascular Changes in Familial Hypercholesterolemia: Analysis with Swept-Source Optical Coherence Tomography Angiography**. *J. Pers. Med.* (2022) **12**. DOI: 10.3390/jpm12060871
26. Yu S., Frueh B.E., Steinmair D., Ebneter A., Wolf S., Zinkernagel M.S., Munk M.R.. **Cataract Significantly Influences Quantitative Measurements on Swept-Source Optical Coherence Tomography Angiography Imaging**. *PLoS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0204501
27. Abay R.N., Akdeniz G.Ş., Katipoğlu Z., Kerimoğlu H.. **Normative Data Assessment of Age-Related Changes in Macular and Optic Nerve Head Vessel Density Using Optical Coherence Tomography Angiography**. *Photodiagnosis Photodyn. Ther.* (2022) **37** 102624. DOI: 10.1016/j.pdpdt.2021.102624
28. Gadde S.G.K., Anegondi N., Bhanushali D., Chidambara L., Yadav N.K., Khurana A., Sinha Roy A.. **Quantification of Vessel Density in Retinal Optical Coherence Tomography Angiography Images Using Local Fractal Dimension**. *Investig. Ophthalmol. Vis. Sci.* (2016) **57** 246. DOI: 10.1167/iovs.15-18287
29. Rao H.L., Pradhan Z.S., Weinreb R.N., Reddy H.B., Riyazuddin M., Sachdeva S., Puttaiah N.K., Jayadev C., Webers C.A.B.. **Determinants of Peripapillary and Macular Vessel Densities Measured by Optical Coherence Tomography Angiography in Normal Eyes**. *J. Glaucoma* (2017) **26** 491-497. DOI: 10.1097/IJG.0000000000000655
30. Wang S., Mendez-Hernandez C., Arribas-Pardo P., Salazar Quiñones L., Fernandez-Perez C., Garcia-Feijoo J.. **Gender-Related Influences on Superficial Papillary Microcirculation Measured with Optical Coherence Tomography Angiography in Patients with Glaucoma**. *Curr. Eye Res.* (2020) **45** 1534-1542. DOI: 10.1080/02713683.2020.1755698
31. Yip V.C.H., Wong H.T., Yong V.K.Y., Lim B.A., Hee O.K., Cheng J., Fu H., Lim C., Tay E.L.T., Loo-Valdez R.G.. **Optical Coherence Tomography Angiography of Optic Disc and Macula Vessel Density in Glaucoma and Healthy Eyes**. *J. Glaucoma* (2019) **28** 80-87. DOI: 10.1097/IJG.0000000000001125
32. Triolo G., Rabiolo A., Shemonski N.D., Fard A., di Matteo F., Sacconi R., Bettin P., Magazzeni S., Querques G., Vazquez L.E.. **Optical Coherence Tomography Angiography Macular and Peripapillary Vessel Perfusion Density in Healthy Subjects, Glaucoma Suspects, and Glaucoma Patients**. *Investig. Ophthalmol. Vis. Sci.* (2017) **58** 5713. DOI: 10.1167/iovs.17-22865
33. Oddone F., Lucenteforte E., Michelessi M., Rizzo S., Donati S., Parravano M., Virgili G.. **Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma**. *Ophthalmology* (2016) **123** 939-949. DOI: 10.1016/j.ophtha.2015.12.041
34. Rezkallah A., Douma I., Bonjour M., Mathis T., Kodjikian L., Denis P.. **Evaluation of the Correlation between Regional Retinal Ganglion Cell Damage and Visual Field Sensitivity in Patients with Advanced Glaucoma**. *J. Clin. Med.* (2022) **11**. DOI: 10.3390/jcm11164880
|
---
title: 'The Effect of Hematocrit on All-Cause Mortality in Geriatric Patients with
Hip Fractures: A Prospective Cohort Study'
authors:
- Yu-Min Zhang
- Kun Li
- Wen-Wen Cao
- Shao-Hua Chen
- Bin-Fei Zhang
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004393
doi: 10.3390/jcm12052010
license: CC BY 4.0
---
# The Effect of Hematocrit on All-Cause Mortality in Geriatric Patients with Hip Fractures: A Prospective Cohort Study
## Abstract
Objective: The present study aimed to evaluate the association between hematocrit (HCT) levels and all-cause mortality in geriatric hip fractures. Methods: Older adult patients with hip fractures were screened between January 2015 and September 2019. The demographic and clinical characteristics of these patients were collected. Linear and nonlinear multivariate Cox regression models were used to identify the association between HCT levels and mortality. Analyses were performed using EmpowerStats and the R software. Results: A total of 2589 patients were included in this study. The mean follow-up period was 38.94 months. Eight hundred and seventy-five ($33.8\%$) patients died due to all-cause mortality. Linear multivariate Cox regression models showed that HCT level was associated with mortality (hazard ratio [HR] = 0.97, $95\%$ confidence interval [CI]: 0.96–0.99, $$p \leq 0.0002$$) after adjusting for confounding factors. However, the linear association was unstable and nonlinearity was identified. A HCT level of $28\%$ was the inflection point for prediction. A HCT level of <$28\%$ was associated with mortality (HR = 0.91, $95\%$ CI: 0.87–0.95, $p \leq 0.0001$), whereas a HCT level > $28\%$ was not a risk factor for mortality (HR = 0.99, $95\%$ CI: 0.97–1.01, $$p \leq 0.3792$$). We found that the nonlinear association was very stable in the propensity score-matching sensitivity analysis. Conclusions: The HCT level was nonlinearly associated with mortality in geriatric hip fracture patients and could be considered a predictor of mortality in these patients. Registration: ChiCTR2200057323.
## 1. Introduction
Hip fractures are common injuries seen in older adults; they are especially common in women who often suffer from osteoporosis and multiple comorbidities [1]. In the United States, more than 320,000 patients are hospitalized per annum with hip fractures [2]. The global annual number of hip fractures is predicted to increase to 6.5 million fractures by 2050 [3]. A similar upward trend in the incidence of hip fractures has been projected for China, where it is estimated that the number will rise to 1.079 million by 2050 [4]. Although hip fractures comprise only $14\%$ of geriatric fractures, they account for $72\%$ of the total cost of orthopedic fracture care in older adults [5]. Most geriatric hip fractures are caused by falls, and they are associated with an increased mortality. The mean one-year global mortality rate in geriatric hip fracture patients is $22\%$ [6]. Given the aging population and high incidence of hip fractures, the costs of geriatric hip fractures are projected to increase dramatically and consume more medical and health resources [7].
The prognosis of geriatric hip fractures is poor, and only about one-fifth of them return to their preinjury functional status one year after surgery [8]. Multiple factors have been proven to directly affect morbidity and mortality after hip surgery; these include delay in surgery, cancer, coronary heart disease, stroke, pneumonia, urinary tract infection, fluid and electrolyte imbalances, and perioperative anemia [9,10]. In addition, a systematic review indicated that malignancy, nursing home residence, time to surgery > two days, pulmonary disease, diabetes, and cardiovascular disease significantly increased the risk of mortality after hip fracture surgery [11]. Song also reported that frailty can predict adverse outcomes effectively in geriatric hip fracture patients [12].
Hematocrit (HCT), is a test that measures the volume of packed red blood cells relative to whole blood [13]. This test can identify conditions such as anemia or polycythemia, monitor response to treatment [14], and as act as a surrogate marker for factors affecting mortality after hip fracture [15].
Anemia, defined by hemoglobin levels in most of these studies, is common in older adults and may increase the risk of death [16]. It was reported that an estimated $40\%$ of geriatric hip fracture patients were anemic on admission, and nearly all of them were anemic postoperatively [17]. Previous studies have identified anemia as a significant prognostic risk factor for patients who undergo surgery for hip fracture [18,19,20,21,22]. Several studies [21,23,24,25] have reported that anemia on admission was associated with short and long-term mortality. A study from Lancet has reported that preoperative low HCT was independently associated with an increased risk of 30-day morbidity and mortality in patients undergoing major surgery [26]. However, as an indicator of anemia, the role of HCT on mortality in hip fracture patients was unclear. Therefore, the specific relationship between HCT levels and the prognosis of patients with hip fractures needs to be further explored.
This study was to assess the influence of HCT level on all-cause mortality in geriatric patients with hip fractures over a long-term follow-up period. we hypothesized that there would be either a linear or a nonlinear association between HCT levels and mortality. In this prospective cohort study, we aimed to identify the role of HCT levels on hip fractures.
## 2.1. Study Design
Older adult patients who had a hip fracture between 1 January 2015, and 30 September 2019, and admitted to the largest trauma center in Northwest China, were enrolled in this study.
This prospective study was approved by the Ethics Committee of Xi’an Honghui Hospital (No. 202201009). All procedures involving human participants were performed in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki).
## 2.2. Participants
Data in-hospital, demographic and clinical data of the patients were obtained from their original medical records. The inclusion criteria were as follows: [1] age ≥ 65 years; [2] a radiographic or computed tomography diagnosis of the femoral neck, intertrochanteric, or subtrochanteric fracture; [3] receiving surgical or conservative treatment in a hospital; [4] availability of clinical data during hospitalization; and [5] availability of contact via telephone. Patients who could not be contacted were excluded from this study.
## 2.3. Hospital Treatment
Patients were examined using blood tests on admission, including HCT level. The ultrasonography of cardiac and lower extremity veins to prepare for surgery. Intertrochanteric fractures are often managed with closed/open reduction and internal fixation (ORIF) using a proximal femoral nail anti-rotation device. Femoral neck fractures are often treated with hemiarthroplasty (HA) or total hip arthroplasty (THA) according to the patient’s age. Prophylaxis for deep vein thrombosis was initiated on admission. Upon discharge, the patients were asked to return monthly for the assessment of fracture union or function.
## 2.4. Follow-Up
After discharge, patients’ family members were contacted by telephone between January 2022 and March 2022, to record data on survival, survival time, and activities of daily living. Follow-up was conducted by two medical professionals (Wen-Wen Cao and Shao-Hua Chen) who were trained in follow-up skills for two weeks. Three attempts were made to get in contact with patients. Failure of family members of the patients to maintain channels of communication with the team, resulted in patients being recorded as lost to follow-up.
## 2.5. Endpoint Events
The endpoint event in this study was all-cause mortality after treatment. We defined all-cause mortality as death reported by patients’ family members.
## 2.6. Variables
The variables in our study were: age, sex, occupation, history of allergy, injury mechanism, fracture classification, presence of hypertension, diabetes, coronary heart disease (CHD), arrhythmia, hemorrhagic stroke, ischemic stroke, cancer, multiple injuries, dementia, chronic obstructive pulmonary disease (COPD), hepatitis, gastritis, age-adjusted Charlson comorbidity index (aCCI), time from injury to admission, time from admission to surgery, HCT level, operation time, treatment strategy, blood loss, infusion, transfusion, length of hospital, and follow-up. The dependent variable was all-cause mortality, while the independent variable was the HCT level. The other variables were potential confounding factors.
## 2.7. Statistics Analysis
Continuous variables are reported as mean ± standard deviation (Gaussian distribution) or median (range, skewed distribution). Categorical variables are presented as numbers with proportions. Chi-square (categorical variables), one-way analysis of variance (normal distribution), and Kruskal–Wallis H test (skewed distribution) were used to detect differences between different HCT levels. Univariate and multivariate Cox proportional hazards regression models (three models) were used to test the association between HCT levels and mortality. Model 1 was not adjusted for covariates, Model 2 was minimally adjusted for sociodemographic variables, and Model 3 was fully adjusted for all covariates. To test the robustness of our results, we performed sensitivity analysis. We converted the HCT level into a categorical variable according to the anemia criteria and calculated the p-value for the trend to verify the results obtained using HCT level as the continuous variable; we also examined the possibility of nonlinearity. Because Cox proportional hazards regression model-based methods are often suspected to be unable to deal with nonlinear models, the nonlinearity between HCT and mortality was assessed using a Cox proportional hazards regression model with cubic spline functions and smooth curve fitting (the penalized spline method). If nonlinearity was detected, we first calculated the inflection point using a recursive algorithm and subsequently constructed a two-piecewise Cox proportional hazards regression model on both sides of the inflection point. In addition, propensity score matching (PSM) was introduced for comparison between matched groups, and confounding factors were adjusted for in the PSM models.
All analyses were performed using statistical software packages R (http://www.R-project.org, accessed on 1 January 2023, R Foundation) and EmpowerStats (http://www.empowerstats.com, accessed on 1 January 2023, X&Y Solutions Inc., Boston, MA, USA). The hazard ratios (HRs) with $95\%$ confidence intervals (CIs) were calculated. A p-value < 0.05 (two-sided) was considered statistically significant.
## 3.1. Patient Characteristics
From the initial sample of 2887 participants who had hip fractures between January 2015 and September 2019, 2589 met the study criteria and were enrolled in our study. The 1-year mortality was $11.05\%$ ($\frac{286}{2303}$). The mean follow-up period was 38.94 months. Of these, 875 ($33.8\%$) patients died due to all-cause mortality. The HCT levels were divided into four groups. Table 1 lists the demographic and clinical characteristics of all enrolled patients, including comorbidities, factors associated with injuries, and treatment.
## 3.2. Univariate Analysis of Association between Variables and Mortality
To identify potential confounding factors and the relationship between variables and mortality, we performed univariate analysis. According to the criteria of $p \leq 0.1$, the following variables were considered in the multivariate Cox regression: age (HR = 1.08; $95\%$ CI: 1.06–1.09); $p \leq 0.0001$), sex (HR = 0.74; $95\%$ CI: 0.65–0.85); $p \leq 0.0001$), time to admission (HR = 1.00; $95\%$ CI: 1.00, 1.00; $$p \leq 0.0531$$), hypertension (HR = 1.13; $95\%$ CI: 0.99–1.29; $$p \leq 0.0643$$), CHD (HR = 1.32; $95\%$ CI: 1.15–1.51; $p \leq 0.0001$), arrhythmia (HR = 1.32; $95\%$ CI: 1.15–1.51; $p \leq 0.0001$), ischemic stroke (HR = 1.42; $95\%$ CI: 1.24–1.64; $p \leq 0.0001$), cancer (HR = 1.77; $95\%$ CI: 1.28–2.44; $$p \leq 0.0005$$), dementia (HR = 2.62; $95\%$ CI: 2.03–3.38; $p \leq 0.0001$), COPD (HR = 1.55; $95\%$ CI: 1.23–1.95; $$p \leq 0.0002$$), hepatitis (HR = 1.62; $95\%$ CI: 1.17–2.23; $$p \leq 0.0033$$), aCCI (HR = 1.51; $95\%$ CI: 1.43–1.61; $p \leq 0.0001$), time to operation (HR = 1.03; $95\%$ CI: 1.00–1.05; $p \leq 0.0481$), treatment strategy (ORIF, HA and THA $p \leq 0.0001$ compared to conservation, respectively), operation time (HR = 1.00; $95\%$ CI: 1.00, 1.00; $$p \leq 0.0433$$), infusion (HR = 1.00; $95\%$ CI: 1.00, 1.00; $$p \leq 0.4246$$), and length in hospital (HR = 1.02; $95\%$ CI: 1.01–1.04; $$p \leq 0.0041$$).
## 3.3. Multivariate Analysis of Association between HCT and Mortality
We used three models (Table 2) to correlate the HCT levels and mortality. Linear regression was observed when HCT level was a continuous variable. The fully adjusted model showed a decrease in mortality risk of $3\%$ (HR = 0.97, $95\%$ CI: 0.96–0.99, $$p \leq 0.0002$$) when HCT level increased by $1\%$ after controlling for confounding factors. When HCT level was used as a categorical variable, we found significant differences in HCT levels among the three models ($p \leq 0.05$). In addition, the p-value for the trend also showed a linear correlation in the three models ($p \leq 0.05$).
We found the interval to be abnormal in the subgroup with an HCT level above the third quartile (Table 2). This instability indicates the possibility of a nonlinear correlation.
As shown in Figure 1, there was a curved association between the HCT levels and mortality after adjusting for confounding factors. We compared two fitting models to explain this association (Table 3). Interestingly, we observed an inflection point. A HCT level < $28\%$ was associated with mortality (HR = 0.91, $95\%$ CI: 0.87–0.95, $p \leq 0.0001$). At a HCT level of >$28\%$, mortality did not change (HR = 0.99, $95\%$ CI: 0.97–1.01, $$p \leq 0.3792$$).
## 3.4. Propensity Score Matching (PSM)
To test the robustness of our results, we performed sensitivity analysis using PSM. Overall, 1400 patients ($54.07\%$) were successfully matched. Age ($p \leq 0.0001$) and aCCI ($p \leq 0.0001$) did not match between the two groups. In the multivariate Cox regression results under the PSM and PSM-adjusted models, the results were stable, and the inflection point was $29.7\%$ (Table 4).
The Kaplan–Meier survival curve is shown in Figure 2.
## 4. Discussion
A few systematic reviews have indicated many risk factors for mortality in geriatric hip fracture patients [11,12,27,28,29], and anemia was an important prognostic risk factor [18,19,20,21,22]. Even though previous studies have proven the association between preoperative hemoglobin and mortality in hip fracture, the results were an almost linear relationship and did not find nonlinearity. In addition, several high-quality studies assessed the effect of preoperative HCT on mortality in patients undergoing major surgery [26,30,31], but the role of HCT on mortality in hip fracture has not been addressed. Therefore, the specific relationship between HCT levels and the prognosis of patients with hip fractures is needed.
Our study showed a nonlinear association between HCT levels and all-cause mortality in geriatric patients after hip fracture treatment. When the HCT level was <$28\%$, geriatric hip fracture patients with lower HCT levels had greater odds of mortality (HR = 0.91, $95\%$ CI: 0.87–0.95; $p \leq 0.0001$). This result suggests that a $1\%$ increase in HCT level was associated with a $9\%$ decrease in mortality in geriatric patients with hip fractures. However, when the HCT level was >$28\%$, no association was found between HCT level and mortality in geriatric hip fracture patients (HR = 0.99, $95\%$ CI: 0.97–1.01; $$p \leq 0.3792$$). Consequently, the HCT level on admission can be used as a clinical predictor of all-cause mortality in geriatric patients with hip fracture.
There were two primary meanings provided in this paper. On the one hand, the HCT level on admission (<$28\%$) can be used as a clinical predictor of all-cause mortality in geriatric patients with hip fractures. When a patient was on admission, the surgeons should notice that this person was at a high risk of a bad prognosis. On the other hand, we would note the importance of HCT from this study. The surgeons could undertake the intervention study on improving the prognosis by transfusion for HCT < $28\%$ in the future. Specifically, rotational thromboelastometry is a laboratory method that is gaining ground on the evaluation of the hemostatic profile of hip fracture patients [32,33].
A number of previous studies have Investigated the effect of anemia on the prognosis of patients undergoing orthopedic surgery. A single-center retrospective study in Singapore reported that anemia was independently associated with prolonged hospitalization and increased perioperative blood transfusions [34]. Nissenholtz et al. [ 23] reported that anemia on admission was associated with short and long-term mortality, in addition to the length of stay, amount of blood transfusions, repeated hospitalizations, post-operative complications, poor functioning, and a reduced quality of life. In addition, few studies have examined the effects of anemia on patients with hip fractures. Ryan et al. [ 21] suggested that geriatric hip fracture patients with preoperative anemia are at an increased risk for morbidity and mortality, especially during the first 30 postoperative days. Similar to the findings of our study, Gruson et al. [ 24] reported that anemia was associated with a longer length of hospital stay and higher rates of 6-month mortality after surgery for hip fracture. Bolton et al. [ 25] reported that anemia was a statistically-significant risk factor for perioperative complications. However, most of these studies used hemoglobin levels to define anemia. The specific relationship between HCT levels and prognosis of patients with hip fractures remains unclear. In a systematic review, Sheehan et al. [ 35] reported frailty was the proposed mechanism for the association between anemia and functional outcome.
In this study, we found a linear relationship between HCT levels and mortality in geriatric hip fractures; however, we also observed that the relationship was unstable. Therefore, we surmised the possibility of a curvilinear relationship from subgroup analysis and curve fitting. Our study found an inflection point on the curve, which was stabilized by PSM sensitivity analysis. The curvilinear relationship more appropriately explains the association between HCT levels and mortality in geriatric hip fracture patients. Many studies have indicated that HCT levels are associated with organ senescence or complications in older population. In a cohort study of geriatric patients undergoing spinal procedures based on the ACS-NSQIP database, Almeida et al. [ 36] concluded that patients with lower HCT levels were at a greater risk for requiring transfusion, renal failure, and infectious complications. Gupta et al. [ 31] found that a HCT level of ≤$39\%$ is associated with an increased risk of 30-day mortality and adverse cardiac events in patients aged 65 years or older undergoing elective vascular procedures. Bodewes et al. [ 37] reported that mortality and major adverse events in patients with chronic limb-threatening ischemia who underwent infrainguinal bypass were inversely associated with preoperative HCT levels.
The patients in our study received blood tests on admission immediately, and the HCT is the first value at admission before treatment. Because of the nature of observational association, we did not give the identified or particular intervention to the patients. However, we gave the transfusion to patients with severe anemia after admission or during the operation in clinical practice. The total volume of the transfusion is 1.84 (U), 1.22 (U), 1.00 (U), and 0.63 (U) in groups Q1–Q4, respectively.
To identify confounders in the study and draw reliable conclusions, we identified factors that affect the HCT level as well as the prognosis of geriatric hip fractures. There were several factors very well known to contribute to mortality in hip fracture population. Age, sex, comorbidities, CHD, arrhythmia, cancer, dementia, time to operation, and treatment strategy were the risk factors for the prognosis of hip fracture which have been reported in previous studies [7,8,10,38,39,40,41,42,43]. The probability of mortality density for a period of 10 years following a hip fracture was $16\%$ for women and $25\%$ for men [44]. A recent systematic review from 81 articles showed that the comorbidities, delay in operation, and type of fractures were important predictors of poor functional outcomes and mortality for patients with hip fractures [28]. In addition, the univariate analysis also found some variables with $p \leq 0.1$ to be associated ischemic stroke, operation time, and infusion volume. Furthermore, considering factors affecting the HCT level, we included hepatitis, COPD, and cancer [45,46,47,48]. Consequently, we have controlled for the vast majority of confounders.
Our study has a few limitations. First, due to the prospective design of our study, there was an inevitable risk for patients to be loss to follow-up. Patients or their families who could not be contacted initially were called two other times to obtain information regarding patients’ prognosis. Second, the causal relationship between the HCT level and prognosis of hip fractures was not identified in our study and requires further confirmation in future studies. It would be meaningful if future studies could establish a causal relationship between HCT levels and all-cause mortality in geriatric hip fractures. Furthermore, the HCT value was not only influenced by anemia, but also by other factors (hematological disorders or fluid intake). Therefore, generalizations of this conclusion for populations from other regions should be made with caution.
In summary, the HCT level was nonlinearly associated with mortality in geriatric hip fractures and could be considered a predictor of the risk of mortality.
## References
1. Ramponi D.R., Kaufmann J., Drahnak G.. **Hip Fractures**. *Adv. Emerg. Nurs. J.* (2018) **40** 8-15. DOI: 10.1097/TME.0000000000000180
2. Mundi S., Pindiprolu B., Simunovic N., Bhandari M.. **Similar mortality rates in hip fracture patients over the past 31 years**. *Acta Orthop.* (2013) **85** 54-59. DOI: 10.3109/17453674.2013.878831
3. Broggi M.S., Oladeji P.O., Tahmid S., Hernandez-Irizarry R., Allen J.. **Depressive Disorders Lead to Increased Complications After Geriatric Hip Fractures**. *Geriatr. Orthop. Surg. Rehabil.* (2021) **12** 21514593211016252. DOI: 10.1177/21514593211016252
4. Si L., Winzenberg T.M., Jiang Q., Chen M., Palmer A.J.. **Projection of osteoporosis-related fractures and costs in China: 2010–2050**. *Osteoporos. Int.* (2015) **26** 1929-1937. DOI: 10.1007/s00198-015-3093-2
5. Burge R., Dawson-Hughes B., Solomon D.H., Wong J.B., King A., Tosteson A.. **Incidence and Economic Burden of Osteoporosis-Related Fractures in the United States, 2005–2025**. *J. Bone Miner. Res.* (2007) **22** 465-475. DOI: 10.1359/jbmr.061113
6. Downey C., Kelly M., Quinlan J.F.. **Changing trends in the mortality rate at 1-year post hip fracture—A systematic review**. *World J. Orthop.* (2019) **10** 166-175. DOI: 10.5312/wjo.v10.i3.166
7. Walsh M., Ferris H., Coughlan T., Hurson C., Ahern E., Sorensen J., Brent L.. **Trends in hip fracture care in the Republic of Ireland from 2013 to 2018: Results from the Irish Hip Fracture Database**. *Osteoporos. Int.* (2021) **32** 727-736. DOI: 10.1007/s00198-020-05636-1
8. Chiang M.-H., Huang Y.-Y., Kuo Y.-J., Huang S.-W., Jang Y.-C., Chu F.-L., Chen Y.-P.. **Prognostic Factors for Mortality, Activity of Daily Living, and Quality of Life in Taiwanese Older Patients within 1 Year Following Hip Fracture Surgery**. *J. Pers. Med.* (2022) **12**. DOI: 10.3390/jpm12010102
9. Chen Y.-P., Kuo Y.-J., Hung S.-W., Wen T.-W., Chien P.-C., Chiang M.-H., Maffulli N., Lin C.-Y.. **Loss of skeletal muscle mass can be predicted by sarcopenia and reflects poor functional recovery at one year after surgery for geriatric hip fractures**. *Injury* (2021) **52** 3446-3452. DOI: 10.1016/j.injury.2021.08.007
10. Groff H., Kheir M.M., George J., Azboy I., Higuera C.A., Parvizi J.. **Causes of in-hospital mortality after hip fractures in the elderly**. *HIP Int.* (2020) **30** 204-209. DOI: 10.1177/1120700019835160
11. Chang W., Lv H., Feng C., Yuwen P., Wei N., Chen W., Zhang Y.. **Preventable risk factors of mortality after hip fracture surgery: Systematic review and meta-analysis**. *Int. J. Surg.* (2018) **52** 320-328. DOI: 10.1016/j.ijsu.2018.02.061
12. Song Y., Wu Z., Huo H., Zhao P.. **The Impact of Frailty on Adverse Outcomes in Geriatric Hip Fracture Patients: A Systematic Review and Meta-Analysis**. *Front. Public Health* (2022) **10** 890652. DOI: 10.3389/fpubh.2022.890652
13. Mondal H., Lotfollahzadeh S.. *StatPearls* (2022)
14. Kiya G.T., Zewudie F.M.. **Comparison of three-fold converted hematocrit and micro-hematocrit in pregnant women**. *PLoS ONE* (2019) **14**. DOI: 10.1371/journal.pone.0220740
15. Bhaskar D., Parker M.J.. **Haematological indices as surrogate markers of factors affecting mortality after hip fracture**. *Injury* (2011) **42** 178-182. DOI: 10.1016/j.injury.2010.07.501
16. Izaks G.J., Westendorp R.G., Knook D.L.. **The definition of anemia in older persons**. *JAMA* (1999) **281** 1714-1717. DOI: 10.1001/jama.281.18.1714
17. Halm E.A., Wang J.J., Boockvar K., Penrod J., Silberzweig S.B., Magaziner J., Koval K.J., Siu A.L.. **The effect of perioperative anemia on clinical and functional outcomes in patients with hip fracture**. *J. Orthop. Trauma* (2004) **18** 369-374. DOI: 10.1097/00005131-200407000-00007
18. Potter L.J., Doleman B., Moppett I.K.. **A systematic review of pre-operative anaemia and blood transfusion in patients with fractured hips**. *Anaesthesia* (2015) **70** 483-500. DOI: 10.1111/anae.12978
19. Grosso M.J., Boddapati V., Cooper H.J., Geller J.A., Shah R.P., Neuwirth A.L.. **The Effect of Preoperative Anemia on Complications After Total Hip Arthroplasty**. *J. Arthroplast.* (2020) **35** S214-S218. DOI: 10.1016/j.arth.2020.01.012
20. Viola J., Gomez M.M., Restrepo C., Maltenfort M.G., Parvizi J.. **Preoperative anemia increases postoperative complications and mortality following total joint arthroplasty**. *J. Arthroplast.* (2015) **30** 846-848. DOI: 10.1016/j.arth.2014.12.026
21. Ryan G., Nowak L., Melo L., Ward S., Atrey A., Schemitsch E.H., Nauth A., Khoshbin A.. **Anemia at Presentation Predicts Acute Mortality and Need for Readmission Following Geriatric Hip Fracture**. *JBJS Open Access* (2020) **5** e20.00048. DOI: 10.2106/JBJS.OA.20.00048
22. Du P., Zhu Y., Guo J., Qi S., Qin J., Zheng C., Hou Z., Zhang Y., Tian Q.-B., Feng Z.. **Incidence and risk factors associated with surgical site infection after surgically treated hip fractures in older adults: A retrospective cohort study**. *Aging Clin. Exp. Res.* (2022) **34** 1139-1148. DOI: 10.1007/s40520-021-02027-2
23. Nissenholtz A., Levy Y., Cooper L., Bugaevsky Y., Weiss A., Beloosesky Y.. **Anemia in Patients after Hip Fracture Repair Surgery**. *Harefuah* (2020) **159** 689-693. PMID: 32955813
24. Gruson K.I., Aharonoff G.B., Egol K.A., Zuckerman J.D., Koval K.J.. **The relationship between admission hemoglobin level and outcome after hip fracture**. *J. Orthop. Trauma* (2002) **16** 39-44. DOI: 10.1097/00005131-200201000-00009
25. Bolton D., Bush C., Wallace M.T.. **Nonagenarian hip fractures: Morbidity and mortality at a single institution**. *J. Clin. Orthop. Trauma* (2021) **14** 69-73. DOI: 10.1016/j.jcot.2020.09.020
26. Musallam K.M., Tamim H.M., Richards T., Spahn D.R., Rosendaal F.R., Habbal A., Khreiss M., Dahdaleh F.S., Khavandi K., Sfeir P.M.. **Preoperative anaemia and postoperative outcomes in non-cardiac surgery: A retrospective cohort study**. *Lancet* (2011) **378** 1396-1407. DOI: 10.1016/S0140-6736(11)61381-0
27. Simunovic N., Devereaux P.J., Sprague S., Guyatt G.H., Schemitsch E., DeBeer J., Bhandari M.. **Effect of early surgery after hip fracture on mortality and complications: Systematic review and meta-analysis**. *CMAJ* (2010) **182** 1609-1616. DOI: 10.1503/cmaj.092220
28. Xu B.Y., Yan S., Low L.L., Vasanwala F.F., Low S.G.. **Predictors of poor functional outcomes and mortality in patients with hip fracture: A systematic review**. *BMC Musculoskelet. Disord.* (2019) **20**. DOI: 10.1186/s12891-019-2950-0
29. Hou M., Zhang Y., Chen A.C., Liu T., Yang H., Zhu X., He F.. **The effects of dementia on the prognosis and mortality of hip fracture surgery: A systematic review and meta-analysis**. *Aging Clin. Exp. Res.* (2021) **33** 3161-3172. DOI: 10.1007/s40520-021-01864-5
30. Musallam K.M., Porter J., Sfeir P.M., Tamim H.M., Richards T., Lotta L.A., Peyvandi F., Jamali F.R.. **Raised haematocrit concentration and the risk of death and vascular complications after major surgery**. *Br. J. Surg.* (2013) **100** 1030-1036. DOI: 10.1002/bjs.9176
31. Gupta P.K., Sundaram A., MacTaggart J.N., Johanning J.M., Gupta H., Fang X., Forse R.A., Balters M., Longo G.M., Sugimoto J.T.. **Preoperative anemia is an independent predictor of postoperative mortality and adverse cardiac events in elderly patients undergoing elective vascular operations**. *Ann. Surg.* (2013) **258** 1096-1102. DOI: 10.1097/SLA.0b013e318288e957
32. Tsantes A.G., Papadopoulos D.V., Trikoupis I.G., Tsante K.A., Mavrogenis A.F., Koulouvaris P., Vaiopoulos A.G., Piovani D., Nikolopoulos G.K., Kokoris S.I.. **The Prognostic Performance of Rotational Thromboelastometry for Excessive Bleeding and Increased Transfusion Requirements in Hip Fracture Surgeries**. *Thromb. Haemost.* (2022) **122** 895-904. DOI: 10.1055/s-0041-1736617
33. Tsantes A.G., Papadopoulos D.V., Roustemis A.G., Trikoupis I.G., Piovani D., Tsante K.A., Mantzios P.G., Mavrogenis A.F., Sokou R., Kokoris S.I.. **Rotational Thromboelastometry Predicts Transfusion Requirements in Total Joint Arthroplasties**. *Semin. Thromb. Hemost.* (2023) **49** 134-144. DOI: 10.1055/s-0042-1753510
34. Abdullah H.R., Sim Y.E., Hao Y., Lin G.Y., Liew G.H.C., Lamoureux E.L., Tan M.H.. **Association between preoperative anaemia with length of hospital stay among patients undergoing primary total knee arthroplasty in Singapore: A single-centre retrospective study**. *BMJ Open* (2017) **7** e016403. DOI: 10.1136/bmjopen-2017-016403
35. Sheehan K.J., Williamson L., Alexander J., Filliter C., Sobolev B., Guy P., Bearne L., Sackley C.. **Prognostic factors of functional outcome after hip fracture surgery: A systematic review**. *Age Ageing* (2018) **47** 661-670. DOI: 10.1093/ageing/afy057
36. Almeida N.D., Lee R., Bestourous D., Klein A.L., Parekh N.R., Sack K., Sherman J.H.. **Perioperative Complications Associated with Severity of Anemia in Geriatric Patients Undergoing Spinal Procedures**. *World Neurosurg.* (2022) **135** e307-e320. DOI: 10.1016/j.wneu.2019.11.159
37. Bodewes T.C., Pothof A.B., Darling J.D., Deery S.E., Jones D.W., Soden P.A., Moll F.L., Schermerhorn M.L.. **Preoperative anemia associated with adverse outcomes after infrainguinal bypass surgery in patients with chronic limb-threatening ischemia**. *J. Vasc. Surg.* (2017) **66** 1775-1785.e2. DOI: 10.1016/j.jvs.2017.05.103
38. Kavak M., Oğuz S., Akkoyun Z., İnan U.. **Predictive factors associated with thirty-day mortality in geriatric patients with hip fractures**. *Acta Orthop. Traumatol. Turc.* (2022) **56** 240-244. DOI: 10.5152/j.aott.2022.21407
39. Zhang P., Li X., Yuan Y., Li X., Liu X., Fan B., Yang M., Wu X.. **Risk factor analysis for in-hospital death of geriatric hip fracture patients**. *Saudi Med. J.* (2022) **43** 197-201. DOI: 10.15537/smj.2022.43.2.20210717
40. Stone A.V., Jinnah A., Wells B., Atkinson H., Miller A.N., Futrell W.M., Lenoir K., Emory C.L.. **Nutritional markers may identify patients with greater risk of re-admission after geriatric hip fractures**. *Int. Orthop.* (2018) **42** 231-238. DOI: 10.1007/s00264-017-3663-3
41. Provenzano G., Jenkins S., Higginbotham W., Markel D.C.. **Factors That Influence Time to Operating Room for Geriatric Hip Fractures: A Quality Improvement Initiative**. *Arthroplast. Today* (2022) **15** 115-119. DOI: 10.1016/j.artd.2022.03.005
42. Lai Y.C., Tang P.L., Kuo T.J., Hsu C.J.. **Different impacts of dementia on two-year mortality after osteosynthesis and hemiarthroplasty in treating geriatric hip fractures**. *Arch. Gerontol. Geriatr.* (2018) **79** 116-122. DOI: 10.1016/j.archger.2018.08.011
43. Ercin E., Bilgili M.G., Sari C., Basaran S.H., Tanriverdi B., Edipoglu E., Celen K.M., Cetingok H., Kural C.. **Risk factors for mortality in geriatric hip fractures: A compressional study of different surgical procedures in 785 consecutive patients**. *Eur. J. Orthop. Surg. Traumatol.* (2017) **27** 101-106. DOI: 10.1007/s00590-016-1843-2
44. Guzon-Illescas O., Fernandez E.P., Villarias N.C., Donate F.J.Q., Peña M., Alonso-Blas C., Vadillo A.G., Mazzucchelli R.. **Mortality after osteoporotic hip fracture: Incidence, trends, and associated factors**. *J. Orthop. Surg. Res.* (2019) **14** 203. DOI: 10.1186/s13018-019-1226-6
45. Gauci R., Hunter M., Bruce D.G., Davis W.A., Davis T.M.E.. **Anemia complicating type 2 diabetes: Prevalence, risk factors and prognosis**. *J. Diabetes Complicat.* (2017) **31** 1169-1174. DOI: 10.1016/j.jdiacomp.2017.04.002
46. Divo M., Celli B.R.. **Multimorbidity in Patients with Chronic Obstructive Pulmonary Disease**. *Clin. Chest Med.* (2020) **41** 405-419. DOI: 10.1016/j.ccm.2020.06.002
47. Cherabuddi M.R., Kurra N., Doosetty S., Gandrakota N.. **Atypical Presentation of Interval Colorectal Cancer/Post-Colonoscopy Colorectal Cancer in a Nursing Home Patient**. *Cureus* (2022) **14** e24849. DOI: 10.7759/cureus.24849
48. Bach V., Schruckmayer G., Sam I., Kemmler G., Stauder R.. **Prevalence and possible causes of anemia in the elderly: A cross-sectional analysis of a large European university hospital cohort**. *Clin. Interv. Aging* (2014) **9** 1187-1196. DOI: 10.2147/cia.S61125
|
---
title: Viscoelastic Properties of Human Facial Skin and Comparisons with Facial Prosthetic
Elastomers
authors:
- Mark W. Beatty
- Alvin G. Wee
- David B. Marx
- Lauren Ridgway
- Bobby Simetich
- Thiago Carvalho De Sousa
- Kevin Vakilzadian
- Joel Schulte
journal: Materials
year: 2023
pmcid: PMC10004410
doi: 10.3390/ma16052023
license: CC BY 4.0
---
# Viscoelastic Properties of Human Facial Skin and Comparisons with Facial Prosthetic Elastomers
## Abstract
Prosthesis discomfort and a lack of skin-like quality is a source of patient dissatisfaction with facial prostheses. To engineer skin-like replacements, knowledge of the differences between facial skin properties and those for prosthetic materials is essential. This project measured six viscoelastic properties (percent laxity, stiffness, elastic deformation, creep, absorbed energy, and percent elasticity) at six facial locations with a suction device in a human adult population equally stratified for age, sex, and race. The same properties were measured for eight facial prosthetic elastomers currently available for clinical usage. The results showed that the prosthetic materials were 1.8 to 6.4 times higher in stiffness, 2 to 4 times lower in absorbed energy, and 2.75 to 9 times lower in viscous creep than facial skin ($p \leq 0.001$). Clustering analyses determined that facial skin properties fell into three groups—those associated with body of ear, cheek, and remaining locations. This provides baseline information for designing future replacements for missing facial tissues.
## 1. Introduction
Skin consists of an outer layer of stratified keratinized epithelium; a middle dermis layer consisting of fibrous, collagenous, and elastic tissue; and a deep hypodermis layer that primarily contains pads of adipose tissue. Skin with a thick epidermal layer (0.8–1.4 mm) is found on the palms of hands and soles of feet, whereas thin epidermis (0.07–0.12 mm) is present in other locations, including the face. Thin epidermis is comprised of four strata (corneum, granulosum, spinosum, and basale). Dermis has an upper papillary layer with connective tissue cells, collagen type III, and a loose elastic fiber network. It is supported by a lower dense reticular layer consisting of collagen type I fiber bundles and coarse elastic fibers. The deep hypodermis is made up of loose connective tissue that changes into adipose tissue of varying thickness depending upon body location and sex. It stores energy and absorbs shock. Facial skin contains sebaceous glands, which provide an oily surface. It also receives the most sun exposure, and its properties are related to the extent of actinic damage. A more detailed description of facial skin histology and function can be found in the publication by Arda et al. [ 1]. Depending upon the measurement method used, facial skin thickness has been reported to be between 0.4 mm and 2.5 mm, and varies by location [2,3,4,5,6].
The biomechanical properties of skin are attributed primarily to the connective tissue present in the dermis. The physical organization and relative amounts of collagen, elastin, glycosaminoglycans, and water contribute to the non-linear stress–strain properties that are accompanied by hysteresis. Time-dependent deformation of skin is characterized by elastic deformation that is followed by viscoelastic creep, and an immediate elastic recovery followed by creeping recovery with residual deformation [7]. Due to the collagenous network oriented along Langer’s lines, skin is anisotropic and behaves differently along different loading directions.
Facial disfigurement arises from trauma, burns, and surgical removal of tumors. It is estimated that annually 400,000 civilian cases of facial fractures are treated in the United States, one to five million burns to the head and neck occur worldwide, and 850,000 new cases of head and neck cancer are diagnosed across the globe [8,9,10]. Treatment modalities include reconstructive surgery, placement of tissue-engineered constructs and biomimetics, and construction of maxillofacial prostheses. For cases where facial disfigurement results, a disfigured person is perceived as less attractive, less intelligent, less capable, and unemployable [11]. This contributes to a lowered self-esteem, which negatively affects the quality of life [12].
Reconstructive surgical techniques have limitations due to their reliance on autogenous and allogeneic materials. When used to replace hard and soft tissues, they are in short supply, may not conform to the intricate geometry required to replicate missing tissue, undergo wound contracture, and, if used as grafts, contribute to donor site morbidity [13]. For this reason, research has focused on replacing missing facial tissues through a combinatorial approach that includes tissue engineering, biomimetics, and prosthetics. Since a desired endpoint is the duplication of the appearance, biology, and function of native tissue, successful engineering of a biological or synthetic replacement requires a set of well-defined parameters known for healthy tissue.
For patients who wear facial prostheses, improvements in pigmentation systems have rendered them aesthetically acceptable. Facial skin tones that are customized chairside produce a prosthesis that has a natural look at the time of delivery. However, polymers used for constructing these prostheses are based on filled poly(dimethyl siloxanes) or polyurethanes that render prostheses having high tensile stiffness and low damping properties. As a result, patient dissatisfaction with comfort and wearability has been reported, along with longevity, function, and discoloration. It has been documented that up to $12\%$ of patients will not wear their prostheses [14,15].
Reports of facial skin properties are limited, and comparisons with mechanical properties of prosthetic elastomers are virtually non-existent. Published data are limited by measurements of cadaveric tissue [16], small numbers of human subjects [17,18], and selection of relatively few facial locations [5,19,20]. What is needed are data derived from vital facial skin at locations where injury occurs. The purpose of this study was to measure viscoelastic properties in facial skin in a subject population equally stratified by age, gender, and race, at six different facial locations. Then, the results were compared with those measured for a group of prosthetic elastomers currently available for clinical usage. The null hypothesis tested was that there are no differences in biomechanical properties in facial skin based on facial location, sex, age, or race. A second hypothesis was that there are no differences between facial skin properties and those measured for prosthetic elastomers.
## 2.1. Facial Skin Measurements
A total of 144 human subjects were enrolled and stratified to produce equal numbers of subjects represented by gender (male, female), age (19–29, 30–49, and 50–70) and race/ethnicity (Asian, African American, Hispanic, and White) ($$n = 6$$). The sample size was calculated from power analyses of preliminary trial data to detect a significant difference ($p \leq 0.05$) of 3 kPa/mm stiffness at different facial locations with $80\%$ statistical power. The study enlisted 52 United States military veterans and 92 non-veterans as participants. All protocols for patient recruitment, informed consent, privacy, and data security were approved by the Omaha VA Subcommittee for Human Studies (IRB Project #00644, approved 11 August 2011), and the Creighton University Institutional Review Board (#13-16724, approved 13 June 2013). Inclusion criteria included healthy facial skin that was not covered with scars or skin lesions where skin measurements were to be made. Exclusion criteria included scarring, facial hair, or prosthesis present in measurement areas. Subjects were asked to refrain from applying facial cream 24 h in advance of the study, and to remove facial jewelry during measurement. After obtaining written informed consent, skin measurements were made at six locations: cheek, chin, tip of the nose, forehead, ear lobe, and body of the ear. An alcohol wipe was used to cleanse the measurement area, and an erasable marker was used to mark locations for the chin, nose, and forehead at approximately the facial midline. Cheek measurements were made two centimeters lateral to the left ala of the nose. Ear lobe measurements were made on the anterior surface of the left ear lobe. Body of left ear measurements were made on the flattest region on the back side, adjacent to the helix and beneath the antihelical fold. Biomechanical measurements were made by placing a hand-held glass chamber with a 1 cm diameter port on the face (BTC-2000, SRLI Technologies, Figure 1), and a vacuum was applied to the skin at a rate of 1.33 kPa/s over a 15 s time period until a maximum (negative) pressure of 20 kPa was reached. The vacuum was held an additional 10 s, the vacuum released, and skin deformation (creep) measured for an additional three seconds.
Stress-displacement and deformation-time data were recorded, and six biomechanical properties determined: percent laxity, stiffness, elastic deformation, creep, absorbed energy, and percent elasticity (Figure 2, following page).
Stress was recorded in units of mm Hg and converted to kPa by multiplying each mm Hg by 0.1333 to achieve the number of kilopascals. No skin thickness measurements were taken. Descriptive statistics and a four-factor linear mixed model with full interaction were applied to each biomechanical property to identify differences based on gender, age, race, and facial location ($p \leq 0.05$). Repeatability was measured on every tenth subject, where biomechanical measurements were obtained at the same cheek location on the right (opposite) side of the face, and paired t-tests compared left and right-side measurements. To gain an understanding of properties representative of the overall subject population, two clustering analyses were performed.
To assess general viscoelastic behaviors, population clusters were derived from the properties of stiffness and creep. To assess the movement and the “feel” of facial skin, a second clustering analysis was applied to the properties of stiffness, elastic deformation, and absorbed energy. The four properties represented resistance to applied vacuum (stiffness), delayed deformation and damping (creep), tissue stretchability under continuous loading (elastic deformation), and texture (energy). For both clustering analyses, all age, sex, race, and facial location measurements were pooled for each biomechanical property. Ward’s minimum variance method was employed as clustering analysis to identify cluster values representative of the subject population [21].
## 2.2. Prosthetic Elastomer Measurements
Elastomers chosen for this study included those cited as the most used by clinicians in a published survey [22] and those with published tensile and hardness properties before and after outdoor weathering [23]. All materials were silica-filled polydimethyl siloxanes (PDMS) with Durometer hardness ranging from 20 (A2000) to 70 (A225-70) (Table 1).
Each product was purchased from Factor II (Lakeside, AZ, USA), mixed according to manufacturers’ instructions, subjected to 5 × 10−3 torr vacuum, poured into circular molds with dimensions 34 mm diameter × 3 mm thickness ($$n = 5$$), and heat polymerized at the temperature recommended for each product (83 °C to 120 °C). The mold was cooled, disc removed, and tested after 72 h to permit post-curing. The sample size was based on a previous study, where a significant difference ($p \leq 0.05$) of 3 units in Durometer hardness could be detected with $80\%$ statistical power [23].
The same hand-held vacuum-generating instrument for measuring facial skin was used to measure viscoelastic properties of the elastomers. Each disc was centered on top of the glass chamber with a 20 g weight to provide adequate seal. The testing regimen applied to the disc was the same as that described for facial skin, and stress-deformation and deformation-time data were recorded. The same six biomechanical properties were determined.
For comparisons of elastomers with facial skin, the facial skin dataset was subdivided by facial location and biomechanical property measurements combined with those obtained for the elastomers. This permitted a direct comparison of a facial location with each elastomer for a given biomechanical property. Descriptive statistics, one-factor general linear model (GLM) and Tukey–Kramer post hoc tests determined significant differences among skin and elastomer group means for each biomechanical property ($p \leq 0.05$).
## 3.1.1. Results from Biomechanical Measurements
One study subject, a Hispanic female, age 30–49, signed an informed consent document, but did not participate in skin measurements. This reduced the subject population from 144 to 143 participants. Significant main effect contributions resulting from the linear mixed model caused by race, age, sex, and facial location on the six measured mechanical properties are presented in Table 2.
Facial location profoundly affected all properties ($p \leq 0.0001$), whereas race did not significantly contribute to any biomechanical property (0.13 ≤ p ≤ 0.96). Differences among age groups generated significant differences for percent laxity and creep (0.003 ≤ p ≤ 0.04), and the results are presented in Figure 3a,b.
The primary differences were associated with the 50–70 age group, where percent laxity was significantly lower than the 30–49 age group, and creep was higher than the 19–29 age group. Gender differences demonstrated significantly lower values for laxity, energy, and elastic deformation in females (0.02 ≤ p ≤ 0.049, Figure 3a,c,d), but none of the other measured properties were significantly affected (0.09 ≤ p ≤ 0.14). Effects rendered by the different facial locations on facial skin properties are summarized in Figure 3 and Table 3.
The body of ear skin was the most rigid and inflexible, as its values were highest for stiffness and lowest for creep, elastic deformation, percent laxity, and energy absorption. Cheek skin was nearly opposite in properties, as its stiffness was 1.8 times lower, whereas creep, elastic deformation, percent laxity, and energy were 1.6, 3.0, 2.5, and 2.0 times higher than the body of ear, respectively. Nose skin was similar to body of ear with its high stiffness, low laxity, low elastic deformation, and low energy, but it demonstrated 1.5 times more creep. Values measured for chin and forehead skin were intermediate to the aforementioned locations for the properties of elastic deformation, creep, and laxity, but forehead skin was stiffer. Ear lobe skin was highest in creep. Percent elasticity was calculated as:percent elasticity = (recovered deformation/elastic deformation) × 100[1] and served as a measure of immediate elastic recovery after force release. Percent elasticity was highest for chin, cheek, and back of ear; intermediate for forehead and nose; and lowest for ear lobe.
Results from paired t-tests demonstrated no significant differences between left and right-side repeatability measurements (0.148 ≤ p ≤ 0.424, not shown).
## 3.1.2. Results from Clustering Analyses
Hierarchical clustering analyses were performed as means to group biomechanical properties within a subject population so that subjects within a cluster were closer to each other than subjects grouped into different clusters [24]. The cluster center, or centroid, is defined by the mean values of properties incorporated within the cluster. Two important aspects of skin biomechanics are its viscoelasticity, or simultaneous stiffness and damping behaviors under load, and its movability when touched or in motion during joint function. Here, stiffness and creep were chosen to define population clusters for viscoelasticity. Stiffness, elastic deformation, and energy absorption were chosen to identify population clusters for facial skin stretchability and texture or “feel.” *Wards analysis* identified three clusters present within the subject population for both sets of analyses. Results are shown in Table 4, and the breakdown for the number of facial locations comprising each cluster is shown in Table 5.
For the viscoelastic properties of combined stiffness and creep, the subject population fell into three distinct clusters (Table 4, upper table). Cluster 1 contained the largest number of observations and its mean values represented a centroid with intermediate stiffness and creep. The mean values were similar to those reported for forehead and chin (Figure 3b, Table 3), and these two locations supplied the highest number of observations for Cluster 1 (Table 5). With the fewest observations, Cluster 2 contained measurements that mostly originated from the back of ear (Table 5) with stiffness being highest and creep lowest of the three clusters. Observations forming Cluster 3 were opposite of those for Cluster 2, as its mean values defining the centroid were lowest for stiffness and highest for creep, with the combination best representing cheek, nose, and ear lobe (Table 5).
For skin stretchability and texture, as represented by the properties of stiffness, elastic deformation, and energy, three distinct population clusters also were identified. The centroid results are presented in the lower table of Table 4. Cluster 1 was comprised of tissues that were highly stiff, while also being low in elastic deformation and absorbed energy. Most of the observations were located at the back of ear (Table 5). Conversely, tissues that were the most easily deformed and stretchable formed Cluster 3, where stiffness was lowest, and elastic deformation and energy being highest. This most closely corresponded to mean values reported for cheek (Figure 3c,d, Table 3), although Table 5 shows that the cluster also included large numbers of forehead, chin and ear lobe observations. Mean values shown for Cluster 2 in (lower) Table 4 were intermediate to those for Clusters 1 and 3. Observations comprising this cluster were largely measured at the tip of the nose, although nearly one-half of measurements taken from the ear lobe and back of ear fell into this cluster (Table 5).
## 3.2.1. Results from Biomechanical Measurements
Table 6 presents comparisons of biomechanical properties between facial skin ($$n = 143$$) and prosthetic elastomers ($$n = 5$$) from the one-factor GLM/Tukey.
Of the biomechanical properties measured, percent laxity showed the most similarities between elastomers and facial skin. Except for A2000, all elastomers were in the same statistical grouping as the back of ear. A2006, A2186, and A588-1 were grouped with chin, ear lobe, and nose, and both A2000 and A2186 were grouped with forehead skin. Only A2000 was in the same statistical grouping as cheek skin. However, these comparisons are misleading, as percentage laxity is a parameter that is normalized to elastic deformation. Consequently, comparisons should be made only within skin locations or within elastomers. Additional explanation is presented in Section 4.2.
For remaining properties, the prosthetic elastomers were vastly different from facial skin, with the exception of the back of the ear, which was grouped with the elastomers in elastic deformation and energy. Otherwise, the prosthetic materials were 1.8 to 6.4 times stiffer, 1.8 to 11.7 times lower in elastic deformation, 2 to 4 times lower in absorbed energy, and 2.75 to 9 times lower in viscous creep. Except for A2000, when vacuum was released, the prosthetic materials immediately recovered $74\%$ to $84\%$ of their original dimension, whereas facial skin recovered $32\%$ to $50\%$—which was 1.5 to 2.5 times lower. Except for back of ear, facial skin was found to be significantly more flexible, stretchable, and viscous in its mechanical response.
## 3.2.2. Comparisons with Clustering Analyses Results
To better visualize the mechanical differences between facial skin and the prosthetic elastomers, two- and three-dimensional scatterplots were constructed. Viscoelastic properties of stiffness and creep were plotted for the three subject population clusters and group of elastomers, and are shown in Figure 4a. For stretchability/texture properties, a 3D scatterplot is presented in Figure 4b.
In Figure 4a, the elastomers are scattered close to the abscissa, demonstrating low creep, and high stiffness compared to the facial skin clusters. Cluster 2 observations (red circles) are closest to the elastomers, and contain predominantly back of ear measurements (Table 5). For stretchability/texture properties shown in Figure 4b, the prosthetic elastomers demonstrate remarkably lower elastic deformation and energy compared to facial skin, and are closest to Cluster 1 (green circles), which contains mostly back of ear measurements (Table 5).
## 4. Discussion
The overall goal of research in this area is to restore the craniofacial complex to its full form and function following injury or disease. This project focused on mechanical property assessment of facial skin, as it determines the feel, comfort, and life-like qualities of a tissue replacement, be it biological, biomimetic, or a synthetic material. The process of successfully engineering replacements requires an understanding of existing properties of healthy tissues, comparing their properties with those that are present in currently used tissue replacements, then invoking an engineering design process to improve results. For prosthetic materials, strategies that impose changes to composition, processing, and placement are expected to be considered during the design process.
Results from this study both accepted and rejected the first null hypothesis, namely, that facial skin properties were not affected by facial location, age, sex, and gender. The hypothesis was accepted for race, partly accepted for age and sex (specific properties were affected), and rejected for facial location. The second hypothesis, that there are no differences in biomechanical properties between facial skin and a group of selected prosthetic materials, was rejected.
## 4.1. Biomechanical Measurements of Facial Skin
A wealth of information has been published from biomechanical tests of skin. Numerous test methods have been employed, and are summarized in reviews by Kalra et al. and Pierard et al. [ 25,26]. In this study, viscoelastic properties were measured with a device based on the application of vacuum. The device chosen for this study (BTC-2000) measures mechanics of both epidermis and dermis. This was considered relevant, as skin biomechanics is largely regulated by the composition and thickness of the epidermis, dermis, and hypodermis, with dermis being the major contributor [27]. This differs from a device often used (Cutometer), which has a smaller port opening (usually 2 mm to 6 mm, versus 10 mm for BTC-2000), and measures only the mechanical response of the epidermis. Barel [7] reported that devices with larger port openings, and hence contact with greater skin surface area, produce larger numbers for all measured biomechanical properties. Smalls et al. [ 27] measured six biomechanical properties in the leg, calf, and thigh with both the Cutometer and BTC-2000. They found that the properties of elastic deformation, energy, and elastic recovery measured with the BTC-2000 positively correlated with similar parameters measured with the Cutometer. Other properties either lacked correlation or were not common to both devices. Consequently, direct comparisons of results between the two instruments are limited.
Past research has identified age, sex, and body location as contributors to skin biomechanics. Race/ethnicity has been studied to a limited extent, with both differences and no differences in skin biomechanics reported. In this study of facial skin, the overwhelming contributor to property differences was facial location (all locations $p \leq 0.0001$), whereas race/ethnicity rendered little effect on any measured property (0.13 ≤ p ≤ 0.96). Age and sex affected two to three selected properties, otherwise their contributions were non-significant.
## 4.1.1. Facial Location
Previous research has reported mechanical properties of facial tissues at a limited number of locations and compared the results with skin located elsewhere on the human body [5,7,19,28,29,30,31]. However, none have characterized multiple viscoelastic properties at six key locations for facial injury, a feature of this study, and only studies by Bellamy and Waters [17] and Farah et al. [ 18] have compared facial skin results with tissue replacements using the same experimental protocol and equipment. Data presented in Figure 3 and Table 3 show the body of ear and cheek to display almost the opposite mechanical behaviors when subjected to applied suction. Skin covering the ear body is thin (1 mm) [6] and tightly bound to underlying hyaline cartilage, which permits little movement and imparts high stiffness with minimum elastic deformation, absorbed energy, and creep. Cheek skin, on the other hand, is thicker (1.5–2 mm), and is more easily stretched/compressed due to its attachment to an adipose layer, which is approximately 5 mm thick [3]. With its loose connective tissue and numerous blood vessels, the fat permits ready skin movement over underlying facial musculature and continues to deform under constant load, thereby imparting flexibility and high creep. These two extremes in mechanics are punctuated by the fact that most ear body and cheek measurements occupy different clusters in Table 5, and create an indelible need to engineer separate tissue replacements capable of mimicking these structures.
The remaining facial locations tested in this study show skin properties that are overlapping and intermediate to those for cheek and ear body (Figure 3, Table 3, Table 4 and Table 5). Chin and forehead skin are thick (1.8–2.5 mm), with underlying fat layers that are one-half that of cheek skin. These skin locations are attached to bone, which serves as an unmovable anchor, permitting deformation of the overlying skin. As a result, elastic and energy properties are more similar to cheek skin than ear body; however, creep is intermediate to the two skin locations. Ear lobe skin is attached to elastic cartilage, which has a substantial elastin network that imparts intermediate flexibility and the ability to continue deforming under a static load, which accounts for its high creep. The tip of nose measurements for elastic properties of stiffness, elastic deformation, and energy are closer to ear body than to cheek and reflect skin attachment to hyaline cartilage. Interestingly, mean creep values are closest to cheek, and inspection of the stiffness-creep table in Table 5 show that nose observations to be nearly equally divided between Cluster 1 (intermediate creep) and Cluster 3 (high creep). The underlying reason for this behavior is unclear.
Laxity characterizes the tightness/looseness of skin and reflects the degree to which skin is bound to underlying tissue, indicating tendency to sag. With the BTC-2000, it is measured as the percentage of elastic deformation that occurs with very low stress and is defined by a change in slope of the stress-deformation curve (Figure 2). Healthy, young skin demonstrates low laxity, and increases in laxity with age are accompanied with wrinkling and folding. For the different facial locations, results presented in Figure 3a show that structures with increased skin and adipose thickness (cheek, forehead, and chin) exhibit higher laxity than those that are bound to cartilage (nose, ear lobe and ear body).
Percentage elasticity reflects the amount of recovered deformation that is normalized to elastic deformation and converted to percentage. It is an indicator of the degree to which skin is able to immediately recover its dimension when the pressure is released. Normalizing the recovered deformation to elastic deformation is sometimes helpful in comparing tissues that differ in dimension, particularly with respect to thickness. The viscoelastic nature of facial skin is evident, as little more than $50\%$ of the deformation is immediately recovered for any facial location. Chin, cheek, and ear body skin demonstrated the highest elastic recovery, whereas ear lobe skin, with its high elastin content, only recovered $27\%$ of its original elastic deformation.
## 4.1.2. Age
Biomechanical tests of aging skin often cannot distinguish between chronological aging and photoaging, where the two different aging processes occur simultaneously. With chronological aging, histological changes include a disappearance of subepidermal oxytalan fibers, larger cystic spaces in the elastin matrix, and clumping of dense microfibrillar areas [32]. The skin becomes more flaccid, which is reflected by an increase in laxity and often manifested as a more wrinkled appearance. Thus, elastic and total deformation increase, elastic recovery decreases, and viscous creep increases [7,29,33]. The tensile relaxation also changes the orientation of Langer’s lines, and the degree of anisotropy increases [34,35]. In photoaging, the elastic fiber network is additionally disrupted through an accumulation of granular elastotic material and accumulated glycosaminoglycans. Echography has shown these accumulations produce subepidermal bands that increase facial skin thickness from 0.1 mm to 0.5 mm [4]. As a result, when chronological aging is accompanied by photoaging (which is expected in highly solar exposed areas such as the face), the reported biomechanical changes include increased elastic modulus, decreased elastic recovery, and increased viscoelastic deformation [7,28,36]. Results for this subject population show agreement for creep as viscous deformation increased with age (Figure 3b). However, age did not significantly affect elastic deformation, stiffness, energy, or percent elasticity, which is in contradiction to the above discussion. This may be partly explained by the fact that this study performed tests on a stratified subject population, where observations are evenly balanced among population subgroups. Most prior research has focused on populations that are limited in number and/or do not include equal numbers of measurements for different genders, ages, races/ethnicities, and body locations. Additionally, differences in measurement equipment and protocols may contribute to these disparities. For percentage laxity, the 50–70 age group was significantly lower than the 19–29 age group, which is perplexing. No explanation can be offered for this occurrence.
## 4.1.3. Sex
Results from published research suggest that sex contribution to skin biomechanics appears to be minimal, as tests of forearm, forehead, hand, and chest skin have demonstrated no significant differences between sexes for elastic modulus, elastic deformation, and elastic recovery [30,37,38]. In a comprehensive report of biomechanical tests conducted using the vacuum method, Barel [7] observed no gender differences for viscoelasticity ratio (creep divided by elastic deformation) and overall elasticity (similar to percentage elasticity) at four different anatomical locations. These results support those previously reported by Cua [29]. Compared to males, female skin extensibility has been reported as higher and the modulus of elasticity is lower, but the reverse has been reported as well [26,39,40]. In this study, male skin was significantly higher in laxity, elastic deformation, and absorbed energy (Figure 3a–c). These differences may be attributed to morphological differences, where the dermis is thicker in males and provides increased volume for displacement under vacuum. Stiffness and creep were not significantly different between males and females (Table 2), which is consistent with measurements reported for other body locations [19,29,37].
## 4.1.4. Race
Few studies have compared biomechanical properties of skin based on race. In a small male population, a 15 kN m rotating torque was applied to forearms of Black, Hispanic, and White subjects. Differences in elastic recovery and extensibility were noted between the dorsal and ventral surfaces for Hispanic and White subjects, but not Black subjects. Melanin protection on dorsal surfaces of Black subjects was attributed to these differences. Black skin demonstrated $26\%$ less elastic recovery compared to other groups [41]. In another study of skin elasticity, the opposite results were found, where no differences in elastic recovery were noted between Black and White subjects on the legs, but skin recovery was 1.5 times higher for Black subjects on the face [42]. Discrepancies between the studies were ascribed to differences in age groups studied, but overall, the results from the two studies have been deemed inconclusive [43]. In this study, none of the six biomechanical properties were significantly affected at any facial location based on differences in race/ethnicity (Table 2). Compared to other studies, this study enlisted a larger subject population that was equally stratified for gender, age, and race/ethnicity. Along with a different measurement method, this likely accounts for differences in reported results.
## 4.2. Facial Prosthetic Elastomers
Comparisons of prosthetic elastomers with facial skin properties presented in Table 6 show the materials to be categorically highly rigid with low deformability, stretchability, and viscous behavior compared to facial skin. Only the ear body, with its thin skin layer attached to hyaline cartilage, behaved similarly as the properties of elastic deformation, absorbed energy, and percent elasticity overlapped with several prosthetic materials. The disparities measured for creep were particularly remarkable as the prosthetic materials were nearly an order of magnitude lower. Similarly, the elastomers exhibited a high degree of elastic recovery following stress removal, between $75\%$ and $85\%$ for all except A2000, whereas facial skin showed only $28\%$ to $49\%$. This speaks to the limitations that are provided by a filled and crosslinked elastic polymer network. Skin has water and GAGs that can flow through collagen and elastin networks under stress, and time-dependent diffusion is required to restore the original tissue dimension following stress removal. The flow-like behavior afforded in the elastomer network is limited to the hysteresis created during unloading, with unrecovered strain remaining when the load falls to zero. The comparisons of prosthetic materials with population clusters shown in Figure 4 visually depict the seemingly one-dimensional nature of the prosthetic elastomer system which produces materials that vary widely in stiffness, but its ability to produce a range of elastic deformations, creep, and absorbed energies representative of facial skin is minimal.
It is interesting that the prosthetic materials overlapped with facial skin in percent laxity, particularly since material stiffness was up to 6.4 times higher and elastic deformation 11.7 times lower. This is attributable to the method by which laxity is measured and percent laxity calculated. As stated, laxity is measured in millimeters at the point where the stress-deformation curve changes slope, and percent laxity is calculated by dividing this deformation by the amount of elastic deformation, then multiplying it by 100. Since the value is normalized to the amount of elastic deformation, similar values can be obtained for stiff surfaces exhibiting low elastic deformation, and flexible surfaces with high elastic deformation. Therefore, this property is useful only for comparing similar systems, such as skin to itself and elastomers to themselves. Comparing dissimilar systems (i.e., skin versus prosthetic materials) can lead to erroneous conclusions.
## 4.3. Engineering Design Considerations
An inspection of facial skin biomechanical properties at various locations and within population clusters suggests that, for the facial locations measured in this study, three types of tissue replacements are required. As discussed, the body of ear and cheek properties are distinctly different from other facial locations, and therefore require separate considerations. Consequently, each location requires the engineering of a skin replacement with its own properties, particularly with respect to stiffness and creep. Property values approaching those listed in Figure 3 and Table 3 for each site would serve as guidelines for the design. Although nose, chin, ear lobe, and forehead skins exhibit their own unique biomechanical behaviors, a considerable overlap in properties is present. Therefore, it seems reasonable to choose a skin replacement that targets properties listed for Cluster 1 (upper table) and Cluster 2 (lower table) of Table 4 for best satisfying the mechanical behaviors of skin present at these facial locations.
Compared to their predecessors, filled PDMS materials have been used as facial prosthetic materials for nearly five decades because of their flexibility, ease of fabrication, biocompatibility, and relative color stability. Based on results presented here, their biomechanical properties fall short of matching those measured for skin at most facial locations. Part of this is owed to the fact that the present processing methods for facial prostheses are able to accommodate only a single material to serve as the entire prosthesis. Facial structures are multi-layered and exhibit a gradation of properties from outer to inner structures. The successful engineering of tissue replacements, be they tissue-engineered, biomimetic, or prosthetic, require an approach that permits the layering of components to produce a composite structure that more closely duplicates the biomechanical behavior of tissue at a given facial location. This very strategy has been studied by Bellamy and Waters [17]. Using uniaxial linear extensometry, facial skin was stretched in undisclosed locations in 15 volunteers, and the results were applied to construct a three-layered composite material. The middle layer contained varying ratios of unreactive silicone fluids and polydimethyl siloxane, and certain formulations produced similar force-decay results as compared to those measured for facial skin. However, similarities were not seen in hysteresis behavior, and the authors concluded continued research was needed. These results demonstrate that a multilayered design holds promise for future facial prosthetic material development.
For prosthetic materials, changes are needed in both material processing and composition. Additive manufacturing is a logical strategy for producing layered structures, and it has been used to produce components for facial reconstruction [44,45,46]. However, these are mostly single composition materials that focus solely on hard or soft tissue replacement, and often without direct comparison to known properties of healthy tissues. Furthermore, advancements in 3D printer development that permit the layering of rigid and flexible polymers that coincide with future material development are needed [47]. For filled PDMS, continued strategies to lower stiffness and increase deformability that include copolymerizing comonomers that widely differ in molecular weight, altering the monomer: crosslinker ratios, exploring combinations of nano- and submicron–sized fillers at various loading levels, and developing filler coatings that promote superior filler dispersion within polymer are needed. Another approach is to combine cellulose fibers with polyisoprene to mimic collagen and elastin networks in skin, which has produced elastomers shown to exhibit nonlinear stress–strain behaviors similar to those reported for ex vivo tests of human skin [48]. All strategies must include considerations that accommodate requirements for fluid rheology during printing, polymerization via ultraviolet and laser energy sources [49,50], and biocompatibility [51].
## 4.4. Study Limitations
Results from this study are limited by patient variability, testing protocols, equipment, and material batches. Inclusion criteria included healthy appearing facial skin, but other factors, such as patient health and habits (e.g., tobacco usage), were not measured nor controlled, which may impact skin mechanics. The vacuum testing protocol was designed to measure creep over ten seconds as a means to obtain sustained viscous behavior, but also was longer than in previous works where shorter time periods were used and a greater elastic response was produced. This limits the ability to compare this study’s results with others regarding the relative contributions from elastic and viscous components of the viscoelastic response. As stated earlier, the BTC-2000 equipment permits measurement of dermal contributions to skin mechanics, but also limits comparisons with studies enlisting other measurement modes. For prosthetic elastomer materials, batch differences are not expected to yield pronounced changes in properties, but these differences may contribute to the disparate results reported among different laboratories.
## 5. Conclusions
Six biomechanical properties of skin were measured at six locations on the faces of 143 human subjects, and the results were compared to those measured for eight prosthetic elastomers currently available for clinical usage. Generally, the elastomers demonstrated a similarity to body of ear skin for the properties of elastic deformation, absorbed energy, and percentage elasticity. Otherwise, the prosthetic materials were significantly different from skin measured at other facial locations, with the most disparate differences noted for stiffness and creep, two key viscoelastic properties. To engineer facial skin replacements, separate considerations are needed for the ear body and cheek, whereas a similar set of biomechanical parameters can be chosen to satisfy those measured for chin, nose, forehead, and ear lobe.
## References
1. Arda O., Goksugur N., Tuzun Y.. **Basic histological structure and functions of facial skin**. *Clin. Derm.* (2014) **32** 3-13. DOI: 10.1016/j.clindermatol.2013.05.021
2. Ha R.Y., Nojima K., Adams W.P., Brown S.A.. **Analysis of facial skin thickness: Defining the relative thickness index**. *Plast. Reconstr. Surg.* (2005) **115** 1769-1773. DOI: 10.1097/01.PRS.0000161682.63535.9B
3. Kim Y.S., Lee K.W., Kim J.S., Gil Y.C., Tanvaa T., Shin D.H., Kim H.J.. **Regional thickness of facial skin and superficial fat: Application to the minimally invasive procedures**. *Clin. Anat.* (2019) **32** 1008-1018. DOI: 10.1002/ca.23331
4. Pellacani G., Seidenari S.. **Variations in facial skin thickness and echogenicity with site and age**. *Acta Derm. Venereol.* (1999) **79** 366-369. DOI: 10.1080/000155599750010283
5. Takema Y., Yorimoto Y., Kawai M., Imokawa G.. **Age-related changes in the elastic properties and thickness of human facial skin**. *Br. J. Derm.* (1994) **131** 641-648. DOI: 10.1111/j.1365-2133.1994.tb04975.x
6. Chopra K., Calva D., Sosin M., Tadisina K.K., Banda A., De La Cruz C., Chaudhry M.R., Legesse T., Drachenberg C.B., Manson P.N.. **A comprehensive examination of topographic thickness of skin in the human face**. *Aesthet. Surg. J.* (2015) **35** 1007-1013. DOI: 10.1093/asj/sjv079
7. Barel A.O., Lambrecht R., Clarys P.. **Mechanical function of the skin: State of the art**. *Curr. Probl. Derm.* (1998) **26** 69-83. DOI: 10.1159/000060577
8. Allareddy V., Allareddy V., Nalliah R.P.. **Epidemiology of facial fracture injuries**. *J. Oral. Maxillofac. Surg. Off. J. Am. Assoc. Oral. Maxillofac. Surg.* (2011) **69** 2613-2618. DOI: 10.1016/j.joms.2011.02.057
9. Hoogewerf C.J., van Baar M.E., Hop M.J., Bloemen M.C., Middelkoop E., Nieuwenhuis M.K.. **Burns to the head and neck: Epidemiology and predictors of surgery**. *Burns* (2013) **39** 1184-1192. DOI: 10.1016/j.burns.2013.03.006
10. Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F.. **Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries**. *CA Cancer J. Clin.* (2021) **71** 209-249. DOI: 10.3322/caac.21660
11. Rankin M., Borah G.L.. **Perceived functional impact of abnormal facial appearance**. *Plast. Reconstr. Surg.* (2003) **111** 2140-2146. DOI: 10.1097/01.PRS.0000060105.63335.0C
12. van Doorne J.M., van Waas M.A., Bergsma J.. **Facial disfigurement after cancer resection: A problem with an extra dimension**. *J. Investig. Surg. Off. J. Acad. Surg. Res.* (1994) **7** 321-326. DOI: 10.3109/08941939409051149
13. Alsberg E., Hill E.E., Mooney D.J.. **Craniofacial tissue engineering**. *Crit. Rev. Oral. Biol. Med. Off. Publ. Am. Assoc. Oral. Biol.* (2001) **12** 64-75. DOI: 10.1177/10454411010120010501
14. Goiato M.C., de Carvalho Dekon S.F., de Faria Almeida D.A., Sanchez D.M., dos Santos D.M., Pellizzer E.P.. **Patients’ satisfaction after surgical facial reconstruction or after rehabilitation with maxillofacial prosthesis**. *J. Craniofacial. Surg.* (2011) **22** 766-769. DOI: 10.1097/SCS.0b013e318207f302
15. Lemon J.C., Kiat-amnuay S., Gettleman L., Martin J.W., Chambers M.S.. **Facial prosthetic rehabilitation: Preprosthetic surgical techniques and biomaterials**. *Curr. Opin. Otolaryngol. Head Neck Surg.* (2005) **13** 255-262. DOI: 10.1097/01.moo.0000172805.48758.0c
16. Griffin M.F., Leung B.C., Premakumar Y., Szarko M., Butler P.E.. **Comparison of the mechanical properties of different skin sites for auricular and nasal reconstruction**. *J. Otolaryngol. Head Neck Surg.* (2017) **46** 33. DOI: 10.1186/s40463-017-0210-6
17. Bellamy K.E., Waters M.G.. **Designing a prosthesis to simulate the elastic properties of skin**. *Biomed. Mater. Eng.* (2005) **15** 21-27. PMID: 15623927
18. Farah J.W., Robinson J.C., Hood J.A., Koran A., Craig R.G.. **Force-displacement properties of a modified cross-linked silicone compared with facial tissues**. *J. Oral. Rehabil.* (1988) **15** 277-283. DOI: 10.1111/j.1365-2842.1988.tb00157.x
19. Luebberding S., Krueger N., Kerscher M.. **Mechanical properties of human skin in vivo: A comparative evaluation in 300 men and women**. *Ski. Res. Technol.* (2014) **20** 127-135. DOI: 10.1111/srt.12094
20. Pierard G.E., Hermanns-Le T., Gaspard U., Pierard-Franchimont C.. **Asymmetric facial skin viscoelasticity during climacteric aging**. *Clin. Cosmet. Investig. Derm.* (2014) **7** 111-118. DOI: 10.2147/CCID.S60313
21. Ward J.H.. **Hierarchical Grouping to Optimize an Objective Function**. *J. Am. Stat. Assoc.* (1963) **58** 236-244. DOI: 10.1080/01621459.1963.10500845
22. Montgomery P.C., Kiat-Amnuay S.. **Survey of currently used materials for fabrication of extraoral maxillofacial prostheses in North America, Europe, Asia, and Australia**. *J. Prosthodont. Off. J. Am. Coll. Prosthodont.* (2010) **19** 482-490. DOI: 10.1111/j.1532-849X.2009.00538.x
23. Willett E.S., Beatty M.W.. **Outdoor weathering of facial prosthetic elastomers differing in Durometer hardness**. *J. Prosthet. Dent.* (2015) **113** 228-235. DOI: 10.1016/j.prosdent.2014.09.009
24. Hendrickson E.L., Lamont R.J., Hackett M.. **Tools for interpreting large-scale protein profiling in microbiology**. *J. Dent. Res.* (2008) **87** 1004-1015. DOI: 10.1177/154405910808701113
25. Kalra A., Lowe A., Al-Jumaily A.M.. **Mechanical Behavior of Skin: A Review**. *J. Mater. Sci. Eng.* (2016) **5** 254. DOI: 10.4172/2169-0022.1000254
26. Pierard G.E., Pierard S.L., Hermanns-Le T., Farage M.A., Miller K.W., Maibach H.I.. **Facial Skin Rheology**. *Textbook of Aging Skin* (2010) 351-361
27. Smalls L.K., Randall Wickett R., Visscher M.O.. **Effect of dermal thickness, tissue composition, and body site on skin biomechanical properties**. *Ski. Res. Technol.* (2006) **12** 43-49. DOI: 10.1111/j.0909-725X.2006.00135.x
28. Ryu H.S., Joo Y.H., Kim S.O., Park K.C., Youn S.W.. **Influence of age and regional differences on skin elasticity as measured by the Cutometer**. *Ski. Res. Technol.* (2008) **14** 354-358. DOI: 10.1111/j.1600-0846.2008.00302.x
29. Cua A.B., Wilhelm K.P., Maibach H.I.. **Elastic properties of human skin: Relation to age, sex, and anatomical region**. *Arch. Derm. Res.* (1990) **282** 283-288. DOI: 10.1007/BF00375720
30. Diridollou S., Black D., Lagarde J.M., Gall Y., Berson M., Vabre V., Patat F., Vaillant L.. **Sex- and site-dependent variations in the thickness and mechanical properties of human skin in vivo**. *Int. J. Cosmet. Sci.* (2000) **22** 421-435. DOI: 10.1111/j.1468-2494.2000.00037.x
31. Krueger N., Luebberding S., Oltmer M., Streker M., Kerscher M.. **Age-related changes in skin mechanical properties: A quantitative evaluation of 120 female subjects**. *Ski. Res. Technol.* (2011) **17** 141-148. DOI: 10.1111/j.1600-0846.2010.00486.x
32. Fazio M.J., Olsen D.R., Uitto J.J.. **Skin aging: Lessons from cutis laxa and elastoderma**. *Cutis* (1989) **43** 437-444. PMID: 2721242
33. Pierard G.E., Henry F., Castelli D., Ries G.. **Ageing and rheological properties of facial skin in women**. *Gerontology* (1998) **44** 159-161. DOI: 10.1159/000022001
34. Pailler-Mattei C., Bec S., Zahouani H.. **In vivo measurements of the elastic mechanical properties of human skin by indentation tests**. *Med. Eng. Phys.* (2008) **30** 599-606. DOI: 10.1016/j.medengphy.2007.06.011
35. Thieulin C., Pailler-Mattei C., Abdouni A., Djaghloul M., Zahouani H.. **Mechanical and topographical anisotropy for human skin: Ageing effect**. *J. Mech. Behav. Biomed. Mater.* (2020) **103** 103551. DOI: 10.1016/j.jmbbm.2019.103551
36. Pedersen L., Hansen B., Jemec G.B.E.. **Mechanical properties of the skin: A comparison between two suction cup methods**. *Ski. Res. Technol.* (2003) **9** 111-115. DOI: 10.1034/j.1600-0846.2003.00021.x
37. Ishikawa T., Ishikawa O., Miyachi Y.. **Measurement of skin elastic properties with a new suction device (I): Relationship to age, sex and the degree of obesity in normal individuals**. *J. Derm.* (1995) **22** 713-717. DOI: 10.1111/j.1346-8138.1995.tb03907.x
38. Diridollou S., Vabre V., Berson M., Vaillant L., Black D., Lagarde J.M., Gregoire J.M., Gall Y., Patat F.. **Skin ageing: Changes of physical properties of human skin in vivo**. *Int. J. Cosmet. Sci.* (2001) **23** 353-362. DOI: 10.1046/j.0412-5463.2001.00105.x
39. Malm M., Samman M., Serup J.. **In vivo skin elasticity of 22 anatomical sites: The vertical gradient of skin extensibility and implications in gravitational aging**. *Ski. Res. Technol.* (1995) **1** 61-67. DOI: 10.1111/j.1600-0846.1995.tb00019.x
40. Salter D.C., McArthur H.C., Crosse J.E., Dickens A.D.. **Skin mechanics measured in vivo using torsion: A new and accurate model more sensitive to age, sex and moisturizing treatment**. *Int. J. Cosmet. Sci.* (1993) **15** 200-218. DOI: 10.1111/j.1467-2494.1993.tb00075.x
41. Berardesca E., de Rigal J., Leveque J.L., Maibach H.I.. **In vivo biophysical characterization of skin physiological differences in races**. *Dermatologica* (1991) **182** 89-93. DOI: 10.1159/000247752
42. Warrier A.G., Kligman A.M., Harper R.A., Bowman J., Wickett R.R.. **A comparison of Black and white skin using noninvasive methods**. *J. Soc. Cosmet. Chem.* (1996) **47** 229-240
43. Wesley N.O., Maibach H.I.. **Racial (ethnic) differences in skin properties: The objective data**. *Am. J. Clin. Derm.* (2003) **4** 843-860. DOI: 10.2165/00128071-200304120-00004
44. Hsieh T.Y., Dedhia R., Cervenka B., Tollefson T.T.. **3D Printing: Current use in facial plastic and reconstructive surgery**. *Curr. Opin. Otolaryngol. Head Neck Surg.* (2017) **25** 291-299. DOI: 10.1097/MOO.0000000000000373
45. Unkovskiy A., Spintzyk S., Brom J., Huettig F., Keutel C.. **Direct 3D printing of silicone facial prostheses: A preliminary experience in digital workflow**. *J. Prosthet. Dent.* (2018) **120** 303-308. DOI: 10.1016/j.prosdent.2017.11.007
46. Jindal S., Manzoor F., Haslam N., Mancuso E.. **3D printed composite materials for craniofacial implants: Current concepts, challenges and future directions**. *Int. J. Adv. Manuf. Technol.* (2021) **112** 635-653. DOI: 10.1007/s00170-020-06397-1
47. Fay C.D., Jeiranikhameneh A., Sayyar S., Talebian S., Nagle A., Cheng K., Fleming S., Mukherjee P., Wallace G.G.. **Development of a customised 3D printer as a potential tool for direct printing of patient-specific facial prosthesis**. *Int. J. Adv. Manuf. Technol.* (2022) **120** 7143-7155. DOI: 10.1007/s00170-022-09194-0
48. Wang Z., Jiang F., Zhang Y., You Y., Wang Z., Guan Z.. **Bioinspired design of nanostructured elastomers with cross-linked soft matrix grafting on the oriented rigid nanofibers to mimic mechanical properties of human skin**. *ACS Nano* (2015) **9** 271-278. DOI: 10.1021/nn506960f
49. Jindal S.K., Sherriff M., Waters M.G., Coward T.J.. **Development of a 3D printable maxillofacial silicone: Part I. Optimization of polydimethylsiloxane chains and cross-linker concentration**. *J. Prosthet. Dent.* (2016) **116** 617-622. DOI: 10.1016/j.prosdent.2016.02.020
50. Jindal S.K., Sherriff M., Waters M.G., Smay J.E., Coward T.J.. **Development of a 3D printable maxillofacial silicone: Part II. Optimization of moderator and thixotropic agent**. *J. Prosthet. Dent.* (2018) **119** 299-304. DOI: 10.1016/j.prosdent.2017.04.028
51. Pagano S., Lombardo G., Caponi S., Costanzi E., Di Michele A., Bruscoli S., Xhimitiku I., Coniglio M., Valenti C., Mattarelli M.. **Bio-mechanical characterization of a CAD/CAM PMMA resin for digital removable prostheses**. *Dent. Mater. Off. Publ. Acad. Dent. Mater.* (2021) **37** e118-e130. DOI: 10.1016/j.dental.2020.11.003
|
---
title: Screening of Potential α-Glucosidase Inhibitors from the Roots and Rhizomes
of Panax Ginseng by Affinity Ultrafiltration Screening Coupled with UPLC-ESI-Orbitrap-MS
Method
authors:
- Hong-Ping Wang
- Chun-Lan Fan
- Zhao-Zhou Lin
- Qiong Yin
- Chen Zhao
- Ping Peng
- Run Zhang
- Zi-Jian Wang
- Jing Du
- Zhi-Bin Wang
journal: Molecules
year: 2023
pmcid: PMC10004417
doi: 10.3390/molecules28052069
license: CC BY 4.0
---
# Screening of Potential α-Glucosidase Inhibitors from the Roots and Rhizomes of Panax Ginseng by Affinity Ultrafiltration Screening Coupled with UPLC-ESI-Orbitrap-MS Method
## Abstract
Panax ginseng was a traditional Chinese medicine with various pharmacological activities and one of its important activities was hypoglycemic activity; therefore, panax ginseng has been used in China as an adjuvant in the treatment of diabetes mellitus. In vivo and in vitro tests have revealed that ginsenosides, which are derived from the roots and rhizomes of panax ginseng have anti-diabetic effects and produce different hypoglycemic mechanisms by acting on some specific molecular targets, such as SGLT1, GLP-1, GLUTs, AMPK, and FOXO1. α-*Glucosidase is* another important hypoglycemic molecular target, and its inhibitors can inhibit the activity of α-Glucosidase so as to delay the absorption of dietary carbohydrates and finally reduce postprandial blood sugar. However, whether ginsenosides have the hypoglycemic mechanism of inhibiting α-Glucosidase activity, and which ginsenosides exactly attribute to the inhibitory effect as well as the inhibition degree are not clear, which needs to be addressed and systematically studied. To solve this problem, affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS technology was used to systematically select α-Glucosidase inhibitors from panax ginseng. The ligands were selected through our established effective data process workflow based on systematically analyzing all compounds in the sample and control specimens. As a result, a total of 24 α-Glucosidase inhibitors were selected from panax ginseng, and it was the first time that ginsenosides were systematically studied for the inhibition of α-Glucosidase. Meanwhile, our study revealed that inhibiting α-Glucosidase activity probably was another important mechanism for ginsenosides treating diabetes mellitus. In addition, our established data process workflow can be used to select the active ligands from other natural products using affinity ultrafiltration screening.
## 1. Introduction
Panax ginseng, which is a traditional medicinal and edible plant, has been widely used in China and Asia for thousands of years. Modern research has proved that panax ginseng has various pharmacological activities, and as well as having anti-tumor [1], anti-aging [2], anti-oxidation [3], anti-fatigue [4], improvement of immune function, intelligence as well as learning ability [5,6], a protective effect on cerebral ischemia, liver as well as kidneys [7,8,9], and regulation of central nervous system [10], panax ginseng also has the effect of decreasing blood sugar and has been used in China as an adjuvant in the treatment of diabetes mellitus. Accumulating evidence has shown that ginsenosides, which are extracted from panax ginseng, exert anti-diabetic effects [11,12,13,14,15]. In vivo and in vitro tests revealed that ginsenosides have anti-diabetic effects mainly because they act on some specific molecular targets. For example, ginsenoside Rg1, Rg3, F2, compound K, and Rh2 could effectively reduce intestinal glucose uptake through the regulation of sodium-glucose cotransporters 1 (SGLT1) gene expression [16,17]; ginsenoside Rg3 reduces blood glucose and increases plasma glucagon-like peptide-1(GLP-1) and plasma insulin through the improvement of insulin resistance, lipid metabolism, energy metabolism, and gut flora metabolism [11]; ginsenoside Rb1 can promote the translocation of glucose transporter to increase glucose uptake in adipocytes, and this reduces fasting glucose through recovery in the expression of glucose transporters (GLUTs) and the phosphorylation of Akt in the adipose tissue of db/db mice [18]; ginsenoside Rg1, Rb3, and compound K can reduce gluconeogenesis through increase AMP-activated protein kinase (AMPK) expression and decreased Forkhead transcription factor 1 (FOXO1) activity [19,20,21], etc. All these modern research results demonstrate that ginsenosides can produce different hypoglycemic mechanisms by acting on different molecule targets. However, besides the above-mentioned pharmacological mechanisms of the anti-diabetic effects of drugs, inhibiting the activity of α-Glucosidase so as to delay the absorption of dietary carbohydrates and finally reduce postprandial blood sugar is another important hypoglycemic mechanism. For ginsenosides, the anti-diabetic effects, whether they have the mechanism of inhibiting α-Glucosidase activity, and which ginsenosides exactly attribute to the inhibitory effect as well as the inhibition degree are not clear and need to be addressed and systematically studied.
In a previous study, the main approach for identifying active ingredients from the roots of panax ginseng was separately testing their biological activity after chemical separation one by one [22]. However, the experiment period is long with a heavy workload, and even some active ingredients with trace amounts are lost during separation. Moreover, it is impossible to achieve high-throughput screening. In order to achieve rapid screening and identification of α-glucosidase inhibitors from natural products, some researchers developed a novel at-line nanofractionation screening platform, in which a time-course bioassay based on high-density well-plates was performed in parallel with high-resolution mass spectrometry (MS), providing a straightforward and rapid procedure to simultaneously obtain chemical and biological information of active compounds, and with this approach, the efficiency and quality of screening can be increased [23]. However, in our study, another much more useful technology for screening α-glucosidase inhibitors, affinity ultrafiltration screening-mass spectrometry, which combined affinity ultrafiltration screening technology with liquid chromatogram-mass spectrometry (LC-MS) technology, was employed. Due to it having the advantage of making the screening of active components from natural products more convenient and effective, it has played an increasingly vital role in early drug discovery [24,25,26,27]. Through affinity ultrafiltration screening technology, the small molecule ligands bound to biological macromolecules can be obtained, and through LC-MS technology, especially through ultra-performance liquid chromatography (UPLC) coupled with ESI-Orbitrap-MS technology which can provide high-resolution MS spectra, the structures of the selected ligands can be rapid analysis and characterization. However, during the selection of ligands from dissociation solution obtained through affinity ultrafiltration screening, some researchers visually compared chromatographic peak intensity on liquid chromatograms of the sample group containing target protein with the control group containing denatured/unadded target protein, and the compounds with higher intensities in the sample group were selected as the ligands [24,25,26,27]. However, when peaks contain many overlapping signals, this approach will lead to false positive or false negative results. Under these circumstances, an effective data process workflow for ligands selection should be established.
Based on the above problems that need to be solved urgently, we used affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS technology to systematically select α-Glucosidase inhibitors from panax ginseng, in which the major components were ginsenosides, and meanwhile established an effective data process workflow to systematically select the small molecule ligands instead of previous visual comparison of chromatographic peak intensity on liquid chromatograms of the sample and the control specimens. Here, through our integrated affinity ultrafiltration screening-MS and established data process workflow, a total of 24 active ingredients were selected from panax ginseng, including 14 known ginsenosides and 10 potential new ginsenosides. Our study first systematically selected and characterized the α-Glucosidase inhibitors from panax ginseng and revealed that inhibiting α-Glucosidase activity probably was another important mechanism for the hypoglycemic effect of ginsenosides, which will improve cognition in people undergoing ginsenosides treatment of diabetes mellitus.
## 2.1. Selection of α-Glucosidase Inhibitors by Affinity Ultrafiltration Screening–LC-UPLC-ESI-Orbitrap-MS
The designed process of selection of α-Glucosidase inhibitors is briefly introduced as follows: when the extract of panax ginseng was incubated with α-Glucosidase, active ligands could bind to the active site of α-Glucosidase forming receptor–ligands complexes, and unbound small molecules were free, which could be separated from receptor–ligands complexes by using an ultrafiltration membrane. Then, the receptor–ligands complexes were disrupted by the addition of dissociation agent, and the released ligands were analyzed by UPLC-ESI-Orbitrap-MS analysis. The full scan data was analyzed by our established data process workflow based on systematic analysis of all compounds in the specimens to select the active ingredients, and the ingredient with the peak area ratio (PAR) value, which was defined as the ratio of peak area of a compound detected in the sample group to that detected in the control group, >1 and $p \leq 0.05$ ($$n = 4$$) was selected as the ligand.
Figure 1 shows the total ion chromatograms of panax ginseng extract, sample, and control specimens. From their total ion chromatograms, we found that there were many overlapping signals due to ginsenosides in panax ginseng owning similar chemical structures and polarity, leading to their poor separation in the reversed chromatographic column. Even in the optimized chromatographic conditions, it is still difficult to achieve their complete separation. Therefore, directly comparing the intensity of each peak in their total ion chromatograms between the sample and the control groups can lead to false positive or false negative results. In our experiment, we established an effective workflow to systematically analyze all compounds in the sample specimens, and compounds not only with high abundance but also with lower intensity even covered by others were analyzed. Each compound of PAR value and its mean PAR value were calculated according to the obtained peak areas yielded by the compounds-extracting workflow. A two-tailed t-test was used to calculate the significant difference (p value) in the peak area of each compound between the sample and the control group. The compounds, whose mean PAR values >1 and $p \leq 0.05$ ($$n = 4$$), were selected as the potential α-Glucosidase inhibitors, and as a result, 24 ligands (shown in Table 1 and Supplementary Materials) including 14 known ginsenosides (R1–R14) and 10 unknown ginsenosides (R15–R24) were successfully selected, and the data of affinity ultrafiltration screening was shown in Supplementary Information Data S1.
From Table 1, we found that the PAR values of the selected were all >1, meaning the intensities of these compounds in the sample group were higher than those in the corresponding control group. In order to confirm that there was indeed a difference in peak intensity between the sample and the control group for the selected ligands, we separately extracted their ion chromatograms in the sample and control specimens, and we found all of the selected ligands exhibiting higher intensities in the sample group, meaning our established data process workflow was reliable and effective. Figure 2 showed the PAR values of some representative ligands. The precursor ions of all the selected ligands were then used to perform targeted MS/MS analysis for structure identification. Although we could characterize the selected ligands based on their high-resolution MS and fragmentation ions produced by targeted MS/MS analysis, unambiguous structural elucidation requires strict confirmation with reference standards. To this end, we obtained the 14 reference standards (R1–R14) (shown in Figure 3) through commercial sources. The identification of these ligands is based on matching both retention time deviation (<0.1 min) and accurate mass deviation (<5 ppm) with the corresponding reference standards. The unknown ligands R15–R24 were elucidated according to the diagnostic ions and fragmentation pathways of ginsenosides, and we found that most of the unknown ligands were the isomers of the known ligands. For example, R15 and R18 showed the same precursor ions at m/z 955.4904 with a mass deviation of 0.10 ppm indicating their molecular formula was C48H76O19. In their MS/MS spectra, their diagnostic ions were observed at m/z 455.3534 suggesting that they were oleanolic-type ginsenosides. The fragmentation ions at m/z 793.4376, 731.4376, 613.3737, 569.3847, and 455.3534 were the same as those of ginsenoside Ro (R8). Thus, R15 and R18 were separately deduced as ginsenoside Ro isomers. Similarly, R16 and R22 were separately deduced as ginsenoside Ra1 isomer or ginsenoside Ra2 isomer due to their fragmentation ions being the same as those of ginsenoside Ra1 or ginsenoside Ra2. R24 was deduced as a quinquenoside R1 isomer due to its fragmentation ions were the same as those of quinquenoside R1. In the MS/MS spectra of R17 and R21, after the loss of Ac (42 Da), their remaining fragmentation ions were the same as those of ginsenoside Rd; thus, R17 and R21 were separately deduced as acetyl-ginsenoside Rd. In the same way, R19 was deduced as (E)-but-2-enoyl ginsenoside Rd because after the loss of (E)-but-2-enoyl (68 Da), the remaining fragmentation ions were the same as those of ginsenoside Rd. The precursor ion of R20 was observed at m/z 943.5251 with a mass deviation of −1.59 ppm indicating its molecular formula was C48H80O18. In its MS/MS spectra, its aglycone ion was obtained at m/z 457.3699, which was 18 Da less than the aglycone of protopanaxatriol (m/z 475 in negative-ion mode); thus, we considered the aglycone ion m/z 457.3699 was dehydrated-protopanaxatriol. The fragmentation ions of m/z 781.4766, 619.4214, and 457.3699 suggested that Glc, Glc, Glc were successively eliminated from the precursor ion. Thus, R20 was deduced as dehydrated-protopanaxatriol + 3Glc. The precursor ion of R23 was observed at m/z 1105.5779 with a mass deviation of −1.45 ppm, suggesting its molecular formula was C54H90O23. In its MS/MS spectra, its aglycone ion was obtained at m/z 457.3698, which was formed by successive losses of 4Glc from the precursor ion m/z 1105.5779. Thus, R23 was deduced as dehydrated-protopanaxatriol + 4Glc. The fragmentation ions of all 24 ligands are shown in Table 1.
As we know, there are mainly three types of ginsenosides in panax ginseng, including protopanaxadiol-, protopanaxatriol-, and oleanolic acid-type. Among the selected ligands, seven ginsenosides including R1, R4–R6, R8, R15, and R18 belong to oleanolic acid-type, whereas thirteen ginsenosides including R2, R7, R10–R14, R16, R17, R19, R21, R22 and R24 belong to protopanaxadiol-type. However, only four ginsenosides, including R3, R9, R20, and R23 are protopanaxatriol-type. Thus, it can be seen that protopanaxadiol-type ginsenosides are the main α-Glucosidase inhibitors, and oleanolic acid-type ginsenosides come second. Nevertheless, protopanaxatriol-type ginsenosides which are one of the most important ginsenosides in panax ginseng own the least number of ligands with much lower abundance.
It is worth noting that through our established data process workflow, much more ligands were selected, and not only were the known compounds with higher amounts (R1–R14) selected and characterized as α-Glucosidase inhibitors but also some unknown compounds (R15–R24) with lower abundance covered by other compounds, and these unknown compounds were probably potential new compounds.
## 2.2. Molecular Docking of α-Glucosidase and Ligands
In order to predict the affinity between the selected ligands and α-Glucosidase, the selected ligands were docked with α-Glucosidase. The compounds (R1–R14) owning the definite structures were downloaded from PubChem and their 3D structures were docked with pre-processed α-Glucosidase. Generally, when the protein–ligand interaction was analyzed using AutoDock vina (version 1.5.6), affinity ≤ −7 kcal/mol indicated the compounds had strong binding with the target protein. After molecular docking with α-Glucosidase, the affinities of R1–R14 were all in the range of −7.2–−9.0 kcal/mol, indicating they all had a strong affinity with α-Glucosidase. In addition, acarbose, which was widely used as an α-Glucosidase inhibitor, was also docked with the pre-processed α-Glucosidase and its affinity was −7.1 kcal/mol. From another aspect, the results mentioned above verified the reliability of the affinity ultrafiltration screening. However, R15–R24 could not be docked with α-Glucosidase due to the fact that they were probably new compounds, and their definite structures were unknown yet.
## 2.3. α-Glucosidase Inhibitory Activity of Ligands
To further verify the α-Glucosidase inhibitory activity of the selected ligands, in vitro enzyme inhibition assay was performed. Due to only the reference standards of R1–R14 being commercially available, these fourteen compounds were performed in vitro enzyme inhibition assay. However, due to R2 and R7 being insoluble in a 0.1 M phosphate buffer (pH 6.8) even though co-solvent DMSO ($2\%$) was added, their in vitro enzyme inhibition assay was not performed. The remaining twelve compounds were easily soluble in a 0.1 M phosphate buffer (pH 6.8) and their α-Glucosidase inhibitory activities were tested. The results are shown in Table 2. From Table 2, we found that oleanolic acid-type ginsenoside zingibroside R1 (R1) exhibited much stronger α-Glucosidase inhibitory activity with the IC50 value of 3.61 mM, even superior to the positive control acarbose (IC50 value of 5.25 mM). Nevertheless, the other selected oleanolic acid-type ginsenosides displayed weaker α-Glucosidase inhibitory activities than acarbose, such as pseudoginsenoside-RT1 (R4) and chikusetsusaponin Iva (R6) with the IC50 values of 39.30 mM and 17.33 mM, respectively, whereas chikusetsusaponin IV (R5) and ginsenoside Ro (R8) separately exhibited $16.20\%$ and $20.23\%$ inhibition rate at 40 mM. For the tested protopanaxadiol-type ginsenosides, all of them displayed lower α-Glucosidase inhibitory activities, and except for ginsenoside Rc (R14) was with the IC50 value 36.83 mM, the inhibition rate of ginsenoside Ra1 (R10), as well as ginsenoside Ra2 (R11), were <$30\%$ at 40 mM whereas the inhibition rate of quinquenoside R1 (R12), as well as ginsenoside Ra3 (R13), were <$20\%$ at 24 mM. For the tested protopanaxatriol-type ginsenosides, ginsenoside F4 (R9) displayed stronger α-Glucosidase inhibitory activity than ginsenoside Rg6 (R3). From Figure 3, we found that the difference in their structures is the double bond position at C21, and ginsenoside F4 (R9) owns a non-terminal double bond whereas ginsenoside Rg6 (R3) owns a terminal double bond. This implied that the double bond position of ginsenosides could affect the activation degree of ginsenosides, and the non-terminal double bond was better than the terminal double bond.
## 3.1. Samples, Reference Standards, and Reagents
α-Glucosidase from *Saccharomyces cerevisiae* was purchased from Sigma (Enzyme Commission number: 3.2.1.20, 100 units, St. Louis, MO, USA), and p-Nitrophenyl-α-d-glucopyranoside (pNPG) was obtained from Macklin Biochemical Technology Co., Ltd. (Shanghai, China). Ammonium acetate buffer (10 mM, pH 6.86) and phosphate buffer (0.1 M, pH 6.8) were obtained from Applygen Technologies Inc. (Beijing, China) LC-MS-grade formic acid was obtained from Fisher-Scientific (Fair Lawn, NJ, USA) whereas LC-MS-grade acetonitrile and methanol were purchased from Merck (Darmstadt, Germany). The distilled water was obtained from Watsons.
The roots and rhizomes of panax ginseng were supplied by the Scientific Research Institute of Beijing Tongrentang Co., Ltd. A total of 14 reference standards (shown in Figure 3), including zingibroside R1 (R1), 20(S)-ginsenoside Rg3 (R2), ginsenoside Rg6 (R3), pseudoginsenoside-RT1 (R4), chikusetsusaponin IV (R5), chikusetsusaponin Iva (R6), ginsenoside Rd (R7), ginsenoside Ro (R8), ginsenoside F4 (R9), ginsenoside Ra1 (R10), ginsenoside Ra2 (R11), quinquenoside R1 (R12), ginsenoside Ra3 (R13), and ginsenoside Rc (R14), were purchased from Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China). The purity of all the reference standards was >$98\%$.
## 3.2. Sample Preparations
The panax ginseng was pulverized into powder (just like flour). The powder of panax ginseng (15.0 g) was ultrasonically extracted for 30 min with 150 mL $70\%$ methanol at 25 °C. The extracted solution was then filtered through a filter paper. This extraction was repeated twice. The filtrate was combined and evaporated to dryness (3.5 g) using a rotary evaporator at 40 °C. The residue (10 mg) was then dissolved in 4 mL of ammonium acetate buffer (10 mM, pH 6.86) and filtered through a 0.22-μm nylon filter membrane to obtain the sample of affinity ultrafiltration screening.
## 3.3.1. Affinity Ultrafiltration Screening
The affinity ultrafiltration procedure was performed according to the modified method of Li [28]. α-Glucosidase was dissolved in 10 mM ammonium acetate buffer (pH 6.86) to obtain the α-Glucosidase solution (40 U/mL). A total of 100 μL of 2.5 mg/mL panax ginseng sample solution was incubated for 30 min at 37 °C with 100 μL α-Glucosidase (40 U/mL). After incubation, the mixture was filtered through an ultrafiltration centrifugal filter (AMICON ULTRA, 0.5 mL, 10 kDa, Millipore, MA, USA) containing a regenerated cellulose ultrafiltration membrane with a 10,000 MW cut-off at 25 °C. The filter was washed with 250 μL ammonium acetate buffer (pH 6.86) by centrifugal force at 14,000 r/min for 10 min to remove the unbound compounds. This process was then repeated four times. Then, 100 μL of methanol–water (50:50; v/v, pH 3.30) was added to release the bound ligands, and centrifugation was followed (14,000 r/min for 15 min). The release process was then repeated twice. All of the dissociation solution was combined and evaporated to dryness using a nitrogen-blowing instrument. The residue was re-dissolved in 50 μL methanol–water (50:50; v/v) for LC-ESI-Orbitrap-MS analysis. The control experiment was carried out with denatured enzyme (in boiling water for 10 min). Each pair of sample and control specimens was prepared in four replicates.
## 3.3.2. UPLC-ESI-Orbitrap-MS Analysis
The re-dissolved solution was analyzed on a Vanquish™ Flex UPLC system (Thermo Scientific, Waltham, MA, USA) equipped with a binary pump and a thermostated column compartment. Multiple components were separated on a Waters ACQUITY UPLC® BEH C18 column (2.1 × 100 mm, 1.7 μm) (Waters, Milford, MA, USA) coupled with an ACQUITY UPLC® BEH C18 VanGuardTM Pre-Column (2.1 × 5 mm, 1.7 μm) using mobile phase A ($0.1\%$ formic acid/water, v/v) and mobile phase B (acetonitrile) by the following gradient elution program: 0–7 min, 2–$20\%$ B; 7–10 min, 20–$25\%$ B; 10–20 min, 25–$40\%$ B; 20–25 min, 40–$65\%$ B; 25–30 min, 65–$95\%$ B. The temperature was set at 35 °C, and the flow rate was 0.3 mL/min. The injection volume was 2 μL.
An Orbitrap Exploris 240 mass spectrometer (Thermo Scientific, Waltham, MA, USA) equipped with a Heated ESI source was used to acquire the mass spectra and negative-ion mode was adopted. The MS parameters were as follows: ion spray voltage: 2500 V, sheath gas: 5.08 L/min, auxiliary gas: 9.37 L/min, ion transfer tube temperature: 320 °C, vaporizer temperature: 350 °C, scan range (m/z): 150–2000, and collision-energy voltage: 35 V. The full scan was operated at a mass resolution of 60,000 whereas the MS2 scan was operated at a mass resolution of 15,000. An internal calibration source, Thermo Scientific EASY-ICTM (Thermo Scientific, Waltham, MA, USA), was adopted to calibrate the entire mass range.
The re-dissolved solution was first analyzed in a full scan mode to minimize signal loss, and then the precursor ions of the selected α-Glucosidase inhibitors were performed targeted MS/MS analysis for structure characterization.
## 3.4. Data Process
Screening ligands from multiple compounds requires some strategies, and in our experiment, we established an effective data process workflow that mainly included three steps: ①Extracting all compounds in each specimen To comprehensively screen the ligands of α-Glucosidase from the crude extract of panax ginseng, we used a workflow to systematically analyze each compound in the sample and control group. The Compound DiscovererTM software (Thermo ScientificTM, version 3.2.0.421) was used for the data process, and all compounds in each specimen were processed by peak alignment and peak extraction based on our extracting workflow “input files → select spectra → align retention time → detect compounds → group compounds”. In the step of “input files,” all LC/MS data files of sample and control specimens were input, whereas in the step of “select spectra,” the entire run time (0–30 min) of the spectra, as well as the negative-ion mode, was selected for further processing. In the “align retention time” step, the retention times of all LC-MS data files were aligned (mass tolerance: 5 ppm), whereas in the “detect compounds” step, all compounds in LC-MS data files were extracted using component elucidator algorithm (mass tolerance: 5 ppm; intensity tolerance: 30; S/N threshold: 3; minimum peak intensity: 100,000; extracted ions: [M-H]−, [M-H+HAc]−; minimum element composition: CHO, and maximum element composition: C90H190O90. The same substances detected in different addition methods were grouped by molecular weight (mass tolerance: 5 ppm) as well as retention time (RT tolerance: 0.1 min) across all files in the “group compounds” step. Running this extracting workflow, we obtained the information of each compound existing in each specimen, including molecular weight, retention time, peak area, etc., which were used for further data analysis.
Ideally, after specific binding to α-Glucosidase, the peaks of compounds incubated with α-Glucosidase showed higher intensities or bigger peak areas than those of compounds incubated with denatured enzyme, meaning the PAR value for a ligand was >1. In this step, we calculated the PAR value of every compound in each specimen, and then the mean PAR value of each compound was calculated. The significant difference in peak area of each compound between the sample and the control group was determined by a two-tailed t-test. Finally, potential α-Glucosidase inhibitors were selected based on a mean PAR > 1 and $p \leq 0.05$ from four replicates.
The precursor ions of the selected potential α-Glucosidase inhibitors were performed targeted MS/MS analysis and the ingredients were characterized according to the MS/MS spectra obtained.
## 3.5. Molecular Docking of α-Glucosidase and Ligands
The 3D coordinate of α-Glucosidase was retrieved from the Protein Data Bank (PDB code: 5ZCB) [29]. The α-Glucosidase structure was pre-processed by removing solvent and adding hydrogen atoms. The 2D structures of ligands were downloaded from PubChem and then their 3D structures were generated in Chemdraw Ultra (version 14.0, http://www.cambridgesoft.com/, accessed on 13 January 2023). Molecular docking was performed with AutoDock vina (version 1.5.6, https://vina.scripps.edu/, accessed on 13 January 2023), and structural cartoons were prepared using PyMOL (version 2.4.0, https://pymol.org/2/, accessed on 13 January 2023).
## 3.6. α-Glucosidase Inhibitory Activity Assay
The α-Glucosidase inhibitory activity assay was performed in 96-well plates according to the modified method of Jiang [30]. Both α-glucosidase and pNPG were dissolved in a 0.1 M phosphate buffer (pH 6.8). Each tested compound was also dissolved in 0.1 M phosphate buffer to give solutions of various concentrations. A total of 40 μL of the tested compound solution was mixed with 40 μL α-Glucosidase solution (0.2 U/mL). After incubation for 5 min at 37 °C, 20 μL of pNPG solution (2 mM), which was used as a substrate, was added and then incubated for 30 min at 37 °C. The amount of released nitrophenyl product was measured on an Epoch 2 microplate spectrophotometer (BioTek) at 405 nm. Controls contained the same reaction mixture, except the same volume of phosphate buffer was added instead of a solution of tested compounds. Acarbose was used as the positive control. The inhibition (%) of the tested ligands on α-Glucosidase was calculated as: (Aa − Ab)/Aa × $100\%$, where Aa was the absorbance of the control, and Ab was the absorbance of the tested compound.
## 4. Conclusions
In our study, α-Glucosidase inhibitors from panax ginseng were systematically selected and characterized using affinity ultrafiltration screening combined with the UPLC-ESI-Orbitrap-MS method, which has the advantages of shortening the experimental period, reducing the workload, and achieving large-scale and high-throughput screening compared to the traditional active ingredient selection method based on multiple extraction and separation. The ligands were selected through our established data process workflow based on systematic analysis of all compounds in the dissociation solution, and as a result, a total of 24 ligands were selected as α-Glucosidase inhibitors, including 14 known ginsenosides and 10 unknown ginsenosides. The α-Glucosidase inhibitor’s activity of ligands with definite structures were verified by molecular docking and in vitro enzyme inhibition assay. For ginsenosides, our study first systematically selected and characterized the α-Glucosidase inhibitors and revealed that inhibiting α-Glucosidase activity probably was another important mechanism for hypoglycemic effect of ginsenosides. In addition, our established data process workflow has two advantages. First, when samples containing many overlapping signals, such as the extract of panax ginseng, our data process workflow could avoid false positive/negative results due to each compound in the specimens being analyzed. The second advantage was much more ligands were selected including ingredients with lower or much lower intensities which were easily neglected by visual comparison of chromatographic peak intensity on liquid chromatograms or total ion chromatograms mode. Therefore, our established data process workflow can be used to select the active ligands from other natural products using affinity ultrafiltration screening.
## References
1. Li M., Wang X., Wang Y., Bao S.C., Chang Q., Liu L.L., Zhang S., Sun L.W.. **Strategies for remodeling the tumor microenvironment using active ingredients of ginseng-A promising approach for cancer therapy**. *Front. Pharmacol.* (2021) **12** 797634. DOI: 10.3389/fphar.2021.797634
2. Hao M.Q., Ding C.B., Peng X.J., Chen H.Y., Dong L., Zhang Y., Chen X.Y., Liu W.C., Luo Y.Q.. **Ginseng under forest exerts stronger anti-aging effects compared to garden ginseng probably via regulating PI3K/AKT/mTOR pathway, SIRT1/NF-κB pathway and intestinal flora**. *Phytomedicine* (2022) **105** 154365. DOI: 10.1016/j.phymed.2022.154365
3. Hyun S.H., Kim S.W., Seo H.W., Youn S.H., Kyung J.S., Lee Y.Y., In G., Park C.K., Han C.K.. **Physiological and pharmacological features of the non-saponin components in Korean Red Ginseng**. *J. Ginseng Res.* (2020) **44** 527-537. DOI: 10.1016/j.jgr.2020.01.005
4. Sung W.S., Kang H.R., Jung C.Y., Park S.S., Lee S.H., Kim E.J.. **Efficacy of Korean red ginseng (**. *Complement. Ther. Med.* (2020) **48** 102246. DOI: 10.1016/j.ctim.2019.102246
5. Chen L.X., Qi Y.L., Qi Z., Gao K., Gong R.Z., Shao Z.J., Liu S.X., Li S.S., Sun Y.S.. **A comparative study on the effects of different parts of**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24061096
6. Fang X.X., Zhang X.Y., Zhang Y.G., Zhang X., Shan M.Y., Guan S.G., Qiu Z.D., Zhu D.F., Luo H.M.. **Exploring the potential of ginseng glycoprotein to improve learning and memory in mice via Notch signaling pathway and structural analysis using multi-information fusion based on liquid chromatography-mass spectrometry**. *J. Ethnopharmacol.* (2023) **303** 115978. DOI: 10.1016/j.jep.2022.115978
7. Yao Y.Q., Hu S., Zhang C.X., Zhou Q., Wang H., Yang Y., Liu C., Ding H.Y.. **Ginsenoside Rd attenuates cerebral ischemia/reperfusion injury by exerting an anti-pyroptotic effect via the miR-139-5p/FoxO1/Keap1/Nrf2 axis**. *Int. Immunopharmacol.* (2022) **105** 108582. DOI: 10.1016/j.intimp.2022.108582
8. Mostafa R.E., Shaffie N.M., Allam R.M.. *PLoS ONE* (2021) **16**. DOI: 10.1371/journal.pone.0260507
9. Jin D., Zhang Y.Q., Zhang Y.H., Duan L.Y., Zhou R.R., Duan Y.Y., Sun Y.T., Lian F.M., Tong X.L.. *Front. Pharmacol.* (2021) **12** 734151. DOI: 10.3389/fphar.2021.734151
10. Lu J., Wang X., Wu A.X., Cao Y., Dai X.L., Liang Y.D., Li X.F.. **Ginsenosides in central nervous system diseases: Pharmacological actions, mechanisms, and therapeutics**. *Phytother. Res.* (2022) **36** 1523-1544. DOI: 10.1002/ptr.7395
11. Zhou P., Xie W.J., He S.B., Sun Y.F., Meng X.B., Sun G.B., Sun X.B.. **Ginsenoside Rb1 as an anti-diabetic agent and its underlying mechanism analysis**. *Cells* (2019) **8**. DOI: 10.3390/cells8030204
12. Park S.J., Nam J., Ahn C.W., Kim Y.S.. **Anti-diabetic properties of different fractions of Korean red ginseng**. *J. Ethnopharmacol.* (2019) **236** 220-230. DOI: 10.1016/j.jep.2019.01.044
13. Yuan H.D., Quan H.Y., Jung M.S., Kim S.J., Huang B., Kim D.Y., Chung S.H.. **Anti-Diabetic Effect of Pectinase-Processed Ginseng Radix (GINST) in High Fat Diet-Fed ICR Mice**. *J. Ginseng Res.* (2011) **35** 308-314. DOI: 10.5142/jgr.2011.35.3.308
14. Hong Y.J., Kim N., Lee K., Sonn C.H., Lee J.E., Kim S.T., Baeg I.H., Lee K.M.. **Korean red ginseng (**. *J. Ethnopharmacol.* (2012) **144** 225-233. DOI: 10.1016/j.jep.2012.08.009
15. Peng M., Wang L.M., Su H., Zhang L., Yang Y., Sun L., Wu Y., Ran L., Liu S.D., Yin M.. **Ginsenoside Rg1 improved diabetes through regulating the intestinal microbiota in high-fat diet and streptozotocin-induced type 2 diabetes rats**. *J. Food Biochem.* (2022) **46** e14321. DOI: 10.1111/jfbc.14321
16. Lai D.M., Tu Y.K., Liu I.M., Chen P.F., Cheng J.T.. **Mediation of β-endorphin by ginsenoside Rh2 to lower plasma glucose in streptozotocin-induced diabetic rats**. *Planta Med.* (2006) **72** 9-13. DOI: 10.1055/s-2005-916177
17. Wang C.W., Su S.C., Huang S.F., Huang Y.C., Chan F.N., Kuo Y.H., Hung M.W., Lin H.C., Chang W.L., Chang T.C.. **An essential role of cAMP response element binding protein in ginsenoside Rg1-mediated inhibition of Na+/glucose cotransporter 1 gene expression**. *Mol. Pharmacol.* (2015) **88** 1072-1083. DOI: 10.1124/mol.114.097352
18. Gao Z., Li Q., Wu X., Zhao X., Zhao L., Tong X.. **New Insights into the mechanisms of Chinese herbal products on diabetes: A focus on the bacteriamucosal immunity-inflammation-diabetes axis**. *J. Immunol. Res.* (2017) **2017** 1813086. DOI: 10.1155/2017/1813086
19. Shang W.B., Guo C., Zhao J., Yu X.Z., Zhang H.. **Ginsenoside Rb1 upregulates expressions of GLUTs to promote glucose consumption in adiopcytes**. *China J. Chin. Mater. Med.* (2014) **39** 4448-4452
20. Liu Q., Zhang F.G., Zhang W.S., Pan A., Yang Y.L., Liu J.F., Li P., Liu B.L., Qi L.W.. **Ginsenoside Rg1 inhibits glucagon-induced hepatic gluconeogenesis through Akt-FoxO1 interaction**. *Theranostics* (2017) **7** 4001-4012. DOI: 10.7150/thno.18788
21. Meng F., Su X., Li W., Zheng Y.. **Ginsenoside Rb3 Strengthens the hypoglycemic effect through AMPK for inhibition of hepatic gluconeogenesis**. *Exp. Ther. Med.* (2017) **13** 2551-2557. DOI: 10.3892/etm.2017.4280
22. Wang H.P., Yang X.B., Yang X.W., Liu J.X., Xu W., Zhang Y.B., Zhang L.X., Wang Y.P.. **Ginsenjilinol, a new protopanaxatriol-type saponin with inhibitory activity on LPS-activated NO production in macrophage RAW 264.7 cells from the roots and rhizomes of**. *J. Asian Nat. Prod. Res.* (2013) **15** 579-587. DOI: 10.1080/10286020.2013.787992
23. Liu R.J., Kool J., Jian J.Y., Wang J.C., Zhao X.L., Jiang Z.J., Zhang T.T.. **Rapid screening α-Glucosidase inhibitors from natural products by at-line nanofractionation with parallel mass spectrometry and bioactivity assessment**. *J. Chromatogr. A* (2021) **1635** 461740. DOI: 10.1016/j.chroma.2020.461740
24. Zhao C.F., Liu Q., Cong D.L., Zhang H., Yu J.J., Jiang Y., Cui X.Y., Sun J.M.. **Screening and determination for potential α-glucosidase inhibitory constituents from Dalbergia odorifera T. Chen using ultrafiltration-LC/ESI-MS**. *Biomed. Chromatogr.* (2013) **27** 1621-1629. DOI: 10.1002/bmc.2970
25. Zhou H., Xing J.P., Liu S., Song F.R., Cai Z.W., Pi Z.F., Liu Z.Q., Liu S.Y.. **Screening and determination for potential α-Glucosidase inhibitors from leaves of acanthopanax senticosus Harms by using UF-LC/MS and ESI-MS**. *Phytochem. Anal.* (2012) **23** 315-323. DOI: 10.1002/pca.1360
26. Zhou X.L., Liang J.S., Zhang Y., Zhao H.D., Guo Y., Shi S.Y.. **Separation and purification of α-Glucosidase inhibitors from Polygonatum odoratum by stepwise high-speed counter-current chromatography combined with Sephadex LH-20 chromatography target-guided by ultrafiltration–HPLC screening**. *J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.* (2015) **15** 149-154. DOI: 10.1016/j.jchromb.2015.01.030
27. Wu B., Song H.P., Zhou X., Liu X.G., Gao W., Dong X., Li H.J., Li P., Yang H.. **Screening of minor bioactive compounds from herbal medicines by in silico docking and the trace peak exposure methods**. *J. Chromatogr. A* (2016) **4** 91-99. DOI: 10.1016/j.chroma.2016.01.062
28. Li H.L., Song F.R., Xing J.P., Tsao R., Liu Z.Q., Liu S.Y.. **Screening and structural characterization of α-Glucosidase inhibitors from Hawthorn leaf flavonoids extract by ultrafiltration LC-DAD-MSn and SORI-CID FTICR MS**. *J. Am. Soc. Mass Spectrom.* (2009) **20** 1496-1503. DOI: 10.1016/j.jasms.2009.04.003
29. Auiewiriyanukul W., Saburi W., Kato K., Yao M., Mori H.. **Function and structure of GH13_31 α-glucosidase with high α-(1→4)-glucosidic linkage specificity and transglucosylation activity**. *FEBS Lett.* (2018) **592** 2268-2281. DOI: 10.1002/1873-3468.13126
30. Jiang W.Y., Kan H., Li P.D., Liu S., Liu Z.Y.. **Screening and structural characterization of potential α-glucosidase inhibitors from**. *Anal. Methods* (2015) **7** 123-128. DOI: 10.1039/C4AY02081B
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---
title: '“The Pupillary (Hippus) Nystagmus”: A Possible Clinical Hallmark to Support
the Diagnosis of Vestibular Migraine'
authors:
- Mauro Gufoni
- Augusto Pietro Casani
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004418
doi: 10.3390/jcm12051957
license: CC BY 4.0
---
# “The Pupillary (Hippus) Nystagmus”: A Possible Clinical Hallmark to Support the Diagnosis of Vestibular Migraine
## Abstract
[1] Background: Hippus (which in this paper will be called “Pupillary nystagmus”) is a well-known phenomenon which has never been related to any specific pathology, so much so that it can be considered physiological even in the normal subject, and is characterized by cycles of dilation and narrowing of the pupil under constant lighting conditions. The aim of this study is to verify the presence of pupillary nystagmus in a series of patients suffering from vestibular migraine. [ 2] Methods: 30 patients with dizziness suffering from vestibular migraine (VM), diagnosed according to the international criteria, were evaluated for the presence of pupillary nystagmus and compared with the results obtained in a group of 50 patients complaining of dizziness that was not migraine-related. [ 3] Results: Among the 30 VM patients, only two cases were found to be negative for pupillary nystagmus. Among the 50 non-migraineurs dizzy patients, three had pupillary nystagmus, while the remaining 47 did not. This resulted in a test sensitivity of $0.93\%$ and a specificity of $0.94\%$. [ 4] Conclusion: we propose the consideration of the presence of pupillary nystagmus as an objective sign (present in the inter-critical phase) to be associated with the international diagnostic criteria for the diagnosis of vestibular migraine.
## 1. Introduction
Vestibular migraine (VM) is characterized by recurrent vestibular attacks that are not associated with migraine headache. VM is now considered as the first cause of episodic vertigo in adults [1], and it is a common diagnosis in children [2]. The diagnosis is primarily based on clinical history, and international guidelines have been developed [3,4]. While the presence or history of migraine is essential for its diagnosis, the headache and dizzy symptoms do not need to temporally coincide. The instrumental examination of patients with VM shows normal results or variable and inconsistent abnormalities, but vestibular testing needs to be performed with the aim of excluding other disorders considered in differential diagnosis. This implies that it is necessary to spend time collecting the detailed clinical history of the patient who, however, is not always able to describe his symptoms exactly with the risk of omitting important details for diagnostic purposes.
Having an instrumental hallmark would be extremely useful, especially in cases where the clinical picture does not fully meet the international diagnostic criteria. In this paper, a sign that has been well known for years and whose origin has never been defined with certainty, so much so that it was classically considered a phenomenon without clinical value, was taken into consideration. It is a very characteristic behavior of the pupil, which dilates and contracts cyclically in the presence of constant lighting, independently of eye movements or change in illumination [5]. It has been called “pupillary hippus” (PH), “pupillary athetosis”, or, in English-speaking countries, “pupillary unrest” or “dancing pupils”, terms that seem, however, to be rather non-specific [6]. PH usually occurs in a physiologically drowsy state, and can range from 0.04 to 2 Hz [5], and the magnitude of the pupil size variations range from not detectable to over 0.5 mm [7]. A precise definition of PH is lacking; the variety of techniques used to assess the pupil movements and the interindividual variation do not allow for validated parameters to consider PH as pathological. PH has been observed in patients suffering from epilepsy [7] or neurotic disorders [8], diabetic autonomic neuropathy [9] and is associated with disorders of the autonomic nervous system [10]. On the other hand, dysautonomia has also been reported to underlie migraine disease [11], and the presence of PH has also been reported in migraineurs [12,13]. In this paper we evaluated the presence of PH in patients suffering from VM with the aim of identifying an objective sign that may be potentially useful in helping the physician with the diagnosis of VM, especially when the criteria indicated by international guidelines are not fully met.
## 2. Materials and Methods
Two series of patients complaining of vertigo and dizziness were considered:30 patients consecutively diagnosed as suffering from definite VM (mean age 52 years, minimum 6 years, maximum 77 years, 11 males and 19 females). The diagnosis of vestibular migraine was made based on international criteria [3]. We excluded from the study patients with probable VM and subjects who had received ear or eye surgery or other significant comorbidities.50 consecutive patients (mean age 58 years, minimum 17 years, maximum 89 years, 23 males and 27 females) affected by vertigo and dizziness not attributable to VM, who constituted the control group. All the patients belonging to the control group did not suffer from migraine or other types of headaches.
The patients belonging to the control group were affected by paroxysmal positional vertigo [15], acute vestibular deficit [8], vascular vertigo [6], Meniere’s disease [6], and acoustic neuroma [1]. A total of 14 patients showed a normal examination, and the dizziness was attributable to diseases that were not strictly vestibular (such as pharmacological dizziness, orthostatic hypotension, and undiagnosed PPPD).
Patients underwent a thorough medical history, otoscopy, neurological evaluation (cerebellar tests and clinical evaluation of the cranial nerves), audiometry, evaluation of the spontaneous and positional nystagmus, and a head shaking test using infrared goggles. The instrumental examination consisted of performing video-HIT, functional-video-HIT, caloric testing, and cervical and ocular VEMPs.
During our experience, we have informally begun to call PH by the heterodox expression: “Pupillary Nystagmus”‘ (PNy). It is well known that no correlation exists between the pupillomotor response and the vestibulo-ocular reflex. From a semeiological point of view, PH could have some similarity with the well-known extra-vestibular nystagmus. Nystagmus is defined as “... a repetitive to and fro movement of the eyes that includes smooth sinusoidal oscillations (pendular nystagmus)” [14]. PH could be defined inductively as “... a repetitive to and fro change in the pupil diameter that includes smooth sinusoidal oscillations”. The only difference is that this phenomenology affects intrinsic rather than extrinsic eye muscles. The semantic expression ‘pupillary nystagmus’ is intended only as a current, but suggestive, variant with the aim of referring to the common traits that the phenomenon of pupillary hippus has with extravestibular nystagmus.
The assessment of pupillary nystagmus (PNy) (presence/absence) was performed under Frenzel glasses, and a video (lasting at least 10 s) was taken. The examiner evaluated the visible amplitude of PNy during the whole observation period under Frenzel glasses. Patients entering the study did not report any other neurological or eye problems or significant head injuries. None of the patients took any drugs that could affect the autonomic nervous system. The presence or absence of pupillary nystagmus was assessed by two different examiners in a double-blind manner: each of them was unaware of the evaluations of the other, including the medical history and examination results. In no case was there a discrepancy in evaluation, demonstrating the ease of observation of the sign. All patients underwent contrast-enhanced brain nuclear magnetic resonance imaging.
A statistical evaluation was performed using a Pearson’s chi-squared test, phi coefficient and Bayesian contingency tables—BF10 [GNU Project [2015]. GNU PSPP (Version 0.8.5) [Computer Software]. Free Software Foundation. Boston, MA, USA; JASP Team [2022]. JASP (Version 0.16.3).
Ethical review and approval by the local Institutional Board (Comitato Etico Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy) were waived for this study. Due to its retrospective nature, it was not set up as part of a research project. Furthermore, the study does not include new experimental diagnostic protocols, and the patients included in the study were diagnosed according to national guidelines. Written informed consent was obtained from all participants, and the study was conducted in accordance with the 1964 Declaration of Helsinki.
## 3. Results
Among the 30 VM patients, only two cases were found to be negative for pupillary nystagmus (Video S1, Supplementary Material). Among the 50 non-migraineur dizzy patients, three had pupillary nystagmus, while the remaining 47 did not. Table 1 shows the results obtained in the two groups.
This resulted in a test sensitivity of $0.93\%$ and a specificity of $0.94\%$.
The positive predictive value is 0.90, and the negative predictive value is 0.96.
A statistical evaluation was undertaken using the chi-square test, and showed a significant difference (X2 value 60.25, $p \leq 0.001$, Phi 0.87, BF10 independent multinomial 8.598 × 10+13). ( Table 2).
## 4. Discussion
The diagnosis of VM is based quite exclusively on the history taking; there is no pathognomonic clinical sign for VM and there are no gold standard diagnostic tests for VM.
The availability of some clinical or instrumental test with a relatively high sensibility and specificity would be very useful, especially when the international criteria are not completely fulfilled. Only the functional video HIT performed with an optokinetic stimulation seems to provide some positive results in VM, indicating a visual dependence in VM patients complaining of visually induced vertigo, head motion–induced vertigo, and head motion–induced dizziness with nausea [15,16]. Usually, the diagnosis of VM is made by an audiologist, otolaryngologist or neurologist. It would be appropriate for the ideal sign associated with vestibular migraine to have all of the following characteristics:○strongly suggestive (even if not pathognomonic) of the condition, therefore present in as many VM patients as possible and absent in most patients with vertigo or dizziness not migraine related;○easily identifiable on otoneurological examination;○present in the inter-critical phase, since it is difficult to examine a VM patient in the acute stage of the disease;○ease of recording and archiving.
In the absence of changes in external influences such as luminance, mood, and fixation, the pupil is in constant motion. An exaggeration of this phenomenon is usually termed Pupillary Hippus: its frequency ranges from 0.04 to 2 Hz [5], and the magnitude of the pupil size variations usually do not surpass 0.5 mm [5]. It is more evident in pupils of medium amplitude and has a periodic pattern (the period measured was 5 seconds5) but the course may not be constant over time (Figure 1). The origin of pupillary hippus is believed to be related to an abnormal activity of the autonomic nervous system because it can be inhibited by pharmacologically antagonizing the parasympathetic system [5,10].
The disruption of the balance between the sympathetic and parasympathetic systems is considered a pathophysiological mechanism underlying migraine disease [13], and changes in pupillary function have been observed in migraine both in the headache attack and in the intercritical phase [17].
It has been reported that the left cerebral hemisphere is mainly involved in parasympathetic activity, and the right in sympathetic system activity. Parasympathetic stimulation in unilateral migraineurs causes significant skin phenomenology on the stimulated side, and sympathetic stimulation does not seem to influence this significantly. Activation in this case would occur through a trigemino-parasympathetic reflex, resulting in vasodilation and the increase in secretory phenomena [18]. There is evidence of a lower sympathetic activity in migraineurs, demonstrated by an increased latency to the light reflex, after apraclonidine administration [19]. Reduced nocturnal activity of the parasympathetic system has also been demonstrated in migraine patients, especially in subjects with aura [20]. Furthermore, cardiac vagal responses via baroreceptors are reduced in migraine patients, but sympathetic system-related responses are not. As a consequence, it appears that the autonomic nervous system may play a role in the pathophysiology of migraine [21]. The pupillary hippus phenomenon can be extinguished with antagonists of the parasympathetic nervous system, whereas antagonists of the sympathetic system dilate the pupils without blocking the hippus: this suggests that the phenomenon originates in the centrally localized parasympathetic system and not in the sympathetic system [10]. Furthermore, parasympathetic activity contributes to the onset of pain in migraine by activating or sensitizing (or both) the intracranial nociceptors [10].
It seems well established in the literature that [10,18,19]:the pupil reacts to asymmetries in the balance between the sympathetic and parasympathetic through changes in its diameter, with particular dependence on vagal tone;An imbalance between the sympathetic and parasympathetic can contribute to the genesis of painful migraine pathology.
It has recently been shown that the pupillary cycle has specific characteristics in the migraine sufferer: in particular, the pupillary cycle period is longer in the migraine. This data allows the differentiation of a migraine patient from a non-migraine patient [21].
The pupillary cycle is a well-known phenomenon [22], and consists in the projection of a luminous dot onto the pupil, very close to the edge of the iris. The photomotor reflex causes miosis, which prevents the light beam from reaching the retina. Consequently, the pupil dilates, and the light reaches the retina again, giving rise to a new cycle. The frequency of the pupillary cycle allows for the evaluation of the sympathetic-parasympathetic balance and, consequently, the predisposition to migraine.
Our results recorded in a group of VM patients demonstrate a very high incidence of PNy (in contrast with the low incidence observed in patients suffering from vertigo and dizziness not migraine related) whose presence could be considered as a hallmark of the disease. The high sensitivity and sensibility of PNy makes this sign highly pathognomonic of VM, and it could be helpful in patients with possible VM. We have found PNy in the only child (six years of age) present in our series of patients suffering from VM. In children, the hippus frequency seems to be higher than it is in adults, suggesting the influence of the sympathetic branch of the autonomic nervous system on it that decreases with age and maturation [23]. For this reason, the presence of PNy in children must be considered with caution. Two patients in definite VM groups did not show PNy; analyzing their clinical and instrumental characteristics, we found no difference compared from those who showed PNy.
As it is not clear in the literature whether the phenomenon is also present in the dark, it is advisable to search for the sign under Frenzel’s glasses or, in any case, in a permanently lit environment (Table 3). However, the use of a binocular system of evaluating the pupillary movements is recommended; a unilateral PH was described in migrainous patients. The search under infra-red video-Frenzel should be avoided entirely, or at least until evidence of the presence of pupillary nystagmus (even in the dark) is obtained.
The sign was always detected in the inter-critical phase (none of the patients examined were in the acute vertiginous crisis phase or reported headache at the time of observation). Little time is required for the examination, as it is to be considered exactly like the ‘bedside’ search for a spontaneous nystagmus. However, it needs to focus specifically on the pupil if one wants to avoid losing the data. In our case series, pupillary nystagmus was present in the great majority of VM patients. It was also present in three patients considered non-migraineurs ($6\%$), but we cannot exclude that in those cases the anamnesis was lacking, given the well-known difficulty in identifying headache crisis as migraine, which is often wrongly attributed to different causes (neck pain, neuralgia, sinusitis, etc.). As an alternative to the study of the pupillary cycle which is not complicated but which requires the help of the Ophthalmologist, we propose the direct search of this sign (using Frenzel glasses) in dizzy patients, especially when a VM is suspected.
The main limitation of this study is the small size of the sample and the heterogeneity of the control group. For this reason, we are planning a study with a large number of patients evaluating additional factors such as age, gender, and course of VM, in order to allow for more significant results. Moreover, the method we have proposed for evaluating the PNy is certainly less precise than an ophthalmological study of the pupillary cycle [21]. Using an infrared pupillometer would be more accurate than assessing pupil size under Frenzel goggles. Nevertheless, as a part of the bedside assessment of patients with suspected VM, this practical and simple evaluation of the pupil movements seems to be sufficiently valuable.
## 5. Conclusions
Even if our results need to be confirmed in a larger series of patients, we propose the observation of pupillary nystagmus as an objective sign helping the physician to diagnose vestibular migraine, being very common in the intercritical phase of this pathology and rarely encountered in dizzy patients that are non-migraine sufferers. This sign is easy to observe and is recordable with a camera or smartphone. We recommend the observation of PNy whose presence could be considered as a supplementary element to reenforce the diagnosis of VM based mainly on the clinical criteria suggested by a joint committee of the International Headache Society (IHS) and the Barany Society [4].
## References
1. Dieterich M., Obermann M., Celebisoy N.. **Vestibular migraine: The most frequent entity of episodic vertigo**. *J. Neurol.* (2016) **263** S82-S89. DOI: 10.1007/s00415-015-7905-2
2. Langhagen T., Lehrer N., Borggraefe I., Heinen F., Jahn K.. **Vestibular migraine in children and adolescents: Clinical findings and laboratory tests**. *Front. Neurol.* (2015) **5** 292. DOI: 10.3389/fneur.2014.00292
3. van de Berg R., Widdershoven J., Bisdorff A., Evers S., Wiener-Vacher S., Cushing S.L., Mack K.J., Kim J.S., Jahn K., Strupp M.. **Vestibular migraine and recurrent vertigo of childhood: Diagnostic criteria consensus document of the Classification Committee of Vestibular Disorders of the Barany Society and the International Headache Society**. *J. Vest. Res.* (2021) **31** 1-9. DOI: 10.3233/VES-200003
4. Lempert T., Olesen J., Furman J., Waterston J., Seemungal B., Carey J., Bisdorff A., Versino M., Evers S., Newman-Toker D.. **Vestibular migraine: Diagnostic criteria**. *J. Vest. Res.* (2012) **22** 167-172. DOI: 10.3233/VES-2012-0453
5. McLaren J.W., Erie J.C., Brubaker R.F.. **Computerized analysis of pupillograms in studies of alertness**. *Investig. Ophthalmol. Vis. Sci.* (1992) **33** 671-676. PMID: 1544791
6. Bouma H., Baghuis J.. **Hippus of the pupil: Periods of slow oscillations of unknown origin**. *Vision Res.* (1971) **11** 1345-1351. DOI: 10.1016/0042-6989(71)90016-2
7. Centeno M., Feldmann M., Harrison N., Rugg-Gunn F.J., Chaudhary U., Falcon C., Lemieux L., Thom M., Smith S.J.M., Sisodiya S.M.. **Epilepsy causing pupillary hippus: An unusual semiology**. *Epilepsia* (2011) **52** 93-96. DOI: 10.1111/j.1528-1167.2011.03137.x
8. Ushakov G.K., Keshokov A.A.. **Pupillary reactions in neurotic disorders**. *Zh. Nevropatol. Psikhiatr. Im. S. S. Korsakova* (1979) **79** 1550-1554. PMID: 516971
9. Smith S.E., Smith S.A., Brown P.M., Fox C., Sonksen P.H.. **Pupillary signs in diabetic autonomic neuropathy**. *BMJ* (1978) **30** 924-927. DOI: 10.1136/bmj.2.6142.924
10. Turnbull F.R.K., Irani N., Lim N., Phillips J.R.. **Origins of pupillary hippus in the autonomic nervous system**. *Investig. Ophthalmol. Vis. Sci.* (2017) **58** 197-203. DOI: 10.1167/iovs.16-20785
11. Gazerani P., Cairns B.E.. **Dysautonomia in the pathogenesis of migraine**. *Expert. Rev. Neurother.* (2018) **18** 153-165. DOI: 10.1080/14737175.2018.1414601
12. Iovetz-Tereshchenko N.N.. **Migraine: A 1950 symposium**. *Postgrad. Med. J.* (1950) **26** 647-654. DOI: 10.1136/pgmj.26.302.647
13. Miglis M.G.. **Migraine and autonomic dysfunction: Which is the horse and which is the jockey?**. *Curr. Pain Headache Rep.* (2018) **22** 19. DOI: 10.1007/s11916-018-0671-y
14. Leigh R.J., Zee D.S.. *The Neurology of Eye Movements* (2015) 657-768
15. Versino M., Mandalaà M., Colnaghi S., Ricci G., Faralli M., Ramat S.. **The integration of multisensory motion stimuli is impaired in vestibular migraine patients**. *J. Neurol.* (2020) **267** 2842-2850. DOI: 10.1007/s00415-020-09905-1
16. Casani A.P., Lazzerini F., Marconi O., Vernassa N.. **The role of the functional head impulse test with and without optokinetic stimuli in vestibular migraine and acute unilateral vestibulopathy: Discovering a dynamic visual depenence**. *J. Clin. Med.* (2021) **10**. DOI: 10.3390/jcm10173787
17. Avnon Y., Nitzan M., Sprecher E., Rogowski Z., Yarnitsky D.. **Autonomic asymmetry in migraine: Augmented parasympathetic activation in left unilateral migraineurs**. *Brain* (2004) **127** 2099-2108. DOI: 10.1093/brain/awh236
18. Cambron M., Maertens H., Paemeleire K., Crevits L.. **Autonomic function in migraine patients: Ictal and interictal pupillometry**. *Headache* (2014) **54** 655-662. DOI: 10.1111/head.12139
19. Matei D., Constantinescu V., Corciova C., Ignat B., Matei R., Popescu C.D.. **Autonomic impairment in patients with migraine**. *Eur. Rev. Med. Pharmacol. Sci.* (2015) **19** 3922-3927. PMID: 26531280
20. Sanya E.O., Brown C.M., von Wilmowsky C., Neundorfer B., Hilz M.J.. **Impairment of parasympathetic baroreflex responses in migraine patients**. *Acta Neurol. Scand.* (2005) **111** 102-107. DOI: 10.1111/j.1600-0404.2004.00358.x
21. Cortez M.M., Rae N., Millsap L., McKean N., Brennan K.C.. **Pupil cycle time distinguishes migraineurs from subjects without headache**. *Front. Neurol.* (2019) **10** 478. DOI: 10.3389/fneur.2019.00478
22. Stern H.S.. **A simple method for the early diagnosis of abnormalities of the pupillary reaction**. *Br. J. Ophthalmol.* (1944) **28** 275-276. DOI: 10.1136/bjo.28.6.275
23. Bufo M.R., Guidotti M., De Faria C., Mofid Y., Bonnet-Brilhault F., Wardak C., Aguillon-Hernadez N.. **Autonomic tone in children and adult: Pupillary, electrodermal and cardiac activity at rest**. *Int. J. Psycophysiol.* (2022) **180** 68-78. DOI: 10.1016/j.ijpsycho.2022.07.009
|
---
title: Effects of Biologic Therapy on Laboratory Indicators of Cardiometabolic Diseases
in Patients with Psoriasis
authors:
- Teppei Hagino
- Hidehisa Saeki
- Eita Fujimoto
- Naoko Kanda
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004419
doi: 10.3390/jcm12051934
license: CC BY 4.0
---
# Effects of Biologic Therapy on Laboratory Indicators of Cardiometabolic Diseases in Patients with Psoriasis
## Abstract
Psoriasis is associated with cardiometabolic and cardiovascular diseases. Biologic therapy targeting tumor necrosis factor (TNF)-α, interleukin (IL)-23, and IL-17 may improve not only psoriasis but also cardiometabolic diseases. We retrospectively evaluated whether biologic therapy improved various indicators of cardiometabolic disease. Between January 2010 and September 2022, 165 patients with psoriasis were treated with biologics targeting TNF-α, IL-17, or IL-23. The patients’ body mass index; serum levels of HbA1c, total cholesterol, high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol, triglyceride (TG), and uric acid (UA); and systolic and diastolic blood pressures were recorded at weeks 0, 12, and 52 of the treatment. Baseline psoriasis area and severity index (week 0) positively correlated with TG and UA levels but negatively correlated with HDL-C levels, which increased at week 12 of IFX treatment compared to those at week 0. UA levels decreased at week 12 after ADA treatment compared with week 0. HDL-C levels decreased 52 weeks after IXE treatment. In patients treated with TNF-α inhibitors, HDL-C levels increased at week 12, and UA levels decreased at week 52, compared to week 0. Thus, the results at two different time points (at weeks 12 and 52) were inconsistent. However, the results still indicated that TNF-α inhibitors may improve hyperuricemia and dyslipidemia.
## 1. Introduction
Psoriasis is a chronic inflammatory skin disease that presents with scaly indurated erythema. The tumor necrosis factor (TNF)-α/interleukin (IL)-23/IL-17 axis is the mainstay of psoriasis pathogenesis. Biologics targeting TNF-α, IL-23, and IL-17 have been developed and have shown significant therapeutic effects in psoriasis. Psoriasis is frequently associated with cardiometabolic diseases such as diabetes mellitus (DM), dyslipidemia (DL), hyperuricemia (HUA), hypertension (HT), and ischemic heart disease, suggesting a pathogenetic link between psoriasis and cardiometabolic diseases [1].
Approximately $40\%$ of patients with psoriasis are obese and develop cardiovascular diseases (CVDs) more frequently than healthy individuals [2,3]. Cytokines, TNF-α, IL-23 and IL-17 are known to be involved in the development of cardiometabolic diseases [4]. Eleven biologics have been approved for psoriasis in Japan, targeting TNF-α (adalimumab [ADA], infliximab [IFX], certolizumab pegol [CZP]), IL-17 (ixekizumab [IXE], secukinumab [SEC], brodalumab [BRO], bimekizumab), and IL-23 (ustekinumab [UST], guselkumab [GUS], risankizumab [RIS], and tildrakizumab). These biologics may improve not only the rash of psoriasis, but also comorbid cardiometabolic diseases. In this study, we retrospectively evaluated whether treatment with these biologics improved the values of laboratory or clinical indicators of cardiometabolic diseases.
## 2.1. Study Design and Data Collection
This study was conducted in accordance with the Declaration of Helsinki [2004] and approved by the Ethics Committee of Nippon Medical School Chiba Hokusoh Hospital. From January 2010 to September 2022, 165 Japanese patients with psoriasis (aged ≥ 18 years; 131 males and 34 females) were treated with any of the biologics (ADA, IFX, CZP, IXE, SEC, BRO, UST, GUS, and RIS) for more than 52 weeks at the outpatient clinic.
In this study, we excluded the patients who had been treated with any of the biologics but stopped the treatments earlier than 52 weeks. The patients included both biologic naïve patients and those who switched from other biologics (regardless of how long they had been treated previously).
Disease severity and laboratory or clinical indicators of cardiometabolic diseases of the patients were recorded during therapy and retrospectively analyzed. Written informed consent was obtained from all the patients. The diagnosis of psoriasis was made by dermatologists based on clinical symptoms and progress. Patient age, body mass index (BMI), disease duration, presence or absence of arthritis, DM, HT, DL, HUA, CVD, and current smoking status were examined before treatment.
Psoriasis area and severity index (PASI), BMI, serum HbA1c, total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), triglyceride (TG), uric acid (UA), and systolic/diastolic blood pressure (sBP/dBP) levels were analyzed at weeks 0, 12, and 52 of treatment. The percentage of patients with a PASI decrease of $75\%$ or more, $90\%$ or more, or $100\%$ or more from the baseline (PASI 75, PASI 90, or PASI 100, respectively) was calculated at weeks 12 and 52.
## 2.2. Statistical Analysis
All statistical analyses were performed using EZR software (Jichi Medical School, Saitama Medical Center). The normality of the data distribution was assessed using the Shapiro–Wilk test. Variables with a normal distribution are expressed as a mean ± standard deviation, and those with a nonparametric distribution are expressed as median [interquartile range]. Correlation analysis was performed using Spearman’s correlation coefficient.
Differences between weeks 0, 12, and 52 were analyzed by repeated measures analysis of variance for variables with a normal distribution and Friedman’s test for variables with a nonparametric distribution. Post hoc analyses were performed using Bonferroni correction. Statistical significance was set at $p \leq 0.05.$
## 3.1. Demographics of Patients with Psoriasis
One hundred and sixty-five Japanese patients with psoriasis (131 men and 34 women) were enrolled in this study (Table 1). The TNF-α inhibitors IFX, ADA and CZP were administered to 28, 17, and 11 patients, IL-17 inhibitors; IXE, SEC, and BRO to 38, 13 and 12 patients; and IL-23 inhibitors, UST, GUS, and RIS to 10, 19, and 17 patients, respectively.
In terms of the overall male/female ratio, the result was a higher proportion of males (approximately $80\%$). Overall, 80 patients were diagnosed with arthritis. Patients with arthritis were more likely to choose TNF-α inhibitors. At baseline, 20, 43, 25, 25, and 6 patients had diabetes, hypertension, dyslipidemia, hyperuricemia, and cardiovascular disease, respectively. A total of 98 patients were smokers. In terms of individual biologics, IXE had the highest number of cases (Table 1). All the patients in the CZP-treated group had arthritis. Risankizumab was selected for patients with pre-existing cardiovascular diseases. Patients administered ADA had the highest smoking rate.
## 3.2. Correlations of PASI with Indicators of Cardiometabolic Diseases before Treatment
Before treatment, the correlations between PASI and individual indicators of cardiometabolic diseases were analyzed (Table 2). The baseline (week 0) PASI was negatively correlated with HDL-C levels and positively correlated with TG and UA levels (Table 2). The other indicators showed no significant correlations with PASI.
## 3.3. Changes in Indicators of Cardiometabolic Diseases after Treatment with TNF-α Inhibitors
After IFX treatment, the HDL-C value increased significantly at week 12, while the value at week 52 was not altered compared with baseline (Table 3). After ADA treatment, the UA value decreased significantly at week 12, whereas the value at week 52 was not altered compared to baseline (week 0). The values of laboratory and clinical indicators examined did not change after CZP treatment compared with the baseline values (week 0). After treatment with all TNF-α inhibitors, the HDL-C value increased significantly at week 12, while the value at week 52 was not altered compared with baseline (week 0). After treatment with all TNF-α inhibitors, the UA value decreased significantly at week 52, whereas the value at week 12 was not altered compared with baseline (week 0). The values of the other indicators were not altered after treatment with TNF-α inhibitors.
## 3.4. Changes in Indicators of Cardiometabolic Diseases after Treatment with IL-17 Inhibitors
After IXE treatment, the HDL-C value decreased significantly at week 52 compared to baseline (week 0) (Table 4). After BRO and SEC treatment, the values of laboratory and clinical indicators examined did not change compared to the baseline values. After treatment with all IL-17 inhibitors, the values of laboratory and clinical indicators examined did not change compared to baseline.
## 3.5. Changes in Indicators of Cardiometabolic Diseases after Treatment with IL-23 Inhibitors
After treatment with GUS, UST, RIS, and all IL-23 inhibitors, the values of laboratory and clinical indicators examined did not change compared with baseline (week 0) (Table 5).
## 3.6. The Improvement of PASI by TNF-α, IL-17, or IL-23 Inhibitors
The PASI 75, PASI 90, and PASI 100 scores after treatment with individual biologics are shown in Figure 1. In PASI 75 and PASI 90, IL-17 inhibitors (SEC, IXE, BRO) and IL-23p19 antibodies (GUS, RIS) were the most effective, while TNF-α inhibitors (ADA, IFX, CZP) and IL-$\frac{12}{23}$p40 antibody (UST) showed lower efficacy. In PASI 100, BRO was the most effective, followed by IXE, with slightly lower efficacy in SEC, IL-23p19 antibodies (GUS, RIS) and p40 antibody (UST), whereas TNF-α inhibitors (ADA, IFX, CZP) showed much lower efficacy. Overall, IL-17 inhibitors (SEC, IXE, BRO) and IL-23p19 antibodies (GUS, RIS) were the most effective in improving PASI, while TNF-α inhibitors (ADA, IFX, CZP) and IL-$\frac{12}{23}$p40 antibody (UST) were inferior.
## 3.7. Correlation between Percent Reduction of PASI Versus Percent Changes of HDL-C or UA
We then analyzed whether the significant changes in HDL-C or UA by TNF-α or IL-17 inhibitors may correlate with the percent reduction in PASI by identical biologics. There was no significant correlation between the percent reduction of PASI and percent changes in HDL-C or UA by the identical biologics (Table 6).
## 4. Discussion
A total of 165 patients were included in the current analysis, which selected 11 biologic treatments used in Japan. There was a clear difference in sex, with $80\%$ of the patients being male. In Japan, $65.8\%$ of psoriasis patients are male, indicating a male predominance in the patient population [5]. This may also be characteristic of the limited area of Chiba.
Before treatment with biologics, PASI significantly correlated positively with serum TG and UA levels and negatively with serum HDL-C levels. Psoriasis skin lesions show hyperproliferation of keratinocytes, which increases the rate of DNA formation and purine synthesis and may lead to HUA [6]. Conversely, urate crystals stimulate keratinocytes to proliferate and produce inflammatory cytokines/chemokines, such as IL-1α or IL-8, indicating the promoting effects of UA on psoriasis [7]. Urate crystals activate the NLR family pyrin domain-containing three inflammasomes in macrophages, resulting in the production of active IL-1β and IL-18 [8], and promote the production of TNF-α in monocytes [9], indicating that UA might increase the levels of inflammatory cytokines promoting psoriasis. A previous study also reported a positive correlation between PASI and serum UA levels [10]. Proinflammatory cytokines, such as TNF-α, promote the production of very low-density lipoprotein-TG by activation of NF-κB in hepatocytes, thus increasing serum TG levels [11,12]. Tumor necrosis factor-α, IL-1, and IL-6 inhibit lipoprotein lipase activity in adipose tissues, thereby decreasing TG clearance and increasing the level of TG in plasma [13,14]. These reports indicate that pro-inflammatory cytokines that induce psoriasis may promote hypertriglyceridemia in parallel. In contrast, HDL is enriched in anti-inflammatory lipids, and interaction of HDL with immune cells induces cholesterol efflux from cell membranes, suppresses immune cell activation, acts on dendritic cells, suppresses their expression of CD40, 80, 86, or class II molecules, and reduces their ability to differentiate naïve T cells into Th1 or Th17 cells, indicating the protective effects of HDL from psoriasis [15]. Previous studies have also reported an association between psoriasis and high TG and low HDL-C levels [16]. The significant correlation between PASI and these cardiometabolic indicators indicates that psoriasis may not only be a skin-limited but also a systemic inflammatory disease.
HDL-C levels increased significantly at weeks 12 and 52 after treatment with IFX or TNF-α inhibitors, respectively. Previous reports have also shown that IFX increases HDL-C levels in patients with psoriasis [17]. TNF-α suppresses the expression of ATP-binding cassette transporter A1 (ABCA1), mediating the rate-controlling step of HDL formation in human intestinal Caco-2 cells, thus decreasing HDL-C levels [18]. Tumor necrosis factor-α inhibitors may counteract the inhibitory effects of TNF-α on HDL formation and thus increase HDL-C levels. HDL prevents the oxidation of LDL, induces efflux of accumulated LDL in the vasculature, and prevents foam cell accumulation, thereby suppressing atherosclerosis [19]. Decreased HDL-C levels are associated with the development of ischemic heart disease [20]. HDL also suppresses the production of inflammatory cytokines and chemokines, such as TNF-α, IL-1β, IL-6, IL-8, CCL3, and CCL4, in human monocytes induced by contact with stimulated T cells [21]. It has previously been reported that ADA treatment decreased serum levels of vascular cell adhesion molecule 1, a biomarker of cardiovascular diseases, in patients with plaque psoriasis [22], indicating the cardioprotective effects of ADA. However, regulatory T lymphocyte dysfunction is important in the development of atherosclerosis, and ADA treatment in patients with psoriasis vulgaris resulted in a decrease in plasma regulatory cytokines (IL-10, transforming growth factor-β1, and IL-35) [23], indicating the pro-atherosclerotic effects of ADA.
After treatment with ADA or TNF-α inhibitors, UA levels decreased significantly at weeks 12 and 52, respectively. Hepatic and plasma TNF-α induce c hepatic parenchymal cell injury, causing de novo purine synthesis and accelerating UA production [24]. Thus, TNF-α may systemically increase UA levels, which may be counteracted by TNF-α inhibitors. Uric acid promotes the progression of atherosclerosis by reducing NO production and inducing superoxide generation in endothelial cells [25]. Meta-analyses have shown a correlation between HUA and CVD risk [26,27]. Patients with psoriatic arthritis with HUA showed greater carotid intima-media thickness than normouricemic patients [28].
Although IFX, ADA, and CZP are all TNF-α inhibitors, the results among these biologics are inconsistent; CZP treatment did not alter HDL-C or UA levels, only ADA decreased UA levels, and only IFX increased HDL-C levels. The inconsistency may at least partially be due to the small sample size and uneven distribution; IFX, ADA, and CZP were administered to 28, 17, and 11 patients, respectively.
Temporarily increased HDL-C or decreased UA levels at week 12 returned to baseline at week 52. Therefore, it is unclear whether these changes will lead to protection against cardiometabolic diseases. In future, we should examine the levels of these indicators for a longer duration in a larger sample size.
HDL-C levels were reduced at week 52 after treatment with the anti-IL17A antibody IXE in patients with psoriasis. These results are contrary to our expectations and indicate that IL-17A may increase HDL levels. To date, conflicting results have been reported regarding the role of IL-17A in cardiovascular and/or metabolic diseases; IL-17A has both proatherogenic and atheroprotective effects [29]. Th17-polarized cells from non-obese diabetic mice following mycobacterial adjuvant immunotherapy delay the development of type 1 diabetes [30], indicating the protective effects of IL-17A against diabetes. It has been reported that metabolic syndromes [31], severe coronary artery diseases [32], and atherosclerosis in rabbits [33] are associated with decreased serum IL-17A levels as well as decreased HDL-C levels, and the improvement of atherosclerosis in a rabbit model leads to an increase in both levels [33], indicating a positive correlation between HDL-C and IL-17A in cardiometabolic diseases. Interleukin-17A acts on mouse endothelial cells and enhances their expression of ABCA1 [34], a transporter mediating HDL synthesis, indicating the possible promoting effects of IL-17A on HDL synthesis. Further studies are required to elucidate the direct effects of IL-17A on HDL expression. Our results also indicate that physicians should use IL-17A inhibitors with caution in patients with psoriasis associated with cardiometabolic disease.
Several studies have indicated that IL-23 may increase serum LDL-C levels [35] or promote the development of DM [36]. It has been reported that anti-IL-$\frac{12}{23}$p40 antibody UST treatment reduced the size of the lipid-rich necrotic core, a high-risk coronary artery plaque in patients with psoriasis [37], while increasing the occurrence of major adverse cardiovascular events [38]. There are few studies on the effects of anti-IL-23p19 antibodies on metabolic or cardiovascular diseases, possibly because these are relatively new biologics. In the present study, IL-23 inhibitors as well as anti-IL-$\frac{12}{23}$p40 and IL-23p19 antibodies did not alter the levels of laboratory or clinical indicators of cardiometabolic diseases in patients with psoriasis. However, treatment with IL-23 inhibitors might alter the levels of different serum indicators or those on imaging, such as coronary CT angiography, and these should be further examined in a larger cohort of patients.
There was no significant correlation between percentage changes in HDL-C or UA and percentage reduction in PASI by TNF-α or IL-17 inhibitors. The results indicate that the effects of TNF-α or IL-17 inhibitors on UA or lipid metabolism may not always be parallel to those on skin rash in psoriasis. Although TNF-α inhibitors were less effective in the improvement of PASI than IL-17 and IL-23 inhibitors, they showed possible beneficial effects on cardiometabolic diseases. However, the improvement in HDL-C or UA by TNF-α inhibitors was transient, and the increase in HDL-C by IFX or decrease in UA by ADA at week 12 was not maintained at week 52. It is possible that the effects of these biologics on lipid or UA metabolism may be unstable and likely to be disturbed by other factors, such as diet, medicines, and other comorbid diseases such as infection. We should further examine the effects of biologics on cardiometabolic indicators over a longer duration (up to five years) and in a larger cohort.
In the present study, we did not investigate new occurrences of DM, HTN, DL, or HUA during the study period. Further studies should investigate the occurrence of these diseases in a larger cohort over a longer duration.
This study had several limitations. First, it was a retrospective study involving a small number of patients. Second, the number of patients for each biologic was biased because of the preferable usage of more effective biologics. Additionally, this study included both bio-naïve and bio-switched patients. We should further examine the influence of prior biologic treatment on the levels of indicators of cardiometabolic diseases in a larger cohort. To compare the effects of different biologics on cardiometabolic indicators, patients should be uniformly assigned to each biologic. Third, we cannot avoid the confounding effects of medications for DM, DL, HT, and HUA, such as insulin or 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors, since some of the patients were taking these medicines during treatment with biologics. Further studies should be conducted to exclude patients taking such medication.
## 5. Conclusions
In conclusion, baseline PASI was significantly positively correlated with serum TG and UA levels and negatively correlated with serum HDL-C levels. At week 12 of IFX treatment, HDL-C level increased, and at week 12 of ADA treatment, UA level decreased compared to week 0. At week 52 of IXE treatment, HDL-C levels decreased compared to those at week 0. After treatment with all TNF-α inhibitors, HDL-C levels increased at week 12, and UA levels were reduced at week 52 compared to week 0. These results indicate that TNF-α inhibitors may improve HUA and dyslipidemia in patients with psoriasis.
## References
1. Rodríguez-Zúñiga M.J.M., García-Perdomo H.A.. **Systematic review and meta-analysis of the association between psoriasis and metabolic syndrome**. *J. Am. Acad. Dermatol.* (2017) **77** 657-666.e8. DOI: 10.1016/j.jaad.2017.04.1133
2. Takahashi H., Takahashi I., Honma M., Ishida-Yamamoto A., Iizuka H.. **Prevalence of metabolic syndrome in Japanese psoriasis patients**. *J. Dermatol. Sci.* (2010) **57** 143-144. DOI: 10.1016/j.jdermsci.2009.11.002
3. Prodanovich S., Kirsner R.S., Kravetz J.D., Ma F., Martínez L., Federman D.G.. **Association of Psoriasis with Coronary Artery, Cerebrovascular, and Peripheral Vascular Diseases and Mortality**. *Arch. Dermatol.* (2009) **145** 700-703. DOI: 10.1001/archdermatol.2009.94
4. Boehncke W.-H., Boehncke S., Tobin A.-M., Kirby B.. **The ‘psoriatic march’: A concept of how severe psoriasis may drive cardiovascular comorbidity**. *Exp. Dermatol.* (2011) **20** 303-307. DOI: 10.1111/j.1600-0625.2011.01261.x
5. Kawada A., Tezuka T., Nakamizo Y., Kimura H., Nakagawa H., Ohkido M., Ozawa A., Ohkawara A., Kobayashi H., Harada S.. **A survey of psoriasis patients in Japan from 1982 to 2001**. *J. Dermatol. Sci.* (2003) **31** 59-64. DOI: 10.1016/S0923-1811(02)00142-1
6. Tripolino C., Ciaffi J., Ruscitti P., Giacomelli R., Meliconi R., Ursini F.. **Hyperuricemia in Psoriatic Arthritis: Epidemiology, Pathophysiology, and Clinical Implications**. *Front. Med.* (2021) **8** 737573. DOI: 10.3389/fmed.2021.737573
7. Uratsuji H., Tada Y., Kawashima T., Kamata M., Hau C.S., Asano Y., Sugaya M., Kadono T., Asahina A., Sato S.. **P2Y6 Receptor Signaling Pathway Mediates Inflammatory Responses Induced by Monosodium Urate Crystals**. *J. Immunol.* (2012) **188** 436-444. DOI: 10.4049/jimmunol.1003746
8. Martinon F., Pétrilli V., Mayor A., Tardivel A., Tschopp J.. **Gout-associated uric acid crystals activate the NALP3 inflammasome**. *Nature* (2006) **440** 237-241. DOI: 10.1038/nature04516
9. Matias M.L., Romão M., Weel I.C., Ribeiro V.R., Nunes P.R., Borges V.T., Araújo J.P., Peraçoli J.C., De Oliveira L., Peracoli M.T.. **Endogenous and Uric Acid-Induced Activation of NLRP3 Inflammasome in Pregnant Women with Preeclampsia**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0129095
10. Li X., Miao X., Wang H., Wang Y., Li F., Yang Q., Cui R., Li B.. **Association of Serum Uric Acid Levels in Psoriasis: A systematic review and meta-analysis**. *Medicine* (2016) **95** e3676. DOI: 10.1097/MD.0000000000003676
11. van Diepen J.A., Wong M.C., Guigas B., Bos J., Stienstra R., Hodson L., Shoelson S.E., Berbée J.F.P., Rensen P.C.N., Romijn J.A.. **Hepatocyte-specific IKK-β activation enhances VLDL-triglyceride production in APOE*3-Leiden mice**. *J. Lipid Res.* (2011) **52** 942-950. DOI: 10.1194/jlr.M010405
12. Feingold K.R., Soued M., Adi S., Staprans I., Shigenaga J., Doerrler W., Moser A., Carl G.. **Tumor Necrosis Factor–Increased Hepatic Very-Low-Density Lipoprotein Production and Increased Serum Triglyceride Levels in Diabetic Rats**. *Diabetes* (1990) **39** 1569-1574. DOI: 10.2337/diab.39.12.1569
13. Greenberg A.S., Nordan R.P., McIntosh J., Calvo J.C., Scow R.O., Jablons D.. **Interleukin 6 reduces lipoprotein lipase activity in adipose tissue of mice in vivo and in 3T3-L1 adipocytes: A possible role for interleukin 6 in cancer cachexia**. *Cancer Res* (1992) **52** 4113-4116. PMID: 1638523
14. Feingold K.R., Marshall M., Gulli R., Moser A.H., Grunfeld C.. **Effect of endotoxin and cytokines on lipoprotein lipase activity in mice**. *Arter. Thromb. A J. Vasc. Biol.* (1994) **14** 1866-1872. DOI: 10.1161/01.ATV.14.11.1866
15. Tiniakou I., Drakos E., Sinatkas V., Van Eck M., Zannis V.I., Boumpas D., Verginis P., Kardassis D.. **High-Density Lipoprotein Attenuates Th1 and Th17 Autoimmune Responses by Modulating Dendritic Cell Maturation and Function**. *J. Immunol.* (2015) **194** 4676-4687. DOI: 10.4049/jimmunol.1402870
16. Ma C., Harskamp C.T., Armstrong E.J., Armstrong A.W.. **The association between psoriasis and dyslipidaemia: A systematic review**. *Br. J. Dermatol.* (2013) **168** 486-495. DOI: 10.1111/bjd.12101
17. Olejniczak-Staruch I., Narbutt J., Ceryn J., Skibińska M., Bednarski I., Woźniacka A., Sieniawska J., Kraska-Gacka M., Ciążyńska M., Śmigielski J.. **AntiTNF-alpha therapy normalizes levels of lipids and adipokines in psoriatic patients in the real-life settings**. *Sci. Rep.* (2021) **11** 9289. DOI: 10.1038/s41598-021-88552-6
18. Field F.J., Watt K., Mathur S.N.. **TNF-α decreases ABCA1 expression and attenuates HDL cholesterol efflux in the human intestinal cell line Caco-2**. *J. Lipid Res.* (2010) **51** 1407-1415. DOI: 10.1194/jlr.M002410
19. Sandoo A., van Zanten J.J.C.S.V., Toms T.E., Carroll D., Kitas G.D.. **Anti-TNFα therapy transiently improves high density lipoprotein cholesterol levels and microvascular endothelial function in patients with rheumatoid arthritis: A Pilot Study**. *BMC Musculoskelet. Disord.* (2012) **13**. DOI: 10.1186/1471-2474-13-127
20. Saito I., Yamagishi K., Kokubo Y., Yatsuya H., Iso H., Sawada N., Inoue M., Tsugane S.. **Association of high-density lipoprotein cholesterol concentration with different types of stroke and coronary heart disease: The Japan Public Health Center-based prospective (JPHC) study**. *Atherosclerosis* (2017) **265** 147-154. DOI: 10.1016/j.atherosclerosis.2017.08.032
21. Gruaz L., Delucinge-Vivier C., Descombes P., Dayer J.-M., Burger D.. **Blockade of T Cell Contact-Activation of Human Monocytes by High-Density Lipoproteins Reveals a New Pattern of Cytokine and Inflammatory Genes**. *PLoS ONE* (2010) **5**. DOI: 10.1371/journal.pone.0009418
22. Zdanowska N., Owczarczyk-Saczonek A., Czerwińska J., Nowakowski J.J., Kozera-Żywczyk A., Owczarek W., Zdanowski W., Placek W.. **Methotrexate and Adalimumab Decrease the Serum Levels of Cardiovascular Disease Biomarkers (VCAM-1 and E-Selectin) in Plaque Psoriasis**. *Medicina* (2020) **56**. DOI: 10.3390/medicina56090473
23. Zdanowska N., Owczarczyk-Saczonek A., Czerwińska J., Nowakowski J.J., Kozera-Żywczyk A., Owczarek W., Zdanowski W., Placek W.. **Adalimumab and methotrexate affect the concentrations of regulatory cytokines (interleukin-10, transforming growth factor-β1, and interleukin-35) in patients with plaque psoriasis**. *Dermatol. Ther.* (2020) **33** e14153. DOI: 10.1111/dth.14153
24. Yee S.B., Ganey P.E., Roth R.A.. **The Role of Kupffer Cells and TNF-alpha in Monocrotaline and Bacterial Lipopolysaccharide-Induced Liver Injury**. *Toxicol. Sci.* (2003) **71** 124-132. DOI: 10.1093/toxsci/71.1.124
25. Papežíková I., Pekarová M., Kolarova H., Klinke A., Lau D., Baldus S., Lojek A., Kubala L.. **Uric acid modulates vascular endothelial function through the down regulation of nitric oxide production**. *Free. Radic. Res.* (2013) **47** 82-88. DOI: 10.3109/10715762.2012.747677
26. Ndrepepa G.. **Uric acid and cardiovascular disease**. *Clin. Chim. Acta* (2018) **484** 150-163. DOI: 10.1016/j.cca.2018.05.046
27. Kim S.Y., Guevara J.P., Kim K.M., Choi H.K., Heitjan D.F., Albert D.A.. **Hyperuricemia and coronary heart disease: A systematic review and meta-analysis**. *Arthritis Care Res.* (2010) **62** 170-180. DOI: 10.1002/acr.20065
28. Gonzalez-Gay M.A., Gonzalez-Juanatey C., Vazquez-Rodriguez T.R., Gomez-Acebo I., Miranda-Filloy J.A., Paz-Carreira J., Martin J., Llorca J.. **Asymptomatic Hyperuricemia and Serum Uric Acid Concentration Correlate with Subclinical Atherosclerosis in Psoriatic Arthritis Patients Without Clinically Evident Cardiovascular Disease**. *Semin. Arthritis Rheum.* (2009) **39** 157-162. DOI: 10.1016/j.semarthrit.2008.06.001
29. Gong F., Liu Z., Liu J., Zhou P., Liu Y., Lu X.. **The paradoxical role of IL-17 in atherosclerosis**. *Cell Immunol.* (2015) **297** 33-39. DOI: 10.1016/j.cellimm.2015.05.007
30. Nikoopour E., Schwartz J.A., Huszarik K., Sandrock C., Krougly O., Lee-Chan E., Singh B.. **Th17 Polarized Cells from Nonobese Diabetic Mice Following Mycobacterial Adjuvant Immunotherapy Delay Type 1 Diabetes**. *J. Immunol.* (2010) **184** 4779-4788. DOI: 10.4049/jimmunol.0902822
31. Surendar J., Aravindhan V., Rao M.M., Ganesan A., Mohan V.. **Decreased serum interleukin-17 and increased transforming growth factor–β levels in subjects with metabolic syndrome (Chennai Urban Rural Epidemiology Study–95)**. *Metabolism* (2011) **60** 586-590. DOI: 10.1016/j.metabol.2010.06.003
32. Liu S., Wang C., Guo J., Yang Y., Huang M., Li L., Wang Y., Qin Y., Zhang M.. **Serum Cytokines Predict the Severity of Coronary Artery Disease without Acute Myocardial Infarction**. *Front. Cardiovasc. Med.* (2022) **9** 896810. DOI: 10.3389/fcvm.2022.896810
33. Yang X., Jia J., Yu Z., Duanmu Z., He H., Chen S., Qu C.. **Inhibition of JAK2/STAT3/SOCS3 signaling attenuates atherosclerosis in rabbit**. *BMC Cardiovasc. Disord.* (2020) **20**. DOI: 10.1186/s12872-020-01391-7
34. Yang J., Kou J., Lalonde R., Fukuchi K.-I.. **Intracranial IL-17A overexpression decreases cerebral amyloid angiopathy by upregulation of ABCA1 in an animal model of Alzheimer’s disease**. *Brain, Behav. Immun.* (2017) **65** 262-273. DOI: 10.1016/j.bbi.2017.05.012
35. Kochumon S., Hasan A., Al-Rashed F., Sindhu S., Thomas R., Jacob T., Al-Sayyar A., Arefanian H., Al Madhoun A., Al-Ozairi E.. **Increased Adipose Tissue Expression of IL-23 Associates with Inflammatory Markers in People with High LDL Cholesterol**. *Cells* (2022) **11**. DOI: 10.3390/cells11193072
36. Khalil R.G., Abdel-Moneim A., Yousef A.I., Abdel-Rahman H., Zanaty M.I., El-Sayed A.. **Association of interleukin-2, interleukin-21 and interleukin-23 with hyperlipidemia in pediatric type 1 diabetes**. *Mol. Biol. Rep.* (2021) **48** 5421-5433. DOI: 10.1007/s11033-021-06545-0
37. Choi H., Uceda D.E., Dey A.K., Abdelrahman K.M., Aksentijevich M., Rodante J.A., Elnabawi Y.A., Reddy A., Keel A., Erb-Alvarez J.. **Treatment of Psoriasis with Biologic Therapy Is Associated with Improvement of Coronary Artery Plaque Lipid-Rich Necrotic Core: Results from a prospective, observational study**. *Circ. Cardiovasc. Imaging* (2020) **13** e011199. DOI: 10.1161/CIRCIMAGING.120.011199
38. Weber B., Merola J.F., Husni M.E., Di Carli M., Berger J.S., Garshick M.S.. **Psoriasis and Cardiovascular Disease: Novel Mechanisms and Evolving Therapeutics**. *Curr. Atheroscler. Rep.* (2021) **23** 67. DOI: 10.1007/s11883-021-00963-y
|
---
title: 'Reduction in the Risk of Peripheral Neuropathy and Lower Decrease in Kidney
Function with Metformin, Linagliptin or Their Fixed-Dose Combination Compared to
Placebo in Prediabetes: A Randomized Controlled Trial'
authors:
- Rafael Gabriel
- Nisa Boukichou-Abdelkader
- Aleksandra Gilis-Januszewska
- Konstantinos Makrilakis
- Ricardo Gómez-Huelgas
- Zdravko Kamenov
- Bernhard Paulweber
- Ilhan Satman
- Predrag Djordjevic
- Abdullah Alkandari
- Asimina Mitrakou
- Nebojsa Lalic
- Jesús Egido
- Sebastián Más-Fontao
- Jean Henri Calvet
- José Carlos Pastor
- Jaana Lindström
- Marcus Lind
- Tania Acosta
- Luis Silva
- Jaakko Tuomilehto
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004435
doi: 10.3390/jcm12052035
license: CC BY 4.0
---
# Reduction in the Risk of Peripheral Neuropathy and Lower Decrease in Kidney Function with Metformin, Linagliptin or Their Fixed-Dose Combination Compared to Placebo in Prediabetes: A Randomized Controlled Trial
## Abstract
Objective: To compare the effect of glucose-lowering drugs on peripheral nerve and kidney function in prediabetes. Methods: Multicenter, randomized, placebo-controlled trial in 658 adults with prediabetes treated for 1 year with metformin, linagliptin, their combination or placebo. Endpoints are small fiber peripheral neuropathy (SFPN) risk estimated by foot electrochemical skin conductance (FESC < 70 μSiemens) and estimated glomerular filtration rate (eGFR). Results: Compared to the placebo, the proportion of SFPN was reduced by $25.1\%$ ($95\%$ CI:16.3–33.9) with metformin alone, by $17.3\%$ ($95\%$ CI 7.4–27.2) with linagliptin alone, and by $19.5\%$ ($95\%$ CI 10.1–29.0) with the combination linagliptin/metformin ($p \leq 0.0001$ for all comparisons). eGFR remained +3.3 mL/min ($95\%$ CI: 0.38–6.22) higher with the combination linagliptin/metformin than with the placebo ($$p \leq 0.03$$). Fasting plasma glucose (FPG) decreased more with metformin monotherapy −0.3 mmol/L ($95\%$CI: −0.48; 0.12, $$p \leq 0.0009$$) and with the combination metformin/linagliptin −0.2 mmol/L ($95\%$ CI: −0.37; −0.03) than with the placebo ($$p \leq 0.0219$$). Body weight (BW) decreased by −2.0 kg ($95\%$ CI: −5.65; −1.65, $$p \leq 0.0006$$) with metformin monotherapy, and by −1.9 kg ($95\%$ CI: −3.02; −0.97) with the combination metformin/linagliptin as compared to the placebo ($$p \leq 0.0002$$). Conclusions: in people with prediabetes, a 1 year treatment with metformin and linagliptin, combined or in monotherapy, was associated with a lower risk of SFPN, and with a lower decrease in eGFR, than treatment with placebo.
## 1. Introduction
Type 2 diabetes (T2D) is a slowly progressive disease characterized by advancing hyperglycemia. Prediabetes, the intermediate stage between normoglycemia and T2D, is a heterogeneous condition with several pathophysiological phenotypes [1]. Progression from prediabetes to T2D ranges between 5–$10\%$ per year [2,3,4] and this rate depends on the sub-phenotype of prediabetes and the risk factor profile; for instance, the level of diabetes risk score [2,3,4].
People with prediabetes have an increased risk for macro- and microvascular (namely nephropathy, neuropathy and retinopathy), complications [5,6,7,8,9,10,11]. The Rotterdam study reported that people with prediabetes have generalized microvascular dysfunction and sequelae representing end-organ damage typical of diabetes [12]. Based on such evidence it has been proposed that the definition of prediabetes should not only be centered on glucose abnormalities but should also incorporate microvascular involvement among its criteria [13,14,15].
Lifestyle intervention studies have shown a reduction in the relative risk of diabetes by 40–$70\%$ in individuals with prediabetes [13]. The Finnish Diabetes Prevention Study (DPS) and the US Diabetes Prevention Program (DPP) showed how lifestyle intervention, as compared to the control group, was associated with a $58\%$ reduction in the risk of diabetes after approximately three years. The benefits of lifestyle modification in delaying the onset of T2D in people with prediabetes have been confirmed in meta-analyses of clinical trials [16]. However, approximately half of the people with hyperglycemia in the DPP failed to achieve normal glycaemia with lifestyle interventions alone, and about $\frac{1}{3}$ of them progressed to clinical T2D in an average of 10 years [3]. Several glucose-lowering drugs such as acarbose, metformin, rosiglitazone, pioglitazone, insulin glargine and liraglutide have also shown some benefits for the prevention of diabetes in people with prediabetes, but this benefit disappeared when the drug therapy was stopped [17,18,19,20,21]. The dipeptidyl peptidase-4 inhibitor (DPP4i) linagliptin has demonstrated its cardiovascular safety in placebo-controlled trials. It also demonstrated non-inferiority using a composite marker for cardiovascular outcome compared to glimepiride. Both cases happened when the drugs were administered as monotherapy in relatively early T2D [22]. Recently, the VERIFY study [23] has reported on the efficacy and safety of combining metformin with the DPP4i vildagliptin, compared with metformin monotherapy, in early–untreated T2D. Evidence of higher efficacy and safety of dual therapy versus monotherapy and/or lifestyle changes in people with prediabetes does not exist. Evidence for the prevention of microvascular complications is also limited in prediabetes. Few prevention trials have evaluated the potential benefits of lifestyle modification or drug treatment for preventing such complications. The Da Qing study in China was the first published trial reporting a significant long-term reduction in the incidence of diabetic retinopathy with lifestyle modification [24]. The evidence regarding the benefit of lifestyle changes or the use of metformin for the prevention of microvascular complications was also inconclusive in the US-DPP [17]. The DPS has reported that lifestyle intervention improved retinopathy status [25]. Experimental studies have reported that linagliptin has a protective effect on the microvasculature of the diabetic retina, most likely due to a combination of neuroprotective and antioxidative beneficial effects [26]. Several clinical trials have also shown that linagliptin may slow down the progression of albuminuria in patients with T2D and renal dysfunction [27,28].
So far, no trial has reported if microvascular function can be better preserved by combining lifestyle interventions with early glucose-lowering drug treatment (multiple or monotherapy) in people with either early diagnosed T2D [29] or with prediabetes [30]. Therefore, the primary objective of the Early Prevention of Diabetes Complications in People with Hyperglycaemia (ePREDICE) trial is to assess the effects of early intensive management of prediabetes on several microvascular parameters in comparison with placebo and lifestyle modification. The primary endpoint was a combination of three microvascular endpoints: retinal and peripheral nerve and kidney functions in adults with prediabetes. In this paper we report 1 year results on the intermediate independent effect of each glucose-lowering drug regimen and placebo (i.e., lifestyle intervention alone) only on the peripheral nerve (sudomotor function) and eGFR.
## 2. Material and Methods
The ePREDICE study design and protocol (including details on the definition and measurement of different variables and outcomes, description of inclusion/exclusion criteria and the recruitment strategy) have been published elsewhere [31]. In sum, the ePREDICE trial is an international, multicenter, randomized, double-blind, parallel-group, placebo-controlled, primary prevention trial initiated by investigators to examine the impact of metformin (Glucophage®, Merck KGaA, Darmstadt, Germany), linagliptin (Trajenta® Boehringer Ingelheim, Ingelheim am Rhein, Germany) and a fixed-dose combination of linagliptin/metformin (Jentadueto® Boehringer Ingelheim, Ingelheim am Rhein, Germany) on microvascular parameters compared with matched-placebo. Participants were randomized with equal probability (1:1:1:1) to metformin 850 mg twice a day; linagliptin 5 mg/once a day plus matched-placebo once a day; fixed-dose combination of linagliptin 2.5 mg/metformin 850 mg twice a day; or matched placebo twice a day. Drug treatment was scheduled for 12 months. All randomized participants were also enrolled in a lifestyle intervention program which included 2 individual sessions followed by 12 group sessions of 1.5 hours duration each. Group sessions were repeated every month. The lifestyle intervention program followed the model developed by the European IMAGE Project [32]. All participants attended in the baseline and 12-month follow-up visits for clinical evaluation of microvascular peripheral and kidney function measurements and OGTT assessment. Primary endpoints for this analysis are the 1 year change in foot electrochemical skin conductance (FESC)—measured in μSiemens by SUDOSCAN®; participants were classified according to FESC as low risk of small fiber neuropathy (SFN: FESC > 70 µS) and high risk of SFN (FESC < 70 µS; includes moderate risk of SFN, FESC 50–70 µS, and severe risk of SFN: FESC < 50 µS)—as well as 1 year change in kidney function with estimated glomerular filtration rate (eGFR). Secondary endpoints are 1 year changes in fasting plasma glucose (FPG), 2 hour post-challenge plasma glucose (2 h-PG) and body weight (BW). The study protocol did not change since the previous publication [32]. Here we are only reporting baseline characteristics of the study participants and effects of the study intervention on two single independent components of the primary study composite outcome, i.e., the estimated glomerular filtration rate (eGFR) and small fiber peripheral neuropathy (SFPN) risk at 12 months. In addition, we report the effect of the intervention on body weight (BW) and fasting plasma glucose (FPG).
## 2.1. Statistical Analysis
The present analysis is based on the full analysis set (FAS) available for the baseline and 12 month follow-up in the central database by September 26, 2019. In this report we analyze the 1 year changes in the variables of interest in those participants randomized at baseline, who also completed the treatment assigned and attended the 1-year appointment for clinical re-assessment ($$n = 658$$). For continuous variables, descriptive statistics (mean, SD, percentages and $95\%$ CI) were used. For the comparison of treatment arms at baseline we used a two-sided Mann–Whitney U-test with significance level ($p \leq 0.05$) for independent samples. We assessed whether each of the 3 active drugs independently (metformin alone, linagliptin alone or the fixed-dose combination of linagliptin/metformin) had a superior effect on the selected variables compared to the placebo. The 1 year changes for paired samples were analyzed using the Wilcoxon test for continuous variables and the McNemar test for categorical variables. In addition, the mean change obtained from the ANCOVA—adjusted for its baseline value—was calculated for the primary outcome variables. The incidence of serious adverse events (SAE) and adverse effects (AE) of special interest to each study drug compared to the placebo are also presented.
## 2.2. Ethical Issues
The study was approved by all local ethic committees of the participating centers and all the National Medicine agencies of the participating countries. All participants received detailed information about the study, including the explanation of their right to withdraw their participation at any time. All participants signed written informed consent forms. All centers followed the European Good Clinical Practice Guidelines and the Declaration of Helsinki as revised in 2008.
## 3. Results
Figure 1 shows the flow-chart of the study population. A total of 1391 potential eligible individuals were screened using a standard 2 h oral glucose tolerance test (OGTT), and 582 ($41.8\%$) of them were excluded: 318 people ($22.8\%$) because they did not meet all the inclusion criteria; 251 ($18.0\%$) declined to sign the informed consent; and 13 people ($0.9\%$) rejected the pre-assigned treatment. In total, 809 participants were fully assessed at baseline and started the assigned drug treatment. After randomization, no demographic differences between the four resulting study groups were observed.
During the first year of the trial, 151 participants ($18.6\%$) withdrew from the study. The discontinuation rates of assigned study drugs were $13.3\%$ (26 participants) in the placebo group; $25.6\%$ (52 participants) in the metformin group; $21.9\%$ (45 participants) in the linagliptin group; and $13.6\%$ (28 participants) in the combined linagliptin/metformin group. After 12 months, 658 participants ($81.3\%$) attended the 1 year follow-up appointment for clinical reassessment. In people who discontinued treatment we did not unmask the assigned drug unless the participant had a medical reason for it. Despite drug withdrawal, we encouraged these participants to continue in the lifestyle intervention program and to attend scheduled clinical appointments. People who discontinued the medication have been excluded from this analysis.
No differences in the variables of interest were observed at baseline between the four study groups, nor among all randomized groups, nor those who completed the 1 year treatment (Table 1).
Table 2 shows the baseline-adjusted mean and proportion differences, and their corresponding $95\%$ confidence intervals ($95\%$ CI), between baseline and 1 year follow-up within each study group and compared to the placebo. After 1 year of treatment, the proportion of participants with high risk of SFPN increased by $29.6\%$ in the placebo group, while the increase was significantly lower in the three active-drug groups: $4.6\%$ in the metformin group; $12.4\%$ in the linagliptin group; $10.1\%$ with the combination linagliptin/metformin; and $9.6\%$ when considering the three active-drug groups together ($p \leq 0.001$ for all comparisons). Compared to the placebo, the proportion of SFPN high-risk estimation by FESC was reduced by $25.1\%$ ($95\%$ CI:16.3; 33.9) with metformin alone; by $17.3\%$ ($95\%$ CI 7.4; 27.2) with linagliptin alone; by $19.5\%$ ($95\%$ CI 10.1; 29.0) with the combination linagliptin/metformin; and by $20.0\%$ (11.5; 28.5) taking together the three active drugs (p value <0.0001 for all comparisons). The 1 year kidney function, measured by eGFR-CKD-EPI per 1.73 m2, remained significantly higher with the fixed-dose combination linagliptin/metformin (3.3 mL/min/year, $95\%$ CI: 0.37; 6.22), $$p \leq 0.0270$$, than with placebo, but not significantly with metformin monotherapy (3.0 mL/min/year: $95\%$ CI; −0.01; 6.01), $$p \leq 0.0511$$, nor with linagliptin monotherapy (1.3 mL/min/year; $95\%$ CI: −2.13; 4.73), $$p \leq 0.4577.$$ After 1 year of treatment, FPG was significantly lower with metformin monotherapy (−0.3 mmol/L, $95\%$CI: −0.48; −0.12), $$p \leq 0.0009$$, with the combination metformin/linagliptin (−0.2 mmol/L, $95\%$ CI: −0.37; −0.03), $$p \leq 0.0219$$, and with the three active drugs taken together (−0.2 mmol/L, $95\%$CI: −0.349; −0.050), $$p \leq 0.008$$, than with placebo. However, no difference between linagliptin monotherapy and placebo was observed. No significant reductions were observed between any of the three active drugs and placebo for 2 h-PG. The 1 year reduction in body weight was significantly greater (−2.0 kg/year, $95\%$ CI: −5.65; −1.65), $$p \leq 0.0006$$, with metformin monotherapy and with the combination metformin/linagliptin (−1.9 kg/year, $95\%$ CI: −3.02; −0.97), $$p \leq 0.0002$$, than with placebo. However, no significant reductions in body weight were observed with linagliptin monotherapy (−0.1 kg/year; $95\%$ CI: 0.15; 0.95; $$p \leq 0.5822$$) nor with the three active drugs taken together (−1.5 kg/year; $95\%$ CI: −4.4; 1.4; $$p \leq 0.316$$) compared to placebo.
In comparison with the placebo, no significant changes after 1 year of treatment were observed in other cardiometabolic risk factors such as blood pressure, waist circumference, serum triglycerides and HbA1c.
## 3.1. Drug Adherence
In a random subsample of 200 participants (50 patients per study arm), we monitored the participants’ drug adherence during the whole treatment period with the electronic Medication Event Monitoring Systems (MEMS®). The compliance with the assigned study medication was considered optimal ($95\%$ or more of the days analyzed) by 75 of the patients. No differences in drug compliance were observed between the four study groups during one year of treatment.
## 3.2. Safety Analysis
Only four SAEs, each in four different patients, were reported, none of them related to the study medication as determined by the responsible clinical investigator. The drug treatment was immediately unmasked in these four people. Additionally, the drug treatment was unmasked by local investigators in seven more people for different medical reasons, such as scheduled surgery or acute illness. Nevertheless, participants were asked to resume the assigned study treatment in an open-label fashion after the resolution of the event that required the unmasking.
A total of 52 participants ($6.0\%$) reported a drug-related AE (24 in the metformin group, 18 in the linagliptin/metformin group, 6 in the placebo group and 4 in the linagliptin group). The most frequent AEs were diarrhea ($46.2\%$) and unspecific digestive intolerance ($36.5\%$). These symptoms were more frequent with metformin ($22.2\%$) and with the combination linagliptin/metformin ($15.3\%$). Symptomatic hypoglycemia, clinically relevant hyperamylasemia or acute pancreatitis were not reported during the period analyzed.
## 4. Discussion
Generally speaking, there is a paucity in the scientific literature of interventional studies on the relationship between prediabetes and microvascular complications, specifically to nephropathy and neuropathy, which are the two main focuses of this manuscript. This makes our study a novel one [30]. The ePREDICE trial is an international, investigator-initiated, randomized, placebo-controlled trial aiming at comparing the effects of different glucose-lowering drugs added to lifestyle management with intervention based on lifestyle management alone on the preservation of microvascular function in individuals with prediabetes. In this report, we focus on the effects of three different therapeutic strategies on kidney and sudomotor functions as well as glycemic parameters.
Participants were predominantly middle-aged, Caucasian, female and overweight/obese. The majority were ex-smokers or current smokers and were taking antihypertensive and lipid-lowering drugs. The randomization procedure efficiently generated well-balanced groups in terms of risk stratification.
The number of participants who completed the assigned drug treatment ($81.3\%$; $\frac{658}{809}$) can be considered high in comparison with other primary prevention trials combining anti-diabetic drugs and lifestyle modification, where a high proportion of withdrawals usually occur [17]. In our study, the proportion of participants who discontinued the assigned study treatment during the 1 year follow-up did not differ between the four study groups. Regarding kidney function preservation, linagliptin did not produce significant changes in eGFR compared to the placebo at weeks 6, 12, 18 and 24 in the MARLINA-T2D study, a randomized, placebo-controlled, multicenter, Phase IIIb clinical trial. This study suggested that linagliptin may not influence kidney function in patients with T2D within 24 weeks of treatment [27]. SGLT2 inhibitors are known to be effective in preventing kidney function decline with an effect of approximately 0.9 ml per minute per 1.73 m2 ($95\%$ CI, 0.61 to 1.25) per year in saved renal function compared to placebo in adults with or without T2D who had an estimated glomerular filtration rate (GFR) of 25 to 75 ml per minute per 1.73 m2 of body surface area [33]. In the DECLARE-TIMI-58 randomized trial [34], patients generally had good eGFR at baseline, which is the case in our study. The authors analyzed the extent to which dapagliflozin and placebo were associated with a decrease in eGFR in people with T2D and eGFR > 90 ml/min at baseline. The difference between groups was 2 ml/min during a 4-year treatment, i.e., 0.5 ml/min per year of preventive effect in favor of dapagliflozin. From this perspective the effect observed in our study seems relatively good. In 1 year of treatment, the eGFR only decreased by 0.3 mL/min in the metformin-alone group, 0.6 mL/min with the combination metformin/linagliptin and 1.8 mL/min in the linagliptin-alone group compared with a greater decrease of 3.2 mL/min per year in the placebo group. Therefore, it is essential to confirm if the preventive effect will persist over a longer time.
A study conducted in active Finnish workers [35] assessed sudomotor function with FESC. Participants with the lowest fitness level were involved in a 12 month training program with recording of their weekly physical activity and a final fitness level evaluation. Significant differences in BMI as well as waist and body fat were seen according to SUDOSCAN risk score classification. Correlation between the SUDOSCAN risk score and estimated VO2max was r = −0.57, $p \leq 0.0001$ for women and −0.48, $p \leq 0.0001$ for men. A significant increase in estimated VO2max in hand and foot ESC and in SUDOSCAN risk score was observed after lifestyle intervention; it was more important in people with the highest weekly activity during the intervention. This was the first study showing that SUDOSCAN could be used to assess cardio-metabolic disease risk status in a working population and to evaluate individual lifestyle interventions. To our knowledge, the ePREDICE trial is the first randomized, controlled trial in prediabetes assessing the effect of lifestyle intervention in combination with glucose-lowering drugs compared with lifestyle modification intervention on peripheral nerve function.
More recently the GRADE study has also reported no differences among the interventions with respect to the development of microvascular outcomes; the mean overall rate (i.e., events per 100 participant-years) of renal impairment was 2.9, and of diabetic peripheral neuropathy, 16.7 [29].
A possible explanation for the small changes in blood glucose observed in our study could be the mix of people with IFG, IGT and IFG + IGT in the study sample. Future analyses should explore whether a stratification by IFG and IGT separately would produce similar results.
Another interesting finding of our study is a greater reduction in body weight observed with metformin monotherapy and with the fixed-dose combination metformin/linagliptin, but not with linagliptin monotherapy, compared to the placebo. The weight loss in the groups containing metformin in our study, approximately $2\%$, was somewhat higher than what has been reported in other randomized controlled trials. A systematic review and meta-analysis reported an average weight loss of 1.1 kg with metformin used for varying periods [36]. The randomized design of the trial makes it unlikely that the difference favoring intervention groups containing metformin can be explained by a better adherence to lifestyle intervention. However, in the US Diabetes Prevention Program, the metformin group also achieved a similar weight loss of $2.1\%$ after 2 years; remarkably, this lasted for the next 10 years [37].
One-year differences in other cardiovascular risk factors such as blood lipids and blood pressure were non-significant between the active drug groups and placebo. This finding is consistent with other drug trials in prediabetes using similar therapeutic regimens [38,39]. Although the study protocol encouraged the use of antihypertensive and lipid-lowering drugs when necessary, according to the current guideline recommendations [40], we do not have information on the proportion of participants taking these drugs during the course of the study.
Recently the VERIFY study reported that early combination of metformin and DPPIVi drugs in patients with untreated T2D was associated with higher reductions of HbA1c and FPG, both short-term and long-term, than with metformin in monotherapy [23]. Evidence on drug therapy and lifestyle modification combined, compared with lifestyle intervention alone, for the prevention of T2D is scarce [41,42,43,44,45,46,47]. The results of available studies should be interpreted with caution because in general they were small, short intervention time, non-randomized or open-label trials where systematic bias cannot be excluded. *In* general, these trials do not support the use of pharmacotherapy in combination with lifestyle intervention to lower the risk in individuals with prediabetes.
## 5. Limitations
The e-PREDICE study was challenging to implement because of its complex multinational, non-commercial design using pharmacologic intervention, carried out by independent academic investigators with 2-pill/day requirements of four different combined pharmacologic regimens in asymptomatic people without medical complaints. Despite these challenges, the mean percentage of days with optimal drug compliance (higher than $80\%$ of the prescribed dose) was $75\%$ in the monitored participants. However, in $25\%$ of participants the compliance with the evening dose was lower than $80\%$. This suboptimal compliance of daily doses may have contributed to the small differences observed between the placebo and active drug groups. Missing data due to participant withdrawal is also an important limitation when interpreting the results of primary prevention trials. However, participants who discontinued drug treatment were asked to maintain the lifestyle recommendations and to remain in the study for future observational follow-up. In the DPP, a similar proportion of participants discontinued metformin during the first six months of treatment, and this proportion remained stable for the next two and five years [17]. In the DPP, the adherence to placebo was consistently higher than adherence to metformin, which contrasts with our study. Using the same definitions than other pharmacological trials conducted in people with prediabetes, we identified a similar number of SEAs and AEs during the treatment period [48]. The number of self-reported gastrointestinal AEs associated with the study medication was slightly higher in the metformin group than in the other study arms, but this was not statistically significant. Similar figures have been reported by other trials in prediabetes [48].
The effects of both lifestyle modification and pharmacologic treatment on diabetes prevention are usually observed after several years of intervention. However, the potential benefits of pharmacological treatment in contrast to lifestyle intervention disappear when glucose-lowering drug therapy is stopped [49,50,51,52].
One year is a short period of time, where fluctuations of blood glucose are common. A longer follow-up is needed before drawing any conclusions on the reduction in diabetes incidence or the regression to normoglycemia. The encouraging results of ePREDICE on risk factors such as body weight and microvascular kidney and peripheral nerve functions can be considered to be of relevance, since the objective was to prevent early microvascular impairment in prediabetes.
## 6. Conclusions
In people with prediabetes, one-year treatment with metformin and linagliptin, either in monotherapy or combination, was associated with a lower risk of small fiber peripheral neuropathy, and with a lower decrease in estimated glomerular filtration rate, than with placebo. In addition, a greater reduction in FPG and body weight was observed with the metformin monotherapy and the fixed-dose combination metformin/linagliptin than with the linagliptin monotherapy or the placebo.
## References
1. Wagner R., Heni M., Tabák A.G., Machann J., Schick F., Randrianarisoa E., de Angelis M.H., Birkenfeld A.L., Stefan N., Peter A.. **Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes**. *Nat. Med.* (2021) **27** 49-57. DOI: 10.1038/s41591-020-1116-9
2. 2.
World Health Organization
Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF ConsultationWorld Health OrganizationGeneva, Switzerland2006. *Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation* (2006)
3. Gernstein H.C., Santaguida P., Raina P., Morrison K.M., Balion C., Hunt D., Yazdi H., Booker L.. **Annual incidence and relative risk of diabetes in people with various categories of dysglycaemia: A systematic overview and meta-analysis of prospective studies**. *Diabetes Res. Clin. Pract.* (2007) **78** 305-312. DOI: 10.1016/j.diabres.2007.05.004
4. Knowler W.C., Fowler S.E., Hamman R.F., Christophi C.A., Hoffman H.J., Brenneman A.T., Brown-Friday J.O., Goldberg R., Venditti E.. **10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study**. *Lancet* (2009) **374** 1677-1686. PMID: 19878986
5. Yeboah J., Bertoni A.G., Herrington D.M., Post W.S., Burke G.L.. **Impaired Fasting Glucose and the Risk of Incident Diabetes Mellitus and Cardiovascular Events in an Adult Population: MESA (Multi-Ethnic Study of Atherosclerosis)**. *J. Am. Coll. Cardiol.* (2011) **58** 140-146. DOI: 10.1016/j.jacc.2011.03.025
6. Heianza Y., Hara S., Arase Y., Saito K., Fujiwara K., Tsuji H., Kodama S., Hsieh S.D., Mori Y., Shimano H.. **HbA1c 5·7–6·4% and impaired fasting plasma glucose for diagnosis of pre-diabetes and risk of progression to diabetes in Japan (TOPICS 3): A longitudinal cohort study**. *Lancet* (2011) **378** 147-155. DOI: 10.1016/S0140-6736(11)60472-8
7. Kanat M., Mari A., Norton L., Winnier D., DeFronzo R.A., Jenkinson C., Abdul-Ghani M.A.. **Distinct β-Cell Defects in Impaired Fasting Glucose and Impaired Glucose Tolerance**. *Diabetes* (2012) **61** 447-453. DOI: 10.2337/db11-0995
8. Plantinga L.C., Crews D.C., Coresh J., Miller E.R., Saran R., Yee J., Hedgeman E., Pavkov M., Eberhardt M.S., Williams D.E.. **Prevalence of chronic kidney disease in US adults with undiagnosed diabetes or pre-diabetes**. *Clin. J. Am. Soc. Nephrol.* (2010) **5** 673-682. DOI: 10.2215/CJN.07891109
9. Singh J.P., Larson M., O’Donnell C.J., Wilson P.F., Tsuji H., Lloyd-Jones D., Levy D.. **Association of hyperglycemia with reduced heart rate variability (The Framingham Heart Study)**. *Am. J. Cardiol.* (2000) **86** 309-312. DOI: 10.1016/S0002-9149(00)00920-6
10. Tapp R.J., Tikellis G., Wong T.Y., Harper C.A., Zimmet P.Z., Shaw J.E.. **Longitudinal association of glucose metabolism with retinopathy: Results from the Australian Diabetes Obesity and Lifestyle (AusDiab) study**. *Diabetes Care* (2008) **31** 1349-1354. DOI: 10.2337/dc07-1707
11. Balkau B., Hu G., Qiao Q., Tuomilehto J., Borch-Johnsen K., Pyorala K.. **Prediction of the risk of cardiovascular mortality using a score that includes glucose as a risk factor. The DECODE Study**. *Diabetologia* (2004) **47** 2118-2128. PMID: 15662552
12. Barr E.L., Zimmet P.Z., Welborn T.A., Jolley D., Magliano D.J., Dunstan D.W., Cameron A.J., Dwyer T., Taylor H.R., Tonkin A.M.. **Risk of Cardiovascular and All-Cause Mortality in Individuals With Diabetes Mellitus, Impaired Fasting Glucose, and Impaired Glucose Tolerance: The Australian Diabetes, Obesity, and Lifestyle Study (AusDiab)**. *Circulation* (2007) **116** 151-157. DOI: 10.1161/CIRCULATIONAHA.106.685628
13. Sorensen B.M., Houben A.J., Berendschot T.T., Schouten J.S., Kroon A.A., van der Kallen C.J., Henry R.M.A., Koster A., Sep S.J.S., Dagnelie P.C.. **Pre-diabetes and type 2 diabetes are associated with generalized microvascular dysfunction: The Maastricht study**. *Circulation* (2016) **134** 1339-1352. DOI: 10.1161/CIRCULATIONAHA.116.023446
14. Tabák A.G., Herder C., Rathmann W., Brunner E.J., Kivimäki M.. **Pre-diabetes: A high-risk state for diabetes development**. *Lancet* (2012) **379** 2279-2290. DOI: 10.1016/S0140-6736(12)60283-9
15. Vas P.R.J., Alberti K.G., Edmonds M.E.. **Pre-diabetes: Moving away from a glucocentric definition**. *Lancet Diabetes Endocrinol.* (2017) **5** 848-849. DOI: 10.1016/S2213-8587(17)30234-6
16. Galaviz K.I., Weber M.B., Straus A., Haw J.S., Narayan K.V., Ali M.K.. **Global Diabetes Prevention Interventions: A Systematic Review and Network Meta-analysis of the Real-World Impact on Incidence, Weight, and Glucose**. *Diabetes Care* (2018) **41** 1526-1534. DOI: 10.2337/dc17-2222
17. Holman R.R., Coleman R.L., Chan J.C.N., Chiasson J.L., Feng H., Ge J., Gerstein H.C., Gray R., Huo Y., Lang Z.. **Effects of acarbose on cardiovascular and diabetes outcomes in patients with coronary heart disease and impaired glucose tolerance (ACE): A randomised, double-blind, placebo-controlled trial**. *Lancet Diabetes Endocrinol.* (2017) **5** 877-886. DOI: 10.1016/S2213-8587(17)30309-1
18. **Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin**. *N. Engl. J. Med.* (2002) **346** 393-403. DOI: 10.1056/NEJMoa012512
19. **Effect of rosiglitazone on the frequency of diabetes in patients with glucose impaired tolerance or impaired fasting glucose: A randomized controlled trial**. *Lancet* (2006) **368** 1096-1105. DOI: 10.1016/S0140-6736(06)69420-8
20. DeFronzo R.A., Tripathy D., Schwenke D.C., Banerji M.A., Bray G.A., Buchanan T.A., Clement S.C., Henry R.R., Hodis H.N., Kitabchi A.E.. **ACT NOW Study**. *N. Engl. J. Med.* (2011) **364** 1104-1115. DOI: 10.1056/NEJMoa1010949
21. Pi-Sunyer X., Astrup A., Fujioka K., Greenway F., Halpern A., Krempf M., Lau D.C., le Roux C.W., Violante Ortiz R., Jensen C.B.. **A Randomized, Controlled Trial of 3.0 mg of Liraglutide in Weight Management**. *N. Engl. J. Med.* (2015) **373** 11-22. DOI: 10.1056/NEJMoa1411892
22. **The prevalence of retinopathy in impaired glucose tolerance and recent-onset diabetes in the Diabetes Prevention Program**. *Diabet. Med.* (2007) **24** 137-144. DOI: 10.1111/j.1464-5491.2007.02043.x
23. Rosenstock J., Kahn S.E., Johansen O.E., Zinman B., Espeland M.A., Woerle H.J., Pfarr E., Keller A., Mattheus M., Baanstra D.. **Effect of Linagliptin vs Glimepiride on Major Adverse Cardiovascular Outcomes in Patients With Type 2 Diabetes: The CAROLINA Randomized Clinical Trial**. *JAMA* (2019) **322** 1155-1166. DOI: 10.1001/jama.2019.13772
24. Matthews D.R., Paldánius P.M., Proot P., Chiang Y., Stumvoll M., Del Prato S.. **Glycaemic durability of an early combination therapy with vildagliptin and metformin versus sequential metformin monotherapy in newly diagnosed type 2 diabetes (VERIFY): A 5-year, multicentre, randomised, double-blind trial**. *Lancet* (2019) **394** 1519-1529. DOI: 10.1016/S0140-6736(19)32131-2
25. Gong Q., Gregg E.W., Wang J., An Y., Zhang P., Yang W., Li H., Li H., Jiang Y., Shuai Y.. **Long-term effects of a randomised trial of a 6-year lifestyle intervention in impaired glucose tolerance on diabetes-related microvascular complications: The China DaQing Diabetes Prevention Outcome Study**. *Diabetologia* (2011) **54** 300-307. DOI: 10.1007/s00125-010-1948-9
26. Aro A., Kauppinen A., Kivinen N., Selander T., Kinnunen K., Tuomilehto J., Keinänen-Kiukaanniemi S., Lindström J., Uusitupa M., Kaarniranta K.. **Life Style Intervention Improves Retinopathy Status—The Finnish Diabetes Prevention Study**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11071691
27. Dietrich N., Kolibabka M., Busch S., Bugert P., Kaiser U., Lin J., Fleming T., Morcos M., Klein T., Schlotterer A.. **The DPP4 Inhibitor Linagliptin Protects from Experimental Diabetic Retinopathy**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0167853
28. Groop P.H., Cooper M.E., Perkovic V., Hocher B., Kanasaki K., Haneda M.. **Linagliptin and its effects on hyperglycaemia and albuminuria in patients with type 2 diabetes and renal dysfunction: The randomized MARLINA-T2D trial**. *Diabetes Obes. Metab.* (2017) **19** 1610-1619. DOI: 10.1111/dom.13041
29. Rosenstock J., Perkovic V., Johansen O.E., Cooper M.E., Kahn S.E., Marx N., Alexander J.H., Pencina M., Toto R.D., Wanner C.. **Effect of Linagliptin vs Placebo on Major Cardiovascular Events in Adults with Type 2 Diabetes and High Cardiovascular and Renal Risk: The CARMELINA Randomized Clinical Trial**. *JAMA* (2019) **321** 69-79. DOI: 10.1001/jama.2018.18269
30. Nathan D.M., Lachin J.M., Bebu I., Burch H.B., Buse J.B., Cherrington A.L., Fortmann S.P., Green J.B., Kahn S.E.. **Glycemia Reduction in Type 2 Diabetes—Microvascular and Cardiovascular Outcomes**. *N. Engl. J. Med.* (2022) **387** 1075-1088. PMID: 36129997
31. Baranowska-Jurkun A., Matuszewski W., Bandurska-Stankiewicz E.. **Chronic Microvascular Complications in Prediabetic States—An Overview**. *J. Clin. Med.* (2020) **9**. DOI: 10.3390/jcm9103289
32. Gabriel R., Boukichou Abdelkader N., Acosta T., Gilis-Januszewska A., Gómez-Huelgas R., Makrilakis K., Kamenov Z., Paulweber B., Satman I., Djordjevic P.. **Early prevention of diabetes microvascular complications in people with hyperglycaemia in Europe. ePREDICE randomized trial. Study protocol, recruitment and selected baseline data**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0231196
33. Lindström J., Neumann A., Sheppard K.E., Gilis-Januszewska A., Greaves C.J., Handke U., Pajunen P., Puhl S., Pölönen A., Rissanen A.. **Take action to prevent diabetes--the IMAGE toolkit for the prevention of type 2 diabetes in Europe**. *Horm. Metab. Res.* (2010) **42** S37-S55. DOI: 10.1055/s-0029-1240975
34. Heerspink H.J.L., Stefánsson B.V., Correa-Rotter R., Chertow G.M., Greene T., Hou F.F., Mann J.F.E., McMurray J.J.V., Lindberg M., Rossing P.. **Dapagliflozin in Patients with Chronic Kidney Disease**. *N. Engl. J. Med.* (2020) **383** 1436-1446. DOI: 10.1056/NEJMoa2024816
35. Mosenzon O., Wiviott S.D., Cahn A., Rozenberg A., Yanuv I., Goodrich E.L., Murphy S.A., Heerspink H.J.L., Zelniker T.A., Dwyer J.P.. **Effects of dapagliflozin on development and progression of kidney disease in patients with type 2 diabetes: An analysis from the DECLARE–TIMI 58 randomised trial**. *Lancet Diabetes Endocrinol.* (2019) **7** 606-617. DOI: 10.1016/S2213-8587(19)30180-9
36. Räisänen A.M., Eklund J., Calvet J.-H., Tuomilehto J.. **Sudomotor Function as a Tool for Cardiorespiratory Fitness Level Evaluation: Comparison with Maximal Exercise Capacity**. *Int. J. Environ. Res. Public Health* (2014) **11** 5839-5848. DOI: 10.3390/ijerph110605839
37. Domecq J.P., Prutsky G., Leppin A., Sonbol M., Altayar O., Undavalli C., Wang Z., Elraiyah T., Brito J.P., Mauck K.F.. **Drugs Commonly Associated With Weight Change: A Systematic Review and Meta-analysis**. *J. Clin. Endocrinol. Metab.* (2015) **100** 363-370. DOI: 10.1210/jc.2014-3421
38. **Long-term safety, tolerability, and weight loss associated with metformin in the Diabetes Prevention Program Outcomes Study**. *Diabetes Care* (2012) **35** 731-737. DOI: 10.2337/dc11-1299
39. Barua M., Pathan F., Nabi M.U., Kabir M.. **Assessment of clinical and biochemical profile of prediabetic subject in Bangladesh, attending in BIRDEM and results of intervention by lifestyle modification, metformin, and DPP4 inhibitor**. *Diabetes Metab. Syndr. Clin. Res. Rev.* (2019) **13** 1603-1608. DOI: 10.1016/j.dsx.2019.03.019
40. Alvarez-Canales M.F.D.L.L., Salazar-López S.S., Farfán-Vázquez D., Martínez-López Y.E., González-Mena J.N., Jiménez-Ceja L.M., Vargas-Ortiz K., Evia-Viscarra M.L., de Oca-Loyola M.L.M., Folli F.. **Effect of linagliptin on glucose metabolism and pancreatic beta cell function in patients with persistent prediabetes after metformin and lifestyle**. *Sci. Rep.* (2021) **11** 8750. DOI: 10.1038/s41598-021-88108-8
41. Cosentino F., Grant P., Aboyans V., Bailey C.J., Ceriello A., Delgado V.. **The Task Force for diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and the European Association for the Study of Diabetes (EASD)**. *Eur. Heart J.* (2020) **41** 255-323. DOI: 10.1093/eurheartj/ehz486
42. Stevens J.W., Khunti K., Harvey R., Johnson M., Preston L., Woods H.B., Davies M., Goyder E.. **Peventing the progression to type 2 diabetes mellitus in adults at high risk: A systematic review and network meta-analysis of lifestyle, pharmacological and surgical interventions**. *Diabetes Res. Clin. Pract.* (2015) **107** 320-331. DOI: 10.1016/j.diabres.2015.01.027
43. Guardado-Mendoza R., Salazar-López S.S., Álvarez-Canales M., Farfán-Vázquez D., Martínez-López Y.E., Jiménez-Ceja L.M., Suárez-Pérez E.L., Angulo-Romero F., Evia-Viscarra M.L., de Oca-Loyola M.L.M.. **The combination of linagliptin, metformin and lifestyle modification to prevent type 2 diabetes (PRELLIM). A randomized clinical trial**. *Metabolism* (2020) **104** 154054. DOI: 10.1016/j.metabol.2019.154054
44. Aroda V.R., Knowler W.C., Crandall J.P., Perreault L., Edelstein S.L., Jeffries S.L., Molitch M.E., Pi-Sunyer X., Darwin C.. **Metformin for diabetes prevention: Insights gained from the Diabetes Prevention Program/Diabetes Prevention Program Outcomes Study**. *Diabetologia* (2017) **60** 1601-1611. DOI: 10.1007/s00125-017-4361-9
45. Ramachandran A., Snehalatha C., Mary S., Mukesh B., Bhaskar A.D., Vijay V.. **The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1)**. *Diabetologia* (2006) **49** 289-297. DOI: 10.1007/s00125-005-0097-z
46. Chiasson J.-L.. **Acarbose for the Prevention of Diabetes, Hypertension, and Cardiovascular Disease in Subjects with Impaired Glucose Tolerance: The Study to Prevent Non-Insulin-Dependent Diabetes Mellitus (Stop-Niddm) Trial**. *Endocr. Pract.* (2006) **12** 25-30. DOI: 10.4158/EP.12.S1.25
47. Le Roux C.W., Astrup A., Fujioka K., Greenway F., Lau D.C.W., Van Gaal L., Ortiz R.V., Wilding J.P.H., Skjøth T.V., Manning L.S.. **3 years of liraglutide versus placebo for type2 diabetes risk reduction and weight management in individuals with prediabetes: A randomised, double-blind trial**. *Lancet* (2017) **389** 1399-1409. DOI: 10.1016/S0140-6736(17)30069-7
48. Knowler W.C., Crandall J.P.. **Pharmacologic Randomized Clinical Trials in Prevention of Type 2 Diabetes**. *Curr. Diabetes Rep.* (2019) **19** 154. DOI: 10.1007/s11892-019-1268-5
49. Griffin S.J., Bethel M.A., Holman R.R., Khunti K., Wareham N., Brierley G., Davies M., Dymond A., Eichenberger R., Evans P.. **Metformin in non-diabetic hyperglycaemia: The GLINT feasibility RCT**. *Health Technol. Assess.* (2018) **22** 18. DOI: 10.3310/hta22180
50. Haw J.S., Galaviz K.I., Straus A.N., Kowalski A.J., Magee M.J., Weber M.B., Wei J., Narayan K.M.V., Ali M.K.. **Long-term Sustainability of Diabetes Prevention Approaches: A Systematic Review and Meta-analysis of Randomized Clinical Trials**. *JAMA Intern. Med.* (2017) **177** 1808-1817. DOI: 10.1001/jamainternmed.2017.6040
51. Holman R.R., Haffner S.M., McMurray J.J., Bethel M.A., Holzhauer B., Hua T.A., Belenkov Y., Boolell M., Buse J.B.. **Effect of nateglinide on the incidence of diabetes and cardiovascular events**. *N. Engl. J. Med.* (2010) **362** 1463-1476. PMID: 20228402
52. Phung O.J., Sobieraj D., Engel S.S., Rajpathak S.N.. **Early combination therapy for the treatment of type 2 diabetes mellitus: Systematic review and meta-analysis**. *Diabetes Obes. Metab.* (2014) **16** 410-417. DOI: 10.1111/dom.12233
|
---
title: Profiling of Taxoid Compounds in Plant Cell Cultures of Different Species of
Yew (Taxus spp.)
authors:
- Dmitry V. Kochkin
- Elena V. Demidova
- Elena B. Globa
- Alexander M. Nosov
journal: Molecules
year: 2023
pmcid: PMC10004465
doi: 10.3390/molecules28052178
license: CC BY 4.0
---
# Profiling of Taxoid Compounds in Plant Cell Cultures of Different Species of Yew (Taxus spp.)
## Abstract
Plant cell cultures of various yew species are a profitable source of taxoids (taxane diterpenoids) with antitumor activity. So far, despite intensive studies, the principles of the formation of different groups of taxoids in cultured in vitro plant cells have not been fully revealed. In this study, the qualitative composition of taxoids of different structural groups was assessed in callus and suspension cell cultures of three yew species (Taxus baccata, T. canadensis, and T. wallichiana) and two T. × media hybrids. For the first time, 14-hydroxylated taxoids were isolated from the biomass of the suspension culture of T. baccata cells, and their structures were identified by high-resolution mass spectrometry and NMR spectroscopy as 7β-hydroxy-taxuyunnanin C, sinenxane C, taxuyunnanine C, 2α,5α,9α,10β,14β-pentaacetoxy-4[20], 11-taxadiene, and yunnanxane. UPLC–ESI-MS screening of taxoids was performed in more than 20 callus and suspension cell lines originating from different explants and grown in over 20 formulations of nutrient media. Regardless of the species, cell line origin, and conditions, most of the investigated cell cultures retained the ability to form taxane diterpenoids. Nonpolar 14-hydroxylated taxoids (in the form of polyesters) were predominant under in vitro culture conditions in all cell lines. These results, together with the literature data, suggest that dedifferentiated cell cultures of various yew species retain the ability to synthesize taxoids, but predominantly of the 14-OH taxoid group compared to the 13-OH taxoids found in plants.
## 1. Introduction
Diterpenoids of the taxane group (taxoids) are specific for plants of the genus Taxus (yew, Taxaceae). More than 300 individual taxoid compounds have been isolated from different yew species [1,2]. These compounds can be divided into several classes based on the structure of the taxane skeleton and the nature and/or arrangement of the functional groups [1,2]. Three to six structural classes of taxoids are usually distinguished; however, the representatives of three groups are the most common among Taxus spp.: 13-hydroxylated taxoids of the baccatin III-type (baccatin III, paclitaxel, etc.), 14-hydroxylated taxoids of the taiwanxan-type (taxuyunnanin C, yunnanxane, etc.), and 11(15→1)-abeo-taxoids [1,2,3,4]. Paclitaxel (commercial synonym Taxol®) is a pharmaceutically important chemical that is widely used in cancer therapy [1].
The mechanism of taxol action is unique as it inhibits microtubule depolymerization. Taxol penetrates into cells and disrupts cytoskeleton functions, which causes the suppression or malfunctioning of various processes in eukaryotic cells. These include inhibition of cell proliferation and intracellular mobility, alterations of membrane structure and function, disruption of intracellular transport, compartmentation, signaling, etc. [ 5]. Taxol is highly effective in treating breast, ovarian, and lung cancer, which are the world’s most common cancer types [6]. Recently, however, the use of taxol has been hampered by a major concern—the emergence and development of tumor cells’ resistance to first-generation chemotherapeutic taxane drugs (13-hydroxylated taxoids (taxol) and its semisynthetic derivatives docetaxel, cabazitaxel, etc.) [ 7]. A pharmacological study of taxoids of other structural types showed that some taxoids, despite being structurally different from taxol, exhibited cytotoxic activity comparable to that of taxol derivatives in relation to certain tumor cell lines [8]. However, it was found that these “unusual” from the perspective of classical taxane diterpenoids chemotherapy agents are effective against tumor cell lines with a multidrug resistance phenotype [8,9]. Several taxoids, including 14-hydroxylated ones, can suppress the resistance of tumor cells to cytotoxic compounds by changing the work of transporters and the plasma membrane (in particular, ABC-transporters) [10]. Taxoids can inhibit epidermal growth factor receptor tyrosine kinase [11] and act as immunomodulators to activate the antitumor properties of effector cells [12]. Consequently, not only taxol but other taxoids of different structural types can be used in cancer chemotherapy as independent drugs or components of complex treatments. In addition to antitumor action, different types of taxoids demonstrate other biological activities, including antidiabetic (by inhibiting alpha-glucosidases and insulin resistance caused by inflammation, and disrupting the lipoxygenase cascade), anti-inflammatory (by impairing migration of leukocytes and development of a granuloma in the site of inflammation), analgesic, antipyretic, anticonvulsant (probably by modulating GABAA receptors), inhibition the formation of superoxide anion radical in neutrophils (by impairing phosphorylation and intracellular transport of proteins-subunits of NADPH oxidase of the plasma membrane), antimicrobial, fungicidal, antileishmanial and several others [13,14,15,16,17,18,19,20]. Based on the information presented, it is important to perform a detailed phytochemical study of not only the baccatin III-group taxoids (paclitaxel and its derivatives), but also other taxane diterpenoids.
The specific biology of yew species, including endemism, slow growth, difficulties in reproduction, and the slow and unstable accumulation of paclitaxel and other taxoids (0.001–$0.03\%$ of the dry weight) in wild plants, significantly limit the industrial production of taxoids from natural plant sources [1,2,3,4].
Plant cell culture can serve as an alternative source of taxoids. There is a pool of publications describing cell cultures of various yew species and their ability to produce taxoids [21]. However, many authors noted either the absence or only trace amounts of taxoids in the cell cultures of Taxus spp. [ 22]. These conclusions were driven primarily by the analysis of 13-hydroxylated taxoids (mostly paclitaxel, baccatin III, and some others) in cell cultures [21,22]. Many 13-OH taxoids are polar compounds, which determines the strategy of their chemical analysis using modern HPLC/UPLC systems with reversed-phase adsorbents [23,24]. However, taxane diterpenoids are very diverse in both structure and physicochemical characteristics. Representatives of several taxoid classes, for example, 14-hydroxylated taxoids, are hydrophobic compounds with a longer elution time in reversed-phase HPLC compared to 13-OH taxoids [23,24]. As a result, these compounds may remain unrevealed during the chemical analysis of cell cultures of Taxus spp. which is usually focused primarily on 13-OH taxoids. The abovementioned considerations suggest that the formation of taxoids of various structural classes in cultured yew cells is relatively underexplored and requires further investigation.
Furthermore, from a scientific viewpoint, isolated plant cells cultured in vitro are not analogous to the cells of whole plants [21]. As a result, many fundamentally important processes, including secondary metabolism, in cell cultures are different from those in plants [21,25]. Several studies demonstrated that the profile of secondary metabolites in plant cell cultures could be altered compared to their donor plants, which is often reflected in the promotion or suppression of the production of certain metabolite groups [21,25]. Meanwhile, there are very few publications related to the composition of structurally different taxoid groups in the in vitro cell cultures of different Taxus species [26,27,28,29].
In this work, we present for the first time a detailed UPLC–ESI-MS analysis of the structural diversity of taxoids in different lines (by explant origin) of callus and suspension cell cultures of three yew species (*Taxus baccata* L., T. canadensis Marshall, and T. wallichiana Zucc.) and two T. × media Rehder hybrids (T. × media cv. Aureovariegata and T. × media cv. Dovastaniana), grown in different nutrient media. The cell lines have been cultured in vitro for over 10 years, except for the cell culture of T. wallichiana, which is 5-years-old.
## 2.1.1. Callus Cell Cultures
In this study, we used callus cell cultures of three yew species (Taxus baccata, T. canadensis, and T. wallichiana) and two T. × media hybrids: T. × media cv. Aureovariegata and T. × media cv. Dovastaniana. All callus and suspension cell cultures, except for T. wallichiana, were maintained by periodic subcultures for over 10 years and are represented by several (in some species, over 20) cell lines that originated from different explants. The T. wallichiana cell culture was maintained in an actively growing state for 5 years and is represented by a single line. The cultures were grown in more than 20 different media; the main differences in the media composition were growth regulators and the presence of antioxidants (polyvinylpyrrolidone) or adsorbents (activated charcoal). A complete list of the cell lines used and cultivation conditions is given in Table 1.
Table 1 presents an increase in the fresh weight of callus cell lines measured on the 56th day of cultivation. For the majority of cultures, the increase in cell biomass ranged from 1.5 to 5.0, which is consistent with the literature [30]. For three cell lines of T. baccata and two cell lines of T. × media, relatively high values of the growth index (7.5–9.1) were recorded, which significantly exceeded those reported in the literature [30]. No correlation was observed between fresh weight accumulation and species, explant, or medium composition (Table 1). However, cell cultures originating from the 40-year-old tree in the botanical garden of the Moscow State University (lines Tb-msu) demonstrated very slow growth. Normally, growing cell cultures of this origin could be developed when the original callus line was cultured in liquid medium to form a cell suspension, followed by placing the cells back on solid medium (the “callus-suspension-callus” cycle). Callus cultures produced through this scheme demonstrated better growth than the original callus lines. *In* general, cell cultures growing in the presence of activated charcoal showed relatively high growth indices (from 3 to over 7.5).
## 2.1.2. Suspension Cell Cultures
Suspension cell cultures developed in this study had different but generally high growth characteristics. The maximum accumulation of dry biomass Mmax for all studied cell lines was within 7–16 g/L; growth index I ranged from 4 to 10; specific growth rate μ was 0.10–0.22 day–1; economic coefficient Y was 0.1–0.3; and biomass productivity P was 0.2–0.8 g/L per day. The highest growth parameters were recorded for suspension cell lines TmA-msu/B5-NB, Tb-msu/B5-NB, Tb-msu/B5-PB, and Tb-msu/B5-DK. Representative growth curves of cell lines Tb-msu/B5-PB-pvp and Tb-msu/B5-NB are shown in Figure 1.
In order to accumulate a sufficient amount of biomass for the isolation of taxoids, suspension cell culture Tb-msu/B5-NB-ac (Taxus baccata) was cultured in a 20-L bubble-type bioreactor operated in a semi-continuous mode for four sequential growth cycles. The growth parameters of the cell culture improved gradually upon cell adaptation to bioreactor conditions (maximum biomass accumulation increased from 6.5 to 15 g/L, specific growth rate—from 0.12 to 0.20 day−1). Cell viability in all growth cycles was above $90\%$. A representative growth curve of the Tb-msu/B5-NB cell line during the third subculture cycle in the bioreactor is given in Figure 1.
The main growth parameters of the suspension cell culture in the subculture cycle shown in Figure 1 are presented in Table 2.
## 2.2.1. Structural Identification of Taxoids in Cell Cultures
The first step in the phytochemical analysis was a detailed structural identification of toxoids present in the cell cultures. A UPLC–ESI-MS analysis of the taxoid composition was performed using biomass from callus cell cultures of T. baccata line Tb-nbg/R-NB-ac, grown on B5-PB-ac medium. This cell line was maintained by periodic subcultures for more than 10 years and showed the highest increase in fresh weight. Callus was taken to the lab for analysis on day 74 of the 42nd subcultivation. The positive ion detection mode (electrospray ionization) was selected to record chromatograms and mass spectra since it allows gathering the most information on the structure of taxane diterpenoids, as well as other natural compounds, during a single run due to molecule fragmentation in the ionization source [25,31].
The UPLC–ESI-MS chromatogram of the alcohol extract from this cell culture recorded in the total ion current mode (positive ions) is presented in Figure 2. Six peaks of compounds were found and eluted from the column within 3.5–11 min. The comparison of their MS spectra with the literature data suggested that they belong to the diterpenoids of the taxane group. These compounds were numbered 1 through 6 in order of increasing hydrophobicity, that is, increasing retention time on a reversed-phase chromatographic column.
The preliminary analysis of the MS spectra of the detected compounds indicated (Table 3) that all of them belong to neutral taxoids with molecules without nitrogen-containing functional groups. This conclusion was supported by the fact that ions with odd m/z values predominated in the MS spectra of all compounds. In addition, intense signals of adduct ions [M + NH4]+ and [M + Na]+ were observed, and there were almost no signals of protonated ions [M + H]+ (Table 3) [23,24,28].
The fragmentation of compounds 1–6 in the ionization source suggested that they all belong to the so-called “regular” taxoids containing a taxa-4[20],11-diene skeleton [1,23,24,28]. Based on the number of substituents in taxa-4[20],11-diene core fragment, compounds 1–6 could be divided into two structural subclasses: [1] compounds 1, 2, and 5, derivatives containing five substituents (the presence of a pair of characteristic ions with m/z 281 and 263), and [2] compounds 3, 4, and 6 containing four substituents (the presence of a pair of characteristic ions with m/z 283 and 265) [1,23,24,28].
By their chemical nature, the substituents in the taxadiene skeleton of the identified compounds are hydroxyl groups esterified (in various combinations) with aliphatic acid residues [1,23,24,28]. The following acyl substituents were identified: acetic acid (identified in all compounds based on the presence of neutral losses of 77 (C2H4O2 + NH3), 60 (C2H4O2), and/or 42 Da (C2H2O) upon fragmentation of the [M+NH4] adduct ion in the ionization source), hydroxymethylbutanoic acid (for compound 3, neutral loss of 118 Da (C5H10O3)), methylbutanoic acid (for compounds 5 and 6, neutral loss of 119 Da (C5H10O2 + NH3)).
The described patterns of MS fragmentation (positive ion mode) suggested that taxoids 1–6 belonged to the structural group of taiwanxan (14-hydroxylated taxoids).
The 14-hydroxylated taxoids were also predominant in the extract of the cell biomass of a suspension culture of T. baccata, line Tb-msu/B5-NB, grown in a 20-L bioreactor, as confirmed by UPLC–ESI-MS (Figure 3 and Table 4). Many of these compounds were identical, in terms of relative chromatographic behavior and mass spectrometry data, to taxoids identified in T. baccata callus culture.
In order to verify the structural identification of the detected taxoids, we isolated major diterpenoids from 93 g of air-dried biomass from a suspension culture of T. baccata (Tb-msu/B5-NB) grown in a 20-L bioreactor.
Five taxoids were present in the cell biomass in amounts sufficient for preparative isolation (analytical TLC results); they were identified by specific staining (pink-lilac color) upon the development of the TLC plate with an anisaldehyde–sulfuric acid reagent [1]. The detected compounds were designed in decreasing order of polarity (relative mobility (Rf) in the ethyl acetate–hexane system (1:1, v/v)) as follows: I (Rf 0.3), II (Rf 0.5), III (Rf 0.7), IV (Rf 0.8), and V (Rf 0.9). Using conventional column chromatography and semipreparative TLC, these compounds were isolated in pure form with the following yields (% of dry cell mass): I $0.0006\%$, II $0.0030\%$, III $0.0050\%$, IV $0.0080\%$, and V $0.014\%$. The structures of the isolated glycosides were determined using high-resolution mass spectrometry and NMR spectroscopy.
The results of the interpretation of the 13C NMR spectrum of taxoid I suggest that the isolated compound has the skeleton of taxa-4[20],11-diene substituted with hydroxyl groups at positions C2, C5, C7, C10, and C14 (Supplementary Table S1). The hydroxyl groups at C2, C5, C10, and C14 are esterified with four acetic acid residues. The order of attachment of acyl fragments to the diterpene backbone was determined by interpreting the results of the 1H–13C HMBC experiment (Figure 4). The stereochemistry of substituents in the molecule of compound I was determined based on the analysis of the spin-spin coupling constants of the corresponding protons and their comparison with the published data (Supplementary Table S2) [27,28,29,32].
Thus, the interpretation of the 1H and 13C NMR spectra of the compound I and analysis of the literature data [27,28,29,32] suggest that this taxoid has the structure of 7β-hydroxy-2α,5α,10β,14β-tetraacetoxy-4[20],11-taxadiene (Figure 4) and corresponds to 7β-hydroxy-taxuyunnanine C, which was first isolated from T. cuspidata cell culture [32]. The described structure is also consistent with the results of high-resolution mass spectrometry of the isolated taxoid: the formula C28H40O9 is confirmed by the presence of a signal of the [M + Na]+ adduct ion at m/z 543.2572 in the spectra of positive ions of this compound (calculated value m/z 543.2565). 7β-Hydroxy-taxuyunnanine C can be classified as a rare and/or unusual taxoid: it is one of a few 14-hydroxylated taxoids having a hydroxyl group at the C7 position of 4[20],11-taxadiene; only two taxoids with a similar structure are currently known [1,2,3,4]. There is only one report on discovering this taxoid first isolated from T. cuspidata callus cell culture [32]. Thus, 7β-hydroxy-taxuyunnanine C was detected for the first time in T. baccata cell culture.
NMR spectroscopy and high-resolution mass spectrometry of compounds II–V were performed in a similar way and revealed that they have the structures of sinenxane C, taxuyunnanine C, 2α,5α,9α,10β,14β-pentaacetoxy-4[20],11-taxadiene, and yunnanxane, respectively (Supplementary Tables S1 and S2). These taxoids were also previously isolated from cell cultures of different yew species (T. chinensis, T. chinensis var. mairei, T. × media, T. wallichiana, and T. cuspidata) [23,24,25,26,27,28,29,33,34]. However, these 14-hydroxylated taxoids were found for the first time in T. baccata cells cultured in vitro.
Thus, the preparative isolation and structural study of individual taxoids confirm the results of their identification performed using UPLC–ESI-MS. 14-hydroxylated taxoids predominated in callus and suspension cultures of T. baccata cells.
## 2.2.2. Screening of Taxoids in the Cell Cultures of Various Yew Species, Provenance and Cultivation Conditions
In order to reveal any general pattern in the accumulation of taxoids of different structural groups among the cell cultures of Taxus spp., we performed the UPLC–ESI-MS phytochemical screening of taxoids from all available cell cultures (over 20 cell lines in total). These cell lines belonged to different yew species and were induced from different donor plants using different explants in different media. Identification of 14-OH taxoids was accomplished by comparing their retention times and mass spectra with standard samples of 14-OH taxoids isolated at the first step of this study. Sinenxane B and taxuyunnanine B were identified by comparing the results of mass spectrometry with the literature [23,24]. Commercial standard samples of Baccatin III, 10-deacetyl-7-xylosyl taxol, cephalomannine, paclitaxel, and taxusin were used to identify 13-hydroxylated taxoids.
The results of screening the biomass of callus cell cultures are presented in Table 5.
As a result, 13-hydroxylated taxoids (paclitaxel, baccatin III, etc.) were not detected in the cell samples of any callus cell cultures of Taxus spp., while 14-hydroxylated taxoids were found in the biomass of almost all studied callus lines. Only four out of the 45 samples tested did not have taxoids.
The screening results emphasized the role of the genotype (the type of plant used for culture induction) in the formation of taxoids in cell cultures. Among the studied callus cultures, T. × media cv. Dovastaniana showed the lowest number of toxoids, and their accumulation in the cell biomass was unstable: only 2 out of 4 biomass samples from this cell culture line contain 14-OH taxoids.
Similar results were obtained for suspension cultures of Taxus spp. grown in flasks (Table 6): 13-OH taxoids were absent in all samples, while 14-OH taxoids were found in most samples.
At the same time, taxoids were not detected in any of the samples of suspension cultures of T. × media cells but were present, with one exception, in all analyzed samples of cell cultures of other species of Taxus spp. This confirmed the earlier conclusion about the low capability of T. × media cell cultures to form taxoids.
The qualitative composition of 14-OH taxoids in the biomass of Taxus spp. may somewhat vary depending on growing conditions. For example, out of the 8 taxoids found in the biomass of the suspension culture of T. baccata cells (Tb-msu/B5-NB) grown in a bioreactor, only sinenxane C is stably present in the cell culture grown in flasks.
## 2.2.3. Screening of Taxoids Released to Cultivation Medium
Paclitaxel and some other 13-OH taxoids can be secreted into the apoplast and the growth medium during the in vitro growth of yew cells [35]. Therefore, we performed the UPLC–ESI-MS screening of culture media of callus and suspension cell cultures studied in this work.
As a result, 13-hydroxylated taxoids were not detected in any culture media samples (Supplementary Tables S4 and S5), while 14-OH were present in most media of the studied callus and suspension cell cultures. 14-OH taxoids were detected in 21 of 28 test samples of callus cultures and in 6 out of 8 samples of suspension cultures (Supplementary Tables S3 and S4). Taxoids were often absent in the culture media of T. × media cells: they were not detected in 4 out of 8 test samples of callus cultures and in none of the suspension cultures.
## 3. Discussion
There are a number of publications on plant cell cultures of different Taxus species. The main goal of the majority of those studies is practical: to obtain well-growing cell cultures and develop strategies for enhanced production of taxoids, primarily of commercially valuable paclitaxel [21].
Some authors reported the development of plant cell cultures accumulating taxol in amounts comparable to its content in plants; however, in most cases, taxol was absent in the cell cultures or present in trace amounts [22]. In suspension cell culture, paclitaxel was first discovered in 1989 in the cell culture of T. brevifolia [36].
Several trends could be found while analyzing the available literature sources on yew cell cultures [22,30,35]:In most publications, studies were performed on relatively "young” cell cultures, 1-2 years after induction, while the content of secondary compounds may change in cell cultures during long-term cultivation. The majority of the studies, with rare exceptions, only focused on the analysis of a few industrially valuable 13-OH-hydroxylated compounds (paclitaxel, baccatin III); other groups of taxoids were not screened. Most studies were performed using cell cultures of one yew species only, which did not allow generalizing on potential trends of taxoid formation in the cell cultures of different Taxus species.
In the present study, taxoids of various groups were screened in the plant cell cultures of different Taxus species, and long-term (at least 10 years) grown cell cultures were mostly used for analysis.
The results indicate that, regardless of the species, cell line, or cultivation conditions, most of the investigated cell cultures of Taxus spp. retain the ability to form taxane diterpenoids. However, in the in vitro cultured yew cells, the metabolism of taxoids was shifted towards the predominant formation of non-polar 14-hydroxylated derivatives (in the form of polyesters), while more hydrophilic and toxic 13-oxygenated (in particular, 13-hydroxylated) taxoids were predominant in the aerial parts of intact plants that were used as explants for culture induction [1,2,3,4,21,22,23,24]. A comparison of the obtained results with literature data [26,27,28,29,33,34] confirms that this is a general pattern and is observed in plant cell cultures of almost all yew species, in which 14-OH taxoids were analyzed.
The reasons for such a metabolic shift may lie in the unique physiology of a plant cell culture as a population of dedifferentiated proliferating cells [21]. Many processes in cells grown in vitro, including secondary metabolism, differ significantly from those in whole plants due to the absence of organismic control, a different signaling system, and altered compartmentation [21]. The formation of 14-OH taxoids in plant cell cultures may be due to their lower toxicity for proliferating cells compared to 13-OH derivatives. For example, paclitaxel and some of its homologues disrupt the functioning of the cytoskeleton, which is lethal for most eukaryotic cells [37].
The pattern of taxoid accumulation described in the present work is unique compared to cell cultures of most other plant taxa, where the secondary metabolism upon cell dedifferentiation is usually shifted towards increased formation of more polar/hydrophilic compounds [21,25]. This might be explained by comparing the results of this study with phytochemical investigations of yew plants. Taxus species tend to accumulate hydrophobic secondary metabolites [4]. For example, phenolic compounds in yews are mainly represented by biflavones, lignans, esters of catechins, etc. In Taxus spp., glycosylated secondary metabolites (except for cyanogenic glycosides, some phenolic minor derivatives, xylosides, and very rarely taxoid glucosides) are less common [4,38] than in angiosperms [39].
The results reported here have both fundamental and applied significance, since they demonstrate the importance of Taxus spp. cell cultures as renewable and environmentally friendly sources of 14-hydroxylated taxoids. Taxoids with 14-hydroxylated structures have a wide range of practical uses. For example, 14-OH taxoids synthesized by in vitro cultured yew cells decrease tumor cell resistance to cytotoxic compounds through disruption of plasma membrane ABC transporters by direct non-covalent binding to these proteins and/or modulation of MAP signaling. Therefore, 14-OH taxoids can be used as components of complex cancer chemotherapy programs [10,40]. Furthermore, 14-OH taxoids isolated from yew cell cultures can be used as intermediate compounds for chemical modifications and biotransformation to generate new taxane drugs [41,42]. 14-OH taxoids can also be used to treat other diseases besides cancer. It has been demonstrated that many natural 14-OH taxoids act as effective alpha-glucosidase inhibitors and are useful in the treatment of diabetes [17]. The 14-OH taxoids can act as nerve growth factor (NGF) mimetics, making them useful in the prevention of side effects associated with classical cytostatics (taxol, cisplatin, and vincristine) and Alzheimer’s disease [4,42]. Accordingly, yew cell cultures can be used as sources of natural compounds important to human physiology and medicine. This study adds to the literature’s already established fact that yew cell cultures in vitro are an excellent source of raw material for isolating unusual and rare (not typical for intact yew plants) taxoids with unique biological properties [8].
## 4.1.1. Callus Cell Cultures
Callus cell cultures of three yew species (Taxus baccata, T. canadensis, and T. wallichiana) and two T. × media hybrids (T. × media cv. Aureovariegata and T. × media cv. Dovastaniana) were used in the study. Callus cultures of T. baccata cells were obtained in 2007–2009 from two plants: a 40-year-old tree from the Botanical Garden of the Moscow State University, Moscow (MSU Botanical Garden; Tb-msu line) and an 800-year-old tree from the Nikitsky Botanical Garden, Crimea (Tb-800 line). Callus cultures of T. canadensis were obtained in 2008 from a 40-year-old plant from the Botanical Garden of Moscow State University (lines Tc-msu); callus cultures of T. × media were developed in 2007–2008 from 30-year-old plants from the Botanical Garden of Moscow State University (lines TmD-msu (cv. Dovastaniana) and TmA-msu (cv. Aureovariegata)). A callus culture of T. wallichiana was induced in 2016 from a 50-year-old tree from the Central Botanical Garden of the National Academy of Sciences of Belarus, Minsk (NASB Botanical Garden; line TW-Bel). Stem segments (a small section of the stem with 1–3 leaves) were used as explants. Each line was obtained from a single explant on a specific medium. For each line, the induction medium is designated as “Im” (initial medium). The conditions for culture induction were described earlier [43].
The developed cell lines were grown on media of different compositions, as described below.
Three mineral salt formulations were used: Gamborg’s medium (B5), Reinart’s medium (R), and White’s medium (W). All media contained vitamins as described by Gamborg (nicotinic acid 0.5 mg/L, pyridoxine 0.1 mg/L, thiamine chloride 0.1 mg/L, Serva, St. Louis, Missouri, USA), $3\%$ sucrose (Merck, Germany), and $0.55\%$ agar (Merck, Germany).
The following combinations of growth regulators were used: NB—1-NAA (2 mg/L) and BAP (0.3 mg/L); PB—picloram (1 mg/L) and BAP (0.3 mg/L); DK—2,4-D (1 mg/L) and kinetin (0.3 mg/L); all growth regulators were purchased from Serva. The media also differed by the presence of polyvinylpyrrolidone (PanReac AppliChem, molecular weight 4000, 1.0 g/L) or activated charcoal (Fluka, 500 mg/L). These media are marked “PVP” and “ac,” respectively. Some callus cultures were obtained from the corresponding suspension cell cultures according to the scheme “callus → suspension → callus.” A complete list of cell lines used and their media are given in Table 1.
Calli was grown in the dark at 26 °C. The fresh biomass gain (the callus-to-transplant weight ratio) of callus cell cultures was determined on the 56th day of growth, as described earlier [43].
## 4.1.2. Suspension Cell Cultures
Suspension cell cultures were obtained from the corresponding callus cultures using a standard procedure [43]. Suspension cultures of T. × media, T. canadensis, and T. baccata cells were obtained in 2009–2010, suspension cell culture of T. wallichiana was developed in 2016 [44].
Suspension cell cultures were grown in media containing Gamborg (B5) mineral salts. The medium composition for each suspension cell line was similar to the corresponding callus line except for the absence of agar. The suspension cell cultures were grown in the dark using an orbital shaker (100 ± 10 rpm) at 26 ± 1 ° C and relative humidity of 70 ± $5\%$. Flasks of 250, 500, and 1000 mL volume filled with, respectively, 40, 80, and 160 mL of culture medium were used for the cultivation of cell suspensions. Cultures were transferred to the fresh medium (subcultured) on the 28th day of cultivation; the inoculum-to-fresh medium ratio was 1:4. To characterize the growth and physiological state of the cell cultures, the dry and fresh weights of the cells and their viability were determined as described earlier [43,44].
The growth index (I), specific growth rate (μ), biomass doubling time (τ), economic coefficient (Y), and biomass productivity (P) were calculated using the following equations [45,46]:I= Xmax/X0, where Xmax and X0 are the maximum and initial values of the growth criterion (dry or fresh weight of cells), respectively; µ (day–1) = (ln X2 − ln X1)/(t2 − t1), where X2 and X1 are the values of the growth criterion (dry or fresh weight of cells) at time points t2 and t1, respectively (calculated for the exponential growth phase); τ (day) = ln 2/µ; Y= (Xmax − X0)/S0, where Xmax and X0 are the maximum and initial concentrations of dry cell biomass (g/L), respectively, and S0 is the initial concentration of the substrate (sucrose) in the medium (g/L of the medium); P(g/L day) = (Xi − X0)/(ti − t0), where X0 and Xi are the amounts of dry biomass at the beginning of cultivation and at time ti, respectively.
In addition, a suspension culture of T. baccata cells (Tb-msu/B5-NB) was grown in a 20-L bubble-type conical bioreactor designed at the Department of Cell Biology and Biotechnology, Timiryazev Institute of Plant Physiology, Russian Academy of Sciences, with a total volume of 20 L and a working volume of 15 L [47]. A cell culture grown in flasks was used as an inoculum; the density of the inoculum was 2 g/L by dry weight. Depending on the growth cycle phase, the air was supplied at a rate of 0.1–1.0 L/L/min. The concentration of dissolved oxygen pO2 was maintained at 10–$40\%$ of saturation in the absence of intense foaming.
## 4.2.1. Sample Preparation for Taxoids Screening
A sample (25 mg) of powdered air-dry biomass or dried culture medium was extracted three times with 1 mL of $96\%$ ethanol for 30 min in an ultrasonic bath (Sapfir, Moscow, Russia), then centrifuged at 130,000 rpm for 10 min; the supernatant was collected into a pear-shaped flask. The combined alcohol extracts were evaporated under vacuum by heating to 50 °C in a water bath. The resulting dry extract was dissolved in 1 mL of distilled water and applied to a Supelclean ENVI-18 solid-phase extraction cartridge (Supelco, St. Louis, Missouri, USA). The cartridge was washed with 3 mL of water, and the analytes were desorbed with 3 mL of ethanol. The resulting solution was evaporated in a vacuum at 50 °C. Before analysis, the extracts were dissolved in an acetonitrile–water mixture (1:1, by volume). When analyzing the culture media of suspension cell cultures, 10 mL of the medium was applied to a cartridge for solid-phase extraction. Further preparation of samples was carried out according to the procedure described above. For most cell cultures, the analysis was carried out for two to four different subcultures (in the tables, the results for different subcultures are presented separately).
## 4.2.2. UPLC–ESI-MS Analysis of Taxoids
The analysis was performed using a Waters Acquity UPLC system (Waters, Milford, MA, USA) equipped with a Xevo QTof hybrid quadrupole time-of-flight mass spectrometer (Waters, Milford, MA, USA). A sample (1 μL) was injected in an ACQUITY UPLC BEH Phenyl column (50 × 2.1 mm, 1.7 μm; Waters, Drinagh, County Wexford, Ireland). The column temperature was 40 °C, and the flow rate of the mobile phase was 0.4 mL/min. A $0.1\%$ (by volume) solution of formic acid in water (solvent A) and a $0.1\%$ (by volume) solution of formic acid in acetonitrile (solvent B) were used as the mobile phase.
Chromatographic separation was performed in the gradient elution mode. The gradient elution was performed by the following program (B, % by volume): 0–1 min, $35\%$; 1–7 min, 35 → $45\%$; 7–17 min, $45\%$; 17–17.5 min, 45 → $95\%$; 17.5–19 min, $95\%$; 19–19.5 min, 95 → $35\%$.
The analysis was carried out in the positive-ion mode in the m/z range of 100–1200. Ionization source parameters were as follows: ionization source temperature 120 °C; desolvation temperature 250 °C; capillary voltage 3.0 kV; sample injection cone voltage 30 V; nitrogen (desolvation gas) flow rate 600 L/h.
Commercial standard samples of baccatin III, cephalomannine, paclitaxel (Sigma-Aldrich, St. Louis, MO, USA), 10-deacetyl-7-xylosyl taxol, and taxusin (ChromaDex, Irvine, CA, USA) were used to identify 13-OH taxoids.
## 4.2.3. Preparative Isolation of Taxoids from T. baccata Cell Culture
Preparative isolation of diterpenoids was performed using 93 g of air-dry biomass from a suspension culture of T. baccata cells (line Tb-msu/B5-NB) grown in a 20-L bubble-type bioreactor. Diterpenoids were separated using a combination of classical column chromatography with silica gel (Silicagel 60, grade 7734, 70-230 mesh, Sigma-Aldrich, St. Louis, MO, USA) and semi-preparative TLC (Uniplate Silica gel GF, Analtech, Newark, DE, USA) according to published procedures [28,29].
## 4.2.4. High-Resolution Mass Spectrometry
High-resolution mass spectra with electrospray ionization were recorded using a Bruker micrOTOF II instrument as described earlier [48].
## 4.2.5. NMR Spectroscopy
The 1H and 13C NMR spectra of the isolated compounds were measured in chloroform-d using a Bruker Avance AV600 instrument (Germany); tetramethylsilane was used as the internal standard. Signals in the 1H and 13C NMR spectra were assigned using two-dimensional NMR experiments (1H–1H COSY, TOCSY, 1H–13C HSQC, and HMBC).
## References
1. Wang Y.-F., Shi Q.-W., Dong M., Kiyota H., Gu Y.-C., Cong B.. **Natural Taxanes: Developments Since 1828**. *Chem. Rev.* (2011) **111** 7652-7709. DOI: 10.1021/cr100147u
2. Lange B.M., Conner C.F.. **Taxanes and taxoids of the genus**. *Phytochemistry* (2021) **190** 112829. DOI: 10.1016/j.phytochem.2021.112829
3. Baloglu E., Kingston D.G.I.. **The Taxane Diterpenoids**. *J. Nat. Prod.* (1999) **62** 1448-1472. DOI: 10.1021/np990176i
4. Appendino G.. **The phytochemistry of the yew tree**. *Nat. Prod. Rep.* (1995) **12** 349-360. DOI: 10.1039/np9951200349
5. Mikuła-Pietrasik J., Witucka A., Pakuła M., Uruski P., Begier-Krasińska B., Niklas A., Tykarski A., Książek K.. **Comprehensive review on how platinum- and taxane-based chemotherapy of ovarian cancer affects biology of normal cells**. *Mol. Life Sci.* (2019) **76** 681-697. DOI: 10.1007/s00018-018-2954-1
6. Amaya C., Luo S., Baigorri J., Baucells R., Smith E.R., Xu X.-X.. **Exposure to low intensity ultrasound removes paclitaxel cytotoxicity in breast and ovarian cancer cells**. *BMC Cancer* (2021) **21**. DOI: 10.1186/s12885-021-08722-7
7. Maloney S.M., Hoover C.A., Morejon-Lasso L.V., Prosperi J.R.. **Mechanisms of Taxane Resistance**. *Cancers* (2020) **12**. DOI: 10.3390/cancers12113323
8. Galletti E., Magnani M., Renzulli M.L., Botta M.. **Paclitaxel and docetaxel resistance: Molecular mechanisms and development of new generation taxanes**. *ChemMedChem* (2007) **2** 920-942. DOI: 10.1002/cmdc.200600308
9. Kobayashi J., Shigemori H., Hosoyama H., Chen Z., Akiyama S., Naito M., Tsuruo T.. **Multidrug resistance reversal activity of taxoids from Taxus cuspidata in KB-C2 and 2780AD cells**. *Jpn. J. Cancer Res.* (2000) **91** 638-642. DOI: 10.1111/j.1349-7006.2000.tb00993.x
10. Hasegawa T., Bai J., Dai J., Bai L., Sakai J., Nishizawa S., Bai Y., Kikuchi M., Abe M., Yamori T.. **Synthesis and structure-activity relationships of taxuyunnanine C derivatives as multidrug resistance modulator in MDR cancer cells**. *Bioorg. Med. Chem. Lett.* (2007) **17** 3722-3728. DOI: 10.1016/j.bmcl.2007.04.030
11. Qayum M., Nisar M., Rauf A., Khan I., Kaleem W.A., Raza M., Karim N., Saleem M.A., Bawazeer S., Uysal S.. **In-vitro and in-silico anticancer potential of taxoids from Taxus wallichiana Zucc**. *Biol. Futur.* (2019) **70** 295-300. DOI: 10.1556/019.70.2019.33
12. Chattopadhyay S.K., Pal A., Maulik P.R., Kaur T., Garg A., Khanuja S.P.. **Taxoid from the needles of the Himalayan yew Taxus wallichiana with cytotoxic and immunomodulatory activities**. *Bioorg. Med. Chem. Lett.* (2006) **16** 2446-2449. DOI: 10.1016/j.bmcl.2006.01.077
13. Nisar M., Khan I., Simjee S.U., Gilani A.H., Obaidullah H.. **Anticonvulsant, analgesic and antipyretic activities of Taxus wallichiana Zucc**. *J. Ethnopharmacol.* (2008) **116** 490-494. DOI: 10.1016/j.jep.2007.12.021
14. Tong J., Lu J., Zhang N., Chi H., Yamashita K., Manabe M., Kodama H.. **Effect of seven tricyclic diterpenoids from needles of Taxus media var. Hicksii on stimulus-induced superoxide generation, tyrosyl or serine/threonine phosphorylation and translocation of cytosolic compounds to the cell membrane in human neutrophils**. *Planta Med.* (2009) **75** 814-822. DOI: 10.1055/s-0029-1185440
15. Polonio T., Efferth T.. **Leishmaniasis: Drug resistance and natural products (review),**. *J. Mol. Med.* (2008) **22** 277-286. DOI: 10.3892/ijmm_00000020
16. Binwal M., Babu V., Israr K.M.M., Kashyap P.K., Maurya A.K., Padalia R.C., Tandon S., Bawankule D.U.. **Taxoids-rich extract from Taxus wallichiana alleviates high-fat diet-induced insulin resistance in C57BL/6 mice through inhibition of low-grade inflammation**. *Inflammopharmacology* (2023) **3** 451-464. DOI: 10.1007/s10787-022-01119-3
17. Dang P.H., Nguyen H.X., Duong T.T.T., Tran T.K.T., Nguyen P.T., Vu T.K.T., Vuong H.C., Phan N.H.T., Nguyen M.T.T., Nguyen N.T.. **α-Glucosidase Inhibitory and Cytotoxic Taxane Diterpenoids from the Stem Bark of Taxus wallichiana**. *J. Nat. Prod.* (2017) **80** 1087-1095. DOI: 10.1021/acs.jnatprod.7b00006
18. Qayum M., Nisar M., Shah M.R., Adhikari A., Kaleem W.A., Khan I., Khan N., Gul F., Khan I.A., Zia-Ul-Haq M.. **Analgesic and antiinflammatory activities of taxoids from Taxus wallichiana Zucc**. *Phytother. Res.* (2012) **26** 552-556. DOI: 10.1002/ptr.3574
19. Sharma H., Garg M.. **A review of traditional use, phytoconstituents and biological activities of Himalayan yew, Taxus wallichiana**. *J. Integr. Med.* (2015) **13** 80-90. DOI: 10.1016/S2095-4964(15)60161-3
20. Küpeli E., Erdemoğlu N., Yeşilada E., Sener B.. **Anti-inflammatory and antinociceptive activity of taxoids and lignans from the heartwood of**. *J. Ethnopharmacol.* (2003) **89** 265-270. DOI: 10.1016/j.jep.2003.09.005
21. Nosov A.M., Popova E.V., Kochkin D.V., Paek K.-Y., Murthy H.N., Zhong J.J.. **Isoprenoid Production via Plant Cell Cultures: Biosynthesis, Accumulation and Scaling-Up to Bioreactors**. *Production of Biomass and Bioactive Compounds Using Bioreactor Technology* (2014) 563-623. DOI: 10.1007/978-94-017-9223-3_23
22. Fu C., Li L., Wu W., Li M., Yu X., Yu L.. **Assessment of genetic and epigenetic variation during long-term**. *Plant Cell Rep.* (2012) **3** 1321-1331. DOI: 10.1007/s00299-012-1251-y
23. Madhusudanan K.P., Chattopadhyay S.K., Tripathi V.K., Sashidhara K.V., Kukreja A.K., Jain S.P.. **LC-ESI-MS analysis of taxoids from the bark of**. *Biomed. Chromatogr.* (2002) **16** 343-355. DOI: 10.1002/bmc.163
24. Madhusudanan K.P., Chattopadhyay S.K., Tripathi V., Sashidhara K.V., Kumar S.. **MS/MS profiling of taxoids from the needles of Taxus wallichiana**. *Phytochem. Anal.* (2002) **13** 18-30. DOI: 10.1002/pca.610
25. Kochkin D.V., Galishev B.A., Titova M.V., Popova E.V., Nosov A.M.. **Chromato-Mass-Spectrometric Identification of Glycosides of Phenylethylamides of Hydroxycinnamic Acids in a Suspension Cell Culture of**. *Rus. J. Plant Physiol.* (2021) **68** 973-980. DOI: 10.1134/S1021443721050083
26. Bai J., Kitabatake M., Toyoizumi K., Fu L., Zhang S., Dai J., Sakai J., Hirose K., Yamori T., Tomida A.. **Production of Biologically Active Taxoids by a Callus Culture of**. *J. Nat. Prod.* (2004) **67** 58-63. DOI: 10.1021/np0301083
27. Bai J., Ito N., Sakai J., Kitabatake M., Fujisawa H., Bai L., Dai J., Zhang S., Hirose K., Tomida A.. **Taxoids and Abietanes from Callus Cultures of**. *J. Nat. Prod.* (2005) **68** 497-501. DOI: 10.1021/np040219h
28. Zhao C.F., Yu L.J., Li L.Q., Xiang F.. **Simultaneous identification and determination of major taxoids from extracts of**. *Z. Naturforsch. C J. Biosci.* (2007) **62** 1-10. DOI: 10.1515/znc-2007-1-201
29. Ma W., Stahlhut R.W., Adams T.L., Park G.L., Evans W.A., Blumenthal S.G., Gomez G.A., Nieder M.H., Hylands P.J.. **Yunnanxane and its homologous esters from cell cultures of**. *J. Nat. Prod.* (1994) **57** 1320-1324. DOI: 10.1021/np50111a027
30. Furmanowa M., Glowniak K., Syklowska-Baranek K., Zgórka G., Józefczyk A.. **Effect of picloram and methyl jasmonate on growth and taxane accumulation in callus culture of**. *Plant Cell Tiss. Organ Cult.* (1997) **49** 75-79. DOI: 10.1023/A:1005858329430
31. Kochkin D.V., Galishev B.A., Glagoleva E.S., Titova M.V., Nosov A.M.. **Rare triterpene glycoside of ginseng (ginsenoside malonyl-Rg1) detected in plant cell suspension culture of**. *Rus. J. Plant Physiol.* (2017) **64** 649-656. DOI: 10.1134/S102144371705003X
32. Fedoreyev S.A., Vasilevskaya N.A., Veselova M.V., Denisenko V.A., Dmitrenok P.S., Ozhigova I.T., Muzarok T.I., Zhuravlev Y.N.. **A new C-14 oxygenated taxane from**. *Fitoterapia* (1998) **69** 430-432
33. Li F.L., Ma X.J., Hu X.L., Hoffman A., Dai J.G., Qiu D.. **Antisense-induced suppression of taxoid 14b-hydroxylase gene expression in transgenic**. *Afr. J. Biotechnol.* (2011) **10** 8720-8728. DOI: 10.5897/AJB11.319
34. Agrawal S., Banerjee S., Chattopadhyay S.K., Kulshrestha M., Madhusudanan K.P., Mehta V.K., Kumar S.. **Isolation of taxoids from cell suspension cultures of**. *Planta Med.* (2000) **66** 773-775. DOI: 10.1055/s-2000-9782
35. Fornalè S., Esposti D.D., Navia-Osorio A., Cusidò R.M., Palazòn J., Teresa Piñol M., Bagni N.. **Taxol transport in**. *Plant Physiol. Biochem.* (2002) **40** 81-88. DOI: 10.1016/S0981-9428(01)01332-8
36. Christen A.A., Bland J., Gibson D.M.. **Cell cultures as a means to produce taxol**. *Proc. Am. Assoc. Cancer Res.* (1989) **30** 566
37. Kim B.J., Gibson D.M., Shuler M.L.. **Relationship of viability and apoptosis to taxol production in Taxus sp. suspension cultures elicited with methyl jasmonate**. *Biotechnol. Prog.* (2005) **21** 700-707. DOI: 10.1021/bp050016z
38. Gai Q.-Y., Jiao J., Wang X., Liu J., Fu Y.-J., Lu Y., Wang Z.-Y., Xu X.-J.. **Simultaneous determination of taxoids and flavonoids in twigs and leaves of three Taxus species by UHPLC-MS/MS**. *J. Pharm. Biomed. Anal.* (2020) **189** 113456. DOI: 10.1016/j.jpba.2020.113456
39. Wilson A.E., Tian L.. **Phylogenomic analysis of UDP-dependent glycosyltransferases provides insights into the evolutionary landscape of glycosylation in plant metabolism**. *Plant J.* (2019) **100** 1273-1288. DOI: 10.1111/tpj.14514
40. Mei M., Xie D., Zhang Y., Jin J., You F., Li Y., Dai J., Chen X.. **A New 2α,5α,10β,14β-tetraacetoxy-4(20),11-taxadiene (SIA) Derivative Overcomes Paclitaxel Resistance by Inhibiting MAPK Signaling and Increasing Paclitaxel Accumulation in Breast Cancer Cells**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0104317
41. Liu X., Chen R., Xie D., Mei M., Zou J., Chen X., Dai J.. **Microbial transformations of taxadienes and the multi-drug resistant tumor reversal activities of the metabolites**. *Tetrahedron* (2012) **68** 9539-9549. DOI: 10.1016/j.tet.2012.09.091
42. Hayakawa K., Itoh T., Niwa H., Mutoh T., Sobue G.. **NGF prevention of neurotoxicity induced by cisplatin, vincristine and taxol depends on toxicity of each drug and NGF treatment schedule: In vitro study of adult rat sympathetic ganglion explants**. *Brain Res.* (1998) **794** 313-319. DOI: 10.1016/S0006-8993(98)00305-9
43. Globa E.B., Demidova E.V., Turkin V.V., Makarova S.S., Nosov A.M.. **Callus and suspension cell culture induction of four yew species:**. *Biotechnology* (2009) **3** 54-59
44. Globa E.B., Demidova E.V., Gaysinskiy V.V., Kochkin D.V.. **Obtainment and characterization of callus and suspension cell culture of**. *Vestn. Severo-Vostochn. Fed. Univ. M.K. Ammosova.* (2018) **2** 18-25. DOI: 10.25587/SVFU.2018.64.12127
45. Nosov A.M., Kuznetsov V.V.. **Methods of evaluation and characterization of growth of cell cultures of higher plants**. *Molecular-Genetic and Biochemical Methods in Modern Plant Biology* (2011) 386-403
46. Demidova E.V., Reshetnyak O.V., Oreshnikov A.V., Nosov A.M.. **Growth and biosynthetic characteristics of ginseng (**. *Rus. J. Plant Physiol.* (2006) **53** 134-140. DOI: 10.1134/S1021443706010171
47. Povydysh M.N., Titova M.V., Ivanov I.M., Klushin A.G., Kochkin D.V., Galishev B.A., Popova E.V., Ivkin D.Y., Luzhanin V.G., Krasnova M.V.. **Effect of Phytopreparations Based on Bioreactor-Grown Cell Biomass of**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13113811
48. Belyakov P.A., Kadentsev V.I., Chizhov A.O., Kolotyrkina N.y.G., Shashkov A.S., Ananikov V.P.. **Mechanistic insight into organic and catalytic reactions by joint studies using mass spectrometry and NMR spectroscopy**. *Mend. Commun.* (2010) **20** 125-131. DOI: 10.1016/j.mencom.2010.05.001
|
---
title: 'Factors associated with poor glycemic control among adult patients with type
2 diabetes mellitus in Gamo and Gofa zone public hospitals, Southern Ethiopia: A
case-control study'
authors:
- Firehiwot Dawite
- Meseret Girma
- Tamiru Shibiru
- Etenesh Kefelew
- Tadiwos Hailu
- Rodas Temesgen
- Getachew Abebe
journal: PLOS ONE
year: 2023
pmcid: PMC10004482
doi: 10.1371/journal.pone.0276678
license: CC BY 4.0
---
# Factors associated with poor glycemic control among adult patients with type 2 diabetes mellitus in Gamo and Gofa zone public hospitals, Southern Ethiopia: A case-control study
## Abstract
### Background
Diabetes mellitus is a serious global public health problem that affects the whole life of people in terms of their biological, psychological, and social effects. Complications and death from diabetes occur from poorly controlled blood glucose levels. Thus, dealing with glycemic control is essential for controlling the development of devastating acute and chronic complications related to diabetes. Therefore, this study aims to assess factors associated with poor glycemic control among type2 diabetes patients in public hospitals of Gamo and Gofa zone southern, Ethiopia, 2021.
### Methods
An institution-based unmatched case-control study was employed among 312 randomly selected participants using a pre-tested, interviewer-administered, and structured questionnaire. Bivariate and multivariable logistic regression analysis was conducted to identify factors associated with poor glycemic control using IBM SPSS version 25. The strength of association was assessed by using an Adjusted odds ratio (AOR) with a $95\%$ confidence interval (CI).
### Result
Factors associated with poor glycemic control based on multivariable analysis were, having comorbidity (AOR = 2.35, $95\%$ CI (1.39–3.95)), adhering to dietary recommendations (AOR = 0.31, $95\%$ CI (089–0.51)), poor social support (AOR = 3.31, $95\%$ CI (1.59–6.85)), physical exercise (AOR = 1.86 $95\%$ CI (1.11–3.12)), and having poly-pharmacy (AOR = 2.83, $95\%$ CI (1.39–5.74)).
### Conclusion and recommendation
This study indicated a significant association of comorbidity, physical exercise, poly-pharmacy, low social support, and adherence to dietary recommendations with poor glycemic control. We suggest that the health care providers and concerned bodies encourage patients to have regular check-ups and work on providing necessary social support.
## Introduction
Non-communicable diseases (NCDs), such as cardiovascular diseases, cancers, diabetes, and chronic respiratory diseases, are now the leading cause of death in most regions of the world [1]. Diabetes is a serious, chronic disease that occurs either when the pancreas does not produce enough insulin (a hormone that regulates blood glucose), or when the body cannot effectively use the insulin it produces [2]. Type 2 diabetes mellitus (T2DM), previously referred to as “noninsulin-dependent diabetes” or “adult-onset diabetes,” accounts for 90–$95\%$ of all diabetes. This form encompasses individuals who have relative insulin deficiency and peripheral insulin resistance [3]. The global diabetes prevalence in 20–79-year-olds in 2021 was estimated to be $10.5\%$ (536.6 million people), rising to $12.2\%$ (783.2 million) in 2045 [4]. The burden of diabetes mellitus (DM) is higher in low and middle-income countries like Ethiopia where, the total unmet need for diabetes care is around $77.0\%$, and rural facilities, particularly in SSA lack access to proper monitoring of glucose like hemoglobin A1c and other key biologic tests [5, 6]. About half ($46.2\%$) of the deaths attributable to diabetes occur in people under the age of 60 years [7]. The Africa Region has the highest ($73.1\%$) proportion of deaths attributable to diabetes in people under the age of 60 years [7]. Furthermore, diabetes carries a significant “double burden” of infectious and chronic diseases in Africa [8]. Diabetes is not fatal if managed effectively, but untreated hyperglycemia results in various multi-organ complications that cause acute and chronic morbidity and death [9]. Poor glycemic control is associated with reduced life expectancy, significant morbidity due to specific diabetes-related microvascular complications, increased risk of macrovascular complications, and diminished quality of life [10].
Despite the availability of a wide range of effective glucose-lowering therapies, approximately half of patients with T2DM in the world do not achieve glycemic targets [11, 12]. A multicenter study conducted in Eastern Europe, Asia, and Latin America showed that $96.4\%$ of study participants had poor glycemic control [13]. similarly, Ethiopia had a consistent and high prevalence of poor glycemic control among diabetic patients ranging from 62.5 in Tigray to 65.5 in the Oromo region [14].
In *Ethiopia a* cross-sectional study has been conducted on factors associated with poor glycemic control Self-monitoring blood glucose, presence of comorbidities, duration of diabetes mellitus, physical activity, total cholesterol of 200 mg/dl or more, waist-to-hip ratio or and types of anti-diabetic medication were identified as factors significantly associated with of poor glycemic control [15–18].
Diabetes management aims to prevent mortality and complications by optimizing the blood glucose level [19]. Clinical trials have shown that tight blood glucose control correlates with a reduction in those complications in a patient with T2DM [20, 21]. Each $1\%$ reduction in the mean Glycated hemoglobin (HbA1c) is associated with a reduction in risk of $21\%$ for deaths related to diabetes, $14\%$ for myocardial infarction, and $37\%$ for microvascular complications [22]. Even if several studies were conducted on factors associated with poor glycemic control in Ethiopia most of them are in the Amhara and Oromia regions which may not represent the southern region of the country very well, so, therefore, there is limited evidence on determinants of poor glycemic control in the southern region of the country. Additionally, the previous studies did not assess the influence of social support and poly-pharmacy on poor glycemic control very well. As well as this study is new in the research area. So then this study aimed to identify factors associated with poor glycemic control among type 2 diabetes mellitus patients in public hospitals of Gamo and Gofa zone, southern, Ethiopia; besides the association of perceived social support with glycemic control was also investigated.
## Study design, setting, and period
This study was conducted in selected three Hospitals in Gamo and Gofa Zones, Southern Ethiopia from March 18 to May 18, 2021. These two Zones are found within the South Nations, Nationalities, and Peoples’ Region (SNNPR) of Ethiopia The administrative center of the two zones, Arba Minch (Gamo zone) and Sawla (Gofa zone) are located at 434 km and 455 km respectively far south of Addis Ababa, the capital city of Ethiopia. The total population of the study area is 2,658,345 in the year $\frac{2017}{18}$ as estimated from the 2007 Ethiopian census. There are two general and four primary hospitals providing curative, preventive, and rehabilitative services for the population in the two zones. The hospitals have a total of 2115 diabetic patients who are under follow up and from those 1091 T2DM have a follow-up in the three study hospitals.
## Population
Cases were T2DM patients in the follow-up clinic that were classified as having poor glycemic control by using an average of three consecutive Fasting Plasma Glucose (FPG) levels and who had an average FPG >130mg/dl. Controls were T2DM in the follow-up clinic that were classified as having good glycemic control by using three consecutive average FPG levels and who had FPG 80–130 mg/dl.
The source population; is for cases of all patients in the follow-up clinic with poor glycemic control. To control all T2DM in the follow-up clinic with good glycemic control.
The study population; for cases all T2DM with poor glycemic control came to the follow-up clinic during data collection time. To control all T2DM patients with good glycemic control who came to the follow-up clinic during data collection time.
## Sampling
The sample size was calculated by using Epi info7 software stat calc. The following assumptions were considered $95\%$ confidence interval, $80\%$ power, a $50\%$ expected proportion of DM patients with adequate physical exercise from a study in northwest 2017 Ethiopia who had good glycemic control (control), and a $67.1\%$ expected proportion of DM patients with inadequate physical exercise who had poor glycemic control (cases), and a case to control the ratio of 1:1. Based on the above assumptions, the sample size calculated was 312(156 cases and 156 controls). Among the variables considered, physical exercise was selected as an associated factor variable for poor glycemic control since it gave a maximum sample size. Both cases and controls were selected by employing a systematic random sampling technique with a proportional allocation of samples [26, 27, 29, 40, 67].
## Data collection procedures and instruments
Structured interviewer-administered questionnaires were used after reviewing all the relevant literature, and recorded review and physical measurements were done. The questionnaire had four parts: socio-demographic information, clinical and anthropometric measure data related to behavioral factors, and a perceived social support scale.
The Summary of diabetic self-care activity (SDCA) was used to measure behavioral factors such as adherence to diabetes-related exercise and self-monitoring of blood glucose levels. It was used in previous studies in evaluating adherence to diabetes medication and diabetes diet among DM patients [23, 24]. Moreover, 10 and 8 items Modified Morisky Scale (MMS) were used to measure other behavioral factors such as adherence to medication and diet [23, 24].
Measurement. A multidimensional scale of perceived social support (MSPSS) was used to measure perceived social support MSPSS. The scale is composed of 12 items in three groups, each of which is composed of four items regarding family (Items 3, 4, 8, and 11), friend (Items 6, 7, 9, and 12), and a special person (Items 1, 2, 5, and 10) [25]. Each item was graded using a 7-point scale. The subscale score is obtained by adding the scores of four items in each scale, and the total scale score is obtained by adding all the subscale scores. The lowest score from the subscales is 4, and the highest score is 28. Based on the mean score it was categorized as good social support if it is above the mean else poor social support [26].
Other DM-related variables that might influence values of glycemic control were taken from medical history records and these are duration with DM, the presence of comorbidity type of medication currently taken, and FPG level.
## Anthropometric measurements
Bodyweight and height were measured by a stadiometer (Seca Germany) a portable weight scale machine. Body weight was measured to an accuracy of 0.1 kg by using Seca Germany and Subjects were measured barefoot. Height was measured in, standing upright by a stadiometer Seca Germany to the nearest 0.1c.m. Body mass index (BMI) was calculated as the ratio of weight in kilograms (kg) to the square of height in meters (m2).
## Waist-to-hip ratio
Waist-to-hip ratio (WHR) was measured after the participants stood with arms at the sides, feet positioned close together, and weight evenly distributed across the feet, the waist circumference (WC) was measured to the nearest 1 cm three times at the approximate midpoint between the lower margin of the last palpable rib and the top of the iliac crest, at the end of a normal exhalation. The mean of the three measurements was calculated and taken at the end. Participants were told to relax and take a few deep, natural breaths before the actual measurement was done to minimize the inward pull of the abdominal contents during the waist measurement. The hip circumference (HC) of the patients was measured three times to the nearest centimeter at the largest circumference of the buttocks. Both hip and waist circumferences were measured with stretch-resistant tape that is wrapped snugly around the participants and the tape was kept level and parallel to the floor at the point of measurement. This protocol of measurement is per the world health organization’s (WHO) Stepwise approach to surveillance [27]. Six trained BSc nurses working in different areas of the study sites were recruited as data collectors and two of them were employed as supervisors for consecutive two months.
## Operational definitions
10.1371/journal.pone.0276678.t001 Glycemic control Patients were categorized based on the American Diabetic Association (ADA) 2019 guideline recommendation into two groups:[15, 28] after taking the average of three consecutive visits of FPG tests. Good glycemic control: average fasting plasma glucose of 80–130 mg/dl. Poor glycemic control: average fasting plasma glucose of > 130 mg/dl. Adherence to exercise Patients were considered to adhere to exercise if the mean scored ≥ 4 days by using SDCA physical exercise adherence questionnaire [29]. Adherence to a diabetic diet A patient was considered to adhere to a dietary regimen when he/she scored at least $50\%$ of the total 10-item MMS dietary-related question [30, 31]. Adherence to diabetic medication A patient was considered to adhere to a medication when he/she scores at least $80\%$ of the total 8 items of MMS questions [32]. Co-Morbidity Patients with any chronic disease that coexisted with their diabetes were considered to be co-morbid [15]. Poly-pharmacy Polypharmacy was defined as the use of a combination of ≥5 medications daily. Simultaneous poly-pharmacy: was used to estimate the number of drugs a patient is receiving at any given point in time [15, 33]. BMI >18.5 underweight, 18.5–24.9 normal, 25.0–29.9 overweight, and ≥30 obese according to the WHO classification for BMI [34]. Central Abdominal obesity Waist-to-hip ratio (central obesity) was considered abnormal if the female participant had more than >0.85 cm of waist circumference and the male participant had more than >1 cm [34]. Social support *From a* total of multidimensional social support scales, respondents who scored below the mean were taken as having poor social support and those who scored above the mean were taken as having good social support from a total of multi-dimensional social support scales.
## Data quality management
The structured questionnaire was developed after reviewing different kinds of literature. Then it was translated from English to Amharic and back-translated into English by the same individual to assure its consistency. Three days of training were given to data collectors and supervisors about the aim, procedure, tool, and ethics before data collection. Whereas pretest was done on patients ($5\%$) on 8 cases and 8 controls at Grease primary hospital and necessary changes were done based on the result of the test. The collected data were reviewed and checked daily for completeness and consistency by the supervisor and principal investigator and ongoing supervision was made.
## Data processing and analysis
Data were recorded in the mobile KOBO toolbox and exported to the SPSS version 25 software package for further management and analysis. Descriptive and analytic statistics were done. Descriptive statistics were used to describe the distribution of explanatory variables among case and control. The findings were presented in narration and tables proportions, and mean and standard deviation. Bi-variable and multivariable analyses using a logistic regression model with odds ratio and its corresponding (CI) $95\%$ confidence interval were done. To identify the significant association variables (p≤0.25) were considered the candidate variables for the multivariable binary logistic regression analysis and finally the strength of association was measured by computing the AOR with a $95\%$ CI. The statistical significance was declared at computing (p≤0.05). And all assumptions of binary logistic regression were checked. Multi-collinearity was assessed by using the variance inflation factor and tolerance. The VIF was <10 and the tolerance was >0.1. Multivariate outliers have been checked by cooked distance and it was < 1 since then there was no problem with a multivariate outlier. In addition assumptions of chi-square were assessed none of the cells has an expected frequency <5. Cross-tabulation was used to summarize descriptive statistics. An odds ratio with $95\%$ CI was used for measuring the strength of the association. P-value < 0.05 was considered as statistically significant. The fitness of the model was checked by Hosmer and Lemeshow goodness of fit test and the p-value of the test were 0.321.
## Ethical considerations
Ethical approval was obtained from the ethical review committee of Arba Minch University, College of Medicine and Health Sciences (IRB/$\frac{1089}{2021}$/reference). Following the approval, an official letter of co-operation was written to concerned bodies by the Department of Public Health of Arba Minch University. Letter of cooperation was obtained from the respective hospitals and written informed consent was obtained from the study participants after informing the purpose of the study.
## Socio-demographic characteristics of study participants
Three hundred twelve study participants (156 cases and 156 controls) were included in the study with a $100\%$ response rate. The majority of controls and cases 98($56.4\%$) and 88($62.8\%$) were females. The mean age of the respondents was 59±14 (years ±SD) and 57±17(years ±SD) for cases and controls respectively. More than one-third of both cases and controls were in the age group >64 years”. Eighty-nine percent [140] of cases and 137($87.8\%$) of control were married. Regarding educational background 30($19.2\%$) of cases and controls had no formal education. Likewise, 89($57.1\%$) of cases and 95($60.9\%$) of controls live in an urban area (Table 1).
**Table 1**
| Variables | Case (n(%)) | Control (n (%)) | Total (n (%)) |
| --- | --- | --- | --- |
| Age | | | |
| 25–34 | 6(3.8) | 6(3.8) | 12(3.8) |
| 35–44 | 25(16) | 16(10.3) | 41(13.1) |
| 45–54 | 38(24.4) | 33(21.2) | 71(22.8) |
| 55–64 | 29(18.6) | 43(27.6) | 72(23.1) |
| >64 | 58(37.2) | 58(37.2) | 116(37.2) |
| Marital status | | | |
| Married | 140(89.7) | 137(87.8) | 277(88.8) |
| Widowed | 9(5.8) | 8(5.1) | 17(5.4) |
| single | 7(4.5) | 11(7.1) | 18(4.8) |
| Sex | | | |
| Male | 58(37.2) | 68(43.6) | 126(40.4) |
| Female | 98(62.8) | 88(56.4) | 186(50.6) |
| Educational status | | | |
| no education | 30(19.2) | 30(19.2) | 60(19.2) |
| Primary | 74(47.4) | 68(44.4) | 142(45.5) |
| Secondary | 44(28.2) | 42(26.1) | 86(27.6) |
| collage and above | 8(5.1) | 16(10.5) | 24(7.7) |
| Occupational status | | | |
| Farmer | 49(31.4) | 52(33.3) | 101(32.4) |
| Housewife | 23(11.7) | 23(14.7) | 46(14.7) |
| Merchant | 33(21.2) | 28(17.9) | 61(19.5) |
| Governmental worker | 39(25) | 36(23.1) | 75(24) |
| Private | 12(7.7) | 17(10.9) | 29(9.3) |
| Religion | | | |
| Orthodox | 103(66.7) | 104(66.7) | 207(66.3) |
| Protestant | 43(27.6) | 36(23.1) | 79(25.3) |
| Muslim | 10(6.4) | 16(102) | 26(7.7) |
| Place of residence | | | |
| Urban | 89(57.1) | 95 (60.9) | 184(59) |
| rural | 67(42.9) | 61 (39.1) | 128(41) |
| Family history of DM | | | |
| Yes | 50(32.1) | 43 (27.6) | 93(29.8) |
| No | 106(67.9) | 113(72.4) | 219(70.2) |
| Income | | | |
| <1500 ETB | 40(25.6) | 42(26.7) | 82(26.3) |
| 1500–3000 ETB | 34 (21.8) | 38 (24.4) | 72(23.1) |
| >3000 ETB | 82(52.6) | 76(48.9) | 158(50.6) |
| Social support | | | |
| Good Supported | 104(66.7) | 134(85.9) | 238(76.3) |
| Poor supported | 52(33.3) | 22(14.1) | 74(23.7) |
## Anti-hyperglycemic medications and poly-pharmacy
Regarding anti-diabetic medications, 77($49.4\%$) of cases and 73($46.8\%$) of control were prescribed with Metformin and Glibenclamide followed by metformin alone 28($17.9\%$) for both cases and controls. Additionally, 35($22.4\%$) for control and 26($10.7\%$) for cases take insulin alone. Regarding poly-pharmacy, twenty-one percent of 34($21.8\%$) of cases and 16($10.3\%$) of control were taking greater than or equal to five medications including medication for blood glucose control. Regarding the duration of diabetes, the duration of diabetes was greater than 10 years in 32($23.1\%$) of the cases and 31($19.2\%$) of the controls. Correspondingly seventy percent of 42($26.9\%$) of the cases and 60($38.5\%$) of the controls had abnormal WHR of ≥ 0.9 for males or ≥ 0.85 for females. Regarding comorbidity 77($49.4\%$) of cases and 46($29.5\%$) of controls had comorbidity (Table 2).
**Table 2**
| Variables | Case (n (%)) | Control (n (%)) | Total (n (%)) |
| --- | --- | --- | --- |
| Type of medication | | | |
| Metformin | 28(17.9) | 28(17.9) | 56(17.9) |
| Insulin and Metformin | 16(10.3) | 12(7.7) | 28(9) |
| Metformin and Glibenclamide | 77(49.4) | 73(46.8) | 150(48.1) |
| Insulin | 26(10.7) | 35(22.4) | 61(19.6) |
| Glibenclamide | 9(5.8) | 8(5.1) | 17(5.4) |
| Poly-pharmacy | | | |
| <5 | 122(78.2) | 140(89.7) | 262(84) |
| >5 | 34(21.8) | 16(10.3) | 50(16) |
| BMI(kg/m2) | | | |
| Normal | 57(36.5) | 61(39.1) | 118(37.8) |
| Overweight | 44(28.2) | 43(27.5) | 87(27.8) |
| Obesity | 55(35.3) | 52(33.4) | 107(34.2) |
| WHR | | | |
| Normal | 114(73.1) | 96(61.5) | 210(67.3) |
| Abnormal | 42(26.9) | 60(38.5) | 102(32.7) |
| Duration of diabetes | | | |
| <5yr | 59(37.8) | 68(43.6) | 127(40.7) |
| 5-10yr | 61(39.1) | 58(37.2) | 119(38.1) |
| >10yr | 36(23.1) | 30(19.2) | 66(21.2) |
| Comorbidity | | | |
| Yes | 77(49.4) | 46(29.5) | 123(39.4) |
| No | 79(50.6) | 110(70.5) | 189(60.6) |
## Diabetes self-care activities and behavioral factors
The proportions of T2DM diabetic patients who adhere to dietary recommendations were 47 ($30.7\%$) among cases and 90($57.7\%$) among controls who had good adherence to the dietary plan. In the assessment of medication adherence, 81(51.9) of cases and 60($38.5\%$) of controls had no adherence to medication. Of the study participants who did adequate exercise 49($31.4\%$) percent of the cases and 77($49.4\%$) of the controls have been involved in, at least 30 min of, exercise for 4 and above- days during the last seven days preceding the study. Moreover, the majority of both cases and controls did not do blood glucose self-monitoring. In addition majority of the study participants never smoked a cigarette and never chew khat in both cases and controls. Regarding alcohol drinking 17($10.9\%$) of cases and 9($5.8\%$) of controls were current drinkers (Table 3).
**Table 3**
| Variables | Case (n (%)) | Control (n (%)) | Total no (n (%)) |
| --- | --- | --- | --- |
| DM association membership | DM association membership | DM association membership | DM association membership |
| Yes | 45(28.8) | 58(32.7) | 103(33) |
| No | 111(71.2) | 98 (62.8) | 209(67) |
| Blood glucose self-monitoring | Blood glucose self-monitoring | Blood glucose self-monitoring | Blood glucose self-monitoring |
| Yes | 27(17.3) | 42(26.9) | 69(22.1) |
| No | 129 (82.7) | 114(73.1) | 243(77.9) |
| Cigarette smoking | Cigarette smoking | Cigarette smoking | Cigarette smoking |
| Never | 144(92.3) | 140(89.7) | 284(91) |
| Past | 6(3.8) | 8(5.1) | 14(4.5) |
| Current | 6(3.8) | 8(5.1) | 14(4.5) |
| Alcohol drinking | Alcohol drinking | Alcohol drinking | Alcohol drinking |
| Never | 104 (66.7) | 104(66.7) | 208(66.7) |
| Past | 35(22.4) | 43(27.6) | 78(25) |
| Current | 17(10.9) | 9(5.8) | 26(8.3) |
| Khat chewing | Khat chewing | Khat chewing | Khat chewing |
| Never | 134(85.9) | 131(84) | 265(84.7) |
| Past | 12(7.7) | 14(9) | 26(8.3) |
| Current | 10(6.4) | 11(7.1) | 21(6.7) |
| Physical exercise | Physical exercise | Physical exercise | Physical exercise |
| Adequate | 49(31.4) | 77(49.4) | 126(40.4) |
| Not adequate | 107(68.6) | 79(50.6) | 186(69.6) |
| Dietary adherence | Dietary adherence | Dietary adherence | Dietary adherence |
| Adhere | 47(30.1) | 90(57.7) | 137(43.9) |
| Not adhere | 109(69.9) | 66(42.3) | 175(56.1) |
| Medication adherence | Medication adherence | Medication adherence | Medication adherence |
| Adhere | 75(48.1) | 96(61.5) | 171(54.8) |
| Not adhere | 81(51.9) | 60(38.5) | 141(45.2) |
## Factors associated with poor glycemic control
In bi-variable logistic regression analysis, educational level, blood glucose self-monitoring, being a member of DM association, social support, comorbidity, dietary adherence, medication adherence, poly-pharmacy, sex, physical exercise, and alcohol drinking were found to have p-value <0.25 and entered into the multivariate logistic analysis. In multivariable binary logistic analysis, poly-pharmacy, dietary adherence, comorbidity, physical exercise, and social support were found to be significantly associated with poor blood sugar control (Table 4).
**Table 4**
| Variables | Cases (n (%)) | Controls (n (%)) | COR (95% CI) | AOR (95% CI) |
| --- | --- | --- | --- | --- |
| Comorbidity | | | | 1.145 |
| Comorbidity | | | | 3.425 |
| Comorbidity | | | | 1.981 |
| Comorbidity | | | | 1.145 |
| Comorbidity | | | | 3.425 |
| Yes | 77(49.4) | 46(29.5) | 2.33(1.46–3.71)* | 2.35(1.39–3.95)** 1.399 |
| Yes | 77(49.4) | 46(29.5) | 2.33(1.46–3.71)* | 3.949 |
| Yes | 77(49.4) | 46(29.5) | 2.33(1.46–3.71)* | 2.06(1.20–3.54)** |
| No | 79(50.6) | 110(70.5) | 1 | 1 |
| Poly-pharmacy | | | | |
| <5 | 122(78.2) | 140(89.7) | 1 | 1 |
| >5 | 34(21.8) | 16(10.3) | 2.44(1.28–4.63)* | 2.83(1.39–5.74)** |
| Physical Exercise | | | | |
| Adequate | 49(31.4) | 77(49.4) | 1 | 11 |
| Inadequate | 107(68.6) | 79(50.6) | 2.13(1.34–3.38)* | 1.86(1.11–3.12)** |
| Social support | | | | |
| Good support | 104(66.7) | 134(85.9) | 1 | 1 |
| Poor support | 52(33.3) | 22(14.1) | 3.04(1.74–5.33)* | 3.05 (1.64–5.65)** |
| Poor support | 52(33.3) | 22(14.1) | 5.334 | 3.05 (1.64–5.65)** |
| Self-blood glucose monitoring | | | | |
| Yes | 27(17.3) | 42(26.9) | 1 | 1 |
| No | 129 (82.7) | 114(73.1) | .57(.33-.98)* | 1.48(.77–2.84) |
| Medication Adherence | | | | |
| Adhere | 75(48.1) | 96(61.5) | 1 | 1 |
| Un-adhere | 81(51.9) | 60(38.5) | 1.73(1.10–2.77)* | 1.16(.66–2.04) |
| Being a member of the DM association | | | | |
| Yes | 45(28.8) | 58(32.7) | 1 | 1 |
| No | 111(71.2) | 98 (62.8) | 1.46(.91–2.35)* | .67(.38–1.17) |
| Educational status | | | | |
| No education | 30(19.2) | 30(19.2) | 1 | 1 |
| Primary | 74(47.4) | 68(44.4) | 1.09(.59–1.99) | 1.17(.55–2.48) |
| Secondary | 44(28.2) | 42(26.1) | 1.05(.54–2.03) | .84(.38–1.88) |
| Collage and above | 8(5.1) | 16(10.5) | .50(.19–1.34)* | .69(.21–2.20) |
| Sex | | | | |
| Male | 58(37.2) | 68(43.6) | 1 | 1 |
| Female | 98(62.8) | 88(56.4) | 1.30(.83–2.05)* | .67(.38–1.16) |
| Dietary adherence | | | | |
| Adhere | 47(30.7) | 90(57.7) | 0.32(0.19–0.50)* | .31(.19-.51)*** |
| Not adhere | 109(69.3) | 66(42.3) | 1 | 1 |
| Alcohol drinking | | | | |
| Never | 104 (66.7) | 104(66.7) | 1 | 1 |
| Past | 35(22.4) | 43(27.6) | .81(.48–1. 37)* | .83(.44–1.56) |
| Current | 17(10.9) | 9(5.8) | 1.88(.80–4.43)* | 1.10(.37–3.32) |
The odds of poor glycemic control were 2.35 times higher among T2DM with comorbidity compared to those who do not have comorbidity (AOR = 2.35, $95\%$ CI (1.39–3.95)). The odds of poor glycemic control were lower for those T2DM diabetic patients who adhere to a dietary recommendation by $69\%$ compared to those T2DM diabetic patients who do not adhere to a dietary recommendation (AOR = 0.31, $95\%$ CI (.19-.51)) (Table 4).
The odds of poor glycemic control were 2.83 times higher among T2DM with poly-pharmacy as compared to participants who had no ploy pharmacy (AOR = 2.83, $95\%$ CI (1.37–5.85)). The odds of poor glycemic control were1.86 times (AOR = 1.86 $95\%$ CI (1.11–3.12)) higher among T2DM patients involved in physical activity only for less than three days as compared to participants doing regular physical activity for more than three days.
The odds of poor glycemic control were 2.35 times higher among T2DM with comorbidity compared to those who do not have comorbidity (AOR = 2.35, $95\%$ CI (1.39–3.95)). The odds of poor glycemic control were lower for those T2DM diabetic patients who adhere to a dietary recommendation by $69\%$ compared to those T2DM diabetic patients who do not adhere to a dietary recommendation (AOR = 0.31, $95\%$ CI (.19-.51)) (Table 4).
## Discussion
This study assessed factors associated with poor glycemic control among T2DM patients in Gamo and Gofa zone public hospitals. The results show that comorbidity, poly-pharmacy, social support, dietary adherence, and physical exercise are factors associated with poor glycemic control.
In this study, the odds of poor glycemic control were 3.05 times higher among T2DM patients with poor social support compared to those with good social support (AOR = 3.05, $95\%$ CI (1.64–5.65)). In line with this study finding a study in China, Ghana, New work also revealed that Social support had significant correlations with glycemic [35–37]. The possible justification can be diabetic patients who have low social support have low self-care practice which indirectly leads to poor glycemic control [38]. Increased social support is directly associated with reduced HbA1c, but it is also indirectly associated with HbA1c through various mechanisms including diet and medication adherence. Social support is important in helping patients with diabetes cope with the disease and improves treatment adherence. In addition results in turkey indicate that social support and empowerment are important for nurses to consider when planning interventions that increase the self-care behavior of individuals with type 2 diabetes and for improving glycemic control [26]. A study was done in turkey also revealed that Social support was a statistically significant predictor of, blood glucose monitoring [42]. In contrast, a study done in South Africa revealed that there was no association between social support and HbA1c this might be because the population might be different in way of life and culture [39].
In this study, the odds of poor glycemic control were 2.83 times higher among T2DM patients with poly-pharmacy as compared to participants who had no ploy pharmacy (AOR = 2.83, $95\%$ CI (1.39–5.74)). A possible justification can be Poly-pharmacy increases the probability of adverse drug events [40, 41]. Therefore poly-pharmacy might decrease compliance to anti-diabetic medications and leads to suboptimal glycemic control [42, 43]. A study done in Romania revealed that T2DM patients who received more drugs than their non-diabetes counterparts were exposed to more drug-drug and food-drug interactions [43]. And another possible justification can be low medication adherence to prescribed medications.
The odds of poor glycemic control were 1.86 times higher among T2DM patients with involvement in physical activity only for less than three days as compared to participants doing regular physical activity for more than three days (AOR = 1.86 $95\%$ CI (1.11–3.12)). This is in line with the study done in Jimma, Nekemte, Saudi Arabia, Jordan, and Thailand [15, 44–47]. This might be because, in people with type 2 diabetes, exercise can improve peripheral insulin sensitivity as well as enhance insulin binding. Exercise also decreases abdominal fat, reduces free fatty acids, and increases insulin-sensitive skeletal muscle, which may result in improved glycemic control [48, 49].
In this study, the odds of poor glycemic control were 2.35 times higher among T2DM patients with comorbidity when compared to those T2DM patients who do not have comorbidity (AOR = 2.35, $95\%$ CI (1.39–3.95)). This finding is consistent with studies done in Mekelle town, Jimma, India, and the Netherlands [15, 50–52]. The possible justification might be because the presence of comorbid illness aggravates disease processes and reduces their quality of life [53]. Another reason might be comorbidity increases poly-pharmacy which increases pill burden and adverse drug reactions. In contrast with this, a study at a police referral hospital in Addis Ababa Ethiopia 2018 revealed that the presence of comorbidity showed a high likelihood of FPG target achievement among the patients this might be because the study participants might have good adherence to medication than comorbid T2DM diabetic study participants in this study [54].
Finally, *In this* study, the odds of poor glycemic control were lower for those T2DM patients who adhere to a dietary recommendation by $69\%$ compared to those type 2 diabetic patients who do not adhere to dietary recommendations (AOR =.31, $95\%$ CI (.19-.51)). This finding is consistent with the studies conducted at Suhul Hospital, Haromaya, Tigray, Debre-Tabour, Ethiopia, which determined that good dietary adherence is significantly associated with having good glycemic control [16, 23, 54, 55]. A possible justification could be avoiding a high-fat diet, especially one high in saturated fats, which has been linked to T2DM and insulin resistance. It appears that saturated fatty acids (but not unsaturated fats) activate immune cells, which produce an inflammatory protein, which in turn then makes cells more insulin resistant so then avoiding a high-fat diet decreases the occurrence of insulin resistance. And also Adherence to diet facilitate weight loss, improving glucose control and lipid profiles in patients with T2DM [56].
## Conclusion and recommendation
In this study, the presence of comorbidity, physical exercise, poly-pharmacy, low social support, and adherence to dietary recommendations were factors associated with poor glycemic control. Therefore, concerned health authorities and health professionals should give special attention to regular follow-up and control of blood glucose levels. Healthcare providers should take aware of psychosocial factors in the treatment regime of the patient. Family members and society should be educated about diabetes, the importance of controlling blood glucose levels consciously, and the long-term complications of the disease. A cohort study is recommended for future researchers to infer substantial evidence of causality.
## Limitations of the study
None of the patients had HbA1c determination due to the unavailability of the laboratory service for the HbA1c determination in the study hospitals. As some parts of the questionnaire depended on the memory of respondents may have resulted in recall bias.
## References
1. Curbing N.. *Noncommunicable diseases in africa: youth are key to curbing the epidemic and achieving sustainable development.* (2015.0)
2. 2World Health Organization. Global report on diabetes:
World Health Organization. Geneva S. 2016.. *Global report on diabetes:* (2016.0)
3. Association AD. **2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2020**. *Diabetes care* (2020.0) **43** S14-S31. DOI: 10.2337/dc20-S002
4. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB. **IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045**. *Diabetes research and clinical practice* (2022.0) **183** 109119. DOI: 10.1016/j.diabres.2021.109119
5. Manne-Goehler J, Geldsetzer P, Agoudavi K, Andall-Brereton G, Aryal KK, Bicaba BW. **Health system performance for people with diabetes in 28 low-and middle-income countries: a cross-sectional study of nationally representative surveys**. *PLoS medicine* (2019.0) **16** e1002751. DOI: 10.1371/journal.pmed.1002751
6. Park PH, Pastakia SD. **Access to Hemoglobin A1c in Rural Africa: A Difficult Reality with Severe Consequences**. *Journal of Diabetes Research* (2018.0) **2018** 6093595. DOI: 10.1155/2018/6093595
7. Saeedi P, Salpea P, Karuranga S, Petersohn I, Malanda B, Gregg EW. **Mortality attributable to diabetes in 20–79 years old adults, 2019 estimates: Results from the International Diabetes Federation Diabetes Atlas**. *Diabetes research and clinical practice* (2020.0) **162** 108086. PMID: 32068099
8. Aikins Ad-G, Unwin N, Agyemang C, Allotey P, Campbell C, Arhinful D. **Tackling Africa’s chronic disease burden: from the local to the global**. *Globalization and health* (2010.0) **6** 1-7. PMID: 20181213
9. Wang C-Y, Neil D, Home P. **2020 vision—An overview of prospects for diabetes management and prevention in the next decade**. *Diabetes research and clinical practice* (2018.0) 143
10. 10Consultation RoaWI. definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. 2006.
11. Braga MF, Casanova A, Teoh H, Dawson KG, Gerstein HC, Fitchett DH. **Treatment gaps in the management of cardiovascular risk factors in patients with type 2 diabetes in Canada**. *Canadian Journal of Cardiology* (2010.0) **26** 297-302. DOI: 10.1016/s0828-282x(10)70393-7
12. Hoerger TJ, Segel JE, Gregg EW, Saaddine JB. **Is glycemic control improving in US adults?**. *Diabetes care* (2008.0) **31** 81-6. PMID: 17934153
13. Chan JC, Gagliardino JJ, Baik SH, Chantelot J-M, Ferreira SR, Hancu N. **Multifaceted determinants for achieving glycemic control: the International Diabetes Management Practice Study (IDMPS).**. *Diabetes care* (2009.0) **32** 227-33. DOI: 10.2337/dc08-0435
14. Teklehaimanot BF, kidanemariam Berhe A, Welearegawi GG. **Poor glycemic control and associated factors among diabetic patients in Ethiopia; A Systemic review and meta-analysis**. *medRxiv* (2019.0) 19004986
15. Mamo Y, Bekele F, Nigussie T, Zewudie A. **Determinants of poor glycemic control among adult patients with type 2 diabetes mellitus in Jimma University Medical Center, Jimma zone, south west Ethiopia: a case control study**. *BMC endocrine disorders* (2019.0) **19** 1-91. PMID: 30606164
16. Fseha B.. **Glycemic control and it’s associated factors in type 2 diabetic patients in Suhul hospital, northwest tigray, Ethiopia**. *J Diabetes Metab* (2017.0) **8** 729
17. Yigazu DM, Desse TA. **Glycemic control and associated factors among type 2 diabetic patients at Shanan Gibe Hospital, Southwest Ethiopia**. *BMC research notes* (2017.0) **10** 1-6. PMID: 28057050
18. Tekalegn Y, Addissie A, Kebede T, Ayele W. **Magnitude of glycemic control and its associated factors among patients with type 2 diabetes at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia**. *PloS one* (2018.0) **13** e0193442. DOI: 10.1371/journal.pone.0193442
19. **Standards of medical care in Diabetes—2018**. *Diabetes Care J Clin Appl Res Educ* (2018.0) **41** S13-28
20. **Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33)**. *The Lancet* (1998.0) **352** 837-53
21. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE. **Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010**. *The lancet* (2013.0) **382** 1575-86. DOI: 10.1016/S0140-6736(13)61611-6
22. Kibirige GPA D., Kampiire L., Kiggundu D. S., Lumu W.. **Frequency and predictors of suboptimal glycemic control in an African diabetic population**. *International Journal of General Medicine* (2017.0) 10
23. Mohammed AS, Adem F, Tadiwos Y, Woldekidan NA, Degu A. **Level of Adherence to the Dietary Recommendation and Glycemic Control Among Patients with Type 2 Diabetes Mellitus in Eastern Ethiopia: A Cross-Sectional Study.**. *Diabetes Metab Syndr Obes* (2020.0) **13** 2605-12. DOI: 10.2147/DMSO.S256738
24. Ali M, Alemu T, Sada O. **Medication adherence and its associated factors among diabetic patients at Zewditu Memorial Hospital, Addis Ababa, Ethiopia**. *BMC research notes* (2017.0) **10** 676. DOI: 10.1186/s13104-017-3025-7
25. Zimet GD, Dahlem NW, Zimet SG, Farley GK. **The multidimensional scale of perceived social support**. *Journal of personality assessment* (1988.0) **52** 30-41
26. Arda Sürücü H, Büyükkaya Besen D, Erbil EY. **Empowerment and social support as predictors of self-care behaviors and glycemic control in individuals with type 2 diabetes**. *Clinical nursing research* (2018.0) **27** 395-413. DOI: 10.1177/1054773816688940
27. 27Organization WH. STEPwise approach to surveillance (STEPS). 2008.
28. Freeman MC, Ogden S, Jacobson J, Abbott D, Addiss DG, Amnie AG. **Integration of water, sanitation, and hygiene for the prevention and control of neglected tropical diseases: a rationale for inter-sectoral collaboration.**. *PLoS neglected tropical diseases* (2013.0) **7** e2439. DOI: 10.1371/journal.pntd.0002439
29. DEBORAH J.. **TOOBERT PSEH, PHD RUSSELL E. GLASGOW, PHD. The Summary of Diabetes Self-Care Activities Measurer esults from 7 studies and a revised scale**. *DIabets care* (2000.0) **23**
30. Berhe. **DIABETES SELF CARE PRACTICES AND ASSOCIATED FACTORS AMONG TYPE 2 DIABETIC PATIENTS IN TIKUR ANBESSA SPECIALIZED HOSPITAL, ADDIS ABABA, ETHIOPIA- A CROSS SECTIONAL STUDY**. *IJPSR* (2012.0) **3** 4219-29
31. Mariya T, Girmay A, Tasew H, Teklay G, Ayele E, Gerensea H. **Determinants of hypertension among diabetic patients in Public Hospitals of the Central Zone, Tigray, Ethiopia 2018: unmatched case-control study.**. *Pan African Medical Journal* (2019.0) **33** 100. DOI: 10.11604/pamj.2019.33.100.17094
32. Yimam M, Desse TA, Hebo HJ. **Glycemic control among ambulatory type 2 diabetes patients with hypertension**. *Co-morbidity in a developing country: A cross sectional study. Heliyon* (2020.0) **6** e05671
33. Bjerrum LR, Hallas J. U., Kragstrup J. **Methods for estimating the occurrence of polypharmacy by means of a prescription database**. *European journal of clinical pharmacology* (1997.0) **53** 7-11. DOI: 10.1007/s002280050329
34. 34Organization WH. Obesity: preventing and managing the global epidemic. 2000.
35. Shao Y, Liang L, Shi L, Wan C, Yu S. **The Effect of Social Support on Glycemic Control in Patients with Type 2 Diabetes Mellitus: The Mediating Roles of Self-Efficacy and Adherence**. *Journal of Diabetes Research* (2017.0) **2017** 2804178. DOI: 10.1155/2017/2804178
36. Botchway M, Davis RE, Merchant AT, Appiah LT, Moore S. **Diabetes-Related Stigma and its Influence on Social Networks, Social Support, and HbA1c in Ghana**. *Ethnicity & Disease* (2021.0) **31** 57-66. DOI: 10.18865/ed.31.1.57
37. Maki KG. **Social support, strain, and glycemic control: A path analysis**. *Personal Relationships* (2020.0) **27** 592-612. PMID: 34108841
38. Yehualashet FA, Tegegne ET, Ayele AD, Takele WW. **Do adult patients with diabetes mellitus living in rural part of Ethiopia and having poor social support have disproportionately poor self-care practice? A systematic review and meta-analysis**. *Primary Care Diabetes* (2021.0). DOI: 10.1016/j.pcd.2021.02.010
39. Ramkisson S, Pillay BJ, Sibanda W. **Social support and coping in adults with type 2 diabetes**. *African Journal of Primary Health Care and Family Medicine* (2017.0) **9** 1-8. DOI: 10.4102/phcfm.v9i1.1405
40. Ahmed B, Nanji K, Mujeeb R, Patel MJ. **Effects of polypharmacy on adverse drug reactions among geriatric outpatients at a tertiary care hospital in Karachi: a prospective cohort study**. *PloS one* (2014.0) **9** e112133. DOI: 10.1371/journal.pone.0112133
41. Nguyen JK, Fouts MM, Kotabe SE, Lo E. **Polypharmacy as a risk factor for adverse drug reactions in geriatric nursing home residents**. *The American journal of geriatric pharmacotherapy* (2006.0) **4** 36-41. DOI: 10.1016/j.amjopharm.2006.03.002
42. Bailey C, Kodack M. **Patient adherence to medication requirements for therapy of type 2 diabetes**. *International journal of clinical practice* (2011.0) **65** 314-22. DOI: 10.1111/j.1742-1241.2010.02544.x
43. Dobrică E-C, Găman M-A, Cozma M-A, Bratu OG, Pantea Stoian A, Diaconu CC. **Polypharmacy in type 2 diabetes mellitus: insights from an internal medicine department**. *Medicina* (2019.0) **55** 436. DOI: 10.3390/medicina55080436
44. Khattab M, Khader YS, Al-Khawaldeh A, Ajlouni K. **Factors associated with poor glycemic control among patients with type 2 diabetes**. *Journal of Diabetes and its Complications* (2010.0) **24** 84-9. DOI: 10.1016/j.jdiacomp.2008.12.008
45. Howteerakul N, Suwannapong N, Rittichu C, Rawdaree P. **Adherence to regimens and glycemic control of patients with type 2 diabetes attending a tertiary hospital clinic**. *Asia Pacific Journal of Public Health* (2007.0) **19** 43-9. DOI: 10.1177/10105395070190010901
46. Alzaheb RA, Altemani AH. **The prevalence and determinants of poor glycemic control among adults with type 2 diabetes mellitus in Saudi Arabia**. *Diabetes* (2018.0) **11** 15. DOI: 10.2147/DMSO.S156214
47. Fekadu G, Bula K, Bayisa G, Turi E, Tolossa T, Kasaye HK. **Challenges and factors associated with poor glycemic control among type 2 diabetes mellitus patients at Nekemte Referral Hospital, Western Ethiopia**. *Journal of multidisciplinary healthcare* (2019.0) **12** 963. DOI: 10.2147/JMDH.S232691
48. Park J-H, Lee Y-E. **Effects of exercise on glycemic control in type 2 diabetes mellitus in Koreans: the fifth Korea National Health and Nutrition Examination Survey (KNHANES V)**. *Journal of physical therapy science* (2015.0) **27** 3559-64. DOI: 10.1589/jpts.27.3559
49. Nayak S, Maiya A, Hande M. **Influence of aerobic treadmill exercise on blood glucose homeostasis in noninsulin dependent diabetes mellitus patients**. *Indian journal of clinical biochemistry* (2005.0) **20** 47-51. DOI: 10.1007/BF02893041
50. Eticha T, Mulu A, Gebretsadik H, Kahsay G, Ali D. **Factors associated with poor glycemic control in type 2 diabetic patients investigated at Ayder Referral Hospital, Mekelle, Ethiopia**. *Ijppr Human* (2016.0) **6** 160-71
51. Sanal T, Nair N, Adhikari P. **Factors associated with poor control of type 2 diabetes mellitus: a systematic review and meta-analysis**. *Journal of diabetology* (2011.0) **3** 1-10
52. Hilde Luijks MB, Hans Bor, van Weel Chris, Lagro-Janssen Toine, de Grauw Wim, Schermer Tjard. **The Effect of Comorbidity on Glycemic Control and Systolic Blood Pressure in Type 2 Diabetes: A Cohort Study with 5 Year Follow Up in Primary Care,netherland**. *plosone* (2015.0) **13**
53. Zinab B, Debalke R. **Acute glycemic level and its association with physical activity recommendation among type 2 diabetic patients in Illubabor zone oromiya, Ethiopia**. *International Journal of Nutrition and Metabolism* (2019.0) **11** 1-10
54. Shimels T, Abebaw M, Bilal AI, Tesfaye T. **Treatment Pattern and Factors Associated with Blood Pressure and Fasting Plasma Glucose Control among Patients with Type 2 Diabetes Mellitus in Police Referral Hospital in Ethiopia**. *Ethiop J Health Sci* (2018.0) **28** 461-72. DOI: 10.4314/ejhs.v28i4.12
55. Gebrie A, Tesfaye B, Sisay M. **Evaluation of glycemic control status and its associated factors among diabetes patients on follow-up at referral hospitals of Northwest Ethiopia: A cross-sectional study, 2020**. (2020.0) **6** e05655
56. Wen H, Gris D, Lei Y, Jha S, Zhang L, Huang MT-H. **Fatty acid–induced NLRP3-ASC inflammasome activation interferes with insulin signaling**. *Nature immunology* (2011.0) **12** 408-15. DOI: 10.1038/ni.2022
|
---
title: Flavonoids from the Roots of Sophora flavescens and Their Potential Anti-Inflammatory
and Antiproliferative Activities
authors:
- Yan-Fei Yang
- Ting-Ting Liu
- Guo-Xian Li
- Xuan-Qin Chen
- Rong-Tao Li
- Zhi-Jun Zhang
journal: Molecules
year: 2023
pmcid: PMC10004487
doi: 10.3390/molecules28052048
license: CC BY 4.0
---
# Flavonoids from the Roots of Sophora flavescens and Their Potential Anti-Inflammatory and Antiproliferative Activities
## Abstract
The phytochemical investigation of the roots of the traditional Chinese medicinal plant Sophora flavescens led to the isolation of two novel prenylflavonoids with an unusual cyclohexyl substituent instead of the common aromatic ring B, named 4′,4′-dimethoxy-sophvein [17] and sophvein-4′-one [18], and 34 known compounds (1–16, 19–36). The structures of these chemical compounds were determined by spectroscopic techniques, including 1D-, 2D-NMR, and HRESIMS data. Furthermore, evaluations of nitric oxide (NO) production inhibitory activity against lipopolysaccharide (LPS)-treated RAW264.7 cells indicated that some compounds exhibited obvious inhibition effects, with IC50 ranged from 4.6 ± 1.1 to 14.4 ± 0.4 μM. Moreover, additional research demonstrated that some compounds inhibited the growth of HepG2 cells, with an IC50 ranging from 0.46 ± 0.1 to 48.6 ± 0.8 μM. These results suggest that flavonoid derivatives from the roots of S. flavescens can be used as a latent source of antiproliferative or anti-inflammatory agents.
## 1. Introduction
The roots of Sophora flavescens are ordinarily served in the traditional Chinese medicine (TCM), “Ku Shen”, for the curing of skin diseases, cancer, dysentery, hematochezia, jaundice, pruritus vulvae, eczema, and hepatitis [1]. Modern pharmacological research shows that it exhibits outstanding activities toward tumors, inflammation, diabetes, and microbial infections [2,3,4,5,6,7]. Phytochemical studies demonstrated that alkaloids and flavonoids are the major chemical classes of S. flavescens compounds [8,9,10]. Matrine, as a reflective component of the alkaloids of S. flavescens, has been used as an antitumor drug (Compound matrine injection) in China. With more in-depth research on the alkaloids of S. flavescens, the anticancer effects of some alkaloids have been shown to be more potent than those of flavonoids of S. flavescens in vitro and in vivo [11].
In order to discover new compounds with antiproliferative and anti-inflammatory activities from S. flavescens, we isolated 36 flavonoids from this medicinal plant (Figure 1), including flavanones, isoflavones, flavonols, flavanonols, and chalcones. Among them, compounds 17 and 18 were two new dihydroflavones with an unusual cyclohexyl substituent instead of the common aromatic ring B. Many studies have shown an interesting link between chronic inflammation and cancers [12]. Thus, all isolated components were evaluated for their anti-inflammatory activity by acting on LPS-stimulated macrophage (RAW 264.7) cell lines in vitro. In addition, all components were assessed for their antiproliferative activities against HepG2 cell lines.
Here, we characterize the isolation, chemical structure elucidation, antiproliferative activity against HepG2 cells, and NO production inhibitory activity of all isolates.
## 2. Results and Discussion
The comprehensive use of separation materials and chromatographic methods such as normal silica gel, hydroxypropyl dextran gel (Sephadex LH-20), MPLC, TLC, MCI reversed-phase column, and HPLC, with the help of MS and NMR and other spectroscopy methods, was carried out to isolate and identify 36 compounds, including 2 new ones: 4′,4′-dimethoxy-sophvein [17] and sophvein-4′-one [18].
## 2.1. Structural Elucidation
Compound 17 was acquired as a white amorphous powder. The HR-ESI-MS demonstrated the molecular ion peak at m/z 429.1894 [M+Na]+ (calcd. for 429.1884), corresponding to the molecular formula C22H30O7, which indicated eight degrees of unsaturation. The 1H spectroscopic data of 17 (Table 1) demonstrated the existence of one aromatic proton signal (δH 5.84 (1H, s)), one isopentenyl side chain signal (δH 1.68 (3H, s); 1.59 (3H, s); 3.14 (2H, d, $J = 6.9$ Hz); 5.11 (1H, br t, $J = 6.9$ Hz)), and two methoxy groups signals (δH 3.15 (3H, s) and 3.10 (3H, s)). By interpreting the DEPT and 13C NMR spectra, 22 carbon signals (Table 1) were observed, including two methoxy groups (δC 47.9 and 48.1), two methyls (δC18.1 (C-5″) and 25.9 (C-4″)), three methylidynes (δC 84.6 (C-2); 96.4 (C-6); 124.4 (C-2″)), six methylenes (δC 22.4 (C-1″); 28.4 (C-3′); 28.5 (C-5′); 29.9 (C-2′); 31.3 (C-6′); 37.0 (C-3)), three oxygenated aromatic carbons (δC161.3 (C-9); 163.0 (C-5); 166.0 (C-7)), and six quaternary carbons (δC 72.2(C-1′); 101.1(C-4′); 103.2 (C-10); 109.0 (C-8); 131.6 (C-3″); 198.8 (C-4)). According to the above NMR data, it is speculated that this compound may be an isopentenyl substituted flavanone. However, from the 1H NMR spectra of compound 17, the proton signal of the typical aromatic flavonoid B-ring in the flavonoid compound completely disappeared, and a complex signal of eight aliphatic protons emerged instead.
These data suggest the presence of a hydrogenated B-ring unit in the compound, and this inference is also confirmed by the HMQC and HMBC spectra (Figure 2). Comparing the 13C NMR data, except for the isopentenyl signal, the remaining 17 signals are very similar to (2S)-4′,4′-dimethoxy-ongokein [13]. The main difference between the two compounds is that the H-8 (δH 5.98 (d, $J = 2.0$ Hz)) signal in (2S)-4′,4′-dimethoxy-ongokein disappears, and the chemical shift of C-8 also decreases to the lower field. These data indicate that the isopentenyl group is attached at the C-8 position. This inference was identified via the HMBC correlation between H-1″ (δH 3.14, 2H, d, $J = 6.9$ Hz) and C-9 (δC 161.3), C-8 (δC 109.0), and C-7 (δC 166.0). On the other hand, the attachment of the two methoxy groups to the C-4′-position was shown by the HMBC correlation peaks between two methoxy signals (δH 3.15 and 3.10) and C-4′ (δC 101.1).
By comparison, the coupling constants of H-2 (δH 4.04 (d, $J = 13.6$ Hz)), H-3a (δH 2.81 (dd, $J = 17.0$, 13.6 Hz)), and H-3b (δH 2.51 (d, $J = 17.0$ Hz)) were found to be very close to (2S)-4′,4′-dimethoxy-ongokein [13], so it is inferred that the C-2 of compound 17 is in the S configuration. Additionally, the configuration of the B-ring of cyclohexane was defined to be the 1′C4′ configuration via the NOE effect in the ROESY experiment (Figure 2). According to the above analysis, the structure of compound 17 was confirmed and named 4′,4′-dimethoxy-sophvein.
Compound 18 was acquired as a white amorphous powder. The HR-ESI-MS displayed the molecular ion peak at m/z 361.1650 [M+H]+ (calcd for 361.1646), suggesting the molecular formula C20H24O6 with 9° of unsaturation. The 1H and 13C NMR spectroscopic data of 18 are similar to 17 (Table 1), with the main difference being the disappearance of the two methoxyl signals in 18. Furthermore, the chemical shift of C-4′ exhibited a downfield shift (from δC 101.1 to 210.3). Combined with HR-ESI-MS, it was found that the molecular weight of compound 18 is 46 less than that of 17. The above data indicate that the C-4′ of the B-ring of compound 18 has a carbonyl group. This inference was further defined by the HMBC correlation of the δH 2.03 (H-3′b), 2.21 (H-2′), and 2.10 (H-6′) proton signals with C-4′ (δC 210.3). Finally, by comparing the NOE effects in the ROESY spectra of the two compounds, it was found that 18 and 17 have the same configuration. The optical rotation values of the two compounds [αD24 = + 49.88 (c 0.25, MeOH) for 17 and αD24 = +38.69 (c 0.29, MeOH)] for 18 also support this inference. Based on the above data analysis, the structure of 18 was confirmed and named sophvein-4′-one. See Supplementary Materials for HR-ESI-MS, 1H and 13C NMR, HMQC, HMBC, 1H-1H COSY, and ROESY of compounds 17 and 18.
The known compounds (1–16 and 19–36) were determined based on a comparison with published NMR data in the references to be sophorafiavanone B [1] [14], isoxanthohumol [2] [15], kenusanonoe I [3] [16], kushenol S [4] [17], leachianone G [5] [18], 5-methoxy-7,2′,4′-trihydroxy-8-[3,3-dimethyl-allyl]flavanone [6] [19], kushenol W [7] [17], 8-(3-Hydroxymethyl-2-butenyl)-5,7,2′,4′-tetra-hydroxyflavanone [8] [20], kushenol V [9] [17], alopecurone G [10] [21], sophoraflavone G [11] [22], leachianone A [12] [23], kurarinone [13] [24], (2S)-2′-methoxykurarinone [14] [25], kushenol E [15] [26], kushenol B [16] [26], noranhyoicaritin [19] [27], sophoflavescenol [20] [28], 8-(3,3-dimethylallyl)-tamarixetin [21] [29], 8-lavandulylkaempferol [22] [30], kushenol Z [23] [31], kushenol C [24] [32], 5-O-methylkushenol C [25] [33], (2R)-3β,7,4′-trihydrox-y-5-methoxy-8-prenyl- flavanone [26] [20], kushenol X [27] [23], kushenol N [28] [34], kushenol I [29] [35], 5,7,4′-trihydroxyisoflavone [30] [36], 7,3′-dihydroxy-4′-methoxyisoflavanone [31] [37], calycosin [32] [36], xanthohumol [33] [38], xanthogalenol [34] [39], kuraridin [35] [34], and kushenol D [36] [40].
## 2.2. Biological Studies
The potential cytotoxicity of compounds on RAW 264.7 macrophages cells was determined before executing further studies. Macrophage cells were treated with compounds, and a mitochondria colorimetric (MTT) assay was used to test cell survival. Cell viability (%) = [(O.DDrug − O.DBlank)/(O.DControl − O.DBlank)] ×$100\%$, and SPSS version 21.0 (probit analysis) was used for calculating CC50 extract. Table 2 shows that some compounds exhibited obvious cytotoxicity on macrophages. Among them, compound 22 was the most toxic composition with a CC50 value of 13.8 ± 0.6 μM.
NO is one of the immune effectors used by macrophages to defend our bodies against intracellular pathogens. To determine if compounds 1–36 can modulate the NO production by macrophages, the Griess reagent was used to analyze the NO levels. The cells were incubated with compounds 1–36 (3.12, 6.25, 12.5, 25, and 50 μM) and then treated with LPS for 24 h. When activated by LPS, NO was produced from macrophages after inducing iNOS genes and subsequent protein expression. NO production inhibition (%) = [(O.DLPS − O.DDrug)/(O.DLPS − O.DBlank)] × $100\%$. The determination of IC50 was calculated using probit analysis in SPSS version 21.0. The research results show that some compounds exhibited obvious NO production inhibitory activity, with IC50 ranging from 4.6 ± 1.1 to 14.4 ± 0.4 μM, as shown in Table 2. Among them, compound 35 displayed the most significant inhibition of NO production, with an IC50 value of 4.6 ± 1.1 μM.
The inhibitory rate of HepG2 cells growth of compounds 1–36 was measured by the MTT assay and compared with a negative control and positive control (cisplatin, IC50 = 24.5 ± 0.8 μM). Inhibitory rate (%) = [(O.DDrug − O.DBlank)/(O.DControl − O.DBlank)] ×$100\%$. The IC50 values were calculated with probit analysis using software SPSS version 21.0. The results show that eight compounds inhibited the growth of HepG2 cells with an IC50 ranging from 0.46 ± 0.1 to 48.6 ± 0.8 μM (Table 3), while others were inactive (IC50 > 50 μM). Compound 22 exhibited the best antiproliferative effects on HepG2 cell lines, with an IC50 value of 0.46 ± 0.1 μM. The present work revealed that many secondary metabolites of S. flavescens have antiproliferative and NO production inhibitory activities, indicating its application in traditional Chinese medicine.
## 3.1. General Experimental Procedure
The optical rotation value was recorded with a Jasco DIP-370 polarimeter (JASCO Corporation, Tokyo, Japan). The ultraviolet (UV) spectrum was recorded by a UV2700 spectrophotometer (Shimadzu, Kyoto, Japan). The infrared (IR) spectrum was obtained by an FT-IR spectrophotometer (PerkinElmer, Waltham, MA, USA) using KBr pellets. High-resolution electrospray ionization mass spectroscopy (HRESIMS) was obtained with an Agilent 6500 LC/Q-TOF mass spectrometer (Agilent, Waldbronn, Germany). The 1H NMR, 13C NMR, HMBC, HSQC, 1H-1H COSY, and ROESY spectra were measured by a Bruker Advance III HD using tetramethylsilane (TMS) as an internal standard (600 MHz, Bruker BioSpin, Zürich, Switzerland). Chemical shifts are demonstrated in δ (ppm) and relative to the residual solvent signals. Silica gel (100–200 and 200–300 mesh, Qingdao Marine Chemical Industry Co., Qingdao, China), polyamide (60–90 mesh, Changfeng Chemical Factory Co., Gulou, Nanjing, China), sephadex LH-20 (Sigma-Aldrich Corp., St. Louis, MO, USA), and RP-18 reverse-phase silica gel (20–45 µm, Fuji Silysia Chemical Ltd., Kasugai-shi, Japan) have been used for column chromatography (CC). The reverse-phase medium-pressure liquid chromatography (RP-MPLC) system comprises a Büchi pump (Büchi Labortechnik AG, Meierseggstrasse 40 Postfach CH-9230, Flawil, Switzerland), column and precolumn (310 × 36 mm and 110 × 36 mm, Soochow high tech chromatography CO., Ltd., Suzhou, China), and MCI-gel CHP-20P (75–150 µm, Mitsubishi Chemical Co., Tokyo, Japan). Thin-layer chromatography (TLC) was performed using precoated silica gel plates (GF254, Qingdao Marine Chemical Factory, Qingdao, China), and the spots were visualized by spraying with $10\%$ sulfuric acid ethanolic solution or α-naphthol-sulfuric acid solution and heating at 110 °C for 3–5 min. Murine macrophage (RAW 264.7) cells were acquired from Kunming Institute of Zoology (KIZ), Chinese Academy of Sciences (CAS). Macrophage cells were placed in a constant temperature incubator and cultured at a temperature of 37 °C and a concentration of $5\%$ CO2. Human hepatoma HepG2 cell lines were also obtained from the Kunming Institute of Zoology (KIZ), Chinese Academy of Sciences (CAS). HepG2 cells were cultured at 37 °C under $5\%$ CO2. Additionally, an inverted phase contrast microscope was used to observe cell morphology.
## 3.2. Plant Material
The roots of S. flavescens were collected in Honghe, Yunnan Province, People’s Republic of China, in June 2019. The sample was identified by one of the authors (Xuan-Qin Chen). A voucher specimen (number: KUMST20190628) was conserved at the Key Laboratory of Phytochemistry, Kunming University of Science and Technology, China.
## 3.3. Extraction and Isolation
The air-dried roots of S. flavescens (20 Kg) were crushed and extracted with $95\%$ aq. ethanol (24 h × 3 times). The ethanol extracts were percolated and evaporated in vacuo to obtain a residue. This residue was suspended in water and then extracted with ethyl acetate (3 times) and concentrated to produce an ethyl acetate phase (493 g). The ethyl acetate phase was segmented and enriched by macroporous resin to obtain 308 g of total flavonoids. The total flavonoid extract (300 g) was segmented using a normal phase silica gel column chromatography (CC) and stepwise gradient elution with a gradient of petroleum ether-ethyl acetate (4:1–1:1) to obtain 7 subfractions (Fr.1–7).
Fr.1 (3.4 g) was further segmented by a silica gel chromatography column (CC) stepwise gradient elution with petroleum ether-dichloromethane (1:2) to yield two subfractions (F1-1~F1-2). F1-1 (1.9 g) was further segmented via Sephadex LH-20 (methanol) to obtain 8 subfractions (F1-1-1~F1-1-8). F1-1-5 (120.6 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (150:1) to obtain compound 3 (14.0 mg). F1-2 (567.0 mg) was further segmented via Sephadex LH-20 (methanol) to obtain 8 subfractions (F1-2-1~F1-2-8). F1-2-5 (59.0 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-ethyl acetate (9:1–2:1) to obtain compounds 1 (141.4 mg) and 4 (33.2 mg). F1-2-8 (80.0 mg) was segmented by CC (silica gel) and stepwise gradient elution with petroleum ether-ethyl acetate (10:1–5:1) to yield compound 22 (30.0 mg).
Fr.2 (1.5 g) was further segmented via Sephadex LH-20 (methanol) to obtain 6 subfractions (F2-1~F2-6). F2-4 (366.0 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (150:1) to obtain 5 subfractions (F2-4-1~F2-4-5). F2-4-4 (28.0 mg) was further segmented via Sephadex LH-20 (methanol) to obtain compounds 15 (8.3 mg) and 16 (7.5 mg). F2-4-5 (15.0 mg) was further segmented via Sephadex LH-20 (methanol) to obtain compound 11 (6.0 mg). F2-6 (246.0 mg) was further segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (100:1–50:1) to obtain 5 subfractions (F2-6-1~F2-6-5). F2-6-3 (32.5 mg) was further segmented via Sephadex LH-20 (methanol) to yield compound 19 (24.3 mg). F2-6-5 (508.6 mg) was further segmented via Sephadex LH-20 (methanol) to obtain compound 21 (468.8 mg).
Fr.3 (30.0 g) was further segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (50:1–10:1) to obtain 2 subfractions (F3-1~F3-2). F3-1 (4.0 g) was further segmented via Sephadex LH-20 (methanol) to yield 7 subfractions (F3-1-1~F3-1-7). F3-1-3 (501.6 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (50:1–20:1) to obtain 4 subfractions (F3-1-3-1~F3-1-3-4). F3-1-3-2 (61.2 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-ethyl acetate (30:1–10:1) to obtain compound 10 (5.6 mg). F3-1-4 (1.1 g) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (50:1–10:1) to obtain 6 subfractions (F3-1-4-1~F3-1-4-6). F3-1-4-5 (642.5 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-ethyl acetate (30:1–10:1) to obtain 3 subfractions (F3-1-4-5-1~F3-1-4-5-3). F3-1-5 (2.3 g) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-ethyl acetate (50:1–30:1) to obtain 6 subfractions (F3-1-5-1~F3-1-5-6). F3-1-5-1 (400.4 mg) was further segmented via Sephadex LH-20 (methanol) to obtain 2 subfractions (F3-1-5-1-1~F3-1-5-1-2). F3-1-5-1-1 (119.2 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-acetone (4:1–1:1) to obtain compound 34 (27.8 mg). F3-1-5-1-2 (70.3 mg), followed by semipreparative high-performance liquid chromatography (HPLC) ($70\%$, MeOH-H2O, 3 mL/min), was used to obtain compound 31 (4.1 mg). F3-2 (25.0 g) was first subjected to reverse-phase RP-18 chromatography with 40–$100\%$ methanol-water as the eluent, and the same fractions were combined by thin-layer chromatography to obtain 8 subfractions (F3-2-1~F3-2-8). F3-2-5 (276.5 mg) was further segmented via Sephadex LH-20 (methanol) to yield compound 30 (140.2 mg).
Fr.4 (11.0 g) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (60:1–10:1) to obtain 6 subfractions(F4-1~F4-6). F4-3 (900.7 mg) was further segmented via Sephadex LH-20 (methanol) to obtain 5 subfractions (F4-3-1-F4-3-5). F4-3-4 (163.3 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-ethyl acetate (20:1–5:1) to obtain compound 36 (68.2 mg). F4-3-5 (78.5 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-ethyl acetate (20:1–5:1) to obtain compound 33 (30.1 mg). F4-4 (900.6 mg) was further segmented via Sephadex LH-20 (methanol) to obtain 3 subfractions (F4-4-1~F4-4-3). F4-4-1 (20.3 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-ethyl acetate (3:1) to obtain compound 17 (9.0 mg). F4-4-2 (102.3 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-ethyl acetate (3:1) to obtain compound 18 (12.2 mg). F4-4-3 (500.1 mg) was segmented by CC (silica gel) stepwise gradient elution with chloroform-methanol (80:1–20:1) to obtain 4 subfractions (F4-4-3-1~F4-4-3-4). F4-5 (1.1 g) was further segmented via Sephadex LH-20 (methanol) to obtain 5 subfractions (F4-5-1~F4-5-5). F4-5-5 (350.4 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-ethyl acetate (4:1–1:1) to obtain 4 subfractions (F4-5-5-1~F4-5-5-4). F4-5-5-1 (60.3 mg) was segmented by CC (silica gel) stepwise gradient elution with trichloromethane-methanol (60:1–10:1) to obtain compound 7 (27.5 mg). F4-5-5-3 (200.3 mg) was further segmented via Sephadex LH-20 (methanol) to obtain compound 9 (123.9 mg). F4-6-4 (2.1 g) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-ethyl acetate (4:1–1:1) to obtain 2 subfractions (F4-6-4-1~F4-6-4-2). F4-6-4-1 (1.2 g) was further segmented via Sephadex LH-20 (methanol) to obtain compound 27 (20.7 mg). F4-6-4-2-2 (1.0 g) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-ethyl acetate (3:1–1:1), and then was further segmented via Sephadex LH-20 (methanol) to obtain compounds 5 (60.6 mg) and 35 (40.2 mg).
Fr.5 (92.0 g) was passed through a normal-phase silica gel column, firstly subjected to reversed-phase RP-18 silica gel eluting with methanol-water (60–$100\%$) to obtain 11 subfractions (F5-1~F5-11). F5-1 (1.1 g) was further segmented via Sephadex LH-20 (methanol) to obtain 4 subfractions (F5-1-1~F5-1-4). F5-2 (2.0 g) was further segmented via Sephadex LH-20 (methanol) to obtain 2 subfractions (F5-2-1~F5-2-2). F5-2-1 (90.8 mg) was repeatedly recrystallized to produce compound 32 (10.3 mg). F5-2-2 (1.3 g) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-acetone (2:1–1:1) to obtain 5 subfractions (F5-2-2-1~F5-2-2-5). F5-2-2-3 (201.4 mg) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (25:1–10:1) and then was further segmented via Sephadex LH-20 (methanol) to obtain compound 8 (5.0 mg). F5-3 (10.0 g) was segmented by CC (silica gel) stepwise gradient elution with trichloromethane-methanol (80:1–30:1) to obtain 6 subfractions (F5-3-1~F5-3-6). F5-3-3-7 (91.5 mg), followed by semipreparative HPLC ($55\%$, MeOH-H2O, 3 mL/min), was used to obtain compound 2 (4.0 mg). F5-3-6 (5.0 g) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-acetone (3:1–1:1) to obtain 4 subfractions (F5-3-6-1~F5-3-6-4). F5-3-6-3, (76.1 mg) followed by semipreparative HPLC ($50\%$, MeOH-H2O, 3 mL/min), was used to obtain compound 26 (4.2 mg). F5-3-6-4 (47.6 mg), followed by semipreparative HPLC ($55\%$, MeOH-H2O, 3 mL/min), was used to obtain compound 28 (4.2 mg) and 29 (4.0 mg). F5-4 (2.0 g) was further segmented via Sephadex LH-20 (methanol) to obtain 9 subfractions (F5-4-1~F5-4-9). F5-4-1 (614.5 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-acetone (3:1–1:1) to obtain compound 13 (500.1 mg). F5-9 (2.0 g) was further segmented via Sephadex LH-20 (methanol) to obtain 10 subfractions (F5-9-1~F5-9-10). F5-9-1 (1.0 g) was segmented by CC (silica gel) stepwise gradient elution with trichloromethane-methanol (50:1–10:1) to obtain compound 14 (806.2 mg). F5-10 (5.5 g) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-acetone (2:1–1:1) to obtain compound 23 (20.1 mg).
Fr.6 (54.0 g) was firstly subjected to reversed-phase RP-18 silica gel eluting with methanol-water (60–$100\%$) to obtain 7 subfractions (F6-1~F6-7). F6-5 (25 g) was segmented by CC (silica gel) stepwise gradient elution with trichloromethane-methanol (100:1–50:1) to obtain compound 13 (10.0 g) and compound 20 (801.5 mg). F6-5-5 (511.6 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-ethyl acetate (3:1–1:1) to obtain 2 subfractions (F6-5-5-1~F6-5-5-2). F6-5-5-2 (105.3 mg) was further segmented via Sephadex LH-20 (methanol) to obtain compound 24 (89.5 mg).
Fr.7 (43.0 g) was segmented by CC (silica gel) stepwise gradient elution with dichloromethane-methanol (15:1–1:1) to obtain 7 subfractions (F7-1~F7-7). F7-5 (4.0 g) was further segmented via Sephadex LH-20 (methanol) to obtain 7 subfractions (F7-5-1~F7-5-7). F7-5-3 (203.5 mg) was further segmented via Sephadex LH-20 (methanol) to obtain 3 subfractions (F7-5-3-1~F9-5-3-3). F7-5-3-3 (30.6 mg) was segmented by CC (silica gel) stepwise gradient elution with petroleum ether-acetone (2:1–1:1) to obtain compound 6 (5.2 mg). F7-5-7 (40.9 mg) was segmented by CC (silica gel) stepwise gradient elution with trichloromethane-acetone (15:1–1:1) to obtain compound 25 (30.7 mg).
4′,4′-Dimethoxy-sophvein [17]: white amorphous powder; αD24 = + 49.88 (c 0.25, MeOH); UV λmax (MeOH) nm (log ε): 214 (4.74), 229 (4.64), 285 (4.58); IR (KBr) νmax: 3456, 2950, 1638, 1400, 1098 cm−1; 1H NMR (Methanol-d4, 600 MHz) and 13C NMR (Methanol-d4, 150 MHz), see Table 1; HRESIMS at m/z 429.1894 [M+Na]+ (calcd for C22H30O7Na+, 429.1884).
Sophvein-4′-one [18]: white amorphous powder; αD24 = +38.69 (c 0.29, MeOH); UV λmax (MeOH) nm (log ε): 214 (4.48), 231 (4.06), 286 (3.97); IR (KBr) νmax: 3446, 2952, 1635, 1406, 1086 cm−1; 1H NMR (Acetone-d6, 600 MHz) and 13C NMR (Acetone-d6, 150 MHz), see Table 1; HRESIMS at m/z 361.1650 [M+Na]+ (calcd for C20H24O6 Na+, 361.1646).
## 3.4. Cell Culture Conditions
Murine macrophages (RAW 264.7) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with the addition of $10\%$ fetal bovine serum (FBS) and $1\%$ streptomycin (10,000 μg/mL)-penicillin (10,000 U/mL) at 37 °C in a humidified incubator under $5\%$ CO2. HepG2 cells were cultured at 37 °C under $5\%$ CO2. The optimal fermentation culture medium comprised DMEM, $1\%$ mixed penicillin (10,000 U mL−1), $1\%$ HEPES (BioFroxx, Hesse, Einhausen), and $10\%$ fetal bovine serum (FBS), as well as streptomycin (SM) (10,000 μg/mL−1) fluid. In all experiments, cells were left to acclimate for 24 h before any treatments.
## 3.5. Cell Viability Examination
To measure cell viability, an MTT assay was performed [41]. Macrophages (4 × 104 per well) were cultured in 96-well plates for 24 h; then, they were treated with several concentrations of compounds 1–36 (3.12, 6.25, 12.5, 25, and 50 μM) for 1 h, and then they were stimulated with LPS (1 μg/mL) for another 24 h. After washing twice with a PBS buffer, 20 μL of the MTT solution was added to each well, and incubation continued for 4 h. Optical density (O.D) was measured at 490 nm by a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). The relative cell viability was calculated in contrast to the normal control group.
## 3.6. NO Production Measurement
Activated macrophage cells could express inducible nitric oxide synthase (iNOS), catalyzing the production of NO from L-arginine. To determine the effect of compounds 1–36 on treatments on NO production, the accumulation of nitrites (NO2−) in the culture medium was recorded as an indicator of NO production [42,43]. Macrophages (8 × 104 per well) were seeded onto 96-well plates and pretreated by compounds 1–36 (3.12, 6.25, 12.5, 25, and 50 μM) 1 h prior to treatment by LPS (1 μg/mL). Afterward, costimulation for 24 h at 37 °C was carried out in an incubator under $5\%$ CO2. Then, Griess reagents I and II (100 μL) were mixed with cell culture medium (70 μL). Prior to measuring the optical density, plates were incubated at room temperature for 10 min, and the absorbance at 540 nm was measured using a Thermo Fisher Scientific microplate reader. N(G)-monomethyl-L-arginine, monoacetate salt (L-NMMA), and the medium were used as positive and negative controls, respectively.
## 3.7. Antiproliferative Assay
Briefly, the cells were incubated in 96-well microplates (1 × 105 per well) and allowed to adhere for 24 h before drug administration. Then, the cell lines were treated with test compounds 1–36 at five concentrations (3.12, 6.25, 12.5, 25, and 50 μM). At 24 and 48 h of incubation, cells were treated with MTT (200 µL, 5.0 mg/mL) and dissolved in the culture medium, for 1 h under at 37 °C under a $5\%$ CO2 humidified atmosphere. The MTT was then removed carefully and resolved with DMSO (150 μL/well). Optical density was recorded using a Thermo Fisher Scientific microplate reader at 490 nm. Cisplatin (CP) and medium were used as positive and negative controls, respectively.
## 3.8. Statistical Analysis
The data were analyzed using Statistical Package for Social Sciences (SPSS Version 21.0) software and are presented as the mean ± S.D. values of three different experiments. The results were analyzed via one-way analysis of variance (ANOVA), and statistical significance was defined as $p \leq 0.05.$
## 4. Conclusions
In summary, the systematic phytochemical study on the roots of S. flavescens led to the isolation of 36 flavonoids (1–36), including 18 dihydroflavonoids (1–18), 7 flavonols (19–25), 4 dihydroflavonols (26–29), 3 isoflavones (30–32), and 4 chalcones (33–36), identified by spectroscopic methods (1H, 13C NMR, HSQC, 1H-1H COSY, HMBC, HRESIMS), and chemical and physical methods. Bioactivity assays indicated that nine compounds exhibited the most significant NO inhibitory activity, better than that of the L-NMMA positive control (IC50 = 21.8 ± 0.9 μM). Furthermore, the antiproliferative activities against the HepG2 hepatoma cell lines of all isolates were assessed using the MTT method. The results demonstrated that compound 22 exhibited significant cytotoxic activity, with an IC50 value of 0.46 ± 0.1 μM (positive control cisplatin: IC50 = 24.5 ± 0.8 μM). This study lays the foundation for studying the potential therapeutic applications of flavonoid derivatives from S. flavescens for inflammatory diseases and hepatoma.
## References
1. Lu K.-Z., Feng Z.-M., Yuan X., Yang Y.-N., Jiang J.-S., Zhang X., Zhang P.-C.. **Five novel pterocarpan derivatives from**. *Chin. J. Chem.* (2021) **39** 2763-2768. DOI: 10.1002/cjoc.202100357
2. He X.-R., Fang J.-C., Huang L.-H., Wang J.-H., Huang X.-Q.. *J. Ethnopharmacol.* (2015) **172** 10-29. DOI: 10.1016/j.jep.2015.06.010
3. Zhou Y.-J., Gou Y.-J., Yang X.-L., Ou Z.-L.. **Anti-cervical cancer role of matrine, oxymatrine and**. *J. Cancer* (2018) **9** 1357-1364. DOI: 10.7150/jca.22427
4. Shen C.-C., Lin T.-W., Huang Y.-L., Wan S.-T., Shien B.-J., Chen C.-C.. **Phenolic constituents of the roots of Sophora flavescens**. *J. Nat. Prod.* (2006) **69** 1237-1240. DOI: 10.1021/np060189d
5. Kwon M., Ko S.K., Jang M., Kim G.H., Ryoo I.J., Son S., Ryu H.W., Oh S.R., Lee W.K., Kim B.Y.. **Inhibitory effects of flavonoids isolated from**. *J. Enzyme Inhib. Med. Chem.* (2019) **34** 1481-1488. DOI: 10.1080/14756366.2019.1640218
6. Zhou H.-P., Lutterodt H.M., Cheng Z.-H., Yu L.-L.. **Anti-inflammatory and antiproliferative activities of trifolirhizin, a flavonoid from**. *J. Agric. Food. Chem.* (2009) **57** 4580-4585. DOI: 10.1021/jf900340b
7. Kim J.H., Cho C.W., Kim H.Y., Kim K.T., Choi G.S., Kim H.H., Cho I.S., Kwon S.J., Choi S.K., Yoon J.Y.. **α-Glucosidase inhibition by prenylated and lavandulyl compounds from**. *Int. J. Biol. Macromol.* (2017) **102** 960-969. DOI: 10.1016/j.ijbiomac.2017.04.092
8. Zhang S.-Y., Li W., Nie H., Liao M., Qiu B., Yang Y.-L., Chen Y.-F.. **Five new alkaloids from the roots of Sophora flavescens**. *Chem. Biodivers.* (2018) **15** e1700577. DOI: 10.1002/cbdv.201700577
9. Li J.-J., Zhang X., Shen X.-C., Long Q.-D., Xu C.-Y., Tian C.-J., Lin Y.. **Phytochemistry and biological properties of isoprenoid flavonoids from**. *Fitoterapia* (2020) **143** 104556-104562. DOI: 10.1016/j.fitote.2020.104556
10. Zhu H., Yang Y.-N., Feng Z.-M., Jiang J.-S., Zhang P.-C.. **Sophoflavanones A and B, two novel prenylated flavanones from the roots of**. *Bioorg. Chem.* (2018) **79** 122-125. DOI: 10.1016/j.bioorg.2018.04.019
11. Yang Y.-N., Zhu H., Yuan X., Zhang X., Feng Z.-M., Jiang J.-S., Zhang P.-C.. **Seven new prenylated flavanones from the roots of**. *Bioorg. Chem.* (2021) **109** 104716. DOI: 10.1016/j.bioorg.2021.104716
12. Coussens L.M., Werb Z.. **Inflammation and cancer**. *Nature* (2002) **420** 860-867. DOI: 10.1038/nature01322
13. Gerold J., Reiner W., Hans A.. **Cyclohexanoid protoflavanones from the stem-bark and roots of**. *Phytochemistry* (2005) **66** 1698-1706. PMID: 15922375
14. Milligan S., Kalita J., Heyerick A., Rong H., Cooman L.D., Keukeleire D.D.. **Identification of a potent phytoestrogen in hops (**. *J. Clin. Endocr. Metab.* (1999) **84** 2249. DOI: 10.1210/jcem.84.6.5887
15. Jeong T.S., Ryu Y.B., Kim H.Y., Curtis-Long M.J., An S.J., Lee J.H., Lee W.S., Park K.H.. **Low Density lipoprotein (LDL)-antioxidant flavonoids from roots of**. *Biol. Pharm. Bull.* (2008) **31** 2097-2102. DOI: 10.1248/bpb.31.2097
16. Wollenweber E., Roitman J.N.. **New reports on surface flavonoids from Chamaebatiaria (Rosaceae), Dodonaea (Sapindaceae), Elsholtzia (Lamiaceae), and Silphium (Asteraceae)**. *Nat. Prod. Commun.* (2007) **2** 385-389. DOI: 10.1177/1934578X0700200407
17. Kuroyanagi M., Arakava T., Hirayama Y., Hayashi T.. **Antibacterial and antiandrogen flavonoids from**. *J. Nat. Prod.* (1999) **62** 1595-1599. DOI: 10.1021/np990051d
18. Iinuma M., Ohyama M., Tanaka T., Lang F.A.. **Three New Phenolic Compounds from the Roots of**. *J. Nat. Prod.* (1993) **56** 2212. DOI: 10.1021/np50102a033
19. Yang X., Yang J., Xu C., Huang M., Zhou Q., Lv J., Ma X., Ke C., Ye Y., Shu G.. **Antidiabetic effects of flavonoids from**. *J. Ethnopharmacol.* (2015) **170** 161-170. DOI: 10.1016/j.jep.2015.05.043
20. Huang R., Liu Y., Zhao L.L., Chen X.X., Wang F., Cai W., Chen L.. **A new flavonoid from**. *Nat. Prod. Res.* (2017) **31** 2228-2232. DOI: 10.1080/14786419.2017.1297992
21. Iinuma M., Ohyama M., Tanaka T.. **Six flavonostilbenes and a flavanone in roots of**. *Phytochemistry* (1995) **38** 519-525. DOI: 10.1016/0031-9422(94)00720-E
22. Shirataki Y., Yokoe I., Noguchi M., Tomimoi T., Komatsu M.. **Studies on the Constituents of Sophora Species. XXII.: Constituents of the root of**. *Chem. Pharm. Bull.* (1988) **36** 2220-2225. DOI: 10.1248/cpb.36.2220
23. Ibieta H.P., Michael W.. **Binding of flavonoids from**. *Planta Med.* (2005) **71** 1065-1068. PMID: 16320211
24. Hatayama K., Komatsu M.. **Studies on the constituents of Sophora species. V. Constituents of the root of**. *Chem. Pharm. Bull.* (1971) **19** 2126-2131. DOI: 10.1248/cpb.19.2126
25. Kang T.H., Jeong S.J., Ko W.G., Kim N.Y., Lee B.H., Inagaki M., Miyamoto T., Higuchi R., Kim Y.C.. **Cytotoxic lavandulyl flavanones from**. *J. Nat. Prod.* (2000) **63** 680-681. DOI: 10.1021/np990567x
26. Shi Y.R., Lee H.S., Kim Y.K., Kim S.H.. **Determination of isoprenyl and lavandulyl positions of flavonoids from**. *Arch. Pharm. Res.* (1997) **20** 491-495. PMID: 18982496
27. Wang Z.Q., Lou Y.J.. **Proliferation-stimulating effects of icaritin and desmethylicaritin in MCF-7 cells**. *Eur. J. Pharmacol.* (2004) **504** 147-153. DOI: 10.1016/j.ejphar.2004.10.002
28. Jung M.J., Kang S.S., Jung H.A., Kim G.J., Choi J.S.. **Isolation of flavonoids and a cerebroside from the stem bark of**. *Arch. Pharm. Res.* (2004) **27** 593-599. DOI: 10.1007/BF02980155
29. Kawamura T., Hayashi M., Mukai R., Terao J., Nemoto H.. **An Efficient Method for C8-Prenylation of Flavonols and Flavanones**. *Synthesis* (2012) **44** 1308-1314
30. Jung J., Kang S.S., Woo J.J., Choi J.S.. **A new lavandulylated flavonoid with free radical and onoo-scavenging activities from**. *Arch. Pharm. Res.* (2005) **28** 1333-1336. DOI: 10.1007/BF02977897
31. Chen H., Yang J., Hao J., Lv Y., Chen L., Lin Q., Yuan J., Yang X.. **A Novel Flavonoid kushenol Z from**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24244425
32. Ohmoto T., Aikawa R., Nikaido T., Sankawa U., Wu L.J., Ueno A., Hukushima S.. **Inhibition of adenosine 3′, 5′-cyclic monophosphate phosphodiesterase by components of**. *Chem. Pharm. Bull.* (1986) **34** 2094-2099. DOI: 10.1248/cpb.34.2094
33. Yagi A., Fukunaga M., Okuzako N., Mifuchi I., Kawamoto F.. **Antifungal substances from**. *Jpn. J. Pharmacogn.* (1989) **43** 343-347
34. Sato S., Takeo J., Aoyama C., Kawahara H.. **Na**. *Bioorgan. Med. Chem.* (2007) **15** 3445-3449. DOI: 10.1016/j.bmc.2007.03.011
35. Wu L., Miyase T., Ueno A., Kuroyanagi M., Noro T., Fukushima S.. **Studies on the Constituents of**. *Yakugaku Zasshi* (1985) **105** 736-741. DOI: 10.1248/yakushi1947.105.8_736
36. Selepe M.A., Drewes S.E., Heerden F.R.. **Total synthesis of the pyranoisoflavone kraussianone I and related isoflavones**. *J. Nat. Prod.* (2010) **73** 1680-1685. DOI: 10.1021/np100407n
37. Umehara K., Nemoto K., Matsushita A., Terada E., Monthakantirat O., De-Eknamkul W., Miyase T., Warashina T., Degawa M., Noguchi H.. **Flavonoids from the heartwood of the Thai medicinal plant**. *J. Nat. Prod.* (2009) **72** 2163-2168. DOI: 10.1021/np900676y
38. Bartmańska A., Tronina T., Popłoński J., Milczarek M., Filip-Psurska B., Wietrzyk J.. **Highly cancer selective antiproliferative activity of natural prenylated flavonoids**. *Molecules* (2018) **23**. DOI: 10.3390/molecules23112922
39. Stevens J.F., Taylor A.W., Nickerson G.B., Ivancic M., Deinzer M.L.. **Prenylflavonoid variation in**. *Phytochemistry* (2000) **53** 759-775. DOI: 10.1016/S0031-9422(00)00005-4
40. Wu L., Miyase T., Ueno A., Kuroyanagi M., Noro T., Fukushima S.. **Studies on the constituents of**. *Chem. Pharm. Bull.* (1985) **33** 3231-3236. DOI: 10.1248/cpb.33.3231
41. El-Readi M.Z., Eid S., Ashour M.L., Tahrani A., Wink M.. **Modulation of multidrug resistance in cancer cells by chelidonine and Chelidonium majus alkaloids**. *Phytomedicine* (2013) **20** 282-294. DOI: 10.1016/j.phymed.2012.11.005
42. Li J.-C., Dai W.-F., Liu D., Jiang M.-Y., Zhang Z.-J., Chen X.-Q., Chen C.-H., Li R.-T., Li H.-M.. **Bioactive ent-isopimarane diterpenoids from**. *Phytochemistry* (2020) **175** 112373. DOI: 10.1016/j.phytochem.2020.112373
43. Cao L., Li R., Chen X., Xue Y., Liu D.. **Neougonin A inhibits lipopolysaccharide -induced inflammatory responses**. *Inflammation* (2016) **39** 1939-1948. DOI: 10.1007/s10753-016-0429-9
|
---
title: Differential effects of NOX2 and NOX4 inhibition after rodent spinal cord injury
authors:
- Guzal Khayrullina
- Sara Bermudez
- Deanna Hopkins
- Young Yauger
- Kimberly R. Byrnes
journal: PLOS ONE
year: 2023
pmcid: PMC10004500
doi: 10.1371/journal.pone.0281045
license: CC0 1.0
---
# Differential effects of NOX2 and NOX4 inhibition after rodent spinal cord injury
## Abstract
Reactive oxygen species (ROS) are a contributing factor to impaired function and pathology after spinal cord injury (SCI). The NADPH oxidase (NOX) enzyme is a key source of ROS; there are several NOX family members, including NOX2 and NOX4, that may play a role in ROS production after SCI. Previously, we showed that a temporary inhibition of NOX2 by intrathecal administration of gp91ds-tat immediately after injury improved recovery in a mouse SCI model. However, chronic inflammation was not affected by this single acute treatment, and other NOX family members were not assessed. Therefore, we aimed to explore the effect of genetic knockout (KO) of NOX2 or acute inhibition of NOX4 with GKT137831. A moderate SCI contusion injury was performed in 3 month old NOX2 KO and wild-type (WT) mice, who received no treatment or GKT137831/vehicle 30 minutes post-injury. Motor function was assessed using the Basso Mouse Scale (BMS), followed by evaluation of inflammation and oxidative stress markers. NOX2 KO mice, but not GKT137831 treated mice, demonstrated significantly improved BMS scores at 7, 14, and 28 days post injury (DPI) in comparison to WT mice. However, both NOX2 KO and GKT137831 significantly reduced ROS production and oxidative stress markers. Furthermore, a shift in microglial activation toward a more neuroprotective, anti-inflammatory state was observed in KO mice at 7 DPI and a reduction of microglial markers at 28 days. While acute alterations in inflammation were noted with GKT137831 administration, this was not sustained through 28 days. In vitro analysis also showed that while GKT137831 reduced ROS production by microglia, it did not translate to changes in pro-inflammatory marker expression within these cells. These data demonstrate that NOX2 and NOX4 play a role in post-injury ROS, but a single dose of NOX4 inhibitor fails to enhance long-term recovery.
## Introduction
Inflammation and oxidative stress occurring after spinal cord injury (SCI) play significant roles in post-injury recovery. An overproduction of reactive oxygen species (ROS) contributes to post-injury oxidative stress and is observed weeks to years after injury [1]. The nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) enzyme family is one of the primary synthesizers of ROS in the injured spinal cord [2, 3]. We and others have shown that the NOX2 and 4 isoforms are up-regulated after brain and SCI in a number of cells [3–5].
In microglia after mouse SCI, NOX2 expression was elevated at 4 and 7 DPI, while NOX4 expression was significantly elevated at 1, 4 and 60 DPI [6]. Inhibition of NOX2, either indirectly with a non-specific inhibitor such as apocynin or via NOX2 knockout (KO), has been shown to reduce acute oxidative stress and improve acute recovery in traumatic brain injury (TBI) models [7]. Also in the brain injury model, delayed administration of gp91ds-tat, which inhibits NOX2 assembly, induced a shift in microglial polarization from pro-inflammatory phenotype to the anti-inflammatory phenotype [8]. We have previously shown that acute inhibition of NOX2 with this same treatment had similar effects in a SCI model, with elevated microglial anti-inflammatory marker expression at subacute periods after moderate SCI in mice [9]. In addition, in this same study acute gp91ds-tat administration reduced acute and sub-acute post-injury inflammation, ROS production and improved motor function after SCI, but chronic inflammation in the injured spinal cord tissue was not found to be altered. Recently, Sabirzhanov et al. [ 5] demonstrated similar results with the NOX2 KO model, with sustained improvements in motor function that was similar to gp91ds-tat administration accompanied by suppressed thermal and mechanical hypersensitivity. This evaluation in NOX2 KO mice also demonstrated sustained white matter sparing at 8 weeks post-injury, as well as acute depression in macrophage infiltration at 24 hours, but sub-acute evaluation was limited.
To date, the contribution of NOX4 to recovery after SCI is unclear. NOX4, which is also upregulated after SCI [3, 4], differs from other NOX isoforms in several ways. This enzyme produces hydrogen peroxide instead of superoxide and is constitutively active independent of cytosolic subunits [10]. This isoform has been reported to be largely regulated on the transcriptional level, however post-transcriptional regulation has also been reported [11, 12]. Originally found in the kidney and named Renox [13], NOX4 has since been described to have a very wide tissue distribution and to be highly expressed in the epithelium, including within brain vessels [14]. In the rat brain, NOX4 was found to be expressed in microglia and astrocytes after injury, and constitutively expressed in neurons [4]. In the healthy rat spinal cord, NOX4 was not found in motor neurons, but was expressed in both astrocytes and microglia, with an elevation in expression acutely after injury in microglia [3, 15]. Clinically, after TBI, Li et al. [ 16] found an acute peak in NOX4 in neurons and a gradual increase in astrocytes that continued until the end of the study, although expression in microglia was not examined. Interestingly, the authors found this increased immunostaining of NOX4 to be correlated with a poorer condition of patients, as measured by the Glasgow Coma Score (GCS).
The role of acute NOX4 expression and the influence of NOX2 knockout during the sub-acute period after SCI, in comparison to our previous work on acute NOX2 inhibition with gp91ds-tat, remains a gap in the SCI research field. This study therefore aimed to investigate the effect of acute inhibition of NOX4 or complete elimination of NOX2 activity through utilization of the NOX4 inhibitor GKT137831 or the NOX2 KO mouse model. While GKT137831 is not specific for NOX4, and may also target NOX1, NOX1 has limited expression within the rodent spinal cord, suggesting effects may be specific for NOX4 [17]. We now show that global genetic ablation of NOX2 has very similar effects as a single acute administration of a NOX2 inhibitor, but that a single dose of a NOX4 inhibitor is less effective. While both NOX2 and NOX4 inhibition reduced acute and sub-acute oxidative stress and inflammation, only NOX2 inhibition led to persistent reductions and functional recovery.
## Experimental design
Adult male NOX2 KO (20-25g, B6.129S-Cybbtm1Din/J, Jackson Laboratories, knock out of cytochrome b-245, beta polypeptide, C57Bl6 background) and wild type (WT, C57Bl6, 20-25g, Taconic Farms, Derwood, MD) mice were utilized in all experiments (Table 1). Groups including KO and WT mice, as well as WT mice that received vehicle or GKT137831. Mice underwent a moderate spinal cord injury contusion at T9, followed by weekly motor assessment using the BMS score. NOX2 KO and WT mice then underwent sub-acute assessment of oxidative stress and inflammatory responses, including detection of protein carbonylation with Oxyblot, pro- and anti-inflammatory marker expression with Western blot, microglial presence and neuronal survival with immunohistochemistry. WT mice administered vehicle or GKT137831 underwent acute and sub-acute assessment of similar outcomes, including detection of acute ROS with CM-H2DCFDA assay, protein carbonylation, and inflammatory cell presence at the lesion site with immunohistochemistry and flow cytometry. In addition, as the results of the GKT137831 study were somewhat surprising, determination of the effect of GKT137831 was evaluated in vitro on isolated microglial cells or the microglial cell line BV2. All outcome measures and n for each experiment are detailed in Table 1.
**Table 1**
| Outcome Measure | BMS (weekly) | Oxyblot/Western blot | CM-H2DCFDA | Immunohisto-chemistry | Flow Cytometry | Cell based Assays |
| --- | --- | --- | --- | --- | --- | --- |
| WT Mice | 10 | 4 (7DPI), 6 (28DPI) | | 3 (28DPI) | | |
| NOX2 KO Mice | 12 | 4 (7DPI), 6 (28DPI) | | 4 (28DPI) | | |
| Vehicle Treated Mice | 6 (1 mouse removed) | 4 (28DPI) | 4.0 | | 6 (7DPI) | |
| GKT137831 Treated Mice | 9 (1 mouse removed) | 4 (28DPI) | 4.0 | | 7 (7DPI) (1 mouse removed) | |
| In vitro—GKT137831/Vehicle | | | | | 3 biological replicates | 3 biological replicates |
## Animal handling and surgical methods
Mice were group housed (4–5 mice per cage) and received free access to food and water with a 12:12 hour light cycle. A total of 82 male mice (NOX2 KO $$n = 16$$, WT $$n = 53$$, WT naïve $$n = 3$$) were used for this study and randomly assigned to experimental groups; due to post-surgical complications, 3 mice (1 WT, 2 WT + GKT137831; removals indicated in Table 1) were removed from the study. Animal numbers were established prior to the study using power analysis based on prior research for BMS scoring, histology and flow cytometry to achieve a power of $80\%$ with an alpha of 0.05. Investigators were blinded to treatment and genetic grouping. All experiments complied fully with the principles set forth in the “Guide for the Care and Use of Laboratory Animals” and were approved by the Uniformed Services University IACUC.
For SCI, mice were anesthetized with isoflurane ($4\%$ induction, $2\%$ maintenance). Mice received a laminectomy at the T9 spinal level, followed by a contusion simulating moderate SCI using the Infinite Horizons Impactor (50 kdyne; Precision Systems and Instrumentation, Fairfax Station, VA) as previously described [9]. The incision was then closed and animals were maintained on heating pads until they regained movement. Acetaminophen (Children’s Tylenol, 200mg/kg) was added to drinking water for 72 hours post-injury. Manual bladder expression was performed daily until normal bladder expression returned.
## GKT137831 administration
At 30 minutes post-injury, either GKT137831 or vehicle ($1.2\%$ methyl cellulose (Sigma, M0262-100g) and $0.1\%$ polysorbate 80 (Sigma, 59924-100G-F) per 100mL de-ionized water) was administered by oral gavage to a cohort of WT mice. GKT137831 was obtained from Genkyotex (Geneva, Switzerland), and reconstituted for a final dose of 60mg/kg and volume of 10ml/kg.
## Functional testing
Locomotor function and recovery was analyzed using the Basso Mouse Scale for Locomotion (BMS) and subscore by investigators blinded to group, as previously described [18]. Injured mice were scored in seven categories including ankle movement, plantar placement, stepping, coordination, paw position, trunk instability and tail position. BMS subscore allows for analysis of paw position, trunk stability and coordination, once mice achieve the threshold of frequent stepping [18], All mice were scored at 24hr post injury and weekly thereafter.
## Oxyblot
At 7 or 28 days post injury (DPI), mice were euthanized and tissue was flushed with 100ml of $0.9\%$ Sodium Chloride. A 5mm spinal cord segment, 2.5mm rostral and 2.5mm caudal to the lesion epicenter, was collected and protein extracted with RIPA (1X) buffer and Halt protease inhibitor single-use cocktail (Thermo Scientific, Rockford, IL). Millipore OxyBlot Protein Oxidation Detection Kit was used according to the manufacturer’s instructions. GAPDH (1:2000, MAB374, Millipore) was used as control for gel loading and protein transfer. Bands were quantified using the Image J software.
## CM-H2DCFDA staining
At 2 hours post-injury, WT-vehicle or GKT137831 treated mice were euthanized with euthasol (sodium pentobarbital mix, 200mg/kg) and spinal cords were rapidly dissected and frozen on dry ice. Fresh frozen spinal cords (10 mm centered on the T9 lesion site) were cut longitudinally with a cryostat and every 3rd 20 μm slice mounted onto charged slides. ROS level in tissue was assessed using fluorescence probe 2’,7’-dichlorodihydrofluorescein diacetate (H2DCFDA; Molecular Probes) as previously described [19]. CM-H2DCFDA is hydrolyzed by nonspecific esterases to release 2′,7′-dichlorodihydrofluorescein (CM-H2DCF), which is oxidized by intracellular ROS, such as hydrogen peroxide, to CM-DCF, which emits a green fluorescence. Briefly, slides were washed with chilled PBS for 5 minutes then incubated at 37°C in a humid chamber covered with 500 μl of 5μM H2DCFDA diluted in PBS. After incubation a last wash with PBS was performed and slides were coverslipped using mounting media containing DAPI (Vector Labs). Fluorescence within the lesion epicenter +/- 1mm was detected and photographed using the NanoZoomer Digital Pathology system (Hamamatsu Photonics, K.K., Japan). Scion Image Analysis was used to assess density of DCF fluorescent pixels above background. Measurement was performed on at least 5 randomly selected images from the dorsal half of the spinal cord obtained per animal in the perilesional region (the 1mm surrounding the lesion epicenter, not including the central cavity) including grey and white matter.
## Western blot
Utilizing the same tissue obtained for Oxyblot, aliquots of 25μg protein were used for western analysis using the following primary antibodies: anti-3NT (1:1000, Ab61392 Abcam, Cambridge, MA), anti-CD86 (1:250, Ab53004 Abcam), anti-iNOS (1:1000, Ab3523 Abcam), anti-GFAP (1:100, Ab4648 Abcam), and anti-CD11b (1:10,000; MCA275R Abd Serotec). Immune complexes were detected with appropriate secondary antibodies and chemiluminescence reagents (Pierce, Rockford, IL). All samples were normalized to GAPDH (1:2000, Millipore, Temecula, CA). ImageJ software was used to quantify bands.
## Immunohistochemistry
At 28 DPI, tissue was assessed for immunohistochemistry. Mice were anesthetized (Euthasol, 0.22ml/kg, I.P) and perfused with 100ml of $0.9\%$ sterile saline, followed by 300ml of $10\%$ buffered formalin phosphate (Fisher Scientific, Fair Lawn, NJ). A 5mm spinal cord segment, 2.5mm caudal and 2.5mm rostral to the injury site, was extracted and kept in formalin for 24 hours and then transferred to a $30\%$ sucrose solution. Spinal cords were then cut into 20μm axial sections. Five sections spanning the lesion site (rostral 2.5mm to caudal 2.5 mm) at regular intervals (every 1mm) were selected for standard fluorescent immunohistochemistry utilizing primary antibodies that had been previously characterized (using lack of primary antibody or blocking antibody and examination of expected cellular morphology and staining profiles) in the laboratory [3, 4], including Iba1 (1:100, 019–19741 Wako), and NeuN (1:100, ABN78 Millipore). Alexa-Fluor secondary antibodies (1:1000, A11010, A11070 or A11017 Invitrogen) were used for visualization. Slides were coverslipped using mounting media containing DAPI to counterstain for nuclei (Vector Labs, Burlingame, CA). Immunofluorescence was detected and photographed in the dorsal column region within the 5mm region of interest using an Olympus DP72 microscope with Olympus cellSens microscopy software (Olympus, Center Valley, PA). NeuN was quantified using ImageJ, using first image threshold to identify only cells positively stained with NeuN, followed by the particle analysis tool, with a cutoff of 500 pixels. All other immunolabeled cells were quantified using threshold analysis and pixel density above threshold with ImageJ as previously described [20].
## Flow cytometry
A 5mm spinal cord segment, 2.5mm caudal and 2.5mm rostral to the injury site, was processed for flow cytometry at 7 days post-injury (DPI), as previously described [9]. Briefly, the OptiPrep density gradient was used to exclude the debris layer from the viable cells. Sytox Blue (Thermo Scientific, Rockford, IL) was used to gate out dead cells. APC/Cy7 conjugated anti-CD45 (1:50, BioLegend, San Diego, CA) was used to gate on population of interest. From there, populations were further gated on microglia/macrophages (CD45+ CD11b+ GR-1-), and neutrophils (CD45+ CD11b+ GR-1+) using PE conjugated anti-CD11b (1:20 eBioscience Inc., San Diego, CA) and FITC conjugated anti-GR-1 (1:200 BioLegend, San Diego, CA). All analysis was performed using FlowJo (FlowJo, LLC, Ashland, OR). OneComp eBeads (one drop per sample, eBioscience Inc., San Diego, CA) were used for single stain controls, while cells were used for unstained control. Further statistical analysis was performed on values/ percentages gathered. Each animal represents one sample with a minimum of 200,000 cells collected.
BV2 microglia (described below) were detached from culture plates with Accutase® cell detachment solution (Innovative Cell Technologies, San Diego, CA). Cells were immunolabeled with PE conjugated anti-CD86 (0.5 μl/ml, BD Biosciences, San Jose, CA, AF647 CD206 (1μl/ml, BD Biosciences, San Jose, CA), BV421 conjugated anti-TGFβ (2 μl/ml, BD Biosciences, San Jose, CA), and Fixable Viability Stain 510 (0.5 μl/ml, BD Biosciences, San Jose, CA).
## In vitro analysis
The BV2 microglial cell line (a generous gift from Dr. Carol Colton) was cultured and replated at passage 13–20. Cells were incubated at 37°C with $5\%$ CO2 in Dulbecco’s modified *Eagle media* (Gibco, Carlsbad, CA) with $10\%$ fetal calf serum (Hyclone, Logan, UT), $1\%$ L-glutamine (Gibco), $1\%$ sodium pyruvate (Gibco), and $1\%$ Pen/Strep (Fisher, Pittsburgh, PA). Cells were used 24 hours after plating for experimentation at a density of 2 x105 cells/ml. BV2 microglia were exposed to LPS (100ng/ml) or vehicle for 1 hour prior to treatment with GKT137831 (0.5μM, 1μM, 5μM, 10μM) or vehicle and incubated at 37°C and $5\%$ CO2 for 24 hours. All drugs were prepared and stored according to the manufacturer’s guidelines.
Primary microglia were obtained as previously described [15]. Briefly, the whole brain was dissected from P2 Sprague Dawley rat pups and homogenized in L15 media (Gibco). Mixed glial cultures were then incubated for 8 to 10 days at 37°C with $5\%$ CO2 in Dulbecco’s modified *Eagle media* (Invitrogen) with $10\%$ fetal calf serum (Hyclone, Logan, UT, USA), $1\%$ L-glutamine (Invitrogen), $1\%$ sodium pyruvate (Invitrogen), and $1\%$ Pen/Strep (Fisher, Pittsburgh, PA, USA). After the initial incubation, cells were shaken for 1 hour at 100 rpm and 37°C and detached microglia were collected and plated at 2 x105.
Twenty-four hours after plating, microglia were exposed to LPS (100ng/ml) or vehicle for 1 hour prior to addition of GKT137831 (10μM) or vehicle and incubation at 37°C and $5\%$ CO2 for 24 hours. All assays were performed in 3 independent trials each in triplicate.
## Nitric oxide and ROS (DCF) assay
Nitric oxide production and intracellular ROS generation were determined 24 hours after treatment. NO• production was assayed using the Griess Reagent Assay kit (Invitrogen) and absorption read at 540nm, according to the manufacturer’s instructions. The intracellular levels of ROS were measured using the fluorescence probe 2’,7’-dichlorodihydrofluorescein diacetate (H2DCFDA; Molecular Probes, Eugene, OR). Cells were incubated with 10 μM H2DCFDA (diluted in PBS) for 45 min at 37°C. Fluorescence was measured using excitation and emission wavelengths of 490 and 535 nm, respectively, according to the manufacturer’s instructions.
## ELISA
At 24 hours after treatment, 150 μl of media was transferred to new plates and frozen at -80°C. In order to assess the changes in cytokine release after treatment with GKT137831, ELISA was used to determine IL-1β (Thermo Fisher) and TNFα (Millipore) release. All assays were performed as per manufacturers’ instructions.
## Statistics
Quantitative data are presented as mean ± standard error of the mean. Normality of data was assessed using the Shapiro-Wilk test within GraphPad Prism. BMS and subscore were analyzed using repeated measures ANOVA with Bonferroni’s multiple comparisons test. All other quantitative data were analyzed using paired t test, unpaired t test, one-way or two-way ANOVA, as appropriate. All statistical tests were performed using the GraphPad Prism Program, Version 9 for Windows (GraphPad Software, San Diego, CA). A p value <0.05 was considered statistically significant.
## NOX2 KO, but not acute NOX4 inhibition, improves functional recovery
Hind limb locomotion recovery post-surgery was measured using the BMS at 1 day and weekly thereafter through 28 DPI (Fig 1). No significant difference in function was found at 1 day post-injury between any group, demonstrating a consistent level of injury among groups. However, the BMS score showed significant ($$p \leq 0.0042$$ WT vs KO over time, two-way ANOVA) increases in score in the NOX2 KO mice compared to WT injured mice at 7 (1.70 +/- 0.2 vs. 3.04+/-0.4), 14 (2.25+/-0.2 vs 3.63+/-0.4), and 28 DPI (2.9+/-0.5 vs. 4.75+/-0.5), suggesting that NOX2 KO improves gross motor function, similar to acute post-injury administration of gp91ds-tat [9]. In addition, the BMS subscore, which evaluates the stepping behavior of the mouse, including frequency of plantar stepping and coordination and paw position during steps, showed a significant improvement with NOX2 KO by 28 days post-injury ($$p \leq 0.0187$$ WT vs KO day 28, repeated measures ANOVA).
**Fig 1:** *Hind limb locomotion recovery post surgery was measured using Basso Mouse Scale (BMS) at 1, 7, 14, 21, and 28 days post injury.The BMS average score showed significant improvement at 7, 14, 21 and 28 days in NOX2 KO injured mice compared to wild type injured mice (A). Subscore analysis showed significant improvement at 28 days post-injury in the KO group (B). Points represent mean +/- SEM., NOX2 KO injured n = 12, WT injured n = 10, Vehicle n = 6, GKT n = 9. *p<0.05, **p<0.01, ****p<0.0001 repeated measures ANOVA.*
In contrast, GKT137831 and vehicle treated mice showed no significant difference between groups or in comparison to the WT ($$p \leq 0.4156$$, repeated measures ANOVA). Similarly, no significant difference between groups was noted in the BMS sub-score ($$p \leq 0.1867$$ Vehicle vs. GKT137831 day 28, repeated measures ANOVA, Fig 1), suggesting that, unlike gp91ds-tat targeting of NOX2, a single administration of NOX4 inhibitor does not markedly change motor function.
## NOX2 KO, but not NOX4 inhibition, reduces oxidative stress after SCI
To determine if the beneficial effects of NOX2 KO were associated with reductions in oxidative stress, oxidative stress markers in tissue surrounding the lesion site was measured at 7 and 28 DPI (Fig 2). Protein carbonylation was measured using the Oxyblot kit, and demonstrated that both in the sub-acute 7 day period ($p \leq 0.01$, unpaired t test, Fig 2A and 2B) and the chronic 28 day period ($p \leq 0.05$, unpaired t test, Fig 2C and 2D), NOX2 KO led to a significant reduction in protein carbonylation in comparison to WT mice, similarly to what was previously observed with gp91ds-tat acute administration [9].
**Fig 2:** *NOX2 knockout and NOX4 inhibition offer acute inhibition of oxidative stress, but only NOX2 knockout reduces chronic post-injury oxidative stress.Oxyblot was used to measure oxidative stress in tissue surrounding the lesion site at 7 (A, B) and 28 days (C, D) in NOX2 WT and KO mice. Representative blots (A, C) and quantitation (B, D) are shown. GAPDH was used to normalize data. CM-H2DCFDA (DCF) staining was performed on tissue at 2 hours after injury to assess acute ROS production with GKT137831 or vehicle treatment. GKT137831 reduced ROS as shown by reduced green DCF fluorescence at the lesion site (lesion epicenter at T9 at the center of the image) compared to vehicle control (E). Pixel density quantification showed this is a significant reduction in DCF fluorescence on GKT137831 treated tissue (F). However, no other marker of oxidative stress was assessed acutely after injury, and oxyblot and 3NT western blotting performed on 28 DPI tissue lysates, shown in (G) and (I), respectively, do not show significant change with treatment. Oxyblot showed protein carbonylation did not significantly differ between vehicle and GKT137831 groups at 28 days (H). 3NT blotting also showed nitrosylation did not significantly differ between the 2 groups (J). *p<0.05, **p<0.01, N = 4/group at 7 dpi (NOX2 KO/WT) and 28dpi (GKT137831, Vehicle), N = 6/group at 28dpi (NOX2/WT). Bars represent mean +/- SEM.*
GKT137831 was not found to improve motor function, so we aimed to determine if NOX4 inhibition reduced acute ROS production in the spinal cord after injury. Examination of the conversion of CM-H2DCF to CM-DCF, indicative of ROS presence, showed an increase with injury at 2 hours (Fig 2E). GKT137831 administration was found to significantly reduce CM-DCF fluorescence ($$p \leq 0.0076$$, unpaired t-test; Fig 2E and 2F), demonstrating an acute reduction in ROS presence with NOX4 inhibition.
However, NOX4 inhibition failed to show a chronic reduction in oxidative stress markers that was observed in NOX2 KO or gp91ds-tat administered mice [9]. Oxyblot was used to measure the protein carbonylation between the vehicle and the GKT137831 treated groups at 28 days post-injury (Fig 2F and 2H), while 3NT was used to measure nitrosylation (Fig 2I and 2J). No significance was seen between groups for either marker ($$p \leq 0.9585$$, 0.5209, respectively, unpaired t test).
## NOX2 KO and NOX4 inhibition reduces sub-acute microglial activation
To determine if NOX activity had an influence on microglial and macrophage activation, expression of various pro- and anti-inflammatory markers was assessed using a variety of methods. Expression of pro-inflammatory markers CD86 and iNOS showed significant reductions in expression in NOX2 KO mice in comparison to WT mice ($$p \leq 0.047$$, $$p \leq 0.013$$, respectively, unpaired t test, Fig 3A–3D). By 28 DPI, no difference was observed between NOX2 KO and WT groups in expression of these same markers (data not shown), similar to our previous findings with gp91ds-tat administration [9]. However, analysis of overall macrophage/microglia presence in the injured spinal cord showed an influence of NOX2 KO on Iba1 expression at the lesion site; quantitation of Iba1 immunoreactivity at 28 DPI showed a significant reduction in KO tissue ($$p \leq 0.034$$, unpaired t-test, Fig 3E and 3F), which was not observed in our prior work [9].
**Fig 3:** *Markers of inflammation were significantly reduced at 7dpi by NOX2 and NOX4 inhibition, but remained so only with NOX2 KO at 28 DPI.Western blotting for inflammatory markers CD86 (A, B) and iNOS (C, D) were significantly altered by NOX2 KO at 7dpi. GAPDH was used to normalize data. N = 4/group. Iba1 immunohistochemistry showed a continuation of inflammation depression with significant reduction at 28 DPI as measured by threshold pixel density analysis (E, F). Images shown 1.5mm rostral to the lesion epicenter. N = 4/group. Size bar = 200μM. Flow cytometry was performed on tissue at 7 DPI to measure inflammatory cell populations in vehicle or GKT137831 treated spinal cords. All inflammatory cells were marked with CD45+ and showed a significant reduction with GKT137831 treatment (G, H). CD45+ CD11b+ GR-1- were interpreted as the macrophage/microglia population and showed a significant reduction (I, J). CD45+ CD11b+ GR-1+ were used to identify the neutrophil population and GKT137831 induced no significant change in these cells (K, L). N = 6-7/group. *p<0.05, unpaired t-test. Bars represent mean +/- SEM.*
Flow cytometry was used to assess immune cell populations in the injured spinal cord 7 DPI with and without GKT137831 administration (Fig 3G–3L). All cells of interest were marked with CD45+ marker (Fig 3G and 3H). GKT137831 treated tissue showed a significant reduction in this population of CD45+ cells ($$p \leq 0.0241$$, unpaired t-test; Fig 3G) in comparison to the vehicle treated group. This reduction was most prominent in the macrophage/microglia populations, labeled as CD45+CD11b+GR1- ($$p \leq 0.0431$$,unpaired t-test; Fig 3I and 3J). Neutrophils (CD45+CD11b+GR1+) showed no significant change in cell population with GKT137831 administration ($$p \leq 0.2912$$, unpaired t-test; Fig 3K and 3L). These reductions were similar to those observed in the subacute period following single dose gp91ds-tat administration [9].
## NOX2 KO improves neuronal viability
To determine if these changes in inflammation and oxidative stress resulted in neuroprotection, NeuN protein expression was assessed at 28 DPI in WT or NOX2 KO mice. In the dorsal horns, near the lesion site, NOX2 KO mice demonstrated more NeuN staining in the dorsal horns (Fig 4A). Quantitation of NeuN pixel density in the dorsal horn showed a significant decrease in WT NeuN expression in comparison to KO ($$p \leq 0.0193$$, unpaired t-test, Fig 4B).
**Fig 4:** *Neuronal viability in the dorsal horn adjacent to the lesion site was greater with NOX2 KO at 28 days post-injury.NeuN is a typically nuclear neuronal marker and was significantly greater in NOX2 KO injured tissue at 28 DPI. Representative images 0.5mm rostral to the lesion site are shown, and demonstrate that NeuN stain is often cytoplasmic in the WT tissue, while it is more nuclear in the KO tissue, as well as being present in a greater amount in the KO tissue. Bars represent mean +/- SEM. N = 3-4/group. *p<0.05, unpaired t test. Size bar = 200μM.*
## Microglia show reduced ROS expression, but not pro-inflammatory markers, in response to GKT137831
The limited effect of NOX4 inhibition in the spinal cord of mice prompted further evaluation of the effect of GKT137831 on microglia. We utilized both the BV2 microglial cell line and a primary rat microglia cell culture stimulated with the pro-inflammatory agent LPS as in vitro models. ROS and nitric oxide release were assessed 24 hours after stimulation and administration of GKT137831. Baseline ROS in BV2 cells was significantly reduced by GKT137831 administration ($p \leq 0.0001$, two-way ANOVA, Fig 5A); while ROS was increased by LPS, this was significantly reduced in a concentration dependent manner by GKT137831 ($$p \leq 0.0023$$, two-way ANOVA; Fig 5A). This effect on ROS production was further confirmed using primary microglia (overall $p \leq 0.0001$, LPS-DMSO vs LPS-GKT137831 $$p \leq 0.0002$$, one-way ANOVA; Fig 5C). Nitric oxide release was not altered by GKT137831 administration ($$p \leq 0.6624$$, two-way ANOVA; Fig 5B), suggesting an incomplete reduction in microglial related inflammation with NOX4 inhibition, unlike what we have previously observed with gp91ds-tat administration in cultured microglia [4].
**Fig 5:** *GKT137831 treatment in microglia reduces LPS induced ROS in a dose-dependent fashion, but has no effect in NO release.BV2 microglial cells were stimulated with LPS and treated with the NOX inhibitor GKT137831 one hour after stimulation. 24 hours after ROS and NO production was assessed using DCF and Greiss assay, respectively. Microglia show a dose dependent significant decrease of ROS release in GKT137831 treated compared to control or LPS alone (A). NO shows no significant reduction with any concentration of GKT137831 (B) ROS production decrease in response to GKT137831 treatment was confirmed in primary microglial cells (C). N = 3. *p<0.05 compared to control, +p<0.05, ++p<0.01, +++p<0.001 compared to LPS, using Two-Way ANOVA (A)(B) and One-Way ANOVA (C) with Dunnett’s Multiple Comparison post-test. Bars represent mean score +/- SEM.*
To further study how microglia respond to GKT137831, the pro-inflammatory cytokines IL-1β and TNFα were examined using ELISA in the BV2 cell line (Fig 6). Surprisingly, IL-1β was induced by GKT137831 in a dose dependent fashion in LPS stimulated cells ($p \leq 0.001$, one-way ANOVA; Fig 6A). On the other hand, LPS induced TNFα release was unaltered by GKT137831 ($$p \leq 0.9999$$, one-way ANOVA; Fig 6B), again suggesting an incomplete regulation of microglial related inflammation with NOX4 inhibition.
**Fig 6:** *GKT137831 treatment has limited effects on microglial related inflammatory responses.BV2 microglia were stimulated with LPS and treated 1 hour after with the NOX inhibitor GKT137831 at 0.5μM, 1μM, 5μM, 10μM. Media was collected 24 hours after stimulation. IL-1β ELISA assay shows no response by microglia to GKT137831 along, but in conjunction with LPS there was a significant dose dependent increase in release (A). GKT137831 showed no effect on TNF-α release, with or without LPS stimulation (B). When BV2 microglia were stimulated with LPS, a significant increase of the pro-inflammatory marker CD86+ cell population (C, D) and reduction in the CD206+ cell population (E, F) was observed GKT137831 did not prevent or induce any polarization changes by itself or after LPS stimulation. N = 3. *p<0.05, **p<0.01, ***p<0.001 using two-way ANOVA. Bars represent mean +/- SEM.*
Finally, we assessed the effects of GKT137831 on standard markers of microglial activation using flow cytometry and the BV2 cell line. We quantified the percent of positive cells for the pro-inflammatory marker CD86 (Fig 6C and 6D) and relative fluorescence of the anti-inflammatory marker CD206 (Fig 6E and 6F). A significant increase in the CD86+ cell population was observed when stimulated with LPS, while the frequency of CD206+ cell population was significantly reduced ($$p \leq 0.033$$ and $$p \leq 0.0002$$, respectively, two-way ANOVA). GKT137831 had no effect on CD86 or CD206 expression ($$p \leq 0.4867$$ and 0.0615, respectively, two-way ANOVA), unlike what we have previously observed with gp91ds-tat administration [6].
## Discussion
Oxidative stress significantly contributes to the pathogenesis of secondary injury by directly damaging cell components and perpetuating the inflammatory process [1]. Our prior work and that of others has demonstrated that NOX2 and NOX4 are significantly elevated in microglia after SCI [3, 21], as well as other cells [22]. Further, inhibition of NOX2 activity reduces oxidative stress and inflammation and improves functional recovery after injury [6, 9]. However it was unclear if long-term reduction using genetic knockout of NOX2 would lead to greater improvement, thus giving important information about therapeutic potential of NOX2 inhibition, or if inhibition of other members of the NOX family, such as NOX4, may improve outcomes.
Similar to the responses seen with acute inhibition, KO of NOX2 reduced ROS acutely and improved motor function. There was also a reduction in inflammation, with an increase in anti-inflammatory markers and a reduction in pro-inflammatory markers. Unlike acute inhibition of NOX2, NOX2 KO led to a chronic reduction in microglia/macrophage number at 28 DPI. However, the overall improvement in locomotor function does not appear to be different between the NOX2 KO and acute inhibition groups [9]. Very similar results have been recently shown by Sabirzhanov et al. [ 5], in which similar motor function improvements and neuronal survival and reduced ROS at 8 weeks post-injury were observed after SCI in NOX2 KO mice. In addition, they found that at 24 hours after injury, NOX2 KO mice demonstrated reductions in macrophage within the injury site, with accompanying very acute changes in pro- and anti-inflammatory markers. We have extended and complemented this work, by showing the same effects of NOX2 KO at the intermediate 7 day and 28 day time points.
In the mouse spinal cord injury model, protein expression of NOX2 peaks at 1–4 days post-injury [6]. It is therefore unsurprising that an acute treatment targeting this early peak would have similar effects to knockout of the gene. In the rat model, however, this expression is more extended, with elevated expression of NOX2 component gene and protein through 6 months post-injury [15, 23]. Therefore, delayed treatment approaches for NOX2 inhibition may be more effective in the rat model than mouse model. Human NOX2 expression after SCI is currently unknown, although after brain injury NOX2 is reported to be elevated acutely [16]; future work is therefore necessary to more clearly define the therapeutic potential of NOX2 inhibition.
NOX2 is directly responsible for producing ROS, particularly after injury [2]. ROS mediate clearance of debris and removal of pathogens, but can also contribute to damage to surrounding healthy tissue by elevating oxidative stress. In vitro, inhibition of NOX2 in microglia significantly reduces ROS production in response to LPS or other stimuli [4, 24]. Acute inhibition of NOX2 in vivo was previously shown to reduce oxidative stress acutely after SCI [9]; KO of NOX2 leads to an extension of that reduction. Oxidative stress can contribute to elevated inflammation and neuronal damage; amelioration of this effect of NOX2 may be a factor in the observed reductions in inflammation and neuronal loss, particularly at 28 days post-injury. These reductions were not observed in the acute inhibition study [9]. Indirect inhibition of NOX2 induced ROS, through knockout of the Hv1 extracellular proton channel, also significantly improves motor function and reduces post-injury histopathology [25].
ROS contribute to propagating inflammation. Studies have shown that ROS and elevations in oxidative stress signal, possibly through the inflammasome, to increase pro-inflammatory cytokine production [26]. NOX2 inhibition has been shown to significantly reduce sub-acute microglial numbers [9]. Interestingly, NOX2 KO was also observed to change pro- vs. anti-inflammatory marker expression, and did reduce overall number by 28 days post-injury. Previous work has suggested that activation state, not number, is more likely to influence outcome after injury [27].
We and others have previously demonstrated that NOX2 inhibition alters pro- and anti-inflammatory properties of microglia, pushing them toward an anti-inflammatory phenotype and reducing pro-inflammatory marker expression [6, 8, 28]. Following controlled cortical impact injury, NOX2 inhibition with apocynin (from 1–4 days post-injury) and NOX2 KO both effectively reduced microglial activation and pushed microglia/macrophages toward an anti-inflammatory phenotype, possibly via an NFκB mechanism. We now show additional support for this, as NOX2 KO reduces pro-inflammatory markers and increases anti-inflammatory markers at 7 days post-injury, similarly to NOX2 inhibition [9].
A number of studies have shown that inhibition of NOX2 can improve recovery after central nervous system injury. In TBI models, acute NOX2 inhibition reduced cognitive impairment and reduced lesion volume [28–30], although Kumar et al. found that inhibition of NOX2 from 1 to 3 days post-injury was not as effective at improving post-TBI function as NOX2 KO. Our current study somewhat agrees with this finding, demonstrating that NOX2 KO results in similar functional outcomes as acute NOX2 inhibition [9] in a mouse SCI model.
In contrast, while the NOX4 inhibitor GKT137831 reduced acute oxidative stress and inflammation after moderate SCI and reduced ROS production by microglia in vitro, this did not translate into sustained reduction in oxidative stress or a significant improvement in motor function. In vitro, GKT137831 has been shown to reduce ROS production by microglia in response to LPS and other stimuli, similar to the current work [24]. However, GKT137831 did not alter microglial pro-inflammatory activation. This lack of functional effect and disparate results between in vivo and in vitro experiments highlights the complexity of SCI and the role of NOX enzymes in post-injury inflammation and oxidative stress.
Oxidative stress markers after SCI can be detected within hours of injury, later peaking at acute time points and remaining present for weeks to months [1, 31]. We observed a significant reduction of oxidative stress markers at 2h in mice treated with GKT137831. This initial acute reduction in ROS release in injured tissue was consistent with our in vitro results in the microglial BV2 cell line and primary microglia, as well as the literature regarding NOX inhibition through GKT137831 [24, 32]. Similarly, administration of a ketogenic diet, which was found to reduce NOX4 expression and activity, potentially via inhibition of NADPH availability via the pentose phosphate shunt or by reducing HDAC function and gene expression, showed a reduction in acute and sub-acute oxidative stress [21]. As GKT137831 has shown no ROS scavenger or antioxidant activity [33], it is most likely that this reduction is a direct result of NOX inhibition in the lesion site. However, by 28 DPI, markers of oxidative stress did not show any reduction with the GKT137831 treatment, suggesting that a single acute administration does not have long-lasting effects. In contrast, a single acute administration of the NOX2 inhibitor gp91ds-tat did demonstrate long-term reductions in oxidative stress, suggesting a differential effect of the 2 isoforms in the acute period after injury [9]. Further, we have previously observed an increase in NOX4 expression at 1 to 7 days after mouse SCI [6]. Therefore, the single acute administration may not have been timed to most appropriately address the NOX4 elevation. Future work may consider delayed or repeated administration.
GKT137831 has good oral bioavailability and high plasma concentrations in vivo and is the most specific NOX4 inhibitor available with a very low affinity for NOX2 [32]. A single acute oral administration of the NOX4 inhibitor GKT137831 significantly reduced several inflammatory markers after a moderate SCI, including CD45+ cells populations at 7 DPI. However, in vitro, GKT137831 treatment did not lead to any changes in microglial polarization response after LPS stimulation. Furthermore, GKT137831 led to an increase in IL-1β release in LPS stimulated microglia in a dose dependent manner, but had no effect on TNF-α release. The mechanism by which IL-1β and TNFα are released by microglia in inflammatory conditions is fundamentally different; the increase in IL-1β but no change in TNFα may be related to this difference in expression properties [34]. Regardless, an increase in IL-1β was unexpected, and additional research into why this cytokine is elevated in cell culture is necessary. In a model of hypoxia-induced retinopathy, daily subcutaneous GKT137831 administration significantly reduced inflammatory mediators; this was also observed in cultured retinal microglia when GKT137831 was present during hypoxia [35]. Importantly, they also found reduced leukocyte infiltration. In agreement with these findings, genetic deletion or pharmacologic inhibition of NOX4 provided renal protection by reducing macrophage infiltration in the diabetic kidney [36]. This is consistent with our findings of a reduced monocyte population after SCI.
There is a high expression of NOX4 in endothelial cells [37]. Furthermore, NOX4 is highly present and active in the neurovasculature [38]. In a NOX4 KO model of stroke, smaller infarct volumes and neuroprotection were observed along with a reduction in the disruption of the blood–brain barrier, although inflammatory cell infiltration was not assessed [39]. NOX4 is also expressed and up-regulated in pericytes acutely after brain ischemia and its upregulation seems to enhance blood-brain barrier breakdown [40]. Furthermore, NOX4 was found to be predominant among the NOX family in human brain pericytes [14]. NOX1 has been suggested to also play a role in the vascular tissue, its expression is induced by IL-1β enhancing monocyte adhesion, while NOX4 seems to be down regulated by IL-1β in vascular cells, authors also suggest that NOX1-derived superoxide, but not hydrogen peroxide, mediates monocyte adhesion to vascular tissue [41]. All of this indicates that the acute GKT137831 administration may also be affecting vascular response in our model in agreement with the reduced inflammatory cell population found in GKT137831 treated mice. The precise mechanisms and the NOX1 and NOX4 interplay in this vascular response remain to be elucidated.
A limitation of this study is that GKT137831 was developed as an inhibitor to both NOX1 and NOX4. With comparable Ki values (140±40 nM for NOX4, 110±30 nM for NOX1), it is likely that NOX1 activity would also be affected after GKT137831 administration. Therefore, additional research is needed to isolate the effect of NOX1 versus NOX4. In addition, the current study only evaluated responses in male rodents; limited information is available to understand the influence of sex on NOX2 or 4 expression or impact in the injured spinal cord. To date, one study has suggested that NOX2 expression is more highly expressed in females after SCI [42].
In conclusion, NOX2 and NOX4 inhibition reduced acute oxidative stress and exerted anti-inflammatory effects in the injured spinal cord. However, these anti-inflammatory effects are not fully mediated through microglia with NOX4 inhibition, and a single treatment does not have long-lasting effects or exert significant functional benefits as single treatment was found to do with NOX2 [9]. Future work is needed to clarify the target cells of GKT137831 and to determine if repeated administration will have beneficial outcomes. In contrast, NOX2 inhibition via genetic knockout was found to be very similar to pharmacological inhibition demonstrated in our previous work [9], suggesting that targeting NOX2 after SCI may have therapeutic benefits.
## References
1. Jia Z, Zhu H, Li J, Wang X, Misra H, Li Y. **Oxidative stress in spinal cord injury and antioxidant-based intervention.**. *Spinal Cord* (2012) **50** 264-74. DOI: 10.1038/sc.2011.111
2. Zhang B, Bailey WM, McVicar AL, Gensel JC. **Age increases reactive oxygen species production in macrophages and potentiates oxidative damage after spinal cord injury**. *Neurobiol Aging* (2016) **47** 157-67. DOI: 10.1016/j.neurobiolaging.2016.07.029
3. Cooney SJ, Zhao Y, Byrnes KR. **Characterization of the expression and inflammatory activity of NADPH oxidase after spinal cord injury**. *Free radical research* (2014) **48** 929-39. DOI: 10.3109/10715762.2014.927578
4. Cooney SJ, Bermudez-Sabogal SL, Byrnes KR. **Cellular and temporal expression of NADPH oxidase (NOX) isotypes after brain injury**. *J Neuroinflammation* (2013) **10** 155. DOI: 10.1186/1742-2094-10-155
5. Sabirzhanov B, Li Y, Coll-Miro M, Matyas JJ, He J, Kumar A. **Inhibition of NOX2 signaling limits pain-related behavior and improves motor function in male mice after spinal cord injury: Participation of IL-10/miR-155 pathways**. *Brain Behav Immun* (2019). DOI: 10.1016/j.bbi.2019.02.024
6. Bermudez S, Khayrullina G, Zhao Y, Byrnes KR. **NADPH oxidase isoform expression is temporally regulated and may contribute to microglial/macrophage polarization after spinal cord injury**. *Mol Cell Neurosci* (2016) **77** 53-64. DOI: 10.1016/j.mcn.2016.10.001
7. Lo W, Bravo T, Jadhav V, Titova E, Zhang JH, Tang J. **NADPH oxidase inhibition improves neurological outcomes in surgically-induced brain injury**. *Neurosci Lett* (2007) **414** 228-32. DOI: 10.1016/j.neulet.2006.12.055
8. Kumar A, Alvarez-Croda DM, Stoica BA, Faden AI, Loane DJ. **Microglial/Macrophage Polarization Dynamics following Traumatic Brain Injury.**. *J Neurotrauma* (2015). DOI: 10.1089/neu.2015.4268
9. Khayrullina G, Bermudez S, Byrnes KR. **Inhibition of NOX2 reduces locomotor impairment, inflammation, and oxidative stress after spinal cord injury**. *J Neuroinflammation* (2015) **12** 172. DOI: 10.1186/s12974-015-0391-8
10. Martyn KD, Frederick LM, von Loehneysen K, Dinauer MC, Knaus UG. **Functional analysis of Nox4 reveals unique characteristics compared to other NADPH oxidases**. *Cell Signal* (2006) **18** 69-82. DOI: 10.1016/j.cellsig.2005.03.023
11. Peshavariya H, Dusting GJ, Di Bartolo B, Rye KA, Barter PJ, Jiang F. **Reconstituted high-density lipoprotein suppresses leukocyte NADPH oxidase activation by disrupting lipid rafts**. *Free radical research* (2009) **43** 772-82. DOI: 10.1080/10715760903045304
12. Serrander L, Cartier L, Bedard K, Banfi B, Lardy B, Plastre O. **NOX4 activity is determined by mRNA levels and reveals a unique pattern of ROS generation**. *Biochem J* (2007) **406** 105-14. DOI: 10.1042/BJ20061903
13. Geiszt M, Kopp JB, Varnai P, Leto TL. **Identification of renox, an NAD(P)H oxidase in kidney**. *Proc Natl Acad Sci U S A* (2000) **97** 8010-4. DOI: 10.1073/pnas.130135897
14. Kuroda J, Ago T, Nishimura A, Nakamura K, Matsuo R, Wakisaka Y. **Nox4 is a major source of superoxide production in human brain pericytes**. *J Vasc Res* (2014) **51** 429-38. DOI: 10.1159/000369930
15. Byrnes KR, Garay J, Di Giovanni S, De Biase A, Knoblach SM, Hoffman EP. **Expression of two temporally distinct microglia-related gene clusters after spinal cord injury**. *Glia* (2006) **53** 420-33. DOI: 10.1002/glia.20295
16. Li Z, Tian F, Shao Z, Shen X, Qi X, Li H. **Expression and clinical significance of non-phagocytic cell oxidase 2 and 4 after human traumatic brain injury**. *Neurol Sci* (2015) **36** 61-71. DOI: 10.1007/s10072-014-1909-z
17. Seredenina T, Nayernia Z, Sorce S, Maghzal GJ, Filippova A, Ling SC. **Evaluation of NADPH oxidases as drug targets in a mouse model of familial amyotrophic lateral sclerosis**. *Free Radic Biol Med* (2016) **97** 95-108. DOI: 10.1016/j.freeradbiomed.2016.05.016
18. Basso DM, Fisher LC, Anderson AJ, Jakeman LB, McTigue DM, Popovich PG. **Basso Mouse Scale for locomotion detects differences in recovery after spinal cord injury in five common mouse strains**. *J Neurotrauma* (2006) **23** 635-59. DOI: 10.1089/neu.2006.23.635
19. Nissan M, Rochkind S, Razon N, Bartal A. **HeNe laser irradiation delivered transcutaneously: its effect on the sciatic nerve of rats**. *Lasers Surg Med* (1986) **6** 435-8. DOI: 10.1002/lsm.1900060502
20. Donnelly DJ, Gensel JC, Ankeny DP, van Rooijen N, Popovich PG. **An efficient and reproducible method for quantifying macrophages in different experimental models of central nervous system pathology**. *J Neurosci Methods* (2009) **181** 36-44. DOI: 10.1016/j.jneumeth.2009.04.010
21. Kong G, Huang Z, Ji W, Wang X, Liu J, Wu X. **The Ketone Metabolite beta-Hydroxybutyrate Attenuates Oxidative Stress in Spinal Cord Injury by Suppression of Class I Histone Deacetylases**. *J Neurotrauma* (2017) **34** 2645-55. DOI: 10.1089/neu.2017.5192
22. Arsenault EJ, McGill CM, Barth BM. **Sphingolipids as Regulators of Neuro-Inflammation and NADPH Oxidase 2.**. *Neuromolecular Med* (2021) **23** 25-46. DOI: 10.1007/s12017-021-08646-2
23. Byrnes KR, Washington PM, Knoblach SM, Hoffman E, Faden AI. **Delayed inflammatory mRNA and protein expression after spinal cord injury**. *J Neuroinflammation* (2011) **8** 130. DOI: 10.1186/1742-2094-8-130
24. Yauger YJ, Bermudez S, Moritz KE, Glaser E, Stoica B, Byrnes KR. **Iron accentuated reactive oxygen species release by NADPH oxidase in activated microglia contributes to oxidative stress in vitro**. *J Neuroinflammation* (2019) **16** 41. DOI: 10.1186/s12974-019-1430-7
25. Li Y, Ritzel RM, He J, Cao T, Sabirzhanov B, Li H. **The voltage-gated proton channel Hv1 plays a detrimental role in contusion spinal cord injury via extracellular acidosis-mediated neuroinflammation**. *Brain Behav Immun* (2021) **91** 267-83. DOI: 10.1016/j.bbi.2020.10.005
26. Naik E, Dixit VM. **Mitochondrial reactive oxygen species drive proinflammatory cytokine production**. *J Exp Med* (2011) **208** 417-20. DOI: 10.1084/jem.20110367
27. Byrnes KR, Loane DJ, Stoica BA, Zhang J, Faden AI. **Delayed mGluR5 activation limits neuroinflammation and neurodegeneration after traumatic brain injury**. *J Neuroinflammation* (2012) **9** 43. DOI: 10.1186/1742-2094-9-43
28. Kumar A, Barrett JP, Alvarez-Croda DM, Stoica BA, Faden AI, Loane DJ. **NOX2 drives M1-like microglial/macrophage activation and neurodegeneration following experimental traumatic brain injury**. *Brain Behav Immun* (2016) **58** 291-309. DOI: 10.1016/j.bbi.2016.07.158
29. Song SX, Gao JL, Wang KJ, Li R, Tian YX, Wei JQ. **Attenuation of brain edema and spatial learning de fi cits by the inhibition of NADPH oxidase activity using apocynin following diffuse traumatic brain injury in rats**. *Molecular medicine reports* (2013) **7** 327-31. DOI: 10.3892/mmr.2012.1147
30. Lu XY, Wang HD, Xu JG, Ding K, Li T. **NADPH oxidase inhibition improves neurological outcome in experimental traumatic brain injury**. *Neurochem Int* (2014) **69** 14-9. DOI: 10.1016/j.neuint.2014.02.006
31. Xiong Y, Rabchevsky AG, Hall ED. **Role of peroxynitrite in secondary oxidative damage after spinal cord injury**. *J Neurochem* (2007) **100** 639-49. DOI: 10.1111/j.1471-4159.2006.04312.x
32. Laleu B, Gaggini F, Orchard M, Fioraso-Cartier L, Cagnon L, Houngninou-Molango S. **First in class, potent, and orally bioavailable NADPH oxidase isoform 4 (Nox4) inhibitors for the treatment of idiopathic pulmonary fibrosis**. *J Med Chem* (2010) **53** 7715-30. DOI: 10.1021/jm100773e
33. Aoyama T, Paik YH, Watanabe S, Laleu B, Gaggini F, Fioraso-Cartier L. **Nicotinamide adenine dinucleotide phosphate oxidase in experimental liver fibrosis: GKT137831 as a novel potential therapeutic agent**. *Hepatology* (2012) **56** 2316-27. DOI: 10.1002/hep.25938
34. Wang WY, Tan MS, Yu JT, Tan L. **Role of pro-inflammatory cytokines released from microglia in Alzheimer’s disease.**. *Ann Transl Med* (2015) **3** 136. DOI: 10.3978/j.issn.2305-5839.2015.03.49
35. Deliyanti D, Wilkinson-Berka JL. **Inhibition of NOX1/4 with GKT137831: a potential novel treatment to attenuate neuroglial cell inflammation in the retina**. *J Neuroinflammation* (2015) **12** 136. DOI: 10.1186/s12974-015-0363-z
36. Jha JC, Gray SP, Barit D, Okabe J, El-Osta A, Namikoshi T. **Genetic targeting or pharmacologic inhibition of NADPH oxidase nox4 provides renoprotection in long-term diabetic nephropathy**. *J Am Soc Nephrol* (2014) **25** 1237-54. DOI: 10.1681/ASN.2013070810
37. Ago T, Kitazono T, Ooboshi H, Iyama T, Han YH, Takada J. **Nox4 as the major catalytic component of an endothelial NAD(P)H oxidase**. *Circulation* (2004) **109** 227-33. DOI: 10.1161/01.CIR.0000105680.92873.70
38. Miller AA, Drummond GR, Schmidt HH, Sobey CG. **NADPH oxidase activity and function are profoundly greater in cerebral versus systemic arteries**. *Circ Res* (2005) **97** 1055-62. DOI: 10.1161/01.RES.0000189301.10217.87
39. Kleinschnitz C, Grund H, Wingler K, Armitage ME, Jones E, Mittal M. **Post-stroke inhibition of induced NADPH oxidase type 4 prevents oxidative stress and neurodegeneration**. *PLoS Biol* (2010) **8**. DOI: 10.1371/journal.pbio.1000479
40. Nishimura A, Ago T, Kuroda J, Arimura K, Tachibana M, Nakamura K. **Detrimental role of pericyte Nox4 in the acute phase of brain ischemia**. *J Cereb Blood Flow Metab* (2016) **36** 1143-54. DOI: 10.1177/0271678X15606456
41. Bayat H, Schroder K, Pimentel DR, Brandes RP, Verbeuren TJ, Cohen RA. **Activation of thromboxane receptor modulates interleukin-1beta-induced monocyte adhesion—a novel role of Nox1**. *Free Radic Biol Med* (2012) **52** 1760-6. DOI: 10.1016/j.freeradbiomed.2012.02.052
42. Stewart AN, Lowe JL, Glaser EP, Mott CA, Shahidehpour RK, McFarlane KE. **Acute inflammatory profiles differ with sex and age after spinal cord injury.**. *J Neuroinflammation* (2021) **18** 113. DOI: 10.1186/s12974-021-02161-8
|
---
title: UPLC-MS Analysis, Quantification of Compounds, and Comparison of Bioactivity
of Methanol Extract and Its Fractions from Qiai (Artemisia argyi Lévl. et Van.)
authors:
- Ting Zhang
- Dingrong Wan
- Yuanyuan Li
- Sisi Wang
- Xiuteng Zhou
- Fatemeh Sefidkon
- Xinzhou Yang
journal: Molecules
year: 2023
pmcid: PMC10004512
doi: 10.3390/molecules28052022
license: CC BY 4.0
---
# UPLC-MS Analysis, Quantification of Compounds, and Comparison of Bioactivity of Methanol Extract and Its Fractions from Qiai (Artemisia argyi Lévl. et Van.)
## Abstract
The Artemisia argyi Lévl. et Van. growing in the surrounding areas of Qichun County in China are called Qiai (QA). Qiai is a crop that can be used both as food and in traditional folk medicine. However, detailed qualitative and quantitative analyses of its compounds remain scarce. The process of identifying chemical structures in complex natural products can be streamlined by combining UPLC-Q-TOF/MS data with the UNIFI information management platform and its embedded Traditional Medicine Library. For the first time, 68 compounds in QA were reported by the method in this study. The method of simultaneous quantification of 14 active components in QA using UPLC-TQ-MS/MS was reported for the first time. Following a screening of the activity of QA $70\%$ methanol total extract and its three fractions (petroleum ether, ethyl acetate, and water), it was discovered that the ethyl acetate fraction enriched with flavonoids such as eupatilin and jaceosidin had the strongest anti-inflammatory activity, while the water fraction enriched with chlorogenic acid derivatives such as 3,5-di-O-caffeoylquinic acid had the strongest antioxidant and antibacterial activity. The results provided the theoretical basis for the use of QA in the food and pharmaceutical industries.
## 1. Introduction
Artemisia argyi Lévl. et Van. is widely distributed in East Asian countries, especially in China. Artemisia argyi is a common flavoring and colorant in the food industry, and also a traditional medicine used to manage dysmenorrhea and inflammation [1]. Another use is in moxibustion, a form of traditional Chinese medicine that involves burning the plant materials over acupuncture points [2]. The mugwort grown in Qichun County, Hubei Province, China, is called “Qiai”. According to Li Shizhen’s “Compendium of Materia Medica”, a classical Chinese medicine work, the quality of *Qiai is* superior to other regions [3]. Modern studies suggest that Qiai contains a wide range of active ingredients, including phenolic acids, terpenes, polysaccharides, and essential oils [4,5,6]. Furthermore, the essential oil, tannins, and flavonoid concentration in Qiai are higher than in other production areas [7,8,9]. Although the prices of Qiai are higher than in other production areas, its demand remains robust. As research progresses, the pharmacological effects of Artemisia argyi, such as anti-inflammatory [10], anti-tumor [11], and obesity improvement [12], become clearer, and more and more Artemisia argyi products are developed and utilized [13]. By 2021, the planting area in Qiai reached 20,000 hectares, with an industrial output value of 1.16 billion dollars.
Phenolic compounds, as the main components in QA, have successfully attracted the attention of most researchers [14,15]. Most studies have made attempts in recent years to indicate the bioactivity of QA’s total phenolic compounds. However, nothing is currently known regarding the qualitative and quantitative analyses of the phenolic compounds in QA. Only 18 phenolic acids were preliminarily identified by ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) [16], 10 phenolic acids were identified and 7 phenolic acids quantified by HPLC [17], and 6 volatile compounds were detected by GC-MS [18]. The complexity of phytochemistry influenced the quantitative results in their research. Their results are deemed inadequate for the ongoing study of QA, so it is necessary to analyze and detect the phenolic components accurately and systematically. An attempt has been made to combine UPLC-Q-Exactive-MS/MS with mass spectrometry databases such as MZVault, MZCloud, and BGI Library for the preliminary identification of 125 chemical components in mugwort leaves from Henan Province, showing that combining UPLC-MS with a phytoconstituent mass spectrometry database can greatly improve the efficiency of compound characterization [19]. In this study, the combination of UPLC-Q-TOF/MS with the UNIFI platform enables rapid and automatic characterization of chemical constituents in plants, which has the advantages of high sensitivity, good selectivity, and easy operation [20]. An efficient qualitative method allowed us to identify 68 phenolic compounds from $70\%$ methanol total extract of QA (QA-TE) by combining UPLC-Q-TOF/MS and the UNIFI platform.
Previous studies have shown that phenolic compounds have various pharmacological activities [21,22]. However, it is not clear which chemical components are responsible for these pharmacological activities. Bioassay data showed that the QA-TE and its water fraction (QA-FWT) had good antioxidant activities, and the ethyl acetate fraction (QA-FEA) and water fraction (QA-FWT) had favorable anti-inflammatory and antibacterial activities. Through further accurate quantitative analysis of the total extract and fractions by UPLC-TQ-MS/MS with superior sensitivity and stability [23], it was revealed for the first time that the antioxidant activity of QA was attributed to phenolic compounds, the anti-inflammatory activity was attributed to flavonoids, and the antibacterial activity was attributed to chlorogenic acid derivatives.
To the best of our knowledge, this is the first study using UPLC-Q-TOF/MS combined with the UNIFI data platform to quickly characterize compounds in QA, and the first study using UPLC-TQ-MS/MS to quantify compounds in QA. Therefore, this work will contribute to the availability of more references for the characterization and quantification of compounds in QA. Beyond that, the work will facilitate providing a theoretical foundation for the application of QA in food, pharmaceutical, and other industries.
## 2.1. Identification of QA Extract by UPLC-Q-TOF/MS
The QA extract solution was detected using UPLC-Q-TOF/MS technology under chromatographic and mass spectrometry conditions. The rapid, efficient and validated UPLC-Q-TOF/MS analytical method was established for the identification of the main chemical components in QA. The base peak ion chromatograms (Figure 1) provide the metabolomic analysis, also known as the analytical fingerprint for plant identification and authentication, a fairly integrated frame.
The collected MS data were imported into the UNIFI information management platform. In the UNIFI information software, the theoretical database of QA leaf compounds and the physical database of reference substances were established. As shown in Table 1 and Table 2, a total of 68 compounds were identified in QA leaves, with 47 compounds identified by positive ion mode collection and 43 compounds identified by negative ion mode (22 compounds were collected by both positive and negative ions). This is the first time that the combination of UPLC-Q-TOF/MS and the UNIFI platform has been applied to characterize the compounds in QA, and the established method has successfully identified the largest number of compounds. Among these are well-known phytochemicals, such as chlorogenic acid, jaceosidin, eupatilin, quercetin, and 3,5-di-O-caffeoylquinic acid, which possess antioxidant, anti-inflammatory, cancer chemopreventive, immunosuppression, and food additive properties.
## 2.2.1. Method Validation
Figure 2 depicts the representative UPLC-TQ-MS/MS total ion chromatogram of standards, QA-TE, QA-FEA, and QA-FWT. Figure 3 depicts the ion chromatograms of 14 standards under the optimal UPLC-TQ-MS/MS conditions. The method’s linearity, sensitivity, precision, and accuracy satisfy international standards. The linearity of the standard solution was assessed by analyzing the standard solution over a concentration range satisfactory for the quantification of the relevant analytes in the sample. All analytes’ regression equations had excellent linearities, with the determination coefficient R2 ≥ 0.9967 (Table 3). All analyte detection limits ranged from 0.48 to 5.32 ng/mL (Table 3), while all analyte quantitation limits ranged from 1.45 to 15.89 ng/mL (Table 3). To the best of our knowledge, this is the lowest limit of the quantification method for the simultaneous quantification of compounds in QA. Additionally, for the peak region of all analytes, the intra-day and inter-day RSDs were less than $2.31\%$ and $2.16\%$, respectively (Table 3).
These findings demonstrate that the approach has good precision whether used to measure on an intra-day or day-to-day basis. Additionally, the range of spiking recoveries for all analytes was $99.79\%$ to $104.37\%$ (Table 3), demonstrating that the method has adequate accuracy. Furthermore, the analyte recovery range was measured to be $97.56\%$ to $101.74\%$ (Table 3). The findings indicate that the adopted methodology has good linearity, sensitivity, precision, accuracy, and stability, and can be used to quantify fourteen characteristic compounds from QA leaves.
## 2.2.2. Quantitative Analysis
The developed UPLC-TQ-MS/MS method was subsequently applied to quantify 14 bioactive compounds in leaves of A. argyi. Table 4 shows the quantification results for extracts and fractions. The p-values for all compounds measured were less than 0.05. Figure 4 depicts the structures of quantified compounds in Qiai. The quantified compounds belonged to two classes, eight flavonoids (chrysoeriol 7-O-glucoside, chrysoeriol, schaftoside, isoschaftoside, hyperoside, hispidulin, eupatilin, and jaceosidin) and six chlorogenic acid derivatives (3,5-di-O-caffeoylquinic acid, 3,4-di-O-caffeoylquinic acid, 4,5-di-O-caffeoylquinic acid, chlorogenic acid, neochlorogenic acid, and 4-Dicaffeoylquinic acid). Among them, hyperoside (Rt = 8.25 min), chrysoeriol 7-O-glucoside (Rt = 11.10 min), and chrysoeriol (Rt = 13.68 min) displayed deprotonated molecules at the m/z ratio of 463.03, 461.10, and 299.03, respectively. This is the first report of the quantification of these three flavonoids in QA that we are aware of. In addition, for the first time, the method of simultaneous quantification of 14 active components in QA using UPLC-TQ-MS/MS was reported.
According to the data in Table 4, chrysoeriol, hispidulin, eupatilin, and jaceosidin in the total extract were enriched in QA-FEA. Hyperoside, schaftoside, isoschaftoside, and six chlorogenic acid derivatives were enriched in QA-FWT after fractionation. This proves that these compounds were mostly extracted using ethyl acetate and methanol. The 14 bioactive compounds include analgesic, anti-inflammatory, and antipyretic properties that can be used to treat a variety of disorders [24,25,26]. Therefore, we can infer that the pharmacological activity of fractions depends on the content of active compounds in them.
## 2.3. Evaluation of Antioxidant Potential
The antioxidant potential of the total extract and fractions was analyzed using the DPPH colorimetric and ABTS colorimetric assays. The details are shown in Table S2. The radical scavenging activities of the total extract, fractions, and trolox were expressed as IC50. Except for QA-FPE, all tested total extracts and fractions had a significant DPPH and ABTS scavenging potential. This may be due to the presence of phenolic compounds in QA. Hydroxyl groups in phenolic compounds react with various kinds of free radicals [27]. In the radical scavenging assay, it was understood that QA-FWT, with IC50 58.34 μg/mL (DPPH) and IC50 270.00 μg/mL (ABTS), was the most active of all the tested samples, which was lower than trolox. The antioxidant activity is closely related to the content of phenolic compounds [28]. It is known that phenolic compounds, particularly chlorogenic acids derivatives, and flavonoids are predominant in QA. Different phenolic components have different solubility in the extraction solvent (petroleum ether, ethyl acetate, and water). The antioxidant activity might be related to the majority quantities of chlorogenic acids derivatives in QA-FWT and flavonoids in QA-FEA.
## 2.4. Inhibition of the NO Release Capacity
NO release inhibition by LPS-stimulated RAW 264.7 cells was performed using five different concentrations of the total extract and fractions at 5, 10, 15, 20, and 25 μg/mL. Details are provided in the Table S3. First, to ensure that the effects on NO release were not caused by reduced cell viability, the potential toxicity of the test materials was evaluated against RAW 264.7 cells. Samples showed cell viability of over $90\%$, indicating that none of the samples were harmful to the cells. Interestingly, among the samples capable of scavenging radicals, QA-FEA and QA-FWT inhibited NO significantly. Furthermore, QA-FEA showed higher activities than QA-FWT. This is because the main components in QA-FEA were flavonoids, whereas the main components in QA-FWT were chlorogenic acids. Moreover, studies have confirmed that the anti-inflammatory activities of eupatilin and jaceosidin [29] were significantly higher than chlorogenic acids. Eupatilin and jaceosidin are the main components of flavonoids enriched in QA-FEA. Inflammatory mediators are important factors to promote the occurrence of inflammation. Eupatilin and jaceosidin can effectively regulate the expression of related enzymes to inhibit the production of inflammatory mediators and prevent future inflammation. This confirms that flavonoids are more responsible for the anti-inflammatory activity of QA than chlorogenic acid derivatives.
## 2.5. Antibacterial Activities
We assessed the diameters of the inhibition zone of the total extract and three fractions against different bacteria (Figure S1). The findings are detailed in Table S4. The diameters of the inhibition zone against P. vulgaris were (in ascending order) QA-FWT (17.7 mm) > QA-TE (17.3 mm) > QA-FEA (16.3 mm) > QA-FPE (13.7 mm). Similarly, the diameters of the inhibition zones against B. subtilis were (in ascending order) QA-FWT (20.3 mm) > QA-FEA (13.3 mm) > QA-TE (11.7 mm) > QA-FPE (10.7 mm). The diameters of the inhibition zone against S. aureus were (in ascending order) QA-FWT (22.3 mm) > QA-TE (20.7 mm) > QA-FEA (18.7 mm) > QA-FPE (14.0 mm). The diameters of the inhibition zone against E. coli were (in ascending order) QA-FWT (20.0 mm) > QA-TE = QA-FEA (16.7 mm) > QA-FPE (14.7 mm). The diameters of the inhibition zone against P. aeruginosa were (in ascending order) QA-FWT (18.7 mm) > QA-FEA (15.7 mm) > QA-TE (14.7 mm) > QA-FPE (12.7 mm). The total extract and fractions of QA inhibited two Gram-positive bacteria (S. aureus, B. subtilis) and three Gram-negative bacteria (E. coli, P. aeruginosa, P. vulgaris), indicating that QA has a wide antibacterial spectrum. QA-FWT had better anti-bacterial activity against different bacteria as evidenced by the diameters of the inhibition zone. This is due to the chlorogenic acid derivatives in QA that can destroy the cell wall and cell membrane structure of bacteria and certainly have an inhibitory effect on bacteria [30,31]. Beyond that, the hydroxylation at C5 and C7 of flavonoid compounds can increase the inhibition of bacterial growth [32]. The C5 and C7 of jaceosidin, eupatilin, and hispidulin riched in QA-FEA are replaced by hydroxyl groups, and the antimicrobial activity of QA-FEA is increased. This provides a theoretical basis for the application of QA as a natural antibacterial agent in food and agriculture.
## 3.1. Chemicals
3,5-di-O-caffeoylquinic acid, 4-dicaffeoylquinic acid, neochlorogenic acid, eupatilin, and 4,5-di-O-caffeoylquinic acid were purchased from Weikeqi (Chengdu, China); chrysoeriol 7-O-glucoside, chlorogenic acid, chrysoeriol, schaftoside, 3,4-di-O-caffeoylquinic acid, hispidulin, and isoschaftoside were purchased from Alfa (Chengdu, China); and hyperoside and jaceosidin were purchased from Yuanye (Shanghai, China). HPLC-grade formic acid, acetonitrile, and leucine enkephalin were purchased from Sigma-Aldrich (St. Louis, MO, USA). All other solvents (petroleum ether, ethyl acetate, methanol, ethanol) were acquired from Chron Chemicals (Chengdu, China). A Milli-Q purification system (Millipore, France) was used to create the ultra-pure water.
Dulbecco’s modified Eagle’s medium (DMEM) was purchased from Servicebio (Wuhan, China), dimethyl sulfoxide (DMSO) was purchased from Aladdin (Shanghai, China), fetal bovine serum was purchased from Newzerun (Wuhan, China), phosphate buffered saline was purchased from Hyclone (Shanghai, China), mueller hinton agar (MHA) and mueller hinton broth (MHB) were purchased from Hopebio (Qingdao, China). The DPPH Free radical Scavenging Ability assay kit and the ABTS Free radical Scavenging Ability assay kit were purchased from Jiancheng Bioengineering Institute (Nanjing, China), and the Nitric Oxide assay kit was purchased from Beyotime (Shanghai, China).
## 3.2. Plant Material
The plant samples (Figure 5) were collected from Zhulin Lake in Qichun County, Huanggang City, Hubei Province, China. The plant was collected in June 2021 and verified by Prof. Dr. Dingrong Wan, South-Central Minzu University (SCMU). Voucher specimens of Qiai plants were deposited in SCMU with the number QA2021060403. The majority of the collected plant leaves was shade dried for 7 days and then pulverized with an electric grinder to give Mugwort leaf powder.
## 3.3. Preparation of Extract and Fractions
Mugwort leaf powder (50.0 g) was extracted with $70\%$ methanol. Extraction (1:20, w/v) was performed by maceration for 3 h at room temperature, heated for reflux three times in a water bath (2.5 h each time), combined with filtrate, and concentrated under vacuum to 7.6 g of the total crude extract (QA-TE). Warm water was used to dissolve 6 g of QA-TE before it was progressively partitioned with 500 mL petroleum ether (PE) and 500 mL ethyl acetate (EtOAc) to produce the PE fraction (QA-FPE, 2.0 g), EtOAc fraction (QA-FEA, 1.2 g), and water fraction (QA-FWT, 2.4 g), respectively. The extract and fractions were stored at −20 °C until use.
## 3.4. UPLC-Q-TOF/MS Analysis
Chromatographic analysis was performed on an ultra-performance liquid chromatography system equipped with a four-element pump, an online degassing machine, an automatic sampler, and a thermostatically controlled column chamber. The separation was performed on an ACQUITY UPLC HSS T3 column (100 × 2.1 mm, 1.8 µm). The mobile phase was composed of solvent A ($0.1\%$ Formic acid in H2O) and solvent B ($0.1\%$ Formic acid in acetonitrile: methanol, 9:1), and the elution gradient system was optimized on this basis. Elution gradient technology was used for the study, with a constant flow rate of 0.4 mL/min. The injection volume was 2 μL. The gradient proceeded as follows: 0–1.0 min, 2–$5\%$ B; 1.0–7.0 min, 5–$20\%$ B; 7.0–9.0 min, $20\%$ B; 9.0–12.5 min, 20–$28\%$ B; 12.5–18.0 min, 28–$40\%$ B; 18.0–22.0 min, 40–$98\%$ B, 22.0–25.0 min, $98\%$ B, 25.0–30.0 min, 98–$2\%$ B. The column and autosampler were kept at 45 and 4 °C, respectively. MS detection was carried out on Synapt-G2-SI MS system. The high collision energy ranged from 15 to 25 eV, whereas the low collision energy was fixed at 6 eV, and the ionization mode was set as ESI+ and ESI−. The mass ranged from 50 to 1200 Da. The cone voltage was 40 V, the capillary voltage was 3.00 kV in the negative mode and 2.59 kV in the positive mode. The desolvation temperature was fixed at 500 °C, while the ion source temperature remained at 150 °C. Desolvation gas (N2) flowed at 800 L/h while cone gas (N2) flowed at 50 L/h.
## 3.5. Construction of UNIFI Theoretical Library on Chemical Constituents of QA
SciFinder, PubMed, PubChem, and Reaxys are a few of the internet databases that were used to compile a list of the compounds mentioned in the literature on QA. Search terms “Artemisia argyi” were employed to search published literature up to April 2022. The process of identifying chemical structures in complex natural products can be streamlined by combining UPLC-Q-TOF/MS data with the UNIFI information management platform and its embedded Traditional Medicine Library. Finally, the structure of 208 compounds reported from A. argyi species was collected and saved in a.sdf file as a theoretical library. The MS data of the QA-TE was imported into the UNIFI platform for rapid matching screening with the theoretical library data of A. argyi compounds.
## 3.6.1. Preparation of Standard Solution and Sample Solution
Flavonoids and chlorogenic acids are important components in QA, which are closely related to the pharmacological action of QA. Therefore, it is significant to quantify the main flavonoids and chlorogenic acids in QA.
A mixed standard stock solution containing hyperoside, chrysoeriol 7-O-glucoside, chlorogenic acid, chrysoeriol, schaftoside, 3,4-di-O-caffeoylquinic acid, 3,5-di-O-caffeoylquinic acid, hispidulin, jaceosidin, 4-dicaffeoylquinic acid, neochlorogenic acid, eupatilin, isoschaftoside, and 4,5-di-O-caffeoylquinic acid was prepared in methanol:water (1:1, v/v). To prepare working standard solutions for plotting the calibration curve, mixed standards were diluted with methanol within the ranges from 3.2 to 1000 ng/mL.
A total of 2–3 mg samples were taken, QA-TE was dissolved in methanol:water (1:1), and QA-FEA and QA-FWT were dissolved in methanol. The sample solution was centrifuged with a centrifuge (Eppendorf 5810R) at 10,000 r/min, and the supernatant was used for the test. The QA-TE and QA-FWT were diluted to 50 μg/mL and the QA-FEA to 10 μg/mL.
## 3.6.2. Instrumentation and Analytical Conditions
Chromatographic analysis was the same as 2.4. The Xevo TQ-S MS/MS system was used to perform the mass spectrometry detection. The ionization mode for was set to ESI+ and ESI− mode for the determination of the main chemical constituents of QA by the UNIFI theoretical library. The quantitative data acquisition mode was set to multiple reaction monitoring (MRM), the ionization mode was set to ESI−, and the other analysis conditions of mass spectrometry were consistent with 2.4. Each analyte’s collision energy and particular fragmentor voltage were tuned in order to produce the strongest quantitative change. Table S1 in the supplementary document includes the optimum values for these critical parameters for the fourteen target compounds.
## 3.7.1. DPPH Assay
The scavenging activities of the total extract and three fractions were evaluated using a 2.2-dy-phenyl-1-picrylhydrazyl (DPPH) Free Radical Scavenging Ability Assay kit with slight modifications [33]. DPPH (600 μL) was admixed with 400 μL of fractions and standard (4.0–426.0 μg/mL), respectively. After being vortexed, the reaction mixture was left at room temperature in the dark for 30 min. After incubation, absorbance was assessed at 517 nm using a spectrophotometer. Methanol was employed as a blank, and trolox served as the positive control (standard). Each blank, samples, and standards’ absorbance were measured in triplicate. The ability to scavenge the DPPH radical was measured by the following equation:%DPPH radical scavenging = (1 − (Ai − Aj) ÷A0) × $100\%$ Ai: absorbance of DPPH radical + fraction/standard; Aj: absorbance of fraction/standard + methanol; A0: absorbance of DPPH radical + methanol.
By graphing the sample concentration vs. the scavenging capacity using a logarithm function, the IC50 (Half-maximal Inhibitory Concentration) value was determined.
## 3.7.2. ABTS Assay
The scavenging activity of the total extract and three fractions was evaluated using a 2,2′-Azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS) Free Radical Scavenging Ability Assay kit. The detection buffer, ABTS solution, and hydrogen peroxide solution (76:5:4) were mixed to prepare the ABTS working solution. Trolox was used as a positive control (standard). ABTS (170 μL), and peroxidase solution (20 μL) were admixed with 10 μL of fractions and standard (51.8–837.0 μg/mL), respectively. The reaction mixture was vortexed and left at room temperature in the dark for 6 min. After incubation, absorbance was measured by an enzyme standard instrument at 405 nm. The ability to scavenge the ABTS radical was measured by the following equation:%DPPH radical scavenging = (A0 − Ai) ÷ A0 × $100\%$ Ai: absorbance of ABTS radical + peroxidase solution+ fraction/standard; A0: absorbance of ABTS radical + peroxidase solution+ H2O.
## 3.8. Determination of Anti-Inflammatory Activity by Inhibition of NO
The inhibiting effect on nitric oxide (NO) production in LPS-stimulated RAW 264.7 (Wuhan, China) macrophage cells served as a metric for the anti-inflammatory action. The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with $10\%$ (v/v) fetal bovine serum and $0.5\%$ penicillin/streptomycin. The cells were cultivated in a humidified incubator at 37 °C with $5\%$ CO2 and $95\%$ air. Measurements were made of the samples’ ability to inhibit NO generation. In 96-well culture plates filled with 100 L of DMEM media, RAW 264.7 cells (6 × 104) were planted. After 2 h of cell adhesion, the cells were starved for 12 h. LPS (1 μg/mL) and different concentrations of sample solution (25, 20, 15, 10, 5 μg/mL) were added simultaneously. The cells were incubated at 37 °C with $5\%$ CO2 for 24 h. After 24 h of incubation, 50 μL of the supernatant was collected for nitrite assay with a NO assay kit by using the Griess reaction [34]. The remaining medium was taken out, and the CCK-8 technique was used to assess the cell viability. The absorbance was measured at 450 nm.
## 3.9. Disc Diffusion Assay
The agar plates’ preparation was performed for the disc diffusion technique to examine the antibacterial activity of the extract and fractions. Two Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis) and three Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa, P. vulgaris) were chosen for antibacterial activities of the total extract and fractions. Each strain was cultivated for 24 h, and the bacterial culture was diluted to a concentration of about 106 CFU/mL. A total of 0.2 mL of the diluted solution was then evenly dispersed over the agar plates. Samples were diluted with methanol at 50 mg/mL. Then, 0.2 mL of the sample solution was injected into a 6 mm diameter hole placed in the agar plates. The plates were cultured at 37 °C for 16 h. To assess the antibacterial activity of the strains, the widths of their inhibition zones were evaluated. Methanol (ME) was used as a negative control, and 5 μg of ciprofloxacin hydrochloride (CH) was used as a positive control.
## 4. Conclusions
In conclusion, this study established a rapid identification method for compounds in QA by combining UPLC-Q-TOF/MS with the UNIFI information management platform. Meanwhile, the study provided an effective method for the quantitative analysis of 14 compounds in QA by UPLC-TQ-MS/MS. This method could quantify 14 compounds simultaneously and be verified by LODs, LOQs, precision, repeatability, stability, and recovery range. The QA-FEA obtained from the QA-TE significantly reduced the NO release by LPS-stimulated RAW 264.7 cells. Meanwhile, QA-FWT has the highest DPPH and ABTS free radical scavenging ability and antibacterial ability. This is because QA-FEA has the highest flavonoid content and QA-FWT has the highest phenolic acid content. The results showed that Artemisia argyi Lévl. et Van., as dietary and traditional Chinese medicine, was an excellent source of natural antioxidants, anti-inflammatory drugs, and antibacterial agents. The results provided the theoretical basis for the use of QA in the food and pharmaceutical industries. The plant material selected for this study was from one production area, so there are some limitations. Factors such as geographical location, variety, and climate can have significant effects on the chemical composition of Artemisia argyi Lévl. et Van. In the future, we will work to improve the information on the chemical composition of Artemisia argyi in terms of different cultivars and origins to provide more comprehensive and reliable information for the research and application of Artemisia argyi.
## References
1. 1.
Editorial Board of Chinese Pharmacopoeia
Pharmacopoeia of the People’s Republic of ChinaChina Pharmaceutical Science and Technology PressBeijing, China201589. *Pharmacopoeia of the People’s Republic of China* (2015) 89
2. Schlaeger J.M., Stoffel C.L., Bussell J.L., Cai H.Y., Takayama M., Yajima H., Takakura N.. **Moxibustion for Cephalic Version of Breech Presentation**. *J. Midwifery Women’s Health* (2018) **63** 309-322. DOI: 10.1111/jmwh.12752
3. Li S.Z.. *Compendium of Materia Medica* (1987) 157
4. Xue Z.J., Guo L.X., Guo M., Yang G.Y., Zhang D., Guo L., Zheng Y.G.. **Study on difference of chemical constituents of Qiai in different harvest periods**. *Zhongguo Zhong Yao Za Zhi* (2019) **44** 5433-5440. PMID: 32237391
5. Chang Y.Q., Xue Z.J., Yang G.Y., Guo M., Zhang D., Zheng Y.G., Guo L.. **Dynamic changes of volatile components of Qiai from different harvest time based on GC-MS and chemometrics analysis**. *Zhongguo Zhong Yao Za Zhi* (2020) **45** 2417-2424. PMID: 32495601
6. Wang X.J., Deng Y.H., Zhang L.P., Zheng G.H., Chen L.L., Fang Y.. **Identification and determination of phenolic acids and flavonoids in**. *Zhongguo Zhong Yao Za Zhi* (2019) **44** 983-989. PMID: 30989859
7. Hu J.Q., Wan D.R., Pu R., Shi N.N., Huang L.Q., Li L.Y., Long W.F.. **Quality evaluation and genuine regional analysis on Artemisia Argyi Folium from different places of China and Korean**. *China J. Tradit. Chin. Med. Pharm.* (2019) **34** 553-556
8. Chen C.J., Luo D.D., Miao Y.H., Kang L.P., Guo L.P., Liu D.H., Huang L.Q.. **Analysis and evaluation of volatile oil content in leaves of different**. *Zhongguo Zhong Yao Za Zhi* (2021) **46** 3814-3823. PMID: 34472254
9. Li M., Chai X., Wang L., Yang J., Wang Y.. **Study of the Variation of Phenolic Acid and Flavonoid Content from Fresh Artemisiae argyi Folium to Moxa Wool**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24244603
10. Kim Y.B., Cho H.J., Yi Y.S.. **Anti-inflammatory role of**. *J. Ethnopharmacol.* (2023) **307** 116231. DOI: 10.1016/j.jep.2023.116231
11. Su S.H., Sundhar N., Kuo W.W., Lai S.C., Kuo C.H., Ho T.J., Lin P.Y., Lin S.Z., Shih C.Y., Lin Y.J.. *J. Ethnopharmacol.* (2022) **299** 115658. DOI: 10.1016/j.jep.2022.115658
12. Han Y., Park H.J., Hong M.K., Shin M.R., Roh S.S., Kwon E.Y.. *Curr. Issues Mol. Biol.* (2022) **4** 6158-6171. DOI: 10.3390/cimb44120420
13. Liu Y., He Y., Wang F., Xu R., Yang M., Ci Z., Wu Z., Zhang D., Lin J.. **From longevity grass to contemporary soft gold: Explore the chemical constituents, pharmacology, and toxicology of**. *J. Ethnopharmacol.* (2021) **279** 114404. PMID: 34246739
14. Xia J.X., Zhao B.B., Zan J.F., Wang P., Chen L.L.. **Simultaneous determination of phenolic acids and flavonoids in**. *J. Pharm. Biomed Anal.* (2019) **175** 112734. DOI: 10.1016/j.jpba.2019.06.031
15. Chang Y., Fan W., Shi H., Feng X., Zhang D., Wang L., Zheng Y., Guo L.. **Characterization of phenolics and discovery of α-glucosidase inhibitors in**. *J. Pharm. Biomed Anal.* (2022) **220** 114982. DOI: 10.1016/j.jpba.2022.114982
16. Luo D.D., Peng H.S., Zhang Y., Huang X.Z., Zhan Z.L., Liu D.H., Kang L.P., Huang L.Q.. **Comparison of chemical components between Artemisia stolonifera and**. *Zhongguo Zhong Yao Za Zhi* (2020) **45** 4057-4064. PMID: 33164389
17. Guo L., Zhang D., Xue Z.J., Jiao Q., Liu A.P., Zheng Y.G., Liu E.H., Duan L.. **Comparison of Artemisiae argyi Folium and Artemisiae lavandulaefoliae Folium by simultaneous determination of multi-components with single reference standard method and chemometric analysis**. *Phytochem. Anal.* (2019) **30** 14-25. DOI: 10.1002/pca.2786
18. Yang M.T., Kuo T.F., Chung K.F., Liang Y.C., Yang C.W., Lin C.Y., Feng C.S., Chen Z.W., Lee T.H., Hsiao C.L.. **Authentication, phytochemical characterization and anti-bacterial activity of two Artemisia species**. *Food Chem.* (2020) **333** 127458. DOI: 10.1016/j.foodchem.2020.127458
19. Cui L.L., Wang X.Z., Lu J., Tian J., Wang L., Qu J.J., Liu Z.H., Wei J.F.. **Rapid Identification of Chemical Constituents in**. *J. Food Qual.* (2021) **7** 5597327. DOI: 10.1155/2021/5597327
20. Anand A., Komati A., Katragunta K., Shaik H., Nagendla N.K., Kuncha M., Mudiam M.K.R., Babu K.S., Tiwari A.K.. **Phytometabolomic analysis of boiled rhizome of**. *Food Chem.* (2021) **342** 128313. PMID: 33067043
21. Nie R.Z., Dang M.Z., Ge Z.Z., Huo Y.Q., Yu B., Tang S.W.. **Interactions of chlorogenic acid and isochlorogenic acid A with model lipid bilayer membranes: Insights from molecular dynamics simulations**. *Chem. Phys. Lipids* (2021) **240** 105136. DOI: 10.1016/j.chemphyslip.2021.105136
22. Wen K., Fang X., Yang J., Yao Y., Nandakumar K.S., Salem M.L., Cheng K.. **Recent Research on Flavonoids and their Biomedical Applications**. *Curr. Med. Chem.* (2021) **28** 1042-1066. DOI: 10.2174/0929867327666200713184138
23. Wu C.Y., Guo Y.Y., Zhou J., Long F., Zhang W., Shen H., Xu J.D., Zhou S.S., Li S.L.. **Holistic quality evaluation of Hibisci Mutabilis Folium by integrating UPLC-QTOF-MS/MS chemical profiling and UPLC-TQ-MS/MS quantification approaches**. *J. Pharm. Biomed Anal.* (2022) **218** 114869. DOI: 10.1016/j.jpba.2022.114869
24. Liu H., Hao J., Wu C., Liu G., Wang X., Yu J., Liu Y., Zhao H.. **Eupatilin Alleviates Lipopolysaccharide-Induced Acute Lung Injury by Inhibiting Inflammation and Oxidative Stress**. *Med. Sci. Monit.* (2019) **25** 8289-8296. DOI: 10.12659/MSM.917406
25. Yun N., Kang J.W., Lee S.M.. **Protective effects of chlorogenic acid against is chemia/reperfusion injury in rat liver: Molecular evidence of its antioxidant and anti-inflammatory properties**. *J. Nutr. Biochem.* (2012) **23** 1249-1255. DOI: 10.1016/j.jnutbio.2011.06.018
26. Su M., Liu F., Luo Z., Wu H., Zhang X., Wang D., Zhu Y., Sun Z., Xu W., Miao Y.. **The Antibacterial Activity and Mechanism of Chlorogenic Acid Against Foodborne Pathogen**. *Foodborne Pathog. Dis.* (2019) **16** 823-830. DOI: 10.1089/fpd.2019.2678
27. Zeb A.. **Concept, mechanism, and applications of phenolic antioxidants in foods**. *J. Food Biochem.* (2020) **44** e13394. DOI: 10.1111/jfbc.13394
28. Fernandez-Panchon M.S., Villano D., Troncoso A.M., Garcia-Parrilla M.C.. **Antioxidant activity of phenolic compounds: From in vitro results to in vivo evidence**. *Crit. Rev. Food Sci. Nutr.* (2008) **48** 649-671. DOI: 10.1080/10408390701761845
29. Min S.W., Kim N.J., Baek N.I., Kim D.H.. **Inhibitory effect of eupatilin and jaceosidin isolated from Artemisia princeps on carrageenan-induced inflammation in mice**. *J. Ethnopharmacol.* (2009) **125** 497-500. DOI: 10.1016/j.jep.2009.06.001
30. Neetu N., Katiki M., Dev A., Gaur S., Tomar S., Kumar P.. **Structural and Biochemical Analyses Reveal that Chlorogenic Acid Inhibits the Shikimate Pathway**. *J. Bacteriol.* (2020) **202** e00248-20. DOI: 10.1128/JB.00248-20
31. Wu Y., Liang S., Zhang M., Wang Z., Wang Z., Ren X.. **The Effect of Chlorogenic Acid on Bacillus subtilis Based on Metabolomics**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25184038
32. Xie Y., Yang W., Tang F., Chen X., Ren L.. **Antibacterial activities of flavonoids: Structure-activity relationship and mechanism**. *Curr. Med. Chem.* (2015) **22** 132-149. DOI: 10.2174/0929867321666140916113443
33. Baliyan S., Mukherjee R., Priyadarshini A., Vibhuti A., Gupta A., Pandey R.P., Chang C.M.. **Determination of Antioxidants by DPPH Radical Scavenging Activity and Quantitative Phytochemical Analysis of Ficus religiosa**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27041326
34. Deng W., Zhu Y., Lin J., Zheng L., Zhang C., Luo M.. **Inhibition of soluble epoxide hydrolase lowers portal hypertension in cirrhotic rats by ameliorating endothelial dysfunction and liver fibrosis**. *Prostaglandins Oth Lipid M.* (2017) **131** 67-74. DOI: 10.1016/j.prostaglandins.2017.08.004
|
---
title: Berberine-Based Carbon Quantum Dots Improve Intestinal Barrier Injury and Alleviate
Oxidative Stress in C57BL/6 Mice with 5-Fluorouracil-Induced Intestinal Mucositis
by Enhancing Gut-Derived Short-Chain Fatty Acids Contents
authors:
- Liang Wu
- Yue Xi
- Man Yan
- Chang Sun
- Jiajun Tan
- Jiayuan He
- Haitao Li
- Dongxu Wang
journal: Molecules
year: 2023
pmcid: PMC10004514
doi: 10.3390/molecules28052148
license: CC BY 4.0
---
# Berberine-Based Carbon Quantum Dots Improve Intestinal Barrier Injury and Alleviate Oxidative Stress in C57BL/6 Mice with 5-Fluorouracil-Induced Intestinal Mucositis by Enhancing Gut-Derived Short-Chain Fatty Acids Contents
## Abstract
This study aims to evaluate the effect of berberine-based carbon quantum dots (Ber-CDs) on improving 5-fluorouracil (5-FU)-induced intestinal mucositis in C57BL/6 mice, and explored the mechanisms behind this effect. Thirty-two C57BL/6 mice were divided into four groups: normal control (NC), 5-FU-induced intestinal mucositis model (5-FU), 5-FU + Ber-CDs intervention (Ber-CDs), and 5-FU + native berberine intervention (Con-CDs). The Ber-CDs improved body weight loss in 5-FU-induced mice with intestinal mucositis compared to the 5-FU group. The expressions of IL-1β and NLRP3 in spleen and serum in Ber-CDs and Con-Ber groups were significantly lower than those in the 5-FU group, and the decrease was more significant in the Ber-CDs group. The expressions of IgA and IL-10 in the Ber-CDs and Con-Ber groups were higher than those in the 5-FU group, but the up-regulation was more significant in the Ber-CDs group. Compared with the 5-FU group, the relative contents of Bifidobacterium, Lactobacillus and the three main SCFAs in the colon contents were significantly increased the Ber-CDs and Con-Ber groups. Compared with the Con-Ber group, the concentrations of the three main short-chain fatty acids in the Ber-CDs group were significantly increased. The expressions of Occludin and ZO-1 in intestinal mucosa in the Ber-CDs and Con-Ber groups were higher than those in the 5-FU group, and the expressions of Occludin and ZO-1 in the Ber-CDs group were more higher than that in the Con-Ber group. In addition, compared with the 5-FU group, the damage of intestinal mucosa tissue in the Ber-CDs and Con-Ber groups were recovered. In conclusion, berberine can attenuate intestinal barrier injury and oxidative stress in mice to mitigate 5-fluorouracil-induced intestinal mucositis, moreover, the above effects of Ber-CDs were more significant than those of native berberine. These results suggest that Ber-CDs may be a highly effective substitute for natural berberine.
## 1. Introduction
Chemotherapy-induced intestinal mucositis (CIM), which is the most common adverse reaction in patients with cancer receiving chemotherapy, manifests as gastrointestinal problems, such as nausea, vomiting, diarrhea, appetite loss, indigestion and malabsorption [1]. CIM increases the pain of patients with cancer and often causes them to discontinue chemotherapy, thus reducing the survival rate of these patients. However, effective prevention and treatment strategies for CIM remain to be devised [2]. CIM has an estimated incidence rate of $40\%$, and $90\%$ of all cases involve the use of 5-fluorouracil (5-FU) and methotrexate [3]. 5-FU is used to treat various gastrointestinal tract tumors, including colon, stomach, and esophageal tumors [4]. Few treatment options are available for 5-FU-induced CIM, thus necessitating studies aimed at developing effective drugs for this condition.
The maintenance of gut homeostasis requires high levels of energy, which is supplied primarily by mitochondria [5], which are not only the primary source of reactive oxygen species (ROS) under oxidative stress but also a key drug target [6]. Under oxidative stress conditions, damaged mitochondria can produce 10 times more ROS than normal mitochondria can, thus aggravating the intestinal histologic damage [7]. In the absence of an adequate antioxidant system, excess levels of ROS can compromise epithelial cell integrity and intestinal barrier function by reducing the number of tight junctions and the quality of cells [8]. ROS and other free radicals can disrupt cell functions by interfering with transcription factors and redox-sensitive signaling pathways. The nucleotide-binding oligomerization domain-, leucine-rich repeat-, and pyrin domain-containing protein 3 (NLRP3) inflammasome is a tripartite protein that regulates the oxidative status and inflammation of cells [9]. ROS are involved in the development of intestinal injury; therefore, cells must effectively alleviate oxidative stress, which may be associated with increased intestinal permeability and short-chain fatty acids (SCFAs) levels, and reduced epithelial apoptosis.
Berberine (Ber) is a quaternary ammonium alkaloid found in 4 families and 10 genera of plants (e.g., Berberis aristate); high levels of Ber have been detected in *Coptis coptidis* and *Phellodendrus chinensis* [10]. Ber exerts strong anti-inflammatory effects, but the exact underlying mechanism remains to be fully understood [11]. Carbon quantum dots (CDs) are surface-modified carbon nanoparticles, which composed a carbon core and an external carbon shell [12]. CDs exhibit high fluorescence stability, photobleaching resistance, broad and continuous fluorescence emission upon excitation, good water solubility, and rapid migration in cells and are thus considered to be ideal biomedical materials [13,14]. Liu et al. [ 15] designed a type of nitrogen-doped CD and demonstrated its biological activity in inhibiting β-amyloid aggregation. The use of traditional Chinese medicine in the preparation of CDs may enhance drug solubility and ensure sustained drug release in vivo, thus substantially increasing the effectiveness of these CD-based drugs [16]. The main disadvantage of *Ber is* its poor solubility, which can be markedly enhanced by using a different dosage form [17].
In this study, we used a mouse model (C57BL/6) of 5-FU-induced intestinal mucositis to investigate the effects of berberine-based carbon quantum dots (Ber-CDs) on intestinal barrier injury, mitochondrial function, and inflammation, and to elucidate the underlying molecular mechanisms.
## 2.1. Preparation and Characterization of Ber-CDs
High-resolution images obtained through transmission electron microscopy showed that the carbon particles were complete crystals without any internal defects, and the Ber-CDs were spherical monodisperse particles with a diameters of about approximately 2–5 nm (Figure 1A). Figure 1B presents the emission spectrum of Ber-CDs obtained under the condition of a fixed excitation light wavelength. The fluorescence emitted by the samples at different wavelengths was measured. A graph was drawn using the wavelength of Ber-CD-emitted fluorescence as the x-axis and the intensity (relative fluorescence units, RFU) as the y-axis. The maximum emission wavelength of Ber-CDs at an excitation wavelength of 380 nm was 518 nm. With a fixed emission wavelength of 450 nm, the maximum excitation wavelength of Ber-CDs was 380 nm (Figure 1C). In addition, the performance of synthesized Ber-CDs with different preservation times were measured by fluorescence spectrometer as shown in Figure S1 (Supplementary Materials), and these results suggest that the synthesized Ber-CDs has good stable performance.
## 2.2. The Body Weight Changes of Experimental Mice
Figure 2 presents the body weight of each group of mice during the experimental period. The normal control (NC) group exhibited a continual increase in body weight throughout the experimental period, whereas the 5-FU, Ber-CD, and native Ber (Con-Ber) groups exhibited significant reductions in body weight 3 days after receiving 5-FU injection (intraperitoneal); in these three group, body weight continued to decrease during 5-FU treatment. On Day 8, the 5-FU group had the lowest body weight, followed by the Con-Ber group; however, no significant difference was observed between the two groups ($p \leq 0.05$). The body weight of the Ber-CD group was significantly higher than that of the 5-FU and Con-Ber groups ($p \leq 0.05$).
## 2.3. Expressions Levels of Inflammatory Factors in the Spleen and Serum of Mice
Figure 3 shows the changes in the expression levels of NLRP3, interleukin (IL)-10, and IL-1β, which indicate the level of inflammatory response in mice, and that of immunoglobulin (Ig)A, which indicates the degree of mucosal immunity in mice. Compared with the findings in 5-FU group, the expression level of NLRP3 in the spleen significantly decreased and that of IL-10 significantly increased in the Ber-CD and Con-Ber groups ($p \leq 0.05$). Compared with the findings in the Con-Ber group, the expression level of NLRP3 significantly decreased and that of IL-10 significantly increased in the Ber-CD group ($p \leq 0.05$). Enzyme-linked immunosorbent assay (ELISA) revealed that compared with the findings for the 5-FU group, the serum level of IL-1β significantly decreased but that of IgA significantly increased in the Ber-CD and Con-Ber groups ($p \leq 0.05$). Compared with the findings in the Con-Ber group, the serum level of IL-1β significantly decreased and that of IgA significantly increased in the Ber-CD group ($p \leq 0.05$).
## 2.4. Morphology of the Intestinal Mucosa and Expression of Tight Junction Proteins
Figure 4A depicts the mouse intestinal mucosa stained with hematoxylin–eosin (HE). In the NC group, the intestinal mucosa was intact and undamaged, the intestinal gland body was arranged regularly, and the mucosal lamina propria was unchanged. In the 5-FU group, the intestinal mucosa was severely necrotic; mucosal atrophy, villi shedding, and mutilated glands were noted; and crypts disappeared. The aforementioned injuries were improved to a certain extent in the Ber-CDs and Con-Ber groups, wherein the small intestinal mucosal epithelium was reduced, glands were relatively neat, and mucosal layer necrosis was inhibited. Figure 4B presents the results of immunohistochemical analyses performed to evaluate the expression levels of the mucosal barrier integrity proteins occludin and zonula occludens (ZO)-1 in the intestinal mucosal tissues of the mice. Compared with the findings for the NC group, the expression levels of ZO-1 and occludin in the small intestine significantly decreased in the 5-FU and Con-Ber groups ($p \leq 0.05$). Compared with the findings for the 5-FU group, the expression levels of both ZO-1 and occludin significantly increased in the Ber-CD group ($p \leq 0.05$) but only that of occludin significantly increased in the Con-Ber group ($p \leq 0.05$).
## 2.5. Relative Abundances of Intestinal Bacteria and Levels of SCFAs in Mouse Feces
The relative abundances of four important bacteria in the colon contents of the mice were evaluated through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). The results are presented in Figure 5A. Compared with the findings for the NC group, the relative abundances of Bifidobacterium and Lactobacillus significantly decreased in the 5-FU group ($p \leq 0.05$); however, no significant between-group difference was noted in the relative abundances of *Escherichia coli* and Enterococcus ($p \leq 0.05$). Compared with the findings for the 5-FU group, the relative abundances of Bifidobacterium and Lactobacillus significantly increased in the Ber-CD and Con-Ber groups ($p \leq 0.05$). However, we noted no significant between-group difference in the relative abundances of E. coli and Enterococcus ($p \leq 0.05$).
The levels of three main SCFAs in colon contents of the mice were measured through liquid chromatography (LC) with tandem mass spectrometry (MS/MS). The results are presented in Figure 5B. Compared with the findings for the NC group, the levels of these SCFAs significantly decreased in the 5-FU group ($p \leq 0.05$). Compared with the findings for the 5-FU group, the levels of these SCFAs significantly increased in the Ber-CD and Con-Ber groups. The levels of propionic and butyric acids were significantly lower in the NC group than in the other groups ($p \leq 0.05$). Compared with the findings for the Con-Ber group, the levels of the three main SCFAs significantly increased in the Ber-CD group ($p \leq 0.05$).
## 2.6. Levels of Plasma Endotoxin, Superoxide Dismutase and Malondialdehyde Concentrations in Mice
Compared with the findings for the NC group, the level of endotoxin significantly increased in the 5-FU, Ber-CD, and Con-Ber groups ($p \leq 0.05$; Figure 6A); however, no significant differences were noted between these three treatment groups ($p \leq 0.05$). Compared with the findings for the NC group, the level of superoxide dismutase (SOD) was significantly decreased only in the 5-FU group ($p \leq 0.05$; Figure 6B); compared with the level of SOD in the Con-Ber group, that in the Ber-CD group was increased significantly ($p \leq 0.05$). Compared with the findings for the NC group, the level of malondialdehyde (MDA) was significantly increased in the 5-FU and Con-Ber groups ($p \leq 0.05$; Figure 6C), but no significant difference was observed among the other groups ($p \leq 0.05$).
## 3. Discussion
Recent studies on CDs, a new nanoscale luminescent carrier, have focused primarily on optimizing their preparation method (e.g., using traditional Chinese medicine) and expanding their range of applications. CDs exhibit good solubility and dispersion properties because of their small size and thus may markedly improve the efficacy of traditional Chinese medicine [12]. CDs with a particle size of < 10 nm can be formed by subjecting traditional Chinese medicine to certain physical and chemical reactions. This indicates that compared with traditional Chinese medicine, CD-based traditional Chinese medicine exhibits enhanced photoluminescence, reduced toxicity, and improved water solubility and biological compatibility. The use of traditional Chinese medicine in the preparation of CDs may resolve the insolubility-related problems of some Chinese medicines and even confer new biological activities [18]. Therefore, the use of traditional Chinese medicine in the preparation of CDs with fluorescence stability and pharmacological activity may increase drug efficacy and expand drug applications.
Ber is a quaternary ammonium alkaloid isolated from the traditional Chinese medicine Coptis coptidis. This compound exhibits a wide range of pharmacological activities, including anti-inflammatory, anti-infection, and anti-tumor activities. Few studies have explored the effects of Ber on CIM [19]. Ber and its hydrochloride or sulfate are insoluble in water, which substantially limits their pharmacological use [20]. In our study, CDs were added to Ber to increase the solubility of native Ber, and excellent therapeutic effects were observed in the mouse model of 5-FU-induced intestinal mucositis.
5-FU exerts antitumor effects by interfering with DNA synthesis in tumor cells, but it also interferes with DNA synthesis in normal tissue cells [21]. We previously demonstrated that 5-FU promotes cellular necrosis and the release of considerable amounts of proinflammatory double-stranded DNA, activates NLRP3 inflammasome, and induces an inflammatory response in mice [22]. NLRP3 inflammasome is a major member of the inflammasome family, which can be activated by bacterial toxins, ATP, reactive oxygen species, urea crystals and other pathogens and danger signal molecules in vivo; it is an important factor in anti-infection immunity and inducing inflammatory diseases [23]. Bauer et al. [ 24] found that NLRP3 inflammasome plays a role in gastrointestinal inflammatory diseases mainly by upregulating the expression of IL-1β, IL-18, and other inflammatory cytokines. In the absence of any intestinal infection, a low level of NLRP3 is expressed in intestinal mucosal epithelial cells and immune cells. During inflammation, the expression level of NLRP3 in immune cells increases rapidly, and NLRP3 further activates the protease caspase-1, which mediates the cleavage and maturation of IL-1β and IL-18 precursors, and eventually induces inflammatory reactions, leading to inflammatory injury in normal tissues [25]. In this study, both native Ber and Ber-CDs inhibited 5-FU-induced activation of NLRP3 and reduced the expression level of the proinflammatory cytokine IL-1β in mice. Furthermore, Ber-CD exhibited potent anti-inflammatory activities. Our findings suggest that Ber-CDs exert strong anti-inflammatory effects, possibly by inhibiting NLRP3 activation, reducing IL-1β expression level, and increasing IL-10 expression level. These compounds further protect normal intestinal mucosal tissues to maintain the integrity of the intestinal mucosal barrier.
The intestinal mucosal barrier is composed of mechanical, biological, immune and chemical barriers. The mechanical barrier comprises a mucus layer on the surface of the intestinal mucosa and tight junctions between closely arranged epithelial cells [26]. 5-FU can severely damage the villi and crypt structures in the small intestine, destroy the tight junctions between mucosal epithelial cells, and damage the intestinal mucosal barrier [27]. 5-FU inhibits the expression of the tight junction proteins occludin and ZO-1 in intestinal epithelial cells, thus increasing the permeability of the intestinal mucosa so that intestinal bacteria and bacterial toxins (e.g., endotoxin and lipopolysaccharides) can enter the bloodstream through the intestinal mucosa and cause systemic inflammation [28]. These changes eventually increase oxidative stress and damage normal tissues. After native Ber and Ber-CDs treatments, considerable increases were noted in the levels of endotoxin and oxidative stress response in the blood of the experimental mice. The condition of mice with CIM was significantly better than that of those with 5-FU-induced intestinal mucositis; furthermore, and the expression levels of ZO-1 and Occludin in intestinal epithelial cells were significantly higher in mice with CIM than in those with 5-FU-induced intestinal mucositis. Thus, Ber can promote the expression of ZO-1 and Occludin to maintain the integrity of mucosal tight junctions and prevent the intestinal bacteria entering the bloodstream. Our findings are consistent with those of other studies [29,30]. However, we found no significant differences in intestinal mucosal barrier integrity or oxidative response indices in mice treated with Ber-CDs and those treated with native Ber. This might be because of the short treatment duration; thus, further studies are necessary.
IgA secreted by intestinal lamina propria plasma cells and intestinal epithelial cells is also an important part of the intestinal mucosal barrier, as it is the first line of defense against pathogen invasion, adhesion, and colonization in the intestinal mucosa [31]. 5-FU markedly inhibited IgA secretion in the experimental mice, thus rendering the host susceptible to various pathogens [32]. We found that the plasma levels of IgA were considerably higher in mice treated with Ber-CDs than in those treated with 5-FU or Con-Ber. These findings indicated that Ber-CDs effectively enhanced mucosal immunity and prevented inflammation induced by intestinal bacteria or their endotoxins.
SCFAs are organic fatty acids with 1–6 carbon atoms. Some microorganisms residing in the human colon produce SCFAs to ferment dietary fibers and resistant starch that cannot be digested by humans [33]. Three major SCFAs found in the human colon are acetic, propionic, and butyric acids, which account for >$95\%$ of all SCFAs [34]. SCFAs can regulate host intestinal mucosal immunity, reduce the colonic inflammatory response, inhibit colon tumor cell proliferation, and induce tumor cell differentiation and apoptosis [35]. Through SCFAs and other metabolites, gut microbes regulate the changes in the physiological functions of their host [36]. SCFAs also participate in the metabolic activities of different organs of the human body and exert various effects. Acetic acid produced through bacterial fermentation can be absorbed and used by the host. It is a key source of host energy, providing approximately $10\%$ of total daily energy [37]. After its absorption in the blood, propionate is catabolized in the liver, and it participates in the conversion of pyruvate to glucose and may inhibit the synthesis of fat [38]. Butyrate can be absorbed by epithelial cells and is the main source of energy for these cells [39]. Butyric acid exerts strong anti-inflammatory effects by activating G-protein-coupled receptors and inhibiting histone deacetylases [40]. In animal studies, butyrate inhibited the bacterial endotoxin–induced activation of neutrophils, the release of various proinflammatory cytokines (e.g., tumor necrosis factor-α), and various inflammation-related signaling pathways (e.g., nuclear factor-κB signaling) and reduced inflammatory reactions [41,42,43]. SCFAs help maintain the integrity of the intestinal mucosal barrier and increase the secretion of mucin by mucosal epithelial cells to enhance a host’s ability to resist attack by pathogens [44,45]. We found that the relative abundances of Bifidobacterium and Lactobacillus in the colon contents of the mice were significantly increased in the Ber-CD and Con-Ber groups. Further LC–MS/MS analysis revealed that the levels of acetic, propionic, and butyric acids in the colon contents of the experimental mice were significantly increased in the Ber-CD and Con-Ber groups; the levels of these three SCFAs were significantly higher in the Ber-CD group than in the Con-Ber group. CDs may improve the effectiveness of native Ber in vivo; however, further studies must be conducted to elucidate the exact mechanisms.
## 4.1. Ber-CD Synthesis and Identification
The nitrogen flow rate of a supersonic collision crushing instrument was set to 600 m/s. Ber was instantaneously crushed into ultrafine powder through super-high-speed collision twice. The temperature of a photoelectric carbonization instrument was set to 120 °C to heat the mixture of Ber powder and water. The mass ratio of Ber powder and water was 1:10 (m/m), the heating time was 180 s, and the far-infrared irradiation speed was 30/s, further give the resulted solution a hydrothermal treatment for 3 h under 180 °C to ensure the uniform dispersion of carbon nanoparticles in water to form a colloidal solution of CDs. The morphology and particle size of Ber-CDs were assessed through transmission electron microscopy (JEM-2100Plus, JEOL, Kitakyushu, Japan). The fluorescence emission and excitation spectra of Ber-CDs were obtained through fluorescence spectrometry (Infinite E Plex, TECAN, Männedorf, Switzerland).
## 4.2. Animal Experiment
Male specific pathogen–free C57BL/6 mice ($$n = 32$$; age, 8 weeks; body weight, 18–22 g) were purchased from the Animal Center of Yangzhou University (Yangzhou, China) and housed in the Animal Experimental Center of Jiangsu University, China. Mice were randomly divided into the following four groups: NC, 5-FU, Ber-CD, and Con-Ber groups. Each group comprised eight mice. The NC group was fed normally and received no treatment. The mice in the 5-FU, Ber-CD, and Con-Ber groups were intraperitoneally injected with 5-FU (30 mg/kg, King York Inc., Tianjin, China) for 5 days to induce intestinal mucositis. The mice in the Ber-CD and Con-Ber groups were intraperitoneally injected with Ber-CDs or native Ber (5 mg/kg), respectively, every day from Day 3. The experiment ended on Day 5. Then, serum, spleen, and intestinal tissues were collected from all groups for subsequent analyses.
## 4.3. qRT-PCR Assay
Ttotal RNA was extracted from the spleen tissues of the experimental mice by following the standard Trizol method (Vazyme, Nanjing, China). cDNA was synthesized through reverse transcription with oligo(dT)n as primer. The mRNA expressions of inflammatory related factors in the spleen were determined by fluorescent quantitative PCR (Vazyme, Nanjing, China). The total system of qRT-PCR reaction was 20 μL, including 10 μL SYBR Green Master Mix, 0.4 μL (10 μmol/L) of upper and lower primers, and 2 μL cDNA template. The following primer sequences were used: NLRP3 (Forward: 5′-ATTACCCGCCCGAGAAAGG-3′, Reverse: 5′-CATGAGTGTGGCTAGATCCAAG-3′), IL-10 (Forward: 5′-GAAGCTCCCTCAGCGAGGACA-3′, Reverse: 5′-TTGGGCCAGTGAGTGAAAGGG-3′) and β-actin (Forward: 5′-ATGACCCAAGCCGAGAAGG-3′, Reverse: 5′-CGGCCAAGTCTTAGAGTTGTTG-3′). The reaction procedure was as follows: pre-denaturation at 95 °C for 5 min, denaturation at 95 °C for 3 s, annealing at 58 °C for 20 s, and extension at 72 °C for 30 s. There were 40 cycles in total. With GAPDH as the internal reference, the relative mRNA expression was calculated by using 2−(ΔΔCT).
## 4.4. ELISA Assay
ELISA assay was used to detect the expressions of IL-1β and IgA in the serum of experimental mice, and enzyme-labeled instrument was used to detect the absorbance value at 450 nm. The standard curves were used to calculated the concentrations of IL-1β and IgA.
## 4.5. HE and Immunohistochemical Staining
Proximal ileocecal intestinal tissues were fixed in $4\%$ paraformaldehyde for 48 h, embedded in paraffin, and stained with HE. The integrity of the intestinal mucosal villus epithelium, separation of the mucosa and lamina propria, edema of the muscle layer, and shedding of intestinal mucosal villi were observed under a microscope.
Paraffin-embedded tissue samples were subjected to immunohistochemical staining. After dewaxing, gradient dehydration, antigen repair, blocking with goat serum, and incubation with antibodies, the tissues samples were stained with 3,3′-diaminobenzidine and counterstained with hematoxylin. The slices were sealed with neutral resin and observed under a microscope. Image-Pro Plus (version 6.0) (Media Cybernetics, Rockville, MD, USA) was used to measure the integral optical density to evaluate the expression levels of occludin and ZO-1.
## 4.6. Measurement of Relative Abundances of Intestinal Bacteria and Levels of SCFAs in Mouse Feces
qRT-PCR was performed to evaluate the relative abundances of intestinal bacteria. For this, primers were synthesized by Genewiz (Suzhou, China). For the investigation, we selected four key intestinal bacteria, namely Bifidobacterium, Lactobacillus, E. coli, and Enterococcus. In this study, the colon contents of the experimental mice were collected. Total DNA was extracted using the Fecal Genomic DNA extraction kit (Tiangen Biotech, Beijing, China). The PCR products of the aforementioned four bacteria were ligated to the pTG19-T vector (Generay Biotech, Shanghai, China) to prepare the gene diluents with known copy number. A series of diluents (102 copy/mL to 1011 copy/mL) were used to generate the amplification standard curve. The copy numbers of the target bacteria in the samples were calculated according to the standard curve.
The levels of the three main SCFAs detected in mouse feces were measured by Microeco Tech Co., Ltd. (Shenzhen, China) through gas chromatography (GC)–MS. In brief, 50 mg of fecal sample was mixed with 500 mL of saturated NaCl solution; next, 20 mL of $10\%$ sulfuric acid solution was added for acidification, which was followed by the addition of 800 mL of diethyl ether before shaking. The mixture was centrifuged at 18,000× g for 15 min at 4 °C. The supernatant was collected, and 0.25 g anhydrous sodium sulfate was added to it; the mixture was shaken and centrifuged again. The supernatant was obtained and added to a gas phase flask for GC–MS analysis.
## 4.7. Detection of Serum Endotoxin, SOD and MDA
Serum endotoxin was detected by the medical laboratory department of Northern Jiangsu People’s Hospital (Yangzhou, China); for this, the Tachypleus amebocyte lysate method was used. The activity of SOD in mouse serum was evaluated using a xanthine oxidase assay kit (Jiancheng Bioengineering Institute, Nanjing, China). Furthermore, the activity of MDA in mouse serum was evaluated using a thiobarbituric acid assay kit (Jiancheng Bioengineering Institute).
## 4.8. Statistical Analysis
Image J and GraphPad Prism8.0 analysis software were used for image analysis and data processing. SPSS 22.0 software was used for statistical analysis. All data were expressed as means ± standard deviations (SD). One-way ANOVA was used for comparison between multiple groups, and LSD-T test was used for statistical analysis for multiple comparisons between groups. $p \leq 0.05$ indicates statistical significance.
## 5. Conclusions
Ber-CDs may alleviate inflammation and maintain intestinal mucosal immunity in vivo. The underlying mechanisms may involve the alteration of intestinal flora to increase the levels of SCFAs. Ber-CDs may alleviate inflammation by inhibiting the activation of intestinal mucosal immune cells and the production of various inflammatory factors. In addition, these compounds increase the levels of ZO-1 and occludin (tight junction proteins found in the intestinal mucosal barrier), thus improving the integrity of the mucosal barrier and preventing the proliferation of intestinal bacteria (Figure 7).
## References
1. Sougiannis A.T., Vanderveen B.N., Davis J.M., Fan D., Murphy E.A.. **Understanding chemotherapy-induced intestinal mucositis and strategies to improve gut resilience**. *Am. J. Physiol. Gastrointest. Liver Physiol.* (2021) **320** 712-719. DOI: 10.1152/ajpgi.00380.2020
2. Chen G., Zeng H., Li X., Liu J., Li Z., Xu R., Ma Y., Liu C., Xue B.. **Activation of g protein coupled estrogen receptor prevents chemotherapy-induced intestinal mucositis by inhibiting the dna damage in crypt cell in an extracellular signal-regulated kinase 1- and 2- dependent manner**. *Cell Death Dis.* (2021) **12** 1034. DOI: 10.1038/s41419-021-04325-z
3. Rosenthal D.I., Mendoza T.R., Fuller C.D., Hutcheson K.A., Wang X.S., Hanna E.Y., Lu C., Garden A.S., Morrison W.H., Cleeland C.S.. **Patterns of symptom burden during radiotherapy or concurrent chemoradiotherapy for head and neck cancer: A prospective analysis using the university of Texas MD Anderson Cancer Center symptom inventory-head and neck module**. *Cancer-Am. Cancer Soc.* (2014) **120** 1975-1984. DOI: 10.1002/cncr.28672
4. Longley D.B., Harkin D.P., Johnston P.G.. **5-fluorouracil: Mechanisms of action and clinical strategies**. *Nat. Rev. Cancer.* (2003) **3** 330-338. DOI: 10.1038/nrc1074
5. Elamin E.E., Masclee A.A., Dekker J., Pieters H.J., Jonkers D.M.. **Short-chain fatty acids activate amp-activated protein kinase and ameliorate ethanol-induced intestinal barrier dysfunction in caco-2 cell monolayers**. *J. Nutr.* (2013) **143** 1872-1881. PMID: 24132573
6. Cao S., Wang C., Yan J., Li X., Wen J., Hu C.. **Curcumin ameliorates oxidative stress-induced intestinal barrier injury and mitochondrial damage by promoting parkin dependent mitophagy through AMPK-TFEB signal pathway**. *Free Radic. Biol. Med.* (2020) **147** 8-22. DOI: 10.1016/j.freeradbiomed.2019.12.004
7. Hytti M., Korhonen E., Hyttinen J., Roehrich H., Kaarniranta K., Ferrington D.A., Kauppinen A.. **Antimycin a-induced mitochondrial damage causes human rpe cell death despite activation of autophagy**. *Oxid. Med. Cell. Longev.* (2019) **2019** 1583656. DOI: 10.1155/2019/1583656
8. Wan Y., Fu Y., Wang F., Sinclair A.J., Li D.. **Protective effects of a lipid extract from hard-shelled mussel (mytilus coruscus) on intestinal integrity after lipopolysaccharide challenge in mice**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10070860
9. Zhen Y., Zhang H.. **NLRP3 inflammasome and inflammatory bowel disease**. *Front. Immunol.* (2019) **10** 276. DOI: 10.3389/fimmu.2019.00276
10. Wang Y., Liu Y., Du X., Ma H., Yao J.. **The anti-cancer mechanisms of berberine: A review**. *Cancer Manag. Res.* (2020) **12** 695-702. DOI: 10.2147/CMAR.S242329
11. Mohammadian H.S., Momtazi-Borojeni A.A.. **Berberine as a promising natural compound for the treatment of periodontal disease: A focus on anti-inflammatory properties**. *J. Cell. Mol. Med.* (2021) **25** 11333-11337. DOI: 10.1111/jcmm.17019
12. Cohen E.N., Kondiah P., Choonara Y.E., du Toit L.C., Pillay V.. **Carbon dots as nanotherapeutics for biomedical application**. *Curr. Pharm. Des.* (2020) **26** 2207-2221. DOI: 10.2174/1381612826666200402102308
13. Song D., Guo H., Huang K., Zhang H., Chen J., Wang L., Lian C., Wang Y.. **Carboxylated carbon quantum dot-induced binary metal–organic framework nanosheet synthesis to boost the electrocatalytic performance**. *Mater. Today* (2022) **54** 42-51. DOI: 10.1016/j.mattod.2022.02.011
14. Zhang B., An G., Chen J., Guo H., Wang L.. **Surface state engineering of carbon dot/carbon nanotube heterojunctions for boosting oxygen reduction performance**. *J. Colloid Interface Sci.* (2023) **637** 173-181. DOI: 10.1016/j.jcis.2023.01.082
15. Liu H., Guo H., Fang Y., Wang L., Li P.. **Rational design of nitrogen-doped carbon dots for inhibiting β-amyloid aggregation**. *Molecules* (2023) **28**. DOI: 10.3390/molecules28031451
16. Luo W.K., Zhang L.L., Yang Z.Y., Guo X.H., Wu Y., Zhang W., Luo J.K., Tang T., Wang Y.. **Herbal medicine derived carbon dots: Synthesis and applications in therapeutics, bioimaging and sensing**. *J. Nanobiotechnol.* (2021) **19** 320. DOI: 10.1186/s12951-021-01072-3
17. Mirhadi E., Rezaee M., Malaekeh-Nikouei B.. **Nano strategies for berberine delivery, a natural alkaloid of berberis**. *Biomed. Pharmacother.* (2018) **104** 465-473. DOI: 10.1016/j.biopha.2018.05.067
18. Wareing T.C., Gentile P., Phan A.N.. **Biomass-based carbon dots: Current development and future perspectives**. *ACS Nano* (2021) **15** 15471-15501. DOI: 10.1021/acsnano.1c03886
19. Hou Q., He W.J., Wu Y.S., Hao H.J., Xie X.Y., Fu X.B.. **Berberine: A traditional natural product with novel biological activities**. *Altern. Ther. Health Med.* (2020) **26** 20-27
20. Zhang X., Han Y., Huang W., Jin M., Gao Z.. **The influence of the gut microbiota on the bioavailability of oral drugs**. *Acta Pharm. Sin. B* (2021) **11** 1789-1812. DOI: 10.1016/j.apsb.2020.09.013
21. Huang Y., Xu W., Zhou R.. **NLRP3 inflammasome activation and cell death**. *Cell Mol. Immunol.* (2021) **18** 2114-2127. DOI: 10.1038/s41423-021-00740-6
22. Xi Y., Sun W., Ding L., Yan M., Sun C., Zhang M., Li S., Qian X., Ma J., Wu L.. **Three important short-chain fatty acids (SCFAs) attenuate the inflammatory response induced by 5-FU and maintain the integrity of intestinal mucosal tight junction**. *BMC Immunol.* (2022) **23**. PMID: 35448938
23. Yang Y., Wang H., Kouadir M., Song H., Shi F.. **Recent advances in the mechanisms of NLRP3 inflammasome activation and its inhibitors**. *Cell Death Dis.* (2019) **10** 128. DOI: 10.1038/s41419-019-1413-8
24. Bauer C., Duewell P., Mayer C., Lehr H.A., Fitzgerald K.A., Dauer M., Tschopp J., Endres S., Latz E., Schnurr M.. **Colitis induced in mice with dextran sulfate sodium (DSS) is mediated by the NLRP3 inflammasome**. *Gut* (2010) **59** 1192-1199. DOI: 10.1136/gut.2009.197822
25. Wang Z., Zhang S., Xiao Y., Zhang W., Wu S., Qin T., Yue Y., Qian W., Li L.. **NLRP3 inflammasome and inflammatory diseases**. *Oxid. Med. Cell. Longev.* (2020) **2020** 4063562. DOI: 10.1155/2020/4063562
26. Camilleri M., Madsen K., Spiller R., Greenwood-Van M.B., Verne G.N.. **Intestinal barrier function in health and gastrointestinal disease**. *Neurogastroenterol. Motil.* (2012) **24** 503-512. DOI: 10.1111/j.1365-2982.2012.01921.x
27. Wu J., Gan Y., Luo H., Xu N., Chen L., Li M., Guan F., Su Z., Lin Z., Xie J.. **Beta-patchoulene ameliorates water transport and the mucus barrier in 5-fluorouracil-induced intestinal mucositis rats via the cAMP/PKA/CREB signaling pathway**. *Front. Pharmacol.* (2021) **12** 689491. DOI: 10.3389/fphar.2021.689491
28. Song M.K., Park M.Y., Sung M.K.. **5-fluorouracil-induced changes of intestinal integrity biomarkers in BALB/c mice**. *J. Cancer Prev.* (2013) **18** 322-329. DOI: 10.15430/JCP.2013.18.4.322
29. Chen H., Zhang F., Li R., Liu Y., Wang X., Zhang X., Xu C., Li Y., Guo Y., Yao Q.. **Berberine regulates fecal metabolites to ameliorate 5-fluorouracil induced intestinal mucositis through modulating gut microbiota**. *Biomed. Pharmacother.* (2020) **124** 109829. DOI: 10.1016/j.biopha.2020.109829
30. Huang R., Ai G., Zhong L., Mai L., Chen J.N., Liu Y., Li Y., Huang X., Su Z., Zhan J.Y.. **Protective effects of oxyberberine in 5-fluorouracil-induced intestinal mucositis in the mice model**. *Evid. Based Complement. Alternat. Med.* (2022) **2022** 1238358. DOI: 10.1155/2022/1238358
31. Perez-Lopez A., Behnsen J., Nuccio S.P., Raffatellu M.. **Mucosal immunity to pathogenic intestinal bacteria**. *Nat. Rev. Immunol.* (2016) **16** 135-148. DOI: 10.1038/nri.2015.17
32. de Barros P., Rabelo A.M., de Vasconcelos G.S., Mendes M.S., Dos R.D., Lacerda L.P., de Sales E.S.E., Dos S.M.F., Da G.M., Cassali G.D.. **Conjugated linoleic acid prevents damage caused by intestinal mucositis induced by 5-fluorouracil in an experimental model**. *Biomed. Pharmacother.* (2018) **103** 1567-1576. DOI: 10.1016/j.biopha.2018.04.133
33. Yi C., Sun W., Ding L., Yan M., Sun C., Qiu C., Wang D., Wu L.. **Short-chain fatty acids weaken ox-ldl-induced cell inflammatory injury by inhibiting the NLRP3/Caspase-1 pathway and affecting cellular metabolism in THP-1 cells**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27248801
34. Zhang Y., Xi Y., Yang C., Gong W., Wang C., Wu L., Wang D.. **Short-chain fatty acids attenuate 5-fluorouracil-induced THP-1 cell inflammation through inhibiting NF-kappaB/NLRP3 signaling via glycerolphospholipid and sphingolipid metabolism**. *Molecules* (2023) **28**. DOI: 10.3390/molecules28020494
35. Rauf A., Khalil A.A., Rahman U.U., Khalid A., Naz S., Shariati M.A., Rebezov M., Urtecho E.Z., de Albuquerque R., Anwar S.. **Recent advances in the therapeutic application of short-chain fatty acids (SCFAs): An updated review**. *Crit. Rev. Food Sci. Nutr.* (2022) **62** 6034-6054. DOI: 10.1080/10408398.2021.1895064
36. Portincasa P., Bonfrate L., Vacca M., De Angelis M., Farella I., Lanza E., Khalil M., Wang D.Q., Sperandio M., Di Ciaula A.. **Gut microbiota and short chain fatty acids: Implications in glucose homeostasis**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23031105
37. Kasubuchi M., Hasegawa S., Hiramatsu T., Ichimura A., Kimura I.. **Dietary gut microbial metabolites, short-chain fatty acids, and host metabolic regulation**. *Nutrients* (2015) **7** 2839-2849. DOI: 10.3390/nu7042839
38. Hernandez M., Canfora E.E., Jocken J., Blaak E.E.. **The short-chain fatty acid acetate in body weight control and insulin sensitivity**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11081943
39. Zhang L., Liu C., Jiang Q., Yin Y.. **Butyrate in energy metabolism: There is still more to learn**. *Trends Endocrinol. Metab.* (2021) **32** 159-169. DOI: 10.1016/j.tem.2020.12.003
40. Getachew B., Csoka A.B., Bhatti A., Copeland R.L., Tizabi Y.. **Butyrate protects against salsolinol-induced toxicity in SH-SY5Y cells: Implication for Parkinson’s disease**. *Neurotox. Res.* (2020) **38** 596-602. DOI: 10.1007/s12640-020-00238-5
41. Chen G., Ran X., Li B., Li Y., He D., Huang B., Fu S., Liu J., Wang W.. **Sodium butyrate inhibits inflammation and maintains epithelium barrier integrity in a TNBS-induced inflammatory bowel disease mice model**. *EBioMedicine* (2018) **30** 317-325. DOI: 10.1016/j.ebiom.2018.03.030
42. Li G., Lin J., Zhang C., Gao H., Lu H., Gao X., Zhu R., Li Z., Li M., Liu Z.. **Microbiota metabolite butyrate constrains neutrophil functions and ameliorates mucosal inflammation in inflammatory bowel disease**. *Gut Microbes* (2021) **13** 1968257. DOI: 10.1080/19490976.2021.1968257
43. Bach K.K., Laerke H.N., Hedemann M.S., Nielsen T.S., Ingerslev A.K., Gundelund N.D., Theil P.K., Purup S., Hald S., Schioldan A.G.. **Impact of diet-modulated butyrate production on intestinal barrier function and inflammation**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10101499
44. Zhou D., Pan Q., Xin F.Z., Zhang R.N., He C.X., Chen G.Y., Liu C., Chen Y.W., Fan J.G.. **Sodium butyrate attenuates high-fat diet-induced steatohepatitis in mice by improving gut microbiota and gastrointestinal barrier**. *World J. Gastroenterol.* (2017) **23** 60-75. DOI: 10.3748/wjg.v23.i1.60
45. Peng L., Li Z.R., Green R.S., Holzman I.R., Lin J.. **Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers**. *J. Nutr.* (2009) **139** 1619-1625. DOI: 10.3945/jn.109.104638
|
---
title: Serum sCD40L and IL-31 in Association with Early Phase of IgA Nephropathy
authors:
- Keiko Tanaka
- Hitoshi Sugiyama
- Hiroshi Morinaga
- Masashi Kitagawa
- Yuzuki Kano
- Yasuhiro Onishi
- Koki Mise
- Katsuyuki Tanabe
- Haruhito A. Uchida
- Jun Wada
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004527
doi: 10.3390/jcm12052023
license: CC BY 4.0
---
# Serum sCD40L and IL-31 in Association with Early Phase of IgA Nephropathy
## Abstract
Background: IgA nephropathy (IgAN) is a major cause of chronic glomerulonephritis worldwide. T cell dysregulation has been reported to contribute to the pathogenesis of IgAN. Methods We measured a broad range of Th1, Th2 and Th17 cytokines in the serum of IgAN patients. We searched for significant cytokines, which were associated with clinical parameters and histological scores in IgAN patients. Results: Among 15 cytokines, the levels of soluble CD40L (sCD40L) and IL-31 were higher in IgAN patients and were significantly associated with a higher estimated glomerular filtration rate (eGFR), a lower urinary protein to creatinine ratio (UPCR), and milder tubulointerstitial lesions (i.e., the early phase of IgAN). Multivariate analysis revealed that serum sCD40L was an independent determinant of a lower UPCR after adjustment for age, eGFR, and mean blood pressure (MBP). CD40, a receptor of sCD40L, has been reported to be upregulated on mesangial cells in IgAN. The sCD40L/CD40 interaction may directly induce inflammation in mesangial areas and may therefore be involved in the development of IgAN. Conclusions: The present study demonstrated the significance of serum sCD40L and IL-31 in the early phase of IgAN. Serum sCD40L may be a marker of the beginning of inflammation in IgAN.
## 1. Introduction
IgA nephropathy (IgAN) is a major cause of chronic glomerulonephritis worldwide [1,2]. Approximately $40\%$ of such patients develop end-stage renal disease (ESRD) within 20 years of the diagnosis [3]. The Oxford classification of pathologic features in IgAN has been proposed and internationally validated and is independently associated with the risk of disease progression [4,5,6]. An international risk-prediction model for disease progression that combines clinical data with pathologic features of IgAN in multiple ethnic groups has recently been reported [7].
Another characteristic of IgAN is that it is an autoimmune disease that is based on the binding of the glycan-specific IgG autoantibody to galactose-deficient IgA1 (Gd-IgA1) as an autoantigen [8,9]. Several pathogenic models of IgAN have been proposed. The dysregulation of the mucosal immune system in response to mucosal antigens results in mucosal B cell proliferation, leading to excessive B cell activating factor (BAFF) and a proliferation-inducing ligand (APRIL) signaling [10,11]. The T cell-dependent production of IgA is mainly stimulated by interleukin (IL)-6, IL-10, transforming growth factor (TGF)-β, BAFF and APRIL produced by intestinal epithelial, dendritic, and stromal cells [2]. Changes in circulating T cell subpopulations, including imbalance of helper T (Th) cells (e.g., Th1 and Th2) and the involvement of T cell cytokines in the posttranslational modification of the IgA1 hinge region may stimulate the production of Gd-IgA1 [12].
Renin-angiotensin blockade can, to some extent, reduce the level of proteinuria and the risk of renal failure in patients with high-risk IgAN. Despite this therapy, a large proportion of patients still develop ESRD. Corticosteroids and other immunosuppressants may be effective treatments for high-risk IgAN patients; however, recent trials did not draw any definitive conclusions [13,14].
To gain insight into the role of T cell cytokines in the disease activity and their potential role in the disease severity of IgAN, we measured a broad range of Th1, Th2 and Th17 cytokines in serum, which could lead to the exploration of novel biomarkers of the disease, as well as disease-specific therapies.
## 2.1. Study Population and Data Collection
The study group consisted of 114 patients with IgAN, 10 patients with autosomal dominant polycystic kidney disease (ADPKD), and 5 healthy subjects. This study focused on primary IgAN and excluded patients considered to have secondary IgAN complicated by gastrointestinal or liver disease, infection, malignancy, IgA vasculitis or other autoimmune abnormalities. Clinical data were obtained from the patients’ medical records. eGFR was calculated by using Modified for Japanese subjects: eGFR (mL/min/1.73 m2) = 194 × serum Creatinine (mg/dL) −1.094 × Age−0.287 (×0.739 for females) [15].
## 2.2. Ethical Issue
The protocol of the present study was approved by the Ethical Committee of the Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences (Approval no. 1009). All subjects gave their written informed consent prior to participation in the study. The study was conducted in accordance with the Declaration of Helsinki.
## 2.3. Multiplex Assay for Cytokines
Cytokines in serum samples were measured by Bio-Plex Pro Human Th17 Cytokine Assays (Bio-Rad Laboratories, Inc., Tokyo, Japan), according to the manufacturer’s instructions. In brief, the samples were diluted 4-fold with the diluting solution and centrifuged at 10,000× g for 5 min. Fifty microliters of the supernatant were used for the following cytokine assays: IL-1β, IL-4, IL-6, IL-10, IL-17A, IL-17F, IL-21, IL-22, IL-23, IL-25, IL-31, IL-33, IFN-γ, soluble CD40 ligand (sCD40L), and TNF-α. The lower limits of detection, according to our standard curves, were 0.27–0.28 pg/mL for IL-1β, 1.3–4.3 pg/mL for IL-4, 0.9–2.6 pg/mL for IL-6, 2.0–3.7 pg/mL for IL-10, 1.5–2.0 pg/mL for IL-17A, 1.8–8.0 pg/mL for IL-17F, 5.0–20.5 pg/mL for IL-21, 4.6–5.0 pg/mL for IL-22, 7.0–26.1 pg/mL for IL-23, 1.2–1.4 pg/mL for IL-25, 2.8–4.8 pg/mL for IL-31, 3.3–7.8 pg/mL for IL-33, 3.2–3.9 pg/mL for IFN-γ, 3.2–7.4 pg/mL for sCD40L, and 0.3–2.4 pg/mL for TNFα.
## 2.4. Serum Gd-IgA1 Measurement
The serum levels of galactose deficient IgA1, a circulating pathogenic molecule in IgAN, were measured with an ELISA kit, according to the manufacturer’s instructions (Immuno-Biological Laboratories, Gunma, Japan), using KM55 monoclonal antibodies [16].
## 2.5. Histological Assessment
Histological findings were evaluated by two independent nephrologists as the MEST-C score according to the Oxford classification of IgAN [5,17,18]. The lesion scores were determined according to mesangial hypercellularity, assessed by the mesangial score (≤0.5 [M0] or >0.5 [M1]), endocapillary hypercellularity (absent [E0] or present [E1]), segmental glomerulosclerosis (absent [S0] or present [S1]), tubular atrophy and interstitial fibrosis (≤$25\%$ [T0], 26–$50\%$ [T1] or >$50\%$ [T2]), and cellular and fibrocellular crescents (absent [C0], present in at least 1 glomerulus [C1] or present in >$25\%$ of glomeruli [C2]). T1 and T2, C1, and C2 were combined, as reported previously [19]. Patients with IgAN who had <8 glomeruli in renal biopsy specimens were excluded from this study.
## 2.6. Statistical Analyses
The results are expressed as the mean ± SD or the median and interquartile range (IQR) for continuous data. p values of <0.05 were considered to indicate statistical significance. For multiple comparisons of the three groups of IgAN and ADPKD patients and healthy controls, every two groups were analyzed using Fisher’s exact test for categorical data and using Student’s t-test or the Mann–Whitney U test for continuous data, as appropriate. Bonferroni correction was then applied (p value of <0.016 was considered significant). Correlations among the cytokines and other variables in IgAN patients were evaluated by Spearman correlation analysis. Multivariate-adjusted regression analysis and multiple logistic regression analysis were performed to determine the factors that were significant independent predictors of proteinuria and renal function in IgAN patients. The statistical analyses were performed using the JMP software program (version 11, SAS Institute Inc., Cary, NC, USA).
## 3. Results
This study included 114 IgAN patients (male, $50\%$), with a mean age of 41.7 ± 15.5 years (range 16–78 years). All IgAN patients received renal biopsies. Their kidney function was relatively preserved, and they had various degrees of proteinuria and histological changes (Table 1). The five healthy controls were similar to the IgAN patients with regard to age, gender and eGFR. The 10 ADPKD patients were older and had lower eGFR values than the IgAN patients (Table 1).
Among the 15 cytokines examined in this study, the serum-soluble CD40 ligand (sCD40L) and IL-31 levels were extremely high and were detectable in almost all IgAN patients (Table S1). In IgAN patients, both serum sCD40L and IL-31 were significantly higher in comparison to those in ADPKD patients or healthy controls ($p \leq 0.0001$ *) (Figure 1). However, since differences in age and renal function may affect serum cytokine levels, multivariate-adjusted regression analyses were performed to determine whether or not cytokines correlated with IgAN, using the age and eGFR for adjustment, in the cohort of IgAN and ADPKD patients and healthy controls. The results showed that IgAN (vs. not IgAN) was a significant factor explaining serum sCD40L ($p \leq 0.0001$ *) and IL-31 ($p \leq 0.0001$ *) levels (Table S2). Therefore, in IgAN patients, both serum sCD40L and IL-31 were significantly higher than in ADPKD patients or healthy control.
Next, we focused on the population of IgAN patients. Univariate analysis of the IgAN patients revealed a significant negative correlation between serum sCD40L and the urinary protein to creatinine ratio (UPCR) ($$p \leq 0.0087$$, R2 = 0.059; Figure 2A). A significant positive correlation was found between serum sCD40L and the estimated glomerular filtration rate (eGFR) ($$p \leq 0.016$$, R2 = 0.050; Figure 2B). The same significant correlations were also observed between serum IL-31 and the UPCR and eGFR (Figure 2C,D). We found that the patients with higher UPCR values had lower eGFR values ($$p \leq 0.0007$$ *) in the IgAN patients. The associations between these cytokines and lower UPCR values may be confounded by the lower eGFR values in patients with high UPCR values, which were correlated with lower cytokines. Interestingly, a high positive correlation was observed between sCD40L and IL-31 ($p \leq 0.0001$, R2 = 0.68) (Figure 2E) in IgAN patients.
In a multivariate-adjusted regression analysis of the IgAN patients, after adjustment for age (Model 1), age and eGFR (Model 2), and age, eGFR and mean blood pressure (MBP) (Model 3), serum sCD40L was independently associated with a lower UPCR (Table 2). Serum sCD40L was significantly associated with lower odds of having proteinuria (≥1.0 g/gCr) in Model 3 (adjusted for age, eGFR and MBP) (Table 3). The multivariate odds ratio for sCD40L (per 100 pg/mL increase) was 0.79 ($95\%$CI: 0.63–0.96, $$p \leq 0.032$$). On the other hand, after adjustment for age, eGFR, and MBP (Model 3), serum IL-31 was an independent determinant of the UPCR (≥1.0 g/gCr) (Table 4) but was not significantly associated with the continuous variable of the UPCR in the multivariate-adjusted regression analysis (Table 5). Serum sCD40L and IL-31 were not independently associated with eGFR in any of the multivariate-adjusted regression analysis models (Tables S3 and S4).
Based on the Oxford classification, both serum sCD40L and IL-31 were significantly higher in IgAN patients with milder tubular atrophy/interstitial fibrosis (lower T-score). Lesion scores other than the T-score were not associated with serum sCD40L or IL-31 (Table 6).
As previously reported [20], the serum Gd-IgA1 levels were positively correlated with serum IgA and the UPCR (Figure S1A,B), but—contrary to our expectations—were not positively correlated with serum sCD40L and IL-31 (Figure S1C,D) in IgAN patients.
TNF-α is a cytokine that was mildly increased and detectable in the serum of $98\%$ of IgAN patients (Table S1). Serum TNF-α had no significant association with the UPCR or eGFR (Figure S2A,B) in IgAN patients. TNF-α was significantly associated with sCD40L and IL-31 (Figure S2C,D), but not with Gd-IgA1 (Figure S2E) in IgAN patients.
## 4. Discussion
The present study indicated that serum sCD40L and IL-31 levels were higher in IgAN patients and were significantly associated with a higher eGFR, lower UPCR, and milder tubulointerstitial lesions (i.e., the early phase of IgAN). Serum sCD40L was independently associated with lower UPCR values in IgAN patients.
It has been recently reviewed that IgAN is characterized by higher proportions of circulatory Th2, T follicular helper (Tfh), Th17, and Th22 cells but lower Th1 and Tregs [12]. The review report mentions that Th2, Th17 and Tfh cytokines contribute to the elevated synthesis of Gd-IgA1 and that the production of anti-Gd-IgA1 autoantibodies may be stimulated by Tfh cells. In the present study, IL-4 (a representative Th2 cytokine), IL-21 (a Tfh cytokine), IL-17 and IL-22 were not detectable in most IgAN patients. IL-6 (a Th2 cytokine) was detected and slightly increased in half of the IgAN patients. TNF-α (a Th1 cytokine) was detectable and mildly increased but was not associated with Gd-IgA1 or any clinical parameters.
CD40L is expressed after activation on all Th subsets (Th1, Th2, Tfh, Th17 cells) other than Tregs [12]. CD40L engages the B-cell receptor CD40 and induces B-cell growth, differentiation, and IgA class switching. IL-31 is another Th2 cytokine, which may be involved in the Gd-IgA1 synthesis as described above. The significant increase of sCD40L and IL-31 in serum may induce B cells to produce excessive Gd-IgA1, leading to IgA deposition in the glomeruli and glomerular injury [21]. However, this study indicated that sCD40L and IL-31 were not positively correlated with serum Gd-IgA1 levels or with the histological scores of glomerular lesions. This suggests that sCD40L and IL-31 have no direct contribution to the production of Gd-IgA1.
In particular, sCD40L was independently associated with lower UPCR values in IgAN patients. CD40L is expressed on activated platelets as well as activated Th cells [22]. Soluble CD40L (sCD40L) is rapidly released from those after activation and has been widely studied as a marker of inflammatory states and autoimmune diseases, including atherosclerosis, rheumatoid arthritis, and systemic lupus erythematosus [23,24,25]. No previous studies have investigated the significance of serum sCD40L in IgAN patients. This study indicates that sCD40L may be a marker of the beginning of inflammation in IgAN.
CD40, a receptor of CD40L, is expressed on antigen-presenting cells (APCs) such as B cells, monocytes, and dendritic cells. CD40/CD40L interaction has a critical role in many aspects of the immune response [22]. CD40/CD40L has been shown to cause the initiation and progression of renal diseases, such as membranous nephritis and lupus nephritis [26,27], which occur due to B cell activation. In the kidney, CD40 expression is induced in mesangial cells, podocytes, and tubular epithelial cells by various pathological stimuli [28,29,30]. The upregulation of CD40 on mesangial cells has been observed in kidney biopsy specimens of IgAN patients [31]. The stimulation with sCD40L and other inflammatory cytokines leads to increased CD40 expression in podocytes and tubular epithelial cells [32,33]. Their upregulation of CD40 contributes to their inflammatory response and the following fibrotic process in kidney disease [28]. Increased sCD40 in serum may directly induce inflammation of mesangial cells in the early phase and may contribute to the progression of proteinuria and interstitial fibrosis in the following phase of IgAN. The reason for the ‘negative’ correlation between serum sCD40L and proteinuria is unclear. However, it may simply be due to the differences in the timing of T cell and platelet activation versus podocyte injury.
Another significantly increased cytokine, IL-31, is not reported to be associated with any autoimmune disease, including IgAN. IL-31 has recently been described as the main cytokine involved in allergies, such as cutaneous allergic reactions and asthma [34,35]. Allergies could be a cause of the development of IgAN but cannot explain all of the increased IL-31 in the serum of IgAN patients. An increase in IL-31 may accompany the significant increase of sCD40L because a high correlation was observed between serum sCD40L and IL-31 in IgAN patients. These cytokines are both Th2 cytokines, but their molecular connection is unknown. The same result has been reported in patients with relapsing-remitting multiple sclerosis (MS) [36]. IL-31 and sCD40L have been shown to be positively correlated with MS severity [37]. In the pathogenesis of MS, IL-31 and sCD40L have been reported to be secreted by activated mast cells, amplifying the T cell immune response in the central nervous system [37]. Mast cells have been suggested to be associated with the progression of interstitial fibrosis in IgAN [38,39], but their role in the early phase of IgAN is not clear. There may be co-stimulators or common pathways that increase the serum levels of both sCD40L and IL-31 but not mast cells in the pathogenesis of IgAN.
The detection of increased serum sCD40L in the early phase of IgAN may give new insight into the effectiveness of antiplatelet therapy. Platelets are also thought to be responsible for the initiation and progression of glomerular injury [40]. Platelets or platelet-releasing growth factors, such as platelet-derived growth factor (PDGF) and transforming growth factor-β (TGF-β), have been shown to induce mesangial proliferation and matrix accumulations [41]. Serum sCD40L may be mostly derived from activated platelets, and antiplatelet therapy (e.g., clopidogrel) is reported to reduce plasma sCD40L [42]. In a past meta-analysis, antiplatelet therapy was suggested to reduce proteinuria and protect against renal dysfunction in patients with IgAN [43]. A therapeutic approach that inactivates platelets may control the inflammation induced by sCD40L/CD40, especially in the early phase of IgAN.
The present study was associated with several limitations. First, we did not histologically analyze the levels of cytokines or their receptors, including IL-31 and sCD40L, in the kidney tissues of IgAN patients. Our preliminary study using assays that were used in this study indicated that these cytokines were not detectable or were at very low levels in urine samples; therefore, we did not evaluate the local levels of these cytokines. Further studies are necessary to determine the CD40 expression in mesangial cells and the surrounding cells, as well as the correlation with the CD40 expression and clinical parameters. Second, we did not compare the serum levels of these cytokines in IgAN to those in other autoimmune glomerulonephritides, such as lupus nephritis, and thus it is possible that the increased serum cytokines may not be specific to IgAN. Further studies are therefore necessary to clarify the local expression of these cytokines and their effects on disease progression in IgAN. Whether these cytokines are prognostic factors for early IgAN or not is particularly important and should be investigated in the prospective study in the next settings In conclusion, serum sCD40L and IL-31 were increased and significantly associated with the early phase of IgAN. Serum sCD40L was independently associated with lower proteinuria and may be a marker of the beginning of inflammation in IgAN.
## References
1. Wyatt R.J., Julian B.A.. **IgA nephropathy**. *N. Engl. J. Med.* (2013) **368** 2402-2414. DOI: 10.1056/NEJMra1206793
2. Kiryluk K., Novak J.. **The genetics and immunobiology of IgA nephropathy**. *J. Clin. Investig.* (2014) **124** 2325-2332. DOI: 10.1172/JCI74475
3. Radford M.G., Donadio J.V., Bergstralh E.J., Grande J.P.. **Predicting renal outcome in IgA nephropathy**. *J. Am. Soc. Nephrol.* (1997) **8** 199-207. DOI: 10.1681/ASN.V82199
4. Cattran D.C., Coppo R., Cook H.T., Feehally J., Roberts I.S., Troyanov S., Alpers C.E., Amore A., Barratt J.. **The Oxford classification of IgA nephropathy: Rationale, clinicopathological correlations, and classification**. *Kidney Int.* (2009) **76** 534-545. DOI: 10.1038/ki.2009.243
5. Roberts I.S., Cook H.T., Troyanov S., Alpers C.E., Amore A., Barratt J., Berthoux F., Bonsib S., Bruijn J.A.. **The Oxford classification of IgA nephropathy: Pathology definitions, correlations, and reproducibility**. *Kidney Int.* (2009) **76** 546-556. DOI: 10.1038/ki.2009.168
6. Coppo R., Troyanov S., Bellur S., Cattran D., Cook H.T., Feehally J., Roberts I.S., Morando L., Camilla R., Tesar V.. **Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments**. *Kidney Int.* (2014) **86** 828-836. DOI: 10.1038/ki.2014.63
7. Barbour S.J., Coppo R., Zhang H., Liu Z.H., Suzuki Y., Matsuzaki K., Katafuchi R., Er L., Espino-Hernandez G., Kim S.J.. **Evaluating a New International Risk-Prediction Tool in IgA Nephropathy**. *JAMA Intern. Med.* (2019) **179** 942-952. DOI: 10.1001/jamainternmed.2019.0600
8. Suzuki H., Fan R., Zhang Z., Brown R., Hall S., Julian B.A., Chatham W.W., Suzuki Y., Wyatt R.J., Moldoveanu Z.. **Aberrantly glycosylated IgA1 in IgA nephropathy patients is recognized by IgG antibodies with restricted heterogeneity**. *J. Clin. Investig.* (2009) **119** 1668-1677. DOI: 10.1172/JCI38468
9. Berthoux F., Suzuki H., Thibaudin L., Yanagawa H., Maillard N., Mariat C., Tomino Y., Julian B.A., Novak J.. **Autoantibodies targeting galactose-deficient IgA1 associate with progression of IgA nephropathy**. *J. Am. Soc. Nephrol.* (2012) **23** 1579-1587. DOI: 10.1681/ASN.2012010053
10. Yeo S.C., Cheung C.K., Barratt J.. **New insights into the pathogenesis of IgA nephropathy**. *Pediatr. Nephrol.* (2018) **33** 763-777. DOI: 10.1007/s00467-017-3699-z
11. Inoue T., Sugiyama H., Hiki Y., Takiue K., Morinaga H., Kitagawa M., Maeshima Y., Fukushima K., Nishizaki K., Akagi H.. **Differential expression of glycogenes in tonsillar B lymphocytes in association with proteinuria and renal dysfunction in IgA nephropathy**. *Clin. Immunol.* (2010) **136** 447-455. DOI: 10.1016/j.clim.2010.05.009
12. Ruszkowski J., Lisowska K.A., Pindel M., Heleniak Z., Debska-Slizien A., Witkowski J.M.. **T cells in IgA nephropathy: Role in pathogenesis, clinical significance and potential therapeutic target**. *Clin. Exp. Nephrol.* (2019) **23** 291-303. DOI: 10.1007/s10157-018-1665-0
13. Rauen T., Eitner F., Fitzner C., Sommerer C., Zeier M., Otte B., Panzer U., Peters H., Benck U., Mertens P.R.. **Intensive Supportive Care plus Immunosuppression in IgA Nephropathy**. *N. Engl. J. Med.* (2015) **373** 2225-2236. DOI: 10.1056/NEJMoa1415463
14. Lv J., Zhang H., Wong M.G., Jardine M.J., Hladunewich M., Jha V., Monaghan H., Zhao M., Barbour S., Reich H.. **Effect of Oral Methylprednisolone on Clinical Outcomes in Patients with IgA Nephropathy: The TESTING Randomized Clinical Trial**. *JAMA* (2017) **318** 432-442. DOI: 10.1001/jama.2017.9362
15. Matsuo S., Imai E., Horio M., Yasuda Y., Tomita K., Nitta K., Yamagata K., Tomino Y., Yokoyama H., Hishida A.. **Revised equations for estimated GFR from serum creatinine in Japan**. *Am. J. Kidney Dis.* (2009) **53** 982-992. DOI: 10.1053/j.ajkd.2008.12.034
16. Yasutake J., Suzuki Y., Suzuki H., Hiura N., Yanagawa H., Makita Y., Kaneko E., Tomino Y.. **Novel lectin-independent approach to detect galactose-deficient IgA1 in IgA nephropathy**. *Nephrol. Dial. Transplant.* (2015) **30** 1315-1321. DOI: 10.1093/ndt/gfv221
17. Haas M., Verhave J.C., Liu Z.H., Alpers C.E., Barratt J., Becker J.U., Cattran D., Cook H.T., Coppo R., Feehally J.. **A Multicenter Study of the Predictive Value of Crescents in IgA Nephropathy**. *J. Am. Soc. Nephrol.* (2017) **28** 691-701. DOI: 10.1681/ASN.2016040433
18. Trimarchi H., Barratt J., Cattran D.C., Cook H.T., Coppo R., Haas M., Liu Z.H., Roberts I.S., Yuzawa Y., Zhang H.. **Oxford Classification of IgA nephropathy 2016: An update from the IgA Nephropathy Classification Working Group**. *Kidney Int.* (2017) **91** 1014-1021. DOI: 10.1016/j.kint.2017.02.003
19. Coppo R., D’Arrigo G., Tripepi G., Russo M.L., Roberts I.S.D., Bellur S., Cattran D., Cook T.H., Feehally J., Tesar V.. **Is there long-term value of pathology scoring in immunoglobulin A nephropathy? A validation study of the Oxford Classification for IgA Nephropathy (VALIGA) update**. *Nephrol. Dial. Transplant.* (2020) **35** 1002-1009. DOI: 10.1093/ndt/gfy302
20. Suzuki Y., Matsuzaki K., Suzuki H., Okazaki K., Yanagawa H., Ieiri N., Sato M., Sato T., Taguma Y., Matsuoka J.. **Serum levels of galactose-deficient immunoglobulin (Ig) A1 and related immune complex are associated with disease activity of IgA nephropathy**. *Clin. Exp. Nephrol.* (2014) **18** 770-777. DOI: 10.1007/s10157-013-0921-6
21. Tang Y., He H., Hu P., Xu X.. **T lymphocytes in IgA nephropathy**. *Exp. Ther. Med.* (2020) **20** 186-194. DOI: 10.3892/etm.2020.8673
22. Elgueta R., Benson M.J., de Vries V.C., Wasiuk A., Guo Y., Noelle R.J.. **Molecular mechanism and function of CD40/CD40L engagement in the immune system**. *Immunol. Rev.* (2009) **229** 152-172. DOI: 10.1111/j.1600-065X.2009.00782.x
23. Nagy B., Miszti-Blasius K., Kerenyi A., Clemetson K.J., Kappelmayer J.. **Potential therapeutic targeting of platelet-mediated cellular interactions in atherosclerosis and inflammation**. *Curr. Med. Chem.* (2012) **19** 518-531. DOI: 10.2174/092986712798918770
24. Aloui C., Prigent A., Sut C., Tariket S., Hamzeh-Cognasse H., Pozzetto B., Richard Y., Cognasse F., Laradi S., Garraud O.. **The signaling role of CD40 ligand in platelet biology and in platelet component transfusion**. *Int. J. Mol. Sci.* (2014) **15** 22342-22364. DOI: 10.3390/ijms151222342
25. Alaaeddine N., Hassan G.S., Yacoub D., Mourad W.. **CD154: An immunoinflammatory mediator in systemic lupus erythematosus and rheumatoid arthritis**. *Clin. Dev. Immunol.* (2012) **2012** 490148. DOI: 10.1155/2012/490148
26. Biancone L., Andres G., Ahn H., DeMartino C., Stamenkovic I.. **Inhibition of the CD40-CD40ligand pathway prevents murine membranous glomerulonephritis**. *Kidney Int.* (1995) **48** 458-468. DOI: 10.1038/ki.1995.314
27. Kalled S.L., Cutler A.H., Datta S.K., Thomas D.W.. **Anti-CD40 ligand antibody treatment of SNF1 mice with established nephritis: Preservation of kidney function**. *J. Immunol.* (1998) **160** 2158-2165. DOI: 10.4049/jimmunol.160.5.2158
28. Zhang S., Breidenbach J.D., Russell B.H., George J., Haller S.T.. **CD40/CD40L Signaling as a Promising Therapeutic Target for the Treatment of Renal Disease**. *J. Clin. Med.* (2020) **9**. DOI: 10.3390/jcm9113653
29. Doublier S., Zennaro C., Musante L., Spatola T., Candiano G., Bruschi M., Besso L., Cedrino M., Carraro M., Ghiggeri G.M.. **Soluble CD40 ligand directly alters glomerular permeability and may act as a circulating permeability factor in FSGS**. *PLoS ONE* (2017) **12**. DOI: 10.1371/journal.pone.0188045
30. Kuo H.L., Huang C.C., Lin T.Y., Lin C.Y.. **IL-17 and CD40 ligand synergistically stimulate the chronicity of diabetic nephropathy**. *Nephrol. Dial. Transplant.* (2018) **33** 248-256. DOI: 10.1093/ndt/gfw397
31. Yellin M.J., D’Agati V., Parkinson G., Han A.S., Szema A., Baum D., Estes D., Szabolcs M., Chess L.. **Immunohistologic analysis of renal CD40 and CD40L expression in lupus nephritis and other glomerulonephritides**. *Arthritis Rheum.* (1997) **40** 124-134. DOI: 10.1002/art.1780400117
32. Rigothier C., Daculsi R., Lepreux S., Auguste P., Villeneuve J., Dewitte A., Doudnikoff E., Saleem M., Bourget C., Combe C.. **CD154 Induces Matrix Metalloproteinase-9 Secretion in Human Podocytes**. *J. Cell. Biochem.* (2016) **117** 2737-2747. DOI: 10.1002/jcb.25571
33. van Kooten C., van der Linde X., Woltman A.M., van Es L.A., Daha M.R.. **Synergistic effect of interleukin-1 and CD40L on the activation of human renal tubular epithelial cells**. *Kidney Int.* (1999) **56** 41-51. DOI: 10.1046/j.1523-1755.1999.00514.x
34. Hartmann K., Wagner N., Rabenhorst A., Pflanz L., Leja S., Forster A., Gehring M., Kapp A., Raap U.. **Serum IL-31 levels are increased in a subset of patients with mastocytosis and correlate with disease severity in adult patients**. *J. Allergy Clin. Immunol.* (2013) **132** 232-235. DOI: 10.1016/j.jaci.2012.11.008
35. Rabenhorst A., Hartmann K.. **Interleukin-31: A novel diagnostic marker of allergic diseases**. *Curr. Allergy Asthma Rep.* (2014) **14** 423. DOI: 10.1007/s11882-014-0423-y
36. Guerrero-García J.D.J., Rojas-Mayorquin A.E., Valle Y., Padilla-Gutierrez J.R., Castaneda-Moreno V.A., Mireles-Ramirez M.A., Munoz-Valle J.F., Ortuno-Sahagun D.. **Decreased serum levels of sCD40L and IL-31 correlate in treated patients with Relapsing-Remitting Multiple Sclerosis**. *Immunobiology* (2018) **223** 135-141. DOI: 10.1016/j.imbio.2017.10.001
37. Pastor Bandeira I., de Almeida Franzoi A.E., Murillo Wollmann G., de Medeiros Junior W.L.G., Nogueira Brandao W., Schatzmann Peron J.P., Becker J., Nascimento O.J.M., Magno Goncalves M.V.. **Interleukin-31 and soluble CD40L: New candidate serum biomarkers that predict therapeutic response in multiple sclerosis**. *Neurol. Sci.* (2022) **43** 6271-6278. DOI: 10.1007/s10072-022-06276-5
38. Ehara T., Shigematsu H.. **Contribution of mast cells to the tubulointerstitial lesions in IgA nephritis**. *Kidney Int.* (1998) **54** 1675-1683. DOI: 10.1046/j.1523-1755.1998.00159.x
39. Konishi Y., Morikawa T., Okada N., Maeda I., Kitabayashi C., Yoshioka K., Okumura M., Nishiyama A., Ueda M., Takai S.. **Evidence for abundant presence of chymase-positive mast cells in the kidneys of patients with immunoglobulin A nephropathy: Effect of combination therapy with prednisolone and angiotensin II receptor blocker valsartan**. *Hypertens. Res.* (2008) **31** 1517-1524. DOI: 10.1291/hypres.31.1517
40. Johnson R.J.. **Platelets in inflammatory glomerular injury**. *Semin. Nephrol.* (1991) **11** 276-284. PMID: 2057642
41. Couser W.G.. **Pathogenesis of glomerular damage in glomerulonephritis**. *Nephrol. Dial. Transplant.* (1998) **13** 10-15. DOI: 10.1093/ndt/13.suppl_1.10
42. Gremmel T., Frelinger A.L., Michelson A.D.. **Soluble CD40 Ligand in Aspirin-Treated Patients Undergoing Cardiac Catheterization**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0134599
43. Taji Y., Kuwahara T., Shikata S., Morimoto T.. **Meta-analysis of antiplatelet therapy for IgA nephropathy**. *Clin. Exp. Nephrol.* (2006) **10** 268-273. DOI: 10.1007/s10157-006-0433-8
|
---
title: 'Extracting patient-level data from the electronic health record: Expanding
opportunities for health system research'
authors:
- Erica Farrand
- Harold R. Collard
- Michael Guarnieri
- George Minowada
- Lawrence Block
- Mei Lee
- Carlos Iribarren
journal: PLOS ONE
year: 2023
pmcid: PMC10004557
doi: 10.1371/journal.pone.0280342
license: CC BY 4.0
---
# Extracting patient-level data from the electronic health record: Expanding opportunities for health system research
## Abstract
### Background
Epidemiological studies of interstitial lung disease (ILD) are limited by small numbers and tertiary care bias. Investigators have leveraged the widespread use of electronic health records (EHRs) to overcome these limitations, but struggle to extract patient-level, longitudinal clinical data needed to address many important research questions. We hypothesized that we could automate longitudinal ILD cohort development using the EHR of a large, community-based healthcare system.
### Study design and methods
We applied a previously validated algorithm to the EHR of a community-based healthcare system to identify ILD cases between 2012–2020. We then extracted disease-specific characteristics and outcomes using fully automated data-extraction algorithms and natural language processing of selected free-text.
### Results
We identified a community cohort of 5,399 ILD patients (prevalence = 118 per 100,000). Pulmonary function tests ($71\%$) and serologies ($54\%$) were commonly used in the diagnostic evaluation, whereas lung biopsy was rare ($5\%$). IPF was the most common ILD diagnosis ($$n = 972$$, $18\%$). Prednisone was the most commonly prescribed medication (911, $17\%$). Nintedanib and pirfenidone were rarely prescribed ($$n = 305$$, $5\%$). ILD patients were high-utilizers of inpatient ($40\%$/year hospitalized) and outpatient care ($80\%$/year with pulmonary visit), with sustained utilization throughout the post-diagnosis study period.
### Discussion
We demonstrated the feasibility of robustly characterizing a variety of patient-level utilization and health services outcomes in a community-based EHR cohort. This represents a substantial methodological improvement by alleviating traditional constraints on the accuracy and clinical resolution of such ILD cohorts; we believe this approach will make community-based ILD research more efficient, effective, and scalable.
## Introduction
Interstitial lung diseases (ILDs) are a diverse group of diffuse parenchymal lung disorders that affect approximately 250,000 people in the United States and result in poor health-related quality of life, increased health care resource utilization, premature respiratory failure, and death [1–4]. Following U.S. Food and Drug Administration (FDA) approval of pharmacologic therapies for major ILD subtypes, increased attention has focused on evaluating treatment efficacy in real world settings [5–9]. However traditional approaches to community-based population health studies require a significant investment in personnel and infrastructure to support participant recruitment, enrollment, data collection and management, particularly in rare diseases such as ILD [10, 11]. As a result ILD research is primarily conducted in tertiary care populations, and there is a limited understanding of ILD diagnostics, management, and outcomes in community-based settings where the majority of patients access care. An innovative approach is required to make ILD research feasible in representative clinical practice settings.
The electronic health record (EHR) provides an efficient, effective, and scalable approach to real-world longitudinal cohort development. EHRs capture an unparalleled breadth and depth of clinical, quality, process, and outcome measures [12–15]. However secondary EHR data use relies heavily on unstructured data and manual extraction which limit its practical use [16, 17]. Automated structured and unstructured data capture is possible using EHR-based tools and algorithms [18]. The data can be rigorously monitored and validated, and the tools iteratively refined. EHR data have been successfully applied in other disease contexts [19–21]. Regional EHR data, with detailed patient-level information, has been particularly impactful in advancing delivery science in other contexts and stands to fundamentally improve population research in ILD [22–24].
In this study, we test whether fully automated data-extraction algorithms and natural language processing (NLP) can be applied to robustly characterize the diagnosis and management of ILD patients. We also highlight several important observations from this approach that we believe will catalyze ILD health research by supporting studies on ILD incidence, prevalence, and health services delivery beyond academic and specialty care centers.
## Materials and methods
The study population was derived from the Kaiser Permanente Northern California (KPNC) population, a non-profit, community-based, integrated health care delivery organization which includes 21 medical centers, 60 outpatient facilities, 110 outpatient pharmacies, and a centralized laboratory. KPNC is a regional healthcare system, currently providing care to over 4.5 million members, representing $30\%$ of the population in the 14-county area of Northern California. Eligible patients were adult (age 18 and older) KPNC members receiving care between January 2012 and December 2019, ensuring all patients had the potential for at least one full year of follow-up. The chosen time frame also aligned with KPNC adoption of the current EHR system (EPIC Systems, Verona, WI). Institutional review boards at the University of California San Francisco (#14–15459), and the KPNC Division of Research (#CN-15-2126-H_05) approved the study protocol. The primary dataset was deidentified prior to access. A subset of patient records was identified for algorithm validation. The IRB waived the requirement for informed consent for this retrospective study of medical records as many of the participants were deceased and the study was determined to pose minimal risk.
## Case identification
Patients with ILD were identified using a previously developed algorithm based on International Classification of Diseases (ICD) codes, ninth and tenth revisions [25]. This highly-specific algorithm requires cases to have at least two claims with an ILD code at least one month apart and chest computed tomography (CT) procedure code (ICD-9-CM 87.41 & CPT-4 71250, 71260, 71270) on or before the date of the second ILD code (S1 Table). Identified cases were censored at the time of death or loss to follow-up, the latter defined as 6 months or greater without an EHR encounter of any type. A random validation sample of 200 cases underwent a structured medical record review by an expert ILD clinician (E.F.) to confirm ILD diagnosis and ILD subtype in order to assess performance of the ILD algorithm.
## EHR data extraction
Data from the EHR were extracted and transformed into a common format according to the Virtual Data Warehouse and Observational Medical Outcomes Partnership (OMOP) Common Data Model [26]. Thereby allowing use of standardized analytics. Data describing baseline demographics and practice patterns at the time of diagnosis (e.g., use of limited autoimmune serologies, chest high-resolution computed tomography (HRCT), pulmonary function tests (PFT) and pathology) were extracted from structured data fields. Autoimmune serologies were limited to antinuclear antibodies, rheumatoid factor, and anti-cyclic citrullinated peptide, three tests recommended as part of a general serologic evaluation in patients with suspected interstitial lung disease that could be reliably extracted from the her [27]. Baseline PFT values included forced expiratory volume (FEV1), forced vital capacity (FVC) and the diffusing capacity (DLCO). Raw values were extracted directly from EHR respiratory flowsheets and percent predicted values were calculated for each individual patient using the Global Lung Function Initiative Network reference values to ensure standardization [28]. Pharmacy data were queried to determine which medications were used in the initial management of ILD. Corticosteroid prescriptions were limited to those reflecting long term use, defined as prescriptions for at least 30 consecutive days and a dose ≥ 20mg daily. Use of nintedanib and pirfenidone were only analyzed beginning in October 2014, corresponding to FDA approval of these medications. Utilization and outcomes data were extracted including follow up chest CTs and PFTs, outpatient visits, hospital admissions, and death records. All data were extracted from existing structured data fields or combination of fields when variable capture was redundant. Internal validation was used to assess variable completeness and concordance between extracted data and free text.
In order to extract data on the presence or absence of radiology "usual interstitial pneumonia" (UIP) pattern (a data element not captured in structured fields), unstructured data sources (e.g., free text) were searched using an open-source NLP system, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES). Radiology reports were searched using the terms "usual interstitial pneumonia" and "UIP". The search algorithm reviewed the text immediately before and after the terms. If the terms were identified and no evidence of negation terms (e.g., "no" or "inconsistent with") was found, the CT was considered to demonstrate UIP pattern. If the terms were not found, or if the terms were found but evidence of negation terms was also found, the CT was considered to not demonstrate a UIP pattern. The NLP algorithm used regular expression and generalized Levenshtein edit distance to identify close misspellings of the key terms of interest [29]. A subset of NLP results ($40\%$) was manually reviewed by an ILD expert (E.F.) and validated in order to assess the performance of the NLP algorithm.
## Statistical analysis
Data processing and analysis were performed in R (version 1.4.1717). The positive predictive value (PPV) and binomial $95\%$ confidence interval (CI) were determined for the ILD algorithm, ILD diagnosis algorithm, and CT radiology NLP algorithm. The odds ratio (OR) of detecting a UIP pattern on a HRCT radiology report versus conventional CT was calculated using logistic regression. For longitudinal health care resource utilization outcomes, we analyzed each year of utilization separately to test whether the results varied over time. We calculated per-comparison P values and pre-specified family-wise adjusted p values to account for multiple comparisons. $P \leq 0.05$ was considered significant, and all P values were 2 sided.
## Results
We identified a total of 5,399 KPNC members (a prevalence of 118 per 100,000) between January 2012 and December 2019 with ILD (referred to subsequently as the ILD Cohort), distributed widely throughout the 14-county area of Northern California (S1 Fig, S2 Table). Twenty-five percent ($$n = 1$$,350) of cases underwent HRCT as part of their diagnostic evaluation, with the remaining $75\%$ receiving a conventional CT chest. Pulmonary function testing ($71\%$, $$n = 3821$$) and limited autoimmune serologies ($54\%$, $$n = 2896$$) were commonly performed during the diagnostic evaluation (Table 2). Only $12\%$ ($$n = 642$$) of patients had a lung biopsy of any type; of these $43\%$ ($$n = 276$$) were surgical biopsies and $57\%$ ($$n = 366$$) were bronchoscopic biopsies (not further characterized). Nineteen percent ($$n = 1004$$) of patients had a bronchoscopy that was not associated with a biopsy.
The ILD Cohort was $54\%$ female, $79\%$ 60 years of age or older, and $59\%$ white (Table 1). The majority of patients were former smokers ($51\%$) and lived in urban ($49\%$) or suburban ($26\%$) locations. The mean (± Standard Deviation (SD)) FVC percent of predicted was 75.19±$18.92\%$, the mean FEV1 percent of predicted was 73.70±$20.12\%$, and the mean DLCO percent of predicted was 51.40 ± $16.70\%$ (Table 2).
Structured case validation revealed a PPV of the ILD algorithm of $95.5\%$ ($95\%$ confidence interval (CI), 95.38, 95.61). The PPV for identifying a specific diagnosis among the cases of ILD was $74\%$ ($95\%$ CI, 73.93, 75.53). The most common ILD diagnosis identified was idiopathic pulmonary fibrosis ($18\%$), followed by connective tissue disease related ILD ($12\%$) and chronic hypersensitivity pneumonitis ($10\%$). One-third of the ILD Cohort ($33\%$) had ILD that did not list a specific diagnosis (S2 Fig). Median survival estimate of the full ILD cohort was 72 months (S3 Fig).
Overall, $99\%$ ($$n = 5366$$) patients in the ILD Cohort had CT reports that could be reviewed using NLP, of which $11\%$ ($$n = 590$$) were classified as UIP pattern and $89\%$ ($$n = 4776$$) were classified as not UIP (Fig 1A). Of the 5366 CT reports, $40\%$ were manually reviewed ($$n = 2168$$) of which $14\%$ ($$n = 312$$) had a UIP pattern and $86\%$ ($$n = 1856$$) did not have a UIP pattern. This corresponds to a PPV for detecting a UIP pattern of $94.29\%$ (90.70, $96.48\%$). During validation of the NLP algorithm it was observed that of the CTs classified as not UIP ($86\%$, $$n = 4776$$), $49\%$ ($$n = 1053$$) specified no UIP pattern while a distinct pattern was not specified for the remaining $37\%$ ($$n = 803$$). Detection of radiologic patterns differed significantly between reports from HRCT and conventional CTs (Fig 1B). The OR of a UIP pattern in the HRCT group ($$n = 501$$) versus the conventional CT scan group ($$n = 1667$$) was 4.83 ($95\%$ CI 3.76, 6.21). On the other hand, patients who received a HRCT were five times less likely to have an indeterminate pattern on report than those who received a conventional CT (OR = 0.21, $95\%$ CI (0.16, 0.27)).
**Fig 1:** *a. Pattern Detection on CT Chest Reports Using Natural Language Processing. Radiographic pattern detection on CT Chest reports from ILD Cohort: detection of usual interstitial pneumonia (UIP) and not UIP patterns using natural language processing (NLP) and manual validation. b. Comparison of Radiographic Patterns in HRCT vs. Conventional CT. Radiographic pattern detection on CT Chest reports from ILD Cohort: comparison of pattern detection in high-resolution CT (HRCT) reports vs. conventional CT reports.*
Long-term corticosteroid use was the most common medication used in the management of ILD patients following diagnosis ($17\%$, $$n = 911$$), followed by mycophenolate mofetil ($13\%$, $$n = 680$$). Only two percent ($$n = 131$$) was prescribed nintedanib and $3\%$ ($$n = 174$$) was prescribed pirfenidone (Table 3).
**Table 3**
| Medications | Total N = 5399 |
| --- | --- |
| Corticosteroids | 911 (17%) |
| Mycophenolate | 680 (13%) |
| Azathioprine | 239 (4%) |
| Rituximab | 169 (3%) |
| Pirfenidone* | 174 (3%) |
| Nintedanib* | 131 (2%) |
| Cyclophosphamide | 15 (0%) |
| Cyclosporine | 18 (0%) |
| Monitoring | Total N = 5399 |
| Pulmonary Visits | 4,446 (82%) |
| CT Chest | 3600 (67%) |
| Pulmonary Function Test | 3,119 (57%) |
| Supplemental Oxygen | 1,775 (32%) |
| Echocardiogram | 1,767 (32%) |
| Pulmonary Rehabilitation | 190 (3%) |
Health care utilization was relatively constant among surviving patients throughout the post-diagnosis study period for patients with ILD (Fig 2). There were no significant differences in percentage of at risk ILD patients utilizing healthcare for all outcomes evaluated (all $P \leq 0.05$). On average, approximately 4 in 5 ILD patients saw a pulmonologist at least once a year, approximately half of ILD patients visited the emergency department at least once a year, and approximately 2 in 5 ILD patients were hospitalized at least once a year. The majority of ILD patients underwent regular PFT testing as part of their longitudinal care while the minority underwent regular chest CTs. The number of patients at risk over time decreased substantially due to a combination of lost-to-follow up, right censoring and death.
**Fig 2:** *Longitudinal health care resource utilization in ILD.Annual percentages of health care utilization for patients with ILD post-diagnosis. PFT = pulmonary function test; ED = emergency department.*
## Discussion
Ensuring high-quality evidence-based care in ILD requires defining and characterizing disease epidemiology, healthcare utilization, and practice patterns in real world settings. Such efforts have historically relied on large-scale recruitment efforts and manual data collection methods that are separate from the clinical enterprise and present a substantial barrier to success. In our study, we aimed to bypass this barrier by applying accurate, automated, and scalable data extraction methodology to a community-based, real world EHR. Our results demonstrate that a code-based EHR algorithm can be used to accurately identify a cohort of ILD patients. ILD subtypes have previously been identified using algorithms, however this is the first study to target a broader cohort of ILD patients more relevant to clinical practice. We also describe the process of building a robust longitudinal ILD patient cohort using baseline, process, and outcome data available in the EHR. We included variables commonly collected in patient registries and clinical trials, as well as data reflecting healthcare utilization and practice patterns. This expanded variable list can be reliably and automatically extracted from the EHR. Further, our results demonstrate that unstructured data sources can be automatically processed through the application of NLP to chest CT reports.
More broadly, our results demonstrate the unique power of the EHR to transform health research. Unlike traditional tertiary cohorts and voluntary registries, community healthcare system EHR-based studies ground our study of ILD diagnosis and management in the real world. Further, EHR-based studies directly inform and enable subsequent implementation efforts to establish best practices in clinical care. Once an automated EHR-based cohort is developed, it can be easily reanalyzed at intervals to assess the impact of clinical interventions on practice patterns and patient outcomes. This pairing of research with care improvement through the EHR is at the heart of what the National Academies has called the Learning Healthcare System [30], and it holds great promise for quality improvement and public health for patients with ILD.
We believe the data reported in this study demonstrate that ILD care in the KPNC system is of high quality. The epidemiology, diagnostic evaluation, and management utilization mirrors a number of the findings from tertiary expert centers [3, 4, 31–33]. As importantly however, these data suggest several areas for care improvement. We highlight a few examples below.
First, an ILD diagnosis is not achieved in a sizable subgroup of patients with ILD. We hypothesize that this may in part stem from underutilization of guideline-recommended diagnostic studies, in particular HRCT. While one hundred percent of the cohort had a CT Chest performed on or before the time of diagnosis (this was part of our case definition), only one quarter had a HRCT as recommended by ILD guidelines. We observed significant differences in the detection of UIP pattern in HRCT reports as compared to conventional CT. We hypothesize that this finding is impacted by both differences in pretest probability of ILD in patients receiving an HRCT vs. conventional CT, as well as differences in test characteristics (i.e. CT precision, experience of radiologists). Overall, resolving knowledge gaps and operational barriers to the use of key diagnostic strategies, such as HRCT, specialty referral, and multidisciplinary case conference discussions, in community-based settings may be highly impactful in improving ILD diagnostic precision.
Second, the use of long-term corticosteroids in patients with ILD was $17\%$, while the use of the nintedanib and pirfenidone was $5\%$ combined. While we expect occasional short-term corticosteroid use in an ILD cohort, their long-term use is associated with substantial morbidity and better tolerated, safer treatment options (e.g., mycophenolate mofetil) exist [34, 35]. Understanding what is driving long-term corticosteroid use and defining when and how non-steroidal immunomodulatory agents such as mycophenolate mofetil are prescribed will help to improve alignment with best practice. Nintedanib and pirfenidone are recommended first-line therapy for patients with idiopathic pulmonary fibrosis and are known to be effective in other forms of progressive fibrosing ILD [36–38]. Understanding patient, provider, and system-level barriers to the use of nintedanib and pirfenidone is a necessary first step to expanding guideline-based use of these medications and developing targeted clinical decision-making support.
Third, high rates of health care utilization are sustained at least eight years post-diagnosis in patients with ILD. This finding expands on our initial study limited to IPF patients, in which we demonstrated significantly higher rates of utilization in IPF patients compared to controls for five years post diagnosis [25]. Sustained health care utilization throughout the disease course suggests the need to develop and implement longitudinal care models and decision aids that meet the shared complex needs of ILD patients. These include guidance on process of care, medication choice and adjustment, management plans for acute worsening of symptoms, longitudinal evaluation of pulmonary function and HRCT, and end of life care planning.
There are limitations to this study. First, in order to develop methods for extracting ILD data from the EHR, we analyzed a single integrated health care system. At this stage algorithms must be developed and/or tailored for a specific health system’s data. However, the algorithms and tools applied in this study were intentionally designed to pull from common EHR data elements, use common free-text terminology, and apply standardized data processing and analytic tools (e.g. OMOP and cTAKES) in order to facilitate the future development of similar EHR-based registries in other health systems. Second, our algorithm validation process was based on retrospective case review. Although these cases were randomly selected for review, if the cases in the validation samples were systematically different than the remaining sample, we could have over or underestimated the PPVs of the algorithms. Third, limitations in our data set precluded analysis of other worthwhile topics including the types of physicians providing care (generalist vs specialist), the impact of multidisciplinary case conference discussion on the diagnosis and management of ILD, important utilization metrics including pulmonary rehabilitation and palliative care, and the extent to which patients accessed ILD care outside of the KPNC system. Future studies combining our real-world EHR cohort with other data sources can expand the types of health care delivery questions that can be answered.
## Conclusion
In summary, these results demonstrate the transformative value of an EHR-based ILD cohort derived from large, community-based practice. By applying automated data extraction tools to alleviate logistical and methodological constraints, such real-world data sets facilitate health research, catalyzing our ability define patient care patterns, evaluate variability in outcomes, identify evidence-practice gaps, and implement solutions.
## References
1. Montesi SB, Fisher JH, Martinez FJ, Selman M, Pardo A, Johannson KA. **Update in Interstitial Lung Disease 2019**. *Am J Respir Crit Care Med* (2020.0) **202** 500-7. DOI: 10.1164/rccm.202002-0360UP
2. Antoniou KM, Margaritopoulos GA, Tomassetti S, Bonella F, Costabel U, Poletti V. **Interstitial lung disease**. *Eur Respir Rev* (2014.0) **23** 40-54. DOI: 10.1183/09059180.00009113
3. Collard HR, Chen SY, Yeh WS, Li Q, Lee YC, Wang A. **Health care utilization and costs of idiopathic pulmonary fibrosis in U.S. Medicare beneficiaries aged 65 years and older.**. *Ann Am Thorac Soc* (2015.0) **12** 981-7. DOI: 10.1513/AnnalsATS.201412-553OC
4. Wu N, Yu YF, Chuang CC, Wang R, Benjamin NN, Coultas DB. **Healthcare resource utilization among patients diagnosed with idiopathic pulmonary fibrosis in the United States.**. *J Med Econ* (2015.0) **18** 249-57. DOI: 10.3111/13696998.2014.991789
5. Durheim MT, Bendstrup E, Carlson L, Sutinen EM, Hyldgaard C, Kalafatis D. **Outcomes of patients with advanced idiopathic pulmonary fibrosis treated with nintedanib or pirfenidone in a real-world multicentre cohort.**. *Respirology* (2021.0). DOI: 10.1111/resp.14116
6. Isshiki T, Sakamoto S, Yamasaki A, Shimizu H, Miyoshi S, Nakamura Y. **Incidence of acute exacerbation of idiopathic pulmonary fibrosis in patients receiving antifibrotic agents: Real-world experience.**. *Respir Med.* (2021.0) **187** 106551. DOI: 10.1016/j.rmed.2021.106551
7. Wright WA, Crowley LE, Parekh D, Crawshaw A, Dosanjh DP, Nightingale P. **Real-world retrospective observational study exploring the effectiveness and safety of antifibrotics in idiopathic pulmonary fibrosis**. *BMJ Open Respir Res* (2021.0) **8**. DOI: 10.1136/bmjresp-2020-000782
8. Tzilas V, Tzouvelekis A, Bouros E, Karampitsakos T, Ntassiou M, Avdoula E. **Clinical experience with antifibrotics in fibrotic hypersensitivity pneumonitis: a 3-year real-life observational study.**. *ERJ Open Res.* (2020.0) **6**. DOI: 10.1183/23120541.00152-2020
9. Kato M, Sasaki S, Tateyama M, Arai Y, Motomura H, Sumiyoshi I. **Clinical Significance of Continuable Treatment with Nintedanib Over 12 Months for Idiopathic Pulmonary Fibrosis in a Real-World Setting.**. *Drug Des Devel Ther.* (2021.0) **15** 223-30. DOI: 10.2147/DDDT.S284819
10. Russo C, Stout L, House T, Santana VM. **Barriers and facilitators of clinical trial enrollment in a network of community-based pediatric oncology clinics**. *Pediatr Blood Cancer* (2020.0) **67** e28023. DOI: 10.1002/pbc.28023
11. Atkinson M, Turkel M, Cashy J. **Overcoming barriers to research in a Magnet community hospital.**. *J Nurs Care Qual* (2008.0) **23** 362-8. DOI: 10.1097/01.NCQ.0000336675.48466.37
12. **Big hopes for big data**. *Nat Med* (2020.0) **26** 1. DOI: 10.1038/s41591-019-0740-8
13. Ross MK, Wei W, Ohno-Machado L. **"Big data" and the electronic health record.**. *Yearb Med Inform* (2014.0) **9** 97-104. DOI: 10.15265/IY-2014-0003
14. Krumholz HM. **Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system.**. *Health Aff (Millwood).* (2014.0) **33** 1163-70. DOI: 10.1377/hlthaff.2014.0053
15. **Healthcare Big Data and the Promise of Value-Based Care**. *Catalyst Carryover.* (2018.0) **4**
16. Holmes JH, Beinlich J, Boland MR, Bowles KH, Chen Y, Cook TS. **Why Is the Electronic Health Record So Challenging for Research and Clinical Care?**. *Methods Inf Med.* (2021.0). DOI: 10.1055/s-0041-1731784
17. Botsis T, Hartvigsen G, Chen F, Weng C. **Secondary Use of EHR: Data Quality Issues and Informatics Opportunities.**. *Summit Transl Bioinform.* (2010.0) **2010** 1-5. PMID: 21347133
18. Gliklich RE, Leavy MB, Dreyer NA. *©2019 United States Government, as represented by the Secretary of the Department of Health and Human Services, by assignment.;* (2019.0)
19. Morrill J, Qirko K, Kelly J, Ambrosy A, Toro B, Smith T. **A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations.**. *J Cardiovasc Transl Res* (2021.0). DOI: 10.1007/s12265-021-10151-7
20. Neves AL, Pereira Rodrigues P, Mulla A, Glampson B, Willis T, Darzi A. **Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol**. *BMJ Open* (2021.0) **11** e046716. DOI: 10.1136/bmjopen-2020-046716
21. Yuan Q, Cai T, Hong C, Du M, Johnson BE, Lanuti M. **Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.**. *JAMA Netw Open* (2021.0) **4** e2114723. DOI: 10.1001/jamanetworkopen.2021.14723
22. Go AS, Tan TC, Horiuchi KM, Laws D, Ambrosy AP, Lee KK. **Effect of Medically Tailored Meals on Clinical Outcomes in Recently Hospitalized High-Risk Adults.**. *Medical Care.* (2022.0). DOI: 10.1097/MLR.0000000000001759
23. Vinson DR, Casey SD, Vuong PL, Huang J, Ballard DW, Reed ME. **Sustainability of a Clinical Decision Support Intervention for Outpatient Care for Emergency Department Patients With Acute Pulmonary Embolism**. *JAMA Network Open* (2022.0) **5** e2212340-e. DOI: 10.1001/jamanetworkopen.2022.12340
24. Ormsby EL, Kojouri K, Chang PC, Lin TY, Vuong B, Ramirez RM. **Association of Standardized Radiology Reporting and Management of Abdominal CT and MRI With Diagnosis of Pancreatic Cancer**. *Clin Gastroenterol Hepatol* (2022.0). DOI: 10.1016/j.cgh.2022.03.047
25. Farrand E, Iribarren C, Vittinghoff E, Levine-Hall T, Ley B, Minowada G. **Impact of Idiopathic Pulmonary Fibrosis on Longitudinal Health-care Utilization in a Community-Based Cohort of Patients.**. *Chest.* (2021.0) **159** 219-27. DOI: 10.1016/j.chest.2020.07.035
26. 26OMOP Common Data Model [Available from: http://ohdsi.github.io/CommonDataModel/.
27. Raghu G, Remy-Jardin M, Myers JL, Richeldi L, Ryerson CJ, Lederer DJ. **Diagnosis of Idiopathic Pulmonary Fibrosis. An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline**. *Am J Respir Crit Care Med* (2018.0) **198** e44-e68. DOI: 10.1164/rccm.201807-1255ST
28. Quanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH. **Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations**. *Eur Respir J* (2012.0) **40** 1324-43. DOI: 10.1183/09031936.00080312
29. Berger B, Waterman MS, Yu YW. **Levenshtein Distance, Sequence Comparison and Biological Database Search**. *IEEE Trans Inf Theory* (2021.0) **67** 3287-94. DOI: 10.1109/tit.2020.2996543
30. Olsen L, Aisner D, McGinnis JM. *Copyright © 2007, National Academy of Sciences.;* (2007.0)
31. Olson AL, Patnaik P, Hartmann N, Bohn RL, Garry EM, Wallace L. **Prevalence and Incidence of Chronic Fibrosing Interstitial Lung Diseases with a Progressive Phenotype in the United States Estimated in a Large Claims Database Analysis.**. *Adv Ther.* (2021.0) **38** 4100-14. DOI: 10.1007/s12325-021-01786-8
32. Nasser M, Larrieu S, Boussel L, Si-Mohamed S, Bazin F, Marque S. **Estimates of epidemiology, mortality and disease burden associated with progressive fibrosing interstitial lung disease in France (the PROGRESS study).**. *Respir Res.* (2021.0) **22** 162. DOI: 10.1186/s12931-021-01749-1
33. Fan Y, Bender SD, Conoscenti CS, Davidson-Ray L, Cowper PA, Palmer SM. **Hospital-Based Resource Use and Costs Among Patients With Idiopathic Pulmonary Fibrosis Enrolled in the Idiopathic Pulmonary Fibrosis Prospective Outcomes (IPF-PRO) Registry.**. *Chest* (2020.0) **157** 1522-30. DOI: 10.1016/j.chest.2019.12.041
34. Oray M, Abu Samra K, Ebrahimiadib N, Meese H, Foster CS. **Long-term side effects of glucocorticoids.**. *Expert Opin Drug Saf* (2016.0) **15** 457-65. DOI: 10.1517/14740338.2016.1140743
35. McDonald CM, Henricson EK, Abresch RT, Duong T, Joyce NC, Hu F. **Long-term effects of glucocorticoids on function, quality of life, and survival in patients with Duchenne muscular dystrophy: a prospective cohort study**. *Lancet* (2018.0) **391** 451-61. DOI: 10.1016/S0140-6736(17)32160-8
36. Richeldi L, du Bois RM, Raghu G, Azuma A, Brown KK, Costabel U. **Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis**. *N Engl J Med* (2014.0) **370** 2071-82. DOI: 10.1056/NEJMoa1402584
37. King TE, Bradford WZ, Castro-Bernardini S, Fagan EA, Glaspole I, Glassberg MK. **A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis**. *N Engl J Med* (2014.0) **370** 2083-92. DOI: 10.1056/NEJMoa1402582
38. Flaherty KR, Wells AU, Cottin V, Devaraj A, Walsh SLF, Inoue Y. **Nintedanib in Progressive Fibrosing Interstitial Lung Diseases**. *N Engl J Med* (2019.0) **381** 1718-27. DOI: 10.1056/NEJMoa1908681
|
---
title: Ratio of extracellular water to intracellular water and simplified creatinine
index as predictors of all-cause mortality for patients receiving hemodialysis
authors:
- Takahiro Yajima
- Kumiko Yajima
journal: PLOS ONE
year: 2023
pmcid: PMC10004563
doi: 10.1371/journal.pone.0282864
license: CC BY 4.0
---
# Ratio of extracellular water to intracellular water and simplified creatinine index as predictors of all-cause mortality for patients receiving hemodialysis
## Abstract
The bioelectrical impedance analysis-measured ratio of extracellular water (ECW) to intracellular water (ICW) reflects fluid volume and malnutrition. It may be an indicator of protein-energy wasting and muscle wasting in hemodialysis patients. We examined the association between the ECW/ICW ratio and simplified creatinine index, which is a new surrogate marker of protein-energy wasting and muscle wasting, and whether their combination can accurately predict mortality. A total of 224 patients undergoing hemodialysis for more than 6 months and having undergone bioelectrical impedance analysis for the assessment of body composition were included. Patients were divided into two groups based on the cut-off values of the ECW/ICW ratio (0.57) and simplified creatinine index (20.4 mg/kg/day) for maximumly predicting mortality. Thereafter, they were cross-classified into four groups with each cut-off point. The ECW/ICW ratio was independently associated with the simplified creatinine index (β = -0.164; $$P \leq 0.042$$). During a follow-up of 3.5 years (2.0–6.0 years), 77 patients died. A higher ECW/ICW ratio (adjusted hazard ratio, 3.66, $95\%$ confidence interval 1.99–6.72, $P \leq 0.0001$) and lower simplified creatinine index (adjusted hazard ratio, 2.25, $95\%$ confidence interval 1.34–3.79, $$P \leq 0.0021$$) were independently associated with an increased risk of all-cause mortality. The adjusted hazard ratio for the higher ECW/ICW ratio and lower simplified creatinine index group vs. the lower ECW/ICW ratio and higher simplified creatinine index group was 12.22 ($95\%$ confidence interval 3.68–40.57, $p \leq 0.0001$). Furthermore, the addition of the ECW/ICW ratio and simplified creatinine index to the baseline risk model significantly improved the C-index from 0.831 to 0.864 ($$p \leq 0.045$$). In conclusion, the ECW/ICW ratio may be a surrogate marker of muscle wasting. Moreover, combining the ECW/ICW ratio and simplified creatinine index may improve the accuracy of predicting all-cause mortality and help stratify the mortality risk of hemodialysis patients.
## Introduction
Fluid overload and malnutrition are common life-threatening concerns for patients undergoing hemodialysis. Volume overload can induce hypertension, left ventricular hypertrophy, and congestive heart failure and can even lead to mortality [1, 2]. Regarding malnutrition, there are several phenotypes, such as protein-energy wasting (PEW) and sarcopenia [3, 4]. PEW, a malnutritional state characterized by the volume reduction of muscle and fat caused by the decreased intake of energy and/or protein and chronic inflammation [5, 6], is highly prevalent and associated with increased risks of all-cause mortality and cardiovascular mortality in hemodialysis patients [7, 8]. Sarcopenia, a condition defined as the loss of skeletal muscle mass and function, is also prevalent and associated with mortality in this population [9–11]. A common element of PEW and sarcopenia is muscle wasting or loss of muscle; therefore, hemodialysis patients can concomitantly have PEW and sarcopenia [3, 4]. We recently reported that computed tomography-measured sarcopenic indices are promising indicators of mortality in hemodialysis patients [12–15]. According to the Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment, muscle function, such as muscle strength, is considered more important than muscle mass for predicting sarcopenia and mortality in the general population [10]. However, muscle function usually deteriorates, and muscle wasting (loss of muscle mass) is now recognized as an important predictor of mortality in the hemodialysis population [16]. The simplified creatinine index (SCI), which is an objective nutritional assessment tool calculated using sex, age, single-pool Kt/V for urea (urea kinetic modeling based on the assumption that urea is located only in one compartment of the body: spKt/Vurea), and serum creatinine level before a hemodialysis session, has been introduced as a marker of muscle mass volume or muscle wasting [17, 18]. The SCI, which helps to predict infection-related, cardiovascular, and all-cause mortality [19, 20], has also been recognized as a new indicator of PEW [21].
Bioelectrical impedance analysis (BIA) has attracted attention as a useful tool to assess the fluid volume and monitor the nutritional status of the hemodialysis population in daily clinical practice [22, 23]. Recently, the BIA-measured ratio of extracellular water (ECW) to intracellular water (ICW), which can simultaneously reflect fluid overload and malnutrition, has emerged as an indicator of PEW in patients receiving hemodialysis [24]. The ECW/ICW ratio can help predict cardiovascular mortality, cardiovascular events, and all-cause mortality in this population [24–26]. Park et al. reported that ECW fluid overload was associated with sarcopenia in the general population [27]. Additionally, ICW may be representative of body cell or skeletal muscle mass [28]; therefore, we hypothesized that the ECW/ICW ratio might be an indicator of muscle wasting. However, the association between the ECW/ICW ratio and muscle wasting in hemodialysis patients remains unclear.
This study aimed to examine the association between the ECW/ICW ratio and the SCI in hemodialysis patients. Moreover, we evaluated whether the combination of the ECW/ICW ratio and SCI could stratify the risks of all-cause mortality and improve the accuracy of predicting mortality in this population.
## Study participants
This retrospective study included patients who had steadily received hemodialysis for more than 6 months (three times per week for four hours) and underwent body composition measurements using BIA at our institution from January 2009 to December 2019. Patient data were anonymized before they were accessed; therefore, the requirement for informed consent was waived by the ethics committee of our institution. This study adhered to the principles of the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of Matsunami General Hospital (approval no. 523).
## Baseline data collection
The following patient data were obtained from medical records: age, sex, hemodialysis vintage, cause of end-stage kidney disease, alcohol consumption, tobacco use, and history of hypertension, diabetes mellitus, and cardiovascular events. In this study, hypertension was defined as the use of any anti-hypertensive medications and/or systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg before a hemodialysis session. Diabetes mellitus was defined as a history of diabetes mellitus and/or treatment with anti-diabetic agents. Cardiovascular disease (CVD) events were defined as a history of angina pectoris, myocardial infarction, congestive heart failure, stroke, or peripheral artery disease. Blood tests were performed with the patient in the supine position before and after the hemodialysis session on a Monday or Tuesday. BIA and chest radiographs were performed on the same day after the hemodialysis session. Body composition data, including the ECW, ICW, and total body water, were measured using a multi-frequency (2.5–350 kHz) body composition analyzer (MLT-550N; SK Medical, Siga, Japan) with the wrist-ankle method. The body mass index (BMI) was calculated as follows: BMI = dry weight (kg)/height squared (m2). The geriatric nutritional risk index (GNRI) was calculated using the serum albumin level and BMI as follows [29–31]: GNRI = 14.89 × serum albumin level (g/dL) + 41.7 × BMI (kg/m2) / 22. When the BMI was greater than 22 kg/m2, the variable BMI (kg/m2)/22 was set to 1. The SCI was calculated using parameters such as age, sex, single-pool Kt/V for urea (spKt/Vurea), and the pre-hemodialysis serum creatinine level as follows [17]: SCI (mg/kg/day) = 16.21 + 1.12 × (1 for males; 0 for females) − 0.06 × age (years) − 0.08 × spKt/Vurea + 0.009 × pre-hemodialysis creatinine level (μmol/L).
## Follow-up and study endpoint
Patients were divided into two groups based on the cut-off values of the ECW/ICW ratio (0.57) and SCI (20.4 mg/kg/day) for maximumly discriminating survival. Thereafter, they were divided into four subgroups with the following cut-off points: G1, ECW/ICW ratio <0.57 and SCI ≥20.4 mg/kg/day; G2, ECW/ICW ratio <0.57 and SCI <20.4 mg/kg/day; G3, ECW/ICW ratio ≥0.57 and SCI ≥20.4 mg/kg/day; and G4, ECW/ICW ratio ≥0.57 and SCI <20.4 mg/kg/day. Patients were followed-up until December 2020. The study endpoint was all-cause mortality.
## Statistical analysis
Normally distributed variables are expressed as mean ± standard deviation, and non-normally distributed variables are expressed as the median and interquartile range. The cut-off values of the ECW/ICW ratio and SCI for maximally predicting mortality were obtained using receiver operating characteristic (ROC) analysis and the Youden index (the maximum sum of sensitivity and specificity minus one). To compare the differences in the baseline characteristics of the patients who were divided into four subgroups based on the cut-off values of the ECW/ICW ratio and SCI, a one-way analysis of variance or the Kruskal–Wallis test was performed for continuous variables, and the chi-squared test was used for categorical variables. A univariate linear regression analysis was performed to examine the correlations between the ECW/ICW and baseline factors. The multivariate linear regression analysis included factors significantly associated with the ECW/ICW ratio in the univariate analysis. The Kaplan–Meier method was used to estimate the survival rate, and the differences were analyzed using the log-rank test. A univariate Cox proportional hazard regression analysis was performed to estimate hazard ratios and $95\%$ confidence intervals [CIs] for all-cause mortality. Multivariate Cox proportional hazard regression analysis was performed by adjusting for age, sex, and variables that had a p value of <0.1 in the univariate *Cox analysis* (hemodialysis vintage, history of CVD events, hemoglobin, phosphorus, C-reactive protein (CRP), and GNRI).
To assess whether the predictive accuracy of all-cause mortality could improve when the ECW/ICW ratio and/or SCI were added to the baseline model, the C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated. As a baseline model, variables, as mentioned above, were included. The C-index was defined as the area under the ROC curve in the logistic regression analysis [32]. The C-index of the enriched model with the addition of the ECW/ICW ratio and/or SCI was compared to that of the baseline model. The NRI was a relative indicator of the number of patients with improved predicted mortality risk, and the IDI was an indicator of the average improvement in the predicted mortality risk after adding the ECW/ICW ratio and/or SCI to the baseline model [33].
All statistical analyses were performed using R version 4.04 (R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 24 (IBM Corp., Armonk, NY, USA); $P \leq 0.05$ was considered to be statistically significant.
## Baseline characteristics
We included 224 patients receiving hemodialysis in the present study. Their baseline characteristics are summarized in Table 1. The mean age was 63.3±13.9 years, and $66.5\%$ of the patients were men. The median hemodialysis vintage was 0.8 years (0.6–4.7 years); $46.9\%$ of the patients had a history of diabetes mellitus, and $62.9\%$ had a history of CVD events. The hemoglobin, total cholesterol, phosphorus, CRP, GNRI, and cardiothoracic ratio (CTR) values were 10.8±1.3 g/dL, 156±35 mg/dL, 5.1±1.3 mg/dL, 0.17 mg/dL (0.06–0.37 mg/dL), 93.2±6.4, and 49.3±$5.0\%$, respectively. The median ECW/ICW ratio and SCI were 0.56 (0.46–0.69) and 19.9 mg/kg/day (18.1–22.1 mg/kg/day), respectively.
**Table 1**
| Unnamed: 0 | All patients (N = 224) | G1 | G2 | G3 | G4 | P value |
| --- | --- | --- | --- | --- | --- | --- |
| | All patients (N = 224) | (N = 63) | (N = 58) | (N = 31) | (N = 72) | P value |
| Age (years) | 63.3 ± 13.9 | 50.2 ± 13.5 | 67.2 ± 10.5 | 64.2 ± 10.5 | 71.4 ± 8.9 | <0.0001 |
| Male sex | 66.5 | 68.3 | 44.8 | 96.7 | 69.4 | <0.0001 |
| Underlying kidney disease | | | | | | 0.033 |
| Diabetic kidney disease | 44.2 | 31.7 | 44.8 | 38.7 | 56.9 | |
| Chronic glomerulonephritis | 29.5 | 42.9 | 31.0 | 32.2 | 15.3 | |
| Nephrosclerosis | 18.8 | 14.3 | 17.2 | 25.8 | 20.8 | |
| Others | 7.6 | 11.1 | 6.9 | 3.2 | 6.9 | |
| HD duration (years) | 0.8 (0.6–4.7) | 1.5 (0.6–9.3) | 0.6 (0.5–1.9) | 5.1 (1.2–9.2) | 0.6 (0.5–1.2) | 0.0002 |
| Alcohol | 21.0 | 22.2 | 20.7 | 25.8 | 18.1 | 0.84 |
| Smoking | 26.8 | 31.7 | 27.6 | 19.4 | 25.0 | 0.61 |
| Hypertension | 95.5 | 93.7 | 94.8 | 93.5 | 98.6 | 0.40 |
| Diabetes mellitus | 46.9 | 31.7 | 51.7 | 45.2 | 56.9 | 0.024 |
| History of CVD | 62.9 | 44.4 | 69.0 | 64.5 | 73.6 | 0.0037 |
| BMI (kg/m2) | 22.0 ± 4.0 | 22.0 ± 3.8 | 22.7 ± 5.1 | 22.1 ± 3.5 | 21.5 ± 3.3 | 0.37 |
| BUN (mg/dL) | 60.5 ± 15.0 | 69.1 ± 17.6 | 58.7 ± 10.5 | 64.1 ± 14.2 | 53.0 ± 11.5 | <0.0001 |
| Creatinine (mg/dL) | 8.9 ± 2.9 | 11.9 ± 2.1 | 7.2 ± 1.8 | 11.1 ± 1.4 | 6.9 ± 1.7 | <0.0001 |
| Albumin (g/dL) | 3.6 ± 0.3 | 3.8 ± 0.3 | 3.7 ± 0.3 | 3.6 ± 0.3 | 3.5 ± 0.4 | <0.0001 |
| Hemoglobin (g/dL) | 10.8 ± 1.3 | 11.1 ± 1.2 | 10.9 ± 1.3 | 10.6 ± 1.1 | 10.5 ± 1.5 | 0.050 |
| T-Cho (mg/dL) | 156 ± 35 | 152 ± 33 | 172 ± 34 | 152 ± 34 | 147 ± 36 | 0.0007 |
| Phosphorus (mg/dL) | 5.1 ± 1.3 | 5.8 ± 1.4 | 4.9 ± 1.1 | 5.3 ± 1.4 | 4.6 ± 1.2 | <0.0001 |
| Glucose (mg/dL) | 138 ± 57 | 132 ± 59 | 139 ± 59 | 136 ± 45 | 143 ± 58 | 0.72 |
| CRP (mg/dL) | 0.17 (0.06–0.37) | 0.11 (0.05–0.21) | 0.18 (0.06–0.32) | 0.23 (0.06–0.55) | 0.21 (0.06–0.53) | 0.034 |
| GNRI | 93.2 ± 6.4 | 95.8 ± 5.6 | 93.6 ± 6.6 | 92.3 ± 5.3 | 90.9 ± 6.3 | <0.0001 |
| Kt/V | 1.3 ± 0.3 | 1.4 ± 0.3 | 1.4 ± 0.3 | 1.3 ± 0.2 | 1.2 ± 0.3 | 0.0013 |
| SCI (mg/kg/day) | 20.1 ± 2.9 | 23.2 ± 2.1 | 18.2 ± 1.5 | 22.0 ± 1.4 | 18.0 ± 1.7 | <0.0001 |
| CTR | 49.3 ± 5.0 | 47.0 ± 4.5 | 50.2 ± 5.3 | 49.0 ± 4.3 | 50.7 ± 5.0 | 0.0001 |
| TBW (kg) | 27.5 ± 5.4 | 28.4 ± 5.8 | 25.1 ± 5.5 | 30.0 ± 4.5 | 27.5 ± 4.5 | 0.0001 |
| ICW (kg) | 17.4 ± 3.7 | 19.8 ± 3.8 | 17.0 ± 3.6 | 17.7 ± 3.0 | 15.5 ± 2.7 | <0.0001 |
| ECW (kg) | 10.0 ± 3.1 | 8.6 ± 2.6 | 8.1 ± 2.2 | 12.3 ± 1.9 | 12.0 ± 2.7 | <0.0001 |
| ECW/ICW ratio | 0.59 ± 0.20 | 0.43 ± 0.10 | 0.47 ± 0.07 | 0.70 ± 0.10 | 0.78 ± 0.19 | <0.0001 |
## Relationship between the ECW/ICW ratio and SCI
The ECW/ICW ratio was positively correlated with age, male sex, history of diabetes, CTR, and log CRP and was negatively correlated with the GNRI and SCI. In the multivariate regression analysis, the ECW/ICW ratio was independently associated with age (β = 0.201; $$P \leq 0.013$$), male sex (β = 0.298; $P \leq 0.0001$), diabetes (β = 0.207; $$P \leq 0.0003$$), CTR (β = 0.134; $$P \leq 0.024$$), GNRI (β = -0.271; $P \leq 0.0001$), and SCI (β = -0.164; $$P \leq 0.042$$) (Table 2).
**Table 2**
| Variables | Univariate | Univariate.1 | Multivariate | Multivariate.1 |
| --- | --- | --- | --- | --- |
| Variables | r | P value | β | P value |
| Age | 0.487 | <0.0001 | 0.201 | 0.013 |
| Male sex | 0.216 | 0.0011 | 0.298 | <0.0001 |
| Diabetes | 0.216 | 0.0011 | 0.207 | 0.0003 |
| CTR | 0.267 | <0.0001 | 0.134 | 0.024 |
| Log CRP | 0.207 | 0.0019 | 0.075 | 0.169 |
| GNRI | -0.349 | <0.0001 | -0.271 | <0.0001 |
| SCI | -0.361 | <0.0001 | -0.164 | 0.042 |
## Association of the ECW/ICW ratio and/or the SCI with all-cause mortality
During a median follow-up of 3.5 years (2.0–6.0 years), 77 patients died because of the following reasons: CVD ($$n = 38$$; $49.4\%$), infection ($$n = 22$$; $28.6\%$), cancer ($$n = 10$$; $13.0\%$), and other causes ($$n = 7$$; $9.1\%$). The univariate Cox proportional hazard regression analysis revealed that the ECW/ICW ratio (continuous) and the SCI (continuous) were significant predictors of all-cause mortality (ECW/ICW ratio: HR 1.05, $95\%$ CI 1.04–1.06, $p \leq 0.0001$; SCI: HR 0.83, $95\%$ CI 0.76–0.90, $p \leq 0.0001$). However, to maximize the predictive value of all-cause mortality, the cut-off points were determined using ROC analysis; ECW/ICW ratio: cut-off value 0.57, AUC 0.737, sensitivity 0.687, specificity 0.741, $p \leq 0.0001$; SCI: cut-off value 20.4 mg/kg/day, AUC 0.623, sensitivity 0.496, specificity 0.715, $$p \leq 0.0006.$$ The 10-year all-cause survival rates were $26.9\%$ in the higher ECW/ICW ratio group and $61.7\%$ in the lower ECW/ICW ratio group ($P \leq 0.0001$) (Fig 1a); these rates were $26.7\%$ in the lower SCI group and $64.8\%$ in the higher SCI group ($$P \leq 0.0002$$) (Fig 1b). The 10-year all-cause survival rates were $76.6\%$ in group 1 (G1), $43.9\%$ in group 2 (G2), $39.4\%$ in group 3 (G3), and $0\%$ in group 4 (G4) ($P \leq 0.0001$). After adjusting for age and sex, hemodialysis vintage, history of CVD, hemoglobin, phosphorus, CRP, and GNRI, a higher ECW/ICW ratio (adjusted hazard ratio [aHR], 3.66; $95\%$ CI, 1.99–6.72; $P \leq 0.0001$) and lower SCI (aHR, 2.25; $95\%$ CI, 1.34–3.79; $$P \leq 0.0021$$) were independently associated with increased risks of all-cause mortality (Table 3). Moreover, compared with G1, the aHRs for G2, G3, and G4 were 4.29 ($95\%$ CI, 1.30–14.18; $$P \leq 0.017$$), 8.61 ($95\%$ CI, 2.61–28.46; $$P \leq 0.0004$$), and 12.22 ($95\%$ CI, 3.68–40.57; $P \leq 0.0001$), respectively (Table 3).
**Fig 1:** *Kaplan–Meier survival curves for all-cause mortality.The Kaplan–Meier method was used to estimate the survival rate, and the differences were analyzed using the log-rank test. All-cause survival rates for the two groups of (ratio of extracellular water to intracellular water) the ECW/ICW ratio <0.57 vs. ECW/ICW ratio ≥0.57 (a), two groups of the simplified creatinine index (SCI) <20.4 mg/kg/day vs. mCI ≥20.4 mg/kg/day (b), and four groups of the combined ECW/ICW and SCI (c). G1 (group 1), ECW/ICW ratio <0.57 and SCI ≥20.4 mg/kg/day; G2 (group 2), ECW/ICW ratio <0.57 and SCI <20.4 mg/kg/day; G3 (group 3), ECW/ICW ratio ≥0.57 and SCI ≥20.4 mg/kg/day; and G4 (group 4), ECW/ICW ratio ≥0.57 and SCI <20.4 mg/kg/day.* TABLE_PLACEHOLDER:Table 3
## Model discrimination
The addition of the SCI alone and ECW/ICW ratio alone into the baseline risk model, including age, sex, hemodialysis vintage, history of CVD, hemoglobin, phosphorus, CRP, and GNRI, did not improve the C-index for predicting all-cause mortality; however, the addition of both significantly improved the C-index from 0.831 to 0.864 ($$P \leq 0.045$$) (Table 4).
**Table 4**
| Variables | C-index | P value | NRI | P value.1 | IDI | P value.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Established risk factors* | 0.831 (0.777–0.886) | Ref. | Ref. | | Ref. | |
| + SCI | 0.838 (0.785–0.891) | 0.31 | 0.348 | 0.011 | 0.008 | 0.24 |
| + ECW/ICW ratio | 0.858 (0.808–0.907) | 0.086 | 0.893 | <0.0001 | 0.056 | 0.0006 |
| + SCI and ECW/ICW ratio | 0.864 (0.816–0.912) | 0.045 | 0.882 | <0.0001 | 0.062 | 0.0002 |
## Discussion
This study demonstrated that the ECW/ICW ratio was independently associated with the SCI and that a higher ECW/ICW ratio and lower SCI were independently associated with increased risks of all-cause mortality. Moreover, the combination of the ECW/ICW ratio and SCI significantly improved the predictability of all-cause mortality and stratified the risk of mortality. These findings suggest that the ECW/ICW ratio may be an indicator of muscle wasting and PEW and support the importance of assessing both the ECW/ICW ratio and SCI for predicting mortality in the hemodialysis population.
In this study, the ECW/ICW ratio was independently associated with diabetes, CTR, GNRI, and SCI. Nakajima et al. reported that the ECW/ICW ratio was independently associated with albuminuria levels in patients with type 2 diabetes mellitus without renal failure and suggested that the ECW/ICW ratio could be helpful to monitor fluid volume imbalance in patients with type 2 diabetes [34]. Additionally, we previously reported the independent association of the ECW/ICW ratio with CTR and GNRI, reflecting volume expansion and malnutrition, respectively [24]. GNRI, which helps to assess the longitudinal nutritional status and predict mortality, is an objective marker of PEW in patients undergoing hemodialysis and is easy to calculate [35]. Moreover, in this study, the ECW/ICW ratio was negatively and independently associated with the SCI. The SCI has been developed as a surrogate marker of muscle mass in hemodialysis patients with anuria [17]. Tsai et al. recently reported that SCI was an independent predictor of PEW [21]. Yamada et al. reported that SCI was highly associated with muscle mass volume, measured using BIA and anthropometry [36]. Furthermore, Yamamoto et al. reported that the SCI was correlated with handgrip strength and gait speed [37]. Therefore, in this study, the independent association of the ECW/ICW ratio with GNRI and SCI suggests that the ECW/ICW ratio may be an indicator of not only PEW but also sarcopenia. However, to clarify the relationship between the ECW/ICW ratio and sarcopenia, direct associations of the ECW/ICW ratio with skeletal muscle mass volume, muscle strength, and gait speed must be investigated in the future. Since the ICW may be used to estimate the skeletal muscle mass volume, methods other than BIA, such as dual-energy X-ray absorptiometry or computed tomography, may be recommended as reference methods.
Possible mechanisms that may explain the association between the ECW/ICW ratio and muscle wasting or sarcopenia have been considered. Fluid overload, which is an increase in the ECW, can lead to intestinal edema and may induce inflammation via the translocation of bowel endotoxin into the circulation [38]. This inflammatory process leads to malnutrition caused by protein catabolism and muscle wasting [39, 40]. Moreover, the fluid overload may impair the absorption of bowel nutrients, such as protein and vitamin D, secondary to bowel edema and may lead to decreased muscle mass and function [41, 42]. Furthermore, a decrease in ICW itself may directly reflect a decrease in muscle mass [28].
In this study, patients with a higher ECW/ICW ratio and those with a lower SCI were independently associated with an increased risk of all-cause mortality in patients undergoing hemodialysis. The ROC-derived cut-off values of the ECW/ICW ratio and SCI for maximally predicting all-cause mortality were 0.57 and 20.4 mg/kg/day, respectively. Kim et al. reported that the cut-off value for maximum discrimination of survival was 0.57 in hemodialysis patients. Canaud et al. demonstrated that the SCI was higher in men than in women, and a higher SCI (>20.5 mg/kg/day) not categorized by sex was independently associated with a decreased risk of mortality in the hemodialysis population. Thus, the cut-off values in the present study were consistent with those that were previously reported. However, the proportion of men in G3 (the group with a higher ECW/ICW ratio and a higher SCI) was relatively high. Therefore, we included sex as a covariate in the multivariate Cox proportional hazard regression analysis, similar to Canaud et al. ’s study. Furthermore, patients with a higher ECW/ICW ratio and a lower SCI were at the highest risk of all-cause mortality in this population. Regarding model discrimination, compared to the baseline risk model, including the GNRI, the addition of both the ECW/ICW ratio and the SCI significantly improved the C-index. Therefore, this study demonstrated that the combined evaluation of the ECW/ICW ratio and SCI enabled the stratification of the risk of all-cause death and improved mortality prediction.
There were some limitations to this study. First, this single-center retrospective study included a relatively small number of patients receiving hemodialysis. In a previous study, the sample size that was used to investigate the relationship of the ECW/ICW ratio or the ECW/ total body water (TBW) ratio with mortality in dialysis patients had a median of 152 (77–234) [minimum: 53 (peritoneal dialysis); maximum: 753 (388 hemodialysis and 365 peritoneal dialysis)] ($$n = 11$$) [43]. Moreover, the median hemodialysis vintage was less than one year. Therefore, our findings may not be applicable to patients with a longer hemodialysis duration. Second, the number of events (death) was small; therefore, full adjustments with covariables were difficult in the multivariate Cox analysis. Third, the ECW/ICW ratio and the SCI were measured only at the time of study enrollment; therefore, changes in these values during follow-up periods were not considered. Fourth, this study included only Japanese patients receiving hemodialysis; therefore, the findings of this study may not be applicable to all patients receiving hemodialysis in other countries. Fifth, residual kidney function, which may affect the SCI value, could not be evaluated because of the retrospective nature of this study. Further prospective, large-scale, multicenter studies may be required to validate our study findings.
## Conclusions
The ECW/ICW ratio was independently associated with the SCI, and both the ECW/ICW ratio and the SCI independently predicted all-cause mortality for patients receiving hemodialysis. Moreover, the combination of the ECW/ICW ratio and SCI was useful for stratifying the mortality risk and improving the accuracy of the mortality prediction. Therefore, the ECW/ICW ratio may be an indicator of muscle wasting, and the combination of the ECW/ICW ratio and SCI may help accurately predict all-cause mortality in this population.
## References
1. Shotan A, Dacca S, Shochat M, Kazatsker M, Blondheim DS, Meisel S. **Fluid overload contributing to heart failure**. *Nephrol Dial Transplant* (2005) **20** vii24-vii27. DOI: 10.1093/ndt/gfh1103
2. Wizemann V, Wabel P, Chamney P, Zaluska W, Moissl U, Rode C. **The mortality risk of overhydration in haemodialysis patients**. *Nephrol Dial Transplant* (2009) **24** 1574-1579. DOI: 10.1093/ndt/gfn707
3. Hara H, Nakamura Y, Hatano M, Iwashita T, Shimizu T, Ogawa T. **Protein energy wasting and sarcopenia in dialysis patients**. *Contrib Nephrol* (2018) **196** 243-249. DOI: 10.1159/000485729
4. Sabatino A, Cuppari L, Stenvinkel P, Lindholm B, Avesani CM. **Sarcopenia in chronic kidney disease: What have we learned so far?**. *J Nephrol* (2021) **34** 1347-1372. DOI: 10.1007/s40620-020-00840-y
5. Fouque D, Kalantar-Zadeh K, Kopple J, Cano N, Chauveau P, Cuppari L. **A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease**. *Kidney Int* (2008) **73** 391-398. DOI: 10.1038/sj.ki.5002585
6. Obi Y, Qader H, Kovesdy CP, Kalantar-Zadeh K. **Latest consensus and update on protein-energy wasting in chronic kidney disease**. *Curr Opin Clin Nutr Metab Care* (2015) **18** 254-262. DOI: 10.1097/MCO.0000000000000171
7. Yajima T, Yajima K, Takahashi H, Yasuda K. **The impact of abdominal fat levels on all-cause mortality risk in patients undergoing hemodialysis**. *Nutrients* (2018) **10** 480. DOI: 10.3390/nu10040480
8. Yajima T, Arao M, Yajima K, Takahashi H, Yasuda K. **The associations of fat tissue and muscle mass indices with all-cause mortality in patients undergoing hemodialysis**. *PLOS ONE* (2019) **14** e0211988. DOI: 10.1371/journal.pone.0211988
9. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T. **Sarcopenia: Revised European consensus on definition and diagnosis**. *Age Ageing* (2019) **48** 16-31. DOI: 10.1093/ageing/afy169
10. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K. **Asian Working Group for Sarcopenia: 2019 Consensus update on sarcopenia diagnosis and treatment**. *J Am Med Dir Assoc* (2020) **21** 300-307.e2. DOI: 10.1016/j.jamda.2019.12.012
11. Ribeiro HS, Neri SGR, Oliveira JS, Bennett PN, Viana JL, Lima RM. **Association between sarcopenia and clinical outcomes in chronic kidney disease patients: A systematic review and meta-analysis**. *Clin Nutr* (2022) **41** 1131-1140. DOI: 10.1016/j.clnu.2022.03.025
12. Yajima T, Arao M, Yajima K, Takahashi H. **Usefulness of computed tomography-measured psoas muscle thickness per height for predicting mortality in patients undergoing hemodialysis**. *Sci Rep* (2021) **11** 19070. DOI: 10.1038/s41598-021-98613-5
13. Yajima T.. **Skeletal muscle density measured by computed tomography as a predictor of mortality in patients receiving hemodialysis**. *J Nephrol* (2022) **35** 1535-1537. DOI: 10.1007/s40620-022-01303-2
14. Yajima T, Arao M, Yajima K. **Psoas muscle index and psoas muscle density as predictors of mortality in patients undergoing hemodialysis**. *Sci Rep* (2022) **12** 10496. DOI: 10.1038/s41598-022-14927-y
15. Yajima T.. **Skeletal muscle gauge and all-cause mortality in hemodialysis patients**. *J Nephrol* (2022). DOI: 10.1007/s40620-022-01480-0
16. Bataille S, Serveaux M, Carreno E, Pedinielli N, Darmon P, Robert A. **The diagnosis of sarcopenia is mainly driven by muscle mass in hemodialysis patients**. *Clin Nutr* (2017) **36** 1654-1660. DOI: 10.1016/j.clnu.2016.10.016
17. Canaud B, Granger Vallée A, Molinari N, Chenine L, Leray-Moragues H, Rodriguez A. **Creatinine index as a surrogate of lean body mass derived from urea Kt/V, pre-dialysis serum levels and anthropometric characteristics of haemodialysis patients**. *PLOS ONE* (2014) **9** e93286. DOI: 10.1371/journal.pone.0093286
18. Canaud B, Ye X, Usvyat L, Kooman J, van der Sande F, Raimann J. **Clinical and predictive value of simplified creatinine index used as muscle mass surrogate in end-stage kidney disease haemodialysis patients-results from the international MONitoring Dialysis Outcome initiative**. *Nephrol Dial Transplant* (2020) **35** 2161-2171. DOI: 10.1093/ndt/gfaa098
19. Arase H, Yamada S, Hiyamuta H, Taniguchi M, Tokumoto M, Tsuruya K. **Modified creatinine index and risk for long-term infection-related mortality in hemodialysis patients: Ten-year outcomes of the Q-Cohort Study**. *Sci Rep* (2020) **10** 1241. DOI: 10.1038/s41598-020-58181-6
20. Arase H, Yamada S, Yotsueda R, Taniguchi M, Yoshida H, Tokumoto M. **Modified creatinine index and risk for cardiovascular events and all-cause mortality in patients undergoing hemodialysis: The Q-Cohort study**. *Atherosclerosis* (2018) **275** 115-123. DOI: 10.1016/j.atherosclerosis.2018.06.001
21. Tsai MT, Tseng WC, Ou SM, Lee KH, Yang CY, Tarng DC. **Comparison of simplified creatinine index and systemic inflammatory markers for nutritional evaluation of hemodialysis patients**. *Nutrients* (2021) **13** 1870. DOI: 10.3390/nu13061870
22. Espinosa Cuevas MA, Navarrete Rodriguez G, Villeda Martinez ME, Atilano Carsi X, Miranda Alatriste P, Tostado Gutiérrez T. **Body fluid volume and nutritional status in hemodialysis: Vector bioelectric impedance analysis**. *Clin Nephrol* (2010) **73** 300-308. PMID: 20353738
23. Chertow GM, Lowrie EG, Wilmore DW, Gonzalez J, Lew NL, Ling J. **Nutritional assessment with bioelectrical impedance analysis in maintenance hemodialysis patients**. *J Am Soc Nephrol* (1995) **6** 75-81. DOI: 10.1681/ASN.V6175
24. Yajima T, Yajima K, Takahashi H, Yasuda K. **Combined predictive value of extracellular fluid/intracellular fluid ratio and the geriatric nutritional risk index for mortality in patients undergoing hemodialysis**. *Nutrients* (2019) **11** 2659. DOI: 10.3390/nu11112659
25. Kim EJ, Choi MJ, Lee JH, Oh JE, Seo JW, Lee YK. **Extracellular fluid/intracellular fluid volume ratio as a novel risk indicator for all-cause mortality and cardiovascular disease in hemodialysis patients**. *PLOS ONE* (2017) **12** e0170272. DOI: 10.1371/journal.pone.0170272
26. Yajima T, Yajima K, Takahashi H. **Annual change in the extracellular fluid/intracellular fluid ratio and mortality in patients undergoing maintenance hemodialysis**. *Sci Rep* (2022) **12** 242. DOI: 10.1038/s41598-021-04366-6
27. Park KS, Lee GY, Seo YM, Seo SH, Yoo JI. **The relationship between extracellular water-to-body water ratio and sarcopenia according to the newly revised Asian Working Group for Sarcopenia: 2019 Consensus Update**. *Aging Clin Exp Res* (2021) **33** 2471-2477. DOI: 10.1007/s40520-020-01766-y
28. Gracia-Iguacel C, González-Parra E, Mahillo I, Ortiz A. **Low intracellular water, overhydration, and mortality in hemodialysis patients**. *J Clin Med* (2020) **9** 3616. DOI: 10.3390/jcm9113616
29. Yamada K, Furuya R, Takita T, Maruyama Y, Yamaguchi Y, Ohkawa S. **Simplified nutritional screening tools for patients on maintenance hemodialysis**. *Am J Clin Nutr* (2008) **87** 106-113. DOI: 10.1093/ajcn/87.1.106
30. Kinugasa Y, Kato M, Sugihara S, Hirai M, Yamada K, Yanagihara K. **Geriatric nutritional risk index predicts functional dependency and mortality in patients with heart failure with preserved ejection fraction**. *Circ J* (2013) **77** 705-711. DOI: 10.1253/circj.cj-12-1091
31. Yajima T, Yajima K, Arao M. **Combined evaluation of geriatric nutritional risk index and modified creatinine index for predicting mortality in patients on hemodialysis**. *Nutrients* (2022) **14** 752. DOI: 10.3390/nu14040752
32. Delong ER, Delong DM, Clarke-Pearson DL. **Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach**. *Biometrics* (1988) **44** 837-845. DOI: 10.2307/2531595
33. Pencina MJ, D’Agostino RB Sr, D’Agostino RB, Vasan RS. **Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond**. *Stat Med* (2008) **27** 157-172. DOI: 10.1002/sim.2929
34. Nakajima H, Hashimoto Y, Kaji A, Sakai R, Takahashi F, Yoshimura Y. **Impact of extracellular-to-intracellular fluid volume ratio on albuminuria in patients with type 2 diabetes: A cross-sectional and longitudinal cohort study**. *J Diabetes Investig* (2021) **12** 1202-1211. DOI: 10.1111/jdi.13459
35. Yajima T, Yajima K, Takahashi H. **Impact of annual change in geriatric nutritional risk index on mortality in patients undergoing hemodialysis**. *Nutrients* (2020) **12** 3333. DOI: 10.3390/nu12113333
36. Yamada S, Taniguchi M, Tokumoto M, Yoshitomi R, Yoshida H, Tatsumoto N. **Modified creatinine index and the risk of bone fracture in patients undergoing hemodialysis: The Q-Cohort Study**. *Am J Kidney Dis* (2017) **70** 270-280. DOI: 10.1053/j.ajkd.2017.01.052
37. Yamamoto S, Matsuzawa R, Hoshi K, Suzuki Y, Harada M, Watanabe T. **Modified creatinine index and clinical outcomes of hemodialysis patients: An indicator of sarcopenia?**. *J Ren Nutr* (2021) **31** 370-379. DOI: 10.1053/j.jrn.2020.08.006
38. Niebauer J, Volk HD, Kemp M, Dominguez M, Schumann RR, Rauchhaus M. **Endotoxin and immune activation in chronic heart failure: A prospective cohort study**. *Lancet* (1999) **353** 1838-1842. DOI: 10.1016/S0140-6736(98)09286-1
39. Pecoits-Filho R, Lindholm B, Stenvinkel P. **The malnutrition, inflammation, and atherosclerosis (MIA) syndrome—The heart of the matter**. *Nephrol Dial Transplant* (2002) **17** 28-31. DOI: 10.1093/ndt/17.suppl_11.28
40. Choi MJ, Seo JW, Yoon JW, Lee SK, Kim SJ, Lee YK. **The malnutrition-inflammation-depression-arteriosclerosis complex is associated with an increased risk of cardiovascular disease and all-cause death in chronic hemodialysis patients**. *Nephron Clin Pract* (2012) **122** 44-52. DOI: 10.1159/000348509
41. Mithal A, Bonjour JP, Boonen S, Burckhardt P, Degens H, El Hajj Fuleihan G. **Impact of nutrition on muscle mass, strength, and performance in older adults**. *Osteoporos Int* (2013) **24** 1555-1566. DOI: 10.1007/s00198-012-2236-y
42. Verlaan S, Maier AB, Bauer JM, Bautmans I, Brandt K, Donini LM. **Sufficient levels of 25-hydroxyvitamin D and protein intake required to increase muscle mass in sarcopenic older adults—The PROVIDE study**. *Clin Nutr* (2018) **37** 551-557. DOI: 10.1016/j.clnu.2017.01.005
43. Wang Y, Gu Z. **Effect of bioimpedance-defined overhydration parameters on mortality and cardiovascular events in patients undergoing dialysis: a systematic review and meta-analysis**. *J Int Med Res* (2021) **49** 3000605211031063. DOI: 10.1177/03000605211031063
|
---
title: Yohimbine Alleviates Oxidative Stress and Suppresses Aerobic Cysteine Metabolism
Elevated in the Rat Liver of High-Fat Diet-Fed Rats
authors:
- Małgorzata Iciek
- Magdalena Górny
- Magdalena Kotańska
- Anna Bilska-Wilkosz
- Marta Kaczor-Kamińska
- Jacek Zagajewski
journal: Molecules
year: 2023
pmcid: PMC10004569
doi: 10.3390/molecules28052025
license: CC BY 4.0
---
# Yohimbine Alleviates Oxidative Stress and Suppresses Aerobic Cysteine Metabolism Elevated in the Rat Liver of High-Fat Diet-Fed Rats
## Abstract
Yohimbine is a small indole alkaloid derived from the bark of the yohimbe tree with documented biological activity, including anti-inflammatory, erectile dysfunction relieving, and fat-burning properties. Hydrogen sulfide (H2S) and sulfane sulfur-containing compounds are regarded as important molecules in redox regulation and are involved in many physiological processes. Recently, their role in the pathophysiology of obesity and obesity-induced liver injury was also reported. The aim of the present study was to verify whether the mechanism of biological activity of yohimbine is related to reactive sulfur species formed during cysteine catabolism. We tested the effect of yohimbine at doses of 2 and 5 mg/kg/day administered for 30 days on aerobic and anaerobic catabolism of cysteine and oxidative processes in the liver of high-fat diet (HFD)-induced obese rats. Our study revealed that HFD resulted in a decrease in cysteine and sulfane sulfur levels in the liver, while sulfates were elevated. In the liver of obese rats, rhodanese expression was diminished while lipid peroxidation increased. Yohimbine did not influence sulfane sulfur and thiol levels in the liver of obese rats, however, this alkaloid at a dose of 5 mg decreased sulfates to the control level and induced expression of rhodanese. Moreover, it diminished hepatic lipid peroxidation. It can be concluded that HFD attenuates anaerobic and enhances aerobic cysteine catabolism and induces lipid peroxidation in the rat liver. Yohimbine at a dose of 5 mg/kg can alleviate oxidative stress and reduce elevated concentrations of sulfate probably by the induction of TST expression.
## 1. Introduction
Yohimbine is a small indole alkaloid derived from the bark of the yohimbe tree (Pausinystalia yohimbe) native to Central and West Africa, Cameroon, and Congo. It is an α2-adrenoceptor antagonist, and it has been documented to possess pharmacological activity. In Africa, it is traditionally used for fever, cough, heart disease, and atherosclerosis. It has been documented that this alkaloid is able to lower blood pressure and possesses anti-inflammatory and immunosuppressive properties [1,2]. The most well-known and widespread use of yohimbine is related to relieving erectile dysfunction and improving sexual stimulation [3]. It is probably connected with the inhibition of the α2 adrenergic receptors in the corpus cavernosum. Studies on animals revealed that yohimbine had a remarkably positive effect on sexual performance [4]. Due to its ability to selectively block α2-adrenoceptors in the brain, yohimbine can increase the release of norepinephrine and dopamine and improve feelings, thus, it is studied as a potential antidepressant [5]. Finally, yohimbine has been shown to induce fat loss and due to its presumed lipolytic properties, it is used for fast weight loss and bodybuilding [6,7].
Obesity induced by improper nutrition, including a high-fat diet (HFD) is a widespread problem in developed countries. A common complication of obesity is non-alcoholic fatty liver disease characterized by chronic inflammation, oxidative stress, and steatosis. Many studies indicate that hydrogen sulfide (H2S), regarded as an important gaseous signaling molecule, plays an important role in the pathophysiology of obesity and obesity-induced liver injury [8]. H2S is synthesized endogenously mainly from L-cysteine. Enzymes involved in this process include cystathionine synthase (CBS), cystathionine γ-lyase (CSE), and 3-mercaptopyruvate sulfur transferase (MST) (Figure 1). CBS and CSE are cytosolic pyridoxal-dependent enzymes, while MST is localized mainly in mitochondria. In the nervous system, CBS and MST play a dominant role in the synthesis of H2S, while in the liver CSE and MST are the main H2S-producing enzymes [8,9,10]. H2S coexists in balance with a pool of reactive sulfane sulfur. This term refers to compounds containing a sulfur atom covalently bound to another sulfur atom. Sulfane sulfur possesses a high reactivity and is easily transferred to appropriate acceptors, such as SO32− or RS−, forming thiosulfate (S2O32−) and persulfides RSS−, respectively. Sulfane sulfur-containing compounds include mainly persulfides and polysulfides as well as elemental sulfur and polythionates [11,12]. Compounds with sulfane sulfur can easily release H2S under reducing conditions. There is a close relationship between H2S and sulfane sulfur compounds, namely both of these reactive sulfur species are responsible for biological effects and both CSE and MST are involved in their formation. Due to the toxicity of high concentrations of H2S [13], it is important to maintain a balance between endogenous H2S synthesis, its storage in the form of sulfane sulfur, and H2S catabolism, thanks to which its concentration in cells can be kept in a low physiological range. Catabolism of H2S takes place in the mitochondria with the participation of sulfide quinone oxidoreductase (SQR), persulfide dioxygenase (ETHE1), and rhodanese (TST). The main end products of H2S catabolism include thiosulfate and inorganic sulfate anions. Sulfate and taurine are also regarded as the main products of aerobic cysteine catabolism (Figure 1). During the mitochondrial catabolism of H2S, sulfane sulfur-containing persulfides of SQRSSH and GSSH are formed which underlines a strict relationship between H2S and persulfides bearing sulfane sulfur.
The impaired endogenous H2S synthesis has been reported to be associated with obesity. It has been demonstrated that exogenous H2S or its donors can alleviate liver injury induced by HFD [14,15,16]. On the other hand, studies by Yang et al. demonstrated that exogenously applied H2S promoted fat accumulation in fruit flies while HFD-induced fat build-up was lost in CSE-deficient mice [17]. Moreover, a recent study by Comas et al. revealed that serum H2S concentration in morbid obesity patients was increased when compared to lean controls [18]. All these facts confirmed that H2S was involved in the pathogenesis of obesity and obesity-induced liver failure, however, the studies analyzed mainly the H2S concentration and expression of H2S-synthesizing enzymes. Sulfane sulfur compounds and sulfates have not been studied in this context.
As the biological activity of yohimbine, especially the regulation of metabolism related to obesity and its role in the treatment of erectile dysfunction, is similar to the well-documented properties of H2S, it seems that the mechanism of yohimbine action can be at least partially associated with the regulatory properties of reactive sulfur species, including H2S and sulfane sulfur. So far, no research has been conducted to clarify this relationship. Therefore, the aim of the present study was to investigate the effect of yohimbine on the level of H2S and sulfane sulfur, and on the activity and expression of enzymes involved in their synthesis in the liver of HFD-induced obese rats. Moreover, the levels of sulfate and thiols (cysteine and glutathione), and the concentration of malondialdehyde (MDA), as a measure of lipid peroxidation, were assayed. We hope that our study will shed new light on the biological potential of yohimbine in the context of HFD-induced liver injury, especially in relation to anaerobic and aerobic cysteine metabolism.
## 2.1. The Effect of HFD and Yohimbine Treatment on the Changes in Body Weight
The mean initial body weight of all 24 young rats was 153.6 ± 3.8 g. Six rats were fed with standard food, while eighteen rats were fed with HFD. After 10 weeks, the weight gain in the control group was 206.7 ± 3.5 g, and in HFD-fed groups, the weight gain was higher (256.8 ± 4.8 g) and this increase was statistically significant (Table 1). During 30 days of the experiment, the weight gain in HFD-fed rats (O) was elevated when compared with control animals (C). Treatment with yohimbine at a dose of 5 mg/kg/day for 30 days caused significantly lowered weight gain when compared to the O group. Yohimbine at a dose of 2 mg/kg/day also diminished the weight gain but the effect was weaker (Table 1).
## 2.2. The Effect of HFD and Yohimbine Treatment on the Level of Free Sulfide, Sulfane Sulfur, and Bound Sulfane Sulfur in the Rat Liver
The level of free sulfide (H2S) did not differ significantly between the studied groups, however, a slight but statistically non-significant increase in H2S level could be seen in the liver of obese rats without yohimbine treatment when compared to healthy animals (Figure 2A).The level of the total pool of sulfane sulfur was significantly decreased in the liver of HFD-induced obese rats when compared to the liver of healthy animals. Treatment with yohimbine at a dose of 5 and 2 mg/kg/day had no effect on the level of sulfane sulfur which remained decreased when compared to control livers (Figure 2B). The level of bound sulfane sulfur, which is a pool of the total sulfane sulfur and includes mainly persulfides and polysulfides, was decreased in the liver of untreated obese animals and obese animals after yohimbine administration. However, the performed statistical analysis revealed a significant decrease in bound sulfane sulfur only in the group of obese animals treated with yohimbine at a dose of 2 mg/kg/day (Figure 2C).
## 2.3. The Effect of HFD and Yohimbine Treatment on the Sulfate Concentration in the Rat Liver
The level of sulfates in the liver of obese rats was significantly increased as compared to the level in the liver of control animals (Figure 3A). Treatment with yohimbine at a dose of 5 mg/kg/day significantly decreased the sulfate level to the control value. Yohimbine at a dose of 2 mg/kg/day also reduced the level of sulfates, however, this effect was not statistically significant (Figure 3A).
## 2.4. The Effect of HFD and Yohimbine Treatment on the Level Malonyl Dialdehyde (MDA) as a Marker of Oxidative Stress
The level of MDA was significantly elevated in the liver of rats fed with HFD when compared to the liver of healthy animals. The treatment with yohimbine at a dose of 5 mg/kg/day resulted in a decrease in MDA level in comparison to obese animals without yohimbine treatment, however, the level of MDA was still higher than MDA level in the liver of healthy rats. In contrast, administration of yohimbine at a lower dose (2 mg/kg/day) had no effect on the level of MDA in the liver of obese rats (Figure 3B).
## 2.5. The Effect of HFD and Yohimbine Treatment on the Activity of Enzymes Involved in Reactive Sulfur Species Formation and Transport (CSE, MST, and TST) in the Rat Liver
The activity of CSE in the liver of obese rats was significantly reduced compared to the liver of control animals. Treatment with yohimbine at a dose of 5 mg/kg/day did not affect the decreased CSE activity but, interestingly, yohimbine administered at a dose of 2 mg/kg/day increased this activity to the control level (Figure 4A). On the other hand, the activity of MST, slightly but non-significant elevated in the liver of obese rats, was reduced by treatment with yohimbine at a dose of 5 mg/kg/day, while the smaller dose of yohimbine did not affect MST activity (Figure 4B). The hepatic activity of TST was not significantly different in any of the studied obese animals, but its slight non-significant increase could be seen in the group after yohimbine administration at a dose of 5 mg/kg/day (Figure 4C).
## 2.6. The Effect of HFD and Yohimbine Treatment on the Expression of Enzymes Involved in the Formation of Reactive Sulfur Species (CSE, MST, CBS, and TST) in the Rat Liver
Gene expression of enzymes involved in reactive sulfur metabolism was another objective of the presented study. Generally, the level of mRNA of the enzymes involved in the synthesis of H2S and sulfane sulfur compounds (CSE, MST, and CBS) was unaffected in the liver of HFD-fed animals when compared to healthy, lean rats, and yohimbine did not affect the expression of these enzymes (Figure 5). On the other hand, our study revealed that expression of TST, the enzyme transporting reactive sulfur and forming thiosulfate was diminished in the liver of obese rats, while treatment with yohimbine at both doses (5 mg and 2 mg) increased TST expression (Figure 5).
## 2.7. The Effect of HFD and Yohimbine Treatment on the Level of the Main Low Molecular Weight Thiols (GSH, CSH)
The obtained results revealed that HFD-induced obesity caused a significant decrease in the concentration of both forms of glutathione: reduced (GSH) and total (tGSH) (Figure 6A). Treatment of obese rats with yohimbine, especially with a dose of 5 mg resulted in a slight enhancement in GSH and tGSH concentration when compared to the liver of untreated obese rats, but this effect was statistically non-significant. Based on the obtained glutathione concentrations, the GSH/GSSG ratio was calculated for each group of rats and presented in Figure 6B. The statistical analysis did not reveal significant differences between groups, however, a tendency towards a decreased GSH/GSSG ratio in the liver of obese animals was visible and yohimbine did not change it.
The level of reduced cysteine (CSH) was significantly lowered in the liver of obese rats and yohimbine regardless of the used dose did not affect this situation (Figure 7A). In the case of total cysteine (tCSH), its level was also decreased in obese rats’ livers and in the liver of obese rats after yohimbine treatment. The calculated CSH/CSSC ratio was significantly decreased in the liver of obese animals (Figure 7B). In the liver of obese rats treated with yohimbine at a dose of 5 mg, the CSH/CSSC ratio was slightly higher than in the untreated obese group, however, this effect was not significant. It can be concluded that obesity results in a significant drop in the level of glutathione and cysteine, especially their biologically active reduced forms and, in this way, it disrupts the redox status. Yohimbine in the doses used in this study was unable to alleviate these disturbances.
## 2.8. The Effect of HFD and Yohimbine Treatment on the Activity of Glutathione S-Transferase
Obesity did not affect the activity of GST in the rat liver when compared to control animals. Administration of yohimbine also did not influence GST activity, however, in the group of rats treated with a higher dose of yohimbine (5 mg) a slight but statistically non-significant elevation of GST activity can be observed (Figure 8).
## 3. Discussion
Our study shows that HFD-induced obesity results in many disturbances in the level of low molecular weight thiols and reactive sulfur species in the rat liver. The study showed that treatment of obese animals with yohimbine for 30 days led to a reduction in body weight (Table 1). Moreover, a previous study revealed that yohimbine administrated at a dose of 5 mg caused a reduction of total food intake and intraperitoneal adipose tissue accumulation [19]. In our study, the effectiveness of yohimbine in alleviating disturbances in cysteine metabolism was not impressive, however, the obtained results demonstrated that it restored sulfate concentration and could affect the expression of rhodanese (TST). Moreover, yohimbine at a dose of 5 mg effectively alleviated oxidative stress by lowering the level of MDA in the liver of obese rats.
The link between hepatic disturbances in obese individuals and H2S production has been the subject of some previous studies that provided conflicting results. Liu et al. reported the downregulation of CSE in the liver of HFD-induced obese mice, while the expression of CBS was unchanged [20]. In turn, Peh et al. reported diminished expression of CSE and MST in the liver of mice fed with a high-fat diet, while the expression of CBS was elevated compared with control mice [21]. On the other hand, Hwang et al. investigating the effect of a 5-week high-fat diet (HFD) on hepatic CBS and CSE expression and H2S level revealed that the CBS and CSE mRNA and protein levels and the level of H2S in the liver of HFD-fed mice were significantly elevated compared to control mice [22]. Our results did not reveal changes in the mRNA levels of enzymes responsible for H2S synthesis, i.e., CSE, MST, and CBS in the liver of HFD-induced obese rats (Figure 5). It seems that these discrepancies between the obtained results can be connected primarily with differences in the duration of HFD feeding (5–24 weeks) and the feed composition (various fat percentages from $16\%$ to $60\%$ and additional diet components, i.e., cholesterol or cholic acid). The use of different species, mice or rats, might also contribute to the observed differences. In our study, the activity of two main enzymes participating in H2S and sulfane sulfur synthesis were also assayed. It should be underlined here that the expression of enzymes does not have to match the activity of enzymes because many proteins can be modified after translation, which affects their activity. Our study revealed that the hepatic activity of CSE was diminished in the liver of obese animals (Figure 4A) despite no change in its expression. Similar results were obtained by Bravo et al. who reported significantly reduced hepatic activity of CSE in the liver of rats fed with HFD [23]. Our results revealed also that the treatment with yohimbine at a dose of 5 mg did not affect CSE activity (Figure 4A). Surprisingly, the treatment with yohimbine at a dose of 2 mg resulted in an increase in CSE activity compared to the liver of obese rats without the yohimbine treatment. In relation to hepatic MST activity, we did not find out significant changes in the liver of HFD-fed rats compared to control animals. The treatment of the obese rats with yohimbine at a dose of 5 mg caused a slight decrease in MST activity (Figure 4B).
Our study did not reveal changes in the level of free H2S in the liver of obese rats compared to control animals and yohimbine at both used doses did not affect hepatic free H2S levels (Figure 2A). On the other hand, our study clearly showed for the first time a significant decrease in sulfane sulfur level in the liver of rats fed with HFD (Figure 2B). None of the used yohimbine doses affected the sulfane sulfur level. Like the total sulfane sulfur, the level of bound sulfane sulfur consisting mainly of persulfides was also diminished in the liver of HFD-fed rats without and after the treatment with yohimbine, however, in this case, the changes were not significant. In light of these results, it can be concluded that the biological activity of yohimbine causing the loss of fat mass is not related to the production of reactive sulfur species including H2S and sulfane sulfur in the rat liver.
The product of lipid peroxidation, malondialdehyde (MDA) is regarded as a biomarker of peroxidative tissue damage. Lipid peroxidation is initiated mainly by reactive oxygen species (ROS) and involves the degradation of polyunsaturated fatty acids (PUFA). ROS detach the hydrogen atom from a PUFA chain transforming fatty acid to free radicals which then generate lipid peroxides. MDA is the well-known end product of lipid peroxides β-oxidation. Our study revealed that the level of MDA was significantly elevated in the liver of HFD-fed rats compared to animals fed a standard diet which indicates lipid peroxidation and suggests that HFD induces oxidative stress in hepatocytes. Similar results were described by other authors previously [16,24,25]. Some of them also reported the decreased activity of SOD in the liver of animals fed with HFD [16,25]. The increased MDA level and diminished activity of SOD were also detected in the serum or plasma of HFD-fed animals [25,26,27]. Our study also revealed a decrease in the hepatic concentration of reduced and total glutathione in HFD-fed rats (Figure 6A). The decrease in GSH level in the liver of animals fed with HFD was also reported by some other researchers [16,28,29]. GSH is the main cellular low molecular weight thiol that can scavenge ROS by oxidizing to glutathione disulfide (GSSG) and in this way, it plays an important antioxidant role. The decrease in GSH level confirms that HFD induces oxidative stress in the liver [16]. However, our study also revealed a diminished concentration of total glutathione (GSH and GSSG) suggesting that in the liver of HFD-fed rats, the synthesis of GSH is inhibited. Similarly to glutathione, both forms of cysteine, reduced and total were diminished in the liver obtained from HFD-fed rats (Figure 7A). The reduced concentration of cysteine, as well as of homocysteine was also reported previously in the liver of mice fed with HFD [30]. As cysteine is the rate-limiting amino acid in the synthesis of GSH, its deficit contributes to a reduction of GSH concentration. On the other hand, cysteine is a substrate for the synthesis of sulfane sulfur; therefore, the decreased cysteine concentration is reflected by a diminished sulfane sulfur level. Cysteine is synthesized from exogenous methionine with the participation of CSE, the activity of which is decreased in the liver of HFD-fed rats, as revealed in our study. The thiol/disulfide ratio is regarded as an indicator of the redox state in the cell. The ratio calculated for glutathione and cysteine showed a decrease in GSH/GSSG and CSH/CSSC ratio in the liver derived from HFD-fed animals, but in the case of the GSH/GSSG ratio these changes were not significant (Figure 6B). These results indicate an impairment of the redox status in the hepatocytes after feeding with HFD which confirms oxidative stress. The decrease in the GSH/GSSG ratio in the liver of HFD-fed animals was also reported by Luo et al. [ 24] however, other authors did not confirm it. Hwang et al. reported that in the liver of HFD-fed mice, lipid peroxides were elevated while the level of total glutathione remained unchanged [22]. In turn, a study by Moreno-Fernández et al. revealed an increase in hepatic GSH level in the liver of HFD-fed rats despite an increase in MDA level [26], while other authors reported diminished GSH concentration in the liver derived from HFD-fed animals like in our study [16,29].
Our results did not show HFD’s impact on GST activity despite the diminished GSH concentration (Figure 8). Similar results were obtained recently by Santativongchai et al. in the liver of rats fed with HFD for 20 weeks [31]. On the other hand, a previous study by Akbay et al. suggested a decreased activity of GST in the liver of rats after 4 weeks of HFD [32]. It seems that relatively short feeding with HFD lasting 4 weeks results in a decrease in GST activity due to the diminished level of GSH being its substrate. Further, induction of GST expression can be evoked as a compensation mechanism, which seems to be confirmed by a previous study reporting that one of the GST isoenzymes, glutathione S-transferase M2 (GSTM2) was highly upregulated in the liver of mice fed with HFD [33]. As a result of the decreased availability of the substrate and upregulated expression, no difference in the activity of total GST was observed between the liver of HFD-fed rats and control rats. According to our research, the treatment with yohimbine did not affect GST activity (Figure 8).
All our above-described results confirmed that HFD led to oxidative stress in the liver. Our study showed that yohimbine at a dose of 5 mg was able to decrease hepatic MDA level, however, it was still increased when compared to control animals (Figure 3B). The antioxidant potential of yohimbine was poorly studied. Most studies reported its use as an α2-adrenoreceptor antagonist to study the therapeutic potential and mechanism of action of some other pharmaceutics, such as dexmedetomidine which is a selective agonist of α2-receptors [34,35]. In this aspect, it has been reported that the blockade of α2-adrenoreceptor by yohimbine aggravated oxidative stress induced by lipopolysaccharide (LPS) in the liver [30] as well as in the nervous system [34,35], while dexmedetomidine attenuated LPS-induced deleterious effects. On the other hand, Shen et al. reported that yohimbine significantly reduced LPS-induced elevations of MDA in the rat brain, suggesting its antioxidant properties [36].
Our results showed that HFD induced an elevation in hepatic sulfate level suggesting an intensification of aerobic cysteine metabolism. It was confirmed by a previous study that reported elevated levels of taurine, which is also the end product of aerobic cysteine transformation [29] (Figure 1). Interestingly, our study revealed that yohimbine at a dose of 5 mg effectively lowered the level of sulfates to the control value (Figure 3A). It has been demonstrated previously in a liposomal and skeletal muscle model that sulfite radicals are able to initiate lipid oxidation [37]. Thus, it is likely that the increase in lipid peroxidation in the liver of HFD-fed rats is at least partially evoked by elevated production of sulfite radicals. This is all the more likely because the treatment of animals with yohimbine at a dose of 5 mg diminished both lipid peroxidation and sulfate production (Figure 3A,B). Moreover, as revealed by our study, the expression of TST mRNA was decreased in the liver of rats fed with HFD, while yohimbine induced this expression (Figure 5). It may be recalled here that TST is the enzyme that can create thiosulfate utilizing sulfite ions (Figure 1). The activity of TST was also slightly increased in the liver of rats treated with yohimbine at a dose of 5 mg when compared to HFD-fed animals without yohimbine treatment, but it was not significant, probably due to diminished concentration of sulfane sulfur.
Moreover, it has been reported previously that the activity of TST can be inhibited by sulfates, H2O2, and oxidative stress. On the other hand, TST is activated by GSH and CSH [38,39]. In our study on the liver of obese rats, oxidative stress decreased GSH and CSH, and elevated concentrations of sulfates were detected, and these factors can explain the lack of increase in TST activity in the O +Y5 group despite the observed increase in TST expression. It seems that yohimbine is able to reduce lipid peroxidation and aerobic cysteine metabolism by inducing the expression of TST and raising thiosulfate production but further studies are necessary to confirm this assumption.
Summing up, our results showed that in the liver of rats fed with HFD, cysteine level and its anaerobic catabolism leading to sulfane sulfur compounds were decreased, while sulfate as a product of aerobic cysteine catabolism was elevated. It can be concluded that HFD shifts the balance between anaerobic and anaerobic catabolism of cysteine and induces lipid peroxidation in the rat liver. Our study revealed for the first time that yohimbine administered to HFD-induced obese rats at a dose of 5 mg/kg for 30 days was able to alleviate oxidative stress and diminish the elevated concentration of sulfate, probably by induction of TST expression. This is summarized in Figure 9.
## 4.1. Animals
The experiments were carried out on 8-week-old male Wistar rats (initial body weight 153.6 ± 3.8. The animals were housed in pairs in plastic cages at a constant temperature, exposed to natural light–dark cycle ($\frac{12}{12}$), and had free access to water and food. All efforts were made to minimize the number and suffering of animals. Experiments were conducted according to the guidelines of the Animal Use and Care Committee of Jagiellonian University and were approved by Local Ethics Committee. ( Permission No $\frac{54}{2012}$). All animals were divided randomly into four groups, each of which consisted of six rats. Obesity was induced in three groups; one group was the healthy control group.
## 4.2. Obesity Induction
Rats were fed a fatty diet consisting of $40\%$ fat blend (Labofeed B with $40\%$ lard, Morawski, Manufacturer Feed, Poland) for 10 weeks and water was available ad libitum. Control rats were fed a standard diet (Labofeed B) for the same period of time. After 10 weeks, the final body weight was statistically significantly higher when compared to control animals (Table 1). Diet-induced obese rats were randomly divided into three groups (mean body weight in each group was the same) and were treated intraperitoneally (i.p.) with yohimbine at a dose of 2 mg/kg/day (Group O + Y2), yohimbine at a dose of 5 mg/kg/day (Group O + Y5) or with vehicle (distilled water in the same volume as yohimbine, i.e., 0.3 mL; Group O), once daily for 30 days. On the 31st day, animals in all groups were anesthetized with thiopental (70 mg/kg) by intraperitoneal injection and the livers were isolated, frozen in liquid nitrogen, and stored at −80 °C for further biochemical assays.
## 4.3. Chemicals
Yohimbine was obtained from Tocris (Bristol, UK), and thiopental from Sandoz International, (France). Potassium cyanide (KCN), potassium thiocyanate (KSCN), p-phenylenediamine, zinc acetate, thionine, dithiothreitol (DTT), pyridoxal phosphate monohydrate (PLP), L-homoserine, sodium thiosulfate (Na2S2O3), 1-chloro-2,4-dinitrobenzene (CDNB), gelatin, glycine-glycine (Gly-Gly), glutathione reduced form (GSH), lactic dehydrogenase (LDH), L-glutamyl-3-carboxy-4-nitroanilide, 3-mercaptopyruvate (3-MP), 3-methyl-2-benzothiazolinone hydrazine hydrochloride monohydrate (MBTH), N-ethylmaleimide (NEM), β-nicotinamide adenine dinucleotide reduced form (NADH), thiobarbituric acid (TBA) and trichloroacetic acid (TCA) were provided by Sigma-Aldrich Chemical Company (St. Louis, MO, USA). Acetic acid, ammonia (NH3), barium chloride (BaCl2), copper sulfate (CuSO4), Folin–Ciocalteu phenol reagent, formaldehyde, ferric chloride (FeCl3), hydrochloric acid (HCl), iron nitrate (Fe(NO3)3), magnesium chloride (MgCl2), nitric acid (HNO3), potassium dihydrogen phosphate (KH2PO4), perchloric acid (HClO4), sodium carbonate (Na2CO3), sodium hydroxide (NaOH), sodium sulfite (Na2SO3) were obtained from the Polish Chemical Reagent Company (P.O.Ch, Gliwice, Poland). HPLC-grade acetonitrile (ACN) and perchloric acid (PCA) were from J.T. Baker (Deventer, The Netherlands). 2-Chloro-1-methylquinolinium tetrafluoroborate (CMQT) was prepared according to the procedure described by Bald and Głowacki [40] in the Department of Environmental Chemistry, University of Łódź (Łódź, Poland).
## 4.4. Preparation of Tissue Homogenates
All experimental procedures involved in the preparation of tissue homogenates were carried out at 4 °C. The frozen liver tissue samples were weighed and homogenized using an IKA-ULTRATURRAX T8 homogenizer for biochemical assays at a ratio of 1 g of tissue to 4 mL of 0.1 M phosphate buffer, pH 7.4, while for HPLC analysis at a ratio of 1 g of tissue to 9 mL of 0.2 M phosphate buffer pH 8.0. The obtained homogenate was centrifuged at 2000× g for 5 min and the supernatant was used for biochemical assays.
## 4.5. Biochemical Assays
The total level of sulfane sulfur was determined by the method of Wood [41], while the level of free sulfide (H2S) was determined using a modified method of Shen et al. [ 42] with fluorometric detection. The level of sulfate was estimated with the sulfate assay kit (Sigma) according to the manufacturer’s instructions. In this method, inorganic sulfate is precipitated by a reaction with barium sulfate in polyethylene glycol for stabilization of turbidity. This method was modified by using gelatin solution instead of polyethylene glycol. The level of sulfide released by reduction with dithiothreitol (DTT) was determined by the modified method of Ogasawara et al. [ 43] and the level of bound sulfane sulfur including mainly persulfides and polysulfides was obtained after subtracting the level of free sulfide. The activity of CSE was assayed by the modified method of Matsuo and Greenberg [44]. L-homoserine was used as a substrate and α-ketobutyric acid formed in this reaction was assayed according to the method of Soda [45]. The activity of MST was determined by the method of Valentine and Frankenfeld [46], while TST activity was assayed according to Sörbo’s method [47]. Estimation of the GST activity was assayed as described previously [48]. The level of MDA was assayed according to Ohkawa [49], while the protein concentration was by the method of Lowry et al. [ 50].
## 4.6. High-Performance Liquid Chromatography (HPLC) Analysis
The levels of reduced and total thiols (glutathione and cysteine) were measured by HPLC after precolumn derivatization with 2-chloro-l-methylquinolinium tetrafluoroborate (CMQT) and with ultraviolet detection [51]. The analysis was performed using an HPLC system (Shimadzu Duisburg, Germany) consisting of two high-pressure pumps LC 10AT vp, a degasser DGU-14 A, an autosampler SIL-10ADvp, a column thermostatic oven CTO-10 Asvp and a diode detector SPD-M10Avp. The entire HPLC system was under the control of Shimadzu’s Lab solution software. The samples were separated using a Zorbax Eclipse XDB-18 column (4.6 × 250 mm, 5 μm) from Agilent (Santa Clara, CA, USA), protected by a guard column with the same packing. The temperature was 25 °C, the flow rate was 1 mL/min, and detector wavelength was 350 nm. The elution profile was as follows: 0–4 min, $12\%$ B; 4–7 min, 12–$40\%$ B; 7–8 min, $40\%$ B; 8–10 min 40–$12\%$ B. Elution solvent A was 0.05 M TCA adjusted to pH 3.2 with lithium hydroxide.
## 4.6.1. Reduced Thiols
For determination of reduced thiols, 200 μL of tissue homogenate was mixed with 15 μL of 0.2 M phosphate buffer, pH 8.2 and 20 μL of 0.1 M CMQT dissolved in distilled water, and the mixture was incubated at room temperature for 5 min. After derivatization, the mixture was acidified with 30 μL of 3 M perchloric acid (PCA) followed by centrifugation. An amount of 20 μL of the obtained supernatant was subjected to HPLC analysis.
## 4.6.2. Total Thiols
For determination of the total thiols, 200 μL of tissue homogenate was mixed with 15 μL of 0.25 M TCEP (dissolved in 0.2 M phosphate buffer, pH 8.2 and adjusted to pH 7–8 with 2.5 M NaOH) and the mixture was incubated at a room temperature for 15 min to reduce disulfide bonds. Next, 20 μL of 0.1 M CMQT was added and the procedure was continued as in the case of reduced thiols. After centrifugation, 20 μL of the supernatant was subjected to HPLC analysis.
## 4.6.3. Oxidized Thiols
The levels of oxidized thiols were estimated by subtracting the level of the reduced thiols from the pool of total thiols and dividing by two (one mol of oxidized thiol consists of two reduced molecules).
## 4.7.1. Isolation of Total RNA
Total RNA was extracted from the tissues using TRI reagent (Sigma-Aldrich, Darmstadt, Germany), according to the protocol provided by the manufacturer. The extracted RNA was suspended in ribonuclease-free water and quantified by measuring the absorbance at 260 nm. After the isolation procedure, every time, the purity of the obtained RNA was checked by estimating the A260 nm/A280 nm ratio. The integrity of the obtained RNA was confirmed by the separation of the 28S and 18S rRNA bands in $2.0\%$ agarose gel electrophoresis. The RNA solutions were stored at −80 °C until further studies were performed.
## 4.7.2. Reverse Transcription of RNA
Total RNA from particular tissues was reverse-transcribed using the GoScriptTM Reverse Transcriptase Kit according to the manufacturer’s protocol (Promega, Madison, WI, USA). For reverse transcription (RT), 2 µg of total RNA was mixed with 1 µL Oligo d(T)15 primer (0.5 µg/µL) and water pretreated with diethylpyrocarbonate (DEPC-H2O), and the mixture was incubated for 5 min at 70 °C. After preincubation, the samples were placed on ice and other components were added to the mixture: 4 µL 5× concentrated RT buffer (250 mM Tris-HCl, 250 mM KCl, 20 mM MgCl2, 50 mM DTT, pH 8.3 at 25 °C), 2 µL deoxyribonucleotide triphosphates (dNTPs, 10 mM) and 1 µL RNase inhibitor (20 U/µL) and 1 µL GoScriptTM Reverse Transcriptase (160 U/µL) in a total volume of 20 µL. After incubation at 25 °C for 5 min, the mixture was incubated for 60 min at 42 °C, and then for the final 10 min at 70 °C. If necessary, the solutions of complementary DNA (cDNA) were stored at −20 °C.
## 4.7.3. Polymerase Chain Reaction (PCR)
The expression of four genes involved in reactive sulfur species metabolism (CSE, MST, CBS, and TST) was analyzed by PCR using the MiniOpticonTM System (Bio-Rad, Hercules, CA, USA). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as a reference gene. Amplification of cDNA was run in a 25 µL reaction volume that contained the following constituents: 2 µL of the synthesized cDNA, 10 µM of each of gene-specific primer pair (Table 2), 2 U/µL Taq DNA polymerase in 10 mM buffer Tris-HCl, pH 8.8 (supplemented with 1.5 mM MgCl2, 50 mM KCl, $0.1\%$ Triton X-100), 10 mM of each dNTPs and DEPC-H2O. The PCR cycling conditions for all selected genes were: 94 °C for 5 min, 26 cycles (for MST, CBS, and TST) or 24 cycles (for CSE and GAPDH) of amplification (94 °C for 30 s, 62 °C for 30 s, and 72 °C for 2 min), and a final extension at 72 °C for 8 min. The PCR reaction conditions for these five genes were established and optimized specifically to address the needs of the present study. In each case, a similar reaction was also performed in the mixture without DNA (negative control) in order to confirm the specificity of the obtained reaction products. All amplification reactions were performed at least three times to ensure the reproducibility of results. All PCR products were analyzed by electrophoresis on a $2.0\%$ agarose gel stained with ethidium bromide and directly visualized under UV light and photographed (ChemiDocTM MP Imaging system with Image Lab Software, version 6.0, Bio-Rad).
## 4.8. Statistical Analysis
The results are presented as the mean values ± standard error of the mean (SEM). For biochemical assays, the mean was calculated from all tissues of animals belonging to a given group, while for enzyme expression the mean was estimated from three determinations. A one-way analysis of variance (ANOVA) followed (if significant) by Tukey test was used for statistical analysis of the data. A p value < 0.05 was considered statistically significant.
## References
1. Tam S.W., Worcel M., Wyllie M.. **Yohimbine: A clinical review**. *Pharm. Ther.* (2001) **91** 215-243. DOI: 10.1016/S0163-7258(01)00156-5
2. Liu Y., Yu H.Y., Xu H.Z., Liu J.J., Meng X.G., Zhou M., Ruan H.L.. **Alkaloids with Immunosuppressive Activity from the Bark of Pausinystalia yohimbe**. *J. Nat. Prod.* (2018) **81** 1841-1849. DOI: 10.1021/acs.jnatprod.8b00324
3. Morales A.. **Yohimbine in erectile dysfunction: The facts**. *Int. J. Impot Res.* (2000) **12** S70-S74. DOI: 10.1038/sj.ijir.3900508
4. Arteaga M., Motte-Lara J., Velázquez-Moctezuma J.. **Effects of yohimbine and apomorphine on the male sexual behaviour pattern of the golden hamster (Mesocricetus auratus)**. *Eur. Neuropsychopharmacol.* (2002) **12** 39-45. DOI: 10.1016/S0924-977X(01)00134-1
5. Tasleem M., Alrehaily A., Almeleebia T.M., Alshahrani M.Y., Ahmad I., Asiri M., Alabdallah N.M., Saeed M.. **Investigation of Antidepressant Properties of Yohimbine by Employing Structure-Based Computational Assessments**. *Curr. Issues Mol. Biol.* (2021) **43** 1805-1827. DOI: 10.3390/cimb43030127
6. Ostojic S.M.. **Yohimbine: The effects on body composition and exercise performance in soccer players**. *Res. Sport. Med.* (2006) **14** 289-299. DOI: 10.1080/15438620600987106
7. McCarty M.F.. **Pre-exercise administration of yohimbine may enhance the efficacy of exercise training as a fat loss strategy by boosting lipolysis**. *Med. Hypotheses* (2002) **58** 491-495. DOI: 10.1054/mehy.2001.1459
8. Loiselle J.J., Yang G., Wu L.. **Hydrogen sulfide and hepatic lipid metabolism—A critical pairing for liver health**. *Br. J. Pharm.* (2020) **177** 757-768. DOI: 10.1111/bph.14556
9. Kabil O., Vitvitsky V., Xie P., Banerjee R.. **The quantitative significance of the transsulfuration enzymes for H2S production in murine tissues**. *Antioxid. Redox Signal.* (2011) **15** 363-372. DOI: 10.1089/ars.2010.3781
10. Rose P., Moore P.K., Zhu Y.Z.. **H**. *Cell Mol. Life Sci.* (2017) **74** 1391-1412. DOI: 10.1007/s00018-016-2406-8
11. Iciek M., Włodek L.. **Biosynthesis and biological properties of compounds containing highly reactive, reduced sulfane sulfur**. *Pol. J. Pharm.* (2001) **53** 215-225
12. Iciek M., Bilska-Wilkosz A., Górny M.. **Sulfane sulfur—New findings on an old topic**. *Acta Biochim. Pol.* (2019) **66** 533-544. DOI: 10.18388/abp.2019_2909
13. Szabo C., Ransy C., Módis K., Andriamihaja M., Murghes B., Coletta C., Olah G., Yanagi K., Bouillaud F.. **Regulation of mitochondrial bioenergetic function by hydrogen sulfide. Part I. Biochemical and physiological mechanisms**. *Br. J. Pharm.* (2014) **171** 2099-2122. DOI: 10.1111/bph.12369
14. Tan G., Pan S., Li J., Dong X., Kang K., Zhao M., Jiang X., Kanwar J.R., Qiao H., Jiang H.. **Hydrogen sulfide attenuates carbon tetrachloride-induced hepatotoxicity, liver cirrhosis and portal hypertension in rats**. *PLoS ONE* (2011) **6**. DOI: 10.1371/journal.pone.0025943
15. Comas F., Moreno-Navarrete J.M.. **The Impact of H**. *Antioxidants* (2021) **10**. DOI: 10.3390/antiox10050633
16. Yu Y., Ye S.M., Liu D.Y., Yang L.Q.. **AP39 ameliorates high fat diet-induced liver injury in young rats via alleviation of oxidative stress and mitochondrial impairment**. *Exp. Anim.* (2021) **70** 553-562. DOI: 10.1538/expanim.21-0056
17. Yang G., Ju Y., Fu M., Zhang Y., Pei Y., Racine M., Baath S., Merritt T.J.S., Wang R., Wu L.. **Cystathionine gamma-lyase/hydrogen sulfide system is essential for adipogenesis and fat mass accumulation in mice. Biochim**. *Biophys. Acta Mol. Cell Biol. Lipids* (2018) **1863** 165-176. DOI: 10.1016/j.bbalip.2017.11.008
18. Comas F., Latorre J., Ortega F., Arnoriaga Rodríguez M., Lluch A., Sabater M., Rius F., Ribas X., Costas M., Ricart W.. **Morbidly obese subjects show increased serum sulfide in proportion to fat mass**. *Int. J. Obes. (Lond.)* (2021) **45** 415-426. DOI: 10.1038/s41366-020-00696-z
19. Dudek M., Knutelska J., Bednarski M., Nowiński L., Zygmunt M., Mordyl B., Głuch-Lutwin M., Kazek G., Sapa J., Pytka K.. **A Comparison of the Anorectic Effect and Safety of the Alpha**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0141327
20. Liu M., Deng M., Su J., Lin Y., Jia Z., Peng K., Wang F., Yang T.. **Specific downregulation of cystathionine β-synthase expression in the kidney during obesity**. *Physiol. Rep.* (2018) **6** e13630. DOI: 10.14814/phy2.13630
21. Peh M.T., Anwar A.B., Ng D.S., Atan M.S., Kumar S.D., Moore P.K.. **Effect of feeding a high fat diet on hydrogen sulfide (H2S) metabolism in the mouse**. *Nitric Oxide* (2014) **41** 138-145. DOI: 10.1016/j.niox.2014.03.002
22. Hwang S.Y., Sarna L.K., Siow Y.L., Karmin O.. **High-fat diet stimulates hepatic cystathionine β-synthase and cystathionine γ-lyase expression**. *Can. J. Physiol. Pharm.* (2013) **91** 913-919. DOI: 10.1139/cjpp-2013-0106
23. Bravo E., Palleschi S., Aspichueta P., Buqué X., Rossi B., Cano A., Napolitano M., Ochoa B., Botham K.M.. **High fat diet-induced non alcoholic fatty liver disease in rats is associated with hyperhomocysteinemia caused by down regulation of the transsulphuration pathway**. *Lipids Health Dis.* (2011) **10** 60. DOI: 10.1186/1476-511X-10-60
24. Luo Z., Gao Q., Li Y., Bai Y., Zhang J., Xu W., Xu J.. **Flammulina velutipes Mycorrhizae Attenuate High Fat Diet-Induced Lipid Disorder, Oxidative Stress and Inflammation in the Liver and Perirenal Adipose Tissue of Mice**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14183830
25. Xu H., Zhang L., Xu D., Deng W., Yang W., Tang F., Da M.. **Knockout of calpain-1 protects against high-fat diet-induced liver dysfunction in mouse through inhibiting oxidative stress and inflammation**. *Food Sci. Nutr.* (2021) **9** 367-374. DOI: 10.1002/fsn3.2002
26. Moreno-Fernández S., Garcés-Rimón M., Vera G., Astier J., Landrier J.F., Miguel M.. **High Fat/High Glucose Diet Induces Metabolic Syndrome in an Experimental Rat Model**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10101502
27. Ding X., Jian T., Li J., Lv H., Tong B., Meng X., Ren B., Chen J.. **Chicoric Acid Ameliorates Nonalcoholic Fatty Liver Disease via the AMPK/Nrf2/NFκB Signaling Pathway and Restores Gut Microbiota in High-Fat-Diet-Fed Mice**. *Oxid. Med. Cell. Longev.* (2020) **2020** 9734560. DOI: 10.1155/2020/9734560
28. Abbas A.M., Sakr H.F.. **Simvastatin and vitamin E effects on cardiac and hepatic oxidative stress in rats fed on high fat diet**. *J. Physiol. Biochem.* (2013) **69** 737-750. DOI: 10.1007/s13105-013-0250-y
29. Yun K.U., Ryu C.S., Oh J.M., Kim C.H., Lee K.S., Lee C.H., Lee H.S., Kim B.H., Kim S.K.. **Plasma homocysteine level and hepatic sulfur amino acid metabolism in mice fed a high-fat diet**. *Eur. J. Nutr.* (2013) **52** 127-134. DOI: 10.1007/s00394-011-0294-0
30. Chen J.H., Yu G.F., Jin S.Y., Zhang W.H., Lei D.X., Zhou S.L., Song X.R.. **Activation of α2 adrenoceptor attenuates lipopolysaccharide-induced hepatic injury**. *Int. J. Clin. Exp. Pathol.* (2015) **8** 10752-10759. PMID: 26617786
31. Santativongchai P., Srisuksai K., Parunyakul K., Thiendedsakul P., Lertwatcharasarakul P., Fungfuang W., Tulayakul P.. **Effects of Crocodile Oil (Crocodylus siamensis) on Liver Enzymes: Cytochrome P450 and Glutathione S-Transferase Activities in High-fat DietFed Rats**. *Vet. Med. Int.* (2022) **2022** 9990231. DOI: 10.1155/2022/9990231
32. Akbay E., Ulusu N.N., Töröner F., Ayvaz G., Taneri F., Aktürk M., Arslan M., Karasu C.. **Effects of rosiglitazone treatment on the pentose phosphate pathway and glutathione-dependent enzymes in liver and kidney of rats fed a high-fat diet**. *Curr. Res. Clin. Exp.* (2004) **65** 79-89. DOI: 10.1016/S0011-393X(04)90007-0
33. Wang C., Tao Q., Wang X., Zhang X.. **Impact of high-fat diet on liver genes expression profiles in mice model of nonalcoholic fatty liver disease**. *Environ. Toxicol. Pharm.* (2016) **45** 52-62. DOI: 10.1016/j.etap.2016.05.014
34. Zhang Y.Z., Zhou Z.C., Song C.Y., Chen X.. **The Protective Effect and Mechanism of Dexmedetomidine on Diabetic Peripheral Neuropathy in Rats**. *Front. Pharm.* (2020) **11** 1139. DOI: 10.3389/fphar.2020.01139
35. Zhang Y., Li M., Cui E., Zhang H., Zhu X., Zhou J., Yan M., Sun J.. **Dexmedetomidine attenuates sevoflurane-induced neurocognitive impairment through α2-adrenoceptors**. *Mol. Med. Rep.* (2021) **23** 38. DOI: 10.3892/mmr.2020.11676
36. Shen K.P., Lo Y.C., Yang R.C., Liu H.W., Chen I.J., Wu B.N.. **Antioxidant eugenosedin-A protects against lipopolysaccharide-induced hypotension, hyperglycaemia and cytokine immunoreactivity in rats and mice**. *J. Pharm. Pharm.* (2005) **57** 117-125. DOI: 10.1211/0022357055137
37. Brannan R.G.. **Reactive sulfur species act as prooxidants in liposomal and skeletal muscle model systems**. *J. Agric. Food Chem.* (2010) **58** 3767-3771. DOI: 10.1021/jf903587n
38. Nandi D.L., Horowitz P.M., Westley J.. **Rhodanese as a thioredoxin oxidase**. *Int. J. Biochem. Cell Biol.* (2000) **32** 465-473. DOI: 10.1016/S1357-2725(99)00035-7
39. Buonvino S., Arciero I., Melino S.. **Thiosulfate-Cyanide Sulfurtransferase a Mitochondrial Essential Enzyme: From Cell Metabolism to the Biotechnological Applications**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23158452
40. Bald E., Glowacki R., Drzewoski J.. **Determination by liquid chromatography of free and total cysteine in human urine in the form of its S-quinolinium derivative**. *J. Chromatogr. A* (2001) **913** 319-329. DOI: 10.1016/S0021-9673(00)01202-4
41. Wood J.L.. **Sulfane sulfur**. *Methods Enzym.* (1987) **143** 25-29. DOI: 10.1016/0076-6879(87)43009-7
42. Shen X., Pattillo C.B., Pardue S., Bir S.C., Wang R., Kevil C.G.. **Measurement of plasma hydrogen sulfide in vivo and in vitro**. *Free Radic Biol. Med.* (2011) **50** 1021-1031. DOI: 10.1016/j.freeradbiomed.2011.01.025
43. Ogasawara Y., Ishii K., Togawa T., Tanabe S.. **Determination of bound sulfur in serum by gas dialysis/high-performance liquid chromatography**. *Anal. Biochem.* (1993) **215** 73-81. DOI: 10.1006/abio.1993.1556
44. Matsuo Y., Greenberg D.M.. **A crystalline enzyme that cleaves homoserine and cystathionine. IV. Mechanism of action, reversibility, and substrate specificity**. *J. Biol. Chem.* (1959) **234** 516-519. DOI: 10.1016/S0021-9258(18)70236-6
45. Soda K.. **A spectrophotometric microdetermination of keto acids with 3-methyl-2-benzothiazolone hydrazone**. *Agric. Biol. Chem.* (1967) **31** 1054-1060. DOI: 10.1080/00021369.1967.10858916
46. Valentine W.N., Frankenfeld J.K.. **3-Mercaptopyruvate sulfurtransferase (EC 2.8.1.2): A simple assay adapted to human blood cells**. *Clin. Chim. Acta* (1974) **51** 205-210. DOI: 10.1016/0009-8981(74)90031-X
47. Sörbo B.. **Rohodanese**. *Methods Enzymol.* (1955) **2** 334-337
48. Rice-Evans C.A., Diplock A.T., Symons M.C.R.. *Techniques in Free Radical Research* (1991)
49. Ohkawa H., Ohishi N., Yagi K.. **Assay for lipid peroxides in animal tissues by thiobarbituric acid reaction**. *Anal. Biochem.* (1979) **95** 351-358. DOI: 10.1016/0003-2697(79)90738-3
50. Lowry O.H., Rosebrough N.J., Farr A.L., Randall R.J.. **Protein measurement with the Folin phenol reagent**. *J. Biol. Chem.* (1951) **193** 265-275. DOI: 10.1016/S0021-9258(19)52451-6
51. Bald E., Chwatko G., Głowacki R., Kuśmierek K.. **Analysis of plasma thiols by high-performance liquid chromatography with ultraviolet detection**. *J. Chromatogr. A* (2004) **1032** 109-115. DOI: 10.1016/j.chroma.2003.11.030
52. Kaczor-Kamińska M., Kaminski K., Wróbel M.. **The Expression and Activity of Rhodanese, 3-Mercaptopyruvate Sulfurtransferase, Cystathionine γ-Lyase in the Most Frequently Chosen Cellular Research Models**. *Biomolecules* (2021) **11**. DOI: 10.3390/biom11121859
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---
title: Green Synthesis of Iridium Nanoparticles from Winery Waste and Their Catalytic
Effectiveness in Water Decontamination
authors:
- Lucia Mergola
- Luigi Carbone
- Tiziana Stomeo
- Roberta Del Sole
journal: Materials
year: 2023
pmcid: PMC10004582
doi: 10.3390/ma16052060
license: CC BY 4.0
---
# Green Synthesis of Iridium Nanoparticles from Winery Waste and Their Catalytic Effectiveness in Water Decontamination
## Abstract
An environmentally friendly procedure was adopted for the first time to prepare green iridium nanoparticles starting from grape marc extracts. Grape marcs, waste of Negramaro winery production, were subjected to aqueous thermal extraction at different temperatures (45, 65, 80, and 100 °C) and characterized in terms of total phenolic contents, reducing sugars, and antioxidant activity. The results obtained showed an important effect of temperature with higher amounts of polyphenols and reducing sugars and antioxidant activity in the extracts with the increase of temperature. All four extracts were used as starting materials to synthesize different iridium nanoparticles (Ir-NP1, Ir-NP2, Ir-NP3, and Ir-NP4) that were characterized by Uv-Vis spectroscopy, transmission electron microscopy, and dynamic light scattering. TEM analysis revealed the presence of very small particles in all samples with sizes in the range of 3.0–4.5 nm with the presence of a second fraction of larger nanoparticles (7.5–17.0 nm) for Ir-NPs prepared with extracts obtained at higher temperatures (Ir-NP3 and Ir-NP4). Since the wastewater remediation of toxic organic contaminants on catalytic reduction has gained much attention, the application of the prepared Ir-NPs as catalysts towards the reduction of methylene blue (MB), chosen as the organic dye model, was evaluated. The efficient catalytic activity of Ir-NPs in the reduction of MB by NaBH4 was demonstrated and Ir-NP2 was prepared using the extract obtained at 65 °C, showing the best catalytic performance, with a rate constant of 0.527 ± 0.012 min−1 and MB reduction of $96.1\%$ in just six min, with stability for over 10 months.
## 1. Introduction
Recently, wastewater remediation of toxic organic contaminants on catalytic reduction has gained great attention. Among others, organic dyes, which are widely used as colorants in several industries, are considered toxic contaminants, and dangerous for human health and aquatic life. A convenient solution for them could be to convert hazardous dyes to non-toxic chemicals and in this context, the use of noble metal nanoparticles (NPs) as todays catalysts represents a proper approach [1,2]. Herein, with the purpose to get novel catalysts for organic dye remediation, iridium NPs were prepared for the first time with a green procedure.
At present, nanoscience represents an emerging area of modern research that studies the structure of materials at a small scale (1–100 nm), evaluating their unique physical and chemical properties compared with the same bulk materials, with a high number of important applications in a wide range of sectors (electronics, packaging, engineering, cosmetics, nanomedicine, and catalysis) [3]. These important characteristics attracted the interest of the scientific community, focusing their attention on the development of new breeds of nanomaterials.
In the last years a lot of works and reviews, on the preparation of noble metal nanomaterials have been published, driven by their peculiar catalytic, optical, and electronic behavior, which allowed their pioneering use in biomedicine and engineering [4,5,6]. Among them, silver and gold NPs were the most investigated thanks to their well-known localized surface plasmon resonance that confers to the nanomaterials’ unique optical properties. Even if noble metal iridium at the nanoscale exhibits important properties such as superior stability, corrosion resistance, and catalytic activity due to the distinctive electronic configuration and the large surface area [7], it has been little investigated. These considerations move us to exploit iridium nanoparticles in the present research.
*In* general, noble metal NPs preparation can be carried out through physical and chemical methods such as microemulsion techniques [8], thermal decomposition [9], UV irradiation, and chemical reduction. Normally, the chemical synthesis of metal NPs needs the reduction of a metal salt by using a reducing agent in the presence of ligands, surfactants, or polymers as stabilizing agents to avoid nanoparticle agglomeration. Sodium borohydride [10], aniline [11], trisodium citrate [12], and formaldehyde [13] are the most common chemical reducing agents. Other elements such as solvent, synthesis temperature, and the type of stabilizing agent, significantly affect metal NPs characteristics. However, the use of these hazardous chemical compounds, dangerous for the environment, considerably hinders their applications, especially in nanomedicine.
Nowadays, the integration of green chemistry principles into nanotechnology is one of the key issues of nanoscience research. Indeed, there is a growing need to develop environmentally friendly and sustainable methods for the synthesis of nanoparticles that utilize nontoxic chemicals, environmentally benign solvents, and renewable materials, which are also in line with some of the fundamental principles of the circular economy [14,15,16]. In this context, recently, some reviews were published describing the possible use of vegetable extracts obtained from plants and agricultural waste as precursors for the green synthesis of metal NPs [17]. Indeed, their high content in terms of sugars, flavonoids, polyphenols, terpenoids, and saponins that can be easily extracted through an aqueous thermal extraction, make vegetable extracts able to reduce and stabilize metal nanoparticles without the use of hazardous chemical compounds, expanding considerably their fields of application [18]. For instance, noble metal nanoparticles were prepared from a lot of vegetable extracts obtained from Tectona Grandis’ seeds [19], *Sphaeranthus indicus* leaves [20], the pericarp of Myristica fragrans fruits [21], *Nigella sativa* plants [22], Malva Verticillata leaves [23], tomatoes and grapefruits [24], and many others. On the contrary, to our knowledge, there are no works on green preparation for iridium NPs starting from natural extracts.
Among many plant sources used for nanoparticle synthesis, there are grape marcs, which are the waste of wine production made of lignocellulosic material and rich in polar substances soluble in hot water with high amounts of tannins and polyphenolic compounds [25,26]. For this reason, grape marcs were also widely studied as starting material for metal nanoparticle preparation [27,28,29]. Although silver and gold nanoparticles are the most commonly studied, only recently iridium nanoparticles have attracted significant interest as selective and active catalysts for different reactions such as CO2 fixation, hydrogenation, and aerobic oxidation [7,30,31]. Noble metal iridium is an element of the platinum group and at the nanoscale exhibit important properties such as superior stability, corrosion resistance, and catalytic activity due to the distinctive electronic configuration and the large surface area [7]. In a recent study, Cui et al. prepared for the first time ultra-small iridium nanoparticles using tannin as a stabilizer and a common chemical reducing agent (sodium borohydride) demonstrating their ability to reduce nitroarenes [31]. With the aim to combine iridium nanoparticles research and green chemistry principles, in this study, we report for the first time the green synthesis of iridium nanoparticles (Ir-NPs) from grape marc extracts with an evaluation of some factors that affect the synthesis and the properties of these NPs. An evaluation of total polyphenol content, antioxidant activity, and reducing sugars of grape marc extracts were made. Moreover, the synthesized iridium nanoparticles were characterized by using transmission electron microscopy (TEM), dynamic light scattering (DLS), Electrophoretic Light Scattering (ELS), and Fourier transform infrared spectroscopy (FTIR) analysis.
With the aim to use the prepared NPs in the wastewater remediation field, the application of the synthesized Ir-NPs to reduce hazardous organic dyes to non-toxic chemicals was studied. In detail, the catalytic effectiveness of the synthesized Ir-NPs in the reduction of MB dye by using NaBH4 as a reducing agent, chosen as an organic dye model, was investigated.
## 2.1. Materials and Chemicals
Grape marc (GM) wastes from Negroamaro winery production were supplied by a local company (Cantina Vecchia Torre S.c.a.) in the Apulia region (southern Italy). Iridium trichloride (IrCl3), gallic acid, methylene blue (MB), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), diammonium salt (ABTS), Sodium Carbonate (Na2CO3) ($99\%$), sodium borohydride (NaBH4), and Potassium Persulphate (K2S2O8) ($99\%$) were purchased from Sigma-Aldrich (Steinheim, Germany).
Folin–Ciocalteu’s phenol reagent and Whatman 41 filter were acquired from Merck KGaA (Darmstadt, Germany). All solutions were prepared using ultrapure water obtained from a water purification system (Human Corporation, Seoul, Korea).
## 2.2. Extract Preparation
GM wastes were firstly sun-dried for six days, milled using a grinder, and finally sieved. Then, different grape marc solution extracts were prepared by adding 5 g of dried GM to 100 mL of ultrapure water. The extraction process was conducted for 1 h in a thermostat oil bath at different temperatures (45, 65, 80, and 100 °C) obtaining four extracts (GM1, GM2, GM3, and GM4, respectively) All extracts were filtered using Whatman 41 filters. Then, centrifuged twice at 8000 rpm for 30 min and finally filtered with a 0.22 µm syringe filter. Afterward, all extracts were aliquoted and stored at −20 °C till further use. Moreover, the pH of all extracts was evaluated using a pH meter Basic 20, (Crison, Alella, Barcelona, Spain, and Europe, www.crisoninstruments.com; accessed on 27 February 2023).
## 2.3. Total Phenolic (TP) Content and Antioxidant Activity of GM Extracts
The TP content of all extracts was determined by a modified Folin–Ciocalteu colorimetric method [32] using gallic acid as standard. Briefly, 0.3 mL of each extract was mixed with Folin–Ciocalteu phenol reagent (1.5 mL) and allowed to stand to react for 6 min at room temperature. The mixtures were neutralized by $7\%$ Na2CO3. Then, all solutions were stirred (200 rpm) for 120 min in the dark at room temperature. Phenolic compounds present in the extracts were oxidized while phosphomolybdic and phosphotungstic acid, contained in the Folin-Ciocalteu reagent, were reduced producing molybdenum (blue-colored) and tungsten oxides. After incubation, the absorbance of all samples was measured at 760 nm using a Jasco V-660 UV-visible spectrophotometer (Jasco, Palo Alto, CA, USA). The standard curves with aqueous gallic acid solutions (ranging from 30 to 600 mg/L) were used for calibration. TP was expressed as the mean of gallic acid equivalents (GAE) per gram of Dried Matter (DM) ± SD for triplicates.
The antioxidant activity of all extracts was evaluated by using an ABTS assay. This methodology is based on the ABTS cation radical formation (ABTS•+) generated through the oxidation of ABTS by potassium persulfate, as reported by Re et al. with slight variations [33]. Briefly, ABTS aqueous solution (7 mM) was prepared and mixed with 2.45 mM of K2S2O8 (final concentration). The mixture was kept in the dark for 12–15 h, under stirring (200 rpm) to favor the ABTS radical formation. After, the solution was diluted with ethanol to obtain an absorbance of 0.7 ± 0.1 at 734 nm. A calibration curve was made using known concentrations of Trolox in ethanol ranging from 0 to 15 µM. Then, 20 µL of Trolox standard solutions or sample extracts were added to 1.980 mL of ABTS•+ working solution, and the absorbance at 734 nm after 2.5 min of incubation, was evaluated. The scavenging capability of ABTS•+ radical was calculated as follows:[1]ABTS•+scavenging effect%=1−AAAB×100 where AB is the absorbance of the initial concentration of the ABTS•+ working solution and AA is the absorbance of the remaining concentration of ABTS•+ in the presence of extract. The activity of extracts was estimated at a minimum of three different concentrations. Finally, the antioxidant activity was calculated as milligrams of gallic acid (GAE) equivalents per 100 g of dried matter (DM).
## 2.4. Evaluation of Reducing Agents
Reducing sugars contained in all extracts were quantified by the Fehling method. 10 mL of Fehling solution was mixed with 3 drops of $1\%$ blue methylene and heated. After boiling, the Fehling solution was titrated with GM extracts until a color change from blue to colorless and the formation of a red precipitate demonstrated the complete reduction of the Fehling solution. The corresponding volume of GM extract was considered for quantitative evaluation using the following equation:[2]monosaccaridesgL−1=0.05154×1000×DA where D and A are respectively the dilution factor and the volume of GM extract used for titration.
## 2.5. Synthesis of Iridium Nanoparticles (Ir-NPs)
Synthesis of iridium nanoparticles was carried out by adding 4 mL of IrCl3 aqueous solution (0.005 M) and 4 mL of the grape marc extract in a glass flask. The mixture was put in a thermostatic oil bath at 80 °C under magnetic stirring (700 rpm) for 5 h. Then, all samples were centrifuged five times at 9500 rpm for 30 min using a PK121 multispeed of Thermo Electron Corporation (Thermo Electron Corporation, Waltham, MA, USA, www.thermoscientific.com; accessed on 27 February 2023). Then, the supernatant was stored at 4 °C until their use.
## 2.6. Characterization Studies
Nanoparticle formation was verified by overlapping UV-visible spectra of precursors (aqueous IrCl3 solution and GM extracts) and reaction product properly diluted and analyzed all at the same concentration to evaluate the disappearance of characteristic peaks of IrCl3 at 440 and 488 nm. To determine the main functional groups of the GM extracts and to observe the functional groups involved in the interactions with the Ir nanoparticle, all extracts and the corresponding Ir-NPs were characterized by using FTIR analysis carried out with a JASCO 660 plus infrared spectrometer. Each sample was deposited on an ATR crystal sampler and dried at 40 °C. Then, FTIR spectra were registered in the range of 4000–650 cm−1. DLS and ELS analysis was conducted using a Malvern Zetasizer Nano ZS 90 (Worcestershire, UK) on diluted samples to evaluate the size and Zeta Potential (ZP) of hydrate nanoparticles. An evaluation of hydrodynamic diameter was made at 25 °C by measuring the autocorrelation function at a 90° scattering angle. Low-magnification TEM analyses were conducted on a Jeol JEM-1400 electron microscope (Jeol Ltd., Akishima, Tokyo, Japan) operating at 120 kV, equipped with a CCD camera ORIUS 831 from Gatan Inc. (Pleasanton, CA, USA). TEM samples for analysis were prepared by initially depositing a few drops of the NP dispersions onto a carbon-coated copper grid and then, after almost 1 min, blotting off the sample with filter paper. The grid was made to air dry in a chemical hood. X-ray powder diffraction (XRD) measurements were carried out with a Rigaku Ultima+ model diffractometer. The X-ray generator was equipped with a copper tube operating at 40 kV and irradiating the sample with a monochromatic CuKα radiation with a wavelength of 0.154 nm. XRD spectra were acquired at room temperature over the 2θ range of 20–80°. Energy dispersive X-ray (EDX) analysis of the Ir-NPs was carried out by Phenom XL SEM microscope. EDX spectra were measured with a Silicon Drift Detector (SDD) thermoelectrically cooled (LN2 free) having an active area of 25 mm2. A voltage of 15 kV was applied in order to perform the X-rays spots analysis. The samples were prepared for EDX analysis by depositing a few drops of the NPs dispersion onto a silicon wafer and subsequently dried in an oven at 40 °C before being transferred to the microscope.
## 2.7. Catalytic Activities of Ir-NPs and Kinetic Studies
The catalytic activity of synthesized IrNPs was evaluated by studying the reduction of MB by using NaBH4 as a reducing agent. Firstly, aqueous solutions of MB (0.0003 M) and NaBH4 (0.1 M) were prepared. Then, 1 mL of NaBH4 was added to 9 mL of MB solution and stirred for 3 min. After, 500 µL of Ir-NPs were added to the mixture and stirred for 1 min. Then, UV-visible spectra were recorded at regular intervals of time (1.5 min) and the absorbance at 664 nm (At) was monitored at reaction time t.
Catalytic activity was also monitored without the presence of Ir-NPs (reference) and in the presence of different extracts used for nanoparticle preparation. Kinetic studies were carried out plotting ln(At/A0) versus reaction time (t) to evaluate the reaction rate constant (k) that was used to compare the catalytic activities of all samples. Moreover, percentages of MB reduction were also evaluated using the following equation:[3]MB reduction%=(C0−Ct)C0×100 where C0 is the initial MB concentration (mol/L) and *Ct is* MB concentration (mol/L) at reaction time t. Catalytic activity studies were conducted in triplicate.
## 3.1. Grape Marc Extracts Preparation and Characterization
Grape marcs and stalks represent an important solid organic waste obtained from winery production. The high add-value of this waste is due to the high contents of sugars and phytochemicals such as polyphenols and pigments with important bioactive and antioxidant properties. In this work, to preserve polyphenolic content sun drying of grape marcs was chosen. Indeed, different studies demonstrated that the TP content, obtained by drying vegetables at 20 °C, was 1.7 times higher than the polyphenolic content observed at 120 °C [34]. After drying, grape marcs were also ground to reduce particle size and increase the amount of the extracted polyphenols.
It is known that the extraction time, temperature, and nature of the extraction solvent can influence the composition in terms of phenolic compounds of the extract [35,36]. Generally, conventional polyphenol extraction methods require the use of organic solvents (methanol and ethanol) often mixed with water or acidified water to form a moderate polar medium that enhances the extraction of phenolic compounds [36,37,38]. Lapornik et al. conducted a detailed study on the influence of water on phenolic extraction from grape marcs, demonstrating as the presence of $70\%$ of organic solvent compared to water, enhances phenolic extraction [39]. However, it was also seen that the presence of organic solvent higher than $70\%$, reduces TP content [40]. Starting from this important information, a green approach, using only water as extraction solvent, was preferred. After sun drying, grinding, and sieving, a thermal extraction of DM in water was conducted for 1 h at different temperatures of extraction. As can be seen in Table 1, four different temperatures were considered with the aim to obtain GM extracts differently in terms of composition and to evaluate how the nature of the extract can influence nanoparticle formation. The pH of all extracts was also determined, showing the presence of an acidic environment due to the prevalence of phenolic acids [35].
The chemical composition of grape marc extracts was extensively studied revealing the presence of polar substances with a high content of tannins and other polyphenolic compounds [37,41]. For the purpose of our work, it is essential to determine the compositions of GM extracts prepared at different extraction temperatures, in terms of both the TP content and amount of reducing sugars, due to their important action as reducing and stabilizing agents in nanoparticle formation.
As can be seen in Table 1, temperature plays a critical influence on the extracted total phenols [42]. Indeed, an enhancement of TP content with the increase in extraction temperature was observed. It can be concluded that, although the use of organic solvents could increase the TP content, the green extraction adopted using only water and the acidic environment typical of GM extracts, was sufficient to obtain TP contents comparable to the results reported using a similar procedure [39]. Moreover, the low drying temperature and the grinding step adopted in this work were fundamental to preventing the loss of polyphenols [42]. A similar trend was also observed for reducing sugars with a concentration that increases with the temperature of extraction in the range of 3.7–12.9 g/L.
The antioxidant capacity of all extracts was also evaluated by measuring the decolorization of ABTS•+ working solution, due to the radical cation reduction, after the addition of sample extracts. After an evaluation of the percentage of inhibition, mg of GAE equivalent per 100 g of DM were calculated. As can be seen in Table 1, the results obtained showed a similar trend of polyphenolic content with an increase in antioxidant activity of the extract, with the temperature of extraction adopted. It can be concluded that the increase in temperature of extraction determines an increase of TP and reduces the sugar content, making these extracts suitable for their use as starting material for nanoparticle preparation.
## 3.2. Synthesis and Characterization of Iridium Nanoparticles
To discriminate how the concentrations of sugars and polyphenols can affect the physicochemical properties of Ir-NPs as well as their catalytic performances, all GM extracts described previously were used as reaction media for their synthesis.
UV-visible spectra of IrCl3 and GM precursor solutions were overlapped with Ir-NP products after appropriate dilution to achieve the same concentration present in the reaction mixture and, for all reactions, the disappearance of characteristic peaks of IrCl3 at 440 and 488 nm was monitored. In particular, in Figure 1 Ir-NP1 formation was reported. As can be seen, peaks typical of IrCl3 completely disappear after thermolytic reduction in reaction media, indicating the complete reduction of Ir3+ ions into Ir[0] [43].
Table 2 summarizes the main features of the Ir-NPs developed by keeping constant the general synthesis procedure occurring at 80 °C for 5 h, however, employing as reaction environment the GM extracts obtained at different temperatures.
After purification, the effective formation of Ir-NPs was ascertained via TEM analysis (Figure 2). The left column of Figure 2 shows bright-field TEM images of all four samples; discrepancies in the extraction temperatures of GM extracts affected feebly the NP shape and more interestingly the particle size. All samples showed one main size component of NPs with a dimension in the range of 3.0–4.5 nm as displayed in Figure 2a,b, and in the insets, in the lower left quadrant of all TEM pictures. In the cases of GM extracts obtained at higher temperatures (GM3 and GM4), also another fraction of NPs was detected exposing larger sizes, as well as shapes with a clear hexagonal section (Figure 2c,d). Histograms on the right side of Figure 2 detail the above distinctly. All such outcomes evidence significant differences in the chemical nature of the GM extracts that operate by favoring diverse iridium ion complexation and then promoting dissimilar paths of NP nucleation and growth. In the examples of Ir-NP3 and Ir-NP4 samples, one may hypothesize a two-stage nucleation occurring with a certain delay time, which contributes to generating two size components; furthermore, it is likewise reasonable to envisage a greater metal ion availability. Ir-NP3 and Ir-NP4 were synthesized starting from extracts prepared at higher temperatures compared to Ir-NP1 and Ir-NP2. Table 1 provides evidence that the chemical composition of each extract is significantly dependent on the extraction temperatures. Higher temperatures sensibly increase the concentration of chemical species that either behave as complexing agents of Ir-ions or as reducing agents. The NP growth occurs as follows: the Ir ions are originally chemically complexed in a more reactive form (generally known as precursors), then, when the system reaches the condition of supersaturation (at that reaction temperature), it homogeneously nucleates forming germs of the NP. The nucleation step is followed by the growth stage, during which the unreacted (and complexed) Ir ions support the enlargement of the NP. Intuitively, the presence of a large number of reducing agents of different chemicals and reductive effectiveness prolongs the times of nucleation (multi-stage nucleation); a large amount of complexing agents makes the Ir-ions largely available for growth. The expansion of the times of the nucleation and the growth thereof, facilitates the occurrence of a multi-modal distribution of the sizes.
Hydrodynamic diameters obtained via DLS analysis confirmed the existence of an organic layer surrounding the NPs, reasonably made of tannins and polyphenols, which stabilized them within the aqueous solutions and provided for an increase in the hydrodynamic size of the particles. In addition, ZP was determined to evaluate the stability of synthesized nanoparticles (Table 2). As can be seen in Table 2, ZP negative values were obtained for all samples, indicating nanoparticle repulsion and demonstrating their stability [44]. The negative value can be due to the capping action of compounds present in the extracts [45].
The XRD patterns of Ir-NP1, Ir-NP2 (Figure S1), Ir-NP3, and Ir-NP4 do not allow to observe distinct diffraction peaks, suggesting that the nanoparticles are amorphous with iridium atoms that are located in globular nanoparticles stabilized by the organic component of the extracts [46]. Even if the information does not help us to estimate the structure of the Ir-NPs, a slight and broad diffraction peak above 40° is visible, corresponding to the [111] planes of Ir[0]. To evaluate the chemical nature of the Ir-NPs, elemental EDX analysis has been performed. Figure 3 shows the EDX spectrum confirming the presence of Ir in the nanoparticles. The elemental EDX analysis has also shown KCl salt and carbon of the organic substances, whereas the presence of Silicon, SiO2, and *Rubidium is* due to the wafer substrate (Figure S2).
Ir-NPs and GM extracts were also characterized by using FTIR analysis. A comparison of FTIR spectra of grape marc extract before (GM2) and after iridium nanoparticles synthesis (Ir-NP2) is shown in Figure 4. As can be seen in Figure 4a complex and broad signals of the extracted spectrum confirms the presence of several compounds in this material ranging from polyphenols, and sugars to lignocellulosic molecules. A broad peak around 3312 and 3276 cm−1 is due to O-H stretching vibrations. Peaks at 2933 and 2889 cm−1 correspond to the C-H stretching of the olefinic chains. The peak at 1716 cm−1 is attributed to the carbonyl C=O stretching in ester groups. The peak at 1595 cm−1 corresponds to the C=C stretching of aromatic rings and together with the peaks at 1337, 1304, and 1261 cm−1 confirm the presence of the lignocellulosic material. Finally, peaks at 1131, 1105, and 1065 cm−1 are attributed to the C-O stretching of alcohols and phenols [29,47,48]. The spectrum of Ir-NP2 in Figure 4b is similar to the previous one with only some variation. It is worth noting the higher intensity of the signal of carbonyl C=O stretching in ester groups and the significant variation in the region of the C-O stretching of alcohols and phenols between 1131 and 1065 cm−1 where the disappearance of the signal at 1105 cm−1 is observed, suggesting iridium interaction with these functional groups of the grape marc extracts. FTIR spectra of the other extracts and Ir-NPs showed similar behavior to the spectra described in this paragraph.
## 3.3. Catalytic Activity
In this paragraph, the applicability of Ir-NPs as catalysts of organic dye reduction was evaluated. The reduction of MB from the water was chosen as the dye model. Although the extracts-correlated differences in terms of TP content and sugar concentrations do not significantly promote major changes in the NP morphology, important side effects on the catalytic activity of the Ir-NPs are observed instead. The catalytic performances of Ir-NPs were spectroscopically investigated by monitoring the reduction of MB aqueous solutions to leuco-MB occurring in the co-presence of sodium borohydride. The reaction was conducted in the dark avoiding air contact to prevent oxidation. It is known that MB, as chloride salt, is a cationic and primary thiazine dye that yields a stable blue water solution at room temperature. MB dye has a pKa of 3.8, which turns the diluted solution slightly acidic [49]. The UV-visible analysis of MB is very important. Generally, aqueous cationic MB shows the most intense adsorption at 664 nm associated with an MB monomer, with a shoulder peak at around 612 nm attributed to MB dimer. An additional two bands appear in the ultraviolet region with peaks at 292 and 245 nm due to the substituted benzene rings. MB, initially blue-colored in an oxidizing environment, became colorless in the presence of a reducing agent because of its reduction to the leuco-MB form and the bands in the visible region disappear. Thus, in this study, the MB reduction reaction was monitored following at 21 °C the decrease of the peak at 664 nm and for more clarity, a wide wavelength range between 400 and 800 nm was registered and shown in Figure 5, Figure 6 and Figure 7, as reported in the literature for similar works [2,50,51]. A detailed study was conducted comparing, under the same experimental conditions, the spectral behavior of the solutions containing Ir-NPs with those lacking in NPs (reference), and with those containing only the corresponding temperature-related extracts in place of NPs. Reduction of MB to leuco-MB is a well-known reversible process promoted by NaBH4 giving a gradual discoloration of the reaction solution and monitored by following the decrease of the peak at 664 nm but also confirmed from the return at a blue-colored solution when the final reaction solution is left in the air and the oxygen turns the leuco-MB form to the initial MB one. This behavior was observed also when the reduction is performed in the presence of Ir-NPs or the extracts. In fact, as can be seen in Figure S3 the increase of the peak at 664 nm is observed when the reaction mixture, after catalytic reaction by NaBH4 in presence of Ir-NP2, is left at the air contact. This result confirms that the reduced form of MB is in the reaction mixture and it is again oxidated to MB by the oxygen.
UV-visible spectra were recorded at regular intervals of time (1.5 min). Firstly, the reduction of MB by NaBH4 in a pristine system (reference) was monitored for 25 min. As shown in Figure 5, a moderate variation of the absorbance intensity at 664 nm was found, indicating a slow and inefficient MB reduction reaction rate in the absence of a nanocatalyst (Table 3). A linear correlation between the ln(At/A0), where At and A0 are the absorbance at reaction time t and 0 respectively, and the reaction time confirmed the presence of a pseudo-first-order reaction (inset of Figure 5) with a constant rate, calculated from its slope, equal to 0.011 ± 0.007 min−1 (Table 3). The pseudo-first-order kinetic model is consistent with the well-known kinetic behavior of MB cation reduction to leuco-MB form by a reducing agent, where it could be hypothesized a simple bimolecular reaction between the reducing molecule and MB cation [52] and it is also similar to kinetic results found in the literature if catalytic metal NPs are used [2].
Similar experiments were repeated in the presence of Ir-NPs. Figure 6 shows the metal NPs’ effectiveness in MB reduction. Indeed, a significant and fast reduction reaction happened for all four cases of Ir-NPs, with narrow discrepancies. Especially compelling the reduction process in the presence of Ir-NP2 (Figure 6b). In this last case, $96.1\%$ of dye reduction was detected within 6 min of reaction (see also sample decolorization in Figure 6b).
Figure 7 compares the performances of all Ir-NP samples after 6 min of UV-visible irradiation: the corresponding percentages of reduction were also calculated. The dependence of the percentages of reduction and rate constants (Figure 7; Table 3) on the temperature of extraction is evident. The experimental outcomes suggest that the extracts prepared in a temperature range between 65 and 80 °C when used as reaction media for the synthesis of Ir-NPs, samples Ir-NP2 and Ir-NP3 respectively, create a chemical environment passivating the metal cores that enhance their catalytic action.
The catalytic role of the environment around the nanoparticles has been also considered. To this aim, similar experiments were reproduced employing only extracts in the process of MB reduction. It is interesting noting for any extract when compared to the reference, that also the extracts showed an increase of the reaction rate in MB reduction, though less significant than IrNPs (Table 3; Figure 8), since the presence of Ir-NPs enhances the catalytic performances of the system more than $300\%$, confirming a significant catalytic activity of the Ir-NPs. Moreover, the trend of the catalytic activity is similar for the extracts and for the corresponding Ir-NPs systems with a maximum observed when an extraction temperature of 65 °C is used. Even if higher extraction temperatures increased total phenolic content, in reducing sugar content and antioxidant activity of the extracts, there was no observed corresponding improvement of the catalytic activity as well in the NPs synthesis suggesting that the chemical environment changes with the extraction temperature and it also affects the catalytic activity. Moreover, the presence of Ir-NP3 and Ir-NP4 of particles with large sizes probably reduces the available surface area reflecting on their catalytic performance.
Table 4 shows a comparison between different noble metal nanoparticles. Since there are only a few works on Ir-NPs preparation and no works on the use of a green synthesis approach, we added to the table also the methodology of other noble metal nanoparticles prepared in green extracts.
Stability and recyclability are important characteristics of a good catalyst [57]. The optimum Ir catalyst synthesized in the present work, Ir-NP2, was stable for a long period of 10 months showing a stable suspension that maintains DLS results and catalytic performance. On the other side, recyclability was not measured because of the small nanoparticle size which hinders their recovery from water. Nevertheless, this preliminary study demonstrated the importance of the extraction conditions to get nanoparticles with optimal catalytic properties for dye degradation application. Further efforts should be devoted to obtaining Ir-NPs in an easily recyclable system such as magnetic bimetallic NPs or by using other supports [57].
## 4. Conclusions
A green procedure for the preparation of ultra-small Ir-NPs using nontoxic starting materials was developed. Antioxidant activity, polyphenolic and sugar contents of the extracts obtained at different extraction temperatures increased as the extraction temperature rose from 45 °C to 100 °C. TEM characterization showed as the discrepancies in the extraction temperatures of GM extracts feebly affected the NP shape and more interestingly affected the particle size. All Ir-NPs showed one main size component of nanoparticles with a dimension in the range of 3.0–4.5 nm with the presence of another fraction of NPs with larger sizes, as well as shapes with a clear hexagonal section for Ir-NP3 and Ir-NP4 prepared with GM extracted at a higher temperature. DLS analysis confirmed the presence of an organic layer surrounding the NPs providing an increase in the hydrodynamic size of the particles. Although all Ir-NPs synthesized showed an effective catalytic activity in the reduction of MB by NaBH4, Ir-NP2 prepared with GM2 (65 °C) evidenced better performance. Probably the composition of GM2 presents the right ratio between reducing and stabilizing agents to promote the formation of NPs with homogeneous size. This study demonstrated the importance of the extraction conditions to get nanoparticles with optimal catalytic properties. An extraction temperature of 65 °C was sufficient to obtain 4.0 nm ultra-small Ir-NP with a catalytic rate of 0.527 min−1 and MB reduction of $96.1\%$ in just six min and high stability for over 10 months. The green and easy preparation procedure and the excellent catalytic performances make Ir-NPs in general and Ir-NP2 in particular optimal systems for dyes degradation application.
## References
1. Shanker U., Rani M., Jassal V.. **Degradation of hazardous organic dyes in water by nanomaterials**. *Environ. Chem. Lett.* (2017) **15** 623-642. DOI: 10.1007/s10311-017-0650-2
2. Azad M., Khan G.A., Ismail F., Ahmed W.. **Facile and efficient dye degradation using silver nanoparticles immobilized cotton substrates**. *Inorg. Chem. Commun.* (2022) **145** 109987. DOI: 10.1016/j.inoche.2022.109987
3. Jeevanandam J., Barhoum A., Chan Y.S., Dufresne A., Danquah M.K.. **Review on nanoparticles and nanostructured materials: History, sources, toxicity and regulations**. *Beilstein J. Nanotechnol.* (2018) **9** 1050-1074. DOI: 10.3762/bjnano.9.98
4. Hammami I., Alabdallah N.M., Al Jomaa A., Kamoun M.. **Gold nanoparticles: Synthesis properties and applications**. *J. King Saud. Univ. Sci.* (2021) **33** 101560. DOI: 10.1016/j.jksus.2021.101560
5. Calderón-Jiménez B., Johnson M.E., Montoro Bustos A.R., Murphy K.E., Winchester M.R., Vega Baudrit J.R.. **Silver nanoparticles: Technological advances, societal impacts, and metrological challenges**. *Front. Chem.* (2017) **5** 6. DOI: 10.3389/fchem.2017.00006
6. Khan K., Saeed K., Khan I.. **Nanoparticles: Properties, applications and toxicities**. *Arab. J. Chem.* (2019) **12** 908-931. DOI: 10.1016/j.arabjc.2017.05.011
7. Cui M.-L., Chen Y.-S., Xie Q.-F., Yang D.-P., Han M.-Y.. **Synthesis, properties and applications of noble metal iridium nanomaterials**. *Coord. Chem. Rev.* (2019) **387** 450-462. DOI: 10.1016/j.ccr.2018.12.008
8. Das A., Natarajan K., Tiwari S., Ganguli A.K.. **Nanostructures synthesized by the reverse microemulsion method and their magnetic properties**. *Mater. Res. Express* (2020) **7** 104001. DOI: 10.1088/2053-1591/abbb55
9. Deshmukh R.D., Composto R.J.. **Surface segregation and formation of silver nanoparticles created in situ in poly(methyl methacrylate) films**. *Chem. Mater.* (2007) **19** 745-754. DOI: 10.1021/cm062030s
10. Samant P., Fernandes J.B.. **In situ FTIR studies for the enhanced activity of Pt(HY) and Pt-Ru(HY) zeolite catalysts for electrooxidation of methanol in fuel cells**. *Chem. Phys. Lett.* (2020) **745** 137277-137282. DOI: 10.1016/j.cplett.2020.137277
11. Rostam-Tapeh-Esmaeil E., Golshan M., Salami-Kalajahi M., Roghani-Mamaqani H.. **Synthesis of copper and copper oxide nanoparticles with different morphologies using aniline as reducing agent**. *Solid State Commun.* (2021) **334–335** 114364-114373. DOI: 10.1016/j.ssc.2021.114364
12. Vega-Baudrit J., Gamboa S.M., Rojas E.R., Martinez V.V.. **Synthesis and characterization of silver nanoparticles and their application as an antibacterial agent**. *Int. J. Biosens. Bioelectron.* (2019) **5** 166-173. DOI: 10.15406/ijbsbe.2019.05.00172
13. Samberg M.E., Orndorff P.E., Monteiro-Riviere N.A.. **Antibacterial efficacy of silver nanoparticles of different sizes, surface conditions and synthesis methods**. *Nanotoxicology* (2011) **5** 244-253. DOI: 10.3109/17435390.2010.525669
14. Del Sole R., Mele G., Bloise E., Mergola L.. **Green aspects in molecularly imprinted polymers by biomass waste utilization**. *Polymers* (2021) **13**. DOI: 10.3390/polym13152430
15. Tarannum N., Divya N., Gautam Y.K.. **Facile green synthesis and applications of silver nanoparticles: A state-of-the-art review**. *RSC Adv.* (2019) **9** 34926-34948. DOI: 10.1039/C9RA04164H
16. Marinescu L., Ficai D., Oprea O., Marin A., Ficai A., Andronescu E., Holban A.-M.. **Optimized synthesis approaches of metal nanoparticles with antimicrobial application**. *J. Nanomater.* (2020) **2020** 6651207. DOI: 10.1155/2020/6651207
17. Castillo-Henríquez L., Alfaro-Aguilar K., Ugalde-Álvarez J., Vega-Fernández L., Montes de Oca-Vásquez G., Vega-Baudrit J.R.. **Green synthesis of gold and silver nanoparticles from plant extracts and their possible applications as antimicrobial agents in the agricultural area**. *Nanomaterials* (2020) **10**. DOI: 10.3390/nano10091763
18. Alvarado M.A., Guzmán O.N., Solís N.M., Vega-Baudrit J.. **Recycling and elimination of wastes obtained from agriculture by using nanotechnology: Nanosensors**. *Int. J. Biosen. Bioelectron.* (2017) **3** 368-375. DOI: 10.15406/ijbsbe.2017.03.00084
19. Rautela A., Rani J., Debnath (Das) M.. **Green synthesis of silver nanoparticles from tectona grandis seeds extract: Characterization and mechanism of antimicrobial action on different microorganisms**. *J. Anal. Sci. Technol.* (2019) **10** 5. DOI: 10.1186/s40543-018-0163-z
20. Balalakshmi C., Gopinath K., Govindarajanc M., Lokesh R., Arumugam A., Alharbi N.S., Kadaikunnan S., Khaled J.M., Benelli G.. **Green synthesis of gold nanoparticles using a cheap sphaeranthus indicus extract: Impact on plant cells and the aquatic crustacean artemia nauplii**. *Coord. Chem. Rev.* (2019) **387** 450-462. DOI: 10.1016/j.jphotobiol.2017.06.040
21. Sasidharan D., Namitha T.R., Johnson S.P., Jose V., Mathew P.. **Synthesis of silver and copper oxide nanoparticles using myristica fragrans fruit extract: Antimicrobial and catalytic applications**. *Sustain. Chem. Pharm.* (2020) **16** 100255. DOI: 10.1016/j.scp.2020.100255
22. Alkhalaf M.I., Hussein R.H., Hamza A.. **Green synthesis of silver nanoparticles by nigella sativa extract alleviates diabetic neuropathy through anti-inflammatory and antioxidant effects**. *Saudi J. Biol. Sci.* (2020) **27** 2410-2419. DOI: 10.1016/j.sjbs.2020.05.005
23. Sk I., Khan M.A., Haque A., Ghosh S., Roy D., Homechuadhuri S., Alam A.. **Synthesis of gold and silver nanoparticles using malva verticillata leaves extract: Study of gold nanoparticles catalysed reduction of nitro-schiff bases and antibacterial activities of silver nanoparticles**. *Curr. Res. Green Sustain. Chem.* (2020) **3** 100006. DOI: 10.1016/j.crgsc.2020.05.003
24. Zia M., Gul S., Akhtar J., Haq I.U., Abbasi B.H., Hussain A., Naz S., Chaudhary M.F.. **Green synthesis of silver nanoparticles from grape and tomato juices and evaluation of biological activities**. *IET Nanobiotechnol.* (2017) **11** 193-199. DOI: 10.1049/iet-nbt.2015.0099
25. Muhlack R.A., Potumarthi R., Jeffery D.W.. **Sustainable wineries through waste valorization: A review of grape marc utilization for value-added products**. *Water Manag.* (2018) **72** 99-118. DOI: 10.1016/j.wasman.2017.11.011
26. Yamamoto H., Maity P., Takahata R., Yamazoe S., Koyasu K., Kurashige W., Negishi Y., Tsukuda T.. **Monodisperse iridium clusters protected by phenylacetylene: Implication for size-dependent evolution of binding sites**. *J. Phys. Chem. C* (2017) **121** 10936-11094. DOI: 10.1021/acs.jpcc.6b12121
27. Skiba M., Vorobyova V.. **Green synthesis of silver nanoparticles using grape pomace extract prepared by plasma-chemical assisted extraction method**. *Mol. Cryst. Liq. Cryst.* (2018) **674** 142-151. DOI: 10.1080/15421406.2019.1578520
28. Krishnaswamy K., Vali H., Orsat V.. **Value-adding to grape waste: Green synthesis of gold nanoparticles**. *J. Food Eng.* (2014) **142** 210-220. DOI: 10.1016/j.jfoodeng.2014.06.014
29. Bastos-Arrieta J., Antonio Florido A., Pérez-Ràfols C., Serrano N., Fiol N., Poch J., Villaescusa I.. **Green synthesis of Ag nanoparticles using grape stalk waste extract for the modification of screen-printed electrodes**. *Nanomaterials* (2018) **8**. DOI: 10.3390/nano8110946
30. Cui M., Zhou J., Zhao Y., Song Q.. **Facile synthesis of iridium nanoparticles with superior peroxidase-like activity for colorimetric determination of H**. *Sens. Actuators B Chem.* (2017) **243** 203-210. DOI: 10.1016/j.snb.2016.11.145
31. Cui M., Huang X., Zhang X., Xie Q., Yang D.. **Ultra-small iridium nanoparticles as active catalysts for the selective and efficient reduction of nitroarenes**. *New J. Chem.* (2020) **44** 18274-18280. DOI: 10.1039/D0NJ03621H
32. Dewanto V., Wu X., Adom K.K., Liu R.H.. **Thermal processing enhances the nutritional value of tomatoes by increasing total antioxidant activity**. *J. Agric. Food Chem.* (2002) **50** 3010-3014. DOI: 10.1021/jf0115589
33. Re R., Pellegrini N., Proteggente A., Pannala A., Yang M., Rice-Evans C.. **Antioxidant activity applying an improved ABTS radical cation decolorization assay**. *Free Radic. Biol. Med.* (1999) **26** 1231-1237. DOI: 10.1016/S0891-5849(98)00315-3
34. Antony A., Farid M.. **Effect of temperatures on polyphenols during extraction**. *Appl. Sci.* (2017) **12**. DOI: 10.3390/app12042107
35. Lafka T.-I., Vassilia Sinanoglou V., Lazos E.S.. **On the extraction and antioxidant activity of phenolic compounds from winery wastes**. *Food Chem.* (2007) **104** 1206-1214. DOI: 10.1016/j.foodchem.2007.01.068
36. Spigno G., Tramelli L., De Faveri D.M.. **Effects of extraction time, temperature and solvent on concentration and antioxidant activity of grape marc phenolics**. *J. Food Eng.* (2007) **81** 200-208. DOI: 10.1016/j.jfoodeng.2006.10.021
37. Yammine S., Delsart C., Vitrac X., Mietton Peuchot M., Ghidossi R.. **Characterisation of polyphenols and antioxidant potential of red and white pomace by-product extracts using subcritical water extraction**. *OENO One* (2020) **54** 263-278. DOI: 10.20870/oeno-one.2020.54..2346
38. Castrica M., Rebucci R., Giromini C., Tretola M., Cattaneo D., Baldi A.. **Total phenolic content and antioxidant capacity of agri-food waste and by-products**. *Ital. J. Anim. Sci.* (2019) **18** 336-341. DOI: 10.1080/1828051X.2018.1529544
39. Lapornik B., Prošek M., Wondra A.G.. **Comparison of extracts prepared from plant by-products using different solvents and extraction time**. *J. Food Eng.* (2005) **71** 214-222. DOI: 10.1016/j.jfoodeng.2004.10.036
40. Wang H., Ye Y.-H., Wang H.-H., Liu J., Liu Y.-J., Jiang B.-W.. **HPLC-QTOF-MS/MS profiling, antioxidant, and α-glucosidase inhibitory activities of pyracantha fortuneana fruit extracts**. *J. Food Biochem.* (2019) **43** e12821. DOI: 10.1111/jfbc.12821
41. Teixeira N., Mateus N., de Freitas V., Oliveira G.. **Wine industry by-product: Full polyphenolic characterization of grape stalks**. *Food Chem.* (2018) **268** 110-117. DOI: 10.1016/j.foodchem.2018.06.070
42. Larrauri J.A., Rupérez P., Saura-Calixto F.. **Effect of drying temperature on the stability of polyphenols and antioxidant activity of red grape pomace peels**. *J. Agric. Food Chem.* (1997) **45** 1390-1393. DOI: 10.1021/jf960282f
43. Kobayashi H., Yamauchi M., Kitagawa H.. **Finding hydrogen-storage capability in iridium induced by the nanosize effect**. *J. Am. Chem. Soc.* (2012) **134** 6893-6895. DOI: 10.1021/ja302021d
44. Yazdi M., Yousefvand A., Hosseini H.A., Mirhosseini S.A.. **Green synthesis of silver nanoparticles using nisin and its antibacterial activity against pseudomonas aeruginosa**. *Adv. Biomed. Res.* (2022) **11** 56. DOI: 10.4103/abr.abr_99_21
45. Raja S., Ramesh V., Thivaharan V.. **Green biosynthesis of silver nanoparticles using Calliandra haematocephala leaf extract, their antibacterial activity and hydrogen peroxide sensing capability**. *Arab. J. Chem.* (2017) **10** 253-261. DOI: 10.1016/j.arabjc.2015.06.023
46. Wang Z.. **Iron complex nanoparticles synthesized by eucalyptus leaves**. *ACS Sustain. Chem. Eng.* (2013) **1** 1551-1554. DOI: 10.1021/sc400174a
47. Pujol D., Liu C., Fiol N., Olivella M.À., Gominho J., Villaescusa I., Pereira H.. **Chemical characterization of different granulometric fractions of grape stalks waste**. *Ind. Crops Prod.* (2013) **50** 494-500. DOI: 10.1016/j.indcrop.2013.07.051
48. Lionetto F., Del Sole R., Cannoletta D., Vasapollo G., Maffezzoli A.. **Monitoring wood degradation during weathering by cellulose crystallinity**. *Materials* (2012) **5** 1910-1922. DOI: 10.3390/ma5101910
49. Khan I., Saeed K., Zekker I., Zhang B., Hendi A.H., Ahmad A., Ahmad S., Zada N., Ahmad H., Ali Shah L.. **Methylene blue: Its properties, uses, toxicity and photodegradation**. *Water* (2022) **14**. DOI: 10.3390/w14020242
50. Suvith V.S., Philiph D.. **Catalytic degradation of methylene blue using biosynthesized gold and silver nanoparticles**. *Spectrochim. Acta A Mol. Biomol. Spectrosc.* (2014) **118** 526-532. DOI: 10.1016/j.saa.2013.09.016
51. Vidhu A.A., Philiph D.. **Spectroscopic, microscopic and catalytic properties of silver nanoparticles synthesized using Saraca indica flower**. *Spectrochim. Acta A Mol. Biomol. Spectrosc.* (2014) **117** 102-108. DOI: 10.1016/j.saa.2013.08.015
52. Mowry S., Ogreen P.J.. **Kinetics of methylene blue reduction by ascorbic acid**. *J. Chem. Edu.* (1999) **76** 970-973. DOI: 10.1021/ed076p970
53. Bonet F., Delmas V., Grugeon S., Herrera Urbina R., Silvert P.-Y.. **Synthesis of monodisperse Au, Pt, Pd, Ru and Ir nanoparticles in ethylene glycol**. *NanoStruct. Mat.* (1999) **11** 1277-1284. DOI: 10.1016/S0965-9773(99)00419-5
54. Chakrapani K., Sampath S.. **Spontaneous assembly of iridium nanochain-like structures: Surface enhanced Raman scattering activity using visible light**. *Chem. Commun.* (2014) **50** 3061-3063. DOI: 10.1039/C3CC49690B
55. Dhanalakshmi M., Lakshmi Prabavathi S., Saravanakumar K., Filip Jones B., Muthuraj V.. **Iridium nanoparticles anchored WO**. *Chem.Phys. Lett.* (2020) **745** 137285. DOI: 10.1016/j.cplett.2020.137285
56. Cui M., Zhao Y., Wang C., Song Q.. **Synthesis of 2.5 nm colloidal iridium nanoparticles with strong surface enhanced Raman scattering activity**. *Microchim. Acta* (2016) **183** 2047-2053. DOI: 10.1007/s00604-016-1846-z
57. Ismail M., Khan M.I., Khan M.A., Akhtar K., Asiri A.M., Khan S.B.. **Plant-supported silver nanoparticles: Efficient, economically viable and easily recoverable catalyst for the reduction of organic pollutant**. *Appl. Organomet. Chem.* (2019) **33** e4971. DOI: 10.1002/aoc.4971
|
---
title: 'Mitotic CDK1 and 4E-BP1 II: A single phosphomimetic mutation in 4E-BP1 induces
glucose intolerance in mice'
authors:
- Simon Cao
- Michael J. Jurczak
- Yoko Shuda
- Rui Sun
- Masahiro Shuda
- Yuan Chang
- Patrick S. Moore
journal: PLOS ONE
year: 2023
pmcid: PMC10004604
doi: 10.1371/journal.pone.0282914
license: CC BY 4.0
---
# Mitotic CDK1 and 4E-BP1 II: A single phosphomimetic mutation in 4E-BP1 induces glucose intolerance in mice
## Abstract
### Objective
Cyclin-dependent kinase 1 (CDK1)/cyclin B1 phosphorylates many of the same substrates as mTORC1 (a key regulator of glucose metabolism), including the eukaryotic initiation factor 4E-binding protein 1 (4E-BP1). Only mitotic CDK1 phosphorylates 4E-BP1 at residue S82 in mice (S83 in humans), in addition to the common 4E-BP1 phospho-acceptor sites phosphorylated by both CDK1 and mTORC1. We examined glucose metabolism in mice having a single aspartate phosphomimetic amino acid knock in substitution at the 4E-BP1 serine 82 (4E-BP1S82D) mimicking constitutive CDK1 phosphorylation.
### Methods
Knock-in homozygous 4E-BP1S82D and 4E-BP1S82A C57Bl/6N mice were assessed for glucose tolerance testing (GTT) and metabolic cage analysis on regular and on high-fat chow diets. Gastrocnemius tissues from 4E-BP1S82D and WT mice were subject to Reverse Phase Protein Array analysis. Since the bone marrow is one of the few tissues typically having cycling cells that transit mitosis, reciprocal bone-marrow transplants were performed between male 4E-BP1S82D and WT mice, followed by metabolic assessment, to determine the role of actively cycling cells on glucose homeostasis.
### Results
Homozygous knock-in 4E-BP1S82D mice showed glucose intolerance that was markedly accentuated with a diabetogenic high-fat diet ($$p \leq 0.004$$). In contrast, homozygous mice with the unphosphorylatable alanine substitution (4E-BP1S82A) had normal glucose tolerance. Protein profiling of lean muscle tissues, largely arrested in G0, did not show protein expression or signaling changes that could account for these results. Reciprocal bone-marrow transplantation between 4E-BP1S82D and wild-type littermates revealed a trend for wild-type mice with 4E-BP1S82D marrow engraftment on high-fat diets to become hyperglycemic after glucose challenge.
### Conclusions
4E-BP1S82D is a single amino acid substitution that induces glucose intolerance in mice. These findings indicate that glucose metabolism may be regulated by CDK1 4E-BP1 phosphorylation independent from mTOR and point towards an unexpected role for cycling cells that transit mitosis in diabetic glucose control.
## Introduction
Eukaryotic Initiation Factor 4E-Binding Protein 1 (4E-BP1), a kinase target for mechanistic target of rapamycin complex 1 (mTORC1) and cyclin-dependent kinase 1 (CDK1), regulates metabolic processes related to cellular energy levels and mitochondrial dynamics [1–4]. When 4E-BP1 is un-phosphorylated, it binds to the mRNA cap-binding protein eIF4E, preventing cap-dependent translation of specific mRNA targets [5]. Poly-phosphorylation of 4E-BP1 by mTORC1 during interphase dissociates 4E-BP1 from eIF4e, resuming cap-dependent protein synthesis [6].
During mitotic entry, CDK1 phosphorylates raptor, which shuts off mTORC1 activity [7]. CDK1 then subsumes the role of mTORC1 to phosphorylate some of the same targets as mTORC1, including 4E-BP1 [8–10]. Both mTORC1 and CDK1 phosphorylate 4E-BP1 at identical residues threonine (T)37, T46, T70, and serine (S)65, except only CDK1 additionally phosphorylates 4E-BP1 at S82 in mice (S83 in humans) [8, 11]. 4E-BP1 S82 phosphorylation by CDK1 is a robust molecular signature for mitosis [8, 9, 11] but its effect on 4E-BP1 function is unknown [11].
Both the mTOR pathway and 4E-BP1 play a critical role glucose tolerance and diabetes [12, 13]. Rapamycin inhibition of mTOR in rodents mimics caloric deprivation and is associated with decreased insulin sensitivity attributable to secondary effects on mTORC2 [12, 14, 15]. This effect has been referred to as “benevolent pseudo-diabetes” and may even be associated with increased longevity [16]. Concordantly, 4E-BP1 overexpression or activation has been linked to increased glucose tolerance in mice while knockout of 4E-BP1 and 4E-BP2 in mice results in insulin resistance, increased sensitivity to diet-induced obesity, and increased muscle lean mass and lipid accumulation [17–20]. The primary target of 4E-BP1, eIF4E, has also been shown to regulate lipid processing, storage, and weight gain in the context of a high fat diet in mice [21].
In this study, we find that a knock-in point mutation replacement of S82 with aspartic acid (S82D) in mice, but not replacement with alanine (S82A), results in reduced glucose tolerance in the setting of intact mTOR and CDK1 activity. These effects appear to be independent of perturbations to mTOR, CDK1, or insulin signaling pathways in lean muscle tissue as measured by reverse phase protein arrays (RPPA). Since the hematopoietic cell compartment is one of the few adult tissues with active cell cycling requiring CDK1/B1 activity, we performed bone marrow transplants between wild-type and transgenic knock-in S82D littermate mice. Glucose tolerance in transplanted mice was more similar to donor than to recipient genotypes in a high fat diet-induced insulin resistance model, suggesting an under-appreciated role for cycling hematopoietic cells in murine total body glucose regulation. Taken together, our findings lend support to studies showing that glucose intolerance in mice [12, 14, 17, 18] may be related to the phosphorylation status of 4E-BP1, particularly at 4E-BP1 residue S82, a residue that is phosphorylated by CDK1 but not mTOR.
## Experimental design
This study was designed to uncover metabolic defects in mice having a 4E-BP1 aspartate substitution (knock-in) point mutation at an amino acid site known to be phosphorylated by CDK1 during mitosis. Knock-in homozygous mice were compared to littermate control WT mice generated by heterozygous breeding. Defects in glucose tolerance for knock-in and control mice fed RCD and HFD were determined by glucose challenge, and metabolic changes were determined by housing in specialized metabolic cages. Longevity was determined by following a cohort of knock-in and control littermate mice under typical animal husbandry conditions until death or $20\%$ peak weight loss.
## Generation of 4E-BP1S82D and 4E-BP1S82A knock-in mouse
Transgenic knock-in mice were generated by a commercial facility (Gen-O-Way) by homologous recombination. A targeting vector contained mutated exon 2 (4E-BP1S82D: S82AGC-to-D82GAC; 4E-BP1S82A: S82AGC-to-A82GCT) with an upstream loxP-Neo-loxP cassette as well as both 5’ and 3’ intronic sequences surrounding the exon 2 (S1a Fig). Briefly, the targeting vector was introduced to the mouse embryonic stem (ES) cells and targeted cells were selected with G418. The successfully targeted ES cells were identified by southern hybridization (S1b and S1c Fig) and injected into blastocysts to develop the chimeric mice. The chimeric male mice were mated with C57BL/6 Cre female mice to excise the loxP-Neo cassette. Mice harboring germline-transmitted 4E-BP1S82D or 4E-BP1S82A allele were selected as the heterozygous founders, which are in a C57BL/6N genetic background. Finally, 4E-BP1S82D and 4E-BP1S82A homozygous mice were established by heterozygous breeding. The identity of 4E-BP1S82D and 4E-BP1S82A was verified by genotyping PCR (S1d Fig). Confirmation of the knock-in mutation and absence of off-target mutation was performed by whole genome sequencing.
## Mouse colony maintenance, irradiation and longevity studies
Mice breeding and long-term monitoring experiment was approved by the Institutional Animal Care and Use Committee (IACUC), University of Pittsburgh (IACUC experimental protocol#18012088). 4E-BP1S82D homozygous and WT mice were established by 4E-BP1S82D/WT heterozygous breeding. The mice were fed a regular chow diet (RC; ProLab IsoPro RMH 3,000; kcal provided as approximately $26\%$ protein, $14\%$ fat, and $60\%$ carbohydrate) though the longevity study. High-fat diet (HFD) was purchased from Research Diets (D12492) and provided kcal as approximately $20\%$ protein, $60\%$ fat and $20\%$ carbohydrate. Body weight and changes in health condition were monitored weekly. Mice with $20\%$ peak weight loss and/or severe illness were euthanized. For irradiation experiments, 11~13-week-old mice were subjected to total body irradiation at 9 Gy. Mice that survived from acute irradiation syndrome were monitored daily. Euthanasia criteria are the same as in long-term observation mice.
Per protocol, mice were sacrificed by using $100\%$ CO2 followed by cervical dislocation if the mice developed tumors greater than 1.8 cm in diameter, or if the mice showed any signs of persistent mobidity such as loss in weight greater than $20\%$, lethargy, unwillingness to ambulate, hunched posturing and ruffled fur. No invasive procedures likely to produce moderate to severe pain were performed; 3–$5\%$ isoflurane inhalant induction and maintenance was performed for genotyping studies.
## Metabolic studies
The major determinants of whole-body energy balance were assessed in the Sable Systems Promethion Multi-plexed Metabolic cage system. Mice were individually housed in a home cage setting for 72 hours during which feeding, activity, energy expenditure and respiratory exchange ratio were continuously monitored. The first 24 h were considered acclimation and not included in the analysis such that data shown represent 48 h of data beginning on day two of housing. Body composition was measured by EchoMRI.
Glucose tolerance tests were performed after a 6 h morning fast (7am-1pm). Following collection of a basal blood sample ($t = 0$) by tail bleed, mice received an intraperitoneal bolus injection of glucose at 1.5 g/kg body weight followed by blood sampling at set intervals (15, 30, 45, 60 and 120 min) for blood glucose measurements and plasma insulin determination. Blood glucose was measured using a Bayer Contour Next glucometer and plasma insulin measured by chemiluminescence ELISA (Stellux, ALPCO).
## Bone marrow transplantation
Recipient mice were exposed to 10 Gy total body irradiation in a Shepherd Mark I Model 68 137Cs gamma-irradiator (J.L. Shepherd & Associates) at a dose rate of ~70 Rad/min. Twenty hours after the irradiation of the recipient mice, donor mice were euthanized and whole bone marrow was flushed from femurs (18-gauge needle) and tibias (28-gauge needle) using cold PBS. Bone marrow was collected by centrifugation and red blood cells were lysed. Bone marrow cells were again collected by centrifugation, counted, and resuspended at 1 x107 cells/mL (10 million/mL) in PBS. 1 x106 (1 million) cells were injected into the tail vein of recipient mice. Bone marrow was fully reconstituted at 6–8 weeks and engraftment of the heterologous donor marrow was confirmed by bone marrow cell PCR [22].
## Protein isolation from tissue for reverse phase protein array (RPPA)
Tissue from the right gastrocnemius was isolated immediately following mouse sacrifice and frozen at -80°C until assayed. Frozen tissue was broken up by tweezers and collected in lysis buffer ($1\%$ Triton X-100, 150 mM NaCl, 1.5 mM MgCl2, 50mM HEPES, pH 7.4, 1 mM EGTA, 100 mM NaF, 10 mM Sodium Pyrophosphate, 1mM Na3VO4, $10\%$ glycerol) [23] with freshly added protease inhibitor (cat. no. 05056489001; Roche) and phosphatase inhibitor (cat. no. 04906837001; Roche) within a Lysing Matrix D tube (cat. no. 116913050-CF; MP Biomedicals). Samples were homogenized using a FastPrep® FP120 Cell Disrupter (Thermo Savant) at speed 6 in three 30 second intervals with incubation on ice for 5 minutes to prevent overheating. Supernatants were sonicated on ice 3 times for 5 seconds, and protein concentrations were quantified using Bio-Rad DC protein assay (cat. no. 5000112).
For RPPA proteomics, tissue lysate prepared in lysis buffer was diluted to a concentration of 1.5 μg/μl. Samples were mixed with 4x SDS sample buffer ($40\%$ glycerol, $8\%$ SDS, 0.25M Tris-HCl, pH 6.8) with freshly added $10\%$ β-mercaptoethanol [23]. Samples were boiled for 5 minutes and stored in -80°C. RPPA analysis and hierarchical clustering analysis was performed by the RPPA Core Facility at MD Anderson Cancer Center using methods described previously [23].
## Statistical analysis
PCA was performed in R using the stats (v3.6.2) package for the protein expression dataset generated by RPPA. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to obtain lists of proteins in the IIS (map04910), mTOR (map04150), and cell cycle (map04110) pathways [24]. Pathway-specific PCA utilized sets of RPPA dataset proteins and associated phosphorylations that corresponded to proteins in each pathway.
PRISM and R were used to produce graphs and perform statistical analyses using unpaired t-tests and log-rank tests, as appropriate. The R survminer v0.4.9 package was used to produce Kaplan-Meier plots. Adjusted P-values from RPPA data were calculated using Benjamini-Hochberg false discovery rate adjustment. P-value < 0.05 was considered significant.
## Generation and characterization of 4E-BP1S82D and 4E-BP1S82A knock-in mice
To examine the effects of a constitutively phosphorylated S82 site, we established C57Bl/6N homozygous mice with a presumed phospho-mimetic amino acid substitution of aspartate for serine (4E-BP1S82D) by homologous recombination (Genoway, France) (S1 Fig). Similarly, we produced mice expressing a constitutively unphosphorylated S82 site by substitution of alanine for serine (4E-BP1S82A). Aspartate substitution is commonly used to functionally mimic the negative charge of a phosphorylated amino acid [25], though it is unknown whether this is the case for 4E-BP1S82D. In 4E-BP1S82A mice, amino acid 82 cannot be phosphorylated and remains uncharged regardless of cell cycle or CDK1 activity while mTOR or CDK1 phosphorylation at other 4E-BP1 sites remain unaffected. Correct knock-in transgenic mutation was confirmed by whole genome sequencing. No gross phenotypic changes were detected for the homozygous 4E-BP1S82D and 4E-BP1S82A knock-in mice, which had similar fertility and weight gain to wild-type (WT) littermates.
Per protocol, animals were sacrificed if reaching $20\%$ peak weight loss, tumors greater than 2 cm2, disability, or distress. The overall survival of 4E-BP1S82D mice was increased compared to WT littermates (29 mice per genotype, $$p \leq 0.03$$, log-rank test) (S2a–S2c Fig), but this was entirely attributed to WT male mice reaching end-point weight loss unexpectedly early rather than an increased longevity for 4E-BP1S82D mice. WT male mice ($$n = 14$$) had a median endpoint survival that was less than WT female mice ($$n = 15$$, $$p \leq 0.01$$, log-rank test). In comparison, 4E-BP1S82A mice overall survival was similar to that of 4E-BP1S82D mice and was not significantly different from their littermate WT mice controls.
This experiment was initiated to determine the effects of human 4E-BP1 substitutions on susceptibility to neoplastic transformation, since expression of a constitutively dephosphorylated S83 site, 4E-BP1S83A, partially reverses rodent cell transformation induced by Merkel cell polyomavirus small T antigen viral oncoprotein [8, 9, 11]. No excess tumors occurred in S82D compared to wild-type littermates, but male 4E-BP1S82D survival was diminished compared to WT males after 9 Gy irradiation (S3 Fig). No excess or unusual neoplasms were noted in either WT or 4E-BP1S82D mice and further studies on tumorigenesis/survival after irradiation were not pursued. Gross pathology of internal organs, including liver and pancreas, was unremarkable for the 29 4E-BP1S82D mice compared to 29 WT mice. Examination of liver sections by microscopy was similarly unremarkable and no tissue pathology was detected among S82D mice.
## Impaired glucose tolerance in male 4E-BP1S82D but not 4E-BP1S82A mice
To examine physiologic changes caused by the 4E-BP1S82D knock-in mutation, eight 4E-BP1S82D and eight WT littermate males aged 8–12 weeks were assessed by glucose tolerance testing (GTT) and metabolic cage analysis after conditioning for two weeks on a regular chow diet (RCD). One WT mouse had a short tail incompatible with GTT tail vein sampling and so was excluded from GTT but included in metabolic cage analyses. After GTT and metabolic cage studies, these mice were switched to a diabetogenic high fat diet (HFD) for 6 weeks pre-conditioning, and then GTT and metabolic analyses were repeated. Similarly, a separate cohort of eight 4E-BP1S82A and nine WT littermate males aged 8–12 weeks were evaluated by GTT under RCD and HFD to compare the effects of a constitutively de-phosphorylated S82 site. Due to metabolic cage space constraints, one WT mouse was dropped at random ($$n = 8$$ in each group) for metabolic cage analysis of 4E-BP1S82A and WT littermates.
On RCD, GTT revealed a non-significant trend towards increased plasma glucose levels in the 4E-BP1S82D group compared to WT male littermates throughout the 120 min study, with significantly elevated plasma glucose levels 30 min after glucose injection (Fig 1a). The area under the curve (AUC), however, was not significant (Fig 1a; $$p \leq 0.07$$, two-tailed t test). No differences in plasma insulin levels were detected (Fig 1b). In comparison, GTT for male 4E-BP1S82A mice fed RCD showed no significant difference in plasma glucose or insulin levels compared to littermate WT mice (Fig 1c and 1d), with one time point of significantly lower plasma glucose levels in the 4E-BP1S82A group at 30 min after glucose injection.
**Fig 1:** *Glucose tolerance tests for male mice fed RCD and HFD showed reduced glucose tolerance for 4E-BP1S82D mice but not 4E-BP1S82A mice.4E-BP1S82D (n = 8) and WT (n = 7) male littermates were conditioned on RCD, and glucose tolerance tests (GTT) were performed with time course and area under the curve measurements for mouse serum glucose levels (a) and mouse serum insulin levels (b). 4E-BP1S82A (n = 8) and WT (n = 9) male littermates on RCD were similarly treated and measured for serum glucose (c) and insulin (d). After HFD preconditioning for 6 weeks, glucose tolerance tests (GTT) were repeated on these mice with time course and area under the curve measurements for mouse serum glucose levels (e, g) and mouse serum insulin levels (f, h). Mean and SEM are shown, and each plotted point represents one mouse. Two-tailed t-tests were used to compare groups. Comparisons at each data point were not significant (p≥0.05) unless noted with an asterisk. * p<0.05, ** p<0.01.*
After conditioning on the HFD [26], fasting plasma glucose levels increased for both 4E-BP1S82D and WT mice as expected (mean, respectively, 197 vs. 171 mg/dl, $$p \leq 0.27$$, two-tailed t test). GTT revealed significantly increased plasma glucose levels for 4E-BP1S82D mice at each time point after glucose injection and significantly increased plasma glucose AUC, with no difference in serum insulin levels (Fig 1e and 1f). In contrast, HFD GTT for male 4E-BP1S82A mice showed no significant difference in plasma glucose or insulin levels compared to littermate WT mice (Fig 1g and 1h). This is consistent with increased insulin resistance in 4E-BP1S82D mice on HFD and may reflect an exacerbation of a modest phenotype seen on RCD. Notably, S82A mice having inactivating mutation at S82 showed reduced mean glucose levels at all time points than their wild-type controls on RCD and HFD GTT although this difference did not reach AUC significance.
In metabolic cage studies, RCD-fed 4E-BP1S82D male mice had lower body weights compared to WT mice, with differences largely attributable to lower lean mass (S4a Fig), although ad libitum RCD-fed mice in the long-term survival study did not show significant body weight differences (S2d and S2e Fig). Under RCD, we detected no differences in energy expenditure or feeding when normalized to body weight, nor differences in respiratory quotient or total activity per mouse for 4E-BP1S82D male mice (S4b–S4d and S5a–S5c Figs).
In metabolic cage studies of HFD-fed 4E-BP1S82D mice, no significant differences in total body weight (S4e Fig) or normalized energy expenditure (S4f and S5d Figs) were present between 4E-BP1S82D and WT mice. HFD-fed 4E-BP1S82D mice displayed decreased overall feeding (due to decreases in feeding during the dark cycle) (S4g and S5e Figs) and a diminished respiratory quotient compared to WT mice during both light and dark cycles (S4h Fig), but showed no differences in activity (S5f Fig).
In contrast to the metabolic changes observed in 4E-BP1S82D mice, 4E-BP1S82A male mice fed RCD and HFD had no changes in the measured metabolic parameters (S4i–S4p and S5g–S5l Figs).
## Absence of significant protein changes between 4E-BP1S82D and WT lean muscle tissues
On completion of HFD metabolic cage experiments, the S82D and WT littermate mice were sacrificed, and right gastrocnemius muscles were subjected to a 419 antibody Reverse Phase Protein Array (RPPA) analysis that includes most major known metabolic pathways having validated antibodies [23] to detect differential protein expression or phosphorylation. After controlling for false-discovery rate (FDR), no significant differences among any of the proteins/phosphoproteins was found (adj. $p \leq 0.05$, S6a Fig).
On unadjusted analysis, 52 proteins showed differences between S82D and WT tissues (S1 Table), including raptor, which was reduced in 4E-BP1S82D compared to WT muscle tissue (ratio: 0.94, $$p \leq 0.0024$$). These differences, however, were non-significant after FDR adjustment for multiple comparisons ($$p \leq 0.26$$). Gene ontology (GO) and pathway analysis based on unadjusted p-values < 0.05 using the DAVID bioinformatics resource [27] produced no significant GO term or pathway hits. Similarly, a Principal Components Analysis (PCA) and hierarchical clustering using all RPPA proteins revealed no distinct clustering for 4E-BP1S82D or WT mice (S6b and S6f Fig). Insulin signaling/insulin-like growth factor, mTOR, and cell cycle/CDK1 pathway-specific PCA also displayed no apparent clustering (S6c–S6e Fig). Taken together, these RPPA data did not reveal lean muscle protein changes or known signaling effects consistent with changes to glucose metabolism for the 4E-BP1S82D mice.
## Effect of bone marrow transplantation on the glucose intolerance phenotype
Since most cells in most non-embryonic tissues are arrested in G0, we sought to determine if reciprocal bone marrow transplantation, in which a portion of cells are actively cycling, could influence total plasma glucose response. Diabetes is a common sequela to human stem cell transplantation, but it is unknown if this is due to immunologic or transplantation drug effects [28, 29]. Conversely, data from mouse studies and case reports have demonstrated bone marrow transplantation as a method to potentially reverse type 1 and type 2 diabetic phenotypes, likely due to a stem-cell related mechanism [30–35].
Reciprocal bone-marrow transplants (BMT) were performed between 7–10 week old male 4E-BP1S82D and WT mice. After a recovery period, surviving 20–23 week old male 4E-BP1S82D mice with WT bone marrow (“4E-BP1S82D/marrowWT”) ($$n = 4$$) and WT mice with 4E-BP1S82D bone marrow (“WT/marrow4E-BP1S82D”) ($$n = 4$$) mice were used for GTT. Spontaneous re-engraftment with 4E-BP1S82D cells occurred in one 4E-BP1S82D mouse transplanted with WT bone marrow and this mouse was dropped from the analysis. Expansion of this experiment was curtailed due to the emergence of the COVID-19 pandemic.
After RCD conditioning, no significant glucose tolerance differences were found for three 4E-BP1S82D/marrowWT mice and four WT/marrow4E-BP1S82D mice (Fig 2b) and no difference in post-GTT insulin levels was observed (Fig 2c). After HFD-conditioning, WT/marrow4E-BP1S82D mice showed an overall nonsignificant trend toward elevated glucose responses during GTT compared to 4E-BP1S82D/marrowWT (AUC glucose, $$p \leq 0.19$$ and AUC insulin level, $$p \leq 0.15$$, two-tailed t test) (Fig 2d and 2e), but WT mice receiving 4E-BP1S82D marrow showed significantly elevated plasma glucose levels at 45 min ($$p \leq 0.0497$$) and 60 min ($$p \leq 0.039$$) after administration. In this small cohort, the comparative glucose intolerance trends seen with untransplanted 4E-BP1S82D mice were attenuated (Fig 1).
**Fig 2:** *WT mice transplanted 4E-BP1S82D bone marrow (WT/marrow4E-BP1S82D, n = 4) trend towards reduced glucose tolerance compared to 4E-BP1S82D mice transplanted WT bone marrow (4E-BP1S82D/marrowWT, n = 3) after HFD challenge.After confirmation of successful reciprocal bone marrow transplantation, mice were preconditioned on RCD and measured for body weight prior to glucose tolerance test (GTT) (a), serum glucose levels during GTT (b), and serum insulin levels during GTT (c). After switching from RCD to HFD, the mice were preconditioned on HFD and measured for body weight prior to glucose tolerance test (GTT) (d), serum glucose levels during GTT (e), and serum insulin levels during GTT (f). Mean and SEM are shown, and each plotted point represents one mouse. GTT results shown as time course and area under the curve graphs. Two-tailed t-tests were used to compare groups. Comparisons at each data point were not significant (p≥0.05) unless noted with an asterisk. * p<0.05.*
Our data provide evidence that a single phosphomimetic amino acid substitution in 4E-BP1 promotes glucose intolerance on HFD. Our observed glucose intolerant phenotype is consistent with previously reported effects of 4E-BP$\frac{1}{2}$ knockout and the anticipated effects of 4E-BP1 inactivation by mTORC1-induced phosphorylation [12, 14, 17, 19, 20]. Phosphorylation of 4E-BP1 at S82, however, is regulated by CDK1, not mTOR, and the mTOR pathway was not significantly affected by 4E-BP1S82D substitution as shown by RPPA analysis on mouse gastrocnemius muscle tissue. Expression of 4E-BP1 proteins for these two genotypes were indistinguishable, indicating that this effect is not due to loss of 4E-BP1 expression. Notably, circulating insulin levels did not significantly differ between S82D and WT mice prior to or during GTT.
Cell-based studies failed to resolve whether 4E-BP1S82D or 4E-BP1S82A substitutions alter cellular cap-dependent translation [11]. Cap-binding assays, for example, are unaffected in cells expressing these substituted proteins, which may indicate a cap-independent signaling role for CDK1 phosphorylation of 4E-BP1S82. Since this is only active during mitosis, 4E-BP1S82 phosphorylation is also likely to affect the small subset of cells (e.g., stem cells) actively undergoing cell division. In a separate study (under review PLoS One), 4E-BP1S82A mice reveal profound polyscystic organ disease with aging that does not occur among 4E-BP1S82D mice. Therefore, despite subtle effects of this mitotic CDK1 phosphorylation site mutation on cellular protein translation, this site has important physiological consequences to the whole animal.
Important caveats are needed for interpreting our data. Phospho-mimetics are imperfect molecular mimics for phosphorylation [25] so 4E-BP1S82D substitution may not accurately model the biological effects of endogenous mitotic S82 phosphorylation. 4E-BP1S82A substitution mutation did not lead to impaired glucose intolerance, providing confidence that a negatively-charged amino acid change at this site specifically contributes to a diabetic phenotype in mice. In contrast to CDK1 phosphorylation of S82, the aspartate substitution will introduce a negative charge at 4E-BP1 residue 82 that will persist throughout the cell cycle and in G0.
Second, our BMT studies on the role of the hematopoietic compartment in glucose tolerance involved small numbers of mice that did not allow clear statistical comparison. The results of these findings were consistent with the hematopoietic compartment having a disproportional impact on 4E-BP1S82D genotype-related tolerance to a glucose challenge after a high-fat diet. We do not know if other cycling tissue compartments (e.g., skin, gut) also contribute to 4E-BP1S82-related glucose metabolism. Any conclusions from these experiments require confirmation due to the small sample numbers available in the BMT experiments.
Mitotic CDK1-mediated 4E-BP1 phosphorylation was found as a consequence of Merkel cell polyomavirus small T antigen sequestration of anaphase promoting complex [9, 36]. This results in 4E-BP1 multi-phosphorylation that is indistinguishable from interphase mTOR phosphorylation, except at residue S82 (S83 in humans). No differences in cap-dependent translation were found by eIF4G immunoprecipitation and refseq for human cell lines having S83 mutations [11]. Further, none of the single amino acid substitutions including S83D, S83E, and S83A in human 4E-BP1 affect 4E-BP1’s 7mGTP cap-binding activity in mitotic and asynchronous cell cycle conditions [8]. Taken together, these findings suggest two non-exclusive possibilities for a mechanistic target for S82D substitution: 1) the S82D substitution affects a tissue other than the gastrocnemius muscle sampled in our RPPA, or 2) S82D substitution affects a nontranslational signaling pathway that our RPPA panel did not interrogate.
The 4E-BP1S82D mice serve as a precise genetic model to study the role of CDK1 and cell cycling in glucose metabolism. *Whole* gene knockout and drug therapies point towards the importance of 4E-BP1 phosphorylation to tumor suppression, longevity, and diabetic control [17, 18, 37–40]. These effects are often assumed to be related to mTOR activity. Here, we show that a point mutation at a site not targeted by mTOR reveals a novel role for 4E-BP1 in diabetes. While mTOR activity has been extensively examined in diabetic models, these data raise the possibility that CDK1 may also contribute to cellular regulation of glucose metabolism.
## References
1. Hulea L, Gravel SP, Morita M, Cargnello M, Uchenunu O, Im YK. **Translational and HIF-1alpha-Dependent Metabolic Reprogramming Underpin Metabolic Plasticity and Responses to Kinase Inhibitors and Biguanides**. *Cell Metab* (2018) **28** 817-32 e8. DOI: 10.1016/j.cmet.2018.09.001
2. Morita M, Gravel SP, Chenard V, Sikstrom K, Zheng L, Alain T. **mTORC1 controls mitochondrial activity and biogenesis through 4E-BP-dependent translational regulation**. *Cell Metab* (2013) **18** 698-711. DOI: 10.1016/j.cmet.2013.10.001
3. Morita M, Prudent J, Basu K, Goyon V, Katsumura S, Hulea L. **mTOR Controls Mitochondrial Dynamics and Cell Survival via MTFP1**. *Mol Cell* (2017) **67** 922-35 e5. DOI: 10.1016/j.molcel.2017.08.013
4. Papadopoli D, Boulay K, Kazak L, Pollak M, Mallette F, Topisirovic I. **mTOR as a central regulator of lifespan and aging**. *F1000Res* (2019) **8**. DOI: 10.12688/f1000research.17196.1
5. Haghighat A, Mader S, Pause A, Sonenberg N. **Repression of cap-dependent translation by 4E-binding protein 1: competition with p220 for binding to eukaryotic initiation factor-4E**. *The EMBO journal* (1995) **14** 5701-9. DOI: 10.1002/j.1460-2075.1995.tb00257.x
6. Thoreen CC, Chantranupong L, Keys HR, Wang T, Gray NS, Sabatini DM. **A unifying model for mTORC1-mediated regulation of mRNA translation**. *Nature* (2012) **485** 109-13. DOI: 10.1038/nature11083
7. Gwinn DM, Asara JM, Shaw RJ. **Raptor is phosphorylated by cdc2 during mitosis**. *PLoS One* (2010) **5** e9197. DOI: 10.1371/journal.pone.0009197
8. Velasquez C, Cheng E, Shuda M, Lee-Oesterreich PJ, Pogge von Strandmann L, Gritsenko MA. **Mitotic protein kinase CDK1 phosphorylation of mRNA translation regulator 4E-BP1 Ser83 may contribute to cell transformation**. *Proc Natl Acad Sci U S A* (2016) **113** 8466-71. DOI: 10.1073/pnas.1607768113
9. Shuda M, Velasquez C, Cheng E, Cordek DG, Kwun HJ, Chang Y. **CDK1 substitutes for mTOR kinase to activate mitotic cap-dependent protein translation**. *Proc Natl Acad Sci U S A* (2015) **112** 5875-82. DOI: 10.1073/pnas.1505787112
10. Odle RI, Walker SA, Oxley D, Kidger AM, Balmanno K, Gilley R. **An mTORC1-to-CDK1 Switch Maintains Autophagy Suppression during Mitosis**. *Mol Cell* (2020) **77** 228-40 e7. DOI: 10.1016/j.molcel.2019.10.016
11. Sun R, Cheng E, Velasquez C, Chang Y, Moore PS. **Mitosis-related phosphorylation of the eukaryotic translation suppressor 4E-BP1 and its interaction with eukaryotic translation initiation factor 4E (eIF4E)**. *J Biol Chem* (2019) **294** 11840-52. DOI: 10.1074/jbc.RA119.008512
12. Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM. **Rapamycin-induced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity**. *Science* (2012) **335** 1638-43. DOI: 10.1126/science.1215135
13. Saxton RA, Sabatini DM. **mTOR Signaling in Growth, Metabolism, and Disease**. *Cell* (2017) **168** 960-76. DOI: 10.1016/j.cell.2017.02.004
14. Deblon N, Bourgoin L, Veyrat-Durebex C, Peyrou M, Vinciguerra M, Caillon A. **Chronic mTOR inhibition by rapamycin induces muscle insulin resistance despite weight loss in rats**. *Br J Pharmacol* (2012) **165** 2325-40. DOI: 10.1111/j.1476-5381.2011.01716.x
15. Liu GY, Sabatini DM. **mTOR at the nexus of nutrition, growth, ageing and disease**. *Nat Rev Mol Cell Biol* (2020) **21** 183-203. DOI: 10.1038/s41580-019-0199-y
16. Blagosklonny MV. **Fasting and rapamycin: diabetes versus benevolent glucose intolerance**. *Cell Death Dis* (2019) **10** 607. DOI: 10.1038/s41419-019-1822-8
17. Tsai S, Sitzmann JM, Dastidar SG, Rodriguez AA, Vu SL, McDonald CE. **Muscle-specific 4E-BP1 signaling activation improves metabolic parameters during aging and obesity**. *J Clin Invest* (2015) **125** 2952-64. DOI: 10.1172/JCI77361
18. Tsai SY, Rodriguez AA, Dastidar SG, Del Greco E, Carr KL, Sitzmann JM. **Increased 4E-BP1 Expression Protects against Diet-Induced Obesity and Insulin Resistance in Male Mice**. *Cell Rep* (2016) **16** 1903-14. DOI: 10.1016/j.celrep.2016.07.029
19. Le Bacquer O, Combe K, Montaurier C, Salles J, Giraudet C, Patrac V. **Muscle metabolic alterations induced by genetic ablation of 4E-BP1 and 4E-BP2 in response to diet-induced obesity**. *Mol Nutr Food Res* (2017) **61**. DOI: 10.1002/mnfr.201700128
20. Le Bacquer O, Petroulakis E, Paglialunga S, Poulin F, Richard D, Cianflone K. **Elevated sensitivity to diet-induced obesity and insulin resistance in mice lacking 4E-BP1 and 4E-BP2**. *J Clin Invest* (2007) **117** 387-96. DOI: 10.1172/JCI29528
21. Conn CS, Yang H, Tom HJ, Ikeda K, Oses-Prieto JA, Vu H. **The major cap-binding protein eIF4E regulates lipid homeostasis and diet-induced obesity**. *Nature Metabolism* (2021) **3** 244-57. DOI: 10.1038/s42255-021-00349-z
22. Leibowitz BJ, Wei L, Zhang L, Ping X, Epperly M, Greenberger J. **Ionizing irradiation induces acute haematopoietic syndrome and gastrointestinal syndrome independently in mice**. *Nat Commun* (2014) **5** 3494. DOI: 10.1038/ncomms4494
23. Tibes R, Qiu Y, Lu Y, Hennessy B, Andreeff M, Mills GB. **Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells**. *Mol Cancer Ther* (2006) **5** 2512-21. DOI: 10.1158/1535-7163.MCT-06-0334
24. Kanehisa M, Goto S. **KEGG: Kyoto Encyclopedia of Genes and Genomes**. *Nucleic Acids Research* (2000) **28** 27-30. DOI: 10.1093/nar/28.1.27
25. Chen Z, Cole PA. **Synthetic approaches to protein phosphorylation**. *Curr Opin Chem Biol* (2015) **28** 115-22. DOI: 10.1016/j.cbpa.2015.07.001
26. Surwit RS, Kuhn CM, Cochrane C, McCubbin JA, Feinglos MN. **Diet-Induced Type II Diabetes in C57BL/6J Mice**. *Diabetes* (1988) **37** 1163-7. DOI: 10.2337/diab.37.9.1163
27. Huang da W, Sherman BT, Lempicki RA. **Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists**. *Nucleic Acids Res* (2009) **37** 1-13. DOI: 10.1093/nar/gkn923
28. Griffith ML, Jagasia M, Jagasia SM. **Diabetes Mellitus after Hematopoietic Stem Cell Transplantation**. *Endocrine Practice* (2010) **16** 699-706. DOI: 10.4158/EP10027.RA
29. Engelhardt BG, Savani U, Jung DK, Powers AC, Jagasia M, Chen H. **New-Onset Post-Transplant Diabetes Mellitus after Allogeneic Hematopoietic Cell Transplant Is Initiated by Insulin Resistance, Not Immunosuppressive Medications**. *Biol Blood Marrow Transplant* (2019) **25** 1225-31. DOI: 10.1016/j.bbmt.2019.02.001
30. Pham PV, Phan NK, Hoang DNC, Tien PV, Doan NP-V, Le PT-B. **A type 2 diabetes mellitus patient was successfully treated by autologous bone marrow-derived stem cell transplantation: A case report**. *Biomedical Research and Therapy* (2019) **6** 2966-9. DOI: 10.15419/bmrat.v6i1.516
31. Arany EJ, Waseem M, Strutt BJ, Chamson-Reig A, Bernardo A, Eng E. **Direct comparison of the abilities of bone marrow mesenchymal versus hematopoietic stem cells to reverse hyperglycemia in diabetic NOD.SCID mice**. *Islets* (2018) **10** 137-50. DOI: 10.1080/19382014.2018.1480285
32. Li M, Ikehara S. **Bone marrow stem cell as a potential treatment for diabetes**. *J Diabetes Res* (2013) **2013** 329596. DOI: 10.1155/2013/329596
33. Lv C-L, Wang J, Xie T, Ouyang J. **Bone marrow transplantation reverses new-onset immunoinflammatory diabetes in a mouse model**. *Int J Clin Exp Pathol* (2014) **7** 5327-36. PMID: 25197419
34. Calne RY, Gan SU, Lee KO. **Stem cell and gene therapies for diabetes mellitus**. *Nat Rev Endocrinol* (2010) **6** 173-7. DOI: 10.1038/nrendo.2009.276
35. Lumelsky N, Blondel O, Laeng P, Velasco I, Ravin R, McKay R. **Differentiation of Embryonic Stem Cells to Insulin-Secreting Structures Similar to Pancreatic Islets**. *Science* (2001) **292** 1389-94. DOI: 10.1126/science.1058866
36. Shuda M, Kwun HJ, Feng H, Chang Y, Moore PS. **Human Merkel cell polyomavirus small T antigen is an oncoprotein targeting the 4E-BP1 translation regulator**. *J Clin Invest* (2011) **121** 3623-34. DOI: 10.1172/JCI46323
37. Qin X, Jiang B, Zhang Y. **4E-BP1, a multifactor regulated multifunctional protein**. *Cell Cycle* (2016) **15** 781-6. DOI: 10.1080/15384101.2016.1151581
38. Musa J, Orth MF, Dallmayer M, Baldauf M, Pardo C, Rotblat B. **Eukaryotic initiation factor 4E-binding protein 1 (4E-BP1): a master regulator of mRNA translation involved in tumorigenesis**. *Oncogene* (2016) **35** 4675-88. DOI: 10.1038/onc.2015.515
39. Zid BM, Rogers AN, Katewa SD, Vargas MA, Kolipinski MC, Lu TA. **4E-BP extends lifespan upon dietary restriction by enhancing mitochondrial activity in Drosophila**. *Cell* (2009) **139** 149-60. DOI: 10.1016/j.cell.2009.07.034
40. Carvalho GB, Drago I, Hoxha S, Yamada R, Mahneva O, Bruce KD. **The 4E-BP growth pathway regulates the effect of ambient temperature on Drosophila metabolism and lifespan**. *Proc Natl Acad Sci U S A* (2017) **114** 9737-42. DOI: 10.1073/pnas.1618994114
|
---
title: 'The impact of influenza vaccination on surgical outcomes in COVID-19 positive
patients: An analysis of 43,580 patients'
authors:
- Susan M. Taghioff
- Benjamin R. Slavin
- Shefali Mehra
- Tripp Holton
- Devinder Singh
journal: PLOS ONE
year: 2023
pmcid: PMC10004617
doi: 10.1371/journal.pone.0281990
license: CC BY 4.0
---
# The impact of influenza vaccination on surgical outcomes in COVID-19 positive patients: An analysis of 43,580 patients
## Abstract
### Background
Multiple recent studies suggest a possible protective effect of the influenza vaccine against severe acute respiratory coronavirus 2 (SARS-CoV-2). This effect has yet to be evaluated in surgical patients. This study utilizes a continuously updated federated electronic medical record (EMR) network (TriNetX, Cambridge, MA) to analyze the influence of the influenza vaccine against post-operative complications in SARS-CoV-2-positive patients.
### Methods
The de-identified records of 73,341,020 patients globally were retrospectively screened. Two balanced cohorts totaling 43,580 surgical patients were assessed from January 2020-January 2021. Cohort One received the influenza vaccine six months-two weeks prior to SARS-CoV-2-positive diagnosis, while Cohort Two did not. Post-operative complications within 30, 60, 90, and 120 days of undergoing surgery were analyzed using common procedural terminology(CPT) codes. Outcomes were propensity score matched for characteristics including age, race, gender, diabetes, obesity, and smoking.
### Results
SARS-CoV-2-positive patients receiving the influenza vaccine experienced significantly decreased risks of sepsis, deep vein thrombosis, dehiscence, acute myocardial infarction, surgical site infections, and death across multiple time points($p \leq 0.05$, Bonferroni Correction $$p \leq 0.0011$$). Number needed to vaccinate (NNV) was calculated for all significant and nominally significant findings.
### Conclusion
Our analysis examines the potential protective effect of influenza vaccination in SARS-CoV-2-positive surgical patients. Limitations include this study’s retrospective nature and reliance on accuracy of medical coding. Future prospective studies are warranted to confirm our findings.
## Introduction
With over 279 million cases and 5.3 million deaths globally, the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues to alter daily life [1]. Given the relative paucity of clinical information coupled with the ubiquitous spread of SARS-CoV-2, the medical community has been challenged to provide answers. Instrumental in finding solutions to combat the current pandemic is the need for reliable and accurate clinical data. One potential answer is federated electronic medical record (EMR) networks. Such technology can analyze millions of deidentified records in minutes thereby helping to guide future prospective studies during health crises, such as the current pandemic.
One aspect of clinical practice that has been deeply impacted by the COVID-19 pandemic is the field of surgery. Unsurprisingly, non-elective surgical procedures in SARS-CoV-2-postive patients have yielded poor post-operative outcomes with significantly increased morbidity and mortality when compared with SARS-CoV-2-negative patients [2, 3]. Specifically, The COVID-Surg Collaborative has published an international cohort study of 1128 SARS-CoV-2 positive patients who underwent surgery, of whom $82\%$ of deaths were attributed to post-operative pulmonary complications; significantly higher than surgical patients negative for SARS-CoV-2 [4].
Given the early indications that SARS-CoV-2-positive status is associated with increased risk for adverse post-operative outcomes, additional insight into ways to combat this effect is essential [2, 3]. With the development of COVID vaccines, there is hope that postoperative outcomes may improve to pre-pandemic levels. However, even with the unprecedented surge in vaccine production, global demand has placed an inevitable strain on the limited supply and distribution around the world. Therefore, a large portion of the global population remains unvaccinated and vulnerable [5].
Recently, several studies have suggested that influenza vaccination is protective against adverse outcomes associated with SARS-CoV-2 including hospitalization, ICU admission, sepsis, stroke, and ED visits [6–8]. Multiple hypotheses regarding the underlying mechanism of influenza vaccination’s potential protective effect against SARS-CoV-2 have been proposed, suggesting an increase in innate immune system acitvation [9–15]. Despite increasing evidence supporting influenza vaccine protection against SARS-CoV-2, this finding has yet to be examined in surgical patients. By retrospectively reviewing over 43,000 de-identified EMRs, this study aims to investigate and characterize the potential protective effect of up-to-date influenza vaccination against various adverse post-operative outcomes in SARS-CoV-2-positive surgical patients.
## Methods
The EMRs of 73,341,020 patients aged 18–99 were retrospectively screened from January 2020-January 2021 in the TriNetX database (TriNetX Inc, Cambridge, MA) (Fig 1). TriNetX is a federated EMR network that aggregates the de-identified medical records of over 73 million patients from 56 participating healthcare organizations into a central, self-updating platform [16].
**Fig 1:** *Study design, illustrating methodology and inclusion criteria.Two equally balanced cohorts of 21,790 created using propensity score matching. Various adverse outcomes were analyzed and compared between the influenza vaccinated and non-influenza vaccinated cohorts.*
## Ethics statement
The methodology of this article was reviewed in full by the Institutional Review Board of the University of Miami. Notably, the authors were completely blinded to any identifiable information associated with the EMRs included on the federated network utilized for this analysis. Given the de-identified nature of the individual EMRs and strict Health Information Portability and Accountability Act (HIPAA)-compliant measures put in place by the federated EMR network platform, this study was granted IRB exemption status and therefore the requirement for written consent forms was waived [16].
## Inclusion criteria
Standardized Logical Observations Identifiers Names and Codes (LOINC) codes were used to identify patients who were positive for SARS-CoV-2 (LOINC: 94500–6) while undergoing surgery (LOINC: 1003143). Additionally, Current Procedural Terminology (CPT) codes were used to capture patients who had received influenza vaccination. Specifically, up-to-date influenza vaccination was defined as administration of either the trivalent live intranasal [90660] or inactivated intramuscular influenza vaccine [90653] within two weeks to six months prior to the date of surgery with a SARS-CoV-2 positive diagnosis. This timeframe was established based upon current CDC guidelines which state that full immunity is achieved two weeks from the date of influenza vaccination, with adequate levels of antibody protection lasting approximately six months prior to observation of a waning effect [17]. Any patients who were either outside of the age range or did not undergo surgery from January 2020-January 2021 while SARS-CoV-2-positive were excluded.
## Cohort balancing
Following application of inclusion and exclusion criteria, one cohort of influenza-vaccinated and one cohort of non-influenza vaccinated, SARS-CoV-2-positive surgical patients were created. Propensity score matching was conducted to minimize confounding and increase external validity. Numerous factors were matched between the two cohorts including: age, race, ethnicity, gender, musculoskeletal disease (M00-M99), hypertension (I10-I16), diabetes (E08-E13), hyperlipidemia (E78), obesity (E65-E68), heart failure (I50), heart disease (I25), chronic obstructive pulmonary disease (J44), and factors influencing health status, including smoking, body mass index (BMI), and socioeconomic status (Z00-Z99).
Using the TriNetX online database platform for real-time analyses, we performed propensity score matching to create cohorts consistent with the aforementioned criteria. Propensity score 1:1 balancing was completed via logistic regression utilizing version 3.7 of Python Software Foundation’s Scikit-Learn package (Python Software Foundation, Delaware, USA). A greedy nearest neighbor matching algorithm approach was used, setting standard differences to a value of less than 0.1 to indicate appropriate matching. To eliminate record order bias, randomization of the record order in a covariate matrix occurred before matching. Baseline characteristics with a standardized mean difference between cohorts lower than 0.1 was considered well-balanced.
## Outcomes assessed
Propensity score matching resulted in two equally-sized cohorts. Cohort One received influenza vaccination within two weeks to six months prior to undergoing surgery with a SARS-CoV-2 positive diagnosis whereas Cohort Two did not. Post-operative complications were then compared between the two cohorts 30, 60, 90, and 120 days after the index event. The following adverse outcomes were assessed using International Classification of Diseases-10 (ICD-10) codes and included Sepsis (A41.9, T81.44), deep vein thrombosis (DVT) (I82.22, I82.40-I82.89, I82.19), acute myocardial infarction (Acute MI) (I21), stroke (I63), acute respiratory distress syndrome (ARDS) (J80), pulmonary embolism (PE) (I26), surgical site infection (SSI) (T81.41, T81.42, T81.49), dehiscence (T81.30, T81.31), hematoma (L76.32), seroma (L76.34), and death.
## Effect size analysis
Using the TriNetX platform’s Analytics function, statistical analysis and logistical regression were performed by comparing indices and relative risks of outcomes only after the successful matching of cohorts with a p-value greater than 0.05. Outcomes for all measures were calculated using $95\%$ confidence intervals (CIs). All p-values were two-sided and the alpha level was set at 0.05. To account for multiple hypothesis testing, post-hoc analysis using a Bonferroni correction of $$p \leq 0.0011$$ was calculated for the 11 adverse outcomes across 4 different time points. Outcomes with p-values less than 0.05 but greater than the Bonferroni correction of $$p \leq 0.0011$$ were deemed to be nominally significant and are indicated as such versus outcomes that were found to be truly significant even following Bonferroni correction (Tables 1–3, Fig 2). Risk ratio (RR) was defined in this study as the ratio of the probability of an adverse post-operative event occurring without history of up-to-date influenza vaccination versus the probability of the adverse post-operative event occurring with history of up-to-date influenza vaccination.
**Fig 2:** *Significantly different (*) and nominally significant (**) risks & risk ratios of post-operative adverse outcomes between influenza vaccinated and non-influenza vaccinated patients within 30–120 days (Fig 2a) and 90–120 days (Fig 2b) of surgery.* denotes statistical significance following Post-Hoc testing for Multiple Hypotheses using Bonferroni Correction to alpha level of $$p \leq 0.0011$$, ** denotes nominally significant values ($p \leq 0.05$) that failed to meet statistical significance following Bonferroni correction of $$p \leq 0.0011.$$* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 Subsequently, Absolute Risk Reduction (ARR), defined as the difference in risk of adverse post-operative outcomes between the influenza vaccinated group versus non-vaccinated group was calculated for each adverse outcome. The reciprocal of ARR was then obtained to determine number needed to treat, defined in this study as number needed to vaccinate (NNV), for all statistically significant variables at 30–120 days post-operatively. The NNV calculation allowed for the quantification of the average number of SARS-CoV-2-positve patients who needed to be vaccinated against influenza two weeks to six months prior to undergoing surgery in order to prevent one adverse post-operative outcome [18, 19].
## Results
A total of 1,435,293 SARS-CoV-2-positive surgery patients met inclusion criteria and were assigned to one of two cohorts based upon influenza vaccination status. 1,413,312 patients were not up-to-date on their influenza vaccination prior to undergoing any type of surgery whereas 21,981 were current (Table 1). Between-group factors for all propensity scoring categories were found to be significantly different ($p \leq 0.0001$) prior to cohort balancing. Following propensity score matching, the differences between cohorts were no longer significant ($p \leq 0.05$), with the exception of a significant difference in the number of Native Hawaiian/Other Pacific Islander individuals ($$p \leq 0.0103$$) (Table 2). After matching, 43,580 patients remained and were divided amongst two equally-sized cohorts of 21,790 patients based upon influenza vaccination status. Each group contained equivalent proportions of females to males, with $59.5\%$ of patients being female and $40.5\%$ being male. The mean age of the influenza vaccinated group was 58.9 years while the non-influenza vaccinated group had a mean age of 59.0 years.
When compared to SARS-CoV-2-positive patients without influenza vaccination prior to surgery, SARS-CoV-2-positive patients with influenza vaccination prior to surgery experienced significantly decreased risk of sepsis within 60–120 days post-operatively with nominal significance within 30 days [($$p \leq 0.0001$$–0.0027, RR = 1.437–1.517, $95\%$ CI:1.133–1.823) NNV:223–400], significantly decreased risk of acute MI within 60–120 days post-operatively with nominal significance within 30 days [($$p \leq 0.0001$$–0.0223, RR = 1.476–2.015, $95\%$ CI:1.055–2.064) NNV: 250–715], significantly decreased risk of dehiscence within 90–120 days post-operatively with nominal significance within 30 and 60 days [($$p \leq 0.0001$$–0.0087, RR = 1.988–2.03, $95\%$CI:1.178–3.355) NNV: 715–1000], and nominally significant decreased risk of DVT within 30–120 days post-operatively [($$p \leq 0.0017$$–0.0351, RR = 1.535–1.607, $95\%$ CI:1.027–2.292) NNV: 476–1000], and (Table 3, Figs 2 & 3).
**Fig 3:** *Number needed to vaccinate with influenza immunization to prevent one of the following post-operative adverse outcomes within 30–120 days (Fig 3a) and 60–120 days (Fig 3b) of surgery in this population.*
Additionally, SARS-CoV-2-positive patients up-to-date on their influenza vaccination prior to surgery experienced a significant reduction in death within 120 days post-operatively with nominal significance at 90 days [($$p \leq 0.0002$$–0.0065, RR = 1.188–1.257, $95\%$ CI:1.049–1.345) NNV: 256–682] and had significantly fewer SSIs within 120 days post-operatively with nominal significance within 60 and 90 days [($$p \leq 0.0001$$–0.0099, RR = 1.66–2.03, $95\%$ CI:1.125–2.46) NNV: 500–833] (Table 3, Figs 2 & 3).
## Discussion
To the best of our knowledge, this study is the first to examine the potential protective effects of influenza vaccination against post-operative complications in SARS-CoV-2-positive patients undergoing any surgical procedure. Our analysis also underscores the possible utility of a global, federated EMR network during worldwide health crises.
The de-identified EMRs included in our study were evaluated for adverse outcomes at 30, 60, 90, and 120 days post-surgery (Fig 1). The upper limit of our study’s timespan was set at 120 days in order to account for the possible presence of Post-Acute COVID-19 Syndrome (PACS). PACS is associated with increased risk of illness-related fatigue, dyspnea, inflammation, and neurologic symptoms. Given previous reports of substantial improvement 16–18 weeks after onset, our 120-day endpoint ensured capture of this phenomenon [20–24].
The potential protective effect of influenza vaccination against adverse outcomes associated with SARS-CoV-2 infection has been well-documented in non-surgical patients [25–31]. One of the first studies to demonstrate this correlation was a single-center retrospective by Yang et al. [ 6]. This study found a significant reduction in hospitalization and ICU admission in influenza-vaccinated patients. Shortly after, Conlon et al. supported this finding, demonstrating that SARS-CoV-2-positive patients immunized against influenza experienced decreased risks of hospitalization, mechanical ventilation, and length of stay [7]. Another key finding was put forth in a retrospective analysis that observed a significantly decreased risk of death in COVID-positive patients current on their influenza immunization [32]. Given our present finding of significant and nominally significant reductions in risk of sepsis, DVT, Acute MI, and dehiscence across all time points 30–120 days (Fig 2A), it appears that the potential protection afforded by influenza vaccination against SARS-CoV-2 may extend to surgical patients in the postoperative period. Further supporting this proposed protective effect are the additional significantly decreased risks of SSIs and death within 120 days following surgery (Fig 2B, Table 3) SARS-CoV-2-positive surgical patients that underwent surgery are more vulnerable to adverse outcomes given the inflammatory and catabolic nature of surgery [33, 34]. Specifically, post-operative patients release an exorbitant amount of pro-inflammatory cytokines along with increased levels of cortisol [35]. This systemic inflammation, when considered alongside the baseline hypercoagulable state induced by endothelial cell invasion by COVID-19, presumably compounds risk of poor post-operative outcomes in the surgical patient population [36].
Although the exact manner by which influenza vaccination confers protection against adverse post-operative outcomes in SARS-CoV-2-positive surgical patients remains undetermined, several mechanistic theories have been hypothesized [9–15]. The proposed mechanisms converge on the premise that the influenza vaccine appears to stimulate the body’s innate immune system, thereby interfering with SARS-CoV-2 replication. One of these theories suggests that influenza immunization activates Toll-Like-Receptor-7 on cells, thereby impeding replication of single-stranded RNA viruses [11]. Alternatively, it has been proposed that influenza vaccination may prime natural killer cells, thus increasing innate immunity to combat viral antigens [14].
Another investigation demonstrated that immunization against influenza upregulates pulmonary angiotensin-converting enzyme 2 (ACE-2) receptors [10]. Downregulation of ACE-2 receptors, (observed in SARS-CoV-2-positive patients) induces pulmonary inflammation and coagulation as fewer angiotensin-II can bind to antioxidant ACE-2 receptors. As a result, the angiotensin-II instead binds to pro-inflammatory angiotensin I receptors resulting in systemic inflammation [15].
Two final postulates are notable in the context of the present study. Firstly, influenza vaccination stimulates an immunologic cascade leading to plaque stabilization, thus, decreasing risk of cardiovascular events. Vaccine-induced antibodies interact with bradykinin-2 receptors leading to increased nitric oxide production and an anti-inflammatory effect [9]. Lastly, the preparation of the trivalent influenza vaccine uses an oil-in-water squalene emulsion, known as MF59, that may induce protection against SARS-CoV-2 via host immune system stimulation [12].
Regardless of mechanism, the strong association between influenza vaccination and decreased risk of adverse post-operative outcomes in SARS-CoV-2-positive patients observed by this study merits further investigation. The hypothesis that individuals current on their influenza immunization have less baseline medical co-morbidities and risk factors for poor post-operative outcomes is supported by the “Before Matching” column in Table 2. In addition, recent literature has suggested that certain characteristics including age, gender, and BMI may dictate SARS-CoV-2 outcomes [37–43]. However, Table 2 also demonstrates the results of the stringent propensity score matching performed, accounting for numerous characteristics, which would have otherwise potentially acted as confounders.
By contextualizing study findings on a global scale, NNV has been an extremely valuable tool for effect size analysis throughout the SARS-CoV-2 pandemic to measure the protective effects of both influenza and coronavirus vaccines [8, 44]. Specifically, our NNV calculations revealed that within 120 days, 223, 250, 323, and 182 individuals would have needed to have been current on their pre-operative influenza immunization to avoid one case of sepsis, acute MI, pneumonia, and death, respectively. Given, the potential benefits elucidated by NNV calculations, ramping up influenza vaccination in parallel with COVID-19 vaccination merits strong consideration.
Even with the unprecedented fast-tracking of multiple vaccines, the fact remains that a majority of the world is not fully vaccinated against SARS-CoV-2 [45]. Furthermore, current projections suggest that numerous countries may not receive sufficient Covid-19 vaccines for years, as partitioning continues to favor nations with the highest gross domestic products [46, 47]. Given the delay of equitable access to the Covid-19 vaccine for the global community, there remains a need for preventative measures to attempt to curb disease burden in affected patients. Influenza vaccination maintains a status as a well-accepted and abundantly available option for the global community. Its low cost and predictable side effect profile make influenza vaccination a valuable provisional option to consider for individuals lacking access to COVID-19 vaccines, and may yield a benefit for individuals with predicted high risk of surgical mortality [43, 48]. Influenza vaccination may also prove useful for citizens with coronavirus vaccine access, but a hesitancy to consent to novel vaccines [49, 50]. Thus, pre-operative influenza vaccination may be beneficial in reducing SARS-CoV-2 morbidity and mortality in post-operative patients world-wide [51–53]. Furthermore, the global population may benefit from influenza vaccination as it can act to prevent a coronavirus and influenza ‘twindemic’ which could overwhelm healthcare resources [54].
This study is limited by its retrospective nature and reliance on the accuracy of medical coding. This study is also limited by its time window of January 2020-January 2021, prior to the widespread availability of SARS-CoV-2 vaccination, thereby prohibiting analysis of any potential synergistic or interactive effects between the influenza and COVID-19 vaccines in this patient population. Federated EMR networks lend themselves to measures of association, but not causation, thus future prospective studies are warranted to validate this study’s finding that an emphasis on influenza vaccination will improve post-operative outcomes in COVID-positive surgery patients.
## Conclusion
Using a federated EMR network of over 73 million patients globally, this analysis examines the potential protective effect of influenza vaccination against adverse post-operative outcomes within 30, 60, 90, and 120 days of SARS-CoV-2-positive surgery patients. Significant findings in favor of the influenza vaccine in mitigating the risks of sepsis, acute MI, and dehiscence across all multiple time points while decreasing the risk of SSI and death by 120 days suggest a potential protective effect that merits further investigation and validation with prospective studies, such as randomized control trials.
## References
1. 1Center JHCR. COVID-19 Map. https://coronavirus.jhu.edu/map.html
2. Hamid HKS. **Surgery during the COVID-19 pandemic**. (2020.0) **396** e73. DOI: 10.1016/S0140-6736(20)32281-9
3. Lei S, Jiang F, Su W. **Clinical characteristics and outcomes of patients undergoing surgeries during the incubation period of COVID-19 infection**. (2020.0) **21** 100331. DOI: 10.1016/j.eclinm.2020.100331
4. Collaborative CO. **Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study**. (2020.0) **396** 27-38. DOI: 10.1016/S0140-6736(20)31182-X
5. 5Center JHCR. Understanding Vaccination Progress by Country. https://coronavirus.jhu.edu/vaccines/international
6. Yang MJ, Rooks BJ, Le TT. **Influenza Vaccination and Hospitalizations Among COVID-19 Infected Adults**. (2021.0) **34** S179-S182. DOI: 10.3122/jabfm.2021.S1.200528
7. Conlon A, Ashur C, Washer L, Eagle KA, Hofmann Bowman MA. **Impact of the influenza vaccine on COVID-19 infection rates and severity**. (2021.0) **49** 694-700. DOI: 10.1016/j.ajic.2021.02.012
8. Taghioff SM, Slavin BR, Holton T, Singh D. **Examining the potential benefits of the influenza vaccine against SARS-CoV-2: A retrospective cohort analysis of 74,754 patients**. (2021.0) **16** e0255541. DOI: 10.1371/journal.pone.0255541
9. Behrouzi B, Araujo Campoverde MV, Liang K. **Influenza Vaccination to Reduce Cardiovascular Morbidity and Mortality in Patients With COVID-19: JACC State-of-the-Art Review**. (2020.0) **76** 1777-1794. DOI: 10.1016/j.jacc.2020.08.028
10. Bourgonje AR, Abdulle AE, Timens W. **Angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 and the pathophysiology of coronavirus disease 2019 (COVID-19)**. (2020.0) **251** 228-248. DOI: 10.1002/path.5471
11. Eldanasory OA, Rabaan AA, Al-Tawfiq JA. **Can influenza vaccine modify COVID-19 clinical course?**. (2020.0) **37** 101872. DOI: 10.1016/j.tmaid.2020.101872
12. Gupta T, Gupta SK. **Potential adjuvants for the development of a SARS-CoV-2 vaccine based on experimental results from similar coronaviruses**. (2020.0) **86** 106717. DOI: 10.1016/j.intimp.2020.106717
13. Jehi L, Ji X, Milinovich A. **Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients**. (2020.0) **158** 1364-1375. DOI: 10.1016/j.chest.2020.05.580
14. Jewett A.. **The Potential Effect of Novel Coronavirus SARS-CoV-2 on NK Cells; A Perspective on Potential Therapeutic Interventions**. (2020.0) **11** 1692. DOI: 10.3389/fimmu.2020.01692
15. Ni W, Yang X, Yang D. **Role of angiotensin-converting enzyme 2 (ACE2) in COVID-19**. (2020.0) **24** 422. DOI: 10.1186/s13054-020-03120-0
16. 16TriNetX https://trinetx.com/about-trinetx/community/.
17. 17Prevention CfDCa. Key Facts About Seasonal Flu Vaccine. https://www.cdc.gov/flu/prevent/keyfacts.htm
18. Hashim A, Dang V, Bolotin S, Crowcroft NS. **How and why researchers use the number needed to vaccinate to inform decision making—a systematic review**. (2015.0) **33** 753-8. DOI: 10.1016/j.vaccine.2014.12.033
19. Tramer MR, Walder B. **Number needed to treat (or harm)**. (2005.0) **29** 576-81. DOI: 10.1007/s00268-005-7916-8
20. Arnold DT, Hamilton FW, Milne A. **Patient outcomes after hospitalisation with COVID-19 and implications for follow-up: results from a prospective UK cohort**. (2020.0). DOI: 10.1136/thoraxjnl-2020-216086
21. Carfi A, Bernabei R, Landi F. **Persistent Symptoms in Patients After Acute COVID-19**. (2020.0) **324** 603-605. DOI: 10.1001/jama.2020.12603
22. Huang C, Huang L, Wang Y. **6-month consequences of COVID-19 in patients discharged from hospital: a cohort study**. (2021.0) **397** 220-232. DOI: 10.1016/S0140-6736(20)32656-8
23. Moreno-Perez O, Merino E, Leon-Ramirez JM. **Post-acute COVID-19 syndrome. Incidence and risk factors: A Mediterranean cohort study**. (2021.0) **82** 378-383. DOI: 10.1016/j.jinf.2021.01.004
24. Nalbandian A, Sehgal K, Gupta A. **Post-acute COVID-19 syndrome**. (2021.0) **27** 601-615. DOI: 10.1038/s41591-021-01283-z
25. Belingheri M, Paladino ME, Latocca R, De Vito G, Riva MA. **Association between seasonal flu vaccination and COVID-19 among healthcare workers**. (2020.0) **70** 665-671. DOI: 10.1093/occmed/kqaa197
26. Del Riccio M, Lorini C, Bonaccorsi G, Paget J, Caini S. **The Association between Influenza Vaccination and the Risk of SARS-CoV-2 Infection, Severe Illness, and Death: A Systematic Review of the Literature**. (2020.0) **17**. DOI: 10.3390/ijerph17217870
27. Erismis B, Karabela SN, Eksi F. **Annual influenza vaccination effect on the susceptibility to COVID-19 infection**. (2021.0) **29** 14-17. DOI: 10.21101/cejph.a6573
28. Massoudi N, Mohit B. **A Case-Control Study of the 2019 Influenza Vaccine and Incidence of COVID-19 Among Healthcare Workers**. (2021.0) **41** 324-334. DOI: 10.1007/s10875-020-00925-0
29. Pawlowski C, Puranik A, Bandi H. **Exploratory analysis of immunization records highlights decreased SARS-CoV-2 rates in individuals with recent non-COVID-19 vaccinations**. (2021.0) **11** 4741. DOI: 10.1038/s41598-021-83641-y
30. Thindwa D, Garcia Quesada M, Liu Y. **Use of seasonal influenza and pneumococcal polysaccharide vaccines in older adults to reduce COVID-19 mortality**. (2020.0) **38** 5398-5401. DOI: 10.1016/j.vaccine.2020.06.047
31. Wolff GG. **Influenza vaccination and respiratory virus interference among Department of Defense personnel during the 2017–2018 influenza season**. (2020.0) **38** 350-354. DOI: 10.1016/j.vaccine.2019.10.005
32. Candelli M, Pignataro G, Torelli E. **Effect of influenza vaccine on COVID-19 mortality: a retrospective study**. (2021.0). DOI: 10.1007/s11739-021-02702-2
33. 33Birmingham Uo. COVID-19 patients who undergo surgery are at increased risk of postoperative death. ScienceDaily. https://www.sciencedaily.com/releases/2020/05/200529190739.htm
34. 34Surgeons RACo. COVID-19 patients who undergo surgery are at greatly increased risk of dying following surgery. Accessed Jun 15, 2020, https://www.surgeons.org/en/News/media-releases/covid-19-patients-who-undergo-surgery-are-at-greatly-increased-risk-of-dying-following-surgery
35. Desborough JP. **The stress response to trauma and surgery**. (2000.0) **85** 109-17. DOI: 10.1093/bja/85.1.109
36. Kichloo A, Dettloff K, Aljadah M. **COVID-19 and Hypercoagulability: A Review**. (2020.0) **26**. DOI: 10.1177/1076029620962853
37. Gerayeli FV, Milne S, Cheung C. **COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis**. (2021.0) **33** 100789. DOI: 10.1016/j.eclinm.2021.100789
38. 38Prevention CfDCa. Older Adults and COVID-19. Accessed June 15, 2021, https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/older-adults.html
39. Takahashi T, Iwasaki A. **Sex differences in immune responses**. (2021.0) **371** 347-348. DOI: 10.1126/science.abe7199
40. Booth A, Reed AB, Ponzo S. **Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis**. (2021.0) **16** e0247461. DOI: 10.1371/journal.pone.0247461
41. McGurnaghan SJ, Weir A, Bishop J. **Risks of and risk factors for COVID-19 disease in people with diabetes: a cohort study of the total population of Scotland**. (2021.0) **9** 82-93. DOI: 10.1016/S2213-8587(20)30405-8
42. Clark CE, McDonagh STJ, McManus RJ, Martin U. **COVID-19 and hypertension: risks and management. A scientific statement on behalf of the British and Irish Hypertension Society**. (2021.0) **35** 304-307. DOI: 10.1038/s41371-020-00451-x
43. **Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score**. (2021.0). DOI: 10.1093/bjs/znab183
44. **SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study**. (2021.0) **108** 1056-1063. DOI: 10.1093/bjs/znab101
45. 45Data OWi. Coronavirus (COVID-19) Vaccinations—Statistics and Research. Accessed June 15, 2021, https://ourworldindata.org/covid-vaccinations
46. Forman R, Shah S, Jeurissen P, Jit M, Mossialos E. **COVID-19 vaccine challenges: What have we learned so far and what remains to be done?**. (2021.0) **125** 553-567. DOI: 10.1016/j.healthpol.2021.03.013
47. Roghani A, Panahi S. **The global distribution of COVID-19 vaccine: The role of macro-socioeconomics measures**. (2021.0). DOI: 10.1101/2021.02.09.21251436
48. Sparrow E, Wood JG, Chadwick C. **Global production capacity of seasonal and pandemic influenza vaccines in 2019**. (2021.0) **39** 512-520. DOI: 10.1016/j.vaccine.2020.12.018
49. Dang A, Sharma J. **Assessing the Low Influenza Vaccination Coverage Rate Among Healthcare Personnel in India: A Review of Obstacles, Beliefs, and Strategies**. (2020.0) **21** 100-104. DOI: 10.1016/j.vhri.2019.08.480
50. Ortiz JR, Neuzil KM. **Influenza Immunization in Low- and Middle-Income Countries: Preparing for Next-Generation Influenza Vaccines**. (2019.0) **219** S97-S106. DOI: 10.1093/infdis/jiz024
51. Abu-Raddad LJ, Chemaitelly H, Butt AA. **Effectiveness of the BNT162b2 Covid-19 Vaccine against the B.1.1.7 and B.1.351 Variants**. (2021.0). DOI: 10.1056/NEJMc2104974
52. Shinde V, Bhikha S, Hoosain Z. **Efficacy of NVX-CoV2373 Covid-19 Vaccine against the B.1.351 Variant**. (2021.0) **384** 1899-1909. DOI: 10.1056/NEJMoa2103055
53. Thompson MG, Burgess JL, Naleway AL. **Interim Estimates of Vaccine Effectiveness of BNT162b2 and mRNA-1273 COVID-19 Vaccines in Preventing SARS-CoV-2 Infection Among Health Care Personnel, First Responders, and Other Essential and Frontline Workers—Eight U.S. Locations, December 2020-March 2021**. (2021.0) **70** 495-500. DOI: 10.15585/mmwr.mm7013e3
54. David Oshinsky AC. *How We Can Avoid a ’Twindemic’ of COVID and Flu* (2020.0)
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---
title: Characterization and Biological Activities of In Vitro Digested Olive Pomace
Polyphenols Evaluated on Ex Vivo Human Immune Blood Cells
authors:
- Claudio Alimenti
- Mariacaterina Lianza
- Fabiana Antognoni
- Laura Giusti
- Onelia Bistoni
- Luigi Liotta
- Cristina Angeloni
- Giulio Lupidi
- Daniela Beghelli
journal: Molecules
year: 2023
pmcid: PMC10004623
doi: 10.3390/molecules28052122
license: CC BY 4.0
---
# Characterization and Biological Activities of In Vitro Digested Olive Pomace Polyphenols Evaluated on Ex Vivo Human Immune Blood Cells
## Abstract
Olive pomace (OP) represents one of the main by-products of olive oil production, which still contains high quantities of health-promoting bioactive compounds. In the present study, three batches of sun-dried OP were characterized for their profile in phenolic compounds (by HPLC-DAD) and in vitro antioxidant properties (ABTS, FRAP and DPPH assays) before (methanolic extracts) and after (aqueous extracts) their simulated in vitro digestion and dialysis. Phenolic profiles, and, accordingly, the antioxidant activities, showed significant differences among the three OP batches, and most compounds showed good bioaccessibility after simulated digestion. Based on these preliminary screenings, the best OP aqueous extract (OP-W) was further characterized for its peptide composition and subdivided into seven fractions (OP-F). The most promising OP-F (characterized for its metabolome) and OP-W samples were then assessed for their potential anti-inflammatory properties in ex vivo human peripheral mononuclear cells (PBMCs) triggered or not with lipopolysaccharide (LPS). The levels of 16 pro-and anti-inflammatory cytokines were measured in PBMC culture media by multiplex ELISA assay, whereas the gene expressions of interleukin-6 (IL-6), IL-10 and TNF-α were measured by real time RT-qPCR. Interestingly, OP-W and PO-F samples had a similar effect in reducing the expressions of IL-6 and TNF-α, but only OP-W was able to reduce the release of these inflammatory mediators, suggesting that the anti-inflammatory activity of OP-W is different from that of OP-F.
## 1. Introduction
Climate change, loss of biodiversity and environmental pollution increase are challenges that must be faced by improving the relationship between humans and ecosystems. With this aim, EU environmental policy and legislation strongly encourages the reuse and recycling of waste, a reduction in harmful chemicals, and the use of environmentally friendly compounds that are also technologically satisfactory and economically convenient.
Agri-food industries are among the principal producers of waste and by-products in the world [1]. The European Union alone produces about 90 million tons of food by-products every year, with an impressively negative effect on the environment [2]. For these reasons, researchers are paying more and more attention to these wastes not only as a potential source of energy, but also as a source of bioactive molecules. In recent years, agri-food by-products have been increasingly considered for the extraction of bioactive compounds such as antioxidants, vitamins, minerals, dietary fiber, essential fatty acids, oligosaccharides and oligopeptides [3,4]. In the Mediterranean area, a huge amount of waste is generated during the olive oil production process [5].
The Mediterranean basin contains approximately $98\%$ of the planted olive (*Olea europea* L.) trees, and together with other European countries, produces $80\%$ of the world’s olive oil [6]. Consequently, the olive oil industry generates significant amounts of olive oil by-products (olive pomace, olive leaves and olive mill wastewater), which need to be managed by these countries according to strategies aimed toward reducing the impact on the environment through the sustainable re-use of agri-food waste.
Olive pomace (OP) is an olive oil by-product rich in high-value compounds (e.g., polyphenols, dietary fiber, unsaturated fatty acids, antioxidants and minerals) and, in the context of a sustainable economy, the interest in recovering and utilizing bioactive compounds to add health benefits to the diet has increased during recent years [7].
Currently, there is indeed a wide bibliography in favor of the beneficial health effects of extra virgin olive oil, as well as on the possibility of obtaining valuable bioactive compounds from the waste products of the olive oil processing process (olive pomace and olive mill wastewater). A search in PubMed using the terms “olive oil AND health” or “olive by-products AND health” produced 3073 and 85 results, respectively (updated on 2 January 2023). Indeed, besides the well-recognized healthy effects of extra virgin olive oil [8,9,10], beneficial properties have also been demonstrated for processing by-products (leaves and olive mill wastewaters) including anti-cancer [11], prevention against age-related diseases [12], cardioprotective, anti-diabetic [13] and anti-inflammatory [14], among others [7,15,16,17]. In a recent study by Markhali et al. [ 18], oleuropein, one of the most common bioactive compounds in olive oil by-products, was found to be effectively capable of rebuilding the tissue damage caused by cisplatin in the stomach and the lungs, whereas Žugčić et al. showed that the compounds found in olive leaves exerted positive effects on gut microbiota [19].
The health-promoting effects of OP have mainly been associated with the presence of antioxidants, especially those belonging to plant-specialized metabolites, attributable to five classes of polyphenols (biophenols) identified as secoiridoids, simple phenols, flavonoids, phenolic acids, and lignans [20]. Interestingly, OP extract has been demonstrated to ameliorate lipid accumulation and lipid-dependent oxidative unbalance [21]. However, high amounts of α-tocopherol (2.63 mg/100 g) and fatty acids have also been identified as bioactive compounds in OP by Nunes et al. [ 22], and a relevant contribution to the reported beneficial effects of OP in preventing cardiovascular and gut diseases has been attributed not only to polyphenols, but also to sugars and minerals present in the pomace by Ribeiro et al. [ 23].
Di Nunzio et al. [ 24] demonstrated that an aqueous OP extract was able to significantly reduce IL-8 secretion, one of the main proinflammatory cytokines, in Caco-2 cells in both basal and inflamed conditions, suggesting OP as a potential low-cost, high added-value ingredient for the formulation of functional and innovative food [24,25].
With a view to a potential use of OP in the formulation of innovative and functional foods or nutraceuticals, in this study we evaluated the prospective anti-inflammatory properties of OP compounds following digestion in the gastrointestinal (GI) compartments and passing the mucosal and intestinal barriers. Indeed, it has been observed that the bioavailability of polyphenols greatly changes during digestion, due to their different degrees of absorption, stability, solubility, and permeability [15,23,26].
To this purpose, three batches of sun-dried OP were characterized for their profiles in phenolic compounds and in vitro antioxidant properties before (methanolic extracts) and after (aqueous extracts) their simulated in vitro digestion and dialysis. The most promising aqueous extract, selected based on its composition of bioactive compounds, was further characterized and tested for its anti-inflammatory potential using PBMC cells.
PBMC cells were chosen because, circulating in the blood stream throughout the body, they represent not only the first systemic cell lines acting in the innate and adaptative immune responses, but also one of the two most represented cell categories (leukocytes vs. red blood cells) of the first tissue in which these bioactive compounds enter the body.
## 2. Results
A preliminary characterization of the three different OP batches (OP1, OP2, and OP3) was carried out to identify the OP batch with the highest potential biological properties before and after in vitro digestion. Only the OP extract with the highest potential biological properties was used for the subsequent experiments.
## 2.1. Total Phenol Content (TPC) and Phenolic Characterization of OP Extracts
The total phenol content and the individual phenolic characterization of each OP extract obtained from the three different batches of OP was determined before (methanol extracts) and after (aqueous extracts) the simulated in vitro digestion.
## 2.1.1. Total Phenol Content (TPC) and Phenolic Characterization of Methanolic OP Extracts
In addition to the evaluation of the total content of phenolic compounds (TPC), a targeted HPLC-DAD analysis was carried out on the three methanolic OP extracts to identify and quantify some of the characteristic compounds of OP belonging to different chemical classes, such as secoiridoids, catechols, diterpens, flavonoids, hydroxycinnamic and phenolic acids (Table 1). Significant differences in the phenolic content were found among the three extracts, with OP1 being the richest for most metabolites. The most abundant compound was luteolin; its highest concentration was found in OP1. The biggest differences among the extracts were found for hydroxytyrosol and tyrosol, which were in the ranges of 4.9–224.6 µg and 8.2–223.0 µg/g, respectively, with OP1 showing the maximum level, and OP3 the minimum level. Regarding hydroxycinnamic acids, caffeic and chlorogenic acids were detected in all extracts. OP2 showed the highest content of caffeic acid, while OP1 was the richest in chlorogenic acid. Gallic acid levels were below the limit of quantification (LOQ) in all samples. Considering the total targeted metabolite index (TTMI), which represents the sum of the identified compounds, its value was significantly higher in OP1 than both OP2 and OP3 (Table 1).
## 2.1.2. Total Phenol Quantification (TPC) and Phenolic Characterization of Aqueous OP Extracts
Table 2 reports the phenolic compositions and TPC of the two types of aqueous extracts (< or >3.5 kDa) obtained from the three OP batches after in vitro digestion and dialysis.
The aqueous OP extracts, characterized by the presence of bioavailable compounds (serum available) with a molecular weight (m.w.) <3.5 kDa after dialysis [23] were indicated as OP-W (1, 2 or 3); whereas the non-available digested aqueous extracts were indicated as OP-W n.a. ( 1, 2 or 3; m.w. > 3.5 kDa).
The distribution of the detected metabolites in the bioavailable and non-bioavailable aqueous extracts obtained after the in vitro digestion varied, depending on the molecule type (Table 2). As a general trend, most metabolites detected in the pomace were also found in the absorbable fraction, with a different percentage of recovery, depending on samples. Only apigenin was not found, neither in the non-absorbable or the absorbable samples.
The bioaccessibility index, calculated as the percentage of the bioactive compound which was solubilised after the intestinal dialysis in reference to its total content in the undigested food, is reported in Table 3. A high bioaccessibility (more than $80\%$) was found for hydroxytyrosol and tyrosol in OP1 and OP2, while undetectable levels of both metabolites were present in OP3, probably due to their low levels in the original pomace extract (Table 2). Similar values were found for myricetin and caffeic acid, while the bioaccessibility of pinoresinol and chlorogenic acid were slightly lower, in the range of 46–$70\%$. Oleuropein and ligstroside were found in the absorbable fraction at percentages ranging from 14 to $50\%$ for the former, and 11 to $43\%$ for the latter. A lower bioaccessibility was observed for luteolin, with a percentage of about $14\%$, with no differences among the three samples.
## 2.2. Antioxidant Properties and Reducing Power of OP Extracts
The antioxidant potential and reducing power of each OP extract obtained from the three different batches of OP before and after the simulated in vitro digestion were evaluated by three different spectrophotometric assays.
## 2.2.1. Antioxidant Properties and Reducing Power (ABTS, DPPH and FRAP Assays) of Methanolic OP Extracts
Table 4 reports the antioxidant properties and reducing powers evaluated in the methanolic extracts obtained from the three crude OP batches (1, 2, and 3). In accordance with the different results obtained for the phenolic content and TTMI index, OP1 methanolic extracts showed the highest antioxidant activity of all the antioxidant assays utilized, whereas OP2 resulted in an intermediate position between the other two extracts. Therefore, the trends of antioxidant responses resembled the trends of TPC found in the three different OP batches.
## 2.2.2. Antioxidant Properties and Reducing Power (ABTS, and FRAP Assays) of Aqueous OP-W n.a. and OP-W Extracts after In Vitro Digestion and Dialysis
Table 5 reports the results of the radical scavenging assays evaluated in the aqueous extracts obtained from the in vitro digestion and dialysis of the three OP batches (1, 2, and 3). All the digested OP samples conserved their own antioxidant properties proportionally to the content of the original bioactive compounds, therefore, the OP1 samples, both absorbable and not, showed the highest antioxidant activity when compared with the other two OP samples.
## 2.2.3. Antioxidant Property (ABTS) and TPC of Aqueous OP-W Fractions (OP-F)
Based on the previous results, OP-W1 was selected as the most promising extract, and further fractionated using reverse-phase chromatography (HPLC-DAD) on a semipreparative C18 column. Figure 1 shows the seven different major chromatographic peaks obtained. All these peaks were characterized by low hydrophobicity, as suggested by the fact that they eluted at a low concentration of acetonitrile (10–$20\%$).
The material was selectively eluted at each peak and then tested for its radical scavenging activity (ABTS assay) and TPC (Table 6). The fifth peak resulted as the most biologically active (*).
## 2.3. OP-W Peptide Identification and Possible Bioactivity
The peptide content of OP-W was detected by mass spectrometry. After digestion, we obtained 78 and 93, or 96 and 112 peptides (before and after further trypsinization) and some of them permitted the identification of 13 and 14 proteins specific to *Olea europaea* olive or to saprophytic microorganisms of the olive tree plant, respectively. Table 7 and Table 8 report the number of peptides identified in the OP-W1 samples, and the corresponding proteins were searched in both “Olea” and “Olea Europea” protein databases. Some of these proteins, such as 50S ribosomal protein L16, amine oxidase, pectinesterase, 2, profilin-1, 4-coumarate-CoA ligase, and putative geraniol 10 hydroxylase, as expected, were derived from Olea europaea; whereas, others from Pseudomonas (sp. PIC125 and PIC 141) belonged to bacteria with potential as a biocontrol tool against pathogenic microorganisms (i.e., *Verticillium dahlia* Kleb.) of olive plants [27].
The profile of these peptides was checked in the open-access tool PeptideRanker (a score higher than 0.6 was considered as potentially “bioactive”) to forecast the eventuality of biological activity of a peptide sequence [28], and two or five peptides of the *Olea europaea* olive received a score between 0.66 and 0.79, or 0.63 and 0.68, before and after trypsinization, respectively. These peptides were in reference to two or four proteins, respectively (putative geraniol 10-hydroxylase, hexosyltransferase or amine oxidase, pectin esterase 2, putative geraniol 10-hydroxylase, and putative LOV domain-containing, respectively). Subsequently, the best scored peptides were submitted to BIOPEP search (accessed on 15 February 2023, h http://www.uwm.edu.pl/biochemia/index.php/pl/biopep/) to hypothesize their possible bioactivities [29], but, so far, no bioactivity has been detected for these peptides.
Furthermore, nine and six peptides (before and after trypsinization, respectively) attributable to Pseudomonas sp. PIC25 resulted in a score higher than 0.6, but none of these were recognized in the proteins identified in Table 8; neither were they present in the BIOPEP database.
## 2.4. Untargeted Metabolomics of the Most Bioactive OP-F
Table 9 reports the percentages of different metabolites identified in the fifth OP1-F sample by GCMS analysis. Among these metabolites, a high percentage (about $25\%$) were represented by antioxidant compounds such as tyrosol and 4 hexylphenol, probably responsible for the highest antioxidant activity observed in the fifth peak (Table 6). Of interest was the $7.6\%$ presence of glutamic acid, which is known to help in maintaining the integrity of the intestinal barrier, as it is incorporated into proteins during their synthesis by the “good” bacteria of the intestinal microbiota, thus, favoring their development [30,31].
## 2.5. Cellular Anti-Inflammatory Activities
To better clarify, at a molecular level, whether OP-W and OP-F aqueous extracts were able to modulate the expression of pro-inflammatory (IL-6 and TNF-α) and anti-inflammatory (IL-10) genes, a real time RT-PCR analysis was carried out on RNA of human PBMCs, which were previously in vitro supplemented, or not supplemented (CTRL), with the aqueous OP extracts (OP-W and OP-F; 2.5 μg/mL of extracts for 24 h) and then stimulated (s.) or not stimulated (n.s.) with LPS (100 ng/mL for 2.5 h). The mRNA expression levels of cytokines in OP-W s. and OP-F s. samples showed significant reductions for IL-6 (in both OP-W s. and OP-F s. cells; $p \leq 0.05$), IL-10 ($p \leq 0.01$ and < 0.05, respectively), and TNF-α ($p \leq 0.01$ and <0.05, respectively), when compared with not supplemented cells (CTRL s.) (Figure 2).
## 2.6. Cytokines Concentrations in Conditioned Medium
To confirm the anti-inflammatory effect of OP aqueous extracts (OP-W and OP-F) in LPS stimulated cells, the concentration of a panel of 16 cytokines (of IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-13, IL-15, IL-17, IL-23, IFNγ, TNF-α and TNF-β) in PBMC culture supernatants was measured by a multiplex ELISA assay. Only the cytokines IL-6, IL-8 and TNF-α resulted as detectable (the remaining were below the detection levels) and, among these, both IL-6 and TNF-α resulted as significantly reduced by the OP-W extracts ($p \leq 0.05$) (Figure 3). Interestingly, the only cytokine that resulted as detectable also in the conditioned medium of cells not stimulated by LPS was IL-8 (means ± S.E.M. were 57.8 ± 13.1, 68.3 ± 16.2, 100.6 ± 54.5 pg/mL in CTRL n.s., OP-W n.s. and OP-F n.s. cells, respectively; p = n.s.).
## 3. Discussion
Our results regarding the phenolic composition of olive pomace extracts confirm that this by-product retains most bioactive compounds present in the fruit, despite the complex chemical transformations occurring during the fruit processing and pomace storage stages.
The chemical profile of this by-product has been widely described by several authors [32,33] and our results allow the conclusion that, despite a huge variability due to the impact of both endogenous (i.e., varieties) and exogenous (i.e., agro-pedoclimatic) factors [34,35], olive pomace represents a good source of natural health-promoting compounds in concentrations much higher than virgin olive oil [36,37]. In the present study too, the evaluation of three different olive pomace samples confirmed the presence of beneficial health compounds in all the batches investigated. These OP samples, although belonging to the same cultivar planted in the same territory, were different in terms of the period of olive harvest and, consequently, their ripening state.
The main phenolic compounds identified in the three pomace samples included hydroxytyrosol, tyrosol, oleuropein, ligstroside, pinoresinol, flavonoids, and hydroxycinnamic acids, and big differences were observed among samples [36]. OP1, the sample characterized by the highest content of unripened olives, was also the sample with the highest levels of target phenols, and with the highest antioxidant activity, based on the three in vitro tests used. Indeed, the ripening state strongly influences the phenolic content and antioxidant properties, that decrease along olive maturation [38].
This OP sample also conserved its highest antioxidant properties in both the aqueous absorbable and not absorbable digested extracts (OP-W n.a. and OP-W, Table 4), being the target phenols here mainly represented. Furthermore, the fifth fraction (OP-F; Table 5), obtained by the same OP1-W sample, also conserved a very high antioxidant property. This was probably due to the high content of tyrosol ($20.49\%$) and 4 hexylphenol ($4.52\%$), that together represented more than $25\%$ of the total OP1-F sample, as testified by the metabolomic analysis (Table 8). Indeed, the free radical scavenging and metal-chelator properties of hydroxytyrosol and secoiridoid derivatives have been well recognized [39], and the high antioxidant efficiency has been attributed to the presence of the O-dihydroxymethyl moyety in the molecule, which mainly acts as a chain breaker by donating a hydrogen atom to peroxyl-radicals (ROO*). However, it has been proposed that hydroxytyrosol may confer an antioxidant protection by also reinforcing the endogenous defence systems against oxidative stress, by boosting different cellular signalling pathways [40]. A good antioxidant capacity has also been demonstrated for the 4-O-monohydroxy compounds ligstroside and tyrosol [41], and several interesting functional properties have been reported for oleuropein, including antioxidant, anti-inflammatory, anti-atherogenic, anti-cancer, and anti-microbial activities, among others [42]. Luteolin and apigenin have been confirmed as relevant components of olive pomace, as previously reported by Peralbo-Molina et al. [ 33], and are known to exert a protective effect against the deleterious effects of reactive oxygen [43].
However, in order to exert beneficial health effects, phenolic compounds in OP samples must be bioaccessible and bioavailable [44]. Bioaccessibility, which is defined as the release of a compound from its natural matrix to be available for intestinal absorption, is the first limiting factor for bioavailability. Ahmad-Qasen et al. [ 45] demonstrated that, after ingestion, the bioaccessibility of total phenolic compounds decreased during the first hour of the digestion process due to the degradation of the bioactive compounds following the pH variation and enzymatic activity, whereas for the rest of the digestion process, a constant value of TPC reached the duodenum. Furthermore, Ribeiro et al., 2020 [23], showed that after gastrointestinal digestion, more than $50\%$ of the water-soluble compounds remained bioaccessible, especially hydroxytyrosol and potassium.
Bioavailability represents the ability of a nutrient or food bioactive to be efficiently digested, absorbed and distributed to provide its beneficial effect to the organism, participating in its physiological processes and storage [46].
Despite the huge research on the biological properties of olive phenolic compounds, information relative to their bioavailability, that strongly influences the pharmacological function, is limited. Recently, however, excellent reviews on the bioavailability of bisphenols and their metabolism have been proposed [46,47,48,49]. It has been shown that after oral administration, hydroxytyrosol is dose-dependently absorbed until the saturation of the phase I metabolic processes of intestinal transporters is reached. Thereafter, the absorption stops and hydroxytyrosol seems to undergo a rapid and intense metabolism [48,50], so that only a small fraction of free (unchanged) hydroxytyrosol is detectable in plasma. Indeed, the biological role of many bioactive compounds in the human organism is attributed to their metabolites, and the intestine represents the main site where it occurs for orally administered compounds [51]. In most human studies, the hydroxytyrosol absorption rate ranges from 55 to $90\%$, circulating bound to lipoproteins, reaching its maximal concentration after 1–2 h following its administration, then the molecule rapidly becomes undetectable. As proof of its absorption, the feces and urinary excretion of hydroxytyrosol and its metabolic derivatives have usually been adopted, with excretion rates resulting as maximum at 0–4 h [52,53].
Tyrosol has generally fewer studies due to its lower bioactivity, and also lower metabolites, according to the available information [46,51].
Our results showed that most phenolic compounds present in the olive pomace extracts were still detected at high levels in the absorbable fraction obtained after in vitro digestion, indicating in most cases a very high bioaccessibility. Hydroxytyrosol, for instance, showed bioaccessibility indexes higher than $80\%$ in two out of three samples, and similar values were found for tyrosol in OP2. These results were in accordance with data reported by Ribeiro et al. [ 23], who obtained very similar values (82 and $77\%$, for hydroxytyrosol and tyrosol, respectively), demonstrating that they were the most bioaccessible compounds present in olive pomace. In a previous investigation, Seiquer et al. [ 54] also reported that the most bioaccessible and stable compounds after in vitro digestion of olive oil were tyrosol and hydroxytyrosol, and specifically, these compounds were absorbed in the intestine by passive diffusion as a result of their polar structure, thus, also demonstrating a good bioavailability, even though dissimilar to each other [47]. The passive diffusion of these compounds in the small intestine was recently confirmed by Sakavitsi et al. [ 51] who also observed a passive diffusion of caffeic acid, homovanillic acid, HT-3-O-sulphate, and 3,4-dihydroxyphenylacetic acid [48] as the main metabolites of hydroxytyrosol.
In OP1, the bioaccessibility index of tyrosol was higher than $100\%$, and the same was observed for caffeic acid. This was not surprising, suggesting that these molecules may be released from the food matrix and/or metabolized from other phenolic compounds with more complex structures [55].
Furthermore, bioavailability studies in the human body have demonstrated that absorbed oleuropein and tyrosol can be metabolized into free hydroxytyrosol, thus, increasing the concentration of HT in circulation [48,56].
However, potential beneficial effects have also been demonstrated for other compounds present in OP such as mineral, proteins and sugars [23]. In this study, the effects of sugars and minerals were not evaluated, while the possible presence of interesting peptides (after digestion of proteins) for their biological effects was investigated.
Mass spectrometry analysis of OP1-W peptide hydrolysates allowed the identification of 13 and 14 proteins specific to *Olea europaea* olive or to saprophytic microorganisms of the olive tree plant, respectively. The profile of these peptides was checked in the open-access PeptideRanker and BIOPEP tools. Overall, seven peptides of *Olea europaea* olive received a significant score. These peptides were referable to putative geraniol 10-hydroxylase, hexosyltransferase or amine oxidase, pectin esterase 2, putative geraniol 10-hydroxylase, and putative LOV domain-containing. However, none of the best scored peptides matched with the BIOPEP database.
Even if the presence of Pseudomonas sp. in OP samples could not be reconducted to any beneficial health effects for “consumers”, it was interesting to find traces of how plants and microorganisms join forces to address environmental pitfalls. Indeed, these strains of Pseudomonas spp. ( PICF141 and PIC25) have shown high in vitro inhibition ability of pathogens’ growth such as V. dahliae, responsible for Verticillium wilt of olive [27]. Furthermore, fifteen peptides attributable to Pseudomonas sp. PIC25 showed a best score, but none were recognized in the proteins identified in Table 8, neither were they present in the BIOPEP database.
Finally, we tested, ex vivo, the effects of OP1-W and OP1-F extracts on cells present in the blood, the first tissue with which these extracts come into contact as soon as they are absorbed by the intestinal wall, even before they undergo phase I and II metabolism that favors their urinary excretion [57].
To date, no data exist on the effects of predigested phenols obtained from olive by-products, neither on human blood immune cells (PBMC) withdrawn from healthy individuals, or on in vitro cell cultures.
We found that the digested absorbable OP1-W extract showed significant anti-inflammatory activity on the ex vivo human PBMC stimulated by LPS. This finding was testified by the significant reduction in pro-inflammatory cytokines IL-6 and TNF-α in conditioned medium ($p \leq 0.05$; Figure 3) and by a lowering trend of IL-8 together with a significantly lower expression of mRNAs encoding IL-6 and TNF-α ($p \leq 0.5$ and $p \leq 0.01$, respectively; Figure 2).
Furthermore, in the ex vivo evaluations, a significant down-regulation of IL-10, a typical anti-inflammatory gene, was also observed. Even if this finding seems in contrast with the previous results, we assumed that it was due to a reduced triggering of the inflammatory process (and its subsequent cascade of pro- and anti-inflammatory signals) in PBMC pre-treated with OP-W (for 22 h) in response to a pro-inflammatory stimulus (LPS), rather than to a lower ability to induce the IL-10 gene to turn off an inflammation fully triggered. This finding was in accordance with results reported by Camargo et al. [ 14], who observed that the dietary assumption of high-phenol virgin olive oil in patients suffering metabolic syndrome, by switching the activity of peripheral blood mononuclear cells to a less deleterious inflammatory profile, was able to repress the inflammatory process, even if it occurred because of the addition of a stimulus of inflammation (i.e., LPS).
Indeed, LPS normally acts by activating the nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) pathways, causing the overexpression of various inflammatory mediators, such TNF-α, IL-1β, IL-6, nitric oxide (NO) and prostaglandin E2 (PGE2) [58,59]. However, Camargo et al. [ 14] found a chemokine repression as a direct consequence of phenols interaction with NF-κB/MAPK/AP-1 inflammation signaling pathways.
Recent research has found evidence of the ability of the phenols of several plants to induce cellular, biochemical, and epigenetic modifications, resulting in modulation of the homeostasis of key cellular processes such as the control of oxidative stress, inflammatory response, and gene expression, among others [57]. Wang et al. [ 60] recently showed that pretreatment with tyrosol markedly inhibited the activation of NF-κB and apolipoprotein-1 (AP-1) in LPS-induced A549 cells.
In the present study, the fifth absorbable OP1-F aqueous extract, which was constituted of more than $20\%$ of tyrosol and utilized at the same concentration of OP-W, significantly reduced the expression of mRNAs encoding IL-6, IL-10, and TNF-α ($p \leq 0.05$); however, it was not able to significantly reduce the cytokine levels in the conditioned medium. Indeed, an increasing trend was observed, although not statistically significant, for all three detectable cytokines. We supposed that the lack of ability of the OP1-F extract (used at the same concentration of OP-W) vs. OP1-W sample to reduce the concentration of cytokines IL-8 and TNF-α was due to a too-high concentration of tyrosol (by using the OP1-F extract at the same concentration of OP1-W extract, the tyrosol level resulted as much higher) or to the presence, in the OP1-W extract, of other compounds eliciting a synergistic effect with tyrosol in quenching the inflammatory response elicited by LPS stimulus.
Indeed, we supposed that a too-high level of tyrosol could induce a sort of paradoxical effect [61,62] as seems stated by the higher IL-8 level in PBMC supplemented with OP1-F aqueous extract, but not triggered by LPS. Indeed, in the present study, as observed by other authors [24], in Caco-2 cell culture maintained at basal condition (control), the only interleukin detectable in the basal medium was IL-8. However, while in the presence of the extract OP1-W extract, the interleukin IL-8 showed a decreasing trend; in the presence of OP1-F extract, the trend was the opposite.
Various reports have stated that diets with high contents of polyphenols are associated with a reduced production of these inflammatory cytokines and a consequent improvement of inflammation [10]. In the present study, for the first time, similar results exerted by digested phenols derived from olive pomace on the ex vivo cultured human (healthy) PBMCs have been demonstrated.
Interestingly, the concentration of phenols in the dose of OP-W extract here adopted on cells was in the order of nanogram. Even if it was quite difficult to find and compare data relative to the bioavailability of oil phenols or phenols of olive by-products orally administered in human in vivo studies [46,47,53], in blood, what was possible to infer from the literature was that it is not impossible to reach this nanogram concentration, although for a very short time, by ingesting, for example, a quantity corresponding to 10 g of olives. Therefore, these preliminary results relative to the anti-inflammatory properties exerted by the mixture of phenols present in the in vitro absorbable digested olive pomace seem promising and worthy of further investigation.
An expanding body of literature has shown that, through increasingly eco-friendly and cost-effective extraction processes applied to olive oil processing by-products [63,64], it is possible to obtain high quantities of bioactive compounds which have been proven to be effective in exerting beneficial effects on health. Our data further demonstrate that olive pomace can, therefore, become a valuable raw material that can be used not only in the energy sectors, food, cosmetic and animal feed [7,65], but also in the nutraceutical field.
## 4.1. Chemicals and Reagents
All reagents were of analytical purity. The 2,2-diphenyl-1-picrylhydrazyl (DPPH), ABTS diammonium salt (2,2-azinobis-3-ethylbenzothiazoline-6-sulphonic acid), Folin–Ciocalteu’s reagent, standards of Trolox and gallic acid were purchased from Sigma-Aldrich Corp. (Milan, Italy). Bile salts were from Oxoid™ (Hampshire, U.K.). Spectrapor 3 45 MM/15 M membrane (cut-off 3.5 kDa) was purchased from Fischer Scientific (Milan, Italy). Pancreatin (from porcine pancreas: P3292-100G), pepsin (from pig gastric mucosa: ≈2500 units/mg protein), α-amylase from human saliva (A0521-500 units/mg), formic acid, potassium sorbate, sodium carbonate, trifluoroacetic acid (TFA), and peptidyl-dipeptidase were purchased from Sigma-Aldrich Corp. (Milan, Italy).
Hydroxytyrosol, tyrosol, oleuropeion, apigenin, luteolin and myricetin were purchased from Extrasynthese (Lyon, France); caffeic acid, chlorogenic acid, pinoresinol, and ligstroside were purchased from Sigma-Aldrich Corp. (Milan, Italy). Acetonitrile was purchased from Pai Acs, Panreac. All other chemicals and solvents were of the highest analytical grade from Sigma-Aldrich Co. (St. Louis, MO, USA).
## 4.2. Olive Pomace Methanolic Extracts
Three batches of sun-dried OP were gently provided by Oil Mill Industry Consoli (Adrano, Catania, Italy) and transported to the laboratory where they were packed in polyethene bags and kept in a freezer at −80 °C until analysis. OP1 was collected in September, OP2 in October, and OP3 in November/December, thus, the ripening state increased with each harvest of olives. These OP samples were stratified (and stabilized with Consoli’s patent), one batch on top of the other, in a unique big pool, and stabilized with Consoli’s patent (Consoli’s patent 0001428707). The pool was well covered until the following summer season, when the pool was opened, and the OP was dried under the sun. Olive pomace samples were collected from the bottom of the pool at the end of June (OP1), July (OP2), and August (OP3). These OP samples were composed mainly of the olive cultivar *Olea europaea* L. (*Nocellara etnea* as main cultivar) and the proximate composition as reported in Chiofalo et al. [ 66].
From each OP sample (OP1, OP2 and OP3; 5 g/batch), methanolic extracts were obtained by using a *Soxhlet apparatus* for 5 h to fall and 200 mL of pure methanol. The obtained solutions were then evaporated with a Rotavapor (Buchi B-490) to collect the OP extracts for further characterization. The crude extracts (methanolic OP extracts) were then weighed (1.228 g, 0.967 g, and 1.004 g, respectively) and the yield calculated ($24.6\%$, $19.34\%$, and $20.1\%$, respectively).
## 4.2.1. Radical Scavenging Activity Assays in Methanolic OP Extracts
By using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) method, the radical scavenging activity of the methanolic OP1, OP2 and OP3 extracts was measured [67]. In a 96-multiwell plate, 50 μL aliquot of each OP extract (0–2 mg/mL) or of the standard Trolox (0–100 mg/mL), in triplicate, was added to 200 μL of DPPH solution (0.1 mM in methanol). After incubation in darkness for 30 min at 37 °C, the absorbance was measured at 490 nm using a UV–VIS microplate reader (FLUOstar Optima, BMG Labtech, Ortenberg, Germany) against DPPH solution as a blank. Values were expressed as Trolox equivalent (μg TE/mg dry extract).
The radical cation scavenging activity of each extract was measured using the 2-2′- azino-bis (3-ethylbenzo-thiazoline-6-sulphonate) diammonium salt (ABTS) method [68]. In a 96-multiwell plate, 50 μL aliquot of sample (0–5 mg/mL) was added to 200 μL of ABTS solution (5 mM). ATBS solution was derived by oxidizing ABTS with MnO2 in distilled water for 30 min in the dark, and then the solution was filtered through filter paper. After 20 min incubation in darkness at room temperature, the absorbance was read at 734 nm using a UV–VIS microplate reader (FLUOstar Optima, BMG Labtech, Ortenberg, Germany) against ABTS solution as a blank. Values were expressed as Trolox equivalent (μg TE/mg dry extract).
## 4.2.2. Ferric Reducing Antioxidant Power (FRAP) Assay in Methanolic OP Extracts
The reducing power of methanolic OP1, OP2 and OP3 extracts was evaluated according to a ferric reducing antioxidant power (FRAP) assay [69]. In a 96-multiwell plate, 25 μL aliquot of sample (0–2 mg/mL) or of standard Trolox (0–100 μg/mL) was added to 175 μL of FRAP working solution containing 20 mmol/L ferric chloride, 300 mmol/L acetate buffer (pH 3.6), and 10 mmol/L TPTZ (2,4,6- tri (2-pyridyl)—S-triazine) made up in 40 mmol/L HCl. The three solutions were mixed at a 10:1:1 ratio (v:v:v). The mixture was incubated in darkness for 30 min at 37 °C and then the absorbance was determined using a UV–VIS microplate reader (FLUOstar Optima, BMG Labtech, Ortenberg, Germany) at 593 against FRAP solution as a blank. Values were expressed as Trolox equivalent (μg TE/mg dry extract).
## 4.2.3. TPC of Methanolic OP Extracts
The Folin–Ciocalteu method was used to determine TPC [70]. Briefly, 25 μL aliquots of OP1, OP2 and OP3 extracts (5 mg/mL) were incubated for 5 min with 125 μL of $10\%$ (w/v) Folin–Ciocalteu reagent. After the addition of 125 μL of Na2CO3 ($10\%$ w/v) and incubation for 30 min in darkness at room temperature, the absorbance was read using a UV–VIS microplate reader (FLUOstar Optima, BMG Labtech, Ortenberg, Germany) at 320 nm. The results were derived from a gallic acid calibration curve (0–1000 ug/mL) prepared from a stock solution (1 mg/mL in ethanol). Values were expressed as mg of gallic acid equivalents (GAE) per gram of dried weight extract (mg of GAE/g extract).
## 4.3. OP In Vitro Digestion and OP Aqueous Extracts
To mimic the in vitro oral, gastric and intestinal digestion of OP samples, the procedure indicated by Diab et al. [ 26] was followed. Briefly, 5 g of each OP batch were mixed with 25 mL SSF and 3 mL (stock 75 U/mL) α-salivary amylase (from human saliva), 5.8 mL distilled H2O, and 0.2 mL CaCl2, and then incubated on a magnetic stirrer for 2 min at 37 °C (oral digestion). For the gastric digestion, 40 mL SGF, 7 mL pepsin (stock 25,000 U/mL), 3 mL distilled H2O, and 0.03 mL CaCl2 were added to the oral outcome and the pH was lowered to 3.0 by HCl; the mixture was incubated for 2 h at 37 °C on a magnetic stirrer, and the pH was checked regularly. Finally, 50 mL of gastric outcome was mixed with 20 mL of pancreatin (stock 100 U/mL), 50 mL of SIF, 6 mL distilled H2O, 10 mL bile salt (stock 10 mM), 0.024 mL CaCl2, and 0.7 mL of 1 M HCl to neutralize the pH to 7.0 to simulate the intestinal digestion.
At the end of this process, the obtained mixture was incubated on a magnetic stirrer for 2 h at 37 °C. Then, to inactivate the enzymes used in the digestion process, the mixture was heated to 90 °C for 10 min. Eventually, the mixture was dialyzed with membrane cut-off 3.5 kDa (Spectra/Por molecular porous membrane tubing, Thermo Fisher Scientific, Milan, Italy) against 250 mL of water for 24 h at 4 °C to separate the high molecular weight (mw) fraction (>3.5 kDa, inside the dialysis membrane) from the low mw fraction (<3.5 kDa, outside the dialysis membrane). At the end of the incubation process, the solution outside the dialysis tubing (OP-W) represented the aqueous OP sample that was available for absorption (whole serum-available sample) and the solution that had not managed to diffuse through the dialysis tubing (OP-W n.a.) represented the whole non-absorbable sample (colon-available) [23]. The dialyzed digested OP-W and OP-W n.a. extracts were then lyophilized, obtaining the OP digested aqueous extracts that were further characterized for their bioactive properties.
## 4.3.1. Radical Scavenging Activity Assays in Aqueous OP-W n.a. and OP-W Extracts
The ABTS and DPPH assays described at Section 4.2.1. were utilized to determine the radical scavenging activities of the three aqueous OP-W n.a. and OP-W (5 mg/mL) extracts.
## 4.3.2. Ferric Reducing Antioxidant Power (FRAP) Assay in Aqueous OP-W n.a. and OP-W Extracts
The FRAP assay described at Section 4.2.2. was utilized to determine the ferric reducing antioxidant powers of the OP-W n.a. and OP-W (5 mg/mL).
## 4.3.3. TPC in OP-W n.a., OP-W
The TPC assay described at Section 4.2.3. was utilized to evaluate the total phenolic contents in the OP-W n.a. and OP-W samples.
## 4.3.4. HPLC-DAD Analysis of Methanolic and Aqueous OP Extracts
The analysis of phenolic compounds in methanolic and aqueous OP samples were carried out with a Jasco (Tokyo, Japan) HPLC-DAD system, consisting of a PU-4180 pump, a MD-4015 PDA detector, and an AS-4050 autosampler. An Agilent Zorbax Eclipse Plus C18 reverse-phase column (Agilent, Santa Clara, CA, USA.) ( 4.6 × 100 mm I.D, 3.5 μm) was used as a stationary phase. Two different methods, modified from that of Peršurić et al. [ 71], were applied for the separation and identification of the analytes in the mixture, both employing water adjusted to pH 2.5 with orto-phosphoric acid (Solvent A) and acetonitrile (Solvent B) as the mobile phase. For the identification and quantification of hydroxytyrosol, tyrosol, oleuropein, apigenin, luteolin, myricetin, ligstroside, and pinoresinol, the following step gradient was used: 90 to $72\%$ A for 10 min, $72\%$ for 15 min, 72 for $70\%$ for 10 min, $70\%$ for 10 min, 70 to $5\%$ for 10 min, and kept constant for 5 min. The gradient was restored to initial conditions and kept constant for 20 min for re-equilibration. The flow rate was 0.5 mL/min, the injection volume for both reference standards and samples was 50 μL, and the detection wavelengths were set at 280 and 360 nm.
For the identification and quantification of phenolic acids (gallic, caffeic, and chlorogenic acid) a different step gradient was used: $97\%$ A for 6 min, 97 to $85\%$ for 11 min, 85 to $82.8\%$ for 10 min, 82.8 to $50\%$ for 13 min, back to initial conditions for 3 min, and constant for 13 min for re-equilibration. The injection volume was 20 μL, the flow rate was 0.7 mL/min, and the PDA wavelengths were set at 280, 329 and 360 nm.
To quantify the analytes, calibration curves were constructed for each standard by injecting six different concentrations (50 ppm, 25 ppm, 12.5 ppm, 6.25 ppm, 3.12 ppm and 1.56 ppm) in duplicate. Stock solutions of hydroxytyrosol, tyrosol, oleuropein, apigenin, luteolin, myricetin, ligstroside, and pinoresinol were prepared with DMSO/acetonitrile ($\frac{20}{80}$) mixture at a concentration of 2 mg/mL. The dilutions were then carried out with water adjusted to pH 2.5 with orto-phosphoric acid to ensure greater stability of the analytes. Gallic acid was dissolved in water, while caffeic and chlorogenic acid were dissolved in methanol and further diluted with the mobile phase.
The bioaccessibility index was calculated as the percentage of the compound detected in the digested and lyophilized samples, with reference to the undigested sample.
## 4.3.5. HPLC-DAD Fractioning of OP-W (OP-F)
The most bioactive OP-W extract (about 8 mg), based on its antioxidant properties, was further purified using reversed-phase high-performance chromatography on a 10-mm × 250 mm semipreparative C18 column (Supelco, Bellefonte, PA, U.S.A.), equilibrated in water and eluted at a flow rate of 2 mL/min with a discontinuous gradient of acetonitrile. In this way, seven OP-W fractions (OP-F) were obtained, and the ABTS and TCP were determined on those OP-Fs.
## 4.3.6. LC-MS/MS Peptide Profiling of OP-W
The most bioactive OP-W extract was resuspended in 50 mM ammonium bicarbonate, pH 8.0, reduced with 10 mM DTT at 56 °C for 45 min, and alkylated with a 55 mM solution of iodoacetamide for 30 min at room temperature in the dark and then desalted by a SEP-PAK chromatography. The main fraction was manually collected and lyophilized. An aliquot of the sample was directly analyzed by LC-MS/MS for protein identification. The remaining portion of the lyophilized fraction was resuspended in 50 mM ammonium bicarbonate, pH 8.0, and incubated with trypsin in a $\frac{1}{50}$ ratio (w/w) at 37 °C for 2 h. The sample was acidified with a final concentration of $0.2\%$ trifluoracetic acid. The peptide mixture was first concentrated and desalted by C18 zip-tip and then was lyophilized. The lyophilized fraction was resuspended in $0.2\%$ HCOOH and analyzed by LC-MS/MS, using a 6530 Q- TOF LC/MS (Agilent) system equipped with a nano-HPLC. After loading, peptide mixtures were first concentrated and desalted on the pre-column. For protein identification, the raw data obtained from the LC-MS/MS analysis were used to search both “Olea” and “Olea Europea” protein databases by an in-house version of the Mascot software.
## 4.3.7. Search of Potential Biological Activities and Peptide Ranking
The potential bioactivities of OP-W peptides were predicted using the open access tool PeptideRanker (accessed on 15 February 2023, http://bioware.ucd.ie/compass/biowareweb/) [72], a web-based tool used to predict the probability of biological activity of peptide sequences. PeptideRanker provides peptide scores in the range of 0–1. The maximum scores indicate the most active peptides, whereas the minimum scores denote the least active peptides. Here, only those peptides with a score higher than 0.6 were considered as potentially “bioactive”. Subsequently, the lists of best-ranked peptides were submitted to the web-available database BIOPEP (accessed on 15 February 2023, http://www.uwm.edu.pl/biochemia/index.php/pl/biopep/) which contains collected data relative to peptides with a recognized bioactivity.
## 4.3.8. Untargeted Metabolomics of OP-F by GCMS Analysis
Due to the small quantity of each of the seven separated OP-F, only the most promising fraction (the fifth fraction), based on ABTS and TPC results, was referred to untargeted metabolomics by GCMS Analysis. GC/MS analysis was performed by a 7820A (Agilent Technologies, Santa Clara, CA, USA) with a HB-5 ms capillary column (30 m × 0.25 mm × 0.25 µm film thickness) (Agilent Technologies). The injector, ion source, quadrupole, and GC/MS interface temperature were 230, 230, 150, and 280 °C, respectively. The flow rate of helium carrier gas was kept at 1 mL/min. An amount of 1 µL of derivatized sample was injected with a 3 min solvent delay time and split ratio of 10:1. The initial column temperature was 40 °C and held for 2 min, ramped to 150 °C at a rate of 15 °C/min, and held 1 min, and then finally increased to 280 °C, at a rate of 30 °C/min, and kept at this temperature for 5 min. The ionization was carried out in the electron impact (EI) mode at 70 eV. The MS data were acquired in full scan mode from m/z 40–400 with an acquisition frequency of 12.8 scans per second. Compound identification was confirmed by injection of pure standards and comparison of the retention time and corresponding EI MS spectra.
## 4.4. Human PBMC Culture, Supplementation and RNA Extraction
Human peripheral blood mononuclear cells were isolated from fresh heparinized blood samples (20 mL) obtained from five healthy donors. Cells were separated by gradient centrifugation and then the number of live mononuclear cells, suspended in complete medium containing RPMI 1640 Medium (Thermo Fischer Scientific, Waltham, MA, USA) with $10\%$ heat inactivated fetal bovine serum (GibcoTM, Thermo Fischer Scientific, Waltham, MA, U.S.A.), 100 μg/mL streptomycin (BiochromAG, Berlin, Germany), 2 mM L-glutamine (Euroclone®, Milan, Italy) and 100 units/mL penicillin (BiochromAG, Berlin, Germany), was determined using a counting chamber and the Trypan blue dye exclusion procedure [70]. The final number of live cells was adjusted to 4 × 106/well (2 mL) and blood cells of each donor were cultured in triplicate at 37 °C in $5\%$ CO2, supplemented or not (CTRL) with OP-W and OP-F aqueous extracts (final concentration of 2.5 μg/mL) that were previously filtered on 0.22 μm acetate cellulose filters. The aqueous extract concentrations were chosen after preliminary tests and, based on the yield obtained after dialysis ($8.9\%$), the OP-W extract contained 0.87, 1.4, 0.3, 0.36, and 0.38 μg/mg of hydroxytyrosol, tyrosol, oleuropein, myricetin, and luteolin, respectively.
After 24 h of incubation, one half of cells/donor (CTRL, OP-W and OP-F cells/donor) was triggered (stimulated: s.) or not (n.s.) with LPS (100 ng/mL) for 2.5 h. Later, cells were transferred in Eppendorf vials (2 mL) and centrifugated (× 300 g) to separate the conditioned media for cytokine measurements. Finally, the residual cells were washed twice with phosphate-buffered saline (GibcoTM, Thermo Fischer Scientific, Waltham, MA, USA) and finally stored at −80 °C until RNA extraction by Mini Kit (QIAGEN GmbH, Hilden, Germany).
A NanoVue Spectrophotometer (GE Healthcare, Milano, Italy) was used to measure RNA yield and purity. Only samples with A260/A280 ratio > 1.8 were used.
## 4.5. Analysis of mRNA Levels by Real Time Reverse Transcriptase-Polymerase Chain Reaction (Real Time RT-PCR)
To obtain cDNA, 1 μg of RNA for each sample was reverse transcribed using an iScript cDNA Synthesis Kit (Bio-Rad Laboratories, Hercules, CA, U.S.A.). The subsequent PCR was performed in a total volume of 10 μL containing 2.5 μL (12.5 ng) of cDNA, 2 μL of RNAsi free dH2O, 5 μL SsoAdvanced Universal SYBR Green Supermix (Bio-Rad Laboratories), and 0.5 μL (500 nM) of each primer. The investigated genes were IL-6, IL-10, and TNFα. All primers (listed in Table 10) were purchased from Sigma-Aldrich Life Science Co. LLC. ( USA) and were intended for human cells. The 18S gene was used as the reference gene.
## 4.6. Measurment of PBMC Cytokines in Conditioned Medium
The pro- and anti-inflammatory cytokines IL-1α, IL-1β, IL-2, IL-4, IL -5, IL-6, IL-8, IL-10, IL-12, IL-13, IL-15, IL-17, IL-23, IFNγ, TNF-α and TNF-β were estimated in the culture conditioned medium of human PBMC, by using multiplex immunoassay (Q-Plex Human Cytokine—Screen 16-plex, Quansys Biosciences, Technogenetics Srl., Milan, Italy), Q-View Imager LS, Q-View software, and following the manufacturer’s instructions. The culture medium was obtained from PBMC previously cultured or not with OP-W and OP-F (2.5 μg/mL for 24 h) and then stimulated (s.) or not (n.s.) by LPS (100 ng/mL for 2.5 h).
## 4.7. Statistical Analysis
Data relative to real time RT-PCR and cytokine concentrations were analyzed by using the two-tailed paired t-test, whereas one-way ANOVA with Tukey’s multiple comparison test was adopted for data relative to radical scavenging activities and phenolic characterization (Prism 7, GraphPad Software Inc., San Diego, U.S.A.) considering significant differences for $p \leq 0.05.$ Values were expressed as mean (S.D.) unless otherwise stated.
## 5. Conclusions
In recent years, there has been growing interest in natural substances as possible sources of active compounds for disease prevention and/or health benefits. The greater awareness of the need to reduce environmental impact and to better exploit resources still available, has led researchers to focus their efforts on the possibility of identifying beneficial molecules for the organism from by-products of the food chain. In this context of a circular economy, the olive processing cycle could provide an example of reuse of waste products. The pomace and vegetative waters are, in fact, rich with very interesting molecules, among which are luteolin, with known anti-tumoral properties; hydroxytyrosol, having hypocholesterolemic action; and tyrosyl, ligstroside, and oleuropein.
In the present research, the phenolic composition and antioxidant activities of three batches of olive pomaces, differing by ripening state, and evaluated both before and after an in vitro simulation of the digestive process, revealed very different contents of phenols (hydroxytyrosol, tyrosol, oleuropein, ligstroside, pinoresinol, flavonoids, and hydroxycinnamic acids) and antioxidant properties, thus, confirming the great influence of the ripening stage and storage conditions on phenolic composition.
Although only part of ingested phenolics can pass the gut barrier, it was noteworthy that these nutraceutical compounds were still present in the digested absorbable aqueous extract of olive pomace and were able to exert an interesting anti-inflammatory activity both at transcriptional level and in the surnatants of human ex vivo PBMC cultures. The data here presented were in line with the in vivo repression of several pro-inflammatory genes observed in the only other work found in the literature that used PBMC cells from patients with metabolic syndrome [14].
However, further studies are necessary to determine the cause of the lower anti-inflammatory response obtained with OP-F, compared with the OP-W sample. Indeed, it could be connected to a higher bioactivity orchestrated by the entire phyto complex (synergistic effect) present in the OP-W sample, probably able to involve different pathways/mechanisms, or conversely, to an excessively high dose of tyrosol in OP-F. Nature and/or evolution could have determined the fast ability to metabolize and eliminate the excessive ingestion of phenols just to reduce deleterious effects when too concentrated in circulation.
The in vitro experiments represent a consistent approach to evaluating the health effect of new functional ingredients, but despite the inherent “défaillance” in in vitro simulation of digestion which is a rather complex physiological process, we believe that compounds under in vitro testing should always undergo a preventive in vitro digestion before their evaluation in cell cultures. Undoubtedly, the in vitro digestion represents one of the experimental approaches to encompass the open questions of bioavailability and metabolism food bioactives, particularly of phenols.
In the present research, however, it was not considered whether, and to what extent, the microbiota could affect digestion and bioavailability of the not absorbable olive pomace extract which, therefore, could also contribute to the increased bioavailability of bioactive compounds.
Furthermore, in the digested and absorbable sample used for cell testing, potentially bioactive peptides were also identified. Those resulting peptides have not yet been described, and they will be further investigated together with the characterization of polysaccharides and minerals, which are also potentially bioactive.
In the future, we would like to evaluate the potential immunomodulatory activity of these aqueous extracts, and, for this purpose, we believe that PBMCs taken from patients with autoimmune diseases could represent a consistent ex vivo model that could provide interesting outcomes.
The results herein reported clearly evidence the anti-inflammatory effect of digested aqueous OP extract and pave the way to an exploitation of the olive pomace by-product as a functional ingredient or as a nutraceutical.
## References
1. Kaza S., Yao L., Bhada-Tata P., Van Woerden F., Lonkova K., Morton J., Poveda R.A., Sarraf M., Malkawi F., Harinath A.S.. *What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050* (2018)
2. Tapia-Quirós P., Montenegro-Landívar M.F., Reig M., Vecino X., Cortina J.L., Saurina J., Granados M.. **Recovery of Polyphenols from Agri-Food By-Products: The Olive Oil and Winery Industries Cases**. *Foods* (2022) **11**. DOI: 10.3390/foods11030362
3. Fava F., Zanaroli G., Vannini L., Guerzoni E., Bordoni A., Viaggi D., Robertson J., Waldron K., Bald C., Esturo A.. **New Advances in the Integrated Management of Food Processing By-Products in Europe: Sustainable Exploitation of Fruit and Cereal Processing by-Products with the Production of New Food Products (NAMASTE EU)**. *New Biotechnol.* (2013) **30** 647-655. DOI: 10.1016/j.nbt.2013.05.001
4. Difonzo G., Gennaro G., Pasqualone A., Caponio F.. **Potential Use of Plant-based By-products and Waste to Improve the Quality of Gluten-free Foods**. *J. Sci. Food Agric.* (2022) **102** 2199-2211. DOI: 10.1002/jsfa.11702
5. Malekjani N., Jafari S.M.. **Valorization of Olive Processing By-Products via Drying Technologies: A Case Study on the Recovery of Bioactive Phenolic Compounds from Olive Leaves, Pomace, and Wastewater**. *Crit. Rev. Food Sci. Nutr.* (2022) 1-19. DOI: 10.1080/10408398.2022.2068123
6. Banias G., Achillas C., Vlachokostas C., Moussiopoulos N., Stefanou M.. **Environmental Impacts in the Life Cycle of Olive Oil: A Literature Review: Environmental Impacts in the Life Cycle of Olive Oil**. *J. Sci. Food Agric.* (2017) **97** 1686-1697. DOI: 10.1002/jsfa.8143
7. Mallamaci R., Budriesi R., Clodoveo M.L., Biotti G., Micucci M., Ragusa A., Curci F., Muraglia M., Corbo F., Franchini C.. **Olive Tree in Circular Economy as a Source of Secondary Metabolites Active for Human and Animal Health Beyond Oxidative Stress and Inflammation**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26041072
8. Barbalace M.C., Zallocco L., Beghelli D., Ronci M., Scortichini S., Digiacomo M., Macchia M., Mazzoni M.R., Fiorini D., Lucacchini A.. **Antioxidant and Neuroprotective Activity of Extra Virgin Olive Oil Extracts Obtained from Quercetano Cultivar Trees Grown in Different Areas of the Tuscany Region (Italy)**. *Antioxidants* (2021) **10**. DOI: 10.3390/antiox10030421
9. Cicerale S., Lucas L., Keast R.. **Antimicrobial, Antioxidant and Anti-Inflammatory Phenolic Activities in Extra Virgin Olive Oil**. *Curr. Opin. Biotechnol.* (2012) **23** 129-135. DOI: 10.1016/j.copbio.2011.09.006
10. Marcelino G., Hiane P.A., Freitas K.d.C., Santana L.F., Pott A., Donadon J.R., de Cássia Avellaneda G.R.. **Effects of Olive Oil and Its Minor Components on Cardiovascular Diseases, Inflammation, and Gut Microbiota**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11081826
11. Bassani B., Rossi T., De Stefano D., Pizzichini D., Corradino P., Macrì N., Noonan D.M., Albini A., Bruno A.. **Potential Chemopreventive Activities of a Polyphenol Rich Purified Extract from Olive Mill Wastewater on Colon Cancer Cells**. *J. Funct. Foods* (2016) **27** 236-248. DOI: 10.1016/j.jff.2016.09.009
12. Omar S., Scott C., Hamlin A., Obied H.. **Olive Biophenols Reduces Alzheimer’s Pathology in SH-SY5Y Cells and APPswe Mice**. *Int. J. Mol. Sci.* (2018) **20**. DOI: 10.3390/ijms20010125
13. Guasch-Ferré M., Hruby A., Salas-Salvadó J., Martínez-González M.A., Sun Q., Willett W.C., Hu F.B.. **Olive Oil Consumption and Risk of Type 2 Diabetes in US Women**. *Am. J. Clin. Nutr.* (2015) **102** 479-486. DOI: 10.3945/ajcn.115.112029
14. Camargo A., Ruano J., Fernandez J.M., Parnell L.D., Jimenez A., Santos-Gonzalez M., Marin C., Perez-Martinez P., Uceda M., Lopez-Miranda J.. **Gene Expression Changes in Mononuclear Cells in Patients with Metabolic Syndrome after Acute Intake of Phenol-Rich Virgin Olive Oil**. *BMC Genom.* (2010) **11**. DOI: 10.1186/1471-2164-11-253
15. Corona G., Spencer J., Dessì M.. **Extra Virgin Olive Oil Phenolics: Absorption, Metabolism, and Biological Activities in the GI Tract**. *Toxicol. Ind. Health* (2009) **25** 285-293. DOI: 10.1177/0748233709102951
16. Castrogiovanni P., Trovato F., Loreto C., Nsir H., Szychlinska M., Musumeci G.. **Nutraceutical Supplements in the Management and Prevention of Osteoarthritis**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17122042
17. Recinella L., Chiavaroli A., Orlando G., Menghini L., Ferrante C., Di Cesare Mannelli L., Ghelardini C., Brunetti L., Leone S.. **Protective Effects Induced by Two Polyphenolic Liquid Complexes from Olive (**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24163002
18. Markhali F.S., Teixeira J.A., Rocha C.M.R.. **Olive Tree Leaves—A Source of Valuable Active Compounds**. *Processes* (2020) **8**. DOI: 10.3390/pr8091177
19. Žugčić T., Abdelkebir R., Alcantara C., Collado M.C., García-Pérez J.V., Meléndez-Martínez A.J., Režek Jambrak A., Lorenzo J.M., Barba F.J.. **From Extraction of Valuable Compounds to Health Promoting Benefits of Olive Leaves through Bioaccessibility, Bioavailability and Impact on Gut Microbiota**. *Trends Food Sci. Technol.* (2019) **83** 63-77. DOI: 10.1016/j.tifs.2018.11.005
20. Leouifoudi I., Harnafi H., Zyad A.. **Olive Mill Waste Extracts: Polyphenols Content, Antioxidant, and Antimicrobial Activities**. *Adv. Pharmacol. Sci.* (2015) **2015** 1-11. DOI: 10.1155/2015/714138
21. Barbaro B., Toietta G., Maggio R., Arciello M., Tarocchi M., Galli A., Balsano C.. **Effects of the Olive-Derived Polyphenol Oleuropein on Human Health**. *Int. J. Mol. Sci.* (2014) **15** 18508-18524. DOI: 10.3390/ijms151018508
22. Antónia Nunes M., Costa A.S.G., Bessada S., Santos J., Puga H., Alves R.C., Freitas V., Oliveira M.B.P.P.. **Olive Pomace as a Valuable Source of Bioactive Compounds: A Study Regarding Its Lipid- and Water-Soluble Components**. *Sci. Total Environ.* (2018) **644** 229-236. DOI: 10.1016/j.scitotenv.2018.06.350
23. Ribeiro T.B., Oliveira A., Campos D., Nunes J., Vicente A.A., Pintado M.. **Simulated Digestion of an Olive Pomace Water-Soluble Ingredient: Relationship between the Bioaccessibility of Compounds and Their Potential Health Benefits**. *Food Funct.* (2020) **11** 2238-2254. DOI: 10.1039/C9FO03000J
24. Di Nunzio M., Picone G., Pasini F., Caboni M.F., Gianotti A., Bordoni A., Capozzi F.. **Olive Oil Industry By-Products. Effects of a Polyphenol-Rich Extract on the Metabolome and Response to Inflammation in Cultured Intestinal Cell**. *Food Res. Int.* (2018) **113** 392-400. DOI: 10.1016/j.foodres.2018.07.025
25. Gullón P., Gullón B., Astray G., Carpena M., Fraga-Corral M., Prieto M.A., Simal-Gandara J.. **Valorization of By-Products from Olive Oil Industry and Added-Value Applications for Innovative Functional Foods**. *Food Res. Int.* (2020) **137** 109683. DOI: 10.1016/j.foodres.2020.109683
26. Diab F., Khalil M., Lupidi G., Zbeeb H., Salis A., Damonte G., Bramucci M., Portincasa P., Vergani L.. **Influence of Simulated In Vitro Gastrointestinal Digestion on the Phenolic Profile, Antioxidant, and Biological Activity of**. *Antioxidants* (2022) **11**. DOI: 10.3390/antiox11091778
27. Gómez-Lama Cabanás C., Legarda G., Ruano-Rosa D., Pizarro-Tobías P., Valverde-Corredor A., Niqui J.L., Triviño J.C., Roca A., Mercado-Blanco J.. **Indigenous Pseudomonas Spp. Strains from the Olive (**. *Front. Microbiol.* (2018) **9** 277. DOI: 10.3389/fmicb.2018.00277
28. Bartolomei M., Capriotti A.L., Li Y., Bollati C., Li J., Cerrato A., Cecchi L., Pugliese R., Bellumori M., Mulinacci N.. **Exploitation of Olive (**. *Antioxidants* (2022) **11**. DOI: 10.3390/antiox11091730
29. Aiello G., Lammi C., Boschin G., Zanoni C., Arnoldi A.. **Exploration of Potentially Bioactive Peptides Generated from the Enzymatic Hydrolysis of Hempseed Proteins**. *J. Agric. Food Chem.* (2017) **65** 10174-10184. DOI: 10.1021/acs.jafc.7b03590
30. Baj A., Moro E., Bistoletti M., Orlandi V., Crema F., Giaroni C.. **Glutamatergic Signaling Along The Microbiota-Gut-Brain Axis**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20061482
31. Tomé D.. **The Roles of Dietary Glutamate in the Intestine**. *Ann. Nutr. Metab.* (2018) **73** 15-20. DOI: 10.1159/000494777
32. Araújo M., Pimentel F.B., Alves R.C., Oliveira M.B.P.P.. **Phenolic Compounds from Olive Mill Wastes: Health Effects, Analytical Approach and Application as Food Antioxidants**. *Trends Food Sci. Technol.* (2015) **45** 200-211. DOI: 10.1016/j.tifs.2015.06.010
33. Peralbo-Molina Á., Priego-Capote F., Luque de Castro M.D.. **Tentative Identification of Phenolic Compounds in Olive Pomace Extracts Using Liquid Chromatography–Tandem Mass Spectrometry with a Quadrupole–Quadrupole-Time-of-Flight Mass Detector**. *J. Agric. Food Chem.* (2012) **60** 11542-11550. DOI: 10.1021/jf302896m
34. Páscoa R.N.M.J., Nunes M.A., Reszczyński F., Costa A.S.G., Oliveira M.B.P.P., Alves R.C.. **Near Infrared (NIR) Spectroscopy as a Tool to Assess Blends Composition and Discriminate Antioxidant Activity of Olive Pomace Cultivars**. *Waste Biomass Valor* (2021) **12** 4901-4913. DOI: 10.1007/s12649-021-01386-1
35. Ryan D., Prenzler P.D., Lavee S., Antolovich M., Robards K.. **Quantitative Changes in Phenolic Content during Physiological Development of the Olive (**. *J. Agric. Food Chem.* (2003) **51** 2532-2538. DOI: 10.1021/jf0261351
36. Nunes M.A., Palmeira J.D., Melo D., Machado S., Lobo J.C., Costa A.S.G., Alves R.C., Ferreira H., Oliveira M.B.P.P.. **Chemical Composition and Antimicrobial Activity of a New Olive Pomace Functional Ingredient**. *Pharmaceuticals* (2021) **14**. DOI: 10.3390/ph14090913
37. Lesage-Meessen L., Navarro D., Maunier S., Sigoillot J.-C., Lorquin J., Delattre M., Simon J.-L., Asther M., Labat M.. **Simple Phenolic Content in Olive Oil Residues as a Function of Extraction Systems**. *Food Chem.* (2001) **75** 501-507. DOI: 10.1016/S0308-8146(01)00227-8
38. Navajas-Porras B., Pérez-Burillo S., Morales-Pérez J., Rufián-Henares J.A., Pastoriza S.. **Relationship of Quality Parameters, Antioxidant Capacity and Total Phenolic Content of EVOO with Ripening State and Olive Variety**. *Food Chem.* (2020) **325** 126926. DOI: 10.1016/j.foodchem.2020.126926
39. Visioli F., Poli A., Gall C.. **Antioxidant and Other Biological Activities of Phenols from Olives and Olive Oil**. *Med. Res. Rev.* (2002) **22** 65-75. DOI: 10.1002/med.1028
40. Bayram B., Ozcelik B., Grimm S., Roeder T., Schrader C., Ernst I.M.A., Wagner A.E., Grune T., Frank J., Rimbach G.. **A Diet Rich in Olive Oil Phenolics Reduces Oxidative Stress in the Heart of SAMP8 Mice by Induction of Nrf2-Dependent Gene Expression**. *Rejuvenation Res.* (2012) **15** 71-81. DOI: 10.1089/rej.2011.1245
41. Torres de Pinedo A., Peñalver P., Morales J.C.. **Synthesis and Evaluation of New Phenolic-Based Antioxidants: Structure–Activity Relationship**. *Food Chem.* (2007) **103** 55-61. DOI: 10.1016/j.foodchem.2006.07.026
42. Haris Omar S.. **Oleuropein in Olive and Its Pharmacological Effects**. *Sci. Pharm.* (2010) **78** 133-154. DOI: 10.3797/scipharm.0912-18
43. Zhao G., Yao-Yue C., Qin G.-W., Guo L.-H.. **Luteolin from Purple Perilla Mitigates ROS Insult Particularly in Primary Neurons**. *Neurobiol. Aging* (2012) **33** 176-186. DOI: 10.1016/j.neurobiolaging.2010.02.013
44. de Bock M., Thorstensen E.B., Derraik J.G.B., Henderson H.V., Hofman P.L., Cutfield W.S.. **Human Absorption and Metabolism of Oleuropein and Hydroxytyrosol Ingested as Olive (**. *Mol. Nutr. Food Res.* (2013) **57** 2079-2085. DOI: 10.1002/mnfr.201200795
45. Ahmad-Qasem M.H., Barrajón-Catalán E., Micol V., Mulet A., García-Pérez J.V.. **Influence of Freezing and Dehydration of Olive Leaves (Var. Serrana) on Extract Composition and Antioxidant Potential**. *Food Res. Int.* (2013) **50** 189-196. DOI: 10.1016/j.foodres.2012.10.028
46. Nikou T., Sakavitsi M.E., Kalampokis E., Halabalaki M.. **Metabolism and Bioavailability of Olive Bioactive Constituents Based on In Vitro, In Vivo and Human Studies**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14183773
47. Galmés S., Reynés B., Palou M., Palou-March A., Palou A.. **Absorption, Distribution, Metabolism, and Excretion of the Main Olive Tree Phenols and Polyphenols: A Literature Review**. *J. Agric. Food Chem.* (2021) **69** 5281-5296. DOI: 10.1021/acs.jafc.1c00737
48. Bender C., Strassmann S., Golz C.. **Oral Bioavailability and Metabolism of Hydroxytyrosol from Food Supplements**. *Nutrients* (2023) **15**. DOI: 10.3390/nu15020325
49. Mechi D., Baccouri B., Martín-Vertedor D., Abaza L.. **Bioavailability of Phenolic Compounds in Californian-Style Table Olives with Tunisian Aqueous Olive Leaf Extracts**. *Molecules* (2023) **28**. DOI: 10.3390/molecules28020707
50. Domínguez-Perles R., Auñón D., Ferreres F., Gil-Izquierdo A.. **Gender Differences in Plasma and Urine Metabolites from Sprague–Dawley Rats after Oral Administration of Normal and High Doses of Hydroxytyrosol, Hydroxytyrosol Acetate, and DOPAC**. *Eur. J. Nutr.* (2017) **56** 215-224. DOI: 10.1007/s00394-015-1071-2
51. Sakavitsi M.E., Breynaert A., Nikou T., Lauwers S., Pieters L., Hermans N., Halabalaki M.. **Availability and Metabolic Fate of Olive Phenolic Alcohols Hydroxytyrosol and Tyrosol in the Human GI Tract Simulated by the In Vitro GIDM–Colon Model**. *Metabolites* (2022) **12**. DOI: 10.3390/metabo12050391
52. Alemán-Jiménez C., Domínguez-Perles R., Medina S., Prgomet I., López-González I., Simonelli-Muñoz A., Campillo-Cano M., Auñón D., Ferreres F., Gil-Izquierdo Á.. **Pharmacokinetics and Bioavailability of Hydroxytyrosol Are Dependent on the Food Matrix in Humans**. *Eur. J. Nutr.* (2021) **60** 905-915. DOI: 10.1007/s00394-020-02295-0
53. Kountouri A.M., Mylona A., Kaliora A.C., Andrikopoulos N.K.. **Bioavailability of the Phenolic Compounds of the Fruits (Drupes) of**. *Phytomedicine* (2007) **14** 659-667. DOI: 10.1016/j.phymed.2007.06.001
54. Seiquer I., Rueda A., Olalla M., Cabrera-Vique C.. **Assessing the Bioavailability of Polyphenols and Antioxidant Properties of Extra Virgin Argan Oil by Simulated Digestion and Caco-2 Cell Assays. Comparative Study with Extra Virgin Olive Oil**. *Food Chem.* (2015) **188** 496-503. DOI: 10.1016/j.foodchem.2015.05.006
55. Nørskov N.P., Hedemann M.S., Lærke H.N., Knudsen K.E.B.. **Multicompartmental Nontargeted LC–MS Metabolomics: Explorative Study on the Metabolic Responses of Rye Fiber versus Refined Wheat Fiber Intake in Plasma and Urine of Hypercholesterolemic Pigs**. *J. Proteome Res.* (2013) **12** 2818-2832. DOI: 10.1021/pr400164b
56. Karković Marković A., Torić J., Barbarić M., Jakobušić Brala C.. **Hydroxytyrosol, Tyrosol and Derivatives and Their Potential Effects on Human Health**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24102001
57. Bucciantini M., Leri M., Nardiello P., Casamenti F., Stefani M.. **Olive Polyphenols: Antioxidant and Anti-Inflammatory Properties**. *Antioxidants* (2021) **10**. DOI: 10.3390/antiox10071044
58. Gilmore T.D.. **Introduction to NF-ΚB: Players, Pathways, Perspectives**. *Oncogene* (2006) **25** 6680-6684. DOI: 10.1038/sj.onc.1209954
59. Kim E.K., Choi E.-J.. **Pathological Roles of MAPK Signaling Pathways in Human Diseases**. *Biochim. Et Biophys. Acta (BBA) Mol. Basis Dis.* (2010) **1802** 396-405. DOI: 10.1016/j.bbadis.2009.12.009
60. Wang W., Xia Y., Yang B., Su X., Chen J., Li W., Jiang T.. **Protective Effects of Tyrosol against LPS-Induced Acute Lung Injury via Inhibiting NF-ΚB and AP-1 Activation and Activating the HO-1/Nrf2 Pathways**. *Biol. Pharm. Bull.* (2017) **40** 583-593. DOI: 10.1248/bpb.b16-00756
61. Martín-Peláez S., Castañer O., Solà R., Motilva M., Castell M., Pérez-Cano F., Fitó M.. **Influence of Phenol-Enriched Olive Oils on Human Intestinal Immune Function**. *Nutrients* (2016) **8**. DOI: 10.3390/nu8040213
62. Bouayed J., Bohn T.. **Exogenous Antioxidants—Double-Edged Swords in Cellular Redox State: Health Beneficial Effects at Physiologic Doses versus Deleterious Effects at High Doses**. *Oxidative Med. Cell. Longev.* (2010) **3** 228-237. DOI: 10.4161/oxim.3.4.12858
63. Cea Pavez I., Lozano-Sánchez J., Borrás-Linares I., Nuñez H., Robert P., Segura-Carretero A.. **Obtaining an Extract Rich in Phenolic Compounds from Olive Pomace by Pressurized Liquid Extraction**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24173108
64. Chanioti S., Tzia C.. **Extraction of Phenolic Compounds from Olive Pomace by Using Natural Deep Eutectic Solvents and Innovative Extraction Techniques**. *Innov. Food Sci. Emerg. Technol.* (2018) **48** 228-239. DOI: 10.1016/j.ifset.2018.07.001
65. Bionda A., Lopreiato V., Crepaldi P., Chiofalo V., Fazio E., Oteri M., Amato A., Liotta L.. **Diet Supplemented with Olive Cake as a Model of Circular Economy: Metabolic and Endocrine Responses of Beef Cattle**. *Front. Sustain. Food Syst.* (2022) **6** 1077363. DOI: 10.3389/fsufs.2022.1077363
66. Chiofalo B., Di Rosa A.R., Lo Presti V., Chiofalo V., Liotta L.. **Effect of Supplementation of Herd Diet with Olive Cake on the Composition Profile of Milk and on the Composition, Quality and Sensory Profile of Cheeses Made Therefrom**. *Animals* (2020) **10**. DOI: 10.3390/ani10060977
67. Srinivasan R., Chandrasekar M.J.N., Nanjan M.J., Suresh B.. **Antioxidant Activity of Caesalpinia Digyna Root**. *J. Ethnopharmacol.* (2007) **113** 284-291. DOI: 10.1016/j.jep.2007.06.006
68. Re R., Pellegrini N., Proteggente A., Pannala A., Yang M., Rice-Evans C.. **Antioxidant Activity Applying an Improved ABTS Radical Cation Decolorization Assay**. *Free Radic. Biol. Med.* (1999) **26** 1231-1237. DOI: 10.1016/S0891-5849(98)00315-3
69. Firuzi O., Lacanna A., Petrucci R., Marrosu G., Saso L.. **Evaluation of the Antioxidant Activity of Flavonoids by “Ferric Reducing Antioxidant Power” Assay and Cyclic Voltammetry**. *Biochim. Et Biophys. Acta (BBA) Gen. Subj.* (2005) **1721** 174-184. DOI: 10.1016/j.bbagen.2004.11.001
70. Nkuimi Wandjou J.G., Lancioni L., Barbalace M.C., Hrelia S., Papa F., Sagratini G., Vittori S., Dall’Acqua S., Caprioli G., Beghelli D.. **Comprehensive Characterization of Phytochemicals and Biological Activities of the Italian Ancient Apple ‘Mela Rosa Dei Monti Sibillini’**. *Food Res. Int.* (2020) **137** 109422. DOI: 10.1016/j.foodres.2020.109422
71. Peršurić Ž., Saftić Martinović L., Zengin G., Šarolić M., Kraljević Pavelić S.. **Characterization of Phenolic and Triacylglycerol Compounds in the Olive Oil By-Product Pâté and Assay of Its Antioxidant and Enzyme Inhibition Activity**. *LWT* (2020) **125** 109225. DOI: 10.1016/j.lwt.2020.109225
72. Mooney C., Haslam N.J., Pollastri G., Shields D.C.. **Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity**. *PLoS ONE* (2012) **7**. DOI: 10.1371/journal.pone.0045012
|
---
title: Evaluation of Mechanical Properties of Glass Ionomer Cements Reinforced with
Synthesized Diopside Produced via Sol–Gel Method
authors:
- Ali Maleki Nojehdehi
- Farina Moghaddam
- Bejan Hamawandi
journal: Materials
year: 2023
pmcid: PMC10004627
doi: 10.3390/ma16052107
license: CC BY 4.0
---
# Evaluation of Mechanical Properties of Glass Ionomer Cements Reinforced with Synthesized Diopside Produced via Sol–Gel Method
## Abstract
This study aimed to fabricate a glass ionomer cement/diopside (GIC/DIO) nanocomposite to improve its mechanical properties for biomaterials applications. For this purpose, diopside was synthesized using a sol–gel method. Then, for preparing the nanocomposite, 2, 4, and 6 wt% diopside were added to a glass ionomer cement (GIC). Subsequently, X-ray diffraction (XRD), differential thermal analysis (DTA), scanning electron microscopy (SEM), and Fourier transform infrared spectrophotometry (FTIR) analyses were used to characterize the synthesized diopside. Furthermore, the compressive strength, microhardness, and fracture toughness of the fabricated nanocomposite were evaluated, and a fluoride-releasing test in artificial saliva was also applied. The highest concurrent enhancements of compressive strength (1155.7 MPa), microhardness (148 HV), and fracture toughness (5.189 MPa·m$\frac{1}{2}$) were observed for the glass ionomer cement (GIC) with 4 wt% diopside nanocomposite. In addition, the results of the fluoride-releasing test showed that the amount of released fluoride from the prepared nanocomposite was slightly lower than the glass ionomer cement (GIC). Overall, the improvement in mechanical properties and optimal fluoride release of prepared nanocomposites can introduce suitable options for dental restorations under load and orthopedic implants.
## 1. Introduction
Glass ionomer cement (GIC) was invented in 1969 by Wilson and Kent in England in a chemical laboratory [1,2]. Since its invention, this cement has been used in clinical dentistry as a restorative biomaterial [3,4,5,6,7]. Glass ionomer cements (GICs) are organic base materials and are known as polyalkenoate cement. This work about these materials is based on the acid–base reaction between calcium fluoroaluminosilicate glass powder and an aqueous solution of polyacrylic acid [1,8]. The initial design of glass ionomer cements (GICs) was a formula of silicate and polycarboxylate cement. Glass ionomer cements (GICs) used aluminosilicate powder from silicate cement and polyacrylic acid liquid from polycarboxylate cement to have the properties of both types of cement together. Hardening of glass ionomer cement (GIC) takes place during three stages: dissolution, gelation, and hardening. Therefore, the hardening mechanism of glass ionomer cement (GIC) includes the dissolution of the surface of glass particles in polymer liquid, then the release of aluminum and calcium ions and finally, the formation of calcium and aluminum polyacrylate chains in the matrix of hardened cement [9].
Glass ionomer cement (GIC) has been used among restorative materials due to some desirable physical, chemical, and biological properties [10]. These materials have permanent adhesion to tooth enamel and dentin, they have the property of releasing fluoride for a long time, and when they are exposed to a solution containing fluoride, they are able to absorb and store it; therefore, they have anti-caries properties [1]. Good biocompatibility in the mouth, color matching with dentin and tooth enamel, acid resistance, thermal expansion coefficient similar to the tooth structure, non-shrinkage due to self-adherence, and non-toxicity are among the other characteristics of these cement [1,11]. Glass ionomers are generally widely used to restore dental structures in dentistry. Apart from dentistry, glass ionomer cement (GIC) has also been suggested as a material for use in bone repair surgeries [12]. In addition, D’Orto et al. [ 13] reported that replacing dental implants with new cement supporting fixed prostheses can also be a useful solution in patients with type I diabetes, provided compensation is performed and recent blood tests are checked by the clinician prior to surgery. Additionally, this replacement can be an effective treatment to prevent mucositis caused by radiotherapy and/or chemotherapy interventions at high risk of oral and pharyngeal mucosa damage [14]. Furthermore, to the advantages of glass ionomer cement (GIC), the most important limitation in the use of these cements is their weak mechanical properties, which limits their use in applications under high stress. Poor mechanical properties cause primary and secondary implant failure [15], which can be achieved by introducing new cement.
In recent years, many efforts have been made to improve the mechanical properties of glass ionomer cement (GIC) [16]. Many researchers have worked in this field with different ideas. These research include adding zirconia particles, bioglass, hydroxyapatite, fluor apatite, forsterite, and titanium diopside to glass ionomer cement (GIC) [1]. In a previous study, we reinforced glass ionomer cement by the forsterite, which showed good mechanical results [10] and was the beam of light for our next studies. Diopside (DIO), with the chemical composition CaMgSi2O6, is one of the important biomaterials that belongs to the group of pyroxenes. All three elements of silicon, calcium, and magnesium, which are needed in the development of the skeletal system, are found in the chemical composition of diopside (DIO). Due to its biocompatibility and mechanical properties, it is used in a wide variety of clinical applications, such as bone and dental root implants, surgery hemostasis applications, drug delivery, and in vivo imaging [17]. In addition, according to Nonami et al. ’s research, the comparison of the mechanical properties and biocompatibility of diopside and hydroxyapatite proved that diopside (DIO) has better mechanical properties than hydroxyapatite. On the other hand, due to its good biocompatibility, it can be used in cases where the use of hydroxyapatite is limited [18]. According to the report of Khandan et al., even the produced hydroxyapatite-diopside (DIO) bio-nanocomposite coatings show favorable biocompatibility and high hardness [19]. Notwithstanding, diopside (DIO) nanoparticles are well known due to their mechanical properties, and they can create good wetting with water because of their superficial hydroxyl groups on their surface; therefore, they have been chosen as a substrate for adsorbents [20].
Another feature of diopside (DIO) is the possibility of obtaining these nanoparticles through natural waste sources such as rice husk as a source of silica and eggshell as a source of calcium oxide by the sol–gel method that is reported by Choudhary et al. [ 21]. The purpose of this study is to fabricate and characterize the diopside (DIO) glass ionomer cement (GIC) nanocomposite and compare the effect of adding these nanoparticles to the ceramic component of glass ionomer cement (GIC) to improve its mechanical properties. Expanding the use of these cement in dentistry and orthopedics due to the improvement of mechanical properties is one of the important achievements of this research. For this purpose, diopside (DIO) nanoparticles were prepared by the sol–gel method, and different weight percentages were added to the commercial glass ionomer cement (GIC) ceramic, and after mixing with cement polymer liquid, the manufactured nanocomposites were subjected to mechanical tests and fluoride release.
## 2.1. Synthesis of Diopside Nanoparticles
Magnesium nitrate (6Mg(NO3)2.6H2O), calcium nitrate (Ca(NO3)2.4H2O), tetraethyl orthosilicate (TEOS, SiC8H20O4), and ethanol in $99\%$ purity were purchased from Merck, Germany. They were used as raw materials for the synthesis of diopside (DIO, CaMgSi2O6) by sol–gel method. To prepare diopside (DIO), the 29.52 g calcium nitrate and 32.05 g magnesium nitrate were added in the 92.14 g ethanol and stirred in a magnetic stirrer for 30 min at 80 °C. After dissolving the salts in ethanol, 52.08 g tetraethyl orthosilicate (TEOS) was added to the solution and stirred under slower rate at 30 °C for 30 min. The produced sol was placed in a dryer at 120 °C for 48 h. Then, the very agglomerated powder from the dryer was manually ground. Based on differential thermal analysis (DTA) results to achieve the desired nanopowder, the sol was heated with the rate of 10 °C/min at 800 °C for 2 h [22]. Then, we ground it by ball-mill. Finally, we reached diopside nanoparticle with 100 nm.
## 2.2. Manufacture of Nanocomposite Samples
Fuji II GC commercial glass ionomer cement (GIC) (a product of Corporation GC, Tokyo, Japan) was purchased. To prepare the nanocomposites, 2, 4, and, 6 wt% diopside nanoparticles were added to glass ionomer cement (GIC)’s powder portion and then mixed with polymeric liquid. First, glass ionomer cement powder was mixed with different weight percentages of diopside nanoparticles for 30 s in an amalgamator. Then, the mixed powders were distributed on the glass plate. Then, they were mixed with the liquid (acrylic acid) in a 2:1 ratio; the mixing method was followed according to the factory instructions under the recommended conditions and time. Through this way, at first, half of the distributed powder, using a plastic spatula, entered the polymer liquid slowly and was quickly mixed within 10 s. Then, the second part of the glass ionomer powder was completely added to the mixture, and this mixing took place within 15 to 20 s. After setting cement for 30 s [23], they were cast in cylindrical molds (6 mm diameter and 12 mm height) at room temperature. The final mixed material had a shiny and wet surface. In the end, the obtained dough was transferred to an aluminum mold containing grooves with specific dimensions according to the relevant standards. In this step, we tried to condense the resulting dough from one side of the mold wall to prevent the formation of air bubbles in the mixture. After the samples were completely set, they were removed from the molds and ground (P600 to P2000) to obtain smoother surfaces. Finally, they were used for tests.
## 2.3. Mechanical Tests
Mechanical tests were evaluated 24 h after setting cement. The whole assembly was stored at 36 °C and at least $95\%$ relative humidity [24]. To perform the compressive strength test, cylindrical samples with a diameter of 6 ± 0.1 mm and a height of 12 ± 1.0 mm were prepared in accordance with the ISO 9917-1 standard. The force was applied to the sample along the longitudinal axis at a speed of 0.5 mm/min. The compressive strength was determined based on Equation [1] [25]:[1]$C = 4$P/πd2 where C is the compressive strength (MPa), P is the maximum fracture force (N), and d is the sample diameter (mm). The Rockwell method is a rapid method for determining hardness of dental materials. Rockwell hardness number (RHN) is designated according to the particular indenter and load applied [26]. In this work, the microhardness test was done by a Rockwell C device by applying a 20 N load. Additionally, to determine the fracture toughness, KOOPA UV1 model macro hardness tester was used [10]. Through this method, we created a crack on the surface of the glass ionomer cement (GIC) 4 wt% diopside (DIO) nanocomposite because, at forces lower than this value, cracks do not form on the surface of the nanocomposite. Our goal was to calculate the toughness by creating a crack on the nanocomposite surface, with the relationship between the applied force and the crack length. It should be noted that the evaluation of the cracks by using an optical microscope and ImageJ software (v 1.53), and the value of the fracture toughness was calculated with the following relationship in this article. Therefore, cylindrical specimens with dimensions of 6 mm φ × 12 mm were mounted by polyester, and hardness effect on the surface was measured. Based on the crack length and applied force, the fracture toughness was calculated (Equation [2]) [27]. For each test, 5 samples were tested, and the average of data with error bar was reported. KIC = 0.0889 [Hv·P/∑ci]$\frac{1}{2}$[2] where KIC is the fracture toughness, HV is microhardness, P is applied load, and C is crack length plus half diameter of the hardening effect.
## 2.4. Fluoride Release Assessment Test (ICP)
The fluoride release test was performed over a period of 14 days. The samples were prepared in the form of a cylinder with a diameter of 6 mm and a height of 12 mm. First, each sample was placed separately in a test tube containing 15 mL of artificial saliva. Plastic tubes were used in 15 mL packages because glass containers absorb and release fluoride. The chemical composition of artificial saliva used is given in Table 1. All materials release their highest proportion of cumulative total fluoride in the first 24 h after mixing [28]. The amount of fluoride released after the first, third, seventh, and fourteenth days of mixing was measured and recorded. At the end of each of these periods, after leaving the test tube, each sample was washed twice with deionized water. Then, to avoid saturation of the solution by fluoride ion after drying, the sample was placed in fresh artificial saliva solution. During the test period, the samples were kept in a bain-marie bath with a temperature of 37 °C [10]. After removing the test tubes, the fluoride sample of the artificial saliva solution was measured using a potentiometric method by a potentiometer (pH/ISE, Meter Thermo Orion, Waltham, MA, USA) and using a specific fluoride ion electrode (Fluoride Combination Electrode, 96-09-00, Thermo Fisher Scientific, Waltham, MA, USA). Before starting the measurement in each period, the potentiometer device was calibrated by fluoride standard solutions containing 0.1, 1, and 10 mg/L of fluoride ion, respectively. The calibration slope of the device was between 58.7 and 60.2. Before measurement, 1 mL of each solution was mixed with 0.1 mL of TISAB III buffer solution (Thermo Orion, Waltham, MA, USA) in a 5 mL polyethylene vial under ambient conditions using a magnetic stirrer [29]. A buffer was added to each solution to remove disturbing ions in the measurement of fluoride ion. To measure, first, the electrode was placed inside the solution, and the container containing the solution was shaken to make the diffusion of fluoride ion in the solution uniform. In the end, the concentration of fluoride ion in terms of ppm for the solution was read and recorded directly from the device. After this operation, the electrode was removed from the solution and completely washed with distilled water, and, after drying, it was used again to measure the next sample. The fluoride concentration of each solution was measured three times and on different days.
## 2.5. Characterizations
To analyze the phase composition and determine the grain size of the synthesized diopside powders, X-ray diffraction (Philips expert) was used. The X-ray diffraction was operated with voltage and current settings of 30 kV and 20 mA, and used Cu-Kα radiation λ = 1.5404 Å in the 2θ range from 10 to 80°. The grain size of the milled diopside powders was measured by evaluating the XRD peaks using Scherer Equation [3] [31]:[3]β=kλL cosθ β is the full width of the diffraction peak under consideration (rad.) at half maximum intensity, k is a constant ($k = 0.89$), λ (nm) is the wavelength of the X-ray, θ(°) is the Bragg diffraction angle, and (L) is the size of the grain (nm).
High magnification FESEM images (TESCAN is located in Brno, Czech Republic) was also used to study the particle size and morphology of the synthesized diopside powders. Additionally, Fourier transform infrared spectrophotometry (FTIR, Bruker Tensor 27 FTIR, Hardtstraße, Karlsruhe, Germany) analysis was performed to evaluate the amount and type of bonds of the material in question to ensure the desired synthesis result.
## 3.1. Characterization of Diopside (DIO) Nanoparticles
The XRD pattern of dried diopside (DIO) at 120 °C for 24 h is shown in Figure 1. As can be seen, the structure is amorphous, and no crystalline phase is created at this temperature. The XRD pattern of nanoparticles prepared by the sol–gel method, after calcination at 800 °C, is presented in Figure 2. The formation of the pure and completely crystalline phase of the diopside (DIO) particles was confirmed by comparing the angles and intensities of scattered peaks with the information contained in the standard JCDPS. Examining the available peaks shows that main peak at 29.9°, 35.5°, 56.7°, 54.9, and 30.8° correspond to the plans of (−221), [002], (−223), [420], and (−311), respectively, and are $100\%$ compatible with the main peaks of the default diopside (DIO) in the 00-003-0860 card [29,32]. According to the phase analysis, there is ~$97\%$ diopside (DIO) and approximately $3\%$ impurity. In previous studies, it has been proven that biomaterials based on calcium magnesium silicates, including diopside (CaMgSi2O6), akermanite (Ca2MgSi2O7), and merwinite (Ca3MgSi2O8), have good properties, such as high biocompatibility and biodegradability and superior mechanical characteristics [33]. Therefore, it was found that the presence of $3\%$ of the merwinite (Ca3MgSi2O8) did not have a negative effect on the mechanical properties. The crystal size of the diopside (DIO) nanoparticles was calculated ~40 nm based on the modified Scherer equation [34]. The results obtained from the phase studies of the produced particles well show that the product of the sol–gel process and the subsequent heat treatment is nanocrystalline diopside (DIO) [35].
The FESEM micrographs of the dispersion of the diopside (DIO) particles are shown in Figure 3. The figure shows that the nanoparticles are formed in very small dimensions with no specific morphology. Similar to many nanometer particles, agglomerations are quite evident in FESEM micrographs due to high surface energy [36]. Achieving this morphology and particle size for diopside (DIO) particles caused by sol–gel method is consistent with the results of other researchers [37]. According to the different shapes and magnifications, the 200 particle sizes were measured in different areas, and it was observed that the particle size is about 140–150 nm, which is consistent with the crystallite size (~40 nm) calculated by XRD pattern.
## 3.2. DTA of Synthesis Diopside (DIO)
The diopside (DIO) solution was dried at 120 °C for 24 h, and DTA was performed to determine the calcination temperature and the crystallization reactions. According to Figure 4, we can see an exothermic peak at ~550 °C, which indicates the heat reaction. Because the system tends to transform into a crystalline structure and lower its energy level. Consequently, it corresponds to our purpose and determines the calcination temperature.
## 3.3. FTIR Analysis of Diopside Nanoparticles
Figure 5 shows FTIR spectrum of diopside nanoparticles. As shown in the figure, the peaks observed at 1000–1200 cm−1, 800 cm−1 and 600 cm−1 wavelengths correspond to the asymmetric tensile vibrations of the groups (Si-O-Si). Additionally, the peak corresponds to symmetric tensile vibrations (Si-O-Si) close to the wavelength of 800 cm−1 [25,38,39,40].
## 3.4. Evaluation of Fluoride Release
The results of the fluoride release test by the ICP method, after 14 days of immersion of glass ionomer cement (GIC) and nanocomposite samples in artificial saliva solution are given in Figure 6. Figure 6 shows the initial high fluoride release on the first day and the slow and steady release of fluoride from the nanocomposite over time. The presence of diopside (DIO) nanoparticles in the glass ionomer structure do not interfere with the ability of glass ionomer cement (GIC) to release fluoride. Additionally, the graph shows that the amount of fluoride released for nanocomposites is less than pure glass ionomer cement (GIC) during the entire measurement period.
One of the factors influencing the release of fluoride from cement is the chemical composition of the ceramic component of glass ionomer cement (GIC). When glass powder is combined with diopside (DIO) nanoparticles and then mixed with polymer liquid, in addition to the surface dissolution of glass particles, the surface of diopside (DIO) nanoparticles is also dissolved in the presence of polymer liquid and calcium, magnesium and even silicon cations from the surface of diopside (DIO) nanoparticles is released. Calcium released from the surface of diopside (DIO) nanoparticles participates in the initial hardening mechanism of glass ionomer cement (GIC) and ultimately leads to the formation of more and stronger calcium and aluminum polyacrylates in the matrix of glass ionomer cement (GIC). In this case, the diffusion of fluoride from the glass ionomer cement (GIC) base is slow and requires more time. Therefore, the release of fluoride from cement decreases. It is important to note that the reactivity of sodium is higher than that of magnesium. Therefore, fluoride is mostly released in the form of sodium fluoride from glass ionomer cement (GIC). It seems that by immersing pure glass ionomer cement (GIC) in artificial saliva solution, more fluoride is available in the form of sodium fluoride, and thus the formation of fluoride is accelerated in the presence of sodium, and the tendency to release fluoride ions is reduced in the presence of diopside (DIO) nanoparticles.
Another factor affecting the release of fluoride from glass ionomer cement (GIC) is the porosity [41]. By adding diopside (DIO) nanoparticles to the ceramic component of glass ionomer cement (GIC), there will be a wider distribution of particle size in the structure of glass ionomer cement (GIC), which will result in greater density of powder particles mixed with the matrix cement polymer and increase mechanical properties of cement. Consequently, the diopside (DIO) particles will occupy the porosity between the glass cement particles and lead to a decrease in the porosity of the glass ionomer cement (GIC) structure [35,42,43]. By reducing the amount of porosity in the cement structure, the entry of artificial saliva into the cement is reduced, and as a result, less fluoride is released from the cement. Sumit et al. [ 44] investigated the relationship between the compressive strength of glass ionomer cement (GIC) and the amount of fluoride released from the cement. Their results showed that there is an inverse relationship between fluoride release and compressive strength, which is also consistent with the results presented in this research. According to Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7, diopside (DIO) glass ionomer cement (GIC) nanocomposite has a higher compressive strength and lower fluoride release than pure glass ionomer cement (GIC).
## 3.5. Mechanical Properties
Figure 7 shows the compressive strength of glass ionomer cement (GIC) diopside (DIO) nanocomposites with different diopside (DIO) content. The results showed that by increasing the diopside (DIO) nanoparticles content up to 4 wt%, the compressive strength increases, but, at higher values (6 wt%), the compressive strength decreases. The glass ionomer cement (GIC) 4 wt% diopside (DIO) nanocomposite has the highest compressive strength compared to other samples, so the compressive strength of composite glass ionomer cement (GIC)-4 wt% diopside (DIO) compared to the plain sample increased from 349.5 MPa to 1155.7 MPa shows ~$230\%$ increase in compressive strength. In addition, by adding 2 wt% and 6 wt% of diopside (DIO) nanoparticles, the compressive strength increased by $42\%$ and $98\%$, respectively, which shows adding diopside (DIO) nanoparticles in quantities less/more than 4wt% glass ionomer cement (GIC) will not have a destructive and reducing effect on the compressive strength of the cement, and it will be higher than the compressive strength of the plain sample. By comparing the results for glass ionomer cement (GIC) 4 wt% diopside (DIO) nanocomposite with the results reported for glass ionomer cement (GIC) 2 wt% forsterite nanocomposite [10], it is clear that the compressive strength has increased by $36\%$.
Figure 8 illustrates the microhardness of glass ionomer cement (GIC) diopside (DIO) nanocomposites with different diopside (DIO) content. The results showed that the glass ionomer cement (GIC) 4 wt% diopside (DIO) nanocomposite has the highest microhardness compared to other nanocomposite samples, and compared to the plain cement, the microhardness increased from 113.7 HV to 148 HV, which means ~$30\%$ increase in microhardness. Moreover, the addition of diopside (DIO) nanoparticles less than 4 wt% glass ionomer cement (GIC) will not have a destructive and reductive effect on the microhardness, and the microhardness has increased by ~$53\%$. By adding more than 4 wt% of diopside (DIO) nanoparticles to glass ionomer cement (GIC), the microhardness is lower than the initial microhardness value. By comparing the results for glass ionomer cement (GIC) 2 wt% diopside nanocomposite with the results reported for glass ionomer cement (GIC) 2 wt% forsterite nanocomposite [10], it indicated that almost the same microhardness is obtained.
Figure 9 and Figure 10 show fracture toughness of glass ionomer cement (GIC) diopside (DIO) nanocomposites with different diopside (DIO) content. The results showed that the glass ionomer cement (GIC) 4 wt% diopside (DIO) nanocomposite has the highest fracture toughness compared to other nanocomposite samples. The fracture toughness increased from 2.743 MPa.m$\frac{1}{2}$ to 5.189 MPa.m$\frac{1}{2}$, which means there is about an $89.17\%$ increase in fracture toughness. Additionally, adding diopside (DIO) nanoparticles in quantities less than 4 wt% glass ionomer cement (GIC) did not have a destructive and reductive effect on the fracture toughness. By adding more than 4 wt% diopside (DIO) nanoparticles to the glass ionomer cement (GIC), the fracture toughness was not lower than the initial fracture toughness. Figure 7, Figure 8 and Figure 9 show that by adding the diopside (DIO) nanoparticles up to 4 wt%, the compressive strength, microhardness, and fracture toughness of glass ionomer cement (GIC) significantly increased.
In justifying the improvement of the mechanical properties of glass ionomer diopside cement nanocomposite in the presence of specific amounts of diopside nanoparticles, the following reason can be mentioned: Diopside particles have high mechanical properties. In most research that aim to improve the mechanical properties of the material, the addition of diopside as a secondary phase has been observed in the matrix. By adding diopside particles to the ceramic component of glass ionomer cement, these particles participate in the hardening mechanism of glass ionomer cement in such a way that in the acid and base reaction between the particles of Acrylic acid glass and polymer liquid, the surface of diopside particles dissolves due to the attack of acidic proton (polymeric liquid) H+, and Ca2+ ions are released from the surface of diopside particles. Therefore, more calcium ions will be available to form crosslinks and acrylate polysalts, which will strengthen the underlying matrix of glass ionomer cement and lead to an increase in cement strength [45,46]. Diopside nanoparticles prepared by the sol–gel method have a crystalline phase structure. Therefore, another reason for increasing the mechanical properties of glass ionomer cement in the presence of diopside nanoparticles is the formation of crystalline phases in the amorphous matrix of glass ionomer cement. By adding nanometer-sized diopside particles to glass ionomer cement powder with larger particle sizes than diopside particles, a wider distribution of particle size will occur in the structure of glass ionomer cement, which results in a greater density of powder particles mixed with the cement polymer matrix, and after that, it will bring better mechanical properties of cement. Diopside particles occupy the empty spaces between the cement glass particles and strengthen the cement base by creating more places to bond with carboxylic acid groups.
The loss of strength in nanocomposites containing diopside nanoparticles in amounts greater than 4 wt% for compressive strength, microhardness, and fracture toughness is due to the reduction of bonding and connecting forces between the ceramic and polymer components of glass ionomer cement. In fact, external diopside particles act as a barrier and prevent the complete connection of glass ionomer cement components. Diopside with nanometer particle size has more surface area compared to glass powder with micron particle size. In the presence of excessive amounts of diopside nanoparticles, the surface dissolution of these particles by polymer liquid is more than the surface dissolution of glass particles. This leads to the decrease in the formation of aluminum polyacrylates, which play a very important role in the final strength of glass ionomer cement. On the other hand, it is possible that the decrease in strength in the presence of excessive amounts of diopside nanoparticles indicates the lack of proper wetting in the interface of matrix and reinforcing particles because, with an increasing amount of nanoparticles in a cement matrix and with increasing surface area, the number of carboxylic acid groups available to bond with nanoparticles decreases. In this case, cracks are created around diopside nanoparticles and with the increase in diopside nanoparticles, the number of cracks in the interface of the matrix and the reinforcement increases. These cracks act as a stress concentration and lead to a decrease in mechanical properties [47,48,49,50]. The results of the mechanical test showed that by adding diopside nanoparticles to glass ionomer cement of up to 4 wt%, the compressive strength, microhardness, and fracture toughness of the nanocomposite produced is higher compared to glass ionomer cement; therefore, adding diopside nanoparticles to glass ionomer cement up to the above amounts is unimpeded.
## 3.6. FESEM Images and EDS Analysis of Glass Ionomer and Glass Ionomer 4 wt% Diopside Nanocomposites
According to the Figure 11, it can be seen that the microstructure of the glass ionomer nanocomposite without the reinforcing phase is in the form of a sheet with irregular corners. By adding diopside nanoparticles, these particles are placed between the particles of the matrix phase and bond with the surrounding particles, and they increase the mechanical properties of the nanocomposite. Additionally, the uniform distribution of the reinforcing phase is observed on the surface of the nanocomposite.
According to the components of glass ionomer cement, which are AlPO4, NaF, CaF2, AlF3, Al2O3, and SiO2, it can be seen that in the composition of glass ionomer cement, the peak of magnesium is very small and minor (Figure 12). Furthermore, according to the composition of diopside and forsterite that both have Mg, in the EDS analysis of glass ionomer cement and glass ionomer diopside nanocomposites, this peak has become a little more intense (Figure 13). Additionally, according to the structure of diopside, which contains calcium, the peak related to *Ca is* observed in the relevant analysis. *In* general, as expected in all three graphs, the highest peak is related to Si because the main composition of glass is SiO2 and diopside also contain Si. In order to justify the absence of a long peak for oxygen, which is one of the main elements of glass and diopside, it can be said that oxygen is relatively light in weight, and EDS and XRF analysis are not able to detect and identify it accurately.
The results of this study were also compared to previous studies on the effect of diopside, zirconium oxide, titanium oxide, and aluminum oxide nanoparticles on the properties of glass ionomer cement, as shown in Table 2. It is apparent that the addition of diopside nanoparticles has significantly improved the properties of glass ionomer.
## 4. Conclusions
In the current study, diopside (DIO) nanoparticles were synthesized, and subsequently, glass ionomer (GIC) diopside (DIO) cement nanocomposites were prepared, and the effect of adding diopside (DIO) nanoparticle on its mechanical properties and fluoride release was investigated. In comparison with previous studies, the obtained results from this research prove that the addition of diopside nanoparticles improves the properties of glass ionomer cement compared to other nanoparticles. The most important results of this study are as follows: *Phase analysis* confirms that pure and crystalline diopside (DIO) nanoparticles were synthesized by the sol–gel method;The optimal percentage of diopside (DIO) nanoparticles to increase the compressive strength, microhardness, and fracture toughness of glass ionomer cement (GIC) was 4 wt%, so the compressive strength, microhardness, and fracture toughness of glass ionomer cement (GIC) saw increases of about $230\%$, $30\%$, and $89\%$;Adding diopside (DIO) nanoparticles to the ceramic component of glass ionomer cement (GIC) causes a slight decrease in the amount of fluoride releases;The produced glass ionomer (GIC) diopside (DIO) cement nanocomposites, due to their mechanical properties, and favorable fluoride release, are suggested as a suitable option for dental restorations and orthopedic implants under load.
## References
1. Khan A.S., Khan M., Rehman I.U.. **Nanoparticles, Properties, and Applications in Glass Ionomer Cements**. *Eur. J. Mol. Clin. Med.* (2012.0) **7** 93-108. DOI: 10.1016/b978-1-4557-3127-5.00005-2
2. Iaculli F., Salucci A., Di Giorgio G., Luzzi V., Ierardo G., Polimeni A., Bossù M.. **Bond Strength of Self-Adhesive Flowable Composites and Glass Ionomer Cements to Primary Teeth: A Systematic Review and Meta-Analysis of In Vitro Studies**. *Materials* (2021.0) **14**. DOI: 10.3390/ma14216694
3. Uzel I., Aykut-Yetkiner A., Ersin N., Ertuğrul F., Atila E., Özcan M.. **Evaluation of Glass-Ionomer versus Bulk-Fill Resin Composite: A Two-Year Randomized Clinical Study**. *Materials* (2022.0) **15**. DOI: 10.3390/ma15207271
4. Ratnayake J., Veerasamy A., Ahmed H., Coburn D., Loch C., Gray A.R., Lyons K.M., Heng N.C.K., Cannon R.D., Leung M.. **Clinical and Microbiological Evaluation of a Chlorhexidine-Modified Glass Ionomer Cement (GIC-CHX) Restoration Placed Using the Atraumatic Restorative Treatment (ART) Technique**. *Materials* (2022.0) **15**. DOI: 10.3390/ma15145044
5. Bilgrami A., Maqsood A., Alam M.K., Ahmed N., Mustafa M., Alqahtani A.R., Alshehri A., Alqahtani A.A., Alghannam S.. **Evaluation of Shear Bond Strength between Resin Composites and Conventional Glass Ionomer Cement in Class II Restorative Technique—An In Vitro Study**. *Materials* (2022.0) **15**. DOI: 10.3390/ma15124293
6. Čulina M.Z., Rajić V.B., Šalinović I., Klarić E., Marković L., Ivanišević A.. **Influence of pH Cycling on Erosive Wear and Color Stability of High-Viscosity Glass Ionomer Cements**. *Materials* (2022.0) **15**. DOI: 10.3390/ma15030923
7. Cheruvathoor J.J., Thomas L.R., Thomas L.A., Shivanna M.M., Machani P., Naik S., Al Kheraif A.A.. **Push-Out Bond Strength of Resin-Modified Glass Ionomer Cement and Flowable Composite Luting Systems on Glass Fiber Post of Root Canal**. *Materials* (2021.0) **14**. DOI: 10.3390/ma14226908
8. Moshaverinia A., Ansari S., Moshaverinia M., Roohpour N., Darr J.A., Rehman I.. **Effects of incorporation of hydroxyapatite and fluoroapatite nanobioceramics into conventional glass ionomer cements (GIC)**. *Acta Biomater.* (2008.0) **4** 432-440. DOI: 10.1016/j.actbio.2007.07.011
9. Wilson A.D., Kent B.E.. **A new translucent cement for dentistry. The glass ionomer cement**. *Br. Dent. J.* (1972.0) **132** 133-135. DOI: 10.1038/sj.bdj.4802810
10. Nojehdehi A.M., Moghaddam F., Hamedani M.T.. **Mechanical properties of glass ionomer cement incorporating forsterite nanoparticles synthesized by the sol-gel method**. *J. Sol-Gel Sci. Technol.* (2022.0) 1-9. DOI: 10.1007/s10971-022-05792-2
11. Werner S., Robert P., Peter J., Oswald G.. **Mixing Component for Dental Glass Ionomer Cements**. *U.S. Patent* (1979.0)
12. Yli-Urpo H., Närhi M., Närhi T.. **Compound changes and tooth mineralization effects of glass ionomer cements containing bioactive glass (S53P4), an in vivo study**. *Biomaterials* (2005.0) **26** 5934-5941. DOI: 10.1016/j.biomaterials.2005.03.008
13. D’Orto B., Polizzi E., Nagni M., Tetè G., Capparè P.. **Full Arch Implant-Prosthetic Rehabilitation in Patients with Type I Diabetes Mellitus: Retrospective Clinical Study with 10 Year Follow-Up**. *Int. J. Environ. Res. Public Health* (2022.0) **19**. DOI: 10.3390/ijerph191811735
14. Rosso M., Blasi G., Gherlone E., Rosso R.. **Effect of Granulocyte-Macrophage Colony-Stimulating Factor on Prevention of Mucositis in Head and Neck Cancer Patients Treated with Chemo-Radiotherapy**. *J. Chemother.* (1997.0) **9** 382-385. DOI: 10.1179/joc.1997.9.5.382
15. Tecco S., Grusovin M.G., Sciara S., Bova F., Pantaleo G., Cappare P.. **The association between three attitude-related indexes of oral hygiene and secondary implant failures: A retrospective longitudinal study**. *Int. J. Dent. Hyg.* (2018.0) **16** 372-379. DOI: 10.1111/idh.12300
16. Xie D., Brantley W., Culbertson B., Wang G.. **Mechanical properties and microstructures of glass-ionomer cements**. *Dent. Mater.* (2000.0) **16** 129-138. DOI: 10.1016/S0109-5641(99)00093-7
17. Srinath P., Azeem P.A., Reddy K.V., Chiranjeevi P., Bramanandam M., Rao R.P.. **A novel cost-effective approach to fabricate diopside bioceramics: A promising ceramics for orthopedic applications**. *Adv. Powder Technol.* (2021.0) **32** 875-884. DOI: 10.1016/j.apt.2021.01.038
18. Nonami T., Tsutsumi S.. **Study of diopside ceramics for biomaterials**. *J. Mater. Sci. Mater. Med.* (1999.0) **10** 475-479. DOI: 10.1023/A:1008996908797
19. Khandan A., Abdellahi M., Ozada N., Ghayour H.. **Study of the bioactivity, wettability and hardness behaviour of the bovine hydroxyapatite-diopside bio-nanocomposite coating**. *J. Taiwan Inst. Chem. Eng.* (2016.0) **60** 538-546. DOI: 10.1016/j.jtice.2015.10.004
20. Harandi Z.J., Nasab S.G., Teimouri A.. **Synthesis and characterisation of magnetic activated carbon/diopside nanocomposite for removal of reactive dyes from aqueous solutions: Experimental design and optimisation**. *Int. J. Environ. Anal. Chem.* (2019.0) **99** 568-594. DOI: 10.1080/03067319.2019.1597867
21. Choudhary R., Venkatraman S.K., Bulygina I., Senatov F., Kaloshkin S., Anisimova N., Kiselevskiy M., Knyazeva M., Kukui D., Walther F.. **Biomineralization, dissolution and cellular studies of silicate bioceramics prepared from eggshell and rice husk**. *Mater. Sci. Eng. C* (2021.0) **118** 111456. DOI: 10.1016/j.msec.2020.111456
22. Yamamoto S., Kawamura N., Nonami T.. **Diopside Synthesized by Sol-gel Method as Phosphorus Adsorption Material: Evaluation of Apatite Deposition in Pseudo Body Solution**. *Trans. Mater. Res. Soc. Jpn.* (2019.0) **44** 17-23. DOI: 10.14723/tmrsj.44.17
23. Kemoli A.M.. **The effects of ambient temperature and mixing time of glass ionomer cement material on the survival rate of proximal ART restorations in primary molars**. *Contemp. Clin. Dent.* (2014.0) **5** 31-36. DOI: 10.4103/0976-237X.128658
24. Peez R., Frank S.. **The physical–mechanical performance of the new Ketac™ Molar Easymix compared to commercially available glass ionomer restoratives**. *J. Dent.* (2006.0) **34** 582-587. DOI: 10.1016/j.jdent.2004.12.009
25. Sayyedan F., Fathi M., Edris H., Doostmohammadi A., Mortazavi V., Hanifi A.. **Effect of forsterite nanoparticles on mechanical properties of glass ionomer cements**. *Ceram. Int.* (2014.0) **40** 10743-10748. DOI: 10.1016/j.ceramint.2014.03.062
26. Vaidya A., Pathak K.. **Mechanical stability of dental materials**. *Appl. Nanocomposite Mater. Dent.* (2019.0) 285-305. DOI: 10.1016/b978-0-12-813742-0.00017-1
27. Moradkhani A., Baharvandi H., Naserifar A.. **Fracture Toughness of 3Y-TZP Dental Ceramics by Using Vickers Indentation Fracture and SELNB Methods**. *J. Kor. Ceram. Soc.* (2018.0) **56** 37-48. DOI: 10.4191/kcers.2019.56.1.01
28. DeSchepper E.J., Berr E.A., Cailleteau J.G., Tate W.H.. **A comparative study of fluoride release from glass-ionomer cements**. *Quintessence Int.* (1991.0) **22** 215-219. PMID: 2068261
29. Lucas M.E., Arita K., Nishino M.. **Toughness, bonding and fluoride-release properties of hydroxyapatite-added glass ionomer cement**. *Biomaterials* (2003.0) **24** 3787-3794. DOI: 10.1016/S0142-9612(03)00260-6
30. Dastjerdi R., Montazer M.. **A review on the application of inorganic nano-structured materials in the modification of textiles: Focus on anti-microbial properties**. *Colloids Surf. B Biointerfaces* (2010.0) **79** 5-18. DOI: 10.1016/j.colsurfb.2010.03.029
31. Cullity B.D.. *Answers to Problems: Elements of X-ray Diffraction* (1978.0)
32. Iwata N.Y., Lee G.-H., Tsunakawa S., Tokuoka Y., Kawashima N.. **Preparation of diopside with apatite-forming ability by sol–gel process using metal alkoxide and metal salts**. *Colloids Surf. B Biointerfaces* (2004.0) **33** 1-6. DOI: 10.1016/j.colsurfb.2003.07.004
33. Alecu A.-E., Costea C.-C., Surdu V.-A., Voicu G., Jinga S.-I., Busuioc C.. **Processing of Calcium Magnesium Silicates by the Sol–Gel Route**. *Gels* (2022.0) **8**. DOI: 10.3390/gels8090574
34. Shoreh S.K.H., Ahmadyari-Sharamin M., Ghayour H., Hassanzadeh-Tabrizi S., Pournajaf R., Tayebi M.. **Two- stage synthesis of SnO**. *Surf. Interfaces* (2021.0) **26** 101326. DOI: 10.1016/j.surfin.2021.101326
35. Fathi M., Kharaziha M.. **Two-step sintering of dense, nanostructural forsterite**. *Mater. Lett.* (2009.0) **63** 1455-1458. DOI: 10.1016/j.matlet.2009.03.040
36. Davoodi D., Emami A.H., Tayebi M., Hosseini S.K.. **Rapid mechanochemical synthesis of nickel-vanadium carbide nanocomposite powder by magnesiothermic reaction**. *Ceram. Int.* (2017.0) **44** 5411-5419. DOI: 10.1016/j.ceramint.2017.12.170
37. Yamamoto A., Arita K., Lucas M., Shinonaga Y., Harada K., Abe Y.. **Development of a New Hydroxyapatite-Ionomer Cement**. *Proceedings of the 2009 World Congress on Preventive Dentistry*
38. Gu Y., Yap A., Cheang P., Khor K.. **Effects of incorporation of HA/ZrO2 into glass ionomer cement (GIC)**. *Biomaterials* (2005.0) **26** 713-720. DOI: 10.1016/j.biomaterials.2004.03.019
39. Dehkordi B.A., Nilforoushan M.R., Talebian N., Tayebi M.. **A comparative study on the self-cleaning behavior and antibacterial activity of Portland cement by addition of TiO**. *Mater. Res. Express* (2021.0) **8** 035403. DOI: 10.1088/2053-1591/abef41
40. Khezrloo A., Tayebi M., Shafiee A., Aghaie A.. **Evaluation of compressive and split tensile strength of slag based aluminosilicate geopolymer reinforced by waste polymeric materials using Taguchi method**. *Mater. Res. Express* (2021.0) **8** 025504. DOI: 10.1088/2053-1591/abe101
41. Goenka S., Balu R., Kumar T.S.. **Effects of nanocrystalline calcium deficient hydroxyapatite incorporation in glass ionomer cements**. *J. Mech. Behav. Biomed. Mater.* (2012.0) **7** 69-76. DOI: 10.1016/j.jmbbm.2011.08.002
42. McDonnell R.D., Spiers C.J., Peach C.J.. **Fabrication of dense forsterite-enstatite polycrystals for experimental studies**. *Phys. Chem. Miner.* (2002.0) **29** 19-31. DOI: 10.1007/s002690100199
43. Ni S., Chou L., Chang J.. **Preparation and characterization of forsterite (Mg2SiO4) bioceramics**. *Ceram. Int.* (2007.0) **33** 83-88. DOI: 10.1016/j.ceramint.2005.07.021
44. Hilton T.J., Ferracane J.L., Broome J.C.. *Summitt’s Fundamentals of Operative Dentistry: A Contemporary Approach* (2013.0)
45. Wang N., Zhao R., Zhang L., Guan X.. **Molecular insights into the adsorption of chloride ions in calcium silicate hydrate gels: The synergistic effect of calcium to silicon ratio and sulfate ion**. *Microporous Mesoporous Mater.* (2022.0) **345** 112248. DOI: 10.1016/j.micromeso.2022.112248
46. Chen Y., Li J., Lu J., Ding M., Chen Y.. **Synthesis and properties of Poly(vinyl alcohol) hydrogels with high strength and toughness**. *Polym. Test.* (2022.0) **108** 107516. DOI: 10.1016/j.polymertesting.2022.107516
47. Xiong Q.-M., Chen Z., Huang J.-T., Zhang M., Song H., Hou X.-F., Li X.-B., Feng Z.-J.. **Preparation, structure and mechanical properties of Sialon ceramics by transition metal-catalyzed nitriding reaction**. *Rare Met.* (2020.0) **39** 589-596. DOI: 10.1007/s12598-020-01385-6
48. Zhang W., Huang Y.. **Three-dimensional numerical investigation of mixed-mode debonding of FRP-concrete interface using a cohesive zone model**. *Constr. Build. Mater.* (2022.0) **350** 128818. DOI: 10.1016/j.conbuildmat.2022.128818
49. Zhang W., Kang S., Huang Y., Liu X.. **Behavior of Reinforced Concrete Beams without Stirrups and Strengthened with Basalt Fiber–Reinforced Polymer Sheets**. *J. Compos. Constr.* (2023.0) **27** 04023007. DOI: 10.1061/JCCOF2.CCENG-4082
50. Qi B., Gao S., Xu P.. **The Application of Recycled Epoxy Plastic Sheets Waste to Replace Concrete in Urban Construction and Building**. *Processes* (2023.0) **11**. DOI: 10.3390/pr11010201
51. Elsaka S.E., Hamouda I.M., Swain M.V.. **Titanium dioxide nanoparticles addition to a conventional glass-ionomer restorative: Influence on physical and antibacterial properties**. *J. Dent.* (2011.0) **39** 589-598. DOI: 10.1016/j.jdent.2011.05.006
52. Khademolhosseini M., Barounian M., Eskandari A., Aminzare M., Zahedi A., Ghahremani D.. **Development of New Al**. *J. Basic Appl. Sci. Res.* (2012.0) **2** 7526-7529
53. Alobiedy A.N., Alhille A.H., Al-Hamaoy A.R.. **Mechanical Properties Enhancement of Conventional Glass Ionomer Cement by Adding Zirconium Oxide Micro and Nanoparticles**. *J. Eng.* (2018.0) **25** 72-81. DOI: 10.31026/j.eng.2019.02.05
54. Sajjad A., Bakar W.Z.W., Mohamad D., Kannan T.P.. **Characterization and enhancement of physico-mechanical properties of glass ionomer cement by incorporating a novel nano zirconia silica hydroxyapatite composite synthesized via sol-gel**. *AIMS Mater. Sci.* (2019.0) **6** 730-747. DOI: 10.3934/matersci.2019.5.730
|
---
title: Comparison of Automated Thresholding Algorithms in Optical Coherence Tomography
Angiography Image Analysis
authors:
- David Prangel
- Michelle Prasuhn
- Felix Rommel
- Salvatore Grisanti
- Mahdy Ranjbar
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004628
doi: 10.3390/jcm12051973
license: CC BY 4.0
---
# Comparison of Automated Thresholding Algorithms in Optical Coherence Tomography Angiography Image Analysis
## Abstract
[1] Background: Calculation of vessel density in optical coherence tomography angiography (OCTA) images with thresholding algorithms varies in clinical routine. The ability to discriminate healthy from diseased eyes based on perfusion of the posterior pole is critical and may depend on the algorithm applied. This study assessed comparability, reliability, and ability in the discrimination of commonly used automated thresholding algorithms. [ 2] Methods: Vessel density in full retina and choriocapillaris slabs were calculated with five previously published automated thresholding algorithms (Default, Huang, ISODATA, Mean, and Otsu) for healthy and diseased eyes. The algorithms were investigated with LD-F2-analysis for intra-algorithm reliability, agreement, and the ability to discriminate between physiological and pathological conditions. [ 3] Results: LD-F2-analyses revealed significant differences in estimated vessel densities for the algorithms ($p \leq 0.001$). For full retina and choriocapillaris slabs, intra-algorithm values range from excellent to poor, depending on the applied algorithm; the inter-algorithm agreement was low. Discrimination was good for the full retina slabs, but poor when applied to the choriocapillaris slabs. The Mean algorithm demonstrated an overall good performance. [ 4] Conclusions: Automated threshold algorithms are not interchangeable. The ability for discrimination depends on the analyzed layer. Concerning the full retina slab, all of the five evaluated automated algorithms had an overall good ability for discrimination. When analyzing the choriocapillaris, it might be useful to consider another algorithm.
## 1. Introduction
Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that provides high-resolution, depth-resolved images of the chorioretinal blood flow [1,2]. OCTA-based vessel density (VD) has been proposed as a promising imaging parameter and biomarker in various clinical studies, in which it has been used to discriminate healthy eyes from diseased ones such as in age-related macular degeneration (AMD), diabetic retinopathy (DR), uveitis, and retinal vein occlusion (RVO) [3,4,5,6].
The calculation of VD is quite heterogeneous: manual, semiautomated, automated thresholding algorithms, fixed thresholds, and machine learning approaches can be applied [5,7,8,9,10]. A study by Rabiolo et al. found significant differences in the determined VD between automated and manual methods [11]. Advantages of automated methods over manual algorithms in terms of repeatability and detection of macular pathologies were found in a recent study by Terheyden et al. [ 12]. Therefore, image processing with automated thresholding appears more promising. Yet, further evaluation is necessary. The different thresholding algorithms can generally be divided into three main groups: firstly cluster-based algorithms such as Otsu, which uses an analysis of variance to split the image into two separate parts, and Default and ISODATA, where clustering is a dynamic process consisting of five sub-steps based on the K-means algorithm; secondly the Mean algorithm, which is a simple histogram-based algorithm, using the mean grey value as the threshold for image binarization; thirdly, Huang uses Shannon’s entropy for image binarization and is therefore entropy-based.
It is of high importance to understand differences and errors in the applied methods. Because OCTA has become an important modality research, but also a clinical routine, it is relevant to achieve comparable results and to apply the methods in a correct and standardized manner. Automated thresholding aids analysis of possible parameters such as VD and therefore needs to be well understood for the various disease entities and devices. Herein, we assess the comparability of five commonly used automated thresholding algorithms regarding reliability, agreement, and ability to discriminate healthy eyes from diseased ones focusing on the retina as well as the choriocapillaris.
## 2. Materials and Methods
Electronic clinical records (Orbis, Agfa Health-Care GmbH; Bonn, Germany) and SD-OCTA (Copernicus Revo NX130; Optopol Technology Ltd., Zawiercie, Poland) images from patients with retinal vein occlusion (RVO), diabetic retinopathy (DR), Uveitis, and neovascular age-related macular degeneration (AMD), who were already enrolled in various other studies and attended our facility from 24 April to 10 May 2019, were reviewed. These studies were approved by the ethics committee of the University of Lübeck, Germany (vote reference #18-102, 18-103 and 19-335). At the time of image acquisition, there was no intra- or subretinal fluid present. No affected eyes of patients with uveitis and RVO were assigned to the control group. General inclusion criteria were age ≥18 years, spherical and cylindrical aberration of ±3 and ±1 diopters, respectively, and 5 × 5 mm OCTA scans with a signal strength ≥ 8. Exclusion criteria were motion and other artifacts on OCTA images as well as the presence of pathological ocular conditions other than RVO, DR, Uveitis, and AMD [13]. Angiograms were taken at the same time of day to avoid distortion due to diurnal changes [14].
Three OCTA images per eye were consecutively obtained using the SD-OCTA device, which operates at 130,000 A-scans per second and a central wavelength of 840 nm. The axial resolution of the system is 5 µm and the transverse resolution 12 µm in tissue. The choriocapillaris angiograms were generated by manually measuring a 20 µm slab starting from the automated RPE segmentation. En face images (512 × 512 pixel) of the full retina slab (superficial and deep retinal layer) and choriocapillaris slab were exported in PNG (Portable Network Graphics) format.
ImageJ (NIH, Version 1.52q, Bethesda, Rockville, MD, USA), an open-source image processing software, was used for image analysis. The OCTA images were converted to 8-Bit format and binarized with five automated thresholding algorithms (Default, Huang, ISODATA, Mean, and Otsu) implemented in ImageJ. Vessel density was calculated based on the results of image binarization for white pixels in relation to all pixels of an image as previously reported (Figure 1) [15].
All statistical analyses were performed with SPSS Statistics, version 24 (IBM Corporation, Armonk, NY, USA) and R software (version 3.6.3, R Foundation for Statistical Computing, Vienna, Austria). A p-value of <0.05 was considered statistically significant. Data were tested for normality with the Shapiro–Wilk test. As OCTA data were found not to be distributed normally, differences between the five automated thresholding algorithms were evaluated with non-parametric testing using LD-F2 analysis [16]. An LD-F2 analysis uses robust rank-based statistics for longitudinal data and small sample sizes in factorial experiments. This study has a two-factorial design in which the eyes of the same patient as one factor and the use of the different algorithms on the same population as the second were included in statistical analysis. Intra-algorithm reliability between the three OCTA images of each eye was evaluated with intraclass correlation coefficients (ICCs). ICC values less than 0.5 indicate poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliability, and values greater than 0.90 indicate excellent reliability [17]. For inter-algorithm agreement, Bland–Altman plots with the limits of agreement (LoA) set at 1.96 standard deviations (SDs), which results in a 95 % confidence interval (CI), were evaluated [18]. The ability to discriminate healthy eyes from disease-affected eyes (DR, RVO, Uveitis, and AMD) in full retina and choriocapillaris slabs was evaluated with receiver operating characteristic (ROC) curves and area under the curve (AUC) values [19,20].
## 3. Results
A total of 91 eyes of 51 patients were enrolled in this study. Demographic and clinical data are reported in Table 1. Twenty-four ($47.3\%$) male and twenty-seven ($52.7\%$) female participants were included in this study, with a mean age of 70.5 years.
Figure 2 and Figure 3 show VD values estimated with the different algorithms as a comparison between groups. Default, ISODATA, and Otsu estimated lower VD values than Huang and Mean in the full retina slab (Figure 2). In the choriocapillaris slabs, estimated VD values differed only slightly, regardless of which algorithm was used (Figure 3). Vessel density differed significantly between the different algorithms for the full retina and choriocapillaris slabs in the LD-F2-analysis ($p \leq 0.001$).
In Table 2 and Table 3, intra-algorithm values of full retina and choriocapillaris angiograms are reported.
Concerning full retina values, Default, Otsu, and ISODATA had excellent reliability for healthy control eyes (ICC > 0.9), while a good reliability (ICC > 0.75) was observed for Mean and Huang. Diseased eyes in total had a good reliability (ICC > 0.75), except for Huang, which only had a poor reliability (ICC < 0.5). An examination of the various subgroups of diseased eyes indicates that all algorithms had an excellent reliability for eyes with DR. In AMD eyes, reliability was only moderate using all five algorithms. Excellent reliability was detected in eyes with uveitis and RVO, except for the Huang algorithm, which had only moderate reliability in uveitis and no reliability in RVO eyes (Table 2).
In choriocapillaris slabs, healthy control eyes had only a poor reliability with all algorithms (ICC < 0.5) except for Huang, which showed a moderate reliability (ICC > 0.5). Diseased eyes had an excellent reliability with Default, ISODATA, and Otsu (ICC > 0.9). Huang und Mean showed a good reliability (ICC > 0.75). Looking into the various subgroups of diseased eyes, Huang had a good reliability in DR and AMD (ICC > 0.75), while all other algorithms had an excellent reliability (ICC > 0.9). All algorithms delivered an excellent reliability in uveitis and RVO eyes (Table 3).
Table 4 and Table 5 show the results of the Bland–*Altman analysis* for the inter-algorithm agreement of the full retina and choriocapillaris angiograms. In the full retina slabs, mean difference (MD) and limits of agreement (LoA) were wider, which indicates a lower level of agreement between algorithms. Default, Otsu, and ISODATA had a good agreement. All other algorithms had a poor agreement, both in the full retina and choriocapillaris slabs.
ROC curves for the discrimination between healthy eyes and eyes affected by DR, AMD, Uveitis, and RVO are illustrated in Figure 4 and Figure 5. A good ability for discrimination between healthy and diseased eyes was detected in the full retina slabs for all algorithms used. The highest AUC values were observed with Huang and Mean (Figure 4). However, a poor ability for discrimination was observed using the choriocapillaris slabs. The highest AUC values were detected with Otsu and ISODATA, while Huang had the lowest AUC values (Figure 5).
## 4. Discussion
In the present study, we compared five different automated thresholding algorithms to calculate the VD in OCTA images of the macula in full retina and choriocapillaris angiograms of eyes of patients with DR, RVO, Uveitis, AMD, and healthy eyes. We applied an LD-F2-analysis, intra-algorithm reliability, inter-algorithm agreement, and ability to discriminate between healthy and diseased eyes in commonly used auto-threshold methods: Default, Huang, ISODATA, Mean, and Otsu as implemented in ImageJ for image processing. As OCTA gains more and more importance in clinical routine, as well as in research, standardized as well as reliable techniques and processing methods are needed in order to restore comparability. Especially as VD is proposed as a new possible surrogate endpoint for clinical trials, it is essential to fully understand and compare clinical as well as technical aspects that may interfere with standardized measurements [21]. Even though VD in OCTA is known to have a good intra- and inter-operator repeatability when we use the same angiocube of the same device, recent studies have proven the dependence on different clinical factors, as well as differences in acquisition and the post-processing methods [11,22]. This includes significant differences in VD calculations based on the applied thresholding strategy [8,11,23,24]. Terheyden et al. found that automated algorithms outperform manual methods on 3 × 3 mm OCTA images to quantify macular perfusion. In addition, they emphasize the need for international standardization in clinical use [12]. A study by Arrigo et al. examined 13 automated algorithms for superficial as well as deep capillary plexus and choriocapillaris slabs. However, the cohort (30 eyes) was relatively small, and they only focused on healthy eyes. The best performing methods for binarization were Huang, Li, Mean, and Percentile, with overall good results [25]. Rabiolo et al. have stressed that studies adapting VD as an outcome should not rely on a normative database [11]. We aimed at evaluating VD more in depth by focusing on specific macular diseases. Diabetic retinopathy, AMD, uveitis, and RVO make up for more than $90\%$ of macular diseases, in which a macular edema results in visual impairment and patients need recurrent intravitreal treatment. Microvascular changes are characteristic of all those four disease entities as microvascular abnormalities can be found in the retina as well as the choriocapillaris [26].
Our study found binarization results estimated with the five algorithms not to be interchangeable ($p \leq 0.001$), and that inter-algorithm agreement for image binarization was low. The results are consistent with existing data that have focused on other ophthalmological conditions [11,12,27].
Intra-algorithm reliability values range from excellent to poor and depend on the applied algorithm and examined retinal layer. For full retina slabs, reliability was excellent to good, except for eyes with AMD and not including Huang, which was poor to not reliable. Reliability results for the choriocapillaris slabs were moderate (Huang) to poor in healthy eyes and good to excellent in eyes with retinal disease. The poor results for healthy eyes are in line with a study by Laiginhas et al., which found significant advantages using local compared to global thresholding methods for binarization of the choriocapillaris angiograms [28]. Previous studies found local thresholding methods such as Phansalkar preferable to global automated methods for the segmentation of the choriocapillaris. Relying on the microvascular architecture of the choriocapillaris, local thresholding strategies lead to more promising results [22,29,30]. However, it remains unclear why the global thresholding algorithms used in the present study worked so much better with regard to reliability in diseased eyes.
The ability to discriminate between healthy and diseased eyes was good in all algorithms for full retina angiograms, and poor for the choriocapillaris slabs. Especially Mean and Huang showed good performances for the retina. Overall, the Mean algorithm detected sufficient values for discrimination, had good reliability and an ability for discrimination on full retina angiograms using the Copernicus Revo NX130 device. This corresponds to previously published data, supporting the theory that the Mean algorithm is a promising automated thresholding algorithm [12,25]. The Huang algorithm also had a good ability for discrimination of the full retina slabs but lacked reliability results. Default and ISODATA showed similar results in our study, which is based on the fact that the former is a slight modification of the latter.
In the future, volume rendered, 3D OCTA assessments will be interesting approaches for a more functional analysis. This method has been applied for a couple of conditions already and seems to be a reliable method for certain study designs [31,32]. However, as far as we know, choroidal sublayer 3D volume angiograms have not been studied yet. This might be an interesting approach for future studies.
Limitations of this study include its retrospective character and the relatively small number of eyes, which led to limited statistical testing such as for age-adjusted statistical comparison. In addition, there is no comparability and evaluation across different OCTA devices. Furthermore, we studied VD in full retina and choriocapillaris OCTA slabs. Other angiogram levels such as a superficial or deep capillary plexus might lead to different results. It is known that vessel density values depend on the device, angiocube size, image averaging, and post-processing methods. Therefore, our data only provide information in this specific setting. From a clinical perspective, we did not account for previous intravitreal medication in diseased eyes. As drugs such as inhibitors of the vascular endothelial growth factor (anti-VEGF) or steroids affect vascular density in the long run, our study cohort might be quite heterogenous. Moreover, the reaction of the vasculature in the different disease entities to those drugs varies [33].
In conclusion, when processing angiograms taken with the Copernicus Revo NX130, automated thresholding algorithms should be preferred for the binarization of full retina angiograms in eyes with DR, AMD, Uveitis, and RVO. When it comes to the choriocapillaris, other approaches should be considered. The Mean algorithm seems to be the most promising candidate for further prospective investigations.
## References
1. Kashani A.H., Chen C.-L., Gahm J.K., Zheng F., Richter G.M., Rosenfeld P.J., Shi Y., Wang R.K.. **Optical Coherence Tomography Angiography: A Comprehensive Review of Current Methods and Clinical Applications**. *Prog. Retin. Eye Res.* (2017) **60** 66-100. DOI: 10.1016/j.preteyeres.2017.07.002
2. Chen C.-L., Wang R.K.. **Optical Coherence Tomography Based Angiography [Invited]**. *Biomed. Opt. Express* (2017) **8** 1056. DOI: 10.1364/BOE.8.001056
3. Lee S.C., Tran S., Amin A., Morse L.S., Moshiri A., Park S.S., Yiu G.. **Retinal Vessel Density in Exudative and Nonexudative Age-Related Macular Degeneration on Optical Coherence Tomography Angiography**. *Am. J. Ophthalmol.* (2020) **212** 7-16. DOI: 10.1016/j.ajo.2019.11.031
4. Xie N., Tan Y., Liu S., Xie Y., Shuai S., Wang W., Huang W.. **Macular Vessel Density in Diabetes and Diabetic Retinopathy with Swept-Source Optical Coherence Tomography Angiography**. *Graefes Arch. Clin. Exp. Ophthalmol.* (2020) **258** 2671-2679. DOI: 10.1007/s00417-020-04832-3
5. Kim A.Y., Chu Z., Shahidzadeh A., Wang R.K., Puliafito C.A., Kashani A.H.. **Quantifying Microvascular Density and Morphology in Diabetic Retinopathy Using Spectral-Domain Optical Coherence Tomography Angiography**. *Investig. Ophthalmol. Vis. Sci.* (2016) **57** OCT362-OCT370. DOI: 10.1167/iovs.15-18904
6. Wintergerst M.W.M., Pfau M., Müller P.L., Berger M., de Sisternes L., Holz F.G., Finger R.P.. **Optical Coherence Tomography Angiography in Intermediate Uveitis**. *Am. J. Ophthalmol.* (2018) **194** 35-45. DOI: 10.1016/j.ajo.2018.06.023
7. Shahlaee A., Samara W.A., Hsu J., Say E.A.T., Khan M.A., Sridhar J., Hong B.K., Shields C.L., Ho A.C.. **In Vivo Assessment of Macular Vascular Density in Healthy Human Eyes Using Optical Coherence Tomography Angiography**. *Am. J. Ophthalmol.* (2016) **165** 39-46. DOI: 10.1016/j.ajo.2016.02.018
8. Giarratano Y., Bianchi E., Gray C., Morris A., MacGillivray T., Dhillon B., Bernabeu M.O.. **Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics**. *Transl. Vis. Sci. Technol.* (2020) **9** 5. DOI: 10.1167/tvst.9.13.5
9. Fragiotta S., Leong B.C.S., Kaden T.R., Bass S.J., Sherman J., Yannuzzi L.A., Freund K.B.. **A Proposed Mechanism Influencing Structural Patterns in X-Linked Retinoschisis and Stellate Nonhereditary Idiopathic Foveomacular Retinoschisis**. *Eye* (2019) **33** 724-728. DOI: 10.1038/s41433-018-0296-8
10. Battaglia Parodi M., Cicinelli M.V., Rabiolo A., Pierro L., Gagliardi M., Bolognesi G., Bandello F.. **Vessel Density Analysis in Patients with Retinitis Pigmentosa by Means of Optical Coherence Tomography Angiography**. *Br. J. Ophthalmol.* (2017) **101** 428-432. DOI: 10.1136/bjophthalmol-2016-308925
11. Rabiolo A., Gelormini F., Sacconi R., Cicinelli M.V., Triolo G., Bettin P., Nouri-Mahdavi K., Bandello F., Querques G.. **Comparison of Methods to Quantify Macular and Peripapillary Vessel Density in Optical Coherence Tomography Angiography**. *PLoS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0205773
12. Terheyden J.H., Wintergerst M.W.M., Falahat P., Berger M., Holz F.G., Finger R.P.. **Automated Thresholding Algorithms Outperform Manual Thresholding in Macular Optical Coherence Tomography Angiography Image Analysis**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0230260
13. Ranjbar M., Plöttner P., Sochurek J.A.M., Lauermann J.L., Alten F., Prasuhn J., Kasten M., Brüggemann N.. **The Impact of Motion Artifacts on Quantitative Optical Coherence Tomography Angiography Analysis in Parkinson’s Disease**. *Park. Relat. Disord.* (2022) **95** 57-58. DOI: 10.1016/j.parkreldis.2022.01.006
14. Rommel F., Rommel F., Rothe M., Rothe M., Kurz M., Kurz M., Prasuhn M., Prasuhn M., Grisanti S., Ranjbar M.. **Evaluating Diurnal Variations in Retinal Perfusion Using Optical Coherence Tomography Angiography**. *Int. J. Retin. Vitr.* (2020) **6**. DOI: 10.1186/s40942-020-00227-y
15. Ranjbar M., Rothe M., Klapa S., Lange T., Prasuhn M., Grisanti S., Riemekasten G., Humrich J.. **Evaluation of Choroidal Substructure Perfusion in Patients Affected by Systemic Sclerosis: An Optical Coherence Tomography Angiography Study**. *Scand. J. Rheumatol.* (2019) **49** 1-5. DOI: 10.1080/03009742.2019.1641616
16. Noguchi K., Gel Y.R., Brunner E., Konietschke F.. **NparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments**. *J. Stat. Soft.* (2012) **50**. DOI: 10.18637/jss.v050.i12
17. Koo T.K., Li M.Y.. **A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research**. *J. Chiropr. Med.* (2016) **15** 155-163. DOI: 10.1016/j.jcm.2016.02.012
18. Giavarina D.. **Understanding Bland Altman Analysis**. *Biochem. Med.* (2015) **25** 141-151. DOI: 10.11613/BM.2015.015
19. Metz C.E.. **Basic Principles of ROC Analysis**. *Semin. Nucl. Med.* (1978) **8** 283-298. DOI: 10.1016/S0001-2998(78)80014-2
20. Obuchowski N.A.. **Receiver Operating Characteristic Curves and Their Use in Radiology**. *Radiology* (2003) **229** 3-8. DOI: 10.1148/radiol.2291010898
21. Cole E.D., Ferrara D., Novais E.A., Louzada R.N., Waheed N.K.. **Clinical trial endpoints for optical coherence tomography angiography in neovascular age-related macular degeneration**. *Retina* (2016) **36** S83-S92. DOI: 10.1097/IAE.0000000000001338
22. Uji A., Balasubramanian S., Lei J., Baghdasaryan E., Al-Sheikh M., Sadda S.R.. **Choriocapillaris Imaging Using Multiple En Face Optical Coherence Tomography Angiography Image Averaging**. *JAMA Ophthalmol.* (2017) **135** 1197-1204. DOI: 10.1001/jamaophthalmol.2017.3904
23. Borrelli E., Sacconi R., Parravano M., Costanzo E., Querques L., Battista M., Grosso D., Giorno P., Bandello F., Querques G.. **Optical coherence tomography angiography assessment of the diabetic macula: A Comparison Study Among Different Algorithms**. *Retina* (2021) **41** 1799-1808. DOI: 10.1097/IAE.0000000000003145
24. Pedinielli A., Bonnin S., Sanharawi M.E., Mané V., Erginay A., Couturier A., Tadayoni R.. **Three Different Optical Coherence Tomography Angiography Measurement Methods for Assessing Capillary Density Changes in Diabetic Retinopathy**. *Ophthalmic. Surg. Lasers Imaging. Retin.* (2017) **48** 378-384. DOI: 10.3928/23258160-20170428-03
25. Arrigo A., Aragona E., Saladino A., Amato A., Bandello F., Battaglia Parodi M.. **The Impact of Different Thresholds on Optical Coherence Tomography Angiography Images Binarization and Quantitative Metrics**. *Sci. Rep.* (2021) **11** 14758. DOI: 10.1038/s41598-021-94333-y
26. Kalra G., Pichi F., Kumar Menia N., Shroff D., Phasukkijwatana N., Aggarwal K., Agarwal A.. **Recent Advances in Wide Field and Ultrawide Field Optical Coherence Tomography Angiography in Retinochoroidal Pathologies**. *Expert Rev. Med. Devices* (2021) **18** 375-386. DOI: 10.1080/17434440.2021.1902301
27. Mehta N., Liu K., Alibhai A.Y., Gendelman I., Braun P.X., Ishibazawa A., Sorour O., Duker J.S., Waheed N.K.. **Impact of Binarization Thresholding and Brightness/Contrast Adjustment Methodology on Optical Coherence Tomography Angiography Image Quantification**. *Am. J. Ophthalmol.* (2019) **205** 54-65. DOI: 10.1016/j.ajo.2019.03.008
28. Laiginhas R., Cabral D., Falcão M.. **Evaluation of the Different Thresholding Strategies for Quantifying Choriocapillaris Using Optical Coherence Tomography Angiography**. *Quant. Imaging Med. Surg.* (2020) **10** 1994005. DOI: 10.21037/qims-20-340
29. Spaide R.F.. **Choriocapillaris Flow Features Follow a Power Law Distribution: Implications for Characterization and Mechanisms of Disease Progression**. *Am. J. Ophthalmol.* (2016) **170** 58-67. DOI: 10.1016/j.ajo.2016.07.023
30. Rochepeau C., Kodjikian L., Garcia M.-A., Coulon C., Burillon C., Denis P., Delaunay B., Mathis T.. **Optical Coherence Tomography Angiography Quantitative Assessment of Choriocapillaris Blood Flow in Central Serous Chorioretinopathy**. *Am. J. Ophthalmol.* (2018) **194** 26-34. DOI: 10.1016/j.ajo.2018.07.004
31. Borrelli E., Grosso D., Parravano M., Costanzo E., Brambati M., Viganò C., Sacconi R., Querques L., Pina A., De Geronimo D.. **Volume Rendered 3D OCTA Assessment of Macular Ischemia in Patients with Type 1 Diabetes and without Diabetic Retinopathy**. *Sci. Rep.* (2021) **11** 19793. DOI: 10.1038/s41598-021-99297-7
32. Borrelli E., Mastropasqua L., Souied E., Sadda S., Vella G., Toto L., Miere A., Corradetti G., Sacconi R., Ferro G.. **Longitudinal Assessment of Type 3 Macular Neovascularization Using 3D Volume-Rendering OCTA**. *Can. J. Ophthalmol.* (2022) **57** 228-235. DOI: 10.1016/j.jcjo.2021.04.020
33. Kalra G., Kar S.S., Sevgi D.D., Madabhushi A., Srivastava S.K., Ehlers J.P.. **Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine**. *J. Pers. Med.* (2021) **11**. DOI: 10.3390/jpm11111161
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---
title: Phytotoxicity of Bisphenol A to Allium cepa Root Cells Is Mediated through
Growth Hormone Gibberellic Acid and Reactive Oxygen Species
authors:
- Valerija Vujčić Bok
- Marko Gerić
- Goran Gajski
- Sanja Gagić
- Ana-Marija Domijan
journal: Molecules
year: 2023
pmcid: PMC10004651
doi: 10.3390/molecules28052046
license: CC BY 4.0
---
# Phytotoxicity of Bisphenol A to Allium cepa Root Cells Is Mediated through Growth Hormone Gibberellic Acid and Reactive Oxygen Species
## Abstract
The aim of this study was to test the phytotoxicity and mode of action of bisphenol A (BPA) on *Allium cepa* using a multibiomarker approach. A. cepa roots were exposed to BPA in concentration range 0–50 mg L−1 for 3 days. BPA even in the lowest applied concentration (1 mg L−1) reduced root length, root fresh weight, and mitotic index. Additionally, the lowest BPA concentration (1 mg L−1) decreased the level of gibberellic acid (GA3) in root cells. BPA at concentration 5 mg L−1 increased production of reactive oxygen species (ROS) that was followed by increase in oxidative damage to cells’ lipids and proteins and activity of enzyme superoxide dismutase. BPA in higher concentrations (25 and 50 mg L−1) induced genome damage detected as an increase in micronucleus (MNs) and nuclear buds (NBUDs). BPA at >25 mg L−1 induced synthesis of phytochemicals. Results of this study using multibiomarker approach indicate that BPA is phytotoxic to A. cepa roots and has shown genotoxic potential to plants, thus its presence in the environment should be monitored.
## 1. Introduction
Bisphenol A (BPA; 4,4′-(propane-2,2-diyl)diphenol) is a carbon-based synthetic chemical that is used as the intermediary in the production of plastic-based materials such as plastic bottles including baby bottles, food cans, electronic items, and medical equipment [1,2,3]. Due to the high demand for plastic-based materials in everyday life, BPA enters the environment, where it becomes ubiquitous. It is estimated that 1 million pounds of BPA are released annually into the environment, mainly due to the polymer industry [4,5]. Consequently, BPA is detected in the atmosphere, in rivers, seawater, and in soil [6,7,8,9]. It is demonstrated that BPA can reside in soil for some time [10] and from the soil, plants easily absorb BPA [11,12]. In 2010, the European Union released a risk assessment report on BPA in the terrestrial ecosystem and indicated that further investigations on the impact of BPA on plants are needed [10].
Studies so far have reported that BPA inhibits the growth or germination of several plants such as Glycine max (soyabean) [12], *Pisum sativum* (pea) [13], *Oryza sativa* (rice) [5], *Cicer arietinum* (chickpea) [14], *Nicotiana tabacum* (tobacco) [15], *Vicia faba* (faba bean) [16], and *Allium cepa* (onion) [17]. It is found that BPA interferes with the production of plants’ hormones [12], affects photosynthesis by inhibiting carbon assimilation [18], induces oxidative stress by increasing production of reactive oxygen species (ROS) [5,7,19,20], and damages the cells’ cytoskeletons by disrupting organization of microtubules [18]. However, further investigations are needed to clarify the mode of action of BPA on terrestrial plants.
The Allium test is a relevant short-term test system for environmental monitoring [21,22]. As a test organism, A. cepa has several advantages: its root growth dynamic is very sensitive to pollutants, the mitotic phases of root meristem cells are clearly visible, it has large chromosomes of a stable and reduced number (2n = 16), and spontaneous chromosomal damage rarely occurs, allowing easy observation of chromosome aberrations and interpreting the mutagenic potential of pollutant [21,23]. Additionally, A. cepa root cells have the metabolic capacity for activating promutagens, thus, in comparison the Ames test addition of S9 mixture (rat liver S9 fraction) is not needed [22]. It is demonstrated that results obtained by A. cepa test correlate well with results obtained on other test organisms, including *Daphnia magna* or the V79 cell line [21,23]. Therefore, A. cepa is frequently used for ecotoxicity testing and has been adopted by the International Program on Plant Bioassays (IPPB) for monitoring or testing environmental pollutants [23,24].
The aim of this study was to investigate BPA phytotoxicity and the impact of BPA on growth hormone, DNA damage, and oxido-reductive balance in A. cepa root cells. By monitoring several biomarkers in root cells in the same experimental set-up we could obtain a better insight into the interplay of assessed parameters and their role in BPA phytotoxicity.
## 2.1. Impact of BPA on A. cepa Root Growth
As root growth parameters, the root length and root fresh weight (FW) were assessed. Exposure to BPA in a concentration range 1–50 mg L−1 for 3 days reduced the growth parameters in a concentration-dependent manner (Figure 1A,B). Even exposure to the lowest applied BPA concentration (1 mg L−1) was phytotoxic and significantly reduced root length in comparison to the control plants (Figure 1A). At higher concentrations (25 mg L−1 and 50 mg L−1), BPA induced changes in the root morphology; roots were curly (bent), brownish, and lost hardness (Figure S1 (Supplementary Materials)).
The negative effect of BPA on root growth was recorded on several plant models. BPA (50 mg L−1, 6 days exposure) reduced the root length of C. arietinum seedlings [14], the radicle length of P. sativum seedlings (2 mg L−1, 24 h exposure) [13], and BPA (100 µM (approximately 23 mg L−1), 8 days exposure) reduced the root length of O. sativa seedlings [5]. A study of G. max revealed that BPA (6 mg L−1, 7 days exposure) reduced root length at all three growth stages tested, at the seedling stage, flowering and podding stage, and seed-filling stage, and the authors observed that the root growth of the seedling stage was the most sensitive to BPA treatment [12]. Thus, our results are in agreement with previous studies.
The mitotic index (MI) represents the number of dividing cells in a cell cycle. The MI of A. cepa roots’ meristem cells is considered a sensitive test for estimating the cytotoxicity of an environmental pollutant [21,22]. In this study, 3 days exposure to the lowest BPA concentration (1 mg L−1) resulted in an MI reduction in the roots’ meristem cells (Figure 1C); 3 days exposure to BPA at concentration 1 mg L−1 reduced MI to $78\%$ while 3 days exposure to BPA at concentration 50 mg L−1 reduced MI to $51\%$ in comparison to control (set at $100\%$). Since an MI below $20\%$ is considered a lethal effect of the pollutant on the tested plant, and an MI under $50\%$ as sublethal [25], it can be concluded that concentrations of BPA tested in this study were under sublethal concentrations. BPA parallel to a concentration-dependent decrease in MI, decreased % of cells in almost all phase of mitosis (Table 1). Earlier studies also found a decrease in the MI of roots’ meristem cells after exposure to BPA. BPA in concentrations 50, 75, and 100 mg L−1 (48 h exposure) reduced the MI of A. cepa meristem cells to 64, 38, and $20\%$, respectively, that is in accordance with our results [17]. A decrease in MI was also observed in root tip cells of P. sativum after 3 days exposure to BPA at a concentration 10 mg L−1 [13].
The inhibition of root growth can be directly related to plant hormone GA3, since 3 days exposure of A. cepa roots to the lowest BPA concentration (1 mg L−1) also reduced the level of GA3 in root cells and the reduction was concentration-dependent (Figure 1D). The relationship between root growth parameters and level of GA3 is confirmed by very strong correlation (r); r between root length and GA3 level was 0.95, and r between root FW and GA3 level was 0.86 (Table S1). Endogenous plant hormones affect plant physiological processes, controlling plant differentiation, development, and growth as well as defense processes by improving plant adaptation to their environment [8,18]. Plant hormone GA3 is synthesized and acts in rapid growing tissues such as root tips by promoting cell division in the proliferation zone, thus is closely related to primary roots’ elongation. A reduction in GA3 level results in plants with shorter roots and smaller root meristems [26], as is the case in our study.
Previously, it was demonstrated that BPA affected the level of plant hormones in the root of G. max at three of the plants’ growth stages tested, the seedling stage, flowering and podding stage, and seed-filling stage [12]. In that study, BPA (6 mg L−1, 7 days exposure) reduced the level of plant hormones, GA3, indole-3-acetic acid (IAA), and ethylene (ETH) and increased the level of abscisic acid (ABA), and the most prominent effect of BPA on root hormones was observed at seedling stage. In comparison to the mentioned study, in our study BPA reduced the GA3 level in plant root at a lower concentration (1 mg L−1 vs. 6 mg L−1) and shorter exposure (3 days vs. 7 days) confirming the Allium test as sensitive model for testing environmental pollutants. More importantly, since in our study the change in GA3 level was an early event, the level of GA3 can be used as a biomarker of plants’ exposure to BPA.
In response to environmental stimuli hormones regulate plant growth and development [26]. In our study, the correlation between root growth parameters and the level of GA3 was very strong (Table S1) indicating that as a response to the unfavorable environmental conditions (presence of BPA), the plant, by reducing GA3 levels, inhibits root growth to avoid damage and ensure growth under adverse conditions [5]. There are several possible ways by which BPA could affect GA3. BPA could interfere with enzymes involved in GA3 biosynthesis or its inactivation, could act directly on the genes, or on GA3 receptor level [26]. In addition, Li et al. [ 12] suggested that an accumulation of ROS induced by BPA could be involved in the change in hormone levels in root cells.
## 2.2. Genotoxic Effect of BPA to A. cepa Root Meristem Cells
The decrease in MI induced by BPA except to hormone GA3 can be linked to the direct action of BPA on the DNA molecule that can result in an inhibition of cell progression in the cell cycle until the DNA damage is repaired; cells can be stopped in the G2 phase of cell cycle and prevented from entering mitosis until the DNA damage is repaired [27]. In this study, an increase in cells in interphase already after treatment with the lowest BPA concentration (1 mg L−1, 3 days exposure) was observed (Table 1). Thus, we further checked the genotoxic potential of BPA to A. cepa root meristem cells.
The genotoxic effect of BPA to roots’ meristem cells of A. cepa exposed to BPA (1–50 mg L−1) was estimated by assessing the frequency of MNs and NBUDs of interphasic nuclei in roots’ meristem cells (Figure 2A,B).
Only two higher concentrations of BPA, 25 mg L−1 and 50 mg L−1, significantly increased frequency of MNs while frequency of NBUDs in meristem cells of A. cepa roots was significantly increased only after exposure to the highest concentration of BPA (50 mg L−1) (Figure 2A,B). MNs and NBUDs are morphological alterations of interphasic nuclei indicating the mutagenic effects of the tested pollutant [22]. MN is easily observed as a similar structure to the main nucleus but in reduced size, while NBUD is recognized as nuclei carrying nuclear bud. MNs and NBUDs are the result of chromosome aberrations such as chromosome breaks or losses (that cannot be incorporated into the main nucleus during cell cycle), as well as an attempt by the cell to eliminate the excess of DNA that is a result of polyploidization. Previously, Trivedi and Chhaya [17] observed that BPA in concentrations above 50 mg L−1 (and 48 h exposure) in A. cepa meristem cells induced various chromosomal aberrations (bridge formation, sticky chromosome) as well as binuclear formation. In root tip mitotic cells of P. sativum seedlings, BPA (2–25 mg L−1, 3 days exposure) produced numerous chromosomal anomalies such as c-mitosis, bridges, laggards, fragments, and sticky chromosomes, and with the increase in BPA concentration the occurrence of chromosomal aberrations increased [9]. Chromosome aberrations (such as laggings, bridges, and sticky chromosomes) can be linked to spindle irregularity caused by BPA treatment. Adamakis et al. [ 18] on *Triticum turgidum* and A. cepa root cells demonstrated that BPA (50 mg L−1, 1 h exposure) affected plant mitosis/cytokinesis by disrupting the microtubule organization. Chromosomal aberrations such as fragments and chromosome losses result in micronucleated cells since fragments or entire chromosome cannot be incorporated into the main nucleus during the cell cycle; thus, MNs and NBUDs are an indicator of the direct action of the pollutant on DNA [22]. The results of our study indicate that DNA damage is induced by BPA only at higher concentrations. Detected DNA damage is involved in a reduction in root growth since a strong negative correlation between growth parameters (root length and root FW) and parameters of genotoxicity is observed; r between root length and MNs frequency was −0.92 and r between root FW and MNs frequency was −0.93 (Table S1). Thus, BPA induced root growth inhibition except by GA3, at higher BPA concentrations is also mediated through DNA damage. Except for mitotic spindle irregularity, DNA damage and consequent chromosome fragments are linked to the excess production of ROS. Therefore, in the next step we assessed the level of ROS in A. cepa roots exposed to BPA.
## 2.3. Impact of BPA on the Level of ROS in A. cepa Root Cells
In this study, by use of fluorescence microscopy, we followed the level of superoxide radical (O2−) and hydrogen peroxide (H2O2). O2− was monitored using fluorescent probe dihydroethidium (DHE) that predominately detects O2− radicals; meanwhile, for detecting H2O2, a fluorescent probe 2′,7′-dichlorofluorescin diacetate (H2DCF-DA) was used. Although exposure to BPA increased O2− level and the increase in red fluorescence was easily observed in the cytoplasm of root cells (Figure 3A–D), a statistically significant increase was observed only after exposure to BPA at 10 mg L−1 (Figure 4A). On the other hand, BPA increased the level of H2O2 already after exposure to 5 mg L−1 (Figure 4B), that was observed as increase in green fluorescence in root cells (Figure 3E–H). Thus, BPA induced the formation of ROS, and an increase in H2O2 was followed by an increase in O2− production. Similarly, BPA (6 mg L−1, 7-days exposure) induced production of H2O2 and O2− in G. max root cells at different plants’ growth stages (seedlings, flowering and podding, and seed-filling stage) [19]. In the study, similar to our observation, at seedling stage BPA induced an increase in H2O2 at a lower concentration (already at concentration 1.5 mg L−1), while an increase in O2− at seedling stage was observed at the higher concentration (6 mg L−1). An increased H2O2 production as a response to BPA exposure (0.3 µg L−1, 2 days) was observed in seagrass *Cymodocea nodosa* intermediate leaves [5], and exposure to BPA (10 µM, 8 days) increased production of H2O2 and hydroxyl radicals (OH−) in the roots of O. sativa seedlings [5]. In our study, a very strong negative correlation of level O2− and root length (r was −0.83), and level of ROS and MI (r between O2− and MI was −0.82; and r between H2O2 and MI was −0.89), as well as level of ROS and GA3 (r between O2− and GA3 was −0.86; and r between H2O2 and GA3 was −0.88) (Table S1) indicate that ROS are involved in inhibition of root growth. Thus, the multibiomarker approach used in this study points to the close relationship of GA3, MI, and ROS in inhibition of root growth induced by BPA.
## 2.4. Impact of BPA on the Level of Oxidative Damage and Superoxide Dismutase (SOD) Activity in A. cepa Root Cells
Overproduction of ROS leads to the activation of antioxidative defenses and damage of cells’ macromolecules such as membrane proteins and phospholipids which consequently negatively affect plant growth [7,19]. Previous studies on different plant models reported increased oxidative damage of lipids upon exposure to BPA. Additionally, an increase, but also a decrease, in antioxidative defenses is observed. Increased levels of malondialdehyde (MDA), as a marker of lipid peroxidation, and an increase in ascorbate peroxidase but a decrease in SOD activity was observed in seagrass *Cymodocea nodosa* intermediate leaves after 2 days exposure to BPA in concentration of 3 µg L−1 [7]. In the study of Ali et al. [ 5], in roots of O. sativa seedlings BPA (10 µM, 8 days exposure) decreased activity of SOD, peroxidase, and catalase and increased activity of ascorbate peroxidase, while MDA was increased at higher BPA concentration (50 µM, 8 days exposure). In G. max roots, BPA (6 mg L−1, 7 days exposure) increased the activity of the antioxidative enzymes, peroxidase, catalase, and SOD, and increased the level of MDA at plants’ different growth stages (seedlings, flowering and podding, and seed-filling stage) [19].
The results of this study indicate that an increased level of ROS, in particular H2O2 and O2−, induce damage of cells’ macromolecules, lipids, and proteins. BPA at a concentration of 5 mg L−1 (3 days exposure) increased production of MDA (Figure 5A). At a higher concentration (10 mg L−1, 3 days exposure), BPA induced the oxidative damage of proteins that was detected as an increase in protein carbonyls (PC) (Figure 5B). The level of O2− correlated very strongly with MDA (r was 0.91) and with PC (r was 0.82) and similarly the level of H2O2 correlated strongly with MDA (r was 0.78) and very strongly with PC (r was 0.83) confirming involvement of ROS in oxidative damage of cells’ lipids and proteins (Table S1). A very strong negative correlation between parameters of oxidative damage (MDA and PC) and growth parameters (root length, root FW and MI; r from −0.82 to −0.97), as well as parameters of oxidative damage (MDA and PC) and GA3 (r = −0.94 and −0.96) indicates involvement of oxidative damage of lipids and proteins in inhibition of root growth. A very strong correlation between MDA and parameters of DNA damage (r between MDA and MNs was 0.85 and between MDA and NBUDs r was 0.89) suggests that DNA damage is induced by MDA, probably by formation of MDA-DNA adducts [28,29].
An increase in ROS activates antioxidative defense that can be detected as increased activity of antioxidative enzymes or decrease in their activity if depletion of antioxidative defense occurs [7,19]. Therefore, in this study we assessed the activity of antioxidative enzyme SOD. Exposure to BPA at a concentration 10 mg L−1 for 3 days increased SOD activity (Figure 5C). Induction of SOD activity by ROS is confirmed by very strong correlation between SOD and level of O2− ($r = 0.87$). Activation of antioxidative defenses in A. cepa root cells can be connected to the increase in protein level (Figure 5D) since protein level correlated well with ROS and oxidative stress parameters (r from 0.88 to 0.97) (Table S1). A strong negative correlation between SOD and growth parameters (root length: −0.91, MI: −0.95 and GA3: −0.95) (Table S1) indicate that plant parallel to inhibition of root growth activated synthesis of antioxidative enzymes. This observation is confirmed with a negative, very strong correlation observed between protein level and root growth parameters (root length: −0.95, root FW: −0.93, MI: −0.92 and GA3: −0.93). The increase in the protein level could point to activation of synthesis of some other antioxidative enzymes, except SOD.
## 2.5. Impact of BPA on the Level of Phytochemicals in A. cepa Root Cells
Polyphenols are secondary metabolites of plants that are distributed through different plant organs and have multiple functions in plants including plant development, growth, pigment generation, and protection against pathogen attack, as well as defense against stress [30]. In response to abiotic stress, the biosynthesis of polyphenols is usually increased in plants [31,32]. Several studies have demonstrated that polyphenols could attenuate the BPA toxic effect in animal model or animal cells [33]. In BPA-treated rats, *Vincetoxicum arnottianum* extract reduced the toxic effect of BPA that authors attributed to the presence of polyphenols and alkaloids [34]. Polyphenols prevent oxidative stress through several ways; phenolic compounds can directly scavenge ROS or prevent oxidative stress by inhibiting oxidizing enzymes, or by complexing metal ions [30].
To our knowledge, only a few studies have investigated the impact of BPA on the level of polyphenols on plant model [7,16]. In the study of Malea et al., on *Cymodocea nodosa* exposure to low concentrations of BPA (0.3 µg L−1 for 1 day) increased the level of total polyphenols (TP), however a higher concentration of BPA (3 µg L−1 for 1 day) decreased their level [7], while in roots of *Vicia faba* exposure to a much higher BPA concentration (30 and 120 µg mL−1) increased the level of TP [16]. Since plants, in order to cope with stress, synthesize polyphenols, Malea et al. explained the increase in TP with the fact that BPA activated plants’ antioxidant protective mechanism. Since different polyphenols have a different mode of action to reduce stress and protect the plant from stress, in this study we except (beside) following impact of BPA on TP, followed impact of BPA on total flavonoids (TF), total flavonols (TFL), and hydroxycinnamic acids (THA).
In root cells of A. cepa, BPA (1–50 mg L−1, 3 days) affected the level of TP, TF, and TFL, while it had no significant effect on THA (Figure 6A–D). Exposure to BPA resulted in a steady increase in the level of TP and TF, and their level was significantly increased only at higher BPA concentrations, 25 and 50 mg L−1, respectively. However, BPA already at a concentration 10 mg L−1 increased the level of TFL in root cells of A. cepa. These results indicate that in roots, in order to prevent oxidative damage for the plant to survive, synthesis of TP occurred, in particular TF and TFL. This was confirmed with very strong correlations between TF and oxidative stress parameters (r between TF and O2− was 0.92, between TF and MDA was 0.94, and between TF and SOD was 0.83). TF also corelated very strongly with TP ($r = 0.87$) and protein level ($r = 0.82$). However, TF negatively correlated with root growth parameters (root length: −0.92, root FW: −0.92, MI: −0.82 and GA3: −0.84) indicating their close negative relationship. The decrease in GA3 level caused by BPA and consequent increase in TF could be linked through common enzymes; gibberellins and flavonoids are characterized by biosynthetic pathways that include similar enzymes [35]. Thus, the plant in a state of stress reassigned enzymes involved in the synthesis of GA3 into biosynthesis of flavonoids. In relation to the increased synthesis of TP and TF upon exposure to a high BPA concentration (25 mg L−1 and 50 mg L−1), it can be concluded that the tested BPA concentrations in this study were not highly toxic to A. cepa.
## 2.6. Principle Component Analysis (PCA)
The PCA plots provide an overview of the similarities and differences among different plant treatments as well as the interrelationships between the measured parameters. Factor 1 and Factor 2 described 77.38 and $10.49\%$ of the variance for BPA (0, 1, 5, 10, 25 and 50 mg L−1)-treated A. cepa root, respectively (Figure S2). Together, the first two Factors represent $87.87\%$ of the total variability. Treatment with two higher concentrations of BPA (25 and 50 mg L−1) had strong loadings with frequency of MNs and TP and treatment with the highest concentration of BPA (50 mg L−1) had strong loadings with distribution of root meristem cells in interphase, NBUDs, accumulation of O2− and H2O2, MDA, PC, proteins, TF and SOD activity. Treatment with the lowest concentration of BPA (1 mg L−1) had strong loadings with roots’ FW, and the control (0 mg L−1) had the highest loadings with root length, MI, GA3, distribution of root meristem cells in prophase, metaphase, anaphase, and telophase. Moderate BPA treatment (10 mg L−1) had strong loadings with THA, TFL, and O2−. The PCA results confirmed our observation that DNA damage is induced by BPA only at higher concentrations. Additionally, the PCA proved that overproduction of ROS leads to the increase in SOD activity, increase in TP and TF, and induces damage of lipids (MDA) and proteins (PC), as observed in earlier studies [7,16,19]. Parameters of root growth and level of plant hormone GA3 as well as the distribution of root meristem cells in prophase, metaphase, anaphase, telophase, and MI were the highest at control samples which indicates the negative influence of BPA on A. cepa root cells. The negative effect of BPA on growth, hormone accumulation, MI, and distribution of root meristem cells in prophase, metaphase, anaphase, and telophase were recorded in previous studies on other plant models [5,12,13,14,17].
## 3.1. Chemicals and Preparation of BPA Solution
BPA (4,4′-(propane-2,2-diyl)diphenol; $99\%$ purity), BSA (bovine albumin serum), Coomassie Brilliant Blue G-250, orcein, 2′,7′-dichlorofluorescin diacetate (H2DCF-DA), dihydroethidium (DHE), Folin–Ciocalteu reagent, gallic acid (GA), quercetin (Q), caffeic acid (CA), 2,4-dinitrophenylhydrazine and thiobarbituric acid (TBA) and trichloroacetic acid (TCA) were procured from Sigma (St. Louis, MO, USA). Other chemicals used in the study were purchased from Kemika (Zagreb, Croatia) and were of p.a. grade or better.
BPA stock solution (200 g L−1) was prepared by dissolving BPA in ethanol ($96\%$). Prior to the experiment, BPA test solutions in concentrations: 1, 5, 10, 25, and 50 mg L−1 were prepared by diluting BPA stock solution in distilled water (de-water). In BPA test solutions the level of ethanol was less than $0.03\%$.
## 3.2. Allium Test
The Allium test was performed according to Fiskesjo [21,36]. Common onion (A. cepa L. var. argenthea) bulbs were purchased at a local store. Equal-sized bulbs (1.5–2.0 cm) were selected for the experiment and their outer scales and brownish bottom plate were removed. Root growth was started by placing bulbs into de-water for 48 h. For the treatment experiment, only bulbs with normally developed roots were selected. Bulbs where the roots had visible morphological abnormalities were discharged from the experiment. For each BPA concentration and control (de-water was used as control) 5 bulbs were selected that were transferred to the appropriate BPA test solution or de-water (control). Biological experiment was repeated three times. The experiment was performed at constant room temperature (20 ± 2 °C) and protected from direct sunlight. Due to short exposure time (3 days), there was no need to renew tested solutions in the test tubes.
After 3 days exposure, each bulb was taken from BPA test solution (or de-water), carefully dried with paper towel and morphology of roots were examined (color, consistency and shape/hooks or twists). Then, roots were removed and root length of two the longest of each bulb were measured, and roots FW were assessed. Afterwards, from each bulb three root tips were placed in ethanol: glacial acetic acid (3:1, v/v) and stored overnight at 4 °C.
For determination of the plant hormone gibberellic acid (GA3), oxidative stress parameters, and phytochemicals, the roots were dried in an oven at 60 °C until a constant weight was reached. Roots of each bulb were kept separately. Dried plant material was stored in a dry place at a constant temperature protected from sun.
## 3.3. Determination of Mitotic Index and Micronucleus
Root tips, fixed in ethanol: glacial acetic acid, were first rinsed in de-water and then placed in aceto-orcein ($1\%$ orcein in $45\%$ glacial acetic acid) for 15 min at 45 °C [21,23]. Microscopic slides were prepared by squashing stained root tips in aceto-orcein. Per each treatment, five microscopic slides were prepared, and the mitotic index (MI) was calculated on 2500 cells per treatment. Microscope slides were examined under bright-field microscope (CX22LED; Olympus, Tokyo, Japan) at magnification 400× and 1000×. The distribution of cells in each phase of the cell cycle (%) was determined by calculating the total number of cells in interphase, prophase, metaphase, anaphase, and telophase and divided by the total number of counted cells (500 cells per slide/root) and multiplied by 100.
Frequency of micronucleus (MNs) and nuclear buds (NBUDs) was determined on the same microscope slides and nuclear abnormalities were identified as morphological alteration of interphase nuclei [22]. Microscope slides were observed under bright-field microscope (CX22LED; Olympus, Tokyo, Japan) at magnification 400× and 1000×. The analysis of MNi and NBUDs was performed on 4000 cells per treatment point and expressed as frequency on 1000 cells.
## 3.4. Determination of GA3 Content
Dried plant material was homogenized in PBS (50 g L−1) and centrifuged (10,000× g 10 min) and supernatant was collected. In supernatant, level of GA3 was determined by use of ELISA commercial kit (MyBioSource, San Diego, CA, USA) according to the producer’s instructions. Absorbance was read on microplate reader (SpectraMax i3x i SpectraMax MiniMax 300; Molecular Devices, San Jose, CA, USA).
## 3.5. Determination of ROS In Situ
The level of ROS in A. cepa root cells in situ and by fluorimetry was assessed by employing fluoresce dyes. Level of superoxide radical (O2−) was assessed using dihydroethidium (DHE) and level of hydrogen peroxide (H2O2) using 2′,7′-dichlorofluorescin diacetate (H2DCF-DA).
Control and BPA-treated roots, fixed in ethanol: glacial acetic acid (3:1, v/v), were first washed with de-water and then incubated in appropriate fluorescence dye. Fluorescence imaging allows localization of ROS within the cells [37]. For H2O2 visualization, the root was incubated in 50 μM H2DCF-DA for 30 min, and for O2− visualization the root was incubated in 10 μM DHE for 30 min. Following incubation in appropriate dye, roots were observed under fluorescent microscope Zeiss Axio Observer 7 equipped with Axiocam 208 camera and Zen 3.4 Pro software (Carl Zeiss, Jena, Germany). To visualize H2O2, the fluorescent microscope was set at λex 475 nm and λem 535 nm and to visualize O2− it was set at λex 555 nm and λem 635 nm. Identical conditions were maintained in all experimental groups.
To quantify O2− and H2O2 in A. cepa root cells, 50 μL of root PBS extract (1 g L−1) was mixed with 10 μM of DHE or 50 μM of H2DCF-DA, respectively, and fluorescence intensity was read on microplate reader (SpectraMax i3x i SpectraMax MiniMax 300; Molecular Devices, San Jose, CA, USA). To quantify O2−, the microplate reader was set at λex 535 and λem 635 nm and to quantify H2O2, the reader was set to λex 485 and λem 535 nm.
## 3.6. Determination of Oxidative Stress Parameters
The level of malondialdehyde (MDA), as a measure of lipid peroxidation, was assessed using the thiobarbituric acid method described by Heath and Packer [38]. The 100 μL of A. cepa roots PBS extract (50 g L−1) was mixed with 400 μL $0.25\%$ (w/v) thiobarbituric acid solution containing $10\%$ (w/v) trichloroacetic acid, heated at 95 °C for 30 min and the reaction was stopped in an ice-bath. The cooled mixtures were centrifuged at 10,000× g for 10 min and the MDA content was calculated from the absorbance at 532 nm (correction was performed by subtracting the absorbance at 600 nm for non-specific turbidity) by using the absorption coefficient 155 mM−1 cm−1.
The level of protein oxidation was estimated in the reaction of protein carbonyl (PC) groups with 2, 4-dinitrophenylhydrazine as described in Levine et al. [ 39]. After the 2,4-dinitrophenylhydrazine reaction, the PC content was calculated based on absorbance coefficient of 22 mM−1 cm−1 and absorbance was measured at 370 nm.
In A. cepa root PBS extracts (1 g·L−1), SOD activity was determined by use of a commercial kit (Cayman Chemical, Ann Arbor, MI, USA) based on the producer’s instructions. Absorbance was read on microplate reader (SpectraMax i3x i SpectraMax MiniMax 300; Molecular Devices, San Jose, CA, USA).
Total soluble proteins in A. cepa roots PBS extract (50 g·L−1) were estimated according to Bradford [40]. The absorbance of reaction mixture was read at 595 nm. The protein content was calculated from the calibration curve and expressed as BSA equivalents (BSAE).
## 3.7. Determination of Phytochemicals
Total polyphenols (TP) of A. cepa roots PBS extracts were determined with Folin–Ciocalteu reagent according to Zhishen et al. [ 41]. Volume of 10 μL of root extract was diluted with 790 μL of deionized water and then 50 μL of Folin–Ciocalteu reagent was added. Afterwards, 150 μL Na2CO3 (1.88 M) was added, and the mixture was incubated for 30 min at 45 °C. The absorbance of the mixture was measured at 765 nm. The TP content was calculated from the calibration curve and expressed as gallic acid equivalent (GAE).
The content of total flavonoids (TF) of root extract prepared in PBS was determined with AlCl3 according to the method described by Zhishen et al. [ 41]. First, a volume of 20 μL A. cepa root extract was diluted in 80 μL of dH2O and then a volume of 6 μL NaNO2 ($5\%$) was added. After 5 min incubation, a volume of 6 μL AlCl3 ($10\%$) was added and the mixture was incubated at room temperature for additional 6 min. Afterwards, 40 μL NaOH (1 M) and distilled water were added to a final volume of 200 μL. The absorbance of the reaction mixture was read at 510 nm. The TF content was calculated from the calibration curve and expressed as quercetin equivalents (QE).
The total content of hydroxycinnamic acids (THA) and total flavonols (TFL) of A. cepa root extracts prepared in PBS were measured according to the method of Howard et al. [ 42], using caffeic acid and quercetin as standards. A volume of 25 mL of the A. cepa root extract (50 g L−1) was mixed with 25 mL HCl (1 g·L−1 in ethanol) and 455 mL HCl (2 g L−1). The absorbance of the solution was read at 320 and 360 nm, respectively. THA and TFL contents were calculated from the corresponding calibration curves and expressed as caffeic acid (CAE) and quercetin equivalents (QAE), respectively.
All absorbances were read on microplate reader (SpectraMax i3x i SpectraMax MiniMax 300; Molecular Devices, San Jose, CA, USA).
## 3.8. Statistical Analysis
A completely random experimental design was performed. The test of normality and the test of homogeneity of variance was carried out. All results were evaluated using Statistica 14.0.1.25 software package (TIBCO Software Inc., Palo Alto, CA, USA) and were subjected to one-way ANOVA for comparison of means and significant differences were calculated according to Duncan’s multiple range test. The data are presented as the mean ± standard deviations (SD). Pearson’s correlation coefficient and principal component analysis (PCA) between all measured parameters was performed. Data were considered statistically significant at p ≤ 0.05.
## 4. Conclusions
The phytotoxicity of BPA to A. cepa was evidenced by the decrease in MI of root meristem cells and the consequent decrease in root growth parameters that was recorded already at the lowest applied BPA concentration. The multibiomarker approach used in this study revealed that inhibition of root growth is closely related to decrease in plant hormone GA3, since BPA applied at the lowest concentration also decreased the level of GA3. Inhibition of root growth and decrease in GA3 was followed by production of H2O2 and O2− and activation of antioxidative defenses detected as an increase in SOD activity. Increased production of ROS resulted in oxidative damage to lipids and proteins. Due to increased oxidative stress, plant cells activated the synthesis of polyphenols, TP and TF. In roots’ meristem cells the genotoxic effect of BPA was observed, however at higher BPA concentrations indicating that genotoxic effect of BPA to A. cepa roots is probably a consequence of several other biochemical changes including oxidative stress. BPA is phytotoxic to A. cepa and its phytotoxicity is mediated through the growth hormone GA3 and ROS that consequently lead to oxidative damage of lipids, proteins, and DNA. Due to its genotoxicity, the potential level of BPA in the environment should be monitored.
## References
1. Naomi R., Yazid M.D., Bahari H., Keong Y.Y., Rajandram R., Embong H., Teoh S.H., Halim S., Othman F.. **Bisphenol A (BPA) Leading to Obesity and Cardiovascular Complications: A Compilation of Current In Vivo Study**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23062969
2. Vehar A., Kovačič A., Hvala N., Škufca D., Levstek M., Stražar M., Žgajnar Gotvajn A., Heath E.. **An Assessment of Mass Flows, Removal and Environmental Emissions of Bisphenols in a Sequencing Batch Reactor Wastewater Treatment Plant**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27238634
3. Cimmino I., Fiory F., Perruolo G., Miele C., Beguinot F., Formisano P., Oriente F.. **Potential Mechanisms of Bisphenol A (BPA) Contributing to Human Disease**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21165761
4. Erler C., Novak J.. **Bisphenol a Exposure: Human Risk and Health Policy**. *J. Pediatr. Nurs.* (2010) **25** 400-407. DOI: 10.1016/j.pedn.2009.05.006
5. Ali I., Liu B., Farooq M.A., Islam F., Azizullah A., Yu C., Su W., Gan Y.. **Toxicological Effects of Bisphenol A on Growth and Antioxidant Defense System in**. *Ecotoxicol. Environ. Saf.* (2016) **124** 277-284. DOI: 10.1016/j.ecoenv.2015.10.027
6. Rotimi O.A., Olawole T.D., De Campos O.C., Adelani I.B., Rotimi S.O.. **Bisphenol A in Africa: A Review of Environmental and Biological Levels**. *Sci. Total Environ.* (2021) **764** 142854. DOI: 10.1016/j.scitotenv.2020.142854
7. Malea P., Kokkinidi D., Kevrekidou A., Adamakis I.-D.S.. **The Enzymatic and Non-Enzymatic Antioxidant System Response of the Seagrass**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23031348
8. Careghini A., Mastorgio A.F., Saponaro S., Sezenna E.. **Bisphenol A, Nonylphenols, Benzophenones, and Benzotriazoles in Soils, Groundwater, Surface Water, Sediments, and Food: A Review**. *Environ. Sci. Pollut. Res.* (2015) **22** 5711-5741. DOI: 10.1007/s11356-014-3974-5
9. Xiao C., Wang L., Zhou Q., Huang X.. **Hazards of Bisphenol A (BPA) Exposure: A Systematic Review of Plant Toxicology Studies**. *J. Hazard. Mater.* (2020) **384** 121488. DOI: 10.1016/j.jhazmat.2019.121488
10. 10.
European Commission
Joint Research Centre
Institute for Health and Consumer Protection
Updated European Union Risk Assessment Report: 4,4′-Isopropylidenediphenol (Bisphenol-A): Human Health Addendum of February 2008Publications OfficeLuxembourg2010. *Updated European Union Risk Assessment Report: 4,4′-Isopropylidenediphenol (Bisphenol-A): Human Health Addendum of February 2008* (2010)
11. Chen L., Chen J., Zhou G., Wang Y., Xu C., Wang X.. **Molecular Dynamics Simulations of the Permeation of Bisphenol A and Pore Formation in a Lipid Membrane**. *Sci. Rep.* (2016) **6** 33399. DOI: 10.1038/srep33399
12. Li X., Wang L., Wang S., Yang Q., Zhou Q., Huang X.. **A Preliminary Analysis of the Effects of Bisphenol A on the Plant Root Growth via Changes in Endogenous Plant Hormones**. *Ecotoxicol. Environ. Saf.* (2018) **150** 152-158. DOI: 10.1016/j.ecoenv.2017.12.031
13. Siddiqui S., Al Amri S.A.M., Al Ghamdy H.A., Alqahtani W.S.S., Alquyr S.M., Yassin H.M.. **Impact of Bisphenol A on Seed Germination, Radicle Length and Cytogenetic Alterations in**. *Caryologia* (2021) **74** 103-109. DOI: 10.36253/caryologia-1230
14. Dogan M., Yumrutas O., Saygideger S., Korkunc M., Gulnaz O., Sokmen A.. **Effects of Bisphenol A and Tetrabromobisphenol A on Chickpea Roots in Germination Stage**. *Am.-Eurasian J. Agric. Environ. Sci.* (2010) **9** 186-192
15. Fu W., Chen X., Zheng X., Liu A., Wang W., Ji J., Wang G., Guan C.. **Phytoremediation Potential, Antioxidant Response, Photosynthetic Behavior and Rhizosphere Bacterial Community Adaptation of Tobacco (**. *Environ. Sci. Pollut. Res.* (2022) **29** 84366-84382. DOI: 10.1007/s11356-022-21765-y
16. Kaźmierczak A., Kornaś A., Mościpan M., Łęcka J.. **Influence of Bisphenol A on Growth and Metabolism of**. *Sci. Rep.* (2022) **12** 20259. DOI: 10.1038/s41598-022-24219-0
17. Chhaya J.T.U.. **Evaluation of Phytotoxic and Clastogenic Potential of Phenolic Pollutant Bisphenol A and Its Bioremediated Product in Triticum Aestivum,**. *Int. J. Curr. Microbiol. Appl. Sci.* (2021) **10** 777-790. DOI: 10.20546/ijcmas.2021.1002.093
18. Adamakis I.-D.S., Panteris E., Eleftheriou E.P.. **Tubulin Acetylation Mediates Bisphenol A Effects on the Microtubule Arrays of**. *Biomolecules* (2019) **9**. DOI: 10.3390/biom9050185
19. Zhang J., Li X., Zhou L., Wang L., Zhou Q., Huang X.. **Analysis of Effects of a New Environmental Pollutant, Bisphenol A, on Antioxidant Systems in Soybean Roots at Different Growth Stages**. *Sci. Rep.* (2016) **6** 23782. DOI: 10.1038/srep23782
20. Adamakis I.-D.S., Sperdouli I., Eleftheriou E.P., Moustakas M.. **Hydrogen Peroxide Production by the Spot-like Mode Action of Bisphenol A**. *Front. Plant Sci.* (2020) **11** 1196. DOI: 10.3389/fpls.2020.01196
21. Fiskesjö G.. **The Allium Test as a Standard in Environmental Monitoring**. *Hereditas* (1985) **102** 99-112. DOI: 10.1111/j.1601-5223.1985.tb00471.x
22. Leme D.M., Marin-Morales M.A.. *Mutat. Res./Rev. Mutat. Res.* (2009) **682** 71-81. DOI: 10.1016/j.mrrev.2009.06.002
23. Firbas P., Amon T.. **Chromosome Damage Studies in the Onion Plant**. *Caryologia* (2014) **67** 25-35. DOI: 10.1080/00087114.2014.891696
24. Aksoy Ö., Yousaf Z.. **Detection of Environmental Mutagens Through Plant Bioassays**. *Plant Ecology—Traditional Approaches to Recent Trends* (2017)
25. Mesi A.D., Kopliku D.. **Cyto- and Genotoxic Activity of Pesticide Cypermex Plus 550 EC on**. *J. Int. Environ. Appl. Sci.* (2015) **10** 475-481
26. Garay-Arroyo A., De La Paz Sánchez M., García-Ponce B., Azpeitia E., Álvarez-Buylla E.R.. **Hormone Symphony during Root Growth and Development**. *Dev. Dyn.* (2012) **241** 1867-1885. DOI: 10.1002/dvdy.23878
27. Štraser A., Filipič M., Novak M., Žegura B.. **Double Strand Breaks and Cell-Cycle Arrest Induced by the Cyanobacterial Toxin Cylindrospermopsin in HepG2 Cells**. *Mar. Drugs* (2013) **11** 3077-3090. PMID: 23966038
28. Marnett L.J.. **Oxy Radicals, Lipid Peroxidation and DNA Damage**. *Toxicology* (2002) **181** 219-222. DOI: 10.1016/S0300-483X(02)00448-1
29. Marnett L.J.. **Lipid Peroxidation—DNA Damage by Malondialdehyde**. *Mutat. Res./Fundam. Mol. Mech. Mutagen.* (1999) **424** 83-95. DOI: 10.1016/S0027-5107(99)00010-X
30. Agati G., Azzarello E., Pollastri S., Tattini M.. **Flavonoids as Antioxidants in Plants: Location and Functional Significance**. *Plant Sci.* (2012) **196** 67-76. DOI: 10.1016/j.plantsci.2012.07.014
31. Sharma A., Shahzad B., Rehman A., Bhardwaj R., Landi M., Zheng B.. **Response of Phenylpropanoid Pathway and the Role of Polyphenols in Plants under Abiotic Stress**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24132452
32. Šamec D., Karalija E., Šola I., Vujčić Bok V., Salopek-Sondi B.. **The Role of Polyphenols in Abiotic Stress Response: The Influence of Molecular Structure**. *Plants* (2021) **10**. DOI: 10.3390/plants10010118
33. D’Angelo S., Scafuro M., Meccariello R.. **BPA and Nutraceuticals, Simultaneous Effects on Endocrine Functions**. *EMIDDT* (2019) **19** 594-604. DOI: 10.2174/1871530319666190101120119
34. Zahra Z., Khan M.R., Majid M., Maryam S., Sajid M.. **Gonadoprotective Ability of**. *Andrologia* (2020) **52** e13590. DOI: 10.1111/and.13590
35. Rademacher W.. **Growth Retardants: Effects on Gibberellin Biosynthesis and Other Metabolic Pathways**. *Annu. Rev. Plant Biol.* (2000) **51** 501-531. DOI: 10.1146/annurev.arplant.51.1.501
36. Fiskesjo G.. **Allium Test for Screening Chemicals; Evaluation of Cytological Parameters**. *Plants Environ. Stud.* (1997) **11** 307-333
37. Brkanac S.R., Gerić M., Gajski G., Vujčić V., Garaj-Vrhovac V., Kremer D., Domijan A.-M.. **Toxicity and Antioxidant Capacity of Frangula Alnus Mill. Bark and Its Active Component Emodin**. *Regul. Toxicol. Pharmacol.* (2015) **73** 923-929. DOI: 10.1016/j.yrtph.2015.09.025
38. Heath R.L., Packer L.. **Photoperoxidation in Isolated Chloroplasts: I. Kinetics and Stoichiometry of Fatty Acid Peroxidation**. *Arch. Biochem. Biophys.* (1968) **125** 189-198. DOI: 10.1016/0003-9861(68)90654-1
39. Levine R., Garland D., Oliver C., Amici A., Climent I.. **Determination of Carbonyl Content in Oxidatively Modified Proteins**. *Methods in Enzymology* (1990)
40. Bradford M.M.. **A Rapid and Sensitive Method for the Quantitation of Microgram Quantities of Protein Utilizing the Principle of Protein-Dye Binding**. *Anal. Biochem.* (1976) **72** 248-254. DOI: 10.1016/0003-2697(76)90527-3
41. Zhishen J., Mengcheng T., Jianming W.. **The Determination of Flavonoid Contents in Mulberry and Their Scavenging Effects on Superoxide Radicals**. *Food Chem.* (1999) **64** 555-559. DOI: 10.1016/S0308-8146(98)00102-2
42. Howard L.R., Clark J.R., Brownmiller C.. **Antioxidant Capacity and Phenolic Content in Blueberries as Affected by Genotype and Growing Season**. *J. Sci. Food Agric.* (2003) **83** 1238-1247. DOI: 10.1002/jsfa.1532
|
---
title: A Novel Method for the Pre-Column Derivatization of Saccharides from Polygonatum
cyrtonema Hua. by Integrating Lambert–Beer Law and Response Surface Methodology
authors:
- Hui Liu
- Yuanyuan Zhao
- Leijing Chen
- Jiao Du
- Hongyan Guo
- Bin Wang
journal: Molecules
year: 2023
pmcid: PMC10004654
doi: 10.3390/molecules28052186
license: CC BY 4.0
---
# A Novel Method for the Pre-Column Derivatization of Saccharides from Polygonatum cyrtonema Hua. by Integrating Lambert–Beer Law and Response Surface Methodology
## Abstract
Traditional Chinese medicine (TCM) safety and effectiveness can be ensured by establishing a suitable quality assessment system. This work aims to develop a pre-column derivatization HPLC method for *Polygonatum cyrtonema* Hua. quality control. In this study, 1-(4′-cyanophenyl)-3-methyl-5-pyrazolone (CPMP) was synthesized and reacted with monosaccharides derived from P. cyrtonema polysaccharides (PCPs), followed by HPLC separation. According to the Lambert–Beer law, CPMP has the highest molar extinction coefficient of all synthetic chemosensors. A satisfactory separation effect was obtained under a detection wavelength of 278 nm using a carbon-8 column and gradient elution over 14 min, with a flow rate of 1 mL per minute. Glucose (Glc), galactose (Gal), and mannose (Man) make up the majority of the monosaccharide components in PCPs, and their molar ratios are 1.73:0.58:1. The confirmed HPLC method has outstanding precision and accuracy, establishing a quality control method for PCPs. Additionally, the CPMP showed a visual improvement from colorless to orange after the detection of reducing sugars, allowing for further visual analysis.
## 1. Introduction
Chemists have frequently shown a significant interest in the design and synthesis of novel chemosensors to determine the analyte in vivo and in vitro [1,2,3]. As a significant pharmacological component in TCM, polysaccharides have strong biological effects, such as immunological modulation and tumor prevention [4,5]. To ensure clinical safety and efficacy, the quality control of TCM polysaccharides is crucial. The polysaccharides’ inherent polydispersity and lack of chromophores make polysaccharide determination difficult [6,7], not to mention the difficulties of establishing a consistent method of quality control for Chinese polysaccharide medicine.
Saccharides are extremely difficult to detect due to their strong polarity, structural similarity, and lack of UV absorption or luminescence group. Generally, the number of total saccharides in crude polysaccharide extracts is determined by colorimetric methods after acidification, based on the Chinese Pharmacopoeia [8]. However, the direct test’s sensitivity and accuracy are low [9]. By comparison, colorimetric chemosensors have received increased attention due to their excellent sensitivity and operational simplicity [10,11,12].
Chemical derivatization can significantly improve the measured sensitivity and selectivity of saccharides in a derivatization reaction between the chemosensor and saccharide, particularly in pre-column derivatization, which requires only a few diverse derivatization reagents, such as 1-phenyl-3-methyl-5-pyrazolone (PMP) [13], fluorescein isothiocyanate (FITC) [14], and others [15,16]. In 1989, Honda et al. [ 17] used PMP to derivatize monosaccharides, and subsequently, researchers used the HPLC-UV technique to examine the PMP derivatives, achieving a desirable result. It should be noted that PMP is currently the pre-column derivatization chemosensor with the highest usage rate. However, the stronger alkaline conditions and lower detecting wavelength result in drawbacks such as undesired byproducts, contaminant interference, and excessively high sensor requirements. As a result, a chemosensor that has a higher sensitivity, lower sensor requirements, and mild reaction conditions is highly desired.
Following our research interests in chemosensor design and quality control [18,19,20,21], particularly for Polygonatum by the HPLC technique [16], herein, we designed and synthesized a variety of PMP derivatives comprising diverse functionalization groups, such as methyl, methoxy, halogen, cyano, and nitro, to address the difficulties stated above. Combining the Lambert–Beer law and response surface methodology (RSM) [22,23], the CPMP with the highest molar extinction coefficient was screened out. By utilizing the RSM combined with the HPLC technique, we established a method for the visual sensing of monosaccharides from PCP. Finally, we established a consistent method of quality control for P. cyrtonema Hua., highlighting the value of the novel technology.
Some efficient and simple reagents for derivatization have been reported [24,25]. In this study, the workflow of sensing monosaccharides was depicted in Scheme 1. The molar extinction coefficient of CPMP is 23382, while that of PMP is 11593, indicating that CPMP is twice as sensitive as PMP. Meanwhile, the maximum absorption wavelength of CPMP is 278 nm, whereas the maximum absorption wavelength of PMP is 245 nm, which indicates less noise interference when CPMP is used. For the developed approach, the limit of detection (LOD) is less than 0.006 μg/mL in terms of the detection of monosaccharides. In contrast, among the other reported approaches [26,27,28], the LOD is greater than 0.4 μg/mL. Consequently, the approach developed in this study offers significant advantages.
## 2.1. UV-Vis Spectra of CPMP
A UV-visible spectrophotometer was utilized to determine the maximum absorption wavelength of the synthesized CPMP. Lambert–Beer’s law was used to calculate the molar extinction coefficient. The PMP molecule exhibits an ultraviolet absorption peak of 245 nm, whereas CPMP exhibits an absorption peak of 278 nm, which is higher in magnitude than PMP (Figure 1A). A comparison of the molar absorption coefficient of PMP (ε = 11593.40 L/mol/cm) and CPMP (ε = 23382.49 L/mol/cm) for CH3OH indicates that CPMP is more sensitive than PMP (Table 1, and Figure 1B). Recently, we have developed a pre-column derivatization HPLC method based on the reaction of 4-hydrazine-1,8-naphthalimide (HAN) as a new chemical sensor with reducing sugar. In the molecule of HAN, the molar extinction coefficient in methanol is 16138.51 L/mol/cm [16]. In contrast to PMP, the synthesized CPMP has a higher sensitivity.
## 2.2. Optimization of Derivatization Conditions by RSM
After performing a single-factor analysis, we used triethyl amine as the best type of alkali from DMAP, Na2CO3, NaOH, K3PO4, and (C2H5)3N. Subsequently, it was determined that reaction temperature, reaction time, alkali concentration, and CPMP concentration played a significant role. The test was designed by the Box–Behnken center combination using Design-Expert 8.0.6 software. Table S1 illustrates the results of the test factors: the peak area of monosaccharide-CPMP varied from 66.3 to 73.45. As the objective function of the regression equation, we obtained the quadratic equation. Subsequently, using the F-test and p-value, it is possible to evaluate the significance of the model’s coefficients. The data obtained from the experiment are fitted multiple times to generate a mathematical model: $Y = 71.55$ + 0.4667A + 1.39B − 0.0917C − 1.98D = 0.0750AB + 0.2250AC + 0.0000AD + 0.2250BC − 0.3750BD + 0.3250CD − 1.44A2 − 1.33B2 − 1.25C2 − 0.7645D2 where Y stands for the peak area of the Glc-PMP derivative, while A, B, C, and D represent reaction temperature, reaction time, alkali concentration, and CPMP concentration, respectively. The significant influencing factors were B (Time, $p \leq 0.0001$) and D (CPMP concentration, $p \leq 0.0001$), as shown in Table 2.
As a result of these data, it is feasible to establish a model through experimentation. As can be seen from the precision value of 10.655, the model is suitable for forecasting the outcomes of experiments. With an R2 adjusted value of 0.8047, the model can predict a response value of $80.47\%$. With a determination coefficient of R2 of 0.9023, the model has excellent suitability and can be used to analyze and predict the peak area of monosaccharide-CPMP. With the R2Pred equal to 0.7327, there is no significant difference between it and the R2, indicating that it was unnecessary to investigate further.
Our study used Design-Expert (11.0) to establish the relationship between the independent and dependent variables. Figure 2 depicts the independent and dependent variables’ 3D response surface and contour plot and presents the response surface and contour map of A (Temperature) and B (Time) about the peak area of monosaccharide-CPMP. Based on the map’s intensity of contours and the response surface’s steepness, the map reflects how many interlacing factors influence the response surface. With increasing density and slope, the impact degree will be more pronounced. Maps in Figure 2b is the steepest (corresponding to Figure 2a–c). These results illustrated that time (B) and CPMP concentration (D) significantly influenced the peak area of monosaccharide-CPMP, while temperature (A) and alkali concentration (C) had a slight influence. Overall, the pre-column derivatization conditions of monosaccharide-CPMP were optimized using RSM during the experiment. As a result, the optimal conditions were established below: time: 60 min, temperature: 70 °C, alkali concentration: 0.4 mol/L, and CPMP concentration: 0.6 mol/L.
## 2.3. Mechanism Analysis of CPMP-Glc by UV-Vis and HRMS
UV-Vis, HRMS, and NMR techniques were used to identify the mechanisms involved in derivatization [29]. As shown in Figure 1A, the maximum absorption wavelength of CPMP is 278 nm, which is redshifted compared with that of PMP. Figure 1B shows that CPMP is attached to the benzene ring with substituent 4-CN, increasing its molar extinction coefficient. In Figure 3A, the methanol solution of CPMP is colorless and transparent, while the solution of CPMP-*Glc is* orange, suggesting the absorption spectrum has changed. Finally, HRMS spectra provide support for the structure of CPMP-Glc. The peak at m/z 561.2101, as shown in Figure 3B, was assigned to [2CPMP-Glc + H]+ (calc. m/z 561.2092).
## 2.4. Quality Control for P. cyrtonema Hua.
When monosaccharides are analyzed directly by HPLC-DAD, they cannot be observed or detected due to the lack of ultraviolet chromophores in saccharides. However, the derivatization reaction between chemosensor CPMP and three monosaccharides, Glc, Man, and Gal, allows the observation of the metabolites of the various monosaccharides conjugated with CPMP. These metabolites are easily separated by the established method.
A comparison of the results displayed in Figure 4A may be sufficient to satisfy the requirements of qualitative and quantitative analysis. Afterwards, an indirect study of monosaccharides in PCPs samples was conducted. The first step involved the hydrolysis of PCPs into monosaccharides with 2 M trifluoroacetic acid, followed by the derivation of the monosaccharides with CPMP. Using the HPLC technique, the obtained samples were analyzed. The HPLC chromatogram shown in Figure 4B exhibited three peaks identified and labeled as follows: peak 1, CPMP-Gal, tR: 4.78 min; peak 2, CPMP-Glc, tR: 5.92 min; and peak 3, CPMP-Man, tR: 12.03 min. A separation degree more significant than 1.5 has been achieved between the three components above-mentioned, thus meeting the requirements for HPLC separation. Therefore, this established method is a practical approach to separating CPMP-monosaccharides and determining indirect monosaccharide content. On the basis of the linear equation, the contents of CPMP-Gal, CPMP-Glc, and CPMP-Man were calculated. As displayed in Table 3, the corresponding molar ratio of Gal, Glc, and *Man is* 0.58:1.73:1.00 in PCP samples. The glucose content was the highest, consistent with literature reports [30,31]. Generally, we were able to successfully detect monosaccharide composition in PCP analytes using the established method, satisfying the goal of providing a method for the quality control of P. cyrtonema Hua. polysaccharide.
## 2.5. Method Validation
To validate the developed method, we conducted experiments examining linearity, LOD, and reproducibility. After plotting the relationship between peak area and spiked concentrations of Glc, Man, and Gal, the linear fitting to the results was completed. Calibration curves are constructed using Glc, Man, and Gal standards with a concentration range of 1 to 104 μg/mL. The linear regression and LOD results are displayed in Table 4. A satisfactory correlation coefficient ranged between 0.9993 and 0.9997 for the three compounds. LOD values were determined for the three substances and ranged from 2.12 × 10−3 to 5.66 × 10−3 μg/mL. In addition, a six-time continuous injection was used to evaluate the precision of the present method, which laid the foundation for calculating the relative standard deviation (RSD) for CPMP-Glc. The RSDs were $0.44\%$ (Table 5). The stability of the established method was also measured during the 24 h after preparation. The RSD of the stability was $0.31\%$, exhibiting that the reproducibility of the presented method is excellent. As a result of its excellent linear relationship, accuracy, sensitivity, and stability, the validated method is able to satisfy the requirements of determining monosaccharides.
## 3.1. Reagent and Instrument
We collected the rhizomes of P. cyrtonema Hua. ( PC) identified by Dr. Jinmei Ou, Anhui University of Chinese Medicine, from Banzhuyuan in Jinzhai County (Luan, Anhui Province, China).
The purity of the 4-phenylhydrazine hydrochloride acquired from Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China) was $95\%$, and it has various substituents (4-Br, 4-CH3, 4-Cl, 4-CN, 4-OCH3, 4-F, 2-F, and 3-F). Shanghai Bide Medical Technology Co., Ltd. (Shanghai, China) supplied the monosaccharide standards, including glucose (Glc), mannose (Man), and galactose (Gal) (purity > $97\%$). Aladdin Reagent Co., Ltd. (Shanghai, China) provided 1-phenyl-3-methyl-5-pyrazolone (PMP, purity $99\%$); reducing sugar-CPMP derivatives were separated using an Agilent ZORBAX SB C8 column (4.6 mm × 250 mm, 5 μm particle); MERCK & Co., Inc. (Shanghai, China) provided the chromatographic methanol and acetonitrile; Waters Xevo G2-XS QTOF spectrometers were used to collect the high-resolution mass spectra (HRMS) (Tolerance = 10.0 ppm). An ultraviolet–visible spectrophotometer, SHIMADZU UV-2550, was used to measure the absorption of ultraviolet-visible light.
## 3.2. Synthesis of CPMP
4-(5-hydroxy-3-methyl-1H-pyrazole-1-yl) benzonitrile 3e was synthesized according to a reported procedure [32,33,34]. In brief, ethyl acetoacetate (2 mmol, 252 μL), glacial acetic acid (0.6 mL), and ethanol (2 mL) were added to commercially available 4-cyanophenylhydrazine hydrochloride (2 mmol, 306.28 mg), and the mixture was reacted at 100 °C for 6 h. A light-yellow solid powder was obtained by silica gel column chromatography, with a yield of $10\%$. NMR analysis of the prepared compound showed that the compound was 4-(5-hydroxy-3-methyl-1H-pyrazole-1-yl) benzonitrile (CPMP). 1H NMR (500 MHz, DMSO) δ 7.98 (d, 2H), 7.88 (d, 2H), 2.51 (s, 2H), 2.14 (s, 3H). This compound was known [31]. Later, we carried out substrate expansion. PMP derivatives with different substituents (such as 4-H, 4-Br, 4-CH3, 4-Cl, 4-OCH3, 4-F, 3-F, and 2-F) were synthesized according to the method described above. These compounds were known [32,33,34].
## 3.3. Investigation of Spectroscopic Characteristics
A total of 20 mg of each PMP derivative with different substituents (4-H, 4-Br, 4-CH3, 4-Cl, 4-CN, 4-OCH3, 4-F, 3-F, and 2-F) was transferred to a 100 mL volumetric flask, and then chromatography-grade methanol was added. The absorbance at the maximum absorption wavelength was measured using a blank methanol solution as a control, and the measurement was repeated three times.
The Lambert–Beer law describes how light absorption intensity, concentration, and light path length are related to wavelength for a given substance. In the classical equation: A = log (1/T) = εbc, ε reflects the degree of light absorption by the absorbing medium, which can be used as the characteristic constant of the substance. When $b = 1$, the value of ε is the slope of this binary first-order equation. The higher the ε value, the more sensitive the chemosensor will be. A Lambert–Beer law can calculate the molar extinction coefficient and absorbance, and the specific values of PMP derivatives can be found in the supplementary material. According to the above method, weighed samples were dissolved in ethanol and acetonitrile, respectively. The results of the three solvent assays are shown in Figure 5, and Tables S2–S4.
The results showed that when methanol was used as the solvent, the molar absorption coefficient of each derivative was higher, and CPMP had less crossover with other peaks at the maximum absorption wavelength. Therefore, CPMP was selected as a novel sugar chemosensor with high sensitivity and was applied to the quality control method concerning the Polygonatum polysaccharide used in TCM.
## 3.4. Optimizing Derivatization Condition by RSM [16]
Variables such as temperature, time, concentration of alkali, and concentration of CPMP significantly affect the efficiency of the reducing sugar-CPMP derivatization reaction; furthermore, these factors can interact. Additionally, a complete experiment designed to explore the relationships between the variables is often time-consuming. To solve this problem, the RSM was proposed to find the most valuable areas for optimization, such as reducing the difficulty of the experimental design, maximizing production, minimizing costs, and minimizing side effects. To optimize the efficiency of the derivatization of saccharides, we selected four single factors, which include reaction temperature (°C), time (h), alkali concentration (mol/L), and CPMP concentration (mol/L). The peak area of CPMP-monosaccharide was explored using Box–Behnken design (BBD)-RSM (Table 6). Based on 29 measurement experiments, we estimated the parameters of the model using the least square method (Table 2). Multiple regression analysis was utilized to analyze the experiment data, resulting in a relationship between the response variable and the test variable of a second-order equation. Using Design-Expert 11.0 software, we can compare multiple regression models and optimize process parameters based on the obtained test data. Thanks to the outstanding value of the software in comparison with alternative methods, optimal conditions were identified for verification and detection.
## 3.5. High-Performance Liquid Chromatography
This study used Agilent ZORBAX SB-C8 (4.6 mm × 250 mm, 5 μm) as a separation column with a temperature of 30 °C. Moreover, the flow rate, the injection volume, and the detection wavelength were 1 mL/min, 5 μL, and 278 nm, respectively. The mobile phase was ammonium acetate (0.02 mol/L)-acetonitrile, gradient elution (0–8 min, CH3CN $20\%$; 10–13 min, CH3CN $28\%$; 13–14 min, CH3CN $20\%$).
## 3.6. Preparation of P. cyrtonema Hua. Polysaccharides (PCPs)
The PCPs were prepared from P. cyrtonema powder. Briefly, the powder was soaked in boiling water (1:4 w/v, 100 °C) for 30 min and then precipitated using ethanol four times. Using Sevag’s method, the precipitates were deproteinized and lyophilized before further analysis.
## 3.7. Monosaccharide Determination
By modifying a previous report [16], we reacted CPMP and reducing sugar, producing the reducing sugar-CPMP derivatives. We then took 19.90 mg of CPMP, dissolved 0.2 mL DMF, and added 0.2 mL (20 mg/mL) of various standard monosaccharide solutions and the solution of 0.2 mL (0.4 mol/L) N(C2H5)3. This mixture was stirred and reacted at 70 °C for 60 min. We then added 0.2 mL (0.3 mol/L) hydrochloric acid and 1 mL chloroform after cooling and mixed for 30 s. The mixture was then transferred to a centrifuge tube and centrifuged (speed 2000 r/min) for 5 min; we removed the lower layer solution, added chloroform, and centrifuged again, repeating this process three times. We aspirated the supernatant and diluted it to 5.0 mL, which was then filtered through a 0.22 μm microporous membrane. An aliquot of 10 μL sample was injected into HPLC to perform the analysis.
## 4.1. Synthesis of CPMP
PMP pre-column derivatization is the main method of quality control for polysaccharides in traditional Chinese medicine. However, the nucleophilicity is insufficient when the derivatization reagent reacts (such as PMP) with monosaccharides; additionally, its maximum absorption wavelength is 245 nm. A large proportion of compounds absorb at this wavelength, which results in a relatively large amount of background interference. To address these issues, a series of PMP derivatives with different substituents (4-H, 4-Br, 4-CH3, 4-Cl, 4-OCH3, 4-F, 3-F, and 2-F) was synthesized according to the method described above.
## 4.2. Establishment of the HPLC Method
Reaction conditions have a significant influence on the results of the reaction. Therefore, it is essential to select the reaction parameters carefully. RSM is widely used today for identifying optimal reaction conditions. The five factors selected for optimizing the efficiency of saccharide derivatization (h), reaction temperature (°C), acid type, acid concentration (eq), and molar ratio (eq) are investigated herein. It was found that the following conditions were optimal: time: 60 min, temperature: 70 °C, alkali concentration: 0.4 mol/L, and CPMP concentration: 0.6 mol/L.
The monosaccharides lack chromogenic groups, and ultraviolet absorption is very weak. The conventional HPLC-UV method cannot directly detect them. Through the derivatization reaction of CPMP and different monosaccharides under optimal conditions, the monosaccharides were labeled. Subsequently, they were separated by an Agilent ZORBAX SB-C8 column (4.6 mm × 250 mm, 5 μm) and were determined by a UV detector at a wavelength of 278 nm.
Next, the monosaccharides in the P. cyrtonema Hua. polysaccharide from Jinzhai of Anhui were examined indirectly. A PCP sample was hydrolyzed, and then a CPMP precursor was derived. As shown in Table 4, the monosaccharides of PCPs include glucose, mannose, and galactose, with a compositional ratio of 1.73:1.00:0.58. Therefore, the method is also appropriate for monosaccharide measurement in PCPs.
## 4.3. Mechanism Analysis
The possible reaction mechanism was further investigated through UV-Vis and HRMS techniques [29]. As shown in Figure 1A, the maximum absorption wavelength of CPMP is 278 nm, which is redshifted compared with that of PMP. Figure 1B shows that CPMP is attached to the benzene ring with the substituent 4-CN, increasing its molar extinction coefficient. In Figure 3A, the methanol solution of CPMP is colorless and transparent, while the solution of CPMP-*Glc is* orange, suggesting the absorption spectrum has changed. In the HRMS spectra of the solution of CPMP-Glc, a peak at m/z 561.2101 was observed, as depicted in Figure 3B. Based on the calculation of [2CPMP-Glc + H]+ is 561.2092, we deduce that the conjugated product of two CPMP and one *Glc is* present in the derivative system. This result further supports the structure of CPMP-Glc.
## 4.4. Comparative Analysis with Existing Methods
According to our approach, the LOD (limit of detection) for the detection of monosaccharides is less than 0.006 μg/mL. On the other hand, among the reported approaches [26,27,28], the LODs are generally higher than 0.4 mg/mL. Based on the quantum dots (QD) technique, the LOD of glucose was 0.811 μg/mL and 0.901 μg/mL, respectively. However, using the reagent TPEA-BAP, the LOD of glucose only reached 0.468 μg/mL, as shown in Table 7. In contrast, the CPMP compound gave a lower detection limit at 0.00522 μg/mL. Additionally, the CPMP reagent possesses higher sensitivity (11593 for PMP; 23,382 for CPMP) and less noise interference (detection at 245 nm for PMP and 278 nm for CPMP). As a result, our developed approach is more widely useful.
## 5. Conclusions
In this study, we have presented a chemosensor method to measure the monosaccharide composition in PCP analytes and achieved satisfactory results for establishing a method of quality control of P. cyrtonema Hua. This approach includes three steps: the synthesis of chemosensor CPMP, the hydrolysis of polysaccharide analyte, and the derivatization and detection of monosaccharides. Among the synthesized PMP derivatives bearing various groups, the CPMP molecule possessed higher sensitivity (ε = 23382.49 L/mol/cm in CH3OH) and less background interference (λmax = 278 nm) and thus was chosen. According to the established HPLC approach, the main monosaccharide components of the P. cyrtonema Hua. polysaccharide were glucose, mannose, and galactose, with a compositional ratio of 1.73:1.00:0.58. The method is appropriate for monosaccharide analysis and content measurement and can be utilized for the quality control of the P. cyrtonema Hua. polysaccharide. Furthermore, the developed approach has a lower LOD (less than 0.006 μg/mL). These features give this approach significant advantages. There is a marked difference in color or transparency between CPMP and CPMP-Glc. Therefore, the adduct’s absorption spectrum is changed, providing the possibility of visualizing different monosaccharides.
## Figures, Scheme and Tables
**Scheme 1:** *The workflow of sensing monosaccharides through a PMP-based chemosensor. (Blue: chemosensor; Black: material and analyte; Red: new bond; Purple: optimal group and ε value).* **Figure 1:** *Spectroscopic properties of pre-column derivatization reagents of PMP and CPMP. (A) UV-Vis spectrum; (B) Molar extinction coefficient in methanol.* **Figure 2:** *Response surface plots showing the effects of variables on the derivatization of CPMP. (a) The response surface of the impact of exaction temperature (A, °C ) and time (B, min ); (b) The response surface of the effects of temperature (B, min) and alkali concentration (C, mol/L); (c) The response surface of the impact of time (B, min) and CPMP concentration (D, mol/L). Red means the effect is the greatest; Yellow means the effect is the moderate, and the *Green is* the weakest.* **Figure 3:** *Mechanism analysis: (A) Photograph of CPMP and CPMP-Glc solutions; (B) HRMS data of CPMP-Glc.* **Figure 4:** *(A) HPLC-DAD chromatogram of mixing CPMP-monosaccharide standards; (B) HPLC-DAD chromatogram of PCPs after derivatization; (peak 1, CPMP-Gla, tR = 4.78 min; peak 2, CPMP-Glc, tR = 5.92 min; peak 3, CPMP-Man, tR = 12.03 min).* **Figure 5:** *UV absorption spectra and molar extinction coefficients of PMP derivatives with different substituents when methanol (A,D), ethanol (B,E), and acetonitrile (C,F) were used as solvents.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 TABLE_PLACEHOLDER:Table 4 TABLE_PLACEHOLDER:Table 5 TABLE_PLACEHOLDER:Table 6 TABLE_PLACEHOLDER:Table 7
## References
1. Teymourian H., Barfidokht A., Wang J.. **Electrochemical glucose sensors in diabetes management: An updated review (2010–2020)**. *Chem. Soc. Rev.* (2020) **49** 7671-7709. DOI: 10.1039/D0CS00304B
2. Li H., Sheng Y., Li W., Yuan L.. **Recent Advances in Molecular Fluorescent Probes for CYP450 Sensing and Imaging**. *Chemosensors* (2022) **10**. DOI: 10.3390/chemosensors10080304
3. Ali A.A., Kharbash R., Kim Y.. **Chemo- and biosensing applications of spiropyran and its derivatives—A review**. *Anal. Chim. Acta* (2020) **1110** 199-223. DOI: 10.1016/j.aca.2020.01.057
4. Yu Y., Shen M., Song Q., Xie J.. **Biological activities and pharmaceutical applications of polysaccharide from natural resources: A review**. *Carbohydr. Polym.* (2018) **183** 91-101. DOI: 10.1016/j.carbpol.2017.12.009
5. Ji X.-L., Guo J.-H., Pan F.-B., Kuang F.-J., Chen H.-M., Guo X.-D., Liu Y.-Q.. **Structural elucidation and antioxidant activities of a neutral polysaccharide from Arecanut (**. *Front. Nutr.* (2022) **9** 853115. DOI: 10.3389/fnut.2022.853115
6. Ji X., Guo J., Ding D., Gao J., Hao L., Guo X., Liu Y.. **Structural characterization and antioxidant activity of a novel high-molecular-weight polysaccharide from**. *J. Food Meas. Charact.* (2022) **16** 2191-2200. DOI: 10.1007/s11694-022-01288-3
7. Pauk V., Pluhacek T., Havlicek V., Lemr K.. **Ultra-high performance supercritical fluid chromatography-mass spectrometry procedure for analysis of monosaccharides from plant gum binders**. *Anal. Chim. Acta* (2017) **989** 112-120. DOI: 10.1016/j.aca.2017.07.036
8. 8.
National Pharmacopoeia Commission
Pharmacopoeia of the People’s Republic of China2020 ed.Part IIIChina Medical Science and Technology PressBeijing, China2020566. *Pharmacopoeia of the People’s Republic of China* (2020) 566
9. Sharma U., Bhandari P., Kumar N., Singh B.. **Simultaneous Determination of Ten Sugars in Tinospora cordifolia by Ultrasonic Assisted Extraction and LC-ELSD**. *Chromatographia* (2010) **71** 633-638. DOI: 10.1365/s10337-010-1520-2
10. Long L.-L., Han Y.-Y., Liu W.-G., Chen Q., Yin D.-D., Li L.-L., Yuan F., Han Z.-X., Gong A.-H., Wang K.. **Simultaneous Discrimination of Hypochlorite and Single Oxygen during Sepsis by a Dual-Functional Fluorescent Probe**. *Anal. Chem.* (2020) **92** 6072-6080. DOI: 10.1021/acs.analchem.0c00492
11. Zheng C., Wu D., Wang T., Xiao J., Yu L.. **A Label-Free Colorimetric Assay Based on Gold Nanoparticles for the Detection of H**. *Chemosensors* (2022) **10**. DOI: 10.3390/chemosensors10030100
12. Zhang X.-X., Ren T.-B., Yang F.-Y., Yuan L.. **Rational design of far red to near-infrared rhodamine analogues with huge Stokes shifts for single-laser excitation multicolor imaging**. *Chin. Chem. Lett.* (2021) **32** 3890-3894. DOI: 10.1016/j.cclet.2021.06.038
13. Dai J., Wu Y., Chen S.-W., Zhu S., Yin H.-P., Wang M., Tang J.. **Sugar compositional determination of polysaccharides from Dunaliella salina by modified RP-HPLC method of precolumn derivatization with 1-phenyl-3-methyl-5-pyrazolone**. *Carbohydr. Polym.* (2010) **82** 629-635. DOI: 10.1016/j.carbpol.2010.05.029
14. Zhang Y., Zhou T., Luo L., Cui Z., Wang N., Shu Y.-M., Wang K.-P.. **Pharmacokinetics, biodistribution and receptor mediated endocytosis of a natural Angelica sinensis polysaccharide**. *Artif. Cells Nanomed. Biotechnol.* (2018) **46** 254-263. DOI: 10.1080/21691401.2017.1421210
15. Zhao P., Li X., Wang Y., Zhang X., Jia H., Guo L., Huang L., Gao W.. **Comparative studies on characterization, saccharide mapping and antiglycation activity of polysaccharides from different**. *J. Pharm. Biomed. Anal.* (2020) **186** 113243. DOI: 10.1016/j.jpba.2020.113243
16. Wang Z., Sun Q.-X., Zhao Y.-Y., Du J., Wang B.. **Synthesis of naphthalimide-type chemsensor and its application in quality evaluation for polygonatum sibiricum Red**. *Front. Chem.* (2022) **10** 969014. DOI: 10.3389/fchem.2022.969014
17. Honda S., Akao E., Suzuki S., Okuda M., Kakehi K., Nakamura J.. **High-performance liquid chromatography of reducing carbohydrates as strongly ultraviolet-absorbing and electrochemically sensitive 1-phenyl-3-methyl-5-pyrazolone derivatives**. *Anal. Biochem.* (1989) **180** 351-357. DOI: 10.1016/0003-2697(89)90444-2
18. Chen L., Wang Z., Liu H., Li X., Wang B.. **tert-Butyl nitrite triggered radical cascade reaction for synthesizing isoxazoles by a one-pot multicomponent strategy**. *Chem. Commun.* (2022) **58** 9152-9155. DOI: 10.1039/D2CC02823A
19. Wang L., Wang Y.L., Tong G.Y., Li Y., Lei M.N., Wu H., Wang B., Hu R.F.. **Development of a novel UHPLC-UV combined with UHPLC-QTOF/MS fingerprint method for the comprehensive evaluation of Nao-Luo-Xin-Tong: Multi-wavelength setting based on traditional Chinese medicinal prescription composition**. *Anal. Methods* (2019) **11** 6092-6102. DOI: 10.1039/C9AY01975H
20. Wang Z., Liu H., Cao Y., Zhang T., Guo H., Wang B.. **A novel method for investigating the mechanism of the anti-rheumatoid arthritis activity of Angelicae pubescentis radix by integrating UHPLC–QTOF/MS and network pharmacology**. *Biomed. Chromatogr.* (2022) **36** e5389. DOI: 10.1002/bmc.5389
21. Shen F., Song Z., Xie P., Li L., Wang B., Peng D.-Y., Zhu G.-Q.. **Polygonatum sibiricum polysaccharide prevents depression-like behaviors by reducing oxidative stress, inflammation, and cellular and synaptic damage**. *J. Ethnopharmacol.* (2021) **275** 114164. DOI: 10.1016/j.jep.2021.114164
22. Huang G., He J.-X., Zhang X., Feng M.-M., Tan Y., Lv C.-C., Huang H., Jin Z.. **Applications of Lambert-Beer law in the preparation and performance evaluation of graphene modified asphalt**. *Constr. Build. Mater.* (2020) **273** 121582. DOI: 10.1016/j.conbuildmat.2020.121582
23. Wang W.-Z., Chen F., Wang Y.-Q., Wang L.-N., Fu H.-Y., Zheng F.-P., Beecher L.. **Optimization of reactions between reducing sugars and 1-phenyl-3-methyl-5-pyrazolone (PMP) by response surface methodology**. *Food Chem.* (2018) **254** 158-164. DOI: 10.1016/j.foodchem.2018.02.001
24. Albukhaty S., Al-Karagoly H., Allafchian A.R., Jalali S.A.H., Al-Kelabi T., Muhannad M.. **Production and characterization of biocompatible nanofibrous scaffolds made of β-sitosterol loaded polyvinyl alcohol/tragacanth gum composites**. *Nanotechnology* (2022) **33** 085102. DOI: 10.1088/1361-6528/ac3789
25. Ji X., Cheng Y., Tian J., Zhang S., Jing Y., Shi M.. **Structural characterization of polysaccharide from jujube (**. *Chem. Biol. Technol. Agric.* (2021) **8** 54. DOI: 10.1186/s40538-021-00255-2
26. Li H., Zhu L., Zhu X., Zhang H.. **Glucose detection via glucose-induced disaggregation of ammonium-modified tetraphenylethylene from polyanion**. *Sens. Actuators B Chem.* (2017) **246** 819-825. DOI: 10.1016/j.snb.2017.02.155
27. Hao T., Wei X., Nie Y., Xu Y., Lu K., Yan Y., Zhou Z.. **Surface modification and ratiometric fluorescence dual function enhancement for visual and fluorescent detection of glucose based on dual-emission quantum dots hybrid**. *Sens. Actuators B Chem.* (2016) **230** 70-76. DOI: 10.1016/j.snb.2016.02.002
28. Qu Z.B., Zhou X., Gu L., Lan R., Sun D., Yu D., Shi G.. **Boronic acid functionalized graphene quantum dots as a fluorescent probe for selective and sensitive glucose determination in microdialysate**. *Chem. Commun.* (2013) **49** 9830-9832. DOI: 10.1039/c3cc44393k
29. Yan Y.-Z., Li S.-Q., Zhang Z.-H., Qu J.-B., Wang J.-Y.. **A sensitive bio-probe for tracking lipid droplets with large Stokes shift and its application in cell imaging**. *Spectrochim. Acta Part A Mol. Biomol. Spectrosc.* (2021) **260** 119988. DOI: 10.1016/j.saa.2021.119988
30. Zhang J.-Y., Chen H.-L., Luo L., Zhou Z.-P., Wang Y.-X., Gao T.-Y., Yang L., Peng T., Wu M.-Y.. **Structures of fructan and galactan from Polygonatum cyrtonema and their utilization by probiotic bacteria**. *Carbohydr. Polym.* (2021) **267** 118219. DOI: 10.1016/j.carbpol.2021.118219
31. Li J., Wang X., Zhou R., Cheng F., Tang X., Lao J., Xu L., He W., Wan D., Zeng H.. **Polygonatum cyrtonema Hua Polysaccharides Protect BV2 Microglia Relief Oxidative Stress and Ferroptosis by Regulating NRF2/HO-1 Pathway**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27207088
32. Chiu W.-J., Chen H.-R., Barve I.J., Sun C.-M.. **Rh(III)-Catalyzed (4 + 1) Annulation of Pyrazol-3-ones with Alkynoates via Ortho-Alkenylation/Cyclization Cascade: Synthesis of Indazole-Fused Pyrazoles**. *J. Org. Chem.* (2022) **87** 12109-12114. DOI: 10.1021/acs.joc.2c01208
33. Fang F.-F., Hu S.-L., Li C.-P., Wang Q., Wang R., Han X., Zhou Y., Liu H.. **Catalytic System-Controlled Divergent Reaction Strategies for the Construction of Diversified Spiropyrazolone Skeletons from Pyrazolidinones and Diazopyrazolones**. *Angew. Chem. Int. Ed.* (2021) **60** 21327-21333. DOI: 10.1002/anie.202105857
34. Zhao X., Zhang L.-P., Li T.-J., Liu G.-Y., Wang H.-M., Lu K.. **p-Toluenesulphonic acid-promoted, I2-catalysed sulphenylation of pyrazolones with aryl sulphonyl hydrazides**. *Chem. Commun.* (2014) **50** 13121-13123. DOI: 10.1039/C4CC05237D
|
---
title: Effect of Sphingomyelinase-Treated LDLs on HUVECs
authors:
- Angelica Giuliani
- Camilla Morresi
- Gabriele Mazzuferi
- Luisa Bellachioma
- Deborah Ramini
- Jacopo Sabbatinelli
- Fabiola Olivieri
- Tiziana Bacchetti
- Gianna Ferretti
journal: Molecules
year: 2023
pmcid: PMC10004656
doi: 10.3390/molecules28052100
license: CC BY 4.0
---
# Effect of Sphingomyelinase-Treated LDLs on HUVECs
## Abstract
Low-density lipoproteins (LDLs) exert a key role in the transport of esterified cholesterol to tissues. Among the atherogenic modifications of LDLs, the oxidative modification has been mainly investigated as a major risk factor for accelerating atherogenesis. Since LDL sphingolipids are also emerging as important regulators of the atherogenic process, increasing attention is devoted to the effects of sphingomyelinase (SMase) on LDL structural and atherogenic properties. The aims of the study were to investigate the effect of SMase treatment on the physical-chemical properties of LDLs. Moreover, we evaluated cell viability, apoptosis, and oxidative and inflammatory status in human umbilical vein endothelial cells (HUVECs) treated with either ox-LDLs or SMase-treated LDLs (SMase-LDLs). Both treatments were associated with the accrual of the intracellular ROS and upregulation of the antioxidant Paraoxonase 2 (PON2), while only SMase-LDLs induced an increase of superoxide dismutase 2 (SOD2), suggesting the activation of a feedback loop to restrain the detrimental effects of ROS. The increased caspase-3 activity and reduced viability observed in cells treated with SMase-LDLs and ox-LDLs suggest a pro-apoptotic effect of these modified lipoproteins on endothelial cells. Moreover, a strong proinflammatory effect of SMase-LDLs compared to ox-LDLs was confirmed by an increased activation of NF-κB and consequent increased expression of its downstream cytokines IL-8 and IL-6 in HUVECs.
## 1. Introduction
Low-density lipoproteins (LDLs) exert a key role in transport of esterified cholesterol to tissues. An elevated plasma concentration of cholesterol associated with LDLs (LDL-C) is a primary causal factor in the development of atherosclerotic cardiovascular disease and significantly contributes to the cardiovascular risk [1,2]. The LDL surface contains several apoproteins and amphipathic lipids. Sphingomyelin and phosphatidylcholine are the main phospholipids, and the LDL hydrophobic core contains cholesterol esters and triglycerides [3]. Previous studies have shown that lipoprotein lipids exert a conformational role on apoproteins. In this regard, alterations of the interactions between lipids and apoprotein B100, together with lipid peroxidation, can contribute to functional alterations of LDLs [4,5]. Among the atherogenic modifications of LDLs, the oxidative modification has been mainly investigated as a major risk factor for accelerating atherogenesis [6]. In fact, several studies have demonstrated that oxidized LDLs (ox-LDLs), regardless of the prooxidant agent or stimulus, display alterations of their physical-chemical properties and altered interactions with cell receptors [4,7]. In macrophages, an enhanced uptake of ox-LDLs by scavenger receptors can lead to accumulation of cholesterol ester and formation of foam cells. Using human umbilical vein endothelial cells (HUVECs), it has been demonstrated that ox-LDLs induce the expression of adhesion molecules on the cell surface, an early event in atherogenesis [4,6]. During their life in circulation, LDLs are susceptible to several compositional changes due to lipid transfer proteins, enzymes of lipoprotein metabolism and cell enzymes. Lipolytic modifications of LDLs triggered by cell enzymes such as the group V secretory phospholipaseA2 (PLA2)–secreted by macrophages [8] and by sphingomyelinase (SMase) released by macrophages and endothelial cells, contribute to a higher atherogenicity [9,10].
Among the lipolytic changes of LDLs, an increasing attention is devoted to the key role attributed to the effects of SMase on LDL structural and atherogenic properties [11,12]. SMase is a sphingomyelin-specific form of phospholipase C that catalyze the cleavage of the phosphodiester bond from sphingomyelin, resulting in the production of ceramide and phosphocholine. LDL-derived sphingolipids are strongly implicated in several pathologic conditions, including insulin resistance and cardiovascular events. For instance, recent studies have demonstrated that the susceptibility of LDL particles to aggregate depends on their sphingomyelin content, and that the latter is associated with future cardiovascular death [13]. One mechanism for the LDL aggregation of SMase-treated LDLs focuses on the formation of hydrophobic domains on the surface of the LDL phospholipid monolayer. This occurs due to SMase-catalyzed hydrolysis of sphingomyelin, which accounts for ∼20–$25\%$ of the total LDL phospholipids [14]. A pathophysiological role of the SMase activity is supported by data which demonstrate that this enzyme is secreted by macrophages and endothelial cells in the vicinity of typical atherosclerotic lesional tissue [15]. Therefore, the hydrolysis of LDL-sphingomyelin by secretory SMase is hypothesized to contribute to the formation of atherosclerotic tissue [15]. SMase treatment of LDL promotes the uptake of lipoproteins and accumulation of cholesterol within macrophages [16]. Inflammation plays a crucial role in all phases of the atherosclerotic process, involving both endothelial and immune cells. IL-6 levels were associated with an increased cardiovascular risk, since IL-6 can affect different type of cells involved in lipid processing and plaque formation [17,18].
The aims of the present study were to investigate the effect of SMase treatment on the physical-chemical properties of LDLs and compare their characteristics and biological activities with the most extensively investigated LDL modification, i.e., oxidation.
Moreover, to gain further insights into the atherogenic potential of SMase-treated LDLs, we compared the effects of ox-LDLs and SMase-treated LDLs on an endothelial cell model (HUVECs) by evaluating markers of cell viability, apoptosis, pro-inflammatory status, oxidative stress, and antioxidant defense.
## 2.1. Sphingomyelinase Treatment Induces Physico-Chemical Modification in LDL
Table 1 summarizes the physico-chemical properties of control LDLs, ox-LDLs and SMase-LDLs. The study of lipid peroxidation has shown a significant increase of levels of Thiobarbituric acid reactive substances (TBARS) only in ox-LDLs. The level of ceramide was significantly increased in SMase-treated LDLs, and the level was slightly increased even in ox-LDLs. Apo B100 structural properties in control LDLs and treated LDLs were investigated using the intrinsic fluorescence of the tryptophan residues (Trp) of the apoprotein. A significant decrease of fluorescence intensity was observed in ox-LDLs. Treatment of LDLs with SMase for 24 h also induced a decrease in the fluorescence intensity of the Trp residues, but to a lower extent compared with ox-LDLs (Table 1). The decrease in fluorescence intensity was significant at the highest enzyme concentration (100 mU/mL). These data demonstrate that changes in the lipid components are reflected by conformational changes of the ApoB100 associated with LDLs. Changes in the apoprotein component of SMase-LDL were also investigated by evaluating hyperchromicity at 282 nm. LDL samples showed an increase in absorbance at 282 nm following treatment with SMase. The increase was significant at the highest enzyme concentration (100 mU/mL) (Table 1).
Using the fluorescence emission spectra of the probe Laurdan, we also observed modifications of the LDL physico-chemical properties with a decrease of the value of generalized polarization (GP) in LDL samples incubated with SMase at the higher concentration (GP = 0.42 ± 0.02 in SMase-LDL), compared with LDLs incubated without the enzyme (C-LDL, GP = 0.52 ± 0.01). The analysis of absorbance at 450 nm of LDL samples treated with SMase has shown a significant increase in turbidity, in comparison with untreated LDLs (LDL-C: 0.291 ± 0.012; LDL + SMase 50 mU/mL: 0.545 ± 0.021 AU; LDL + SMase 100 mU/mL: 0.797 ± 0.025 AU). No significant changes of turbidity were observed in ox-LDLs.
## 2.2. Modified LDLs Decreased Cell Viability and Increased Caspase-3 Expression in HUVECs
Figure 1 shows the viability of HUVECs, detected by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay after 24 h of incubation with LDLs treated in different experimental conditions. Viability was not significantly modified in cells incubated with control LDLs with respect to cells incubated without lipoproteins. A decrease of viability was observed in cells incubated with modified LDLs compared to cells incubated in the presence of control LDLs ($p \leq 0.05$). The effect on cell viability was dependent on the LDL concentration. The decrease in cell viability was seen to a greater extent using the highest concentration of ox-LDL (50 µg/mL). A significant decrease was also observed after incubation for 24 h with LDLs treated with the highest concentration of SMase (100 mU/mL).
To evaluate a potential activation of the proteins involved in apoptosis, the expression of the caspase-3 protein in HUVECs was carried out following incubation with normal and modified LDLs. A significant increase of caspase-3 expression was observed in cells incubated with ox-LDLs and with SMase-LDL 100 mU/mL (Figure 2).
## 2.3. Modified LDLs Modulate Oxidative Status and Antioxidant Defense of HUVECs
In order to study whether ox-LDLs and SMase-LDLs are able to induce alterations of the oxidative status of endothelial cells, the intracellular levels of reactive oxygen species (ROS) were compared in HUVECs incubated in different experimental conditions. Incubation of HUVECs for 24 h with 20 µg/mL LDL had no significant effects on the intracellular levels of ROS assessed using the 2′,7′-dichlorodihydrofluorescein diacetate (DCFH2-DA, DCF) probe compared to untreated cells. A higher concentration of 50 µg/mL LDL caused a significant increase in DCF fluorescence (about $60\%$, $p \leq 0.05$) (Figure 3). Similar to ox-LDLs, a significant increase of ROS was also observed in cells incubated with SMase-LDLs ($p \leq 0.05$; Figure 3).
The expression of the antioxidant enzyme Paraoxonase-2 (PON2) was significantly increased in HUVECs incubated with ox-LDLs and SMase-treated LDLs, while the expression of the antioxidant enzyme Superoxide dismutase (SOD 2) was upregulated only after treatment with SMase-LDLs. Our findings suggest that endothelial cells are likely to initiate a response against oxidative stress (Figure 4).
## 2.4. Ox-LDLs and SMase-LDLs Induce a Proinflammatory Response in HUVECs
To further study the molecular mechanisms that can contribute to the alterations of cell viability and to the increase of ROS in HUVECs in response to incubation with modified LDLs, we analyzed the expression of the transcription factor NF-κB and of its downstream proinflammatory cytokines IL-8 and IL-6. The expression of the phosphorylated p65 subunit, which reflects NF-κB activation, was significantly increased in HUVECs in the presence of ox-LDLs and SMase-LDLs at a concentration of 50 µg/mL (Figure 5).
An increased expression of IL-6 and IL-8 was observed in HUVECs treated with either ox-LDLs or SMase-LDLs, with the latter having a greater effect (Figure 6). Accordingly, IL-6 secretion in cellular media confirmed a greater proinflammatory action of SMase-LDL compared to ox-LDL on HUVECs.
## 3. Discussion
The lipid peroxidation of LDLs occurs in vivo and is triggered by different factors [19,20]. The lipid composition changes and the atherogenic properties of ox-LDLs have been previously studied using different cell models [21,22,23], and the effects have been extensively reviewed [24,25]. The uptake of oxidized low-density lipoprotein through scavenger receptors by endothelial cells drives the activation of transcription factors, such as nuclear factor-κB (NF-κB), that evoke proinflammatory adhesion molecule expression in endothelial cells [23]. Among the atherogenic alterations, an increasing attention is devoted to the effects of sphingomyelinase (SMase) released by macrophages and endothelial cells on LDLs [9]. In fact, the plasma levels of sphingomyelin, upon enzymatic hydrolysis by sphingomyelinase yields ceramide, have been shown to correlate with the severity of coronary artery disease [13]. The increase in LDL ceramide levels is considered as a key factor contributing to the aggregation of LDLs within the arterial wall. In fact, biophysical studies have shown that ceramide has a pronounced tendency to self-aggregation. Therefore, the formation of ceramide from sphingomyelin is considered a critical step in atherosclerosis [26]. In our experimental conditions, alterations of the lipid composition and physico-chemical properties of LDLs induced by treatment with SMase have been confirmed by a significant increase in ceramide level compared with untreated LDLs, which is in good agreement with other studies [27]. In addition, we demonstrated an increase of turbidity and a decrease of molecular order on the phospholipid surface of the LDLs in the microenvironment of the probe Laurdan. The effect was dependent on the concentration of SMase and was significant at 100 mU/mL SMase. LDLs treated with SMase also showed alterations of ApoB100 as shown by the decrease in Tryptophan fluorescence and by the increase of hyperchromicity at 282 nm. All these results suggest alterations of ApoB100 structure and a greater exposure of ApoB100 aromatic amino acid residues in LDLs treated by SMase, which is in good agreement with other authors [28].
We confirmed a cytotoxic effect exerted by ox-LDLs on HUVECs, with higher ROS levels and activation of NF-κB in our experimental conditions, which is in agreement with Cominacini et al. [ 23]. Previous studies have shown that ox-LDLs cause an increase of ROS production in different cell models, including fibroblasts and endothelial cells [29,30]. It has been suggested that mitochondria could be a possible source of ROS in endothelial cells following incubation with ox-LDLs. It was also shown that ox-LDLs can activate nitric oxide synthase (eNOS), leading to the activation of c-Jun NH2-terminal kinase (JNK) [31]. In addition, ROS have been proposed to selectively activate phosphorylation of NF-κB via a redox-regulated tyrosine kinase [32].
The effects of SMase-LDLs on HUVEC have not been previously studied. We confirmed that in our experimental conditions, aggregation occurs at the end of incubation between LDLs and SMase. The literature data demonstrate that aggregated LDLs can be internalized by an LDL receptor or other mechanisms involving plasma membrane invaginations. Previous studies have demonstrated uptake of SMase-LDLs mediated by LDL receptors in macrophages [16,33]. In addition, using cytochalasin D during incubations, it has been demonstrated that endocytosis, not phagocytosis, was involved in the internalization of SMase-treated LDLs. Boyanovsky et al. [ 34] have confirmed that the uptake of ceramide-enriched LDLs by human microvascular endothelial cells in a receptor-mediated fashion. Further studies are necessary to investigate the molecular mechanisms of the interactions between SMase-LDLs and HUVEC. However, we demonstrated that SMase-LDLs exert a cytotoxic effect on HUVEC, cause a significant increase of ROS, and stimulate NF-κB activation, as well as the production of IL-6 and IL-8, with respect to unmodified LDLs. Importantly, IL-6 secretion in cellular media confirmed a greater proinflammatory action of SMase-LDLs compared to ox-LDLs on endothelial cells. IL-6 is a pivotal cytokine of innate immunity, modulating a broad set of physiological functions traditionally associated with host defense, immune cell regulation, proliferation, and differentiation [35]. Extensive literature supports the proatherogenic role for IL-6 in cardiovascular disease, and IL-6 inhibition has consequently been proposed as a novel method for vascular protection [36]. Therefore, we can hypothesize that interactions between SMase-LDLs and the plasma membrane of HUVECs could activate cytotoxic mechanisms, increase ROS, and trigger proinflammatory responses mediated by the NF-κB pathway. In addition, the increased expression of IL-8, the most relevant chemokine in the framework of in vivo inflammation, strongly suggests a proinflammatory activity of SMase-LDLs. The effect of SMase-LDLs could be related to alterations of ApoB100 structure and/or to the increase of ceramide levels. Our hypothesis is supported by literature data. A cytotoxic effect was observed from LDL(−) particles isolated from animal models [37]. As previously mentioned, LDL(−) have a higher level of ceramide and show structural alterations of the apoB100 [14]. A potential role in inflammatory signaling exerted by SMase-LDLs has been observed in monocytes with an SMase-induced monocyte arachidonic acid release and cPLA2 activation, accompanied by increased TNF-α secretion [38] We also demonstrated a significant increase of expression of caspase-3 in HUVECs treated with ox-LDLs or SMase-LDLs. The activation of caspases has been previously observed in human coronary artery endothelial cells (HCAEC) incubated with ox-LDLs [39]. Caspase activation can damage the permeability and integrity of the mitochondrial membrane, decreasing the mitochondrial membrane potential and destroying the structure of the mitochondrial membrane. Some hypotheses can be formulated to explain the effect of SMase-LDLs on caspases. We hypothesize that the interactions between SMase-LDLs and HUVECs result in the activation of the expression of the caspase and the potential activation of apoptosis. The effect could be attributed to the higher level of ceramide, as hypothesized by Pettus et al. [ 40]. Other authors have shown that ceramide-rich LDLs are taken up by receptor-mediated mechanisms and can deliver excess ceramide to the cells [34]. The accumulation of LDL-derived ceramide within cells induces apoptosis of HME-1 cells [34]. Ceramide has also been shown to activate reactive oxygen species (ROS), mitochondrial oxidative damage, and apoptosis in vascular cells [41].
PON2 is an antioxidant intracellular enzyme localized in mitochondria, endoplasmic reticulum and in plasma membrane, and is expressed in several cells [42,43,44]. PON2 exerts a protective effect against oxidative damage triggered under different experimental conditions [42,43,44]. A significant increase in PON2 expression was observed in cells incubated with ox-LDLs or SMase-LDLs. An increased expression of PON2 has been previously observed in HUVEC in response to oxidative stress caused by glycation end products such as glycated albumin (GA) and Nε-(carboxymethyl) lysine (CML) [45]. We suggest that the higher expression of PON2 could represent a defense mechanism activated by the cell in response to higher ROS levels. This hypothesis is supported by previous studies on Caco-2 cells with a higher PON2 expression in response to inflammatory agents and to higher expression of NF-κB [46], and further supported by the increased expression of the antioxidant enzyme SOD2.
In conclusion, although ox-LDL and SMase-treated LDLs show different compositional and physico-chemical properties, they trigger ROS increase in HUVECs and increase phosphorylation of NF-κB. We suggest that SMase-LDLs could trigger free radical signaling through activation of one or more of the many enzymatic sources for ROS which are present in almost all cell types and that these free radicals may play a key role in the cellular pathways leading to NF-κB expression.
Our results could have a physiological relevance. Beyond their structural role, it is now clearly established that sphingolipids serve as bioactive signaling molecules to regulate diverse processes including inflammatory signaling and proliferation. An increase of SMase activity has been demonstrated in the plasma of patients affected by dysmetabolic diseases [47,48]. Secretory SMase activity is increased in the serum of patients with type 2 diabetes, chronic heart failure, and acute coronary syndromes [47,48]. In addition to the Mg2+-dependent SMase, a Zn2+-dependent SMase is secreted by macrophages and endothelial cells in the vicinity of typical atherosclerotic lesional tissue [49]. Tissue containing LDLs from atherosclerotic lesions contains 10–50 times more ceramide than intact plasma LDLs within lesional tissue [10,15]. In patients with metabolic syndrome or diabetes, circulating plasma ceramide levels are significantly higher than in normal individuals. Electronegative LDLs show SMase activity, which leads to increased ceramide levels that can produce pro-inflammatory effects and susceptibility to aggregation [50].
## 4. Conclusions
In conclusion, although ox-LDLs and SMase-treated LDLs show different compositional and physico-chemical properties, they trigger ROS increase in HUVEC and increase the phosphorylation of NF-κB. We suggest that alterations of the interactions between SMase-LDLs and cell plasma membrane could trigger free radical signaling through activation of one or more of the many ubiquitarian enzymatic sources of ROS and that these free radicals may play a key role in the cellular pathways leading to NF-κB expression. Moreover, in our experimental conditions SMase-LDLs exert a proinflammatory effect that has not been previously shown.
## 5. Materials and Methods
LDLs from human plasma were purchased from Prospec-TanyTechnoGene Ltd. (Ness-Ziona, Israel). Copper sulphate, methylglyoxal, Hepes, PBS, EDTA, and NaCland sphingomyelinase (SMase) from B. cereus were acquired from Merk Life Science S.r.l.(Milan, Italy). 6-Dodecanoyl-2-Dimethylaminonaphthalene (Laurdan) probe was purchased from Thermo Fisher Scientific (Waltham, MA, USA); Bradford reagent was purchased from Bio-Rad Laboratories S.r.l. ( Segrate (MI), Italy). Amplex Red kit was purchased from Thermo Fisher Scientific (Waltham, MA USA). HUVECs (human umbilical vein endothelial cells) were purchased from Clonetics Corporation (Lonza, Basel, Switzerland) and cultured in a special growth medium for endothelial cells (Endothelial Growth Medium-2-EGM, by Lonza, Basel, Switzerland). Caspase-3 (CAS3), nuclear factorkB p65 phosphorylated (Fosfo-NF-κB p65), actin, vinculin, and GADPH antibodies were acquired from Euroclone (Pero (MI), Italy), while Paraoxonase 2 (PON2) antibody was from Merk Life Science S.r.l. ( Milan, Italy).
## 5.1.1. Oxidation of LDLs
LDL (1 mg/mL) was oxidized by incubation for 24 h at 37 °C with 10 μM copper sulphate according to the literature [51]. At the end of incubation, the oxidation was stopped by addition of EDTA (final concentration: 10 mM). Treated LDLs were dialysated in PBS at pH 7.4, for 24 h at 4 °C.
## 5.1.2. Treatment of LDL Sphingomyelinase
LDLs were treated with SMase from Bacillus cereus. Incubations were conducted using LDLs (1 mg/mL) in 5 mMHepes, 150 mMNaCl for 24 h at 37 °C. Two concentrations of SMase (50 mU/mL and 100 mU/mL) were used [52]. At the end of incubation, the lipolysis was stopped by addition of EDTA (final concentration: 10 mM). The hydrolysis of sphingomyelin was followed using the Amplex Red-phosphorylcholine-coupled SMase fluorescence assay kit. LDL samples were treated with1 unit/mL horseradish peroxidase, 0.1 unit/mL choline oxidase, 4 units/mL alkaline phosphatase, and 50.5 μM Amplex Red Reagent in HEPES buffer. The progress of the sphingomyelin hydrolysis was observed as an increase of resorufin fluorescence using excitation and emission wavelengths, 530–569 nm and 590 nm, respectively. As resorufin is produced in equimolar amounts with the Phosphocholine and ceramide, the fluorescence is proportional to ceramide generation.
A calibration curve was made using increasing concentrations of phosphorylcholine in HEPES buffer in order to measure ceramide formation in LDL treated in the absence or in the presence of SMase [52].
## 5.2. Measurement of LDL Peroxidation
The formation of thiobarbituric acid (TBA) products were evaluated in LDLs treated in different experimental conditions using absorbance at 535 nm [53]. 1,1,3,3-Tetramethoxypropane was used as standard. All measurements were conducted in triplicate. Data are presented as mean ± SD.
## 5.3. Measurement of LDL Aggregation
Measurement of LDL aggregation was analyzed monitoring turbidity at an absorbance of 450 nm. All measurements were conducted in triplicate [28].
## 5.4. Measurement of LDL Hyperchromicity
Hyperchromicity of LDLs treated in the different experimental conditions was evaluated at 280 nm. The hyperchromicity of sample at 280 nm reflects the exposure of chromophoric aromatic amino acid residues due to the unfolding and fragmentation of Apo B [19].
## 5.5. Physico-Chemical Properties of Untreated and Modified LDLs
Laurdan probe was dissolved in a $100\%$ methanol solution (concentration 1 mM) and stored at −20 °C. Briefly, an aliquot of Laurdan was incorporated with LDL (100 μg/mL of protein) for 30 min at 37 °C, using a final probe concentration of 1 μM. [51].
From the emission spectra obtained, the intensities at 440 nm and 490 nm were considered for the calculation of the GP (generalized polarization) parameter through the equation:GP = (I 440 − I 490)/(I 440 + I 490). A high GP value is associated with a lower membrane fluidity and a lower polarity of the microenvironment surrounding the probe [54]. All measurements were conducted in triplicate.
## 5.6. Intrinsic Fluorescence Spectroscopy
The intrinsic fluorescence resulting from the presence of aromatic amino acids (tryptophan and tyrosine) of Apo-B100 was used to study apoprotein properties using emission spectra (excitation wavelength = 295 nm). The position of the maximum emission of tryptophan fluorescence is sensitive to the hydrophobicity of the surrounding environment. The samples of untreated LDLs and modified LDLs were resuspended in 5 mM Hepes buffer, 150 mM NaCl at pH 7.4. The emissions spectra were evaluated using a Perkin Elmer LS 55 spectrofluorimeter. All measurements were conducted in triplicate [51].
## 5.7. Cell Culture
Cryopreserved HUVECs obtained from a pool of three donors were purchased from Clonetics (CC-2519, Lonza, Basel, Switzerland) and maintained in EBM-2 (CC-3156, Lonza) supplemented with SingleQuot Bullet Kit (CC-4176, Lonza) and maintained in a humidified atmosphere of $5\%$ CO2 at 37 °C. Cells were seeded in flasks up to passage five at a density of 5000/cm2 and sub-cultured when they reached 70–$80\%$ confluence. All cell cultures were regularly tested for mycoplasma contamination.
8 × 105 HUVECs were cultured in 6-well plates and allowed to attach overnight before the treatment. Then, cells were exposed to buffer (lipoprotein free, negative control) or incubated with untreated LDLs, oxidized LDLs (ox-LDLs), or LDLs treated with sphingomyelinase (SMase-LDLs). Before incubation with cells, all LDL samples were passed in 0.2 µm filters under sterile conditions. Cells were treated in absence or in the presence of control or modified LDLs (20 µg/mL and 50 µg/mL) for 24 h; cells were then harvested and used for subsequent analysis [55].
## 5.8. Cell Viability Assay
Cell viability was determined using the colorimetric assay based on the reduction of a yellow tetrazolium salt (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium or MTT) to purple formazan crystals by metabolically active cells. HUVECs were cultured into 96 well plates at density of 8 × 103 cells/cm2 [56]. After 24 h, the cells were washed with fresh medium and then treated in absence of lipoproteins, or with normal LDLs, ox-LDLs, and SMase-LDLs. After 24 h of treatment, the MTT solution (1 mg/mL) was added and incubated for an additional 4 h. The obtained product was solubilized with 200 μL of dimethyl sulfoxide (DMSO). Absorbance was measured by a microplate reader (MPT Reader, Invitrogen, Milano, Italy) at the optical density of 540 nm. All measurements were conducted in triplicate.
## 5.9. Intracellular ROS Levels
ROS levels were analyzed using 2′,7′-dichlorodihydrofluorescein diacetate (DCFH2-DA) probe (Sigma-Aldrich, St. Louis, MO, USA). Upon reaching p4, the cells were passed into 96 well plates (density 2 × 104 cells/mL), with a volume of 100 μL of medium for each well. After 24 h, the cells were washed with fresh medium and then treated without lipoproteins or incubated with normal LDLs, oxidized LDLs, or SMase-treated LDLs using LDL concentrations of 20 μg/mL and 50 μg/mL. After 24-h incubation, the medium was removed, and after washing with PBS, the samples were incubated in the dark at 37 °C with 25 μM DCFH2-DA for 30 min. Cells were washed to remove extracellular DCFH2-DA and then phosphate-buffered saline was added. The fluorescence of the cells from each well was measured and recorded on a fluorescence plate reader at λex/λem ($\frac{485}{535}$ nm) (Multi-Mode Microplate Reader SynergyTM HT, Agilent Technologies Italia S.p. A. Cernusco sul Naviglio (MI), Italy) [57]. All measurements were conducted in triplicate.
## 5.10. Western Blot
RIPA buffer (150 mMNaCl, 10 mM Tris, pH 7.2, $0.1\%$ SDS, $1.0\%$ Triton X-100, 5 mM EDTA, pH 8.0) containing a protease inhibitor cocktail (Roche Applied Science, USA) and a phosphatase (Sigma-Aldrich) inhibitor cocktail was used to obtain the cell lysates. Protein concentration was determined using Bradford Reagent (Sigma-Aldrich, Milano, Italy). Total protein extracts (25 μg) were separated by SDS-PAGE and then transferred to a nitrocellulose membrane using the Trans-Blot Turbo™ Transfer system (Bio-Rad). Membranes were then blocked for 1 h at room temperature (RT) in TBS with $0.1\%$ of Tween-20 containing $5\%$ non-fat dried milk, and subsequently incubated overnight at 4 °C with the primary antibodies of interest. All primary antibodies were probed with a secondary horseradish peroxidase (HRP)-conjugated antibody (Vector, USA). Proteins were visualized by ECL and the chemiluminescent signaling acquired using ChemiDoc XRS + System (Bio-RadLaboratories, Hercules, CA, USA) and analyzed using Image J software (Version 1.50i, National Institute of Health, Bethesda, MD, USA).
The primary antibodies used were Caspase-3 (CAS3) (# 9664) (Cell Signaling Tecnologies), Phospho-NF-κB p65 (# 5970), NF-κB p65 (Sc-8008) (Santa Cruz Biotechnologies) Paraoxonase 2 (PON2) (# SAB2700275). Actin, vinculin, and glyceraldehyde-3-phosphate dehydrogenase (GADPH) were used as normalizers.
## 5.11. ELISA Assay
Culture medium from the cell cultures was collected at the end of each incubation, centrifuged at 14.000 × RPM for 20 min, and stored at −80 °C until use. IL-6 concentration was measured using a commercially available enzyme-linked immunosorbent assay (ELISA) kit. IL-6 concentration was determined in triplicate according to the instructions from the manufacturers (#501030, Cayman Chemical Ann Arbor, MI, USA).
## 5.12. RNA Isolation and MRNA Expression
Total RNA was isolated using the NorgenBiotek Kit (#37500, Thorold, ON, Canada), according to the manufacturer’s instructions. RNA was stored at −80 °C until use. RNA amount was determined by spectrophotometric quantification with Nanodrop ONE (NanoDrop Technologies, Wilmington, NC, USA). Total RNA (1000 ng) was reverse-transcribed using TAKARA Kit (PrimeScript™ RT reagent Kit with gDNA Eraser, Cat: RR047A) based on the manufacturer’s instructions. RT-PCR was performed in a Rotor-Gene Q (Qiagen, Hilden, Germany) using TB Green™ Premix Ex Taq™ (Cat: RR420A) in a 10 µL reaction volume. mRNA quantification was assessed using the 2−ΔCT method. β-actin were used as an endogenous control. Each reaction was run in duplicate and always included a no-template control.
The primers’ sequences (written 5′-3′) were: IL-6, Fw: CCAGCTACGAATCTCCGACC, Rv: CATGGCCACAACAATGACG; IL-8, Fw: TCTGCAGCTCTGTGTGTGAAGG, Rv: TGGGGTGGAAAGGTTTGGA; β-actin, Fw: TGCTATCCCTGTACGCCTCT, Rv: GTGGTGGTGAAGCTGTAGCC; SOD2, Fw: GTT GGG GTT GGC TTG GTT TC, Rv: ATA AGG CCT GTT GTT CCT TGC.
## 5.13. Statistical Analysis
Data are presented as mean ± standard deviation (SD) of at least 3 independent experiments. Two-tailed paired Student’s t test was applied to determine differences between samples. p values < 0.05 were considered significant.
## References
1. Ference B.A., Ginsberg H.N., Graham I., Ray K.K., Packard C.J., Bruckert E., Hegele R.A., Krauss R.M., Raal F.J., Schunkert H.. **Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel**. *Eur. Heart J.* (2017) **38** 2459-2472. DOI: 10.1093/eurheartj/ehx144
2. Werner R.M., Pearson T.A.. **LDL-cholesterol: A risk factor for coronary artery disease--from epidemiology to clinical trials**. *Can. J. Cardiol.* (1998) **14** 3B-10B. PMID: 9627535
3. Hevonoja T., Pentikainen M.O., Hyvonen M.T., Kovanen P.T., Ala-Korpela M.. **Structure of low density lipoprotein (LDL) particles: Basis for understanding molecular changes in modified LDL**. *Biochim. Biophys. Acta* (2000) **1488** 189-210. DOI: 10.1016/S1388-1981(00)00123-2
4. Steinberg D., Parthasarathy S., Carew T.E., Khoo J.C., Witztum J.L.. **Beyond cholesterol. Modifications of low-density lipoprotein that increase its atherogenicity**. *N. Engl. J. Med.* (1989) **320** 915-924. PMID: 2648148
5. Sommer A., Prenner E., Gorges R., Stutz H., Grillhofer H., Kostner G.M., Paltauf F., Hermetter A.. **Organization of phosphatidylcholine and sphingomyelin in the surface monolayer of low density lipoprotein and lipoprotein(a) as determined by time-resolved fluorometry**. *J. Biol. Chem.* (1992) **267** 24217-24222. DOI: 10.1016/S0021-9258(18)35752-1
6. Steinbrecher U.P., Witztum J.L., Parthasarathy S., Steinberg D.. **Decrease in reactive amino groups during oxidation or endothelial cell modification of LDL. Correlation with changes in receptor-mediated catabolism**. *Arteriosclerosis* (1987) **7** 135-143. DOI: 10.1161/01.ATV.7.2.135
7. Esterbauer H., Gebicki J., Puhl H., Jurgens G.. **The role of lipid peroxidation and antioxidants in oxidative modification of LDL**. *Free Radic. Biol. Med.* (1992) **13** 341-390. DOI: 10.1016/0891-5849(92)90181-F
8. Plihtari R., Hurt-Camejo E., Oorni K., Kovanen P.T.. **Proteolysis sensitizes LDL particles to phospholipolysis by secretory phospholipase A2 group V and secretory sphingomyelinase**. *J. Lipid Res.* (2010) **51** 1801-1809. DOI: 10.1194/jlr.M003103
9. Marathe S., Schissel S.L., Yellin M.J., Beatini N., Mintzer R., Williams K.J., Tabas I.. **Human vascular endothelial cells are a rich and regulatable source of secretory sphingomyelinase. Implications for early atherogenesis and ceramide-mediated cell signaling**. *J. Biol. Chem.* (1998) **273** 4081-4088. DOI: 10.1074/jbc.273.7.4081
10. Schissel S.L., Jiang X., Tweedie-Hardman J., Jeong T., Camejo E.H., Najib J., Rapp J.H., Williams K.J., Tabas I.. **Secretory sphingomyelinase, a product of the acid sphingomyelinase gene, can hydrolyze atherogenic lipoproteins at neutral pH. Implications for atherosclerotic lesion development**. *J. Biol. Chem.* (1998) **273** 2738-2746. DOI: 10.1074/jbc.273.5.2738
11. Kornhuber J., Rhein C., Muller C.P., Muhle C.. **Secretory sphingomyelinase in health and disease**. *Biol. Chem.* (2015) **396** 707-736. DOI: 10.1515/hsz-2015-0109
12. Marathe S., Kuriakose G., Williams K.J., Tabas I.. **Sphingomyelinase, an enzyme implicated in atherogenesis, is present in atherosclerotic lesions and binds to specific components of the subendothelial extracellular matrix**. *Arterioscler. Thromb. Vasc. Biol.* (1999) **19** 2648-2658. DOI: 10.1161/01.ATV.19.11.2648
13. Ruuth M., Nguyen S.D., Vihervaara T., Hilvo M., Laajala T.D., Kondadi P.K., Gistera A., Lahteenmaki H., Kittila T., Huusko J.. **Susceptibility of low-density lipoprotein particles to aggregate depends on particle lipidome, is modifiable, and associates with future cardiovascular deaths**. *Eur. Heart J.* (2018) **39** 2562-2573. DOI: 10.1093/eurheartj/ehy319
14. Sneck M., Nguyen S.D., Pihlajamaa T., Yohannes G., Riekkola M.L., Milne R., Kovanen P.T., Oorni K.. **Conformational changes of apoB-100 in SMase-modified LDL mediate formation of large aggregates at acidic pH**. *J. Lipid Res.* (2012) **53** 1832-1839. DOI: 10.1194/jlr.M023218
15. Schissel S.L., Tweedie-Hardman J., Rapp J.H., Graham G., Williams K.J., Tabas I.. **Rabbit aorta and human atherosclerotic lesions hydrolyze the sphingomyelin of retained low-density lipoprotein. Proposed role for arterial-wall sphingomyelinase in subendothelial retention and aggregation of atherogenic lipoproteins**. *J. Clin. Investig.* (1996) **98** 1455-1464. DOI: 10.1172/JCI118934
16. Xu X.X., Tabas I.. **Sphingomyelinase enhances low density lipoprotein uptake and ability to induce cholesteryl ester accumulation in macrophages**. *J. Biol. Chem.* (1991) **266** 24849-24858. DOI: 10.1016/S0021-9258(18)54306-4
17. Akita K., Isoda K., Sato-Okabayashi Y., Kadoguchi T., Kitamura K., Ohtomo F., Shimada K., Daida H.. **An Interleukin-6 Receptor Antibody Suppresses Atherosclerosis in Atherogenic Mice**. *Front. Cardiovasc. Med.* (2017) **4** 84. DOI: 10.3389/fcvm.2017.00084
18. Kong P., Cui Z.Y., Huang X.F., Zhang D.D., Guo R.J., Han M.. **Inflammation and atherosclerosis: Signaling pathways and therapeutic intervention**. *Signal Transduct. Target. Ther.* (2022) **7** 131. DOI: 10.1038/s41392-022-00955-7
19. Alouffi S., Faisal M., Alatar A.A., Ahmad S.. **Oxidative Modification of LDL by Various Physicochemical Techniques: Its Probable Role in Diabetes Coupled with CVDs**. *Biomed Res. Int.* (2018) **2018** 7390612. DOI: 10.1155/2018/7390612
20. Hodis H.N., Kramsch D.M., Avogaro P., Bittolo-Bon G., Cazzolato G., Hwang J., Peterson H., Sevanian A.. **Biochemical and cytotoxic characteristics of an in vivo circulating oxidized low density lipoprotein (LDL-)**. *J. Lipid Res.* (1994) **35** 669-677. DOI: 10.1016/S0022-2275(20)41181-2
21. Auge N., Garcia V., Maupas-Schwalm F., Levade T., Salvayre R., Negre-Salvayre A.. **Oxidized LDL-induced smooth muscle cell proliferation involves the EGF receptor/PI-3 kinase/Akt and the sphingolipid signaling pathways**. *Arterioscler. Thromb. Vasc. Biol.* (2002) **22** 1990-1995. DOI: 10.1161/01.ATV.0000043453.21629.3B
22. Brand K., Eisele T., Kreusel U., Page M., Page S., Haas M., Gerling A., Kaltschmidt C., Neumann F.J., Mackman N.. **Dysregulation of monocytic nuclear factor-kappa B by oxidized low-density lipoprotein**. *Arterioscler. Thromb. Vasc. Biol.* (1997) **17** 1901-1909. DOI: 10.1161/01.ATV.17.10.1901
23. Cominacini L., Pasini A.F., Garbin U., Davoli A., Tosetti M.L., Campagnola M., Rigoni A., Pastorino A.M., Lo Cascio V., Sawamura T.. **Oxidized low density lipoprotein (ox-LDL) binding to ox-LDL receptor-1 in endothelial cells induces the activation of NF-kappaB through an increased production of intracellular reactive oxygen species**. *J. Biol. Chem.* (2000) **275** 12633-12638. DOI: 10.1074/jbc.275.17.12633
24. Jiang H., Zhou Y., Nabavi S.M., Sahebkar A., Little P.J., Xu S., Weng J., Ge J.. **Mechanisms of Oxidized LDL-Mediated Endothelial Dysfunction and Its Consequences for the Development of Atherosclerosis**. *Front. Cardiovasc. Med.* (2022) **9** 925923. DOI: 10.3389/fcvm.2022.925923
25. Hajri T.. **Effects of oxidized lipids and lipoproteins on cardiac function**. *Front. Biosci. (Landmark Ed.)* (2018) **23** 1822-1847. DOI: 10.2741/4675
26. Bismuth J., Lin P., Yao Q., Chen C.. **Ceramide: A common pathway for atherosclerosis?**. *Atherosclerosis* (2008) **196** 497-504. DOI: 10.1016/j.atherosclerosis.2007.09.018
27. Guarino A.J., Lee S.P., Tulenko T.N., Wrenn S.P.. **Aggregation kinetics of low density lipoproteins upon exposure to sphingomyelinase**. *J. Colloid Interface Sci.* (2004) **279** 109-116. DOI: 10.1016/j.jcis.2004.06.066
28. Rivas-Urbina A., Rull A., Montoliu-Gaya L., Perez-Cuellar M., Ordonez-Llanos J., Villegas S., Sanchez-Quesada J.L.. **Low-density lipoprotein aggregation is inhibited by apolipoprotein J-derived mimetic peptide D-[113-122]apoJ**. *Biochim. Biophys. Acta Mol. Cell Biol. Lipids* (2020) **1865** 158541. DOI: 10.1016/j.bbalip.2019.158541
29. Maziere C., Meignotte A., Dantin F., Conte M.A., Maziere J.C.. **Oxidized LDL induces an oxidative stress and activates the tumor suppressor p53 in MRC5 human fibroblasts**. *Biochem. Biophys. Res. Commun.* (2000) **276** 718-723. DOI: 10.1006/bbrc.2000.3528
30. Maziere C., Salle V., Gomila C., Maziere J.C.. **Oxidized low density lipoprotein increases RANKL level in human vascular cells. Involvement of oxidative stress**. *Biochem. Biophys. Res. Commun.* (2013) **440** 295-299. DOI: 10.1016/j.bbrc.2013.09.072
31. Gliozzi M., Scicchitano M., Bosco F., Musolino V., Carresi C., Scarano F., Maiuolo J., Nucera S., Maretta A., Paone S.. **Modulation of Nitric Oxide Synthases by Oxidized LDLs: Role in Vascular Inflammation and Atherosclerosis Development**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20133294
32. Morgan M.J., Liu Z.G.. **Crosstalk of reactive oxygen species and NF-κB signaling**. *Cell Res.* (2011) **21** 103-115. DOI: 10.1038/cr.2010.178
33. Gupta A.K., Rudney H.. **Sphingomyelinase treatment of low density lipoprotein and cultured cells results in enhanced processing of LDL which can be modulated by sphingomyelin**. *J. Lipid Res.* (1992) **33** 1741-1752. DOI: 10.1016/S0022-2275(20)41332-X
34. Boyanovsky B., Karakashian A., King K., Giltiay N., Nikolova-Karakashian M.. **Uptake and metabolism of low density lipoproteins with elevated ceramide content by human microvascular endothelial cells: Implications for the regulation of apoptosis**. *J. Biol. Chem.* (2003) **278** 26992-26999. DOI: 10.1074/jbc.M301536200
35. Uciechowski P., Dempke W.C.M.. **Interleukin-6: A Masterplayer in the Cytokine Network**. *Oncology* (2020) **98** 131-137. DOI: 10.1159/000505099
36. Ridker P.M., Rane M.. **Interleukin-6 Signaling and Anti-Interleukin-6 Therapeutics in Cardiovascular Disease**. *Circ. Res.* (2021) **128** 1728-1746. DOI: 10.1161/CIRCRESAHA.121.319077
37. Demuth K., Myara I., Chappey B., Vedie B., Pech-Amsellem M.A., Haberland M.E., Moatti N.. **A cytotoxic electronegative LDL subfraction is present in human plasma**. *Arterioscler. Thromb. Vasc. Biol.* (1996) **16** 773-783. DOI: 10.1161/01.ATV.16.6.773
38. Oestvang J., Bonnefont-Rousselot D., Ninio E., Hakala J.K., Johansen B., Anthonsen M.W.. **Modification of LDL with human secretory phospholipase A2 or sphingomyelinase promotes its arachidonic acid-releasing propensity**. *J. Lipid Res.* (2004) **45** 831-838. DOI: 10.1194/jlr.M300310-JLR200
39. Chen J., Mehta J.L., Haider N., Zhang X., Narula J., Li D.. **Role of caspases in Ox-LDL-induced apoptotic cascade in human coronary artery endothelial cells**. *Circ. Res.* (2004) **94** 370-376. DOI: 10.1161/01.RES.0000113782.07824.BE
40. Pettus B.J., Chalfant C.E., Hannun Y.A.. **Ceramide in apoptosis: An overview and current perspectives**. *Biochim. Biophys. Acta* (2002) **1585** 114-125. DOI: 10.1016/S1388-1981(02)00331-1
41. Matsunaga T., Kotamraju S., Kalivendi S.V., Dhanasekaran A., Joseph J., Kalyanaraman B.. **Ceramide-induced intracellular oxidant formation, iron signaling, and apoptosis in endothelial cells: Protective role of endogenous nitric oxide**. *J. Biol. Chem.* (2004) **279** 28614-28624. DOI: 10.1074/jbc.M400977200
42. Furlong C.E., Marsillach J., Jarvik G.P., Costa L.G.. **Paraoxonases-1, -2 and -3: What are their functions?**. *Chem. Biol. Interact.* (2016) **259 Pt B** 51-62. DOI: 10.1016/j.cbi.2016.05.036
43. Bacchetti T., Sartini D., Pozzi V., Cacciamani T., Ferretti G., Emanuelli M.. **Exploring the role of paraoxonase-2 in bladder cancer: Analyses performed on tissue samples, urines and cell cultures**. *Oncotarget* (2017) **8** 28785-28795. DOI: 10.18632/oncotarget.15674
44. Morresi C., Cianfruglia L., Sartini D., Cecati M., Fumarola S., Emanuelli M., Armeni T., Ferretti G., Bacchetti T.. **Effect of High Glucose-Induced Oxidative Stress on Paraoxonase 2 Expression and Activity in Caco-2 Cells**. *Cells* (2019) **8**. DOI: 10.3390/cells8121616
45. Ravi R., Ragavachetty Nagaraj N., Subramaniam Rajesh B.. **Effect of advanced glycation end product on paraoxonase 2 expression: Its impact on endoplasmic reticulum stress and inflammation in HUVECs**. *Life Sci.* (2020) **246** 117397. DOI: 10.1016/j.lfs.2020.117397
46. Precourt L.P., Seidman E., Delvin E., Amre D., Deslandres C., Dominguez M., Sinnett D., Levy E.. **Comparative expression analysis reveals differences in the regulation of intestinal paraoxonase family members**. *Int. J. Biochem. Cell Biol.* (2009) **41** 1628-1637. DOI: 10.1016/j.biocel.2009.02.013
47. Pan W., Yu J., Shi R., Yan L., Yang T., Li Y., Zhang Z., Yu G., Bai Y., Schuchman E.H.. **Elevation of ceramide and activation of secretory acid sphingomyelinase in patients with acute coronary syndromes**. *Coron. Artery Dis.* (2014) **25** 230-235. DOI: 10.1097/MCA.0000000000000079
48. Gorska M., Baranczuk E., Dobrzyn A.. **Secretory Zn2+-dependent sphingomyelinase activity in the serum of patients with type 2 diabetes is elevated**. *Horm. Metab. Res.* (2003) **35** 506-507. PMID: 12953170
49. Wong M.L., Xie B., Beatini N., Phu P., Marathe S., Johns A., Gold P.W., Hirsch E., Williams K.J., Licinio J.. **Acute systemic inflammation up-regulates secretory sphingomyelinase in vivo: A possible link between inflammatory cytokines and atherogenesis**. *Proc. Natl. Acad. Sci. USA* (2000) **97** 8681-8686. DOI: 10.1073/pnas.150098097
50. Ke L.Y., Chan H.C., Chen C.C., Lu J., Marathe G.K., Chu C.S., Chan H.C., Wang C.Y., Tung Y.C., McIntyre T.M.. **Enhanced Sphingomyelinase Activity Contributes to the Apoptotic Capacity of Electronegative Low-Density Lipoprotein**. *J. Med. Chem.* (2016) **59** 1032-1040. DOI: 10.1021/acs.jmedchem.5b01534
51. Ferretti G., Bacchetti T., Masciangelo S., Bicchiega V.. **Effect of phytosterols on copper lipid peroxidation of human low-density lipoproteins**. *Nutrition* (2010) **26** 296-304. DOI: 10.1016/j.nut.2009.04.015
52. Walters M.J., Wrenn S.P.. **Effect of sphingomyelinase-mediated generation of ceramide on aggregation of low-density lipoprotein**. *Langmuir* (2008) **24** 9642-9647. DOI: 10.1021/la800714w
53. Ferretti G., Rabini R.A., Bacchetti T., Vignini A., Salvolini E., Ravaglia F., Curatola G., Mazzanti L.. **Glycated low density lipoproteins modify platelet properties: A compositional and functional study**. *J. Clin. Endocrinol. Metab.* (2002) **87** 2180-2184. DOI: 10.1210/jcem.87.5.8466
54. Parasassi T., De Stasio G., d’Ubaldo A., Gratton E.. **Phase fluctuation in phospholipid membranes revealed by Laurdan fluorescence**. *Biophys. J.* (1990) **57** 1179-1186. DOI: 10.1016/S0006-3495(90)82637-0
55. Ferretti G., Bacchetti T., Moroni C., Vignini A., Nanetti L., Curatola G.. **Effect of homocysteinylation of low density lipoproteins on lipid peroxidation of human endothelial cells**. *J. Cell. Biochem.* (2004) **92** 351-360. DOI: 10.1002/jcb.20069
56. Sladowski D., Steer S.J., Clothier R.H., Balls M.. **An improved MTT assay**. *J. Immunol. Methods* (1993) **157** 203-207. DOI: 10.1016/0022-1759(93)90088-O
57. Bacchetti T., Campagna R., Sartini D., Cecati M., Morresi C., Bellachioma L., Martinelli E., Rocchetti G., Lucini L., Ferretti G.. *Molecules* (2022) **27**. DOI: 10.3390/molecules27196488
|
---
title: Amino Acid Substitutions at P1 Position Change the Inhibitory Activity and
Specificity of Protease Inhibitors BmSPI38 and BmSPI39 from Bombyx mori
authors:
- Youshan Li
- Meng Wei
- Jie Zhang
- Rui Zhu
- Yuan Wang
- Zhaofeng Zhang
- Changqing Chen
- Ping Zhao
journal: Molecules
year: 2023
pmcid: PMC10004685
doi: 10.3390/molecules28052073
license: CC BY 4.0
---
# Amino Acid Substitutions at P1 Position Change the Inhibitory Activity and Specificity of Protease Inhibitors BmSPI38 and BmSPI39 from Bombyx mori
## Abstract
It was found that silkworm serine protease inhibitors BmSPI38 and BmSPI39 were very different from typical TIL-type protease inhibitors in sequence, structure, and activity. BmSPI38 and BmSPI39 with unique structure and activity may be good models for studying the relationship between the structure and function of small-molecule TIL-type protease inhibitors. In this study, site-directed saturation mutagenesis at the P1 position was conducted to investigate the effect of P1 sites on the inhibitory activity and specificity of BmSPI38 and BmSPI39. In-gel activity staining and protease inhibition experiments confirmed that BmSPI38 and BmSPI39 could strongly inhibit elastase activity. Almost all mutant proteins of BmSPI38 and BmSPI39 retained the inhibitory activities against subtilisin and elastase, but the replacement of P1 residues greatly affected their intrinsic inhibitory activities. Overall, the substitution of Gly54 in BmSPI38 and Ala56 in BmSPI39 with Gln, Ser, or Thr was able to significantly enhance their inhibitory activities against subtilisin and elastase. However, replacing P1 residues in BmSPI38 and BmSPI39 with Ile, Trp, Pro, or Val could seriously weaken their inhibitory activity against subtilisin and elastase. The replacement of P1 residues with Arg or Lys not only reduced the intrinsic activities of BmSPI38 and BmSPI39, but also resulted in the acquisition of stronger trypsin inhibitory activities and weaker chymotrypsin inhibitory activities. The activity staining results showed that BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) had extremely high acid–base and thermal stability. In conclusion, this study not only confirmed that BmSPI38 and BmSPI39 had strong elastase inhibitory activity, but also confirmed that P1 residue replacement could change their activity and inhibitory specificity. This not only provides a new perspective and idea for the exploitation and utilization of BmSPI38 and BmSPI39 in biomedicine and pest control, but also provides a basis or reference for the activity and specificity modification of TIL-type protease inhibitors.
## 1. Introduction
Proteases exist widely in organisms, and participate in the decomposition of proteins to maintain the normal life activities of organisms. Protease inhibitors are the main regulators of protease catalytic activity in vivo, which can bind protease molecules and inhibit their physiological activity, thereby terminating the unwanted proteolytic process [1]. The interaction between protease and inhibitor is highly specific, that is, each protease has its specific inhibitor as its regulatory factor. The location and activity of these protease inhibitors greatly affect the function of the corresponding proteases, and thus determine the fate of various tissues and cells at specific stages of growth and development. The mutation of protease inhibitors will affect their biological effects. The analysis of the key active sites of protease inhibitors will not only deepen people’s understanding of their active mechanism of action, but is also conducive to their bioengineering transformation and application [2].
The silkworm, Bombyx mori, is a silk-spinning insect with important economic value, which has accumulated a large amount of basic research, and has become one of the ideal models of Lepidoptera in the fields of the biochemistry, genetics, and genomics [3,4,5,6]. Unlike mammals, insects lack lymphocytes or immunoglobulins, and serine protease inhibitors are thought to play important roles in insect immunity [7,8,9]. In our previous study, 80 serine protease inhibitor (SPI) genes were identified from the silkworm genome sequence, named BmSPI1 to BmSPI80, respectively. Ten SPI domains were identified from these serine protease inhibitors, including Serpin, Kunitz, Kazal, Trypsin inhibitor-like cysteine-rich (TIL), Amfpi, Bowman-Birk, Antistasin, WAP, Pacifast, and Alpha-macroglobulin [10]. Further studies revealed that serine protease inhibitors BmSPI38 and BmSPI39 have a unique TIL domain, which can inhibit the activity of the virulence protease CDEP-1 from Beauveria bassiana, inhibit the germination of B. bassiana conidia, and thus enhance the antifungal capacity of silkworm [11,12]. Protease inhibitors are abundant in silkworm silk. TIL family protease inhibitors, represented by BmSPI39, can enter the cocoon layer during the process of silk secretion, endow the cocoon with strong anti-microbial protease hydrolysis properties, and provide long-term protection for the development of pupa in the cocoon [13,14].
The TIL domain of most TIL family protease inhibitors in nature usually contains 10 conserved cysteine residues, forming 5 intramolecular disulfide bonds. The disulfide bridge positions are 1–7, 2–6, 3–5, 4–10, and 8–9 [15,16,17]. Such typical TIL-type protease inhibitors mainly inhibit cathepsin, trypsin, chymotrypsin, and other mammalian proteases [18,19,20]. However, our previous studies found that the TIL domain of BmSPI38 and BmSPI39 lack the second and sixth cysteine residues, which can strongly inhibit the activities of microbial proteases such as subtilisin, proteinase K, and *Aspergillus melleus* protease, but have no inhibitory effect on trypsin and chymotrypsin [11,12]. It was suggested that the absence of Cys2nd and Cys6th may be the important reason for them to obtain inhibitory activity against microbial proteases. Cys2nd and Cys6th were introduced into the corresponding sites of BmSPI38 and BmSPI39 by site-directed mutagenesis. The results showed that the introduction of cysteines not only affected the polymerization state of BmSPI38 and BmSPI39, but also significantly reduced their inhibitory activity against microbial proteases [21]. Unfortunately, the introduction of Cys2nd and Cys6th did not change the inhibitory specificity of BmSPI38 and BmSPI39. By systematically summarizing the sequence characteristics and activities of reported TIL family protease inhibitors, it is found that the inhibitory specificity of these inhibitors may have a certain rule. The inhibitory activity and specificity of TIL family protease inhibitors may be determined jointly by the two missing cysteines (Cys2nd and Cys6th), and the physicochemical properties of P1 and P1′ residues in the reactive center [21]. However, the above speculation still needs more experimental evidence to support it.
It is well known that small-molecule serine protease inhibitors bind to proteases as substrate analogues, and at the same time the peptide bond in their reactive center is cleaved. Previous studies have shown that P1 residues in the reactive center of serine protease inhibitors largely determine their inhibitory specificity, and any change in P1 residues may affect the specificity and inhibitory ability of the inhibitor [22,23,24,25,26]. So far, the activity and function of BmSPI38 and BmSPI39 have been relatively clear, but the mechanism of action is not fully understood, and the key sites affecting their inhibitory activity and specificity need to be further explored.
In this study, mutant proteins were obtained by site-directed mutagenesis and prokaryotic expression techniques, and the effects of amino acid substitutions at the P1 position on the inhibitory activity and specificity of protease inhibitors BmSPI38 and BmSPI39 were investigated in vitro. This was carried out in order to provide a basis or reference for the inhibitory activity and specificity modification of TIL family protease inhibitors, and to promote the production and application of such inhibitors.
## 2.1. BmSPI38 and BmSPI39 Have Inhibitory Activity against Elastase
It was found that elastase has a strong hydrolysis ability for silk fibroin protein, and its hydrolysis ability was significantly stronger than that of trypsin, chymotrypsin, papain, collagenase, and alkaline protease, which may be due to the large number of hydrolysis sites of elastase in silk fibroin protein [27,28,29,30]. To explore whether BmSPI38 and BmSPI39, which are highly expressed in the silk glands of B. mori, have elastase inhibitory activity, an in-gel activity staining technique was used to examine the inhibitory activity of recombinant BmSPI38 and BmSPI39 proteins towards porcine pancreatic elastase. Three strong inhibitory-active bands were detected for both recombinant BmSPI38 and BmSPI39, indicating that they could strongly inhibit elastase activity (Figure 1A,B). This is the first time that the inhibitory activity of silkworm protease inhibitors against elastase has been defined.
## 2.2. Design, Expression Vector Construction, and Prokaryotic Expression of the P1 Mutants of BmSPI38 and BmSPI39
As shown in Figure 2A, the TIL domains of BmSPI38 and BmSPI39 contain eight conserved cysteines that form four intramolecular disulfide bridges. In contrast to typical TIL-type protease inhibitors, BmSPI38 and BmSPI39 lack the second and sixth cysteines. It was noted that Cys2nd and Cys6th formed exactly a disulfide bond bridge in the typical TIL domain [15,16,17]. The putative P1 residues in the reactive centers of BmSPI38 and BmSPI39 are Gly54 and Ala56, respectively. In order to explore the effects of P1 residues on the inhibitory activity and specificity of BmSPI38 and BmSPI39, saturation mutagenesis was performed on Gly54 in BmSPI38 and Ala56 in BmSPI39 by utilizing site-directed mutagenesis (Figure 2B). Based on amino acid properties, The P1 residues in BmSPI38 and BmSPI39 were replaced by acidic (Glu and Asp), basic (Arg, Lys and His), small polar neutral (Cys, Ser and Thr), small non-polar (Ala/Gly, Pro, and Val), larger polar neutral (Asn, Gln and Tyr), and larger non-polar amino acids (Met, Leu, Ile, Phe, Trp), respectively.
To obtain active recombinant mutant proteins for subsequent studies, P1-mutant expression vectors of BmSPI38 and BmSPI39 were transformed into E. coli Origami 2(DE3) and BL21(DE3) cells for induced expression, respectively. The P1 mutant proteins recombinantly expressed in E. coli cells were separated and detected by $16.5\%$ reducing SDS-PAGE. The results showed that the P1 mutant proteins of BmSPI39 were mainly expressed in soluble form in BL21(DE3) cells, and obvious protein expression bands were detected at the size of its monomer (about 9.3 kDa) and dimer (about 18.6 kDa) (Figure 2C). All P1 mutant proteins of BmSPI38 were detected in the supernatant of Origami 2(DE3) cell lysate, and their apparent molecular weights were basically consistent with their monomers (about 7.5 kDa) and dimers (about 15 kDa) sizes (Figure 2D). These results indicated that folded or functional P1 mutants of BmSPI38 and BmSPI39 could be obtained by prokaryotic expression technology.
## 2.3. Inhibition Activity of the P1 Mutants of BmSPI38 against the Serine Protease
To investigate the effect of amino acid substitutions at the P1 position on the inhibitory activity and specificity of BmSPI38, we analyzed the inhibitory activity of these mutants against subtilisin, elastase, trypsin, and chymotrypsin using in-gel activity staining of protease inhibitor (Figure 3). Combined with the relative expression levels of the mutant proteins in E. coli cells (Figure 3, CB), the inhibitory activities of these mutants against different serine proteases were preliminarily compared according to the strength of the inhibitory-active bands. All mutants, except G54C, G54I, and G54W, showed inhibitory activities against subtilisin. All the mutants exhibited inhibitory activity against elastase, but the inhibitory activities of G54I and G54W were extremely weak. The inhibitory activity of G54C was only detected against elastase, but not against subtilisin. The inhibitory activities of G54Q, G54S, G54T, and G54A against subtilisin and elastase were significantly stronger than those of wild-type BmSPI38. The inhibitory activities against subtilisin were roughly G54C, G54I, G54W << G54H, G54P, G54V < wild type, while the inhibitory activities against elastase were roughly G54I, G54W << G54P, G54V, G54H < G54C, G54D < wild type. When P1 residues were replaced with strong basic amino acids Arg or Lys, G54R and G54K not only retained strong inhibitory activities against subtilisin and proteinase K, but also gained inhibitory activities against trypsin. The inhibitory activity of G54K towards trypsin was stronger than that of G54R. All mutants except G54R and G54K failed to inhibit trypsin activities. None of the mutants showed an inhibitory activity against chymotrypsin. These results indicate that the replacement of P1 residues not only affects the intrinsic activities of BmSPI38 (the inhibitory activities against subtilisin and elastase), but also changes its inhibitory specificity by replacing Gly with Arg or Lys.
## 2.4. Inhibition Activity of the P1 Mutants of BmSPI39 against the Serine Protease
Similarly, the effects of amino acid replacements at the P1 site on the intrinsic activity and inhibitory specificity of BmSPI39 were explored by using in-gel activity staining technology (Figure 4). The results showed that all mutant proteins could inhibit the activities of subtilisin and elastase, but not chymotrypsin. Overall, the inhibitory activities of A56Q, A56S, and A56T against subtilisin and elastase were significantly stronger than those of wild-type BmSPI39, while the inhibitory activities of A56D, A56P, A56V, A56I, and A56W were significantly weaker than those of wild-type BmSPI39. Although A56Y, A56M, and A56L showed little difference in elastase inhibitory activity compared to wild-type BmSPI39, their subtilisin inhibitory activity was significantly stronger than wild-type BmSPI39. When P1 residues were replaced by Arg or Lys, the mutant proteins not only retained strong inhibitory activities against subtilisin and elastase, but also acquired the trypsin inhibitory activity. Similar to the results of BmSPI38, BmSPI39(A56K) showed stronger inhibitory activity against trypsin than BmSPI39(A56R). In conclusion, the replacements of P1 residues not only greatly affect the intrinsic activity of BmSPI39, but also change the inhibitory specificity of the mutants when P1 residues are replaced with strong basic amino acids.
## 2.5. The Replacement of P1 Residue with Lysine or Arginine Enables BmSPI38 and BmSPI39 to Obtain Trypsin Inhibitory Activity
To further confirm the inhibitory activities of the mutant proteins with a strong basic amino acid at the P1 position against serine proteases represented by trypsin, recombinant BmSPI38, BmSPI38(G54R), BmSPI38(G54K), BmSPI39, BmSPI39(A56R), and BmSPI39(A56K) were purified by immobilized-nickel affinity chromatography. In addition to the monomer forms of wild-type and mutant proteins, the dimers, trimers, and a small amount of higher-order multimeric forms were also detected in SDS-PAGE (Figure 5A). Protease inhibition assays confirmed that BmSPI38(G54R), BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) not only retained strong inhibitory activities against subtilisin, proteinase K, and elastase, but also obtained strong inhibitory activities against trypsin (Figure 5B,C). *In* general, the mutants with Lys at P1 have stronger inhibitory activity against trypsin than that of Arg. In-gel activity staining of P1 mutant proteins further confirmed the inhibitory activity of BmSPI38(G54R), BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) against trypsin (Figure 5D). Notably, BmSPI38(G54R) and BmSPI38(G54K) also achieved a certain degree of chymotrypsin inhibitory activities compared with wild-type BmSPI38 (Figure 5B). Unexpectedly, BmSPI39 also showed weak chymotrypsin inhibitory activity, which was somewhat enhanced by alkaline amino acid substitution (Figure 5C).
## 2.6. Comparison of the Inhibitory Ability of Mutant BmSPI38 Proteins to Different Serine Proteases
To further compare the intrinsic inhibitory activity of P1 mutants of BmSPI38, different molar concentrations of protease inhibitors were incubated with subtilisin, proteinase K, and elastase, respectively, and the residual enzyme activities were measured. With the increase in BmSPI38, BmSPI38(G54R), and BmSPI38(G54K) concentrations, the residual enzyme activities of subtilisin, proteinase K, and elastase gradually decreased, indicating that all of them could strongly inhibit the activities of the above three proteases (Figure 6A,C,E). Overall, the replacement of Gly at the P1 site with Arg or Lys resulted in a significantly reduced inhibition of BmSPI38 against subtilisin (Figure 6A,B), proteinase K (Figure 6C,D), and elastase (Figure 6E,F). Among them, the inhibitory capacity of BmSPI38(G54R) on subtilisin, proteinase K, and elastase was significantly lower than that of BmSPI38(G54K). At the same time, we also compared the ability of BmSPI38 P1 mutants to inhibit trypsin and chymotrypsin. As the molar ratio of inhibitor to protease increased, BmSPI38 did not show any inhibition towards trypsin and chymotrypsin (Figure 6G,I). BmSPI38(G54R) and BmSPI38(G54K) obtained strong trypsin inhibitory activity (Figure 6G) and weak chymotrypsin inhibitory activity (Figure 6I). The inhibitory capacity of BmSPI38(G54K) towards trypsin was significantly stronger than that of BmSPI38(G54R), while its inhibitory capacity towards chymotrypsin was significantly weaker than that of BmSPI38(G54R) (Figure 6G–J). The above results suggested that replacing the P1 residue with a strong basic amino acid could not only reduce the intrinsic activity of BmSPI39, but also alter its inhibitory specificity.
## 2.7. Comparison of the Inhibitory Ability of Mutant BmSPI39 Proteins to Different Serine Proteases
To further investigate the effect of replacing Ala at the P1 position with a strong basic amino acid on the intrinsic activity of BmSPI39, the protease inhibitors with different molar concentrations were incubated with subtilisin, proteinase K, or elastase, respectively, and the residual enzyme activities were determined. With the increase in the molar ratio of inhibitor to protease, the residual enzyme activities of subtilisin (Figure 7A), proteinase K (Figure 7C), and elastase (Figure 7E) gradually decreased, indicating that BmSPI39, BmSPI39(A56R), and BmSPI39(A56K) could strongly inhibit the activities of the above three proteases. The inhibitory activity of BmSPI39(A56R) towards the above three proteases was significantly lower than that of BmSPI39(A56K), while the activity of BmSPI39(A56K) was significantly lower than that of wild-type BmSPI39 (Figure 7B,D,F). Meanwhile, we also compared the ability of BmSPI39, BmSPI39(A56R), and BmSPI39(A56K) to inhibit trypsin and chymotrypsin. Wild-type BmSPI39 had no trypsin inhibitory activity, but showed very weak chymotrypsin inhibitory activity. After replacement of Ala 56 with Arg or Lys, the mutant protein of BmSPI39 acquired strong trypsin inhibitory activity and weak chymotrypsin inhibitory activity (Figure 7G–J). BmSPI39(A56K) showed significantly stronger inhibition against trypsin than BmSPI39(A56R), but significantly weaker inhibition against chymotrypsin than BmSPI39(A56R) (Figure 7G–J). The results presented above indicated that substitution of P1 residues with strong basic amino acids not only reduced the intrinsic activity of BmSPI39, but also enabled the mutant protein to acquire the inhibitory activity against trypsin and chymotrypsin.
## 2.8. P1 Mutants of BmSPI38 and BmSPI39 Have Extremely High Acid–Base and Thermal Stability
Previous studies have confirmed that wild-type BmSPI38 and wild-type BmSPI39 have extremely high acid–base stability and thermal stability [11,12]. To explore the effect of P1 residue substitution on the physicochemical properties of BmSPI38 and BmSPI39, BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) treated with different pH or temperature were separated by alkaline Native PAGE and stained for activity in the gel. The results showed that BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) had no significant changes in the strength of the active bands against trypsin in the range of pH 3 to 11, indicating that they all had extremely high acid–base stability (Figure 8A–C). The inhibitory activity of BmSPI38(G54K) did not change significantly after treatment at 37–90 °C, but decreased slightly after treatment at 100 °C for 10 min (Figure 8D). BmSPI39(A56R) and BmSPI39(A56K) still maintained strong inhibitory activities against trypsin after treatment at 37–100 °C (Figure 8E,F). The above results showed that BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) have extremely high thermal stability.
## 3. Discussion
Elastase is a serine protease with broad specificity that hydrolyzes connective tissue components such as elastin, proteoglycan, fibronectin, and collagen I, II, III, and IV, etc. Elastin is the major component of elastic fibers. Elastic fibers mainly exist in ligaments and vascular walls, endowing tissues with elasticity and tensile capacity, and are important components of tissues and organs such as skin, lungs, large arteries, and ligaments. The excessive production of elastase can lead to different degrees of tissue damage, and can cause or aggravate emphysema [31,32], acute respiratory distress syndrome [33], pancreatitis [34], rheumatoid arthritis [35,36], chronic bronchitis [37], nephritis [38], atherosclerosis [39,40], fatty liver [41], autoimmune diabetes [42], and other diseases, which seriously endanger human health. Elastase can also destroy collagen fibers and the basement membrane layer of tissues, thus causing cancer [43,44]. In addition, elastase can hydrolyze the elastin fibers of the skin, leading to skin wrinkling or sagging, and inhibiting the activity of elastase can prevent skin aging [45,46]. Elastase inhibitors can effectively inhibit the activity of elastase, and have important application value in the research and development of anti-inflammation, anti-tumor, and anti-skin aging drugs [47,48,49,50].
Silk protein has broad application prospects in many fields due to its unique characteristics of biocompatibility, biodegradability, self-assembly, mechanical stability, controllable structure, and morphology [51,52]. The silk cocoon extracts of Antheraea assamensis, B. mori, and Philosamia ricini have a certain inhibitory effect on porcine pancreatic elastase [50]. The treatment of ultraviolet-irradiated human skin fibroblasts with cocoon silk extracts can not only down-regulate the gene expressions of proinflammatory cytokines and matrix metalloproteinase-1, but also enhance the production of collagen, thus preventing skin photoaging caused by ultraviolet radiation [50]. Studies have shown that there are abundant protease inhibitors in B. mori silk, and TIL protease inhibitors represented by BmSPI39 and BmSPI38 enter the cocoon layer during the process of silk secretion, which endows the cocoon with strong anti-microbial protease hydrolysis properties [13,14,53,54]. The present study confirmed that BmSPI38 and BmSPI39 can strongly inhibit the activity of porcine pancreatic elastase, which was the first time the inhibitory activity of silkworm protease inhibitors against elastase was clarified (Figure 1). We speculated that the anti-elastase activity of cocoon silk extract might be closely related to the abundance of BmSPI38 and BmSPI39 in cocoon silk. As potent inhibitors of elastase, BmSPI38 and BmSPI39 not only provide a powerful tool for studying the relationship between the structure and function of elastase, but also have important research value in the fields of biomedicine and skin care. However, as a potential template for anti-inflammatory and anti-aging leading drugs, in vivo evaluation is essential.
It is found that BmSPI38 and BmSPI39 are very different from typical TIL protease inhibitors in structure and activity [11,12]. The second and sixth cysteines are absent in the TIL domain of BmSPI38 and BmSPI39. They cannot inhibit the activities of mammalian proteases such as trypsin and chymotrypsin but have strong microbial protease inhibitory activities. BmSPI38 and BmSPI39 with unique structure and activity may be good models for studying the relationship between the structure and function of small-molecule TIL-type protease inhibitors, which can provide a basis or reference for the activity and specificity modification of such inhibitors, and promote the production and application of TIL-type inhibitors. The simultaneous introduction of conserved Cys2nd and Cys6th resulted in a dramatic decrease in the inhibitory activities of BmSPI38 and BmSPI39 against subtilisin and proteinase K, but failed to alter their inhibitory specificity [21]. Obviously, there should be other amino acid sites involved in determining the inhibitory specificity of BmSPI38 and BmSPI39. Studies have shown that the P1 residue in the reactive center is usually one of the key sites that determines the inhibitory specificity of serine protease inhibitors [22,23,24,25,26]. In this study, the P1 residues of BmSPI38 and BmSPI39 were replaced by amino acids with different properties by site-directed mutagenesis, in order to explore the effect of P1 sites on the inhibitory activity and specificity of BmSPI38 and BmSPI39.
Studies have shown that different P1 residues may affect the conformation of protease inhibitors [55,56,57]. However, the P1 mutants of BmSPI38 and BmSPI39 still had inhibitory activity. This study found that all mutants of BmSPI38 and BmSPI39 could inhibit the activity of elastase. Except for BmSPI38(G54C), other mutants of BmSPI38 and BmSPI39 retained inhibitory activity against subtilisin (Figure 3 and Figure 4). This may reflect the broad specificity of elastase and subtilisin because the pockets of their active sites are large enough. Studies have shown that B. mori fungal protease inhibitor-F (BmFPI-F) has similar sequence characteristics and inhibitory activities with BmSPI38 and BmSPI39, and only has one TIL domain containing eight cysteine residues, which can inhibit the activities of microbial proteases such as subtilisin, proteinase K, and A. melleus protease [58]. When the P1 residues (Thr29) of BmFPI-F were replaced by six different amino acids (Glu, Arg, Gly, Leu, Met, and Phe), all the mutant proteins still retained the ability to inhibit subtilisin [59]. The saturation mutation of Met at the P1 position of Streptomyces subtilisin inhibitor (SSI) was carried out, and all mutants also showed different degrees of subtilisin inhibitory activity [60]. These findings are generally consistent with our results. Although almost all mutant proteins retained the inhibitory activities against subtilisin and elastase, the P1 residue substitution could greatly affect the strength of the intrinsic activity of BmSPI38 and BmSPI39. *In* general, when Gly54 in BmSPI38 and Ala56 in BmSPI39 were replaced with Gln, Ser, or Thr, their inhibitory activities against subtilisin and elastase were significantly enhanced. However, replacing P1 residues in BmSPI38 and BmSPI39 with Ile, Trp, Pro, or Val severely weakened their inhibitory activities against subtilisin and elastase. It has been reported that SSI mutants with P1 residues of polar amino acids Gln, Ser, or Thr have extremely strong subtilisin inhibitory activity, while mutants with P1 residues of Pro, Ile, Gly, or Val have weak inhibitory activity against subtilisin [60]. The replacement of Thr at the P1 position of BmFPI-F with Gly resulted in a greatly reduced [56] potency against subtilisin [59], which was exactly verified by the fact that replacing Gly at P1 with Thr could enhance the inhibitory activity of BmSPI38 against subtilisin.
Trypsin, chymotrypsin, and elastase are serine proteases with high sequence and structural similarity, but with different substrate specificity [61]. Trypsin is one of the endopeptidases that selectively hydrolyzes the peptide bond formed by the carboxyl group of the basic amino acid Arg or Lys in proteins. This is because trypsin has an open acidic S1 pocket, and the conserved Asp189 at the bottom of the S1 pocket can form a salt-bridge interaction with arginine or lysine at the P1 position of the substrate [62]. It has been shown that serine protease inhibitors whose P1 residues in the reactive center are a basic amino acid tend to inhibit trypsin. CmPI II is a Kazal protease inhibitor isolated from Caribbean snail Cenchritis muricatus, which can inhibit not only trypsin activity, but also subtilisin and elastase activity [24]. The substitution of Arg at the P1 site of CmPI II with Ala not only abolished its trypsin inhibitory activity, but also increased its inhibitory activity against subtilisin and elastase [24]. SSI mutants with P1 residues replaced by Arg and Lys obtained inhibitory activity against trypsin, while mutants with Tyr and Trp gained inhibitory activity against chymotrypsin [63]. The winged bean chymotrypsin inhibitors (WCI) belong to the Kunitz family, and replacing Leu at P1 with Arg can transform WCI(L56R) into a potent trypsin inhibitor [22]. This rule also applies to TIL-type protease inhibitors. In this study, it was found that replacing the P1 residues of BmSPI38 and BmSPI39 with Arg or Lys could not only reduce their inhibitory activities against subtilisin and elastase, but also enable the mutant proteins to obtain strong trypsin inhibitory activity and weak chymotrypsin inhibitory activity (Figure 5, Figure 6 and Figure 7). Egf1.0, a TIL-type protease inhibitor in Microplitis demolitor bracovirus, has only eight conserved cysteines in the TIL domain. The mutation of the P1 residue of Egf1.0 from Arg to Ala resulted in the loss of its inhibitory activity against prophenoloxidase activating proteinase-3 (PAP3, belonging to trypsin) [64]. Cotesia vestalis teratocytes can secrete a TIL-type protease inhibitor CvT-TIL, which can strongly inhibit the activation of prophenoloxidase in the hemolymph of the host insect by interacting with PAP3 of the phenoloxidase (PO) cascade pathway [65]. CvT-TIL also has a TIL domain containing eight conserved cysteine residues, and its P1 residue is Arg, which can not only inhibit the activity of subtilisin, but also inhibit elastase and trypsin to some extent [65]. It should be pointed out that BmSPI38(G54R), BmSPI38(G54K), BmSPI39(A56R), BmSPI39(A56K), and CvT-TIL have similar sequence characteristics. They all have a TIL domain containing eight cysteines, and P1 residues are strongly basic amino acids. BmSPI38(G54R), BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) all showed much better inhibition of trypsin, subtilisin, and elastase than CvT-TIL. Can BmSPI38(G54R), BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) inhibit the PO cascade in insects? *What is* the specific mechanism by which they inhibit the PO cascade? Can P1 mutants of BmSPl38 and BmSPl39 be used as target molecules to improve the virulence of biopesticides? All of these scientific questions need to be answered urgently.
It should be noted that the mutant proteins of BmSPI38 and BmSPI39 cannot be excluded from acquiring other new activities and functions due to the sensitivity of in-gel activity staining of protease inhibitor, and the limited types of proteases tested. For example, no inhibitory bands of BmSPI38(G54R), BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) against chymotrypsin were detected by in-gel activity staining, while the protease inhibition assays confirmed that the above four mutants had weak inhibitory activities of chymotrypsin. Because BmSPI38(G54R) has relatively weak inhibitory activity against trypsin, only the physicochemical properties of BmSPI38(G54K), BmSPI39(A56R), and BmSPI39(A56K) with strong activity were investigated in this study. Similar to the wild-type BmSPI38 and BmSPI39 [11,12], the mutants with Arg or Lys at the P1 position have extremely high acid–base stability and thermal stability, which may be related to the abundant disulfide bonds in BmSPI38 and BmSPI39.
The results presented above indicate that the amino acid properties of P1 residues play an important role in the inhibitory activity and specificity of BmSPl38 and BmSPl39, which may be related to the conformational changes around the reactive center. Future studies should be carried out to obtain more mutants, reveal the structure–function relationship between BmSPl38, BmSPl39, and their target proteases, and obtain more potent and specific inhibitors using BmSPl38 and BmSPl39 as scaffolds for biomedicine and agroforestry pest control.
## 4.1. Escherichia coli Strains, Plasmids, and Reagents
E. coli Trans-T1, BL21(DE3), and Origami 2(DE3) were purchased from TransGen Biotech (Beijing, China), Sangon Biotech (Shanghai, China) and Invitrogen (Carlsbad, CA, USA), respectively. TransStart® FastPfu DNA polymerase (FastPfu), TransStart® FastPfu Fly DNA polymerase (FastPfu Fly) and EasyPfu DNA Polymerase (EasyPfu) were purchased from TransGen Biotech. Proteinase K from *Tritirachium album* limber and elastase from the porcine pancreas were purchased from Roche (Mannheim, Germany) and Sangon Biotech, respectively. Subtilisin A from Bacillus licheniformis, trypsin and α-chymotrypsin from the bovine pancreas, N-acetyl-D,L-phenylalanine-β-naphthylester (APNE) and Fast Blue B Salt were purchased from Sigma-Aldrich (St. Louis, MO, USA). Fluorescein isothiocyanate (FITC)-labeled casein was purchased from Thermo Fisher Scientific (Waltham, MA, USA). Recombinant expression plasmids BmSPI38-p28 and BmSPI39-p28 were preserved by the College of Biological Science and Engineering, Shaanxi University of Technology.
## 4.2. Expression Vector Construct of the P1 Mutants
According to the manual of the QuikChange II XL Site-Directed Mutagenesis Kit (Agilent, Santa Clara, CA, USA), 19 pairs of mutagenic primers were designed for site-directed saturation mutagenesis at the P1 position of BmSPI38 and BmSPI39, respectively. The templates, desired mutations, DNA polymerases, and primer sequences involved in P1-site mutations of BmSPI38 and BmSPI39 are shown in Table 1 and Table 2, respectively. BmSPI38-p28 and BmSPI39-p28 recombinant expression plasmids were transferred into dam+ E. coli strain Trans1-T1. After overnight culture at 37 °C 220 r/min, methylated BmSPI38-p28 and BmSPI39-p28 plasmids were extracted again. Then, the BmSPI38-p28 or BmSPI39-p28 plasmid was used as a template for PCR amplification with FastPfu DNA polymerase to introduce target mutations. The PCR products were purified using EasyPure® PCR Purification Kit (TransGen Biotech) or EasyPure® Quick Gel Extraction Kit (TransGen Biotech), and then digested with the Dpn I restriction endonuclease (Thermo Fisher Scientific) at 37 °C for 30 min to completely remove the methylated template DNA. The Dpn I digested products were transformed into Trans1-T1 competent cells, and the monoclones were selected for sequencing verification. As shown in Table 1 and Table 2, the mutants that failed in the first round of PCR amplification can be obtained by changing the DNA polymerases (FastPfu Fly or EasyPfu) or templates (the mutant expression plasmid that has been successfully constructed) in the PCR. Finally, all the mutant expression vectors were constructed.
## 4.3. Expression and Purification of the Mutants
The expression plasmids of the P1 mutants of BmSPI38 and the expression plasmids of the P1 mutants of BmSPI39 were transferred into E. coli Origami 2(DE 3) and BL21(DE3) competent cells for induced expression, respectively. When the OD600 of the culture reached 0.6–1.0, protein expression was induced using IPTG at a final concentration of 0.2 mmol/L at 37 °C for 5 h or at 16 °C for 20 h. The E. coli cells were harvested by centrifugation at 6000× g for 30 min, washed, and suspended with binding buffer (20 mmol/L Tris-HCl, 500 mmol/L NaCl, pH 7.9). The E. coli cells were lysed by sonication, and bacterial precipitates and supernatants were collected by centrifugation at 16,000× g for 30 min. The expressions of mutant proteins were analyzed by $16.5\%$ SDS-PAGE. The mutated proteins were purified with Ni2+-NTA (nitrilotriacetic acid) affinity chromatography (Sangon Biotech, Shanghai, China) as described for the wild-type proteins [11,12]. The lysate supernatant was loaded onto a 1 mL Ni2+-NTA affinity chromatography column. It was then washed and eluted sequentially by binding buffer supplemented with 20, 50, 100, and 400 mmol/L imidazole. The eluted fraction enriched with the target protein was pooled, and the imidazole was removed by over-night dialysis at 4 °C in binding buffer. The second round of immobilized-nickel affinity chromatography was performed. The purified mutant proteins were finally collected and dialyzed to 10 mmol/L PBS buffer (pH 7.4) for activity detection.
## 4.4. In-Gel Activity Staining of Protease Inhibitor
In-gel activity staining of protease inhibitor was performed as previously described, with a slight modification [11,66]. After separating using a $10\%$ alkaline Native PAGE, the gels were placed in 5 mg/mL elastase, 0.07 mg/mL subtilisin A, 0.07 mg/mL proteinase K, 0.07 mg/mL trypsin, or 0.07 mg/mL chymotrypsin solution, and incubated in the dark for 30 min at 37 °C, with shaking at 45 rpm. After recovering the protease solution, the gels were washed with ddH2O, and then were allowed to stand in the dark at 37 °C for 30 min. The gels were stained for 15 min at 37 °C in the dark with a mixture of substrate (20 mg APNE dissolved in 10 mL of N,N′-dimethylformamide) and a staining solution (100 mg Fast Blue B Salt dissolved in 100 mL of 0.1 mol/L pH 8.0 Tris-HCl buffer containing 20 mmol/L CaCl2) at a ratio of 1:10. The gels were stained fuchsia due to the diazotization-coupling reaction of β-naphthol that was produced by protease hydrolyzed substrates (APNE) on the gel. Due to protease inhibition, the positions of protease inhibitors will not be stained and will be shown as white bands.
## 4.5. Protease Inhibition Assays
Depending on the hydrolytic capacity of different serine proteases to FITC-casein substrate, 3 pmol of subtilisin, 3 pmol of proteinase K, 750 pmol of elastase, 15 pmol of trypsin, or 15 pmol of chymotrypsin were used per reaction (per well) in the protease inhibition assay. To determine whether P1 residue replacement could alter the inhibitory specificity of BmSPI38 and BmSPI39, a relatively high dose of the protease inhibitors was incubated with the specified proteases. The molar ratios of protease inhibitor to subtilisin, proteinase K, elastase, trypsin, and chymotrypsin were set as follows: 15:1, 15:1, 0.2:1, 150:1, 150:1. In order to further compare the inhibitory capacity of the mutant proteins on different proteases, different molar concentrations of protease inhibitors were incubated with the specified proteases. The molar ratios of protease inhibitors to subtilisin or proteinase K were set as 0.2, 0.5, 1, 2, 5, 15, and 25. The molar ratios with elastase were set as 0.004, 0.02, 0.04, 0.08, 0.2, 0.4, and 0.6. The molar ratios with chymotrypsin were set as 1, 10, 25, 50, 100, and 150. The molar ratio of BmSPI38 mutants to trypsin were set as 1, 5, 25, 55, 100, and 150; the molar ratio of BmSPI39 mutants to trypsin were set as 0.2, 0.5, 1, 2, 5, 15, and 25. The protease inhibitor and protease were mixed and supplemented with 0.1 mol/L Tris-HCl (pH 7.5) to 100 μL, and then incubated at 37 °C for 30 min. Then, 100 μL FITC-casein was added and incubated at 37 °C for 60 min in darkness. The fluorescence intensity was measured at the excitation and emission wavelengths of $\frac{485}{535}$ nm, and the residual enzyme activity was calculated. The inhibitory activity of the protease inhibitor against protease was assessed by the following formula: residual enzyme activity% = enzyme activity of experimental group/enzyme activity of control group × $100\%$. In the control group, the protease inhibitor was replaced by an equal volume of 10 mmol/L PBS buffer (pH 7.4).
## 4.6. Acid–Base and Thermal Stability Analysis
Equal masses of purified mutant proteins were mixed with Britton–Robinson buffer of different pH values (pH 3–11) and incubated at room temperature for 24 h. The purified mutant proteins were treated at different temperatures (37 °C, 40 °C, 50 °C, 60 °C, 70 °C, 80 °C, 90 °C, and 100 °C) for 10 min. Then, the treated protein samples were separated using $10\%$ alkaline Native PAGE. Finally, the activities of mutant proteins were analyzed by in-gel activity staining.
## 4.7. Statistical Analysis
All statistical analyses of the data were performed using the Data Processing System (DPS) software version 9.01. Statistically significant differences were assessed by one-way analysis of variance (ANOVA). The error bar represents the standard error of the mean ($$n = 3$$). Marks with different lowercase letters “a to d” indicate significant differences between treatment groups at $p \leq 0.05$, while marks with one identical letter indicate no significant differences between treatment groups at $p \leq 0.05.$
## 5. Conclusions
In conclusion, this study demonstrated, for the first time, that silkworm protease inhibitors BmSPI38 and BmSPI39 can strongly inhibit the activity of elastase, and confirmed that the replacements of P1 residues not only greatly affect the intrinsic activity of BmSPI38 and BmSPI39, but also alter their inhibitory specificity when P1 residues are replaced with strong basic amino acids. This not only provides a new perspective and idea for the exploitation and utilization of BmSPI38 and BmSPI39 in biomedicine and pest control, but also provides a basis or reference for the activity and specificity modification of TIL-type protease inhibitors.
## References
1. Hibbetts K., Hines B., Williams D.. **An overview of proteinase inhibitors**. *J. Vet. Intern. Med.* (1999) **13** 302-308. DOI: 10.1111/j.1939-1676.1999.tb02185.x
2. Zhang J., Yang Y., Li Y.. **Key active sites of proteases and protease inhibitors: A review**. *Chin. J. Biotechnol.* (2021) **37** 561-579
3. Xia Q., Zhou Z., Lu C., Cheng D., Dai F., Li B., Zhao P., Zha X., Cheng T., Chai C.. **A draft sequence for the genome of the domesticated silkworm (**. *Science* (2004) **306** 1937-1940. PMID: 15591204
4. Xia Q., Cheng D., Duan J., Wang G., Cheng T., Zha X., Liu C., Zhao P., Dai F., Zhang Z.. **Microarray-based gene expression profiles in multiple tissues of the domesticated silkworm,**. *Genome Biol.* (2007) **8** R162. DOI: 10.1186/gb-2007-8-8-r162
5. Duan J., Li R., Cheng D., Fan W., Zha X., Cheng T., Wu Y., Wang J., Mita K., Xiang Z.. **SilkDB v2.0: A platform for silkworm (**. *Nucleic Acids Res.* (2010) **38** D453-D456. DOI: 10.1093/nar/gkp801
6. Xia Q., Li S., Feng Q.. **Advances in silkworm studies accelerated by the genome sequencing of**. *Annu. Rev. Entomol.* (2014) **59** 513-536. DOI: 10.1146/annurev-ento-011613-161940
7. Kanost M.R.. **Serine proteinase inhibitors in arthropod immunity**. *Dev. Comp. Immunol.* (1999) **23** 291-301. DOI: 10.1016/S0145-305X(99)00012-9
8. Fullaondo A., Garcia-Sanchez S., Sanz-Parra A., Recio E., Lee S.Y., Gubb D.. **Spn1 regulates the GNBP3-dependent Toll signaling pathway in**. *Mol. Cell. Biol.* (2011) **31** 2960-2972. DOI: 10.1128/MCB.01397-10
9. Cerenius L., Kawabata S., Lee B.L., Nonaka M., Soderhall K.. **Proteolytic cascades and their involvement in invertebrate immunity**. *Trends Biochem. Sci.* (2010) **35** 575-583. DOI: 10.1016/j.tibs.2010.04.006
10. Zhao P., Dong Z., Duan J., Wang G., Wang L., Li Y., Xiang Z., Xia Q.. **Genome-wide identification and immune response analysis of serine protease inhibitor genes in the silkworm,**. *PLoS ONE* (2012) **7**. DOI: 10.1371/journal.pone.0031168
11. Li Y., Zhao P., Liu S., Dong Z., Chen J., Xiang Z., Xia Q.. **A novel protease inhibitor in**. *Insect Biochem. Mol. Biol.* (2012) **42** 766-775. DOI: 10.1016/j.ibmb.2012.07.004
12. Li Y., Zhao P., Liu H., Guo X., He H., Zhu R., Xiang Z., Xia Q.. **TIL-type protease inhibitors may be used as targeted resistance factors to enhance silkworm defenses against invasive fungi**. *Insect Biochem. Mol. Biol.* (2015) **57** 11-19. DOI: 10.1016/j.ibmb.2014.11.006
13. Li Y.S., Liu H.W., Zhu R., Xia Q.Y., Zhao P.. **Protease inhibitors in**. *Insect Sci.* (2016) **23** 835-842. DOI: 10.1111/1744-7917.12241
14. Guo X., Dong Z., Zhang Y., Li Y., Liu H., Xia Q., Zhao P.. **Proteins in the Cocoon of Silkworm Inhibit the Growth of**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0151764
15. Huang K., Strynadka N.C., Bernard V.D., Peanasky R.J., James M.N.. **The molecular structure of the complex of**. *Structure* (1994) **2** 679-689. DOI: 10.1016/S0969-2126(00)00068-X
16. Cierpicki T., Bania J., Otlewski J.. **NMR solution structure of Apis mellifera chymotrypsin/cathepsin G inhibitor-1 (AMCI-1): Structural similarity with**. *Protein Sci.* (2000) **9** 976-984. DOI: 10.1110/ps.9.5.976
17. Rosengren K.J., Daly N.L., Scanlon M.J., Craik D.J.. **Solution structure of BSTI: A new trypsin inhibitor from skin secretions of**. *Biochemistry* (2001) **40** 4601-4609. DOI: 10.1021/bi002623v
18. Bania J., Stachowiak D., Polanowski A.. **Primary structure and properties of the cathepsin G/chymotrypsin inhibitor from the larval hemolymph of**. *Eur. J. Biochem.* (1999) **262** 680-687. DOI: 10.1046/j.1432-1327.1999.00406.x
19. Mignogna G., Pascarella S., Wechselberger C., Hinterleitner C., Mollay C., Amiconi G., Barra D., Kreil G.. **BSTI, a trypsin inhibitor from skin secretions of**. *Protein Sci.* (1996) **5** 357-362. DOI: 10.1002/pro.5560050220
20. Babin D.R., Peanasky R.J., Goos S.M.. **The isoinhibitors of chymotrypsin/elastase from**. *Arch. Biochem. Biophys.* (1984) **232** 143-161. DOI: 10.1016/0003-9861(84)90530-7
21. Li Y., Liu H., Zhu R., Xia Q., Zhao P.. **Loss of second and sixth conserved cysteine residues from trypsin inhibitor-like cysteine-rich domain-type protease inhibitors in**. *Peptides* (2016) **86** 13-23. DOI: 10.1016/j.peptides.2016.09.013
22. Khamrui S., Dasgupta J., Dattagupta J.K., Sen U.. **Single mutation at P1 of a chymotrypsin inhibitor changes it to a trypsin inhibitor: X-ray structural (2.15 A) and biochemical basis**. *Biochim. Biophys. Acta* (2005) **1752** 65-72. DOI: 10.1016/j.bbapap.2005.06.012
23. Legowska A., Debowski D., Lesner A., Wysocka M., Rolka K.. **Introduction of non-natural amino acid residues into the substrate-specific P1 position of trypsin inhibitor SFTI-1 yields potent chymotrypsin and cathepsin G inhibitors**. *Bioorg. Med. Chem.* (2009) **17** 3302-3307. DOI: 10.1016/j.bmc.2009.03.045
24. Rojas L., Cabrera-Munoz A., Gil Pradas D., Gonzalez J.B., Alonso-Del-Rivero M., Gonzalez-Gonzalez Y.. **Arginine substitution by alanine at the P1 position increases the selectivity of CmPI-II, a non-classical Kazal inhibitor**. *Biochem. Biophys. Rep.* (2021) **26** 101008. DOI: 10.1016/j.bbrep.2021.101008
25. Kojima S., Fushimi N., Ikeda A., Kumagai I., Miura K.. **Secretory production of chicken ovomucoid domain 3 by**. *Gene* (1994) **143** 239-243. PMID: 8206380
26. Ding L., Hao J., Luo X., Chen Z.. **Engineering varied serine protease inhibitors by converting P1 site of BF9, a weakly active Kunitz-type animal toxin**. *Int. J. Biol. Macromol.* (2018) **120** 1190-1197. DOI: 10.1016/j.ijbiomac.2018.08.178
27. Hu C., Cui J., Ren F., Peng C.. **Enzyme hydrolysis of silk fibroin and the anti-diabetic activity of the hydrolysates**. *Int. J. Food Eng.* (2008) **4** 13. DOI: 10.2202/1556-3758.1298
28. Jeong J., Hur W.. **Even-numbered peptides from a papain hydrolysate of silk fibroin**. *J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.* (2010) **878** 836-840. DOI: 10.1016/j.jchromb.2010.01.034
29. Xu Y., He G.. **Properties of elastase from**. *Food Ferment. Ind.* (2008) **34** 28-31
30. Cheng W., Chen J., Zhang Y.. **Comparison of hydrolysis of silk fibroin by different proteases**. *J. Silk* (2003) **1** 30-32
31. Spix B., Butz E.S., Chen C.C., Rosato A.S., Tang R., Jeridi A., Kudrina V., Plesch E., Wartenberg P., Arlt E.. **Lung emphysema and impaired macrophage elastase clearance in mucolipin 3 deficient mice**. *Nat. Commun.* (2022) **13** 318. DOI: 10.1038/s41467-021-27860-x
32. Suki B., Bartolak-Suki E., Rocco P.R.M.. **Elastase-Induced Lung Emphysema Models in Mice**. *Methods Mol. Biol.* (2017) **1639** 67-75. PMID: 28752447
33. Wang Z., Chen F., Zhai R., Zhang L., Su L., Lin X., Thompson T., Christiani D.C.. **Plasma neutrophil elastase and elafin imbalance is associated with acute respiratory distress syndrome (ARDS) development**. *PLoS ONE* (2009) **4**. DOI: 10.1371/journal.pone.0004380
34. John D.S., Aschenbach J., Kruger B., Sendler M., Weiss F.U., Mayerle J., Lerch M.M., Aghdassi A.A.. **Deficiency of cathepsin C ameliorates severity of acute pancreatitis by reduction of neutrophil elastase activation and cleavage of E-cadherin**. *J. Biol. Chem.* (2019) **294** 697-707. DOI: 10.1074/jbc.RA118.004376
35. Trzybulska D., Olewicz-Gawlik A., Graniczna K., Kisiel K., Moskal M., Cieslak D., Sikora J., Hrycaj P.. **Quantitative analysis of elastase and cathepsin G mRNA levels in peripheral blood CD14(+) cells from patients with rheumatoid arthritis**. *Cell. Immunol.* (2014) **292** 40-44. DOI: 10.1016/j.cellimm.2014.08.009
36. Bazzichi L., Ciompi M.L., Betti L., Rossi A., Melchiorre D., Fiorini M., Giannaccini G., Lucacchini A.. **Impaired glutathione reductase activity and levels of collagenase and elastase in synovial fluid in rheumatoid arthritis**. *Clin. Exp. Rheumatol.* (2002) **20** 761-766. PMID: 12508766
37. Fujita J., Nelson N.L., Daughton D.M., Dobry C.A., Spurzem J.R., Irino S., Rennard S.I.. **Evaluation of elastase and antielastase balance in patients with chronic bronchitis and pulmonary emphysema**. *Am. Rev. Respir. Dis.* (1990) **142** 57-62. DOI: 10.1164/ajrccm/142.1.57
38. Kumasaka R., Nakamura N., Fujita T., Murakami R., Shimada M., Osawa H., Yamabe H., Okumura K.. **Beneficial effect of neutrophil elastase inhibitor on anti-Thy1.1 nephritis in rats**. *Nephrology* (2008) **13** 27-32. DOI: 10.1111/j.1440-1797.2007.00843.x
39. Henriksen P.A., Sallenave J.M.. **Human neutrophil elastase: Mediator and therapeutic target in atherosclerosis**. *Int. J. Biochem. Cell. Biol.* (2008) **40** 1095-1100. DOI: 10.1016/j.biocel.2008.01.004
40. Marino F., Tozzi M., Schembri L., Ferraro S., Tarallo A., Scanzano A., Legnaro M., Castelli P., Cosentino M.. **Production of IL-8, VEGF and Elastase by Circulating and Intraplaque Neutrophils in Patients with Carotid Atherosclerosis**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0124565
41. Mirea A.M., Toonen E.J.M., van den Munckhof I., Munsterman I.D., Tjwa E., Jaeger M., Oosting M., Schraa K., Rutten J.H.W., van der Graaf M.. **Increased proteinase 3 and neutrophil elastase plasma concentrations are associated with non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes**. *Mol. Med.* (2019) **25** 16. DOI: 10.1186/s10020-019-0084-3
42. Shu L., Zhong L., Xiao Y., Wu X., Liu Y., Jiang X., Tang T., Hoo R., Zhou Z., Xu A.. **Neutrophil elastase triggers the development of autoimmune diabetes by exacerbating innate immune responses in pancreatic islets of non-obese diabetic mice**. *Clin. Sci.* (2020) **134** 1679-1696. DOI: 10.1042/CS20200021
43. Moroy G., Alix A.J., Sapi J., Hornebeck W., Bourguet E.. **Neutrophil elastase as a target in lung cancer**. *Anti-Cancer Agents Med. Chem.* (2012) **12** 565-579. DOI: 10.2174/187152012800617696
44. Sato T., Takahashi S., Mizumoto T., Harao M., Akizuki M., Takasugi M., Fukutomi T., Yamashita J.. **Neutrophil elastase and cancer**. *Surg. Oncol.* (2006) **15** 217-222. DOI: 10.1016/j.suronc.2007.01.003
45. Tsuji N., Moriwaki S., Suzuki Y., Takema Y., Imokawa G.. **The role of elastases secreted by fibroblasts in wrinkle formation: Implication through selective inhibition of elastase activity**. *Photochem. Photobiol.* (2001) **74** 283-290. DOI: 10.1562/0031-8655(2001)074<0283:TROESB>2.0.CO;2
46. Imokawa G.. **Mechanism of UVB-induced wrinkling of the skin: Paracrine cytokine linkage between keratinocytes and fibroblasts leading to the stimulation of elastase**. *J. Investig. Dermatol. Symp. Proc.* (2009) **40** 36-43
47. Shinguh Y., Yamazaki A., Inamura N., Fujie K., Okamoto M., Nakahara K., Notsu Y., Okuhara M., Ono T.. **Biochemical and pharmacological characterization of FR134043, a novel elastase inhibitor**. *Eur. J. Pharmacol.* (1998) **345** 299-308. DOI: 10.1016/S0014-2999(98)00028-4
48. Patel A., Gandhi K., Shah S., Patel D., Chhatbar S., Shah D., Patel S., Patel H., Bambharoliya T.. **In silico Study and Solvent-free one-pot Synthesis of Tetrahydropyrimidine derivatives by Mechanochemistry Approach for Targeting Human Neutrophil Elastase against Lung Cancer**. *Curr. Comput.-Aided Drug Des.* (2022) **18** 293-306. DOI: 10.2174/1573409918666220622232501
49. Sahebnasagh A., Saghafi F., Safdari M., Khataminia M., Sadremomtaz A., Talaei Z., Rezai Ghaleno H., Bagheri M., Habtemariam S., Avan R.. **Neutrophil elastase inhibitor (sivelestat) may be a promising therapeutic option for management of acute lung injury/acute respiratory distress syndrome or disseminated intravascular coagulation in COVID-19**. *J. Clin. Pharm. Ther.* (2020) **45** 1515-1519. DOI: 10.1111/jcpt.13251
50. Kumar J.P., Mandal B.B.. **Inhibitory role of silk cocoon extract against elastase, hyaluronidase and UV radiation-induced matrix metalloproteinase expression in human dermal fibroblasts and keratinocytes**. *Photochem. Photobiol. Sci.* (2019) **18** 1259-1274. DOI: 10.1039/c8pp00524a
51. Saric M., Scheibel T.. **Engineering of silk proteins for materials applications**. *Curr. Opin. Biotechnol.* (2019) **60** 213-220. DOI: 10.1016/j.copbio.2019.05.005
52. Kundu B., Kurland N.E., Yadavalli V.K., Kundu S.C.. **Isolation and processing of silk proteins for biomedical applications**. *Int. J. Biol. Macromol.* (2014) **70** 70-77. DOI: 10.1016/j.ijbiomac.2014.06.022
53. Zhang Y., Zhao P., Dong Z., Wang D., Guo P., Guo X., Song Q., Zhang W., Xia Q.. **Comparative proteome analysis of multi-layer cocoon of the silkworm,**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0123403
54. Dong Z., Zhao P., Wang C., Zhang Y., Chen J., Wang X., Lin Y., Xia Q.. **Comparative proteomics reveal diverse functions and dynamic changes of**. *J. Proteome Res.* (2013) **12** 5213-5222. DOI: 10.1021/pr4005772
55. Zhao B., Xu P., Jiang L., Paaske B., Kromann-Hansen T., Jensen J.K., Sorensen H.P., Liu Z., Nielsen J.T., Christensen A.. **A cyclic peptidic serine protease inhibitor: Increasing affinity by increasing peptide flexibility**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0115872
56. Lin Z., Jiang L., Yuan C., Jensen J.K., Zhang X., Luo Z., Furie B.C., Furie B., Andreasen P.A., Huang M.. **Structural basis for recognition of urokinase-type plasminogen activator by plasminogen activator inhibitor-1**. *J. Biol. Chem.* (2011) **286** 7027-7032. DOI: 10.1074/jbc.M110.204537
57. Hosseini M., Jiang L., Sorensen H.P., Jensen J.K., Christensen A., Fogh S., Yuan C., Andersen L.M., Huang M., Andreasen P.A.. **Elucidation of the contribution of active site and exosite interactions to affinity and specificity of peptidylic serine protease inhibitors using non-natural arginine analogs**. *Mol. Pharmacol.* (2011) **80** 585-597. DOI: 10.1124/mol.111.072280
58. Eguchi M., Itoh M., Chou L.-Y., Nishino K.. **Purification and characterization of a fungal protease specific protein inhibitor**. *Comp. Biochem. Physiol.* (1993) **104B** 537-543
59. Taniguchi M., Atiwetin P., Hirai T., Itoh M., Harada S., Hara S., Kamei K.. **Interaction of subtilisin BPN’ and recombinant fungal protease inhibitor F from silkworm with substituted P1 site residues**. *Biosci. Biotechnol. Biochem.* (2006) **70** 1262-1264. DOI: 10.1271/bbb.70.1262
60. Kojima S., Nishiyama Y., Kumagai I., Miura K.. **Inhibition of subtilisin BPN’ by reaction site P1 mutants of**. *J. Biochem.* (1991) **109** 377-382. DOI: 10.1093/oxfordjournals.jbchem.a123389
61. Perona J.J., Craik C.S.. **Structural basis of substrate specificity in the serine proteases**. *Protein Sci.* (1995) **4** 337-360. DOI: 10.1002/pro.5560040301
62. Lin S.J., Dong K.C., Eigenbrot C., van Lookeren Campagne M., Kirchhofer D.. **Structures of neutrophil serine protease 4 reveal an unusual mechanism of substrate recognition by a trypsin-fold protease**. *Structure* (2014) **22** 1333-1340. DOI: 10.1016/j.str.2014.07.008
63. Kojima S., Obata S., Kumagai I., Miura K.. **Alteration of the specificity of the**. *Bio/Technology* (1990) **8** 449-452. DOI: 10.1038/nbt0590-449
64. Beck M.H., Strand M.R.. **A novel polydnavirus protein inhibits the insect prophenoloxidase activation pathway**. *Proc. Natl. Acad Sci. USA* (2007) **104** 19267-19272. DOI: 10.1073/pnas.0708056104
65. Gu Q.J., Zhou S.M., Zhou Y.N., Huang J.H., Shi M., Chen X.X.. **A trypsin inhibitor-like protein secreted by**. *J. Insect Physiol.* (2019) **116** 41-48. DOI: 10.1016/j.jinsphys.2019.04.009
66. Luo Z., Yang J., Zhang J., Meng G., Lu Q., Yang X., Zhao P., Li Y.. **Physicochemical Properties and Elimination of the Activity of Anti-Nutritional Serine Protease Inhibitors from Mulberry Leaves**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27061820
|
---
title: Obesity Is Indirectly Associated with Sudden Cardiac Arrest through Various
Risk Factors
authors:
- Yun Gi Kim
- Joo Hee Jeong
- Seung-Young Roh
- Kyung-Do Han
- Yun Young Choi
- Kyongjin Min
- Jaemin Shim
- Jong-Il Choi
- Young-Hoon Kim
journal: Journal of Clinical Medicine
year: 2023
pmcid: PMC10004688
doi: 10.3390/jcm12052068
license: CC BY 4.0
---
# Obesity Is Indirectly Associated with Sudden Cardiac Arrest through Various Risk Factors
## Abstract
Although obesity is a well-established risk factor of cardiovascular event, the linkage between obesity and sudden cardiac arrest (SCA) is not fully understood. Based on a nationwide health insurance database, this study investigated the impact of body weight status, measured by body-mass index (BMI) and waist circumference, on the SCA risk. A total of 4,234,341 participants who underwent medical check-ups in 2009 were included, and the influence of risk factors (age, sex, social habits, and metabolic disorders) was analyzed. For 33,345,378 person-years follow-up, SCA occurred in 16,352 cases. The BMI resulted in a J-shaped association with SCA risk, in which the obese group (BMI ≥ 30) had a $20.8\%$ increased risk of SCA compared with the normal body weight group (18.5 ≤ BMI < 23.0) ($p \leq 0.001$). Waist circumference showed a linear association with the risk of SCA, with a 2.69-fold increased risk of SCA in the highest waist circumference group compared with the lowest waist circumference group ($p \leq 0.001$). However, after adjustment of risk factors, neither BMI nor waist circumference was associated with the SCA risk. In conclusion, obesity is not independently associated with SCA risk based on the consideration of various confounders. Rather than confining the findings to obesity itself, comprehensive consideration of metabolic disorders as well as demographics and social habits might provide better understanding and prevention of SCA.
## 1. Introduction
Obesity, which is associated with various medical diseases such as hypertension, diabetes mellitus, dyslipidemia, and coronary artery disease, is a major health concern in developed countries [1,2,3,4]. The association between obesity and coronary artery disease is of concern since it can lead to myocardial infarction or sudden cardiac arrest (SCA) [5,6,7,8]. A prospective cohort study involving 1 million adults in the United States revealed that the risk of all-cause death and cardiovascular death was significantly increased in obese people [8]. Another study with 2.3 million adolescents from Israel with 40 years of follow-up demonstrated that obesity during adolescence was associated with significantly increased all-cause and cardiovascular mortality in adults [7]. A study from the Republic of Korea also found a significant association between obesity and all-cause mortality [9]. In these studies, body weight status such as normal weight, overweight, and obesity was defined using body-mass index (BMI) criteria, an indicator of general obesity [7,8,9].
Using BMI can have several limitations [10]. It cannot divide (i) lean body mass from fat and (ii) abdominal fat from the fat of other body sites [10,11]. Furthermore, abdominal obesity, which is not fully reflected in BMI, can have a better predictive value for medical diseases such as diabetes mellitus and myocardial infarction compared with general obesity [12,13].
Sudden cardiac arrest is a medical emergency that imposes a significant burden on both the victim and the society [14,15,16]. Although both high BMI and waist circumference are known to be associated with the increased risk of cardiovascular death, whether this association is a direct effect of obesity or the result of metabolic comorbidities frequently associated with obesity, such as hypertension, diabetes mellitus, and dyslipidemia, is debated. In addition, prior studies only adjusted a limited number of covariates such as age, sex, height, smoking status, alcohol consumed, educational level, and level of physical exercise, and the influence of other important covariates such as blood pressure, fasting blood glucose, dyslipidemia, and estimated glomerular filtration rate (eGFR) were not taken into account [7,8,17]. We aimed to evaluate the association between obesity measured as BMI and waist circumference and the risk of SCA under adjustment of various metabolic comorbidities using a large prospective cohort from the Korean National Health Insurance Service (K-NHIS) database.
## 2.1. K-NHIS Database
This study is a retrospective analysis based on the K-NHIS database, which represents the entire population of South Korea. The K-NHIS is the single and exclusive medical insurance system managed by the government which mandates virtually the entire Korean population to subscribe to. The system is paid for by a nationwide tax system; it also covers those who are not able to afford it, and guarantees basic health care services (citizens are all registered in the system and, therefore, there is less chance of selection bias). The K-NHIS database offers a prospective cohort of subscribed citizens with medical records and various medical measurements during national health check-ups. Therefore, medical data derived from the K-NHIS database are a valuable source for a range of medical research.
If the protocols of the study are approved by both the institutional review board and the official review committee of the K-NHIS (https://nhiss.nhis.or.kr/, accessed on 21 January 2022), researchers are permitted to utilize the K-NHIS database to perform medical research. The Institutional Review Board of Korea University Medicine Anam Hospital and official review committee of the K-NHIS approved this specific study (IRB No.: 2021AN0185). The requirement for written informed consent was waived by the Institutional Review Board of Korea University Medicine Anam Hospital. This study complied with the Declaration of Helsinki and the legal regulations of South Korea.
The K-NHIS provides a regular, biennial, nationwide health check-up to its subscribers. The national health check-up is free of charge for the subscribers as it is covered by the government tax system. During the health check-up, various medical measurements are taken that include height, body weight, waist circumference, blood pressure, serum creatinine, liver function tests, fasting blood glucose (FBG), lipid profile, smoking and alcohol habits, level of income, and physical activity. In the K-NHIS database, various diagnostic codes of the International Classification of Disease, 10th revision (ICD-10) such as hypertension, diabetes mellitus, or heart failure, and prescription history of drugs are recorded. The capability of utilizing these covariates is a distinguished feature of medical research studies based on the K-NHIS database [18,19].
## 2.2. Participants
In 2009, $66\%$ of people who were meant to undergo the nationwide health check-up actually underwent the check-up. Among adult citizens who underwent nationwide health check-ups in 2009, $40\%$ were randomly sampled and enrolled in this study. Exclusion criteria were participants who were younger than 20 years or those with a diagnosis of SCA prior to enrollment (day of 2009 health check-up). Data obtained from 1 January 2002 to 31 December 2008 were used to identify baseline demographics such as presence of hypertension and diabetes mellitus. Medical follow-up duration was between the day of the 2009 health check-up of each participant and 31 December 2018. No follow-up losses were present except for emigrations.
## 2.3. Primary Outcome
The primary outcome is the occurrence of SCA during the follow-up period (the day of the 2009 health check-up of each patient and 31 December 2018). The incidence of SCA was defined as event numbers per 1000 person-years of follow-up. Identification of SCA events was based on claims of the following ICD-10 codes: I46.0 (cardiac arrest with successful resuscitation); I46.1 (sudden cardiac arrest); I46.9 (cardiac arrest, cause unspecified); I49.0 (ventricular fibrillation and flutter); R96.0 (instantaneous death); and R96.1 (death occurring less than 24 h from onset of symptoms). According to the definition of SCA, only claims that occurred at emergency department visit were identified as SCA event, and claims during in-hospital admission were excluded.
In order to conform to the definition of SCA, any possible non-cardiac causes of sudden arrest were excluded from the primary outcome [20]. If participants had a prior diagnosis of cerebral hemorrhage, ischemic stroke, asphyxia, suffocation, drowning, gastrointestinal bleeding, sepsis, anaphylaxis, major trauma, hit by lightning, electric shock, or burn within six months of the diagnosis of SCA, the event was not counted as a primary outcome.
## 2.4. Definitions
The influence of waist circumference and BMI on risk of SCA was evaluated. Waist circumference was measured as the mid-point between the rib cage and the iliac crest. Waist circumference was classified into six stages: waist circumference < 80.0 (cm), 80.0 ≤ waist circumference < 85.0, 85.0 ≤ waist circumference < 90.0, 90.0 ≤ waist circumference < 95.0, 95.0 ≤ waist circumference < 100.0, and waist circumference ≥ 100.0 for males, and waist circumference < 75.0, 75.0 ≤ waist circumference < 80.0, 80.0 ≤ waist circumference < 85.0, 85.0 ≤ waist circumference < 90.0, 90.0 ≤ waist circumference < 95.0, and waist circumference ≥ 95.0 for females. Body-mass index was classified into five groups: low body weight (BMI < 18.5 [kg/m2]); normal body weight (18.5 ≤ BMI < 23.0); pre-obesity (23.0 ≤ BMI < 25.0); obesity class I (or mild obesity, 25.0 ≤ BMI < 30.0); and obesity class II-III (or moderate to severe obesity, BMI ≥ 30.0) [21,22].
Alcohol consumption status was defined as follows: (i) non-drinker, 0 g of alcohol per week; (ii) mild to moderate drinker, <210 g of alcohol per week; and (iii) heavy drinker, ≥210 g of alcohol per week.
For smoking status: (i) current smokers were defined as those who smoked ≥ 100 cigarettes in their lifetime and continued smoking within one month of the 2009 nationwide health check-up; (ii) ex-smokers were those who smoked ≥ 100 cigarettes in their lifetime, but had not smoked within one month of the 2009 nationwide health check-up; and (iii) never-smokers were those who smoked < 100 cigarettes in their lifetime.
Diabetes mellitus and hypertension were classified into three stages each: (i) non-diabetic (FBG < 100 mg/dL); (ii) impaired fasting glucose (IFG) (FBG 100–125 mg/dL); and (iii) diabetes mellitus (FBG ≥ 126 mg/dL or a prior claim of ICD-10 codes for diabetes mellitus) for diabetes mellitus, and (i) non-hypertension (systolic blood pressure [SBP] < 120 [mmHg] and diastolic blood pressure [DBP] < 80); (ii) pre-hypertension (either 120 ≤ SBP < 140 or 80 ≤ DBP < 90); and (iii) hypertension (either SBP ≥ 140, DBP ≥ 90, or a prior claim of ICD-10 codes for hypertension) for hypertension.
Estimated glomerular filtration rate (eGFR) was calculated based on measured creatinine level during the 2009 health check-up, and chronic kidney disease (CKD) was defined as eGFR < 60 mL/min/1.73 m2 based on the Modification of Diet in Renal Disease (MDRD) equation.
Defining regular physical activity was based on a self-questionnaire acquired during the 2009 health check-up: people who had one or more sessions in a week with high (such as running, climbing, intense bicycle activities) or moderate (such as walking fast, tennis, or moderate bicycle activities) physical activity. The quality of physical measurement and laboratory tests are guaranteed and legally certified by K-NHIS, and the robustness of the aforementioned definitions was validated in our prior studies [19,23,24,25,26,27].
## 2.5. Statistical Analysis
The categorical variables are presented as number and percentage, and the continuous variables are presented as mean and standard deviation, or median value with interquartile range. The Student’s t-test was used for comparison of continuous variables, and the Chi-square test or Fisher’s exact test was used for comparison of the categorical variables as indicated. The Cox proportional hazards model was used to calculate unadjusted and adjusted hazard ratios (HR) and $95\%$ confidence intervals (CI). In addition to the un-adjusted model, five multivariate models were adopted: (i) multivariate model 1: adjusted for age and sex; (ii) multivariate model 2: adjusted for model 1 plus smoking, alcohol, regular exercise, and income; (iii) multivariate model 3: adjusted for model 2 plus hypertension, diabetes mellitus, and dyslipidemia; (iv) multivariate model 4: adjusted for model 2 plus hypertension, diabetes mellitus, dyslipidemia, and chronic kidney disease; and (v) multivariate model 5: adjusted for model 4 plus ɣ-GTP. All tests were two-tailed, and statistical significance was defined as p values ≤ 0.05. All statistical analyses were performed with SAS version 9.2 (SAS Institute, Cary, NC, USA).
## 3.1. Study Population
A total of 4,234,341 participants were randomly sampled from participants that underwent 2009 nationwide health screening (Figure 1). People with prior diagnosis of SCA ($$n = 491$$) and with missing data ($$n = 177$$,427) were excluded from the study and 4,056,423 people were followed until December 2018. Sudden cardiac arrest occurred in 16,352 subjects among 33,345,378 person-years of follow-up, with an incidence of 0.490 (per 1000 person-years). The flow of the study is summarized in Figure 1. Significant differences in the baseline demographics between people who did and did not experience SCA are summarized in Supplementary Table S1: people with SCA were older and had higher prevalence of male sex, current smokers, hypertension, diabetes mellitus, dyslipidemia, and CKD [25]. The baseline demographics according to BMI status demonstrated a significant difference across all parameters such as age, sex, smoking and alcohol consumption status, regular exercise, income level, hypertension, diabetes mellitus, dyslipidemia, CKD, and ɣ-glutamyl transferase (ɣ-GTP) (Table 1). A similar pattern of difference in the baseline demographics was observed according to waist circumference, which is described in Table 2.
## 3.2. BMI and SCA
Body weight status measured by BMI was significantly associated with the risk of SCA for both men and women (Table 3, Figure 2a). Moderate to severe obesity (BMI ≥ 30) had $20.8\%$ increased rate of SCA compared with normal weight (18.5 ≤ BMI < 23) ($95\%$ CI = 1.12–1.31; $p \leq 0.001$: Table 3). After adjustment of the influence of age and sex, the increased rate of SCA in moderate to severe obese people was elevated to $35.5\%$ from $20.8\%$ ($95\%$ CI = 1.25–1.47; $p \leq 0.001$: Table 3). The relative risk of SCA in moderate to severe obese people was $38.8\%$ higher after further adjusting for smoking and alcohol consumption status, regular exercise, and income level ($95\%$ CI = 1.28–1.50; $p \leq 0.001$: Table 3). However, the association between BMI and the risk of SCA was lost after adjusting for the influence of hypertension, diabetes mellitus, dyslipidemia, and CKD (HR = 1.05; $95\%$ CI = 0.96–1.13; $$p \leq 0.286$$: Table 3, Figure 2a). Furthermore, people with pre-obesity (23 ≤ BMI < 25) and mild obesity (25 ≤ BMI < 30) showed significantly lower risk of SCA compared with people with normal body weight (18.5 ≤ BMI < 23) after multivariate adjustment (HR = 0.80 and 0.79, respectively; $p \leq 0.001$ for both: Table 3). The multivariate model further adjusting for ɣ-GTP showed similar results, reflecting no association between obesity and risk of SCA (HR = 0.94; $95\%$ CI = 0.87–1.02; $$p \leq 0.130$$: Table 3, Figure 2a) and decreased risk of SCA in the pre-obesity and mild obesity groups (HR = 0.78 and 0.74, respectively; $p \leq 0.001$ for both: Table 3, Figure 2a).
## 3.3. Waist Circumference and SCA
Participants were classified into six groups according to waist circumference measured during their health check-up. Without adjustment of covariates, waist circumference showed a significant linear association with the risk of SCA with higher waist circumference associated with increased risk of SCA for both men and women (Table 3, Figure 2b). However, such association was significantly weakened after adjusting age, sex, smoking, alcohol, regular exercise, and income (Table 3, Figure 2b). After further adjusting the influence of metabolic disorders (hypertension, diabetes mellitus, dyslipidemia, and CKD), the highest waist circumference group no longer showed an increased rate of SCA (HR = 1.04; $95\%$ CI = 0.96–1.12; $$p \leq 0.346$$). Compared with the reference group (<80 cm and 75 cm for men and women, respectively), middle-level waist circumference (between 80 cm and 100 cm for men, and 75 cm and 95 cm for women) was associated with lower risk of SCA (Table 3). Adjustment of ɣ-GTP further affected the association between waist circumference and the risk of SCA, with all other groups showing lower risk of SCA compared with the reference group (Table 3).
## 3.4. Obesity, Metabolic Syndrome and SCA
The association of obesity and SCA was further analyzed according to the presence of the classic metabolic syndromes—hypertension, diabetes mellitus, and dyslipidemia. Participants were divided into (i) those who had the three metabolic syndromes triad (hypertension, diabetes mellitus, and dyslipidemia) and (ii) those who had only one or two, or none of the metabolic syndrome triad Supplementary Table S2. Participants with the metabolic syndrome triad revealed a higher incidence of SCA across all subgroups. After adjusting covariates, participants with the metabolic syndrome triad did not show any significant association between obesity (measured with either BMI or waist circumference) and increased risk of SCA.
## 3.5. Multivariate Model
In a multivariate Cox-proportional-hazards model, age, sex, smoking status, alcohol consumption, regular exercise, low income, hypertension, diabetes mellitus, dyslipidemia, CKD, and ɣ-GTP were independently associated with SCA risk (Table 4). Influence on SCA based on the degree of hazard ratio was most prominent in age, sex, smoking, hypertension, diabetes mellitus, and CKD. Waist circumference also showed an independent association with the risk of SCA, but the association was a negative correlation with a $0.6\%$ lower rate of SCA per 1 cm increase in waist circumference (Table 4).
## 4. Discussion
This study investigated the association of SCA with obesity, which is represented as BMI and waist circumference, based on a nationwide health insurance cohort of South Korea. Before consideration of the mediating risk factors, both general and central obesity were positively associated with risk of SCA. General obesity measured as BMI resulted in a J-shaped association with SCA, with highest SCA risk in the low body weight group (BMI < 18.5) followed by the obesity class II-III group (30.0 ≤ BMI), and the lowest SCA risk in the normal body weight group (18.5 ≤ BMI < 23). In contrast, central obesity measured as waist circumference reflected a linear association with SCA risk, resulting in a 2.6-fold increased risk of SCA in the highest waist circumference subgroup ($\frac{100}{95}$ cm ≤ waist circumference) compared with the reference group (waist circumference < $\frac{80}{75}$ cm). However, the association between obesity and SCA risk was lost after adjustment of the covariates of metabolic disease and its surrogate marker (ɣ-GTP). In other words, a positive association of obesity and SCA was not present after adjustment of the covariates, which accentuates the mediating effect of metabolic disease and sociodemographic factors on SCA rather than the effect of obesity itself. Our study features discriminative strength through assessing the single exclusive nationwide health insurance system, comprising approximately 4.2 million participants, which is the largest population study assessing the association between obesity and SCA. Although the overall incidence of SCA was not high (<$0.5\%$), sufficient cases of SCA ($$n = 16$$,352) were analyzed.
## 4.1. Obesity and SCA
Obesity is a well-established risk factor for mortality as well as atherosclerotic cardiovascular disease, which is represented as BMI, waist circumference, or waist-to-hip ratio. A J-shaped association between BMI and all-cause mortality is shown in prior studies [8,9]. Furthermore, central obesity assessed as waist circumference or waist-to-hip ratio reflected a robust association with mortality after adjustment of BMI, which led to a more comprehensive understanding of the linkage between obesity and mortality by evaluating both general obesity and central obesity [17]. However, both general and central obesity are strongly associated with various metabolic disorders such as hypertension, diabetes mellitus, dyslipidemia, and CKD, as shown in this study. It was unclear whether obesity itself or an associated metabolic disorder is the culprit risk factor for SCA.
Our study found a J-shaped association of SCA with BMI before adjustment of the covariates. Waist circumference also showed a linear association with SCA before covariate adjustment, which is a finding that is consistent with previous studies. However, the association of obesity and SCA was no longer present after adjustment for demographic factors (age and sex) and social habits, which was weakened even more after further consideration of the metabolic conditions and its surrogate marker, ɣ-GTP. Waist circumference showed a negative association with SCA after covariate adjustment ($0.6\%$ decreased risk of SCA per 1 cm increase in waist circumference: Table 4). Our findings suggest that obesity itself is not an independent risk factor for SCA, but is a surrogate marker of metabolic disorders and people demographics. Therefore, not only reducing body weight and waist circumference but also gaining a comprehensive understanding of the metabolic risk factors as a whole in each individual, and the successful management of metabolic disorders, may be important for primary prevention of SCA.
Obesity holds a strong correlation with metabolic disease as well as cardiovascular disease including coronary artery disease, heart failure, and cardiac arrhythmia. However, decreased risk of adverse cardiovascular outcomes in obese patients had been observed in various cardiovascular diseases, known as the obesity paradox [28,29]. The obesity paradox partly explains this loss of association of obesity and SCA. The exact mechanisms of the obesity paradox are yet to be established, but several hypotheses support the obesity paradox. Increased obesity alters hormonal and lipid mediators and cytokines—increased lipoproteins have a protective effect on inflammatory response, such as binding to endotoxin and increase of lymphocytes [30,31]. A decrease of adiponectin level and catecholamine response in obese patients also supports better clinical outcomes [32,33]. In addition, tumor necrosis factor-α I and II receptors produced by adipose tissue may promote an anti-arrhythmic environment, which may lead to decreased lethal arrhythmic events [34,35]. Furthermore, excessive fat and serum cholesterol in obese patients may serve as a reserve for acute inflammatory stress conditions that may provoke SCA [36]. Nonetheless, the use of BMI for measuring obesity should be interpreted with caution since BMI does not accurately reflect different components of body composition such as muscle mass and visceral fat. For instance, a previous study on cancer patients revealed that the obesity paradox was present when it was measured as BMI, but not in sarcopenic obesity patients [37]. It should also be acknowledged that most of the previous studies on the obesity paradox had focused on BMI as an assessment tool of obesity [38]. Therefore, to clarify the obesity paradox, further investigations that utilize more accurate methods to assess body composition and nutritional status are needed.
## 4.2. Prevention of SCA
Although the consequences of SCA events are highly dependent on the geographical accessibility of emergency medical services and the degree of training of citizens, the majority of SCA events impose considerable socioeconomic costs on their victims and family members [14]. In addition, even after the return of spontaneous circulation, neurologically complete recovery is challenging. Primary prevention of SCA with recognition of the underlying risk factors represents a key strategy in reducing the socioeconomic burden of SCA on public health.
Increased adiposity not only aggravates cardiovascular hemodynamics and leads to structural change of myocardium but also accelerates metabolic condition by the dysregulation of lipid metabolism, elevation of blood pressure, increased insulin resistance, and pro-inflammatory response [39,40]. Our study demonstrated a clear association between obesity (both by BMI and waist circumference) and various metabolic disorders. The increase of BMI and waist circumference was associated with an increased prevalence of metabolic disorders, including diabetes mellitus, hypertension, dyslipidemia, and chronic kidney disease. Clarifying whether obesity is independently associated with SCA or is indirectly associated with SCA through the influence of metabolic disorders is important for the prevention of SCA. If coexisting metabolic disorders are the culprit risk factor for SCA, weight reduction itself may not be sufficient to effectively prevent SCA, and concomitant management of metabolic disorders such as hypertension and diabetes may be more important.
## 4.3. Limitations
There are several limitations in this study. First, obesity defined through BMI and waist circumference was measured at the time of enrollment [2009], and temporal change of the parameters was not considered. Certain populations with systemic conditions such as malignancy or tuberculosis might have experienced acute change of body weight. The results of the current study cannot demonstrate cause and effect relationships. Consequently, further analysis of temporal change of BMI and waist circumference may provide more valuable clinical implications. In a similar vein, although obesity was quantified as BMI and waist circumference, it was not further analyzed specifically, such as analysis of fat mass and proportion of visceral fat. Second, since the outcome was restricted to out-of-hospital cardiac arrests, this study might have underestimated the actual incidence of SCA. Due to heterogenous etiologies of in-hospital cardiac arrests, it is difficult to distinguish the predisposing condition of in-hospital cardiac arrest by ICD-10 codes—whether it was a sudden, unexplained cardiac arrest or a hemodynamic collapse due to non-cardiac underlying conditions. Moreover, the major reason for excluding in-hospital cardiac arrest is due to its different clinical characteristics compared with out-of-hospital cardiac arrest [41]. Patients with in-hospital cardiac arrests are reported to have older age, higher proportion of non-shockable rhythm, and also a higher proportion of chronic illness such as infection, malignancy, or chronic respiratory disease [42]. Therefore, we restricted the analysis to out-of-hospital cardiac arrest to decrease heterogeneity and reduce other possible confounding factors. Last, our cohort exclusively consisted of an East Asian population and extrapolation to other ethnic groups should be undertaken with caution.
## 5. Conclusions
Obesity assessed as BMI and waist circumference did not show an independent association with SCA risk after adjustment of mediating risk factors. In conclusion, rather than focusing on obesity per se, an integrated approach with consideration of pre-existing metabolic disorders as well as people demographics and social habits might provide a better understanding and prevention of SCA.
## References
1. Heymsfield S.B., Wadden T.A.. **Mechanisms, Pathophysiology, and Management of Obesity**. *N. Engl. J. Med.* (2017) **376** 254-266. DOI: 10.1056/NEJMra1514009
2. Hall J.E., da Silva A.A., do Carmo J.M., Dubinion J., Hamza S., Munusamy S., Smith G., Stec D.E.. **Obesity-induced hypertension: Role of sympathetic nervous system, leptin, and melanocortins**. *J. Biol. Chem.* (2010) **285** 17271-17276. DOI: 10.1074/jbc.R110.113175
3. Knowler W.C., Fowler S.E., Hamman R.F., Christophi C.A., Hoffman H.J., Brenneman A.T., Brown-Friday J.O., Goldberg R., Venditti E., Nathan D.M.. **10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study**. *Lancet* (2009) **374** 1677-1686. DOI: 10.1016/S0140-6736(09)61457-4
4. Stecker T., Sparks S.. **Prevalence of obese patients in a primary care setting**. *Obesity* (2006) **14** 373-376. DOI: 10.1038/oby.2006.49
5. Flint A.J., Rexrode K.M., Hu F.B., Glynn R.J., Caspard H., Manson J.E., Willett W.C., Rimm E.B.. **Body mass index, waist circumference, and risk of coronary heart disease: A prospective study among men and women**. *Obes. Res. Clin. Pract.* (2010) **4** e171-e181. DOI: 10.1016/j.orcp.2010.01.001
6. Wilson P.W., D’Agostino R.B., Sullivan L., Parise H., Kannel W.B.. **Overweight and obesity as determinants of cardiovascular risk: The Framingham experience**. *Arch. Intern. Med.* (2002) **162** 1867-1872. DOI: 10.1001/archinte.162.16.1867
7. Twig G., Yaniv G., Levine H., Leiba A., Goldberger N., Derazne E., Ben-Ami Shor D., Tzur D., Afek A., Shamiss A.. **Body-Mass Index in 2.3 Million Adolescents and Cardiovascular Death in Adulthood**. *N. Engl. J. Med.* (2016) **374** 2430-2440. DOI: 10.1056/NEJMoa1503840
8. Calle E.E., Thun M.J., Petrelli J.M., Rodriguez C., Heath C.W.. **Body-mass index and mortality in a prospective cohort of U.S. adults**. *N. Engl. J. Med.* (1999) **341** 1097-1105. DOI: 10.1056/NEJM199910073411501
9. Jee S.H., Sull J.W., Park J., Lee S.Y., Ohrr H., Guallar E., Samet J.M.. **Body-mass index and mortality in Korean men and women**. *N. Engl. J. Med.* (2006) **355** 779-787. DOI: 10.1056/NEJMoa054017
10. Cerhan J.R., Moore S.C., Jacobs E.J., Kitahara C.M., Rosenberg P.S., Adami H.O., Ebbert J.O., English D.R., Gapstur S.M., Giles G.G.. **A pooled analysis of waist circumference and mortality in 650,000 adults**. *Mayo Clin. Proc.* (2014) **89** 335-345. DOI: 10.1016/j.mayocp.2013.11.011
11. Snijder M.B., van Dam R.M., Visser M., Seidell J.C.. **What aspects of body fat are particularly hazardous and how do we measure them?**. *Int. J. Epidemiol.* (2006) **35** 83-92. DOI: 10.1093/ije/dyi253
12. Wang Y., Rimm E.B., Stampfer M.J., Willett W.C., Hu F.B.. **Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men**. *Am. J. Clin. Nutr.* (2005) **81** 555-563. DOI: 10.1093/ajcn/81.3.555
13. Yusuf S., Hawken S., Ounpuu S., Bautista L., Franzosi M.G., Commerford P., Lang C.C., Rumboldt Z., Onen C.L., Lisheng L.. **Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: A case-control study**. *Lancet* (2005) **366** 1640-1649. DOI: 10.1016/S0140-6736(05)67663-5
14. Myat A., Song K.J., Rea T.. **Out-of-hospital cardiac arrest: Current concepts**. *Lancet* (2018) **391** 970-979. DOI: 10.1016/S0140-6736(18)30472-0
15. Cummins R.O., Ornato J.P., Thies W.H., Pepe P.E.. **Improving survival from sudden cardiac arrest: The “chain of survival” concept. A statement for health professionals from the Advanced Cardiac Life Support Subcommittee and the Emergency Cardiac Care Committee, American Heart Association**. *Circulation* (1991) **83** 1832-1847. DOI: 10.1161/01.CIR.83.5.1832
16. Ong M.E.H., Perkins G.D., Cariou A.. **Out-of-hospital cardiac arrest: Prehospital management**. *Lancet* (2018) **391** 980-988. DOI: 10.1016/S0140-6736(18)30316-7
17. Pischon T., Boeing H., Hoffmann K., Bergmann M., Schulze M.B., Overvad K., van der Schouw Y.T., Spencer E., Moons K.G., Tjonneland A.. **General and abdominal adiposity and risk of death in Europe**. *N. Engl. J. Med.* (2008) **359** 2105-2120. DOI: 10.1056/NEJMoa0801891
18. Kim Y.G., Han K.D., Kim D.Y., Choi Y.Y., Choi H.Y., Roh S.Y., Shim J., Kim J.S., Choi J.I., Kim Y.H.. **Different Influence of Blood Pressure on New-Onset Atrial Fibrillation in Pre- and Postmenopausal Women: A Nationwide Population-Based Study**. *Hypertension* (2021) **77** 1500-1509. DOI: 10.1161/HYPERTENSIONAHA.120.16513
19. Kim Y.G., Han K.-D., Choi J.-I., Choi Y.Y., Choi H.Y., Boo K.Y., Kim D.Y., Lee K.-N., Shim J., Kim J.-S.. **Non-genetic risk factors for atrial fibrillation are equally important in both young and old age: A nationwide population-based study**. *Eur. J. Prev. Cardiol.* (2021) **28** 666-676. DOI: 10.1177/2047487320915664
20. Al-Khatib S.M., Stevenson W.G., Ackerman M.J., Bryant W.J., Callans D.J., Curtis A.B., Deal B.J., Dickfeld T., Field M.E., Fonarow G.C.. **2017 AHA/ACC/HRS Guideline for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society**. *J. Am. Coll. Cardiol.* (2018) **72** 1677-1749. DOI: 10.1016/j.jacc.2017.10.053
21. 21.
World Health Organization
Regional Office for the Western Pacific
the International Association for the Study of Obesity
the International Obesity Task Force
The Asia-Pacific Perspective: Redefining Obesity and Its TreatmentWorld Health OrganizationGeneva, Switzerland200256p. *The Asia-Pacific Perspective: Redefining Obesity and Its Treatment* (2002)
22. Seo M.H., Lee W.Y., Kim S.S., Kang J.H., Kang J.H., Kim K.K., Kim B.Y., Kim Y.H., Kim W.J., Kim E.M.. **2018 Korean Society for the Study of Obesity Guideline for the Management of Obesity in Korea**. *J. Obes. Metab. Syndr.* (2019) **28** 40-45. DOI: 10.7570/jomes.2019.28.1.40
23. Kim Y.G., Oh S.K., Choi H.Y., Choi J.I.. **Inherited arrhythmia syndrome predisposing to sudden cardiac death**. *Korean J. Intern. Med.* (2021) **36** 527-538. DOI: 10.3904/kjim.2020.481
24. Roh S.-Y., Choi J.-I., Park S.H., Kim Y.G., Shim J., Kim J.-S., Do Han K., Kim Y.-H.. **The 10-year trend of out-of-hospital cardiac arrests: A Korean nationwide population-based study**. *Korean Circ. J.* (2021) **51** 866-874. DOI: 10.4070/kcj.2021.0127
25. Kim Y.G., Han K., Jeong J.H., Roh S.-Y., Choi Y.Y., Min K., Shim J., Choi J.-I., Kim Y.-H.. **Metabolic Syndrome, Gamma-Glutamyl Transferase, and Risk of Sudden Cardiac Death**. *J. Clin. Med.* (2022) **11**. DOI: 10.3390/jcm11071781
26. Kim Y.G., Roh S.Y., Han K.-D., Jeong J.H., Choi Y.Y., Min K., Shim J., Choi J.-I., Kim Y.-H.. **Hypertension and diabetes including their earlier stage are associated with increased risk of sudden cardiac arrest**. *Sci. Rep.* (2022) **12** 12307. DOI: 10.1038/s41598-022-16543-2
27. Lee J., Lee J.S., Park S.H., Shin S.A., Kim K.. **Cohort Profile: The National Health Insurance Service-National Sample Cohort (NHIS-NSC), South Korea**. *Int. J. Epidemiol.* (2017) **46** e15. DOI: 10.1093/ije/dyv319
28. Lavie C.J., McAuley P.A., Church T.S., Milani R.V., Blair S.N.. **Obesity and cardiovascular diseases: Implications regarding fitness, fatness, and severity in the obesity paradox**. *J. Am. Coll. Cardiol.* (2014) **63** 1345-1354. DOI: 10.1016/j.jacc.2014.01.022
29. Zeller M., Steg P.G., Ravisy J., Lorgis L., Laurent Y., Sicard P., Janin-Manificat L., Beer J.-C., Makki H., Lagrost A.-C.c.. **Relation between body mass index, waist circumference, and death after acute myocardial infarction**. *Circulation* (2008) **118** 482-490. DOI: 10.1161/CIRCULATIONAHA.107.753483
30. Ravnskov U.. **High cholesterol may protect against infections and atherosclerosis**. *Int. J. Med.* (2003) **96** 927-934. DOI: 10.1093/qjmed/hcg150
31. Cavaillon J., Fitting C., Haeffner-Cavaillon N., Kirsch S., Warren H.S.. **Cytokine response by monocytes and macrophages to free and lipoprotein-bound lipopolysaccharide**. *Infect. Immun.* (1990) **58** 2375-2382. DOI: 10.1128/iai.58.7.2375-2382.1990
32. Kistorp C., Faber J., Galatius S., Gustafsson F., Frystyk J., Flyvbjerg A., Hildebrandt P.. **Plasma adiponectin, body mass index, and mortality in patients with chronic heart failure**. *Circulation* (2005) **112** 1756-1762. DOI: 10.1161/CIRCULATIONAHA.104.530972
33. Csige I., Ujvárosy D., Szabó Z., Lőrincz I., Paragh G., Harangi M., Somodi S.. **The impact of obesity on the cardiovascular system**. *J. Diabetes Res.* (2018) **2018** 3407306. DOI: 10.1155/2018/3407306
34. Lee S.-H., Chen Y.-C., Chen Y.-J., Chang S.-L., Tai C.-T., Wongcharoen W., Yeh H.-I., Lin C.-I., Chen S.-A.. **Tumor necrosis factor-α alters calcium handling and increases arrhythmogenesis of pulmonary vein cardiomyocytes**. *Life Sci.* (2007) **80** 1806-1815. DOI: 10.1016/j.lfs.2007.02.029
35. Mohamed-Ali V., Goodrick S., Bulmer K., Holly J.M., Yudkin J.S., Coppack S.W.. **Production of soluble tumor necrosis factor receptors by human subcutaneous adipose tissue in vivo**. *Am. J. Physiol. -Endocrinol. Metab.* (1999) **277** E971-E975. DOI: 10.1152/ajpendo.1999.277.6.E971
36. Goossens C., Marques M.B., Derde S., Vander Perre S., Dufour T., Thiessen S.E., Güiza F., Janssens T., Hermans G., Vanhorebeek I.. **Premorbid obesity, but not nutrition, prevents critical illness-induced muscle wasting and weakness**. *J. Cachexia Sarcopenia Muscle* (2017) **8** 89-101. DOI: 10.1002/jcsm.12131
37. Gonzalez M.C., Pastore C.A., Orlandi S.P., Heymsfield S.B.. **Obesity paradox in cancer: New insights provided by body composition**. *Am. J. Clin. Nutr.* (2014) **99** 999-1005. DOI: 10.3945/ajcn.113.071399
38. Prado C.M., Gonzalez M.C., Heymsfield S.B.. **Body composition phenotypes and obesity paradox**. *Curr. Opin. Clin. Nutr.* (2015) **18** 535-551. DOI: 10.1097/MCO.0000000000000216
39. Bastien M., Poirier P., Lemieux I., Despres J.P.. **Overview of epidemiology and contribution of obesity to cardiovascular disease**. *Prog. Cardiovasc. Dis.* (2014) **56** 369-381. DOI: 10.1016/j.pcad.2013.10.016
40. Parto P., Lavie C.J.. **Obesity and Cardiovascular Diseases**. *Curr. Prob. Cardiol.* (2017) **42** 376-394. DOI: 10.1016/j.cpcardiol.2017.04.004
41. Penketh J., Nolan J.P.. **In-hospital cardiac arrest: The state of the art**. *Crit. Care* (2022) **26** 376. DOI: 10.1186/s13054-022-04247-y
42. Andersson A., Arctaedius I., Cronberg T., Levin H., Nielsen N., Friberg H., Lybeck A.. **In-hospital versus out-of-hospital cardiac arrest: Characteristics and outcomes in patients admitted to intensive care after return of spontaneous circulation**. *Resuscitation* (2022) **176** 1-8. DOI: 10.1016/j.resuscitation.2022.04.023
|
---
title: Liver Protection of a Low-Polarity Fraction from Ficus pandurata Hance, Prepared
by Supercritical CO2 Fluid Extraction, on CCl4-Induced Acute Liver Injury in Mice
via Inhibiting Apoptosis and Ferroptosis Mediated by Strengthened Antioxidation
authors:
- Weibo Dai
- Xiaoyan Pang
- Weiwen Peng
- Xinyi Zhan
- Chang Chen
- Wenchang Zhao
- Congyan Zeng
- Quanxi Mei
- Qilei Chen
- Weihong Kuang
- Zhanping Gou
- Xianjing Hu
journal: Molecules
year: 2023
pmcid: PMC10004706
doi: 10.3390/molecules28052078
license: CC BY 4.0
---
# Liver Protection of a Low-Polarity Fraction from Ficus pandurata Hance, Prepared by Supercritical CO2 Fluid Extraction, on CCl4-Induced Acute Liver Injury in Mice via Inhibiting Apoptosis and Ferroptosis Mediated by Strengthened Antioxidation
## Abstract
Ficus pandurata Hance (FPH) is a Chinese herbal medicine widely used for health care. This study was designed to investigate the alleviation efficacy of the low-polarity ingredients of FPH (FPHLP), prepared by supercritical CO2 fluid extraction technology, against CCl4-induced acute liver injury (ALI) in mice and uncover its underlying mechanism. The results showed that FPHLP had a good antioxidative effect determined by the DPPH free radical scavenging activity test and T-AOC assay. The in vivo study showed that FPHLP dose-dependently protected against liver damage via detection of ALT, AST, and LDH levels and changes in liver histopathology. The antioxidative stress properties of FPHLP suppressed ALI by increasing levels of GSH, Nrf2, HO-1, and Trx-1 and reducing levels of ROS and MDA and the expression of Keap1. FPHLP significantly reduced the level of Fe2+ and expression of TfR1, xCT/SLC7A11, and Bcl2, while increasing the expression of GPX4, FTH1, cleaved PARP, Bax, and cleaved caspase 3. The results demonstrated that FPHLP protected mouse liver from injury induced by CCl4 via suppression of apoptosis and ferroptosis. This study suggests that FPHLP can be used for liver damage protection in humans, which strongly supports its traditional use as a herbal medicine.
## 1. Introduction
According to a retrospective study in China, the annual incidence of liver injury is ~23 per 100,000, which inevitably leads to a substantial socioeconomic burden [1]. A series of factors, such as alcohol, drugs, viruses, and being overweight, can cause ALI (acute liver injury) [2]. The common symptoms of ALI include weakness, fatigue, nausea, swollen abdomen, itching, and jaundice [3], and the clinical symptoms include abnormal levels of alanine transaminase (ALT) and aspartate aminotransferase (AST), which increase the risk of liver fibrosis, cirrhosis, and even acute hepatic failure [4,5].
It was previously reported that the pathological mechanisms of liver injury involve cytochrome P450 (CYP450) metabolism disorder, inflammation, oxidative stress, and cell apoptosis [6]. The majority of drug metabolism is mediated by the CYP450 enzyme system in the liver, and oxidative damage induced by free radicals has become an important factor in hepatotoxicity. The mitochondria is the main organelle that produces oxidative metabolites and is also susceptible to ROS (reactive oxygen species) attack, which can lead to structural and functional disturbances, thereby inducing hepatocyte apoptosis [7]. Ferroptosis, a regulated form of cell death induced by iron-dependent lipid peroxidation, has become an important factor in many diseases [8]. Increasing evidence indicates that ferroptosis is also involved in the process of liver injury [9]. Under the depletion of glutathione (GSH) and inactivation of glutathione peroxidase 4 (GPX4), Fe3+ can be reduced to Fe2+ and iron overloading promotes lipid peroxidation and the generation of mitochondrial ROS [10]. The accumulated redox-active iron plays an important role in the Fenton reaction and the production of lipid peroxidation, as well as the disintegration of ferritin [11], which leads to ferroptosis [12]. Therefore, it is exceptionally critical to prevent liver injury by inhibiting hepatocyte apoptosis and ferroptosis via targeting of oxidative stress.
Currently, the therapeutic strategies for ALI mainly include gastric lavage, activated charcoal, ipecacuanha (an emetic) [13], hemodialysis [14], as well as pharmacotherapies (e.g., glutathione, glycyrrhizin, S-adenosylmethionine) [15]. Pharmacotherapy can only prevent further liver damage from gastric residues, but it cannot repair the damaged liver and may cause discomfort to patients [16]. The drugs used in the clinic can induce a series of side effects, such as hypertension, obesity, and insulin resistance [17]. N-acetyl-L-cysteine (NAC) is a commonly used drug for treating liver injury in the clinic but cannot be administered to children because it induces rash and nausea [18,19]. New drugs or alternative remedies are still largely in demand. Traditional ethnic herbal medicines have a long-history of application in humans and are beneficial for liver health due to their edible properties and few adverse effects [10]. Hence, developing new drugs from ethnic herbal medicines is a good approach for protecting the liver from injury.
Ficus pandurata Hance (FPH), a Chinese ethnic herbal medicine with medicine and food homology, has been widely utilized for liver health care in Southeast China. In addition, the “Atlas of 100 kinds of well-chosen wild vegetables in Zhejiang” also recorded that FPH possessed the functions of “regulating Qi”, i.e., activating blood circulation along with dispelling dampness and detoxication. In this study, the low-polarity fraction from *Ficus pandurata* Hance (FPHLP) was obtained by supercritical CO2 fluid extraction technology. Gas chromatography-mass spectrometry (GC-MS) was applied to analyze the chemical composition of FPHLP and the antioxidant capacity of FPHLP was evaluated by 2,2-diphenyl-1-picrylhydrazil (DPPH), total antioxidant capacity (T-AOC), and flow cytometry assays. The alleviation function of FPHLP against CCl4-induced ALI in mice was evaluated via biochemical assay and hematoxylin & eosin (H&E) staining, and its mechanism was systematically determined by enzyme-linked immunosorbent assay (ELISA), western blotting, and immunohistochemistry (IHC) assays.
## 2.1. Antioxidation Effects of FPHLP
The antioxidation properties of FPHLP were evaluated by DPPH radical scavenging activity and T-AOC assays, as well as ROS detection via flow cytometry. As a result, FPHLP exhibited good DPPH radical scavenging activity in a concentration-dependent manner, with the highest clearance rate of $141.5\%$ at a concentration of 40 mg/mL (Figure 1A). The T-AOC activity of FPHLP was measured according to the ferric reducing antioxidant power (FRAP) method, and the results showed that FPHLP exhibited good antioxidant activity in a dose-dependent manner (Figure 1B). The flow cytometry results also showed that the ROS level was increased in the H2O2-treated group, indicating that the model of H2O2-induced oxidative stress in HepG2 cells was successfully established. After FPHLP treatment (6, 8, and 10 µg/mL), the high levels of ROS induced by H2O2 were significantly reversed (Figure 1C,D). These results suggested that FPHLP had good antioxidative stress potency.
## 2.2. FPHLP Protects against CCl4-Induced Liver Injury
To explore the protective effects of FPHLP against liver injury, C57BL6/J mice were intraperitoneally injected with CCl4 to establish the acute liver injury (ALI) model and intragastrically administered FPHLP. As a result, the appearance of the liver tissue obviously changed after CCl4 induction; most of the hepatic lobules appeared yellow and granular lesions were observed. Meanwhile, the appearance of injury in the liver tissues of the FPHLP- and silibinin-treated groups was alleviated (Figure 2B), showing that FPHLP protected against ALI induced by CCl4. Furthermore, as shown in Figure 2C,D, the levels of ALT and AST in the serum of the model group were sharply enhanced after CCl4 induction and the level of LDH in the liver homogenate of the model group was significantly higher than that of the control group (Figure 2E), indicating that the liver injury mouse model was successfully established. Meanwhile, the ALT, AST, and LDH levels of the FPHLP- and silibinin-treated groups were significantly reversed, suggesting that FPHLP and silibinin had good protective effects against liver injury in mice. Additionally, the H&E staining assay showed that the liver tissues of the model group had typical features of liver injury, such as inflammatory infiltration and enlarged vacuoles. After FPHLP and silibinin treatments, the classical features of pathological changes in the liver tissues were remarkably ameliorated (Figure 2F). These data indicated that FPHLP could significantly alleviate the acute liver injury induced by CCl4 in mice.
## 2.3. FPHLP Protects Liver Injury via Inhibiting Apoptosis
Liver injury can cause hepatocyte death, which is irreversible. Hence, the protective effect of FPHLP against hepatocyte apoptosis was evaluated via western blotting and IHC assays. The results showed that the expression of cleaved PARP and Bax in the liver tissues of mice was upregulated, while that of Bcl2 was reduced after CCl4 injection. However, after FPHLP treatment, the expression of cleaved PARP, Bax, and Bcl2 was significantly reversed, suggesting that FPHLP effectively suppressed hepatocyte apoptosis (Figure 3A,B). Meanwhile, the expression of cleaved caspase-3 in the liver tissues was detected via IHC assay, and the results showed that the expression of cleaved caspase-3 was increased in the CCl4-treated group compared to that in the control group, while that in the FPHLP-treated groups was sharply suppressed (Figure 3C). These results suggested that FPHLP effectively protected against apoptosis.
## 2.4. FPHLP Protects Liver Injury via Strengthening Antioxidative Activity
Oxidative stress has been recognized as an important mechanism underlying the pathophysiology of ALI [20]. In the present study, the influence of FPHLP on oxidative stress in the liver tissues of CCl4-induced ALI model mice was investigated via ELISA assays, western blotting, and IHC assays. As shown in Figure 4A–D, the levels of SOD and GSH, two important antioxidation parameters, were significantly decreased and the levels of ROS and MDA were significantly increased after CCl4 induction ($p \leq 0.05$ vs. control group), while those of the FPHLP-treated groups were significantly reversed ($p \leq 0.05$ vs. model group). Additionally, the effect of FPHLP on the expression of oxidative stress-related proteins, including Keap1, Nrf2, HO-1, and Trx-1, was assessed. The results showed that CCl4 markedly increased the expression of Keap1 and decreased the expression of Nrf2 and its downstream factor HO-1 as well as Trx-1, while FPHLP treatment strongly reversed these effects (Figure 4E,F), indicating that FPHLP effectively suppressed oxidative stress in the liver tissues. The IHC assay was also used to detect the expression of Nrf2, the critical factor of the Keap1/Nrf2/HO-1 pathway, in the liver tissues of ALI model mice, and the results showed that FPHLP enhanced the expression of Nrf2 in mice with CCl4-induced ALI (Figure 4G). Altogether, these data demonstrated that FPHLP strongly alleviated the acute liver injury in mice by enhancing the antioxidative capacity.
## 2.5. FPHLP Protects Liver Injury via Inhibiting Ferroptosis
In our study, the effect of FPHLP on ferroptosis in ALI model mice was evaluated via western blotting and biochemical assays. The results showed that the level of Fe2+ in the liver tissue of the CCl4-treated group was markedly elevated compared to that of the control group, while those in the FPHLP-treated groups were significantly decreased (Figure 5A). Additionally, the effect of FPHLP on ferroptosis-related protein expression, including GPX4, xCT, FTH1, and TfR1, was assessed via western blotting and the results showed that CCl4 markedly downregulated the expression of GPX4 and FTH1, two important proteins that chelate Fe2+, and upregulated the expression of xCT and TfR1, while FPHLP remarkably reversed the effect (Figure 5B,C). In all, our findings suggested that FPHLP significantly alleviated ALI via suppression of ferroptosis in mice.
## 2.6. GC-MS Analysis for FPHLP
The components of FPHLP were analyzed by GC-MS, and the results showed that a total of 47 major compounds were identified, including neophytadiene (peak 1), 6,10,14-trimethyl-2-pentadecanone (peak 2), phytol (peak 3), ficusin (peak 4), 3,7,11,15-tetramethyl-2-hexadecen-1-ol (peak 5), hexadecanoic acid (peak 6), ethyl palmitate (peak 7), 5-methoxypsoralen (peak 8), linoleic acid (peak 9), oleic acid (peak 10), ethyl linolenate (peak 11), octadecanoic acid (peak 12), ethyl linoleate (peak 13), ethyl oleate (peak 14), 4,8,12,16-tetramethylheptadecan-4-olide (peak 15), hercoyn D (peak 16), stigmasta-3,5-diene (peak 17), stigmasta-3,5-diene (peak 18), 1,4-benzenedicarboxylic acid,bis(2-ethylhexyl)ester (peak 19), di-isononyl phthalate (peak 20), squalene(peak 21), Di-isononyl phthalate (peak 22), di-isononyl phthalate (peak 23), dinonyl phthalate (peak 24), β-sitosterol acetate (peak 25), lupeol (peak 26), olean-12-en-3-yl acetate (peak 27), β-amyrin-3-acetate (peak 28), trifluoroacetate (peak 29), trifluoroacetate (peak 30), stigmasta-3,5-diene (peak 31), beta-amyrone (peak 32), psi-taraxasterol (peak 33), trifluoroacetate (peak 34), lanosta-8,24-dien-3-ol (peak 35), 13,27-cyclours-11-en-3-ol (peak 36), acetate (peak 37), lanosteryl acetate (peak 38), olean-12-en-3-ol (peak 39), taraxerol acetate (peak 40), lanosteryl acetate (peak 41), alpha-amyrenyl acetate (peak 42), friedelan-3-one (peak 43), (3S,6aR,6bR,8aS,12S,14bR)-4,4,6a,6b,8a,11,12,14b-octamethyl-1,2,3,4,4a,5,6,6a,6b,7,8,8a,9,12,12a,12b,13,14,14a,14-bicosahydropicen-3-yl acetate (peak 44), dotriacontanal (peak 45), β-amyrenonol acetate (peak 46), and 3beta-acetoxy-11-oxoursan-12-ene (peak 47) (Table 1 and Figure 6). The information of all compounds is shown in Table 1. It is reported that the compounds of FPHLP, such as neophytadiene [21], ethyl linoleate [22], squalene [23], phytol [24] and 5-methoxypsoralen [25], have several bioactivities, including anti-inflammation, antioxidation, and liver protection.
## 3.1. Materials and Reagents
The whole plant was collected from Dajin, Kaiping, Guangdong, China. It was identified as FPH by Professor Huang Haibo from the Department of Traditional Chinese Medicine Identification of Guangzhou University of Chinese Medicine. CCl4 (#C11588428) was obtained from Macklin Biochemical Co., Ltd. (Shanghai, China). ALT (#C009-2-1), AST (#C010-2-1), iron (#A039-2-1), and LDH (#A020-2-2) assay kits were purchased from Nanjing Jiancheng Bioengineering Institution (Nanjing, China). GSH (#311210610), SOD (#535210610), and MDA (#417210825) ELISA kits were purchased from Tianjin Anoric Biotechnology Co., Ltd. (Tianjin, China). The IL-6 assay kit (#$\frac{01}{2022}$) was purchased from Shanghai Enzyme-linked Biotechnology Co., Ltd. (Shanghai, China). The ROS assay kit (#MM-43700M1) was purchased from Jiangsu Meimian Industrial Co., Ltd. (Yancheng, China). Primary antibodies against cleaved PARP (AF7023), Bcl2 (AF6139), GPX4 (DF6701), xCT (DF12509), FTH1 (DF6278), TfR1 (AF5343), HO-1 (AF5393), GAPDH (AF7021), and Bax (AF0120) were obtained from Affinity Biosciences Ltd. (Cincinnati, OH, USA). The primary antibody against Keap1 (D199574) was obtained from Sangon Biotech Ltd. (Shanghai, China). The primary antibody of Trx-1 (C63L6) and anti-rabbit secondary antibody (7074P2) were obtained from Cell Signaling Technology Inc. (Boston, MA, USA). The primary antibody against Nrf2 (A0674) was obtained from ABclonal Biotechnology Co., Ltd. (Wuhan, China).
## 3.2. FPHLP Preparation
FPHLP supercritical fluid extraction of FPH was performed by Nantong Huaan Supercritical Extraction Co., Ltd. (Nangtong, China) using a supercritical fluid extractor (HA220-40-11, Nangtong, China). Briefly, 2212.5 g FPH was placed into the extraction tank and carbon dioxide was pressurized by a pressurizing pump. The conditions were as follows: 45 °C extraction temperature, 55 °C separation temperature, 28 MPa extraction pressure, and 8 MPa separation pressure [26]. The extraction time was set at 3 h and the extracted essential oil was collected every 1 h. A total of 6.5 g of paste was obtained with a yield of $0.294\%$. According to ref. [ 27], the daily dosage of FPH is 100~400 g for adults, which was converted to a mice dosage of 12~50 g/kg (calculated based on the quantity of crude material). According to the yield of FPHLP ($0.3\%$), crude drug dosages of 12 and 50 g/kg were converted into extract dosages of 36 and 150 mg/kg. We selected 50 and 100 mg/kg as the testing dosages for the animal study after a pilot study.
## 3.3. DPPH Radical Scavenging Activity Assay
A DPPH assay kit (G0128W) obtained from Suzhou Grace Biotechnology Co., Ltd. (Suzhou, China) was used to evaluate the DPPH scavenging ability of FPHLP. Briefly, 150 µL of FPHLP (40, 20, 10, 5, 2.5, and 1.25 mg/mL) was added to DPPH working solution (150 µL) and $80\%$ methanol served as the control, according to the manufacturer’s instructions. The mixtures were co-incubated for 30 min at room temperature, then centrifuged at 5000× g for 5 min. A 200 µL aliquot of supernatant was transferred into a 96-well plate to measure the absorbance at 517 nm, and the DPPH scavenging rate was calculated according to the following equation:DPPH scavenging rate=1−OD of measurement group−OD of control group of blank group×$100\%$
## 3.4. Total Antioxidant Capacity (T-AOC) Analysis
According to the instructions of the T-AOC detection kit (#BC1315, Biobox, Beijing, China) [28], different concentrations of FeSO4 were used to establish a standard curve. The FPHLP samples were prepared in DMSO to obtain 10, 8, 6, 4, 2, and 1 mg/mL solution, and VC at 400 µg/mL was used as the positive control. The process was performed following the instructions of the T-AOC commercial kit. The blank group contained 180 µL of mixed working solution (control group) and 24 µL of distilled water. The measurement group containing 180 µL of mixed working solution, 6 µL of sample solution, and 18 µL of distilled water. The samples were co-incubated for 10 min after gentle mixing and 200 µL of the mixture was transferred to a 96-well plate to measure the absorbance of Fe2+-TPTZ at 592 nm. According to the standard curve, the absorbance of Fe2+-TPTZ was substituted to calculate the corresponding iron ion concentration. The final T-AOC was determined as follows:T-AOC (µM) = 34 × corresponding to the concentration of iron ions.
The concentration of T-AOC was regarded as the total antioxidant capacity of FPHLP.
## 3.5. Cell Lines and Culture
HepG2 cells were purchased from the Cell Bank of Shanghai Institute of Cell Biology, Chinese Academy of Sciences (Shanghai, China). Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (#8120364, Gibco) containing $10\%$ fetal bovine serum (FBS) (#2176404, Gibco) and $1\%$ penicillin-streptomycin and maintained in an incubator under $5\%$ CO2 at 37 °C. The logarithmically growing cells were used for the following experiments.
## 3.6. ROS Detection
HepG2 cells were used to evaluate the effect of FPHLP on ROS production according to ref. [ 29]. FPHLP was dissolved in DMSO and diluted with DMEM. Briefly, HepG2 cells were incubated with different concentrations of FPHLP (10, 8, and 6 µg/mL) for 24 h and then treated with 400 µM of H2O2 for 4 h to induce oxidative stress. The ROS level was determined using a commercial ROS assay kit (#S0033S, Beyotime Biotechnology, Shanghai, China). The cells were dyed with DCFH-DA solution and the fluorescence was detected via flow cytometry.
## 3.7. Animal Experiment Design
The animal experiment was approved by the Animal Ethics and Welfare Committee (AEWC) of the Zhongshan Hospital of Traditional Chinese Medicine. The experimental procedures and animal treatments were carried out strictly following the principle of Laboratory Animal Care and the guidelines of the AEWC of Zhongshan Hospital of Traditional Chinese Medicine (AEWC-2021026). Eight-week-old C57BL/6 male mice were provided by Zhuhai BesTest Bio-Tech Company Limited (SYXK2020-0109). All mice had access to food and water ad libitum and were kept under a 12 h light/dark cycle. The mice were randomly divided into 5 groups: control group, $0.2\%$ CCl4 group, $0.2\%$ CCl4 + silibinin (120 mg/kg) group, $0.2\%$ CCl4 + FPHLP high dosage group (FPHLP-H, 100 mg/kg), and $0.2\%$ CCl4 + FPHLP low dosage group (FPHLP-L, 50 mg/kg). The FPHLP samples and silibinin were prepared in $0.5\%$ CMC-Na and the control group was given $0.5\%$ CMC-Na orally from days 8 to 14. Silibinin was given orally from days 8 to 14. FPHLP was given orally from days 8 to 14, twice a day. Mice were injected with $0.2\%$ CCl4 on day 13 and sacrificed after the FPHLP treatment was terminated. The experimental design is shown in Figure 2A.
## 3.8. Sample Collection
After treatment, mice were anesthetized with pentobarbital to collect blood samples and then sacrificed for dissection [30]. Blood was collected and serum was obtained by centrifugation at 4000 rpm at 4 °C for 10 min. The livers were collected and photographed. Part of the liver tissue was fixed in $4\%$ paraformaldehyde (PFA) solution for hematoxylin and eosin (H&E) staining, and the rest was stored at −80 °C.
## 3.9. Enzyme-Linked Immunosorbent Assay (ELISA) and Biochemical Analyses
Biochemical parameters, including the concentrations of ALT and AST in serum, were determined using commercial kits according to the manufacturer’s instructions. Fresh liver tissue (50 mg) was weighed and homogenized with cold phosphate-buffered saline (0.01 M, pH 7.4, w/v, 1: 10). The samples were then centrifuged at 5000× g for 10 min at 4 °C, and the supernatants were collected for biochemical analysis. The levels of LDH, ROS, MDA, SOD, and GSH in liver tissues were evaluated following the manufacturer’s instructions.
## 3.10. Western Blot
Liver tissues were weighed and lysed with RIPA lysis buffer (P0013B, Beyotime Biotechnology, Shanghai, China) supplemented with $1\%$ protease inhibitor and phosphatase inhibitor (P1046, Beyotime Biotechnology, Shanghai, China) for 30 min at 4 °C [31]. The protein samples were collected after centrifugation at 12,000 rpm for 10 min in 4 °C, and the protein concentrations were measured using a bicinchoninic acid (BCA) protein assay kit (#23225, Thermo, Rockford, USA). An equal amount of protein was loaded and separated on 8~$15\%$ sodium dodecyl sulfate-polyacrylamide gels and transferred onto polyvinylidene difluoride membranes (PVDF, Merck Millipore Ltd., IPVH00010, Darmstadt, Germany). The transferred membranes were blocked with QuickBlockTM solution (P0252, Beyotime Biotechnology, Shanghai, China) for 15 min at room temperature, washed in PBST ($0.1\%$ Tween-20 in PBS), and incubated with primary antibodies of cleaved PARP, Bcl2, Bax, Keap1, HO-1, Trx-1, Nrf2, GPX4, xCT, FTH1, TfR1, and GAPDH overnight at 4 °C. After being washed with PBST 3 times, the membranes were then incubated with secondary antibodies conjugated with horseradish peroxidase (HRP) for 2 h at room temperature. The protein blots were detected using an enhanced chemiluminescence (ECL) kit (KF8003, Affinity Biosciences, Cincinnati, OH, USA). All analyses of the protein blots were performed using Image J software.
## 3.11. Hematoxylin and Eosin (H&E) Staining
Histopathological examination was performed according to the reference with minor modifications [32]. Briefly, liver tissues were fixed in $4\%$ PFA for 24 h, dehydrated by gradient ethanol, paraffin-embedded, sectioned (~4 μm), stained with H&E, and mounted with neutral gum. The morphological changes in tissues were observed under an optical microscope (Nikon Corporation, ECLIPSE Ti2-A, Tokyo, Japan) and photos were taken (magnification, 200×).
## 3.12. Immunohistochemistry (IHC) Study
IHC analysis was performed to examine the protein expression of Nrf2 and cleaved caspase-3 in the liver tissues [33]. Briefly, the paraffin-embedded samples were cut into sections (~4 μm) and sealed with $3\%$ H2O2 at room temperature to inactivate the enzyme, then boiled in 10 mM sodium citrate buffer (pH 6.0) for 10 min and cooled at room temperature. The sections were blocked with normal goat serum, incubated with anti-Nrf2 and anti-cleaved caspase-3 (1:200) overnight at 4 °C, and then with corresponding secondary antibodies for 1 h. The expression of Nrf2 and cleaved caspase-3 in the liver tissues was evaluated under an optical microscope and photos were taken (magnification, 200×).
## 3.13. GC/MS Analysis
To analyze the compounds, FPHLP was dissolved in chloroform for GC/MS analysis. The extraction process consisted of vortexing for 10 min, ultrasound for 30 min at room temperature, and centrifugation at 5000× g at 4 °C for 10 min. The characteristic components of FPHLP were analyzed using an Agilent 7890A-5975C GC/MS. The analytes were separated on a DB-5MS capillary column (60 m × 250 μm × 0.25 μm, HP-5MS, Agilent Technologies) coated with phenyl arylene polymer. The chromatographic temperature conditions used for separation were selected according to the reference, with the instrument parameters of 310 °C for the injection port temperature, 310 °C for the gas chromatography-mass spectrometer interface temperature, and N2 drying gas at a flow rate of 1.5 mL/min [34]. Conditions for the GC-MS analysis: 70 eV of electron energy of electron impact (EI), 35~550 of the scanning range of mass charge ratio, and 230 °C ion source temperature. MSD ChemStation software was used to process data.
## 3.14. Statistical Analysis
All data were expressed as the mean ± standard error of the mean (SEM). Statistical differences between the two groups were compared by Student’s t-test. Differences at $p \leq 0.05$ were considered statistically significant.
## 4. Discussion
A series of factors in life can lead to liver injury, such as excessive drinking, toxins, and drugs. The CCl4-induced liver injury model is a classical model commonly used to study liver function and liver-injury protection [35]. CCl4 is a potent toxin metabolized by the cytochrome P450 system that can be transformed into free radicals in the body, which disturbs the metabolism of lipids on the liver cell membrane, resulting in the production of high levels of ROS and MDA, leading to inflammation and oxidative stress [36], and eventually inducing apoptosis and necrosis of hepatocytes [37].
The treatment of liver injury mainly includes scavenging free radicals, detoxifying, reducing transaminase levels, and regulating immunity [38]. Chinese herbal medicines have been used to treat liver diseases with little side effects by suppressing inflammation and oxidative stress. For example, *Coptis chinensis* inflorescence extract exerts a hepatoprotective function by reducing ROS generation induced by CCl4 in HepG2 cells [39]. FPH was reported to have heat-clearing, detoxifying, and anti-inflammatory effects in the Chinese medicinal literature [31]. Our previous studies reported that the aqueous extract of FPH protected against alcohol-induced acute liver injury and alleviated colitis accompanied by secondary liver injury induced by dextran sulfate sodium (DSS) in mice [24,33]. However, no study has reported the bioactivity of FPHLP. This study showed that the low-polarity components of FPH effectively alleviated the acute liver injury in mice induced by CCl4.
ALT and AST leak into the serum when hepatocytes or their cell membranes are destroyed [40]. LDH, an important enzyme in glycolysis, is another key indicator of impaired liver function [41]. Inflammation dysregulation drives the liver pathology associated with acute liver injury [42], during which proinflammatory factors, such as IL-6 and LPS, promote chronic inflammation in the liver [30]. Under the pathological conditions of liver injury, excessive LPS activates innate immune cells, including Kupffer cells, leading to the release of proinflammatory cytokines such as IL-6 and TNFα, thereby reinforcing the inflammatory response [43]. Hence, inhibiting the acute inflammatory response and retarding the evolution of chronic inflammation can prevent hepatic failure from occurring. In this study, the levels of AST, ALT, LDH, IL-6, and LPS were significantly increased in the ALI mouse model induced by CCl4, while they were significantly reduced after FPHLP treatment, suggesting that FPHLP showed excellent efficiency against liver injury and could be regarded as a therapeutic candidate for ALI.
The activation of apoptosis is closely connected to mitochondria [44]. The activation of pro-apoptotic genes, such as Bax, results in the enhancement of mitochondrial permeabilization and release of cytochrome c, which induces caspase-3 stimulation and hepatocyte apoptosis [45]. The expression of pro-apoptotic proteins, such as Bax, cleaved caspase-3, and cleaved PARP, would be increased and that of anti-apoptotic proteins, such as Bcl2, would be decreased in impaired liver tissues [46,47], which was verified in the CCl4-induced ALI mouse model. In our study, FPHLP significantly reversed these effects, suggesting that FPHLP showed good efficiency in alleviating ALI via suppression of apoptosis.
Oxidative stress has become an important factor in hepatotoxicity, and increasing evidence shows that oxidative stress can promote ALI [46]. Oxidative stress can cause the imbalance of cystine/glutamate antiporter system and decrease cystine intake, thus blocking GSH synthesis [48]. GSH depletion also leads to more extensive hepatotoxicity when ROS excessively accumulates [49]. Excessive MDA covalently modifies proteins, nucleic acids, and other lipids, resulting in destruction of structural integrity or cell death [50,51]. The Keap1/Nrf2/ARE signaling pathway plays a critical role in protecting cells from endogenous and exogenous oxidative stresses [52], among which the transcription factor Nrf2 takes charge of detoxification and antioxidation and regulates GSH metabolism, while Keap1, a highly redox-sensitive member of the BTB-Kelch family, is takes responsibility for Nrf2 degradation when oxidative stress occurs [39]. Nrf2 can evade Keap1-mediated degradation, translocate to the nucleus, and activate a series of ARE-dependent genes, such as the antioxidative and cytoprotective genes HO-1 and SOD [40]. In this study, FPHLP significantly increased the levels of SOD and GSH and enhanced the expression of Nrf2 and HO-1, while inhibiting the expression of Keap1 and reducing the levels of ROS and MDA, indicating that FPHLP could alleviate ALI by suppressing oxidative stress.
Ferroptosis, a kind of non-apoptotic cell death characterized by GSH depletion and iron overloading [53], can be induced by the accumulation of redox-active iron, glutathione depletion, and lipid peroxidation [54]. Ferroptosis is involved in the occurrence and development of a series of liver diseases, including acute or chronic liver injury and liver cancer [55]. GPX4 is the key antioxidant enzyme that quenches phospholipid hydroperoxide by reducing lipid hydroperoxide to nontoxic lipid alcohol directly in the membrane. Once GSH is depleted, the Xc−-glutathione (GSH)-GPX4-dependent antioxidant defense system will be inactivated, leading to the accumulation of lipid hydroperoxides [53,56]. In addition, ferrous iron catalyzes and enhances the occurrence of lipid peroxidation [39]. TfR1, an identified ferroptosis marker, controls cellular iron absorption by carrying transferrin-bound iron into cells via receptor-mediated endocytosis, and then Fe3+ can be reduced to Fe2+ by STEAP3 [57]. The labile iron released from organelles upon various stresses is incorporated into enzymes, leading to production of excessive ROS that results in lipid peroxidation [40]. FTH1, a major iron storage protein harboring ferroxidase activity, also plays an important antioxidative role in maintaining the balance of the redox system [58], by taking responsibility for the oxidation of Fe2+ to Fe3+ and reducing the accumulation of Fe2+ [59]. As the major sites of iron utilization and master regulators of oxidative metabolism, mitochondria are the main source of ROS, which would induce apoptosis and ferroptosis of cells. Ferroptosis is also reportedly associated with severe damage of mitochondrial morphology, bioenergetics, and metabolism [60]. Our study revealed that FPHLP significantly reduced the expression of TfR1 and accumulation of Fe2+ and increased the expression of GPX4 and FTH1, suggesting that FPHLP effectively suppressed ferroptosis when treating ALI in mice. Furthermore, ferroptosis can also propagate lipid peroxidation chain reactions, leading to hepatocyte destruction and apoptotic death [61]. Injured hepatocytes not only jeopardize liver function, but they also activate Kupffer cells, resulting in proinflammatory and fibrogenic responses and the vicious cycle of liver damage [62].
Altogether, our study indicated that FPHLP significantly protected the liver from injury in mice with CCl4-induced ALI by suppressing oxidative stress, inflammation, ferroptosis, and apoptosis, suggesting that FPHLP may be regarded as a potential hepatoprotective drug. However, the key active ingredient and target of FPHLP remain undefined, which will be investigated in the follow-up study.
## 5. Conclusions
In conclusion, this is the first study to explore the suppression of ALI by FPHLP as well as the underlying mechanism. The in vitro data showed that FPHLP efficiently suppressed oxidative stress, and the in vivo study indicated that FPHLP showed excellent inhibition against CCl4-induced ALI in a mouse model. FPHLP improved liver function by reducing ALT and ALT levels in ALI model mice and improved the antioxidant capacity by increasing the levels of antioxidant enzymes, such as SOD and GSH. In addition, FPHLP reduced the accumulation of Fe2+ by enhancing the iron transport mechanism (Figure 7). Our study provides experimental evidence for the therapeutic function of FPHLP against ALI, suggesting that FPHLP might serve as a novel candidate for the treatment of liver injury.
## References
1. Shen T., Liu Y., Shang J., Xie Q., Li J., Yan M., Xu J., Niu J., Liu J., Watkins P.B.. **Incidence and Etiology of Drug-Induced Liver Injury in Mainland China**. *Gastroenterology* (2019) **156** 2230-2241.e2211. DOI: 10.1053/j.gastro.2019.02.002
2. Williams R., Alexander G., Armstrong I., Baker A., Bhala N., Camps-Walsh G., Cramp M.E., de Lusignan S., Day N., Dhawan A.. **Disease burden and costs from excess alcohol consumption, obesity, and viral hepatitis: Fourth report of the Lancet Standing Commission on Liver Disease in the UK**. *Lancet* (2018) **391** 1097-1107. DOI: 10.1016/S0140-6736(17)32866-0
3. Kim J.B., Sohn J.H., Lee H.L., Kim J.P., Han D.S., Hahm J.S., Lee D.H., Kee C.S.. **Clinical characteristics of acute toxic liver injury**. *Korean J. Hepatol.* (2004) **10** 125-134. PMID: 15218346
4. Ibanez L., Perez E., Vidal X., Laporte J.R.. **Prospective surveillance of acute serious liver disease unrelated to infectious, obstructive, or metabolic diseases: Epidemiological and clinical features, and exposure to drugs**. *J. Hepatol.* (2002) **37** 592-600. DOI: 10.1016/S0168-8278(02)00231-3
5. Krawitz S., Lingiah V., Pyrsopoulos N.T.. **Acute Liver Failure: Mechanisms of Disease and Multisystemic Involvement**. *Clin. Liver Dis.* (2018) **22** 243-256. DOI: 10.1016/j.cld.2018.01.002
6. Yang Y.M., Cho Y.E., Hwang S.. **Crosstalk between Oxidative Stress and Inflammatory Liver Injury in the Pathogenesis of Alcoholic Liver Disease**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23020774
7. Pan X., Zhou J., Chen Y., Xie X., Rao C., Liang J., Zhang Y., Peng C.. **Classification, hepatotoxic mechanisms, and targets of the risk ingredients in traditional Chinese medicine-induced liver injury**. *Toxicol. Lett.* (2020) **323** 48-56. DOI: 10.1016/j.toxlet.2020.01.026
8. Yi J., Wu S., Tan S., Qin Y., Wang X., Jiang J., Liu H., Wu B.. **Berberine alleviates liver fibrosis through inducing ferrous redox to activate ROS-mediated hepatic stellate cells ferroptosis**. *Cell Death Discov.* (2021) **7** 374. DOI: 10.1038/s41420-021-00768-7
9. Wu J., Wang Y., Jiang R., Xue R., Yin X., Wu M., Meng Q.. **Ferroptosis in liver disease: New insights into disease mechanisms**. *Cell Death Discov.* (2021) **7** 276. DOI: 10.1038/s41420-021-00660-4
10. Thomas C.E., Morehouse L.A., Aust S.D.. **Ferritin and superoxide-dependent lipid peroxidation**. *J. Biol. Chem.* (1985) **260** 3275-3280. DOI: 10.1016/S0021-9258(19)83617-7
11. Dixon S.J., Lemberg K.M., Lamprecht M.R., Skouta R., Zaitsev E.M., Gleason C.E., Patel D.N., Bauer A.J., Cantley A.M., Yang W.S.. **Ferroptosis: An iron-dependent form of nonapoptotic cell death**. *Cell* (2012) **149** 1060-1072. DOI: 10.1016/j.cell.2012.03.042
12. Brok J., Buckley N., Gluud C.. **Interventions for paracetamol (acetaminophen) overdoses**. *Cochrane Database Syst. Rev.* (2002) CD003328. DOI: 10.1002/14651858.CD003328
13. Wu J., Lu A.D., Zhang L.P., Zuo Y.X., Jia Y.P.. **Study of clinical outcome and prognosis in pediatric core binding factor-acute myeloid leukemia**. *Zhonghua Xue Ye Xue Za Zhi* (2019) **40** 52-57. DOI: 10.3760/cma.j.issn.0253-2727.2019.01.010
14. Li M., Luo Q., Tao Y., Sun X., Liu C.. **Pharmacotherapies for Drug-Induced Liver Injury: A Current Literature Review**. *Front. Pharmacol.* (2021) **12** 806249. DOI: 10.3389/fphar.2021.806249
15. Stine J.G., Lewis J.H.. **Current and future directions in the treatment and prevention of drug-induced liver injury: A systematic review**. *Expert Rev. Gastroenterol. Hepatol.* (2016) **10** 517-536. DOI: 10.1586/17474124.2016.1127756
16. Oray M., Abu Samra K., Ebrahimiadib N., Meese H., Foster C.S.. **Long-term side effects of glucocorticoids**. *Expert Opin. Drug Saf.* (2016) **15** 457-465. DOI: 10.1517/14740338.2016.1140743
17. Garcia-Cortes M., Ortega-Alonso A., Andrade R.J., Spanish Group for the Study of Drug-Induced Liver D.. **Safety of treating acute liver injury and failure**. *Expert Opin. Drug Saf.* (2022) **21** 191-203. DOI: 10.1080/14740338.2021.1955854
18. Chalasani N.P., Maddur H., Russo M.W., Wong R.J., Reddy K.R.. **ACG Clinical Guideline: Diagnosis and Management of Idiosyncratic Drug-Induced Liver Injury**. *Am. J. Gastroenterol.* (2021) **116** 878-898. DOI: 10.14309/ajg.0000000000001259
19. Singh D., Cho W.C., Upadhyay G.. **Drug-Induced Liver Toxicity and Prevention by Herbal Antioxidants: An Overview**. *Front. Physiol.* (2015) **6** 363. DOI: 10.3389/fphys.2015.00363
20. Zhang Y., Luo J., Zeng F.. **Volatile composition analysis of tree peony (Paeonia section Moutan DC.) seed oil and the effect of oxidation during storage**. *J. Food Sci.* (2021) **86** 3467-3479. DOI: 10.1111/1750-3841.15790
21. Dai W., Chen C., Feng H., Li G., Peng W., Liu X., Yang J., Hu X.. **Protection of Ficus pandurata Hance against acute alcohol-induced liver damage in mice via suppressing oxidative stress, inflammation, and apoptosis**. *J. Ethnopharmacol.* (2021) **275** 114140. DOI: 10.1016/j.jep.2021.114140
22. Liu Y., Huang Z.Z., Min L., Li Z.F., Chen K.. **The BRD4 inhibitor JQ1 protects against chronic obstructive pulmonary disease in mice by suppressing NF-kappaB activation**. *Histol. Histopathol.* (2021) **36** 101-112. DOI: 10.14670/HH-18-283
23. Tan J., Li P., Xue H., Li Q.. **Cyanidin-3-glucoside prevents hydrogen peroxide (H(2)O(2))-induced oxidative damage in HepG2 cells**. *Biotechnol. Lett.* (2020) **42** 2453-2466. DOI: 10.1007/s10529-020-02982-2
24. Zatroch K.K., Knight C.G., Reimer J.N., Pang D.S.. **Refinement of intraperitoneal injection of sodium pentobarbital for euthanasia in laboratory rats (**. *BMC Vet. Res.* (2017) **13**. DOI: 10.1186/s12917-017-0982-y
25. Dai W., Zhan X., Peng W., Liu X., Peng W., Mei Q., Hu X.. **Ficus pandurata Hance Inhibits Ulcerative Colitis and Colitis-Associated Secondary Liver Damage of Mice by Enhancing Antioxidation Activity**. *Oxid. Med. Cell. Longev.* (2021) **2021** 2617881. DOI: 10.1155/2021/2617881
26. Zhan X., Peng W., Wang Z., Liu X., Dai W., Mei Q., Hu X.. **Polysaccharides from Garlic Protect against Liver Injury in DSS-Induced Inflammatory Bowel Disease of Mice via Suppressing Pyroptosis and Oxidative Damage**. *Oxid Med. Cell. Longev.* (2022) **2022** 2042163. DOI: 10.1155/2022/2042163
27. Chen Y., Zhang P., Chen W., Chen G.. **Ferroptosis mediated DSS-induced ulcerative colitis associated with Nrf2/HO-1 signaling pathway**. *Immunol. Lett.* (2020) **225** 9-15. DOI: 10.1016/j.imlet.2020.06.005
28. Yu X., Zhang H., Wang J., Wang J., Wang Z., Li J.. **Phytochemical Compositions and Antioxidant Activities of Essential Oils Extracted from the Flowers of Paeonia delavayi Using Supercritical Carbon Dioxide Fluid**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27093000
29. Wang K.. **Molecular mechanisms of hepatic apoptosis**. *Cell Death Dis.* (2014) **5** e996. DOI: 10.1038/cddis.2013.499
30. Bhardwaj M., Sali V.K., Mani S., Vasanthi H.R.. **Neophytadiene from Turbinaria ornata Suppresses LPS-Induced Inflammatory Response in RAW 264.7 Macrophages and Sprague Dawley Rats**. *Inflammation* (2020) **43** 937-950. DOI: 10.1007/s10753-020-01179-z
31. Park S.Y., Seetharaman R., Ko M.J., Kim D.Y., Kim T.H., Yoon M.K., Kwak J.H., Lee S.J., Bae Y.S., Choi Y.W.. **Ethyl linoleate from garlic attenuates lipopolysaccharide-induced pro-inflammatory cytokine production by inducing heme oxygenase-1 in RAW264.7 cells**. *Int. Immunopharmacol.* (2014) **19** 253-261. DOI: 10.1016/j.intimp.2014.01.017
32. Chen Y.P., Gu Y.F., Zhao H.R., Zhou Y.M.. **Dietary squalene supplementation alleviates diquat-induced oxidative stress and liver damage of broiler chickens**. *Poult. Sci.* (2021) **100** 100919. DOI: 10.1016/j.psj.2020.12.017
33. Kim E.N., Trang N.M., Kang H., Kim K.H., Jeong G.S.. **Phytol Suppresses Osteoclast Differentiation and Oxidative Stress through Nrf2/HO-1 Regulation in RANKL-Induced RAW264.7 Cells**. *Cells* (2022) **11**. DOI: 10.3390/cells11223596
34. Liang Y., Xie L., Liu K., Cao Y., Dai X., Wang X., Lu J., Zhang X., Li X.. **Bergapten: A review of its pharmacology, pharmacokinetics, and toxicity**. *Phytother. Res.* (2021) **35** 6131-6147. DOI: 10.1002/ptr.7221
35. Xu G., Han X., Yuan G., An L., Du P.. **Screening for the protective effect target of deproteinized extract of calf blood and its mechanisms in mice with CCl4-induced acute liver injury**. *PLoS ONE* (2017) **12**. DOI: 10.1371/journal.pone.0180899
36. Unsal V., Cicek M., Sabancilar I.. **Toxicity of carbon tetrachloride, free radicals and role of antioxidants**. *Rev. Environ. Health* (2021) **36** 279-295. DOI: 10.1515/reveh-2020-0048
37. Kawakami K., Moritani C., Hatanaka T., Suzaki E., Tsuboi S.. **Hepatoprotective Activity of Yellow Chinese Chive against Acetaminophen-Induced Acute Liver Injury via Nrf2 Signaling Pathway**. *J. Nutr. Sci. Vitaminol.* (2020) **66** 357-363. DOI: 10.3177/jnsv.66.357
38. Muniz Santana Bastos E., Bispo da Silva A., Cerqueira Coelho P.L., Pereira Borges J.M., Amaral da Silva V.D., Moreau da Cunha V.H., Costa S.L.. **Anti-inflammatory activity of Jatropha curcas L. in brain glial cells primary cultures**. *J. Ethnopharmacol.* (2021) **264** 113201. DOI: 10.1016/j.jep.2020.113201
39. Lam P., Cheung F., Tan H.Y., Wang N., Yuen M.F., Feng Y.. **Hepatoprotective Effects of Chinese Medicinal Herbs: A Focus on Anti-Inflammatory and Anti-Oxidative Activities**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17040465
40. Kew M.C.. **Serum aminotransferase concentration as evidence of hepatocellular damage**. *Lancet* (2000) **355** 591-592. DOI: 10.1016/S0140-6736(99)00219-6
41. Puri B.K., Kingston M.C., Monro J.A.. **Fructose-associated hepatotoxicity indexed by the lactate dehydrogenase isoenzyme LDH-5**. *Med. Hypotheses* (2019) **124** 40-41. DOI: 10.1016/j.mehy.2019.02.019
42. Robinson M.W., Harmon C., O’Farrelly C.. **Liver immunology and its role in inflammation and homeostasis**. *Cell Mol. Immunol* (2016) **13** 267-276. DOI: 10.1038/cmi.2016.3
43. Thakur V., McMullen M.R., Pritchard M.T., Nagy L.E.. **Regulation of macrophage activation in alcoholic liver disease**. *J. Gastroenterol. Hepatol.* (2007) **22** S53-S56. DOI: 10.1111/j.1440-1746.2006.04650.x
44. Ahmadian E., Eftekhari A., Kavetskyy T., Khosroushahi A.Y., Turksoy V.A., Khalilov R.. **Effects of quercetin loaded nanostructured lipid carriers on the paraquat-induced toxicity in human lymphocytes**. *Pestic. Biochem. Physiol.* (2020) **167** 104586. DOI: 10.1016/j.pestbp.2020.104586
45. Ahmadian E., Khosroushahi A.Y., Eftekhari A., Farajnia S., Babaei H., Eghbal M.A.. **Novel angiotensin receptor blocker, azilsartan induces oxidative stress and NFkB-mediated apoptosis in hepatocellular carcinoma cell line HepG2**. *Biomed. Pharmacother.* (2018) **99** 939-946. DOI: 10.1016/j.biopha.2018.01.117
46. Ma J.Q., Ding J., Zhang L., Liu C.M.. **Hepatoprotective properties of sesamin against CCl4 induced oxidative stress-mediated apoptosis in mice via JNK pathway**. *Food Chem. Toxicol.* (2014) **64** 41-48. DOI: 10.1016/j.fct.2013.11.017
47. Volarevic V., Misirkic M., Vucicevic L., Paunovic V., Simovic Markovic B., Stojanovic M., Milovanovic M., Jakovljevic V., Micic D., Arsenijevic N.. **Metformin aggravates immune-mediated liver injury in mice**. *Arch. Toxicol.* (2015) **89** 437-450. DOI: 10.1007/s00204-014-1263-1
48. Lewerenz J., Hewett S.J., Huang Y., Lambros M., Gout P.W., Kalivas P.W., Massie A., Smolders I., Methner A., Pergande M.. **The cystine/glutamate antiporter system x(c)(-) in health and disease: From molecular mechanisms to novel therapeutic opportunities**. *Antioxid. Redox Signal.* (2013) **18** 522-555. DOI: 10.1089/ars.2011.4391
49. Eftekhari A., Ahmadian E., Azarmi Y., Parvizpur A., Hamishehkar H., Eghbal M.A.. **In vitro/vivo studies towards mechanisms of risperidone-induced oxidative stress and the protective role of coenzyme Q10 and N-acetylcysteine**. *Toxicol. Mech. Methods* (2016) **26** 520-528. DOI: 10.1080/15376516.2016.1204641
50. Del Rio D., Stewart A.J., Pellegrini N.. **A review of recent studies on malondialdehyde as toxic molecule and biological marker of oxidative stress**. *Nutr. Metab. Cardiovasc. Dis.* (2005) **15** 316-328. DOI: 10.1016/j.numecd.2005.05.003
51. Patlevic P., Vaskova J., Svorc P., Vasko L., Svorc P.. **Reactive oxygen species and antioxidant defense in human gastrointestinal diseases**. *Integr. Med. Res.* (2016) **5** 250-258. DOI: 10.1016/j.imr.2016.07.004
52. Rosowsky A., Wright J.E., Holden S.A., Waxman D.J.. **Influence of lipophilicity and carboxyl group content on the rate of hydroxylation of methotrexate derivatives by aldehyde oxidase**. *Biochem. Pharmacol* (1990) **40** 851-857. DOI: 10.1016/0006-2952(90)90326-G
53. Yang W.S., Stockwell B.R.. **Ferroptosis: Death by Lipid Peroxidation**. *Trends Cell Biol.* (2016) **26** 165-176. DOI: 10.1016/j.tcb.2015.10.014
54. Bertrand R.L.. **Iron accumulation, glutathione depletion, and lipid peroxidation must occur simultaneously during ferroptosis and are mutually amplifying events**. *Med. Hypotheses* (2017) **101** 69-74. DOI: 10.1016/j.mehy.2017.02.017
55. Capelletti M.M., Manceau H., Puy H., Peoc’h K.. **Ferroptosis in Liver Diseases: An Overview**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21144908
56. Chen X., Li J., Kang R., Klionsky D.J., Tang D.. **Ferroptosis: Machinery and regulation**. *Autophagy* (2021) **17** 2054-2081. DOI: 10.1080/15548627.2020.1810918
57. Zheng J., Conrad M.. **The Metabolic Underpinnings of Ferroptosis**. *Cell Metab.* (2020) **32** 920-937. DOI: 10.1016/j.cmet.2020.10.011
58. Wei L., Zuo Z., Yang Z., Yin H., Yang Y., Fang J., Cui H., Du Z., Ouyang P., Chen X.. **Mitochondria damage and ferroptosis involved in Ni-induced hepatotoxicity in mice**. *Toxicology* (2022) **466** 153068. DOI: 10.1016/j.tox.2021.153068
59. Fang Y., Chen X., Tan Q., Zhou H., Xu J., Gu Q.. **Inhibiting Ferroptosis through Disrupting the NCOA4-FTH1 Interaction: A New Mechanism of Action**. *ACS Cent. Sci.* (2021) **7** 980-989. DOI: 10.1021/acscentsci.0c01592
60. Battaglia A.M., Chirillo R., Aversa I., Sacco A., Costanzo F., Biamonte F.. **Ferroptosis and Cancer: Mitochondria Meet the “Iron Maiden” Cell Death**. *Cells* (2020) **9**. DOI: 10.3390/cells9061505
61. Su L.J., Zhang J.H., Gomez H., Murugan R., Hong X., Xu D., Jiang F., Peng Z.Y.. **Reactive Oxygen Species-Induced Lipid Peroxidation in Apoptosis, Autophagy, and Ferroptosis**. *Oxid. Med. Cell. Longev.* (2019) **2019** 5080843. DOI: 10.1155/2019/5080843
62. Czaja A.J.. **Hepatic inflammation and progressive liver fibrosis in chronic liver disease**. *World J. Gastroenterol.* (2014) **20** 2515-2532. DOI: 10.3748/wjg.v20.i10.2515
|
---
title: Potential Anticancer Activity of Juniperus procera and Molecular Docking Models
of Active Proteins in Cancer Cells
authors:
- Sultan Alhayyani
- Abdullah Akhdhar
- Amer H. Asseri
- Abdelhafeez M. A. Mohammed
- Mostafa A. Hussien
- L. Selva Roselin
- Salman Hosawi
- Fahad AlAbbasi
- Khadijah H. Alharbi
- Roua S. Baty
- Abdulaziz A. Kalantan
- Ehab M. M. Ali
journal: Molecules
year: 2023
pmcid: PMC10004709
doi: 10.3390/molecules28052041
license: CC BY 4.0
---
# Potential Anticancer Activity of Juniperus procera and Molecular Docking Models of Active Proteins in Cancer Cells
## Abstract
Medicinal plants provide a wide range of active compounds that can be exploited to create novel medicines with minimal side effects. The current study aimed to identify the anticancer properties of *Juniperus procera* (J. procera) leaves. Here, we demonstrate that J. procera leaves’ methanolic extract suppresses cancer cells in colon (HCT116), liver (HepG2), breast (MCF-7), and erythroid (JK-1) cell lines. By applying GC/MS, we were able to determine the components of the J. procera extract that might contribute to cytotoxicity. Molecular docking modules were created that used active components against cyclin-dependent kinase 5 (Cdk5) in colon cancer, aromatase cytochrome P450 in the breast cancer receptor protein, the -N terminal domain in the erythroid cancer receptor of the erythroid spectrin, and topoisomerase in liver cancer. The results demonstrate that, out of the 12 bioactive compounds generated by GC/MS analysis, the active ingredient 2-imino-6-nitro-2H-1-benzopyran-3-carbothiamide proved to be the best-docked chemical with the chosen proteins impacted by DNA conformational changes, cell membrane integrity, and proliferation in molecular docking studies. Notably, we uncovered the capacity of J. procera to induce apoptosis and inhibit cell growth in the HCT116 cell line. Collectively, our data propose that J. procera leaves’ methanolic extract has an anticancer role with the potential to guide future mechanistic studies.
## 1. Introduction
Cancer is a condition that can start in any organ or tissue of the body and then spreads to other organs due to the uncontrolled growth of cells. It is the second-most common cause of death, just after heart disease. By 2030, it is anticipated that there will be 26 million cancer patients [1]. Malignant tumors can be treated surgically, with chemotherapy, and at various stages of development with radiotherapy. Myelotoxicity, cardiotoxicity, renal toxicity, pulmonary toxicity, skin toxicity, intestinal damage, and hair loss are just a few of the negative effects of chemotherapy, which can harm healthy cells [2,3]. Globally, intensive research is being conducted to create a medication that is effective at specifically destroying cancer cells. Plant extracts are not poisonous in large quantities. Plant extracts are typically mixed with chemotherapy medications to lessen the dosage and the side effects of the latter [4]. Medicinal plants’ active ingredients can be used to treat various illnesses and as adjuvants to reduce the side effects of chemotherapy [5,6].
A wide variety of plants and animals characterize the Kingdom of Saudi Arabia. One of Saudi Arabia’s most valued medicinal plants is *Juniperus procera* Hochst. ex Endl. ( Cupressaceae), also known as Arar in Arabic and as African pencil cedar in English. J. procera has a pleasant scent since it contains a significant amount of volatile oils [7,8]. The Cupressaceae plant family contains 70 different plant species, including the juniper plant. J. procera is found in Saudi Arabia, Yemen, Sudan, Eritrea, and Ethiopia, as well as in Eastern Africa, from the Eastern Democratic Republic of the Congo all the way to Malawi and Zimbabwe. It is also grown in South Africa, France, the United Kingdom, the United States, India, and Australia, among other places [7,9,10]. Juniperus species have traditionally been used to treat hypoglycemia [11], anti-inflammatory disorders [12], cancer [13], tuberculosis, bronchitis, pneumonia, ulcers, intestinal worms, wounds, and liver disease [7].
The essential oils of J. procera Hochst. ex Endl. leaves and stems, as well as its ripe and unripe fruits, were evaluated for pinene, -3-carene [14]. The essential oil of J. procera is larvicidal against Anopheles arabiensis, Patton instar larvae, the primary malaria vector [15], and insecticidal against Aedes aegypti [16].
Plant bioactive substances in the diet may be helpful as cancer-prevention agents. Bioactive compounds have been proposed to suppress cancer-cell development through two mechanisms: the alteration of redox status and interference with basic cellular functions, such as apoptosis, cell cycle, angiogenesis, invasion, and metastasis [17]. Plants are used to make four types of anticancer drugs: camptothecin derivatives (camptotecin and irinotecan), taxanes (paclitaxel and docetaxel), vinca alkaloids (vinblastine, vincristine, and vindesine), and epipodophyllotoxins (etoposide and teniposide) [18]. It is feasible to develop even more potent medications from various plants. Numerous plants with anticancer properties have been examined to determine how medicinal plants cause cell-cycle arrest and prevent angiogenesis in tumor cells, in addition to inducing apoptosis and inhibiting invasiveness and metastasis [19]. The cytotoxic effect of J. procera leaves and fruit extracts with silver nanoparticles was investigated against the colon cancer (Caco2) cell line. The fruit extract was more effective than the leaves, and combining AgNPs with leaves or fruits demonstrated considerable anticancer efficacy [20]. Juniperus communis extract is also being studied to prevent melanoma carcinogenesis as it inhibits tumor development and promotes apoptosis. The inhibitory action of biochemicals on cancer cells via apoptosis is thought to be an excellent mechanism for optimum anticancer medications since apoptosis can remove damaged cells without generating inflammation [21].
The present study demonstrates, for the first time, the possible anticancer activity of the methanolic extract of J. procera leaves against human cell lines from the colon (HCT116), liver (HepG2), breast (MCF-7), and erythroid (JK-1) tissues through apoptosis and antiproliferation. The bioactive elements in the leaf extract from J. procera were also shown to interact with proteins involved in conformational changes in DNA, cell membrane integrity, and proliferation via molecular docking studies.
## 2.1. Extraction of Bioactive Compounds
From 50 g of dried, crude, powdered leaves of J. procera, the methanol-extracted J. procera yielded the most (2.2 g) bioactive compounds, followed by n-hexane (1.5 g), ethyl acetate (1.1 g), and dichloromethane (0.9 g).
## 2.2. Effect of Methanolic Extract of J. procera on the Proliferation of Several Cancer Cell Lines
The percentage viability of HCT116, HepG2, MCF-7, and JK-1 cells after 48 h of treatment with different dosages of J. procera methanolic extract is shown in Figure 1. J. procera extract cytotoxicity was assessed in terms of IC50 values for each cell line, which were 115, 75, 112, and 124 μg/mL in HCT116, HepG2, MCF-7, and JK-1 cells, respectively (Table 1).
## 2.3. GC/MS Analysis of Juniperus procera Leaf Extract and Molecular Docking Study
GC/MS analysis was used to determine the phytochemical contents of J. procera leaf extract. The chromatogram in Figure 2 shows that 12 distinct chemicals were found. The retention time and mass spectra of the reference substances in software libraries were compared to identify each peak. The compounds’ identification and structure were determined by comparing the retention time (RT) and fragmentation pattern in mass spectra to the NIST library database. Table 2 displays the chemical name, molecular formula, and retention time in the chromatogram of bioactive components found in J. procera extract. The molecular structure of these bioactive chemicals is depicted in Figure 3. These substances are classified as alkaloids, terpenoids, polyphenols, glycosides, flavonoids, and amino acids.
The GC/MS analysis confirmed the presence of 12 distinct active components in the methanolic extract of J. procera leaves (Table 2 and Figure 3). These substances were utilized in molecular docking experiments.
Table 3 displays the docking score (S), the root-mean-square deviation (RMSD), and the energy values (E) acquired during the docking analysis. The S results show that, the more negative the docking score, the better the docking between bioactive chemicals and all various proteins. Furthermore, lower RMSD values indicate a more stable docking complex. The energy values are related to the energy required for bioactive chemical binding to all proteins. As a result, less energy is required for binding, resulting in easier interaction between bioactive chemicals and proteins. The redocking data indicated that ligands were coupled to their targets in very near proximity to their real conformation, confirming the dependability of the docking techniques and settings.
According to the docking data, the chemical 2-imino-6-nitro-2H-1-benzopyran-3-carbothiamide (Hit 1 or Compd. 1) is best docked with all of the distinct proteins of 3ig7, 1woa, 3eqm, and 4fm9, with docking scores of -5.56-, -5.88-, -671-, and -5.66-, respectively. The best docking score of Hit 1 was achieved against 3eqm, representing erythroid cancer protein, which matches the experimental findings.
Figure 4 depicts the molecular docking of 2H-1-benzopyran-3-carbothiamide (Hit 1) with 3ig7 CDK-5 (receptor). The CDK-5/Hit 1 complex was found in the residues Leu83, Lys33, Asp145, and Val18. The Hit 1 compound (ligand) interacts with Lys33 and Asp145 in CDK-5 (receptor) through a hydrogen bond. Lys33 is identified as an H-donor to the -NH group, whereas Asp145 is an H-acceptor from the -NH2 group. A hydrophobic interaction between Val18 and the aromatic ring of the Hit 1 chemical was found. The Hit 1 chemical interacts with Leu83 through a backbone interaction via an electron-withdrawing nitro group. In the instance of the erythroid spectrin/Hit 1 (1woa/Hit 1) complex, the residues contribute the most to Ser52 and Tyr53. The interaction of Ser52 and Tyr53 with the aromatic ring of the Hit 1 chemical was recognized as a hydrophobic interaction.
The aromatase cytochrome P450/Hit 1 (3eqm/Hit 1) complex in the residues was mostly responsible for Val373 and Met374. The Hit 1 compound (ligand) interaction with Val373 and 3eqm (receptor) were found to be an H-donor to the -C=S group.
In contrast, the topoisomerase/Hit 1 (4fm9/Hit 1) complex in the residues contributes significantly to Arg673. The Hit 1 compound (ligand) interactions with Arg673 and 4fm9 (receptor) were discovered to act as an H-donor to the -C=S group.
## 2.4. Flow-Cytometry Assessment of Apoptosis and Cell-Cycle Analysis
To evaluate the antiproliferative role of J. procera in human cancer cells, apoptosis and cell-cycle analysis were investigated in HCT116 cells treated with J. procera methanolic extract. As shown in Figure 5 and Table 4, the proportion of apoptosis and necrosis of HCT116 cells treated with J. procera methanolic extract in the upper and lower right quadrants of Figure 5 depicts late and early apoptotic cells, respectively. In the HCT116 cell line, the proportion of early and late apoptotic cells treated with J. procera methanolic extract was $3.8\%$ and $44.8\%$, respectively. J. procera methanolic extract caused apoptosis and necrosis in $20\%$ and $48.6\%$ of HCT116 cells, respectively, compared to only $3.5\%$ in untreated cells. Doxorubicin (DOX), a well-known chemotherapy drug, was used as a positive control.
To further explore the notion that J. procera has metabolic activity and a viable role in HCT116 cells, a cell-cycle assay was performed. As shown in Figure 6 and Figure 7 and Table 5 and Table 6, the percentages of untreated HCT116 cells were $61.2\%$, $12.3\%$, and $25.8\%$ in G1/G0, S, and G2/M, respectively. However, when treated with the IC50 of J. procera methanolic extract, HCT116 cells arrested in a lower proportion in the G1/G0 phase ($33.6\%$), in a higher percentage in the S phase ($30.3\%$), and in a slightly higher percentage in G2/M ($36.1\%$).
The cell percentages in G1/G0 for DOX (an anticancer medication) and J. procera methanolic extract were 46.5 and 33.6, respectively. In the S phase, the percentage of cells treated with J. procera extracts was $30\%$, whereas the percentage of HCT116 cells treated with DOX was $17.9\%$. These results indicate that the percentages of HCT116 cells in S phases was more elevated in cells treated with J. procera than in those treated with DOX. Furthermore, in the G2/M phase, DOX ($31.3\%$) and the extract had nearly identical effects ($36.1\%$). Taken together, these data lead us to propose that J. procera may play an antiproliferative role in HCT116 cells via apoptosis and interruption of the cell-cycle process.
## 3. Discussion
The nature of the extraction solvents utilized is essentially what determines the yield of bioactive compounds, the type of compounds isolated, and the impact of biological activity *In this* investigation, J. procera extract was prepared using solvents of various polarities, including methanol, n-hexane, dichloromethane, and ethyl acetate. A previous study showed similar higher-yield results [13,22,23,24,25]. The largest concentration of extract in methanol solvent was associated with stronger polarity, and it is assumed that methanol may dissolve both hydrophilic and lipophilic elements in plants, resulting in a larger yield. Phytochemical analysis and cytotoxicity were carried out with a methanolic extract of J. procera.
The IC50 values were ranked as follows: JK-1 > HCT116 > MCF-7 > HepG2. The cytotoxicity of J. procera fruit and leaf extract has been compared in previous studies. Previous researchers found that J. procera fruit extract was more cytotoxic than J. procera leaf extract against breast (MCF-7 and MDA-MB-231) and ovarian (SKOV-3) cancer cells. However, the leaf extract was more cytotoxic to liver (HepG2) and cisplatin-resistant ovarian cancer cell lines (A2780CP) [20].
IC50 values of doxorubicin and a leaf extract of J. procera combined with doxorubicin in a treated ovarian cancer cell line (A2780CP) were almost similar, at 1.2 and 0.9 μg/mL. The methanolic extract of J. procera leaves demonstrated cytotoxicity on oral SCC-9 cell lines, with an IC50 value of 208.7 g/mL [13].
By combining silver nanoparticles with the J. procera extract, the cytotoxicity of the extract against the colon cancer (Caco2) cell line was improved [20]. The cytotoxicity of J. procera extract transformed into ZnO nanocomposites was also considerably increased [24]. Extracts from a different Juniperus species are believed to have anticancer properties. Juniperus communis has differing degrees of cancer-cell-proliferation inhibition that may be extracted using various solvents. Juniperus phoenicea extracts created with different solvents, such as n-hexane, chloroform, and methanol, demonstrated that cell-proliferation was suppressed in human lung (A549), breast (MCF-7), and liver (HepG2) cancer cells, with the MCF-7 cell line being the most sensitive, with IC50 values of 24.5 μg/mL [25].
Stankovic and colleagues found that the different species of Teucrium extract on the HCT116 cell line displayed increased cytotoxicity at higher doses after 72 h of exposure. At lower doses and with longer exposure times, the extract stimulates some proliferative effects in surviving cells [26]. These findings show that the cytotoxic impact is affected by various parameters, including the kind of solvent used for extraction, plant components utilized, cell lines tested, and the treatment period.
Figure 4 depicts the molecular docking of 2H-1-benzopyran-3-carbothiamide (Hit 1) with 3ig7 CDK-5 (receptor). The CDK-5/Hit 1 complex was found in the residues Leu83, Lys33, Asp145, and Val18. The Hit 1 compound (ligand) interacts with Lys33 and Asp145 in CDK-5 (receptor) through a hydrogen bond. Lys33 was identified as an H-donor to the -NH group, whereas Asp145 was an H-acceptor from the -NH2 group. A hydrophobic interaction between Val18 and the aromatic ring of the Hit 1 chemical was found. The Hit 1 chemical interacts with Leu83 through a backbone interaction via an electron-withdrawing nitro group. In the erythroid spectrin/Hit 1 (1woa/Hit 1) complex, the residues contribute the most to Ser52 and Tyr53. The interaction of Ser52 and Tyr53 with the aromatic ring of the Hit 1 chemical was recognized as a hydrophobic interaction.
The molecular docking approach is used to anticipate the probable orientation of the ligand and receptor that results in the formation of a stable complex [27]. The chemical in a plant extract is referred to as a ligand, while the protein in cancer cells is referred to as a receptor. The binding affinity of the ligand and receptor can be used to predict the affinity and activity of a therapeutic molecule. It is also essential for comprehending the anticancer processes via which the active substance in the plant leaves may be detected.
In this study, molecular docking was performed on 12 different molecules identified by GC/MS and proteins in cancer cells, such as cyclin-dependent kinase 5, cytochrome P450 aromatase, erythroid spectrin, and topoisomerase. We found that 2-imino-6-nitro-2H-1-benzopyran-3-carbothiamide docked with all four targeted receptors mentioned above.
Cdk5 has lately been implicated in the formation and progression of several malignancies, including colon tumors [24,28]. Cdk5 (3ig7) is a viable therapeutic target receptor for developing novel cancer medicines due to its extensive protumorigenic activity. Cytochrome P450 aromatase is an enzyme responsible for catalyzing the estrogen hormone, which is known as a proliferative factor in breast cancer. Based on our docking data, Hit 1 docks with cytochrome P450 aromatase, which may lead to a reduction in the oncogenic activity of estrogen [29]. The erythroid spectrin is known for its important role in maintaining cell-membrane integrity and its contribution to the cell cycle and cell spreading. Here, we have shown that the chemical Hit 1 docked with the erythroid spectrin, which may prevent its role in the proliferation and spreading of cancer cells [30]. Topoisomerase is a critical enzyme in DNA strand cleavage that acts as a cellular controller during replication and transcription. We found that the Hit 1 compound docked with topoisomerase, which may result in a reduction in cancer-cell proliferation [31].
Nitroaromatic compounds are thought to be prodrugs for cancer treatment [32]. Nitroaromatic groups are thought to be trigger units that can take up to six electrons from reductase enzymes. This results in the creation of different reduced species and radicals, with a significant shift in electron density at nitrogen-carrying substituents, which might increase cellular toxicity as they act as DNA crosslinking agents and undergo sequential inhibition in the cell cycle.
Flow cytometry was used to examine apoptosis and necrosis in colon cancer cells (HCT116). Apoptosis is a type of programmed cell death that may be detected via DNA damage. It is a valuable marker for selecting chemicals for further research as potential anticancer medicines. The cell releases phosphatidylserine on the extracellular surface during apoptosis, which may be detected using Annexin V-FITC/propidium iodide (PI) fluorescence. Annexin V is a protein linked to a fluorescent green dye that indicates apoptosis. Propidium iodide (PI) is a fluorescent red dye that stains necrotic and late-apoptotic DNA. Comparative research was conducted under three distinct settings, including a control (untreated), an IC50 dosage of doxorubicin (DOX), and J. procera methanolic extract, to evaluate the amount of cell apoptosis and necrosis in the colon cancer cell (HCT116). These findings suggest that J. procera leaf extract may efficiently trigger apoptosis and necrosis in HCT116 cells.
To determine the level of cell-cycle arrest caused by J. procera extract at a certain phase, the percentages of the cell population in the interphase (G0, G1, S, and G2) and mitotic phase (M) were measured using flow cytometry and propidium iodide (PI) labeling. HCT116 cells treated with J. procera methanolic extract halted an increase in the S and G2/M phases by 2.5 and 1.4 times, respectively, and they reduced levels by 1.8 fold in the G1/G0 phases.
DOX is less effective than J. procera extract at moving cells from the G1/G0 phase to the next phase of the cell cycle. In contrast to DOX, the extract increases the number of cells in S-phase J. procera extracts ($30\%$). The number of cells in the G2/M phase increases significantly following JP extract treatment when compared to control and DOX. Furthermore, in the G2/M phase, DOX and the extract have nearly identical effects. It has been observed that anticancer medicines halt the cell cycle through a series of processes that occur at distinct stages in G1 or G2/M, followed by cell death via apoptosis [33]. During cell-cycle inhibition, anticancer drugs may cause DNA damage by causing cell stasis at various stages of the cell cycle, such as at G1 or G2/M, and thereby induce apoptosis. J. procera extracts have a cytotoxic impact on cancer cells via cell-cycle arrest, which is produced by DNA damage and the stalling of cells at the G1 or G2/M phase, resulting in apoptosis. As a result, J. procera methanolic extract has apoptosis-inducing properties. The response of the extract and reductions in cell growth depend on the cell line, the concentration of the extract, and the treatment time [18,34]. The proportion of cells in the cell-cycle phase was determined after 24 h in the current investigation. By prolonging the treatment duration, it is feasible to reach the level of the most arrested cells at various stages of the cell cycle. Collectively, our demonstration of the anticancer action of J. procera methanolic extract emphasizes the need for more studies on the bioactive compounds that may inhibit specific oncogenic targets in cancer cells.
## 4.1. Plant Material
The herbalist Mr. Ali Mdawei collected fresh J. procera Hochst ex Endl. ( Cupressaceae) leaves in April 2021 from Bahat Rabia, Asir region, Saudi Arabia (GPS coordinates 18.326245 0N, 42.321546 0E). The leaves were washed under running water, dried in the shade, and then ground into powder.
## 4.2. Extraction of Plant Material
The solvent n-hexane, dichloromethane, ethyl acetate, or methanol was used to extract air-dried, milled J. procera leaves (50 g/500 mL) for 24 h in a Soxhlet extractor, yielding 1.5, 0.9, 1.1, and 2.2 g of extract, respectively. To obtain phenolic bioactive components, methanol J. procera extract was treated using the GC/MS spectroscopic method.
## 4.3. Human Cell Line and Culture Conditions
Four human cell lines were available from the Tissue Culture Unit, Department of Biochemistry, Faculty of Science, King Abdulaziz University: human colorectal carcinoma cell line (HCT116), hepatoma G2 (HepG2), breast cancer cell line (Michigan Cancer Foundation–7) (MCF-7), and hemopoietic erythroid cell line (JK-1). The attached human cell lines were grown for 24 h in complete media, Dulbecco’s Modified Eagle Medium (DMEM), and JK human cell lines in Roswell Park Memorial *Institute medium* (RPMI 1640, which contains $10\%$ fetal bovine serum and $1\%$ antibiotic). The DMEM and RPMI 1640 were supplied by Life Technologies Gibco. The cells were incubated in a $5\%$ CO2 incubator at 37 °C and $95\%$ humidity.
After receiving 4 mL of $0.25\%$ trypsin with EDTA, $90\%$ of the confluent cells were collected and incubated in a CO2 incubator for 5 min. After 5 mL of complete medium was added, the trypsin process was stopped. The media-containing unattached cells were centrifuged, and the pellets were washed twice with sterile phosphate-buffered saline (PBS) [35,36].
The number of cells was determined using a hemocytometer and counted in the four primary squares after 20 μL of this cell-containing media were stained with $0.4\%$ trypan blue. The number of cells per ml was calculated using the following equation: $\frac{1}{4}$ × 104 × 2. A total of 0.1 mL of 5000 cells suspended in complete media was placed in each well of a 96-well microplate, and the plate was then incubated in the incubator for 24 h.
## 4.4. Evaluations of the IC50 of J. procera in Human Cell Lines Using the MTT Assay
Using the MTT assay to evaluate the metabolic activity and cell viability of cancer cells, J. procera methanolic extract’s potency as an anticancer agent was assessed. Different amounts of J. procera methanolic extract, ranging from 12.5 to 200 μg/mL, were applied to the media once $70\%$ of the cells in each well had reached confluence. We repeated each concentration 4 times. Then, 96-well plates were incubated for 48 h before the media in each well were replaced with 100 μL of free media containing 0.5 mg of MTT/mL for 4 h in the incubator. Dimethylsulfoxide (DMSO) at a dosage of 100 μL was added to each well and left to remain at room temperature for 15 min before being detected at 595 nm with a microplate reader (Bio-RAD microplate reader, Hercules, CA, USA). Using the curve of cell viability vs. different concentrations of J. procera extract, the $50\%$ inhibitory concentration (IC50) of J. procera methanolic extract against cell lines was calculated [35,36].
## 4.5. Assessment of Apoptosis in HCT116 Cells Treated with J. procera
For 24 h, HCT116 was grown in a CO2 incubator. Cells were divided and counted using trypsin. In a 6-well plate, 2 × 105 cells were grown for 24 h. The well’s medium was changed to complete media containing the IC50 of the methanolic extract of J. procera. Trypsin was used to separate the HCT116 cells after 24 h, and the medium from each well containing cells was then gathered into tubes and centrifuged. After that, phosphate-buffered saline (PPS) solution was used to wash the pellets. To 100 μL of suspended treated HCT116 cells, 400 μL of binding buffer and 25 μL of Annexin V-FITC/propidium iodide (PI) solution were added. The cells were detected using a flow-cytometry device. The software module computed data automatically [37].
## 4.6. Evaluation of the Cell Cycle in HCT116 Treated with J. procera
PI from ThermoFisher Scientific can attach to DNA, stain it, and measure cellular aggregation throughout the cell cycle using flow cytometry [38]. A total of 1 × 106 HCT116 cells were cultivated on a 6-well plate for 24 h. The medium was replaced with a medium containing J. procera extract at the IC50 level. To collect the treated HCT116 cells after 24 h, 0.5 mL of $0.25\%$ trypsin was added to each well, and trypsin activity was stopped with 0.5 mL of complete medium. The suspended cells were rinsed twice with PBS after centrifuging for 5 min at 1500 rpm. The cells were placed in 1 mL of ice-cold $70\%$ ethanol and frozen for at least 4 h at −20 °C. After a 100 μL wash with cold PBS containing RNase A, the suspended cells were stained with 250 μL of PI solution (50 mg/mL PI) and allowed to rest in the dark for 1 h. Every cell that had been designated was read using a flow cytometer (Applied Bio-system, Hercules, CA, USA).
## 4.7. GS/MS Examination of Methanolic Extract of J. procera Leaves
J. procera extract was phytochemically analyzed using a gas chromatograph (Agilent Technologies, Santa Clara, CA USA) with mass spectrometry (GC/MS 7890B). The GC/MS device has a 59,778 mass-selective detector and an HP-5MS 30 m GC column (30 m length, 0.25 mm inner diameter, and 0.25 m film thickness). Helium ($99.99\%$ purity) was used as the carrier gas, with a column flow rate of 1 mL/min. Agilent Technologies’ ChemStation program (Agilent Technologies 7890B) was utilized for system control and data processing. The splitless injection mode was used, with a 1 L injection volume and a split ratio of 1:10. The temperature of the input injection was fixed to 250 °C. The column temperature schedule was as follows: 50 °C for 1 min, 50 °C to 200 °C for 10 min and held at this temperature for 5 min, and 200 °C to 300 °C for 15 min and held at this temperature for 10 min, for a total run time of 37.6 min. Electron impact ionization (EI) at 70 eV was used to ionize the ions in mass spectrometry, and spectra were monitored in the selected ion monitoring (SIM) mode with an m/z ratio of 40 to 500 or a time-of-flight detector. The bioactive components’ chemical names, molecular formulas, and molecular structures were determined by comparing them to the spectrum of known components listed in the NIST library (National Institute of Standards and Technology) (NIST 2.0).
## 4.8. Molecular Docking Study of J. procera Extract with Four Different Cancer Proteins
The molecular interactions between the active substances extracted from J. procera that theoretically bind to four different active proteins impact cell proliferation, receptors, the integrity of cell membranes, and DNA conformation. Molecular docking was applied to investigate cyclin-dependent kinase 5 (Cdk5) in colon cancer (PDB code = 3ig7 [28], aromatase cytochrome P450 in breast cancer (PDB code = 3eqm) [29], the N-terminal domain of the erythroid spectrin in erythroid cancer (PDB code = 1owa) [30], and topoisomerase in liver cancer (PDB code = 4fm9)” [31]. The MOE 2019.102 platform was used for all docking research. Each bioactive compound and all protein interactions were simulated in 2D and 3D. The binding energies (E) and chemical interactions of the 12 drug-like compounds docked to the protein targets were thoroughly examined.
Protein 3D structures were retrieved as pdb files from the Protein Data Bank https://www.rcsb.org/ (accessed on 3 February 2023) after eliminating all solvent molecules and correcting all structures and charges, as previously reported [39]. Active sites were defined as the presence of the active medication or cocrystalline ligand and were isolated as dummy atoms. The docking results were generated by utilizing Triangle Matcher with stiff protein, and the docking score was determined for 30 postures using the London dG method, with the best 5 poses abstracted. The docking score (S), the root-mean-square deviation (RMSD) between the cocrystal and docked conformation, and the binding energies (Es) of each plant-derived molecule were used to calculate the findings. To determine the variations in binding affinities, the binding energies of each chemical were compared. The molecular interactions of the best-docked compounds with the target proteins were thoroughly investigated (Figure 8).
The cocrystalline molecule N-1-[cis-3-(acetylamino)cyclobutyl]-1H-imidazol-4-yl-2-(4-methoxyphenyl)acetamide was obtained from the database and used for the validation of the docking methods. The docked structure had a −7.86 kcal/mol docking score and an RMSD of 1.198.A0. The machine used for this investigation was configured with Windows 10 and an Intel (R) Core (TM) i7-8550U CPU running at 1.80 GHz and 1.99 GHz.
## 4.9. Statistical Analysis
The results are presented as the mean SDs of the viability of treated cells. The absorbance of treated cells * 100 divided by the absorbance of untreated cells was used to compute the percentage of viability. GraphPad Prism Software (version 9.0, San Diego, CA, USA) was used to determine the drug IC50. The flow-cytometry software from Applied Biosystems determined the percentage of cells in each phase, as well as the quantity of necrotic and apoptotic cells, automatically.
## 5. Conclusions
This study demonstrates that J. procera leaf extract is cytotoxic to the cancer cell lines HepG2, MCF-7, HCT116, and JK. Additionally, J. procera leaf extracts cause the death of HCT116 cells by arresting the cell cycle, which is caused by DNA damage and results in cell-stalling at the G1 or G2/M phase. A GC/MS analysis of J. procera leaf extracts showed 12 unique bioactive components. These findings imply that the extract of J. procera leaves contains bioactive substances that may be used as anticancer medications. A decrease in breast cancer cell-proliferation may also result from Hit 1 inhibition of aromatase, a cytochrome P450 enzyme that catalyzes estrogen generation in breast cancer patients with positive estrogenic receptors (MCF-7). Additionally, the function of erythroid spectrin in maintaining the integrity and adherence of cell membranes may be impacted by Hit 1’s reduction in production on the JK cell line. The results of molecular docking suggest that Hit 1’s reduction in proliferation and cell-cycle arrest in liver cancer cells may be caused by topoisomerase inactivation. Purification of the bioactive compound 2-Imino-6-nitro-2H-1-benzopyran-3-carbothiamide from J. procera is required to investigate its cytotoxicity against various cancer cell lines in vitro and in vivo.
## References
1. Ferlay J., Colombet M., Soerjomataram I., Mathers C., Parkin D.M., Piñeros M., Znaor A., Bray F.. **Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods**. *Int. J. Cancer* (2019) **144** 1941-1953. DOI: 10.1002/ijc.31937
2. Dong J., Chen H.. **Cardiotoxicity of anticancer therapeutics**. *Front. Cardiovasc. Med.* (2018) **5** 9. DOI: 10.3389/fcvm.2018.00009
3. Kilickap S., Akgul E., Aksoy S., Aytemir K., Barista I.. **Doxorubicin-induced second degree and complete atrioventricular block**. *EP Eur.* (2005) **7** 227-230. DOI: 10.1016/j.eupc.2004.12.012
4. Al-Zahrani A., Ibraheem F., El-Senduny F.F.. **Anticancer activities of Saudi**. *Res. Sq.* (2022) 1-25. DOI: 10.21203/rs.3.rs-1666736/v1
5. Hosseinzadeh S., Jafarikukhdan A., Hosseini A., Armand R.. **The application of medicinal plants in traditional and modern medicine: A review of Thymus vulgaris**. *Int. J. Clin. Med.* (2015) **6** 635. DOI: 10.4236/ijcm.2015.69084
6. Mintah S.O., Asafo-Agyei T., Archer M.-A., Junior P.A.-A., Boamah D., Kumadoh D., Appiah A., Ocloo A., Boakye Y.D., Agyare C.. **Medicinal plants for treatment of prevalent diseases**. *Pharmacogn. Med. Plants* (2019) **9** 1-19
7. Al-Attar A.M., Alrobai A.A., Almalki D.A.. **Effect of Olea oleaster and**. *Saudi J. Biol. Sci.* (2016) **23** 363-371. DOI: 10.1016/j.sjbs.2015.08.011
8. Dailah H.G.. **The ethnomedicinal evidences pertaining to traditional medicinal herbs used in the treatment of respiratory illnesses and disorders in Saudi Arabia: A review**. *Saudi J. Biol. Sci.* (2022) **29** 103386. DOI: 10.1016/j.sjbs.2022.103386
9. Jansen P.C.M.. *Spices, Condiments and Medicinal Plants in Ethiopia, Their Taxonomy and Agricultural Significance* (1981)
10. Topçu G., Erenler R., Çakmak O., Johansson C.B., Çelik C., Chai H.-B., Pezzuto J.M.. **Diterpenes from the berries of**. *Phytochemistry* (1999) **50** 1195-1199. DOI: 10.1016/S0031-9422(98)00675-X
11. Alkhedaide A., Nassan M.A., Ismail T.A., Soliman M.M., Mohamed E.H., Amer H.H., Aldhahrani A.. **Hypoglycemic and antioxidant effect of**. *Pathophysiology* (2019) **26** 361-368. DOI: 10.1016/j.pathophys.2019.11.001
12. Alkhedaide A.Q.. **Anti-inflammatory effect of**. *Anti-Inflamm. Anti-Allergy Agents Med. Chem. Former. Curr. Med. Chem. Anti-Inflamm. Anti-Allergy Agents* (2019) **18** 71-79. DOI: 10.2174/1871523018666181126124336
13. AlShahrani A., AlShahrani I., Hosmani J., Togoo R.A., Sakinatulain T., Alam T., Hameed M.S.. **Anticancer activity**. *Pharmacogn. Mag.* (2020) **16** 499
14. El-Said H., Ashgar S.S., Bader A., AlQathama A., Halwani M., Ascrizzi R., Flamini G.. **Essential oil analysis and antimicrobial evaluation of three aromatic plant species growing in Saudi Arabia**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26040959
15. Karunamoorthi K., Girmay A., Fekadu S.. **Larvicidal efficacy of Ethiopian ethnomedicinal plant**. *Asian Pac. J. Trop. Biomed.* (2014) **4** S99-S106. DOI: 10.12980/APJTB.4.2014C687
16. Almadiy A.A.. **Chemical composition, insecticidal and biochemical effects of two plant oils and their major fractions against Aedes aegypti, the common vector of dengue fever**. *Heliyon* (2020) **6** e04915. DOI: 10.1016/j.heliyon.2020.e04915
17. Kampa M., Nifli A.-P., Notas G., Castanas E.. **Polyphenols and cancer cell growth**. *Rev. Physiol. Biochem. Pharmacol.* (2007) **159** 79-113. PMID: 17551696
18. Balan K.V., Demetzos C., Prince J., Dimas K., Cladaras M., Han Z., Wyche J.H., Pantazis P.. **Induction of apoptosis in human colon cancer HCT116 cells treated with an extract of the plant product, Chios mastic gum**. *Vivo* (2005) **19** 93-102
19. Thuwaini M.M.. **Natural sources as promising future anticancer therapies-A review**. *GSC Biol. Pharm. Sci.* (2022) **19** 084-113. DOI: 10.30574/gscbps.2022.19.2.0178
20. Magdah G.. **Cytogenetic toxicity of**. *Int. J. Pharmacol.* (2019) **15** 576-585
21. Galati G., Teng S., Moridani M.Y., Chan T.S., O’Brien P.J.. **Cancer chemoprevention and apoptosis mechanisms induced by dietary polyphenolics**. *Drug Metab. Drug Interact.* (2000) **17** 311-350. DOI: 10.1515/DMDI.2000.17.1-4.311
22. Adnan M., Oh K.K., Azad M.O.K., Shin M.H., Wang M.-H., Cho D.H.. **Kenaf (**. *Life* (2020) **10**. DOI: 10.3390/life10100223
23. Salih A.M., Al-Qurainy F., Nadeem M., Tarroum M., Khan S., Shaikhaldein H.O., Al-Hashimi A., Alfagham A., Alkahtani J.. **Optimization method for phenolic compounds extraction from medicinal plant (Juniperus procera) and phytochemicals screening**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26247454
24. Alghamdi M.D., Nazreen S., Ali N.M., Amna T.. **ZnO Nanocomposites of**. *Nanomaterials* (2022) **12**. DOI: 10.3390/nano12040664
25. Fernandez A., Cock I.E.. **The therapeutic properties of**. *Pharmacogn. J.* (2016) **8** 273-280. DOI: 10.5530/pj.2016.3.17
26. Stankovic M.S., Curcic M.G., Zizic J.B., Topuzovic M.D., Solujic S.R., Markovic S.D.. **Teucrium plant species as natural sources of novel anticancer compounds: Antiproliferative, proapoptotic and antioxidant properties**. *Int. J. Mol. Sci.* (2011) **12** 4190-4205. DOI: 10.3390/ijms12074190
27. Alamri M.A., Al-Jahdali M., Al-Radadi N.S., Hussien M.A.. **Characterization, theoretical investigation, and biological applications of Mn (II), Co (II), Ni (II), Cu (II), and Zn (II) complexes of a triazene ligand containing a benzothiazole ring**. *Appl. Organomet. Chem.* (2022) **36** e6466. DOI: 10.1002/aoc.6466
28. Pozo K., Bibb J.A.. **The emerging role of Cdk5 in cancer**. *Trends Cancer* (2016) **2** 606-618. DOI: 10.1016/j.trecan.2016.09.001
29. Sharfalddin A.A., Emwas A.-H., Jaremko M., Hussien M.A.. **Transition metal complexes of 6-mercaptopurine: Characterization, Theoretical calculation, DNA-Binding, molecular docking, and anticancer activity**. *Appl. Organomet. Chem.* (2021) **35** e6041. DOI: 10.1002/aoc.6041
30. Das D., Patra M., Chakrabarti A.. **Binding of hemin, hematoporphyrin, and protoporphyrin with erythroid spectrin: Fluorescence and molecular docking studies**. *Eur. Biophys. J.* (2015) **44** 171-182. DOI: 10.1007/s00249-015-1012-2
31. Eweas A.F., Khalifa N.M., Ismail N.S., Al-Omar M.A., Soliman A.M.M.. **Synthesis, molecular docking of novel 1, 8-naphthyridine derivatives and their cytotoxic activity against HepG2 cell lines**. *Med. Chem. Res.* (2014) **23** 76-86. DOI: 10.1007/s00044-013-0604-6
32. Denny W.A.. **Nitroaromatic Hypoxia-Activated Prodrugs for Cancer Therapy**. *Pharmaceuticals* (2022) **15**. DOI: 10.3390/ph15020187
33. Demarcq C., Bunch R.T., Creswell D., Eastman A.. **The role of cell cycle progression in cisplatin-induced apoptosis in Chinese hamster ovary cells**. *Cell Growth Differ.-Publ. Am. Assoc. Cancer Res.* (1994) **5** 983-994
34. Smith E., Palethorpe H.M., Tomita Y., Pei J.V., Townsend A.R., Price T.J., Young J.P., Yool A.J., Hardingham J.E.. **The purified extract from the medicinal plant Bacopa monnieri, bacopaside II, inhibits growth of colon cancer cells in vitro by inducing cell cycle arrest and apoptosis**. *Cells* (2018) **7**. DOI: 10.3390/cells7070081
35. Ali E.M., Elashkar A.A., El-Kassas H.Y., Salim E.I.. **Methotrexate loaded on magnetite iron nanoparticles coated with chitosan: Biosynthesis, characterization, and impact on human breast cancer MCF-7 cell line**. *Int. J. Biol. Macromol.* (2018) **120** 1170-1180. DOI: 10.1016/j.ijbiomac.2018.08.118
36. Plumb J.A.. **Cell sensitivity assays: The MTT assay**. *Cancer Cell Culture* (2004) 165-169
37. Rieger A.M., Nelson K.L., Konowalchuk J.D., Barreda D.R.. **Modified annexin V/propidium iodide apoptosis assay for accurate assessment of cell death**. *JoVE J. Vis. Exp.* (2011) **50** e2597
38. Fried J., Perez A.G., Clarkson B.D.. **Flow cytofluorometric analysis of cell cycle distributions using propidium iodide. Properties of the method and mathematical analysis of the data**. *J. Cell Biol.* (1976) **71** 172-181. DOI: 10.1083/jcb.71.1.172
39. Gurung A.B., Bhattacharjee A., Ali M.A.. **Exploring the physicochemical profile and the binding patterns of selected novel anticancer Himalayan plant derived active compounds with macromolecular targets**. *Inform. Med. Unlocked* (2016) **5** 1-14. DOI: 10.1016/j.imu.2016.09.004
|
---
title: Influence of High-Pressure Homogenization on the Physicochemical Properties
and Betalain Pigments of Red Beetroot (Beta vulgaris L.) Juice
authors:
- Bartosz Kruszewski
- Ewa Domian
- Małgorzata Nowacka
journal: Molecules
year: 2023
pmcid: PMC10004726
doi: 10.3390/molecules28052018
license: CC BY 4.0
---
# Influence of High-Pressure Homogenization on the Physicochemical Properties and Betalain Pigments of Red Beetroot (Beta vulgaris L.) Juice
## Abstract
High-pressure homogenization (HPH) is considered an innovative and modern method of processing and preserving liquid and semi-liquid foods. The aim of this research was to examine the impact of HPH processing on the content of betalain pigments and physicochemical properties of beetroot juice. Combinations of the following HPH parameters were tested: the pressure used (50, 100, 140 MPa), the number of cycles (1 and 3) and the applied cooling or no cooling. The physicochemical analysis of the obtained beetroot juices was based on the determination of the extract, acidity, turbidity, viscosity and color values. Use of higher pressures and a greater number of cycles reduces the turbidity (NTU) of the juice. Moreover, in order to maintain the highest possible extract content and a slight color change of the beetroot juice, it was crucial to perform sample cooling after the HPH process. The quantitative and qualitative profiles of betalains have been also determined in the juices. In terms of the content of betacyanins and betaxanthins, the highest values were found in untreated juice at 75.3 mg and 24.8 mg per 100 mL, respectively. The high-pressure homogenization process resulted in a decrease in the content of betacyanins in the range of 8.5–$20.2\%$ and of betaxanthins in the range of 6.5–$15.0\%$, depending on the parameters used. Studies have shown that that the number of cycles was irrelevant, but an increase in pressure from 50 MPa to 100 or 140 MPa had a negative effect on pigment content. Additionally, juice cooling significantly limits the degradation of betalains in beetroot juice.
## 1. Introduction
Red beetroots (*Beta vulgaris* L.) and juice made from them are becoming more and more valuable to consumers worldwide due to the growing number of scientific reports on the health benefits of their consumption [1,2]. Beetroot of many varieties and shapes is cultivated on all continents in the temperate climate zone. According to recent reports, *Poland is* the largest producer of red beets in the European Union (EU) with a $35\%$ share in total production [3]. This vegetable is exported primarily to Slovakia (4.3 thousand tons, $24\%$ share of export volume), Czech Republic (3.6 thousand tons, $20\%$) and Romania (2.3 thousand tons, $13\%$). Ukraine was also a significant buyer (1.8 thousand tons, $10\%$) [3]. Beetroot is eagerly consumed in Poland and Europe (about $8\%$ of total EU vegetables volume consumed). Due to its good storability, it can be available fresh for almost the entire year.
Red beetroot is considered a health-promoting food due to the presence of nutritional and bioactive components such as vitamins, minerals, phenols, nitrates and betalains [4]. It contains vitamins such as C, A, E and K and is also abundant in vitamins from the B group [5]. Beetroot is not only a source of vitamins but also of minerals, which include manganese, magnesium, potassium, sodium, phosphorus, iron, zinc, copper, boron, silicon and selenium [5]. Thanks to its high fiber content, it has a beneficial effect on digestive processes [6]. Beetroot is among the top ten vegetables most abundant in antioxidant compounds [4,7]. The flesh and juice contain high amount of flavonoids, flavonols, otho-diphenols, condensed tannins and other substances classified to antioxidants [8]. It is also easily digestible and low in calories [5]. Red beetroot is a valuable raw material commonly used in the processing industry for the production of various types of dried, frozen, fermented and canned foods, as well as juices and their concentrates [9]. Most importantly, it is used in food industry in the form of juice concentrate as a coloring food and as a raw material for the extraction of the natural food additive betanin dye E162 [6].
The main source of betalains in nature is beetroot (B. vulgaris L.), especially its peel, but they are also found in some parts of amaranth and the fruits of Opuntia and Hylocerasus cactus, as well as in mushroom species such as *Amanita muscaria* [10]. The pigments are water-soluble and divided into two groups in terms of their molecular structure: red betacyanins and yellow betaxanthins. Their quantitative ratio determines the color and it depends on the plant variety [11]. The predominant pigment is betanin, which belongs to the betacyanins group. However, all betalains exhibit antioxidant properties, with proven lipid peroxidation preventive activity [9,12]. Scientific studies have shown that the stability of betalains is affected by pH, water activity, metal cations (such as iron, copper, tin, aluminum), oxygen concentration, light availability and the presence of endogenous enzymes and antioxidant compounds [6,9]. Processing parameters that need to be monitored because they affect betalain content in food include temperature and duration of heating, oxygen availability and pigment concentration [10,12].
High-pressure homogenization (HPH) together with high hydrostatic pressure (HHP) belongs to a group of innovative food processing and preservation methods based on application of high pressure [13,14]. Originally, HPH was only used to produce good quality emulsions and homogenize complex liquid products as a standardization step. It involves forcing liquid or semi-liquid products under pressure through a valve with a narrow gap of different geometries [13,15]. As a result of physical phenomena such as friction, collision, cavitation and turbulence, a mechanical reduction of particles and disruption of microbial cells occurs [16,17]. A side effect is a temperature increase on the valve and the product, dependent on height of the pressure drop within the valve. Therefore, in order to reduce the effect of temperature on bioactive compounds in the product, cooling of the valve or product after it exits the device is used [18,19].
Previous studies show a different effect of HPH on biologically active compounds in food, in relationship to the pressure applied, the temperature of the product at the input, and the number of product passes through the homogenizer [20,21]. Depending on the parameters and type of the pigments, experiments showed no effect or a decrease in content at level of 10–$30\%$. An increase in pigments concentration up to $10\%$ of the original value was also reported in some cases [18,19]. However, there is no information in the literature about the effect of the HPH method on the concentration of betalains. Therefore, the main objective of the study was to evaluate the effect of HPH betalain pigments of beetroot juice. Changes in basic physicochemical parameters (total soluble solids, pH, titratable acidity, direct turbidity, serum cloudiness, color and viscosity) were also investigated.
## 2.1. Temperature Changes during the HPH Processing
High-pressure homogenization treatment increased the temperature of the juices. The temperature increase was proportional to the increase of pressure used during processing. For homogenized samples at 50, 100 and 140 MPa, the juice temperature measured at the outlet of the device after cycle increased by a maximum of 7.4, 14.4 and 21.1 °C, respectively (Table 1). The influence of applied pressure is very evident. This is due to the physical phenomena occurring in the homogenizing valve such as shear, cavitation, turbulence and impacts with surfaces, that become more intense as the pressure increases. According to Dumay et al. [ 15] a total increase of temperature of various products falls within the range of 17–21 °C per 100 MPa. It comprises [1] fluid temperature increase with the homogenization pressure by 2–3 °C per 100 MPa, due to the heat of compression generated during the pressure build-up in the pressure intensifier; [2] linear increase with the homogenization pressure by 14–18 °C per 100 MPa, due to shear effects and partial conversion of mechanical energy into heat. Most of the pressure is dissipated as heat, and only a small part is used for mechanical disintegration of particles. In the present study there was obtained a temperature rise lower than the given relation, but it should be noted that the intensity of physical phenomena during the process is affected by the architecture of the valve, valve construction material characteristics and the composition of the homogenized product. Our homogenizer used for experiments had a sharp-angle type valve, and was made of abrasion- and corrosion-resistant ceramic and Duplex stainless steel.
The use of relatively low inlet temperatures of the product in the HPH processing is one of the methods to preserve thermolabile bioactive components. However, it is not always advisable to use a low inlet temperature because less deactivation of microorganisms and enzymes is obtained [22,23]. A second method is cooling after the process with a heat exchanger or cooling the valve [19,24].
HPH processing at 140 MPa without cooling after one cycle caused a +21.2 °C rise in juice temperature. However, after three cycles, it resulted in a relatively small temperature rise (change +15.8 °C) compared to the temperature recorded after one cycle under the same conditions (Table 1). This was surprisingly less than expected. The temperature of the product was not increasing by 21 °C on each cycle, because the beetroot juice was passively cooled from the temporary collection container awaiting the next entry into the homogenizer. In addition, the juice was also passively cooled on the components of the device, as only the valve itself had an elevated temperature. The ambient temperature during the processing was about 19 °C.
## 2.2. Total Soluble Solids (TSS), pH and Titratable Acidity (TA)
According to the results (Table 2), the high-pressure homogenization has not affected the TSS content of the beetroot juice when cooling was applied. Significant changes in the reduction of TSS occurred (−0.4 °Brix) when the same treatment was applied but without cooling. Apparently, the presence of heat reduced the extract. The literature states that under the influence of elevated temperature residual protein precipitation and depletion of reducing sugars takes place [25,26]. No effect of pressure parameters and number of cycles was observed on the TSS content of the juice. In a previous study on blackcurrant juice [18], as well as in the present research, there was no impact of the number of cycles on extract content, but the effect of pressure was significant. However, the reduction in TSS occurred only as a result of the pressure change to the level of 220 MPa. Other researchers have found no change in TSS in juices under high-pressure homogenization [17,21] or have pointed to a direct impact of pressure and number of cycles [26].
pH and TA are very important parameters of beverages from a safety point of view and have a direct impact on the choice of preservation methods and storage conditions. The results show no change in the TA of the juice, while changes were observed in pH (Table 2). The pH value slightly but statistically significantly increased by an average of 0.13 in all homogenized samples regardless of whether cooling was used. This could have been due to the extraction of alkaline-forming compounds from residual particles. Our results are in contrast with those reported by Velázquez-Estrada et al. [ 24] who observed no changes in pH and significant decrease of TA in HPH-processed orange juice. The authors pointed to a greater influence of inlet temperature than pressure alone on pH and TA characteristics. However, it should be noted that beetroot juice, in contrast to orange juice, has a different composition of sugars, organic acids and bioactive compounds, so the effect of HPH may have been different.
## 2.3. Direct Turbidity and Serum Cloudiness
Measurement of turbidity (NTU) indicated that very cloudy beetroot juice was obtained (Figure 1a). Turbidity is usually formed by pectin, fats, cellulose compounds, proteins and their complexes with various substances, as well as other compounds [27]. Based on the results of the study, there is a significant effect of the homogenization process on the turbidity of all juice samples. The number of cycles was relevant only at 50 MPa. Increasing the pressure from 50 MPa to 100 or 140 MPa resulted in an even greater reduction in turbidity, although no statistical difference was observed between 100 and 140 MPa. The best effect of reducing NTU among all juice samples was achieved in the variant with a pressure of 140 MPa, especially the three cycles with cooling, and the one cycle without cooling. The HPH process mechanically reduces the size of particles suspended in the juice matrix [27], which allows more light to pass through without reflection. Additionally, in HPH samples without cooling, the elevated temperature may have led to particle precipitation as suggested by the TSS values (Table 2). In an earlier publication, the impact of pressure parameters and number of cycles on the direct turbidity of blackcurrant juice was also observed, but was more intense [18].
Serum cloudiness of the raw beetroot juice, presented in the Figure 1b, was high (1.83) compared to the values obtained in other juices reported in the literature. Velázquez-Estrada et al. [ 24] obtained serum cloudiness at the level of 0.46 in orange juice, while Silva et al. [ 27] measured it at an average of 0.26 in pineapple pulp. These values suggest that the beetroot juice from our study had a more colloidal matrix. Homogenization resulted in an increase in serum turbidity in all variants (Figure 1b). The sample with the most severe parameters (140 MPa, three cycles, no cooling) in the experiment had the highest value of about 1.5 times the original value. In view of the above, the matrix of the juice has become even more colloidal, which potentially stabilized the juice and delayed sedimentation during further storage. Some researchers link the increase in serum turbidity to a decrease in particle size [20,24].
## 2.4. Influence of HPH on Juice Color and Viscosity
HPH treatment had a direct effect on the color of the red beetroot juice (Table 3). Samples processed at 50, 100 and 140 MPa but with only one cycle had a similar color, slightly altered from the raw juice (ΔE* in range 0.13–0.17). Greater, statistically significant changes in color were noted when the juice was homogenized three times (ΔE* in range 0.29–0.43). Samples that were not cooled after homogenization had the highest ΔE* values, 0.69 and 0.94 for 140 MPa/1 cycle and 140 MPa/3 cycles, respectively. The only color parameter that changed in all samples was the a*, which was decreasing as a result of more severe HPH parameters. The reason is the degradation of the juice’s betalain pigments, particularly betacyanins, as discussed in Section 2.5. Scientific reports describe various influences of HPH on product color parameters. In processed blackcurrant juice, both L* and b* values increased, while the a* value decreased, resulting in obtained ΔE* value at 3.33 [18]. HPH treatment of strawberry-based smoothie impacted L* and a* value positively [28]. The changes were explained by modifications in particle size and shape, their aggregation, oxidative reactions and caramelization of fruit sugars. However, all of these studies were conducted on different HPH equipment, and in addition, the products had different formulation.
The viscosity of a liquid is the internal friction that occurs during its flow. It can be described also as a resistance of a liquid to a change in shape, or movement of neighboring portions relative to one another. Knowledge of the physical properties, including rheological parameters such as viscosity of liquid food products is very important during the design of processes and industrial equipment, as well as at the stage of product development. Based on the study results, it can be stated that the HPH process reduced the viscosity of the beetroot juice. The cooling intervention during HPH did not significantly affect the viscosity values (Table 3). The process of HPH breaks up solid particles in suspensions, and consequently, the viscosity of the fluid decreases or increases depending on the type of matrix and the particles contained in it, as well as their size. Szczepańska et al. [ 29] obtained a significant decrease in the viscosity of apple juice after applying HPH; they observed the lowest viscosity at 200 MPa. According to the observations, this could be related to more significant changes in particle size distribution (greater reduction in particle size). In contrast, other researchers reported an increase of apparent viscosity in mango juice after HPH processing [30]. However, they suggested that the increase could have been partially due to an increase in the solubility of high molecular weight carbohydrates such as starch and pectin. Furthermore, inactivation of pectin-degrading enzymes by HPH prevents pectin depolymerization, resulting in higher serum viscosity and higher juice consistency [30].
## 2.5. Qualitative and Quantitative Determinations of Betalains
High-performance liquid chromatography with diode-array detection (HPLC-DAD) analysis allowed identification of the betacyanins compounds in beetroot juice samples: betanin, isobetanin (Figure A1) and the betaxanthins vulgaxanthin I and II (Figure A2). The conditions of the chromatographic analysis allowed clear separation of the individual components. Due to the lack of commercially available quantitative standards, the concentration of betalain pigments was determined spectrophotometrically. Changes in betalain pigments determined both by the chromatographic and spectrophotometric methods after the HPH process are shown in Table 4.
Losses of both groups of betacyanins were observed in juice samples. Among the betacyanins, the betanin was the most resistant pigment to the process conditions. However, the losses of betanin, defined as the change in peak area, were $12.7\%$, $15.2\%$ and $16.2\%$ for pressures of 50, 100 and 140 MPa with cooling, respectively. For isobetanin, the losses were $14.2\%$, $18.2\%$ and $19.8\%$ for 50, 100 and 140 MPa with cooling, respectively. There was statistically significant impact of pressure, but no effect of the number of cycles on the betanin or isobetanin peak area. Increased heat dosage in samples without cooling resulted in even greater betanin and isobetanin degradation. Moreover, in this type of treatment, three cycles resulted in a statistically significantly higher degradation of isobetanin compared to one cycle (Table 4). Quantification of total betacyanins confirms chromatographic studies. The linear correlation analysis performed between these two determinations resulted in a Pearson’s correlation coefficient of $r = 0.981$ and determination coefficient r2 = 0.962. The content of betacyanins dropped from 75.3 mg/100 mL to the minimum value of 65.3 mg/100 mL (about $13\%$) in HPH cooled juice samples. Additionally, an increased degrading effect of lack of cooling during HPH process on betacyanins was observed (total $20\%$ reduction).
The chromatographic determination of vulgaxanthin I and II also showed an effect of HPH treatment. However, losses were less than recorded for betanin and isobetanin (Table 4). There was an influence of homogenization pressure but no effect of the number of cycles on the peak area of vulgaxanthins. The smallest degradation was caused by a pressure of 50 MPa, followed by pressures of 100 and 140 MPa (these two caused similar decreases). HPH samples without cooling showed even more progressive vulgaxanthin degradation. The linear correlation analysis performed between chromatographic and spectrophotometric determinations of betaxanthin pigments resulted in a Pearson’s correlation coefficient of $r = 0.960$ and determination coefficient r2 = 0.921. The content of betaxanthins dropped from 24.8 mg/100 mL to the minimum value of 22.1 mg/100 mL (about $11\%$) in HPH cooled juice samples. The not-cooled samples had minimally lower concentrations of these substances (total $15\%$ reduction).
According to the literature, betalain pigments are more stable to negative processing and storage conditions such as temperature, pH, light and presence of oxygen than anthocyanins commonly present in food [31,32]. This is confirmed after comparing to the results of anthocyanin concentrations in blackcurrant juices previously processed under similar HPH parameters [18]. Both individual anthocyanin monomers and their total content recorded higher levels of degradation than content of betalains in present study. Furthermore, it was observed that betaxanthins were more resistant to HPH processing conditions than betacyanins. We presume that the degradation of betalains in the beetroot juice during HPH treatment was partially due to oxidation reactions with the oxygen present in the juice, and partially due to exposure of these compounds to endogenous enzymes. To some extent these reactions were limited by reducing substances such as flavonoids, flavonols, phenolic acids and other antioxidants, which are abundant in red beetroot. In addition, in the HPH samples without cooling, there is an additional degradation mechanism in the form of the thermal induction of hydrolysis of the aldimine bond of betanin and isobetanin with production of the betalamic acid and cyclo-Dopa 5-O-β-glucoside [31]. This is favored by a pH > 6, which characterized the samples. According to Skalicky et al. [ 33], the effect of high temperature can also cause the loss of conjugated sugar, which leads to the formation of labile aglycones with a different λmax. Another mechanism of thermal degradation of betanin and isobetanin involves decarboxylation and dehydrogenation. However, the loss of one carboxyl group did not affect the betanidin chromophore, and the resulting molecule is even more stable [33,34]. Other researchers confirm the effect of elevated temperatures on betalains’ degradation [35,36].
## 2.6. Comprehensive Overwiew of All Samples—PCA Analysis Results
Principal component analysis (PCA) figures, based on the first two principal components which explained $94.11\%$ of the total variance, demonstrate grouping of the beetroot juice samples according to parameters of HPH treatment (Figure 2a,b).
Based on the PCA figures, the pressure parameters of 50, 100 and 140 MPa in combination with one or three cycles, when the samples were cooled, have formed a single group on the created plane. Samples of individual HPH juices differed in their composition and properties, as the group they formed is not so compact. Figure 2a shows that these samples are located far from the control sample, which means that each combination of HPH processing parameters significantly affected the characteristics of the juice. A separate group was formed by samples after HPH treatment but without cooling applied. These samples had the lowest content of betalain compounds and the highest difference in color compared to the raw juice, but some similar values of pH, NTU, viscosity and serum cloudiness compared to HPH samples with applied cooling. Based on PCA analysis, the pressure of 140 MPa and one or three cycles without cooling are not recommended for use.
## 3.1. Juice Preparation and HPH Treatment
The material for the study was the juice from red beetroots of the “Czerwona Kula” variety, harvested on a farm in the Mazovia Province, Poland. This variety was chosen for the study because it is rich in betalain pigments and dedicated to industrial processing. The raw material was washed, allowed to dry and then weighed. The juice was pressed using a RAVEN EWW002 slow-speed juicer (Poland) and filtered on a sterile 17-strand gauze. The process yield was $49\%$ (v/m). The obtained juice was poured into a collecting vessel, then separated by volume into a control sample and samples for processing.
The high-pressure homogenization (HPH) process was carried out on a PANDA 2K NS1001L manufactured by GEA NIRO SOAVI (Parma, Italy). Only the first stage valve was used by setting pressures of 50, 100 and 140 MPa, at flow rate of 160 mL/min. According to the technical specification, the homogenizer has a sharp-angle type valve. Prior to the processing, the device was cleaned with $70\%$ ethanol. At each pressure parameter, beetroot juice at an inlet temperature of 20.5 ± 0.5 °C was passed through the homogenization valve one and three times (1 and 3 cycles). All juice samples were cooled immediately in a container with an ice water bath after each cycle to reduce heat influence and adjust temperature to 20.5 ± 0.5 °C. In addition, for a pressure of 140 MPa, an additional series of HPH processing was carried out (1 and 3 cycles) but without cooling after each cycle. The temperature of all juice samples at the outlet of the device was monitored (Figure 3). The experiment with all HPH parameters variants was performed in two independent replicates.
## 3.2. Analysis of Total Soluble Solids, pH and Titratable Acidity
The extract content expressed as total soluble solids (TSS) was determined by placing few drops of juice on the measuring prism of the Refracto 30PX refractometer from Mettler-Toledo (Switzerland). The result was read at 20 °C directly from the device in °Brix. The measurement was performed in triplicate for each sample.
Both pH and titratable acidity (TA) of beetroot juice samples were analyzed in triplicate using an automated titrator TitroLine® 5000 (SI Analytics®, Mainz, Germany). Before analyses, the titrator was calibrated with buffer solutions, and temperature of juice samples was adjusted to 23 °C. Titratable acidity was determined by titrating juice to pH 8.1 using 0.1 M sodium hydroxide. Results are expressed as g of citric acid per 100 mL of juice.
## 3.3. Direct Turbidity and Serum Cloudiness
The direct turbidity of the juice expressed in nephelometric turbidity units (NTU) was tested using a 2100 Q turbidimeter from HACH Lange GmbH (Berlin, Germany) based on the instrument’s instructions, at a range of 0–2000 NTU. Before analyses, the turbidimeter was calibrated against standards, and temperature of juice samples was adjusted to 23 °C. The analysis was made by placing into device a glass cuvette filled with diluted juice.
The adopted method of Wang et al. [ 21] was used to measure turbidity of the juice serum. Briefly, 6 mL of juice was placed in 15 mL tubes and centrifuged (20 °C, 10 min, 4200× g) on an MPW–352R device (MPW Med. Instruments, Warsaw, Poland). The supernatant was transferred into optical glass cuvettes and its absorbance was analyzed using a Shimadzu UV-1650PC spectrophotometer (Shimadzu Corp., Kyoto, Japan) at a wavelength of 660 nm. The absorbance result was directly related to the serum cloudiness.
Both measurements were carried out in triplicate.
## 3.4. Viscosity Measurement
Viscosity was measured using a Brookfield DV-II viscometer (AMETEK Brookfield, Middleborough, MA, USA) with adapter for a low viscosity samples and spindle No. 2. The result at a speed of 60 rpm was read from the display of the device in the mPa s unit. Measurements were carried out according to the device’s instructions in triplicate for each juice variant.
## 3.5. Color Parameters
Instrumental measurement of the beetroot juice’s color parameters was performed in the CIE L*a*b* system (L*—lightness; a*—red to green; b*—yellow to blue) using a Konica Minolta CM-3600d colorimeter (Osaka, Japan) [18]. Determination in fivefold repetition for each sample was made in transmission mode, with the following settings: an illuminant D65, an observation angle of 10°, using a glass cuvette with a layer thickness of 2 mm. The total color difference ΔE* between untreated and HPH-processed juice was calculated by the application of Equation [1]. [ 1]ΔE*=ΔL*2+Δa*2+Δb*2
## 3.6. Chromatographic Determination of Betalains
The qualitative determination of betalain compounds was carried out using a high-performance liquid chromatography coupled with diode array detector (HPLC-DAD) based on the methodology of Kujala et al. [ 37]. For this purpose, the beetroot juice was diluted with distilled water so that the absorbance values of the individual compounds will not be supersaturated in the DAD detector at 480 and 538 nm. The juice was filtered through an Alfatec hydrophilic PTFE syringe filter. The first four drops were discarded. Then, 1 mL of the diluted juice was taken into chromatography vials and capped.
Analysis of betalain compounds was carried out in a Shimadzu Modular HPLC (Shimadzu Corp., Japan) equipped with an LC-10ATvp pump, SPD-M20A DAD detector, CTD-10AsVp column thermostat and DGU-20A5R degasser. A Luna C18[2] 250 × 4.6 mm column from Phenomenex (Torrance, CA, USA) was used, with a pre-column mounted with the same characteristics. Two solvents were used: acetonitrile (A) and formic acid/water (0.4: 99.6, v/v) (B). The elution profile was 0–5 min, $100\%$ B; 5–35 min, 0–$13\%$ A in B; 35–40 min, 13–$27\%$ A in B; 40–50 min, $100\%$ B. The injection volume was 20 µL, the column temperature was set at 35 °C and the flow rate was 1.0 mL/min. The time of single analysis was 50 min.
Betanin and isobetanin (betacyanins representatives) were identified using a red beet extract standard (Sigma-Aldrich, Schnelldorf, Germany), and beetroot juice reconstituted from concentrate at a characteristic wavelength of 538 nm. Betaxanthins in the form of the sum of vulgaxanthins I and II were identified by comparing the chromatograms obtained with those published in the literature [37], taking into account the 480 nm wavelength. Three independent replicates were performed for each sample.
## 3.7. Spectrophotometric Quantification of Betalains
The content of betalain pigments was determined spectrophotometrically using a differential method according to Stintzing et al. [ 38]. With this method, pigments such as betacyanins (red-violet) and betaxanthins (yellow) can be determined simultaneously. All juice samples were diluted with the previously prepared McIlvaine buffer (citrate-phosphate buffer, pH adjusted to 6.5) in such a proportion that the absorbance value at 538 nm was in the range of 0.4–0.8. The blank sample was McIlvaine buffer alone. Absorbance was measured at three wavelengths: 476, 538 and 600 nm. All determinations were made in triplicate. The absorbance value of betacyanins (BC), taking into account light absorption due to the presence of various impurities in the matrix, was calculated according to Equation [2]. [ 2]ABC=1.095×A538−A600 The content of betacyanins, expressed in mg of betanin in 100 mL of juice, was calculated according to Equation [3]. [ 3]CBC=ABC⋅DF⋅MW⋅100ε⋅L where DF—dilution factor; MW—molecular mass of betanin, 550 g/mol; ε—extinction coefficient for betanin, 60,000 L/mol*cm; L—thickness of the layer of the measured solution, 1 cm.
The absorbance value for betaxanthins (BX) at 476 nm taking into account the light absorbance due to the presence of impurities and betacyanins was calculated based on Equation [4]. [ 4]ABX= A476−A538+0.677⋅ABC The content of betaxanthins, expressed in mg of vulgaxanthin in 100 mL of juice, was calculated using Equation [5]. [ 5]CBX=ABX⋅DF⋅MW⋅100ε⋅L where DF—dilution factor; MW—molecular mass of vulgaxanthin, 390 g/mol; ε—extinction coefficient for vulgaxanthin, 48,000 L/mol*cm; L—thickness of the layer of the measured solution, 1 cm.
## 3.8. Statistics
All data are presented as a mean with standard deviation. Statistical analyses were conducted using Statistica 13.3 (TIBCO Software Inc., Palo Alto, CA, USA). The effect of HPH treatment on the physicochemical properties and betalain content of beetroot juice was analyzed using ANOVA analysis of variance. Any differences between the obtained values of the different juice variants were compared using the Tukey HSD test (α = $95\%$). Pearson’s correlation coefficients and determination coefficients between qualitative and quantitative data of betalains analyses were determined. The gathered data from the study were used in the principal component analysis (PCA) in order to comprehensively show the changes in physicochemical and betalain pigment profiles in the beetroot juice samples processed with different parameters using the HPH method. The gathered data (except titratable acidity values) were qualified for PCA analysis based on a correlation score with the first or second principal component of at least 0.6 [39].
## 4. Conclusions
*In* general, high-pressure homogenization (HPH) has a great potential to preserve the bioactive and physicochemical qualities of beetroot juice, but adequate parameters should be considered depending on the desired final characteristics of the juice. In our experiment, in terms of balance between good physicochemical qualities and betalains quantities in the juice, the best HPH treatment parameters were 100 and 140 MPa for one cycle with cooling. As the study proved, cooling of product during or after the HPH process is essential to reduce the level of loss in betalain pigments and to prevent the deterioration of the juice quality because of heating. As also shown, some physicochemical parameters and bioactive compounds are affected only by applied pressure, or the number of cycles, but at a certain pressure. There was a significant effect of HPH treatment on the juice turbidity (NTU), serum cloudiness, viscosity and pH.
The most resistant to the application of high pressure during homogenization are the vulgaxanthins I and II, as well as the entire group of betaxanthins. Among the betacyanins, it was betanin that had the lowest degradation rate. This is important, positive information for food and dietary supplement manufacturers because betanin makes up the majority of betalain pigments and is used as a food colorant.
## References
1. Wang Y., Do T., Marshall L.J., Boesch C.. **Effect of Two-Week Red Beetroot Juice Consumption on Modulation of Gut Microbiota in Healthy Human Volunteers—A Pilot Study**. *Food Chem.* (2023.0) **406** 134989. DOI: 10.1016/j.foodchem.2022.134989
2. Aliahmadi M., Amiri F., Bahrami L.S., Hosseini A.F., Abiri B., Vafa M.. **Effects of Raw Red Beetroot Consumption on Metabolic Markers and Cognitive Function in Type 2 Diabetes Patients**. *J. Diabetes Metab. Disord.* (2021.0) **20** 673-682. DOI: 10.1007/s40200-021-00798-z
3. **EUROSTAT Database**
4. Clifford T., Howatson G., West D., Stevenson E.. **The Potential Benefits of Red Beetroot Supplementation in Health and Disease**. *Nutrients* (2015.0) **7** 2801-2822. DOI: 10.3390/nu7042801
5. Hadipour E., Taleghani A., Tayarani-Najaran N., Tayarani-Najaran Z.. **Biological Effects of Red Beetroot and Betalains: A Review**. *Phytother. Res.* (2020.0) **34** 1847-1867. DOI: 10.1002/ptr.6653
6. Ceclu L., Nistor O.-V.. **Red Beetroot: Composition and Health Effects—A Review**. *J. Nutr. Med. Diet Care* (2020.0) **6** 1-9. DOI: 10.23937/2572-3278.1510043
7. Wootton-Beard P.C., Ryan L.. **A Beetroot Juice Shot Is a Significant and Convenient Source of Bioaccessible Antioxidants**. *J. Funct. Foods* (2011.0) **3** 329-334. DOI: 10.1016/j.jff.2011.05.007
8. Fidelis M., Santos J.S., Coelho A.L.K., Rodionova O.Y., Pomerantsev A., Granato D.. **Authentication of Juices from Antioxidant and Chemical Perspectives: A Feasibility Quality Control Study Using Chemometrics**. *Food Control* (2017.0) **73** 796-805. DOI: 10.1016/j.foodcont.2016.09.043
9. Chhikara N., Kushwaha K., Sharma P., Gat Y., Panghal A.. **Bioactive Compounds of Beetroot and Utilization in Food Processing Industry: A Critical Review**. *Food Chem.* (2019.0) **272** 192-200. DOI: 10.1016/j.foodchem.2018.08.022
10. Naseer S., Hussain S., Abid A.. **Betalain as a Food Colorant: Its Sources, Chemistry and Health Benefits**. *Proc. Pak. Acad. Sci. B (Life Environ. Sci.)* (2019.0) **56** 31-37
11. Szopińska A.A., Gawęda M.. **Comparison of Yield and Quality of Red Beet Roots Cultivated Using Conventional, Integrated and Organic Method**. *J. Hortic. Res.* (2013.0) **21** 107-114. DOI: 10.2478/johr-2013-0015
12. Masih D., Singh N., Singh A.. **Red Beetroot: A Source of Natural Colourant and Antioxidants: A Review**. *J. Pharmacogn. Phytochem.* (2019.0) **8** 162-166
13. Roobab U., Shabbir M.A., Khan A.W., Arshad R.N., Bekhit A.E.-D., Zeng X.-A., Inam-Ur-Raheem M., Aadil R.M.. **High-Pressure Treatments for Better Quality Clean-Label Juices and Beverages: Overview and Advances**. *LWT* (2021.0) **149** 111828. DOI: 10.1016/j.lwt.2021.111828
14. Fei Y., Yang Z., Niazi S., Chen G., Nasir M.A., Khan I.M., Rehman A., Aadil R.M., Trif M., Coşier V.. **Proteolysis of β-Lactoglobulin Assisted by High Hydrostatic Pressure Treatment for Development of Polysaccharides-Peptides Based Coatings and Films**. *Coatings* (2022.0) **12**. DOI: 10.3390/coatings12101577
15. Dumay E., Chevalier-Lucia D., Picart-Palmade L., Benzaria A., Gràcia-Julià A., Blayo C.. **Technological Aspects and Potential Applications of (Ultra) High-Pressure Homogenisation**. *Trends Food Sci. Technol.* (2013.0) **31** 13-26. DOI: 10.1016/j.tifs.2012.03.005
16. Mesa J., Hinestroza-Córdoba L.I., Barrera C., Seguí L., Betoret E., Betoret N.. **High Homogenization Pressures to Improve Food Quality, Functionality and Sustainability**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25143305
17. Benjamin O., Gamrasni D.. **Microbial, Nutritional, and Organoleptic Quality of Pomegranate Juice Following High-pressure Homogenization and Low-temperature Pasteurization**. *J. Food Sci.* (2020.0) **85** 592-599. DOI: 10.1111/1750-3841.15032
18. Kruszewski B., Zawada K., Karpiński P.. **Impact of High-Pressure Homogenization Parameters on Physicochemical Characteristics, Bioactive Compounds Content, and Antioxidant Capacity of Blackcurrant Juice**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26061802
19. Szczepańska J., Skąpska S., Połaska M., Marszałek K.. **High Pressure Homogenization with a Cooling Circulating System: The Effect on Physiochemical and Rheological Properties, Enzymes, and Carotenoid Profile of Carrot Juice**. *Food Chem.* (2022.0) **370** 131023. DOI: 10.1016/j.foodchem.2021.131023
20. Moscovici Joubran A., Katz I.H., Okun Z., Davidovich-Pinhas M., Shpigelman A.. **The Effect of Pressure Level and Cycling in High-Pressure Homogenization on Physicochemical, Structural and Functional Properties of Filtered and Non-Filtered Strawberry Nectar**. *Innov. Food Sci. Emerg.* (2019.0) **57** 102203. DOI: 10.1016/j.ifset.2019.102203
21. Wang X., Wang S., Wang W., Ge Z., Zhang L., Li C., Zhang B., Zong W.. **Comparison of the Effects of Dynamic High-pressure Microfluidization and Conventional Homogenization on the Quality of Peach Juice**. *J. Sci. Food Agric.* (2019.0) **99** 5994-6000. DOI: 10.1002/jsfa.9874
22. Levy R., Okun Z., Shpigelman A.. **High-Pressure Homogenization: Principles and Applications Beyond Microbial Inactivation**. *Food Eng. Rev.* (2021.0) **13** 490-508. DOI: 10.1007/s12393-020-09239-8
23. Liu J., Bi J., Liu X., Liu D., Verkerk R., Dekker M., Lyu J., Wu X.. **Modelling and Optimization of High-Pressure Homogenization of Not-from-Concentrate Juice: Achieving Better Juice Quality Using Sustainable Production**. *Food Chem.* (2022.0) **370** 131058. DOI: 10.1016/j.foodchem.2021.131058
24. Velázquez-Estrada R., Hernández-Herrero M., Guamis-López B., Roig-Saguès A.. **Influence of Ultra-high Pressure Homogenisation on Physicochemical and Sensorial Properties of Orange Juice in Comparison with Conventional Thermal Processing**. *Int. J. Food Sci. Technol.* (2019.0) **54** 1858-1864. DOI: 10.1111/ijfs.14089
25. Suárez-Jacobo Á., Rüfer C.E., Gervilla R., Guamis B., Roig-Sagués A.X., Saldo J.. **Influence of Ultra-High Pressure Homogenisation on Antioxidant Capacity, Polyphenol and Vitamin Content of Clear Apple Juice**. *Food Chem.* (2011.0) **127** 447-454. DOI: 10.1016/j.foodchem.2010.12.152
26. Karacam C.H., Sahin S., Oztop M.H.. **Effect of High Pressure Homogenization (Microfluidization) on the Quality of Ottoman Strawberry (F. Ananassa) Juice**. *LWT* (2015.0) **64** 932-937. DOI: 10.1016/j.lwt.2015.06.064
27. Silva V.M., Sato A.C.K., Barbosa G., Dacanal G., Ciro-Velásquez H.J., Cunha R.L.. **The Effect of Homogenisation on the Stability of Pineapple Pulp: Homogenisation of Pineapple Pulp**. *Int. J. Food Sci. Technol.* (2010.0) **45** 2127-2133. DOI: 10.1111/j.1365-2621.2010.02386.x
28. de Oliveira Ribeiro L., Almeida A.C.S., de Carvalho C.W.P., Borguini R.G., Ferreira J.C.S., Freitas S.P., da Matta V.M.. **Effect of Processing on Bioactive Compounds, Physicochemical and Rheological Characteristics of Juçara, Banana and Strawberry Smoothie**. *Plant Foods Hum. Nutr.* (2018.0) **73** 222-227. DOI: 10.1007/s11130-018-0681-2
29. Szczepańska J., Skąpska S., Marszałek K.. **Continuous High-Pressure Cooling-Assisted Homogenization Process for Stabilization of Apple Juice**. *Food Bioprocess Technol.* (2021.0) **14** 1101-1117. DOI: 10.1007/s11947-021-02611-4
30. Zhou L., Guan Y., Bi J., Liu X., Yi J., Chen Q., Wu X., Zhou M.. **Change of the Rheological Properties of Mango Juice by High Pressure Homogenization**. *LWT—Food Sci. Technol.* (2017.0) **82** 121-130. DOI: 10.1016/j.lwt.2017.04.038
31. Ravichandran K., Saw N.M.M.T., Mohdaly A.A.A., Gabr A.M.M., Kastell A., Riedel H., Cai Z., Knorr D., Smetanska I.. **Impact of Processing of Red Beet on Betalain Content and Antioxidant Activity**. *Food Res. Int.* (2013.0) **50** 670-675. DOI: 10.1016/j.foodres.2011.07.002
32. Herbach K.M., Stintzing F.C., Carle R.. **Betalain Stability and Degradation -Structural and Chromatic Aspects**. *J. Food Sci.* (2006.0) **71** R41-R50. DOI: 10.1111/j.1750-3841.2006.00022.x
33. Skalicky M., Kubes J., Shokoofeh H., Tahjib-Ul-Arif M., Vachova P., Hejnak V.. **Betacyanins and Betaxanthins in Cultivated Varieties of Beta Vulgaris L. Compared to Weed Beets**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25225395
34. Aztatzi-Rugerio L., Granados-Balbuena S.Y., Zainos-Cuapio Y., Ocaranza-Sánchez E., Rojas-López M.. **Analysis of the Degradation of Betanin Obtained from Beetroot Using Fourier Transform Infrared Spectroscopy**. *J. Food Sci. Technol.* (2019.0) **56** 3677-3686. DOI: 10.1007/s13197-019-03826-2
35. Bianchi F., Pünsch M., Venir E.. **Effect of Processing and Storage on the Quality of Beetroot and Apple Mixed Juice**. *Foods* (2021.0) **10**. DOI: 10.3390/foods10051052
36. Wang T., Liu L., Rakhmanova A., Wang X., Shan Y., Yi Y., Liu B., Zhou Y., Lü X.. **Stability of Bioactive Compounds and in Vitro Gastrointestinal Digestion of Red Beetroot Jam: Effect of Processing and Storage**. *Food Biosci.* (2020.0) **38** 100788. DOI: 10.1016/j.fbio.2020.100788
37. Kujala T., Vienola M., Klika K., Loponen J., Pihlaja K.. **Betalain and Phenolic Compositions of Four Beetroot (**. *Eur. Food Res. Technol.* (2002.0) **214** 505-510. DOI: 10.1007/s00217-001-0478-6
38. Stintzing F.C., Schieber A., Carle R.. **Evaluation of Colour Properties and Chemical Quality Parameters of Cactus Juices**. *Eur. Food Res. Technol.* (2003.0) **216** 303-311. DOI: 10.1007/s00217-002-0657-0
39. Raczyk M., Kruszewski B., Zachariasz E.. **Effect of Tomato, Beetroot and Carrot Juice Addition on Physicochemical, Antioxidant and Texture Properties of Wheat Bread**. *Antioxidants* (2022.0) **11**. DOI: 10.3390/antiox11112178
|
---
title: 'Association between Dietary Habits and Pancreatitis among Individuals of European
Ancestry: A Two-Sample Mendelian Randomization Study'
authors:
- Xiaotong Mao
- Chunyou Huang
- Yuanchen Wang
- Shenghan Mao
- Zhaoshen Li
- Wenbin Zou
- Zhuan Liao
journal: Nutrients
year: 2023
pmcid: PMC10004739
doi: 10.3390/nu15051153
license: CC BY 4.0
---
# Association between Dietary Habits and Pancreatitis among Individuals of European Ancestry: A Two-Sample Mendelian Randomization Study
## Abstract
Dietary factors are believed to potentially influence the risk of pancreatitis. Here, we systematically investigated the causal relationships between dietary habits and pancreatitis by using two-sample Mendelian randomization (MR). Large-scale genome-wide association study (GWAS) summary statistics for dietary habits were obtained from the UK Biobank. GWAS data for acute pancreatitis (AP), chronic pancreatitis (CP), alcohol-induced AP (AAP) and alcohol-induced CP (ACP) were from the FinnGen consortium. We performed univariable and multivariable MR analyses to evaluate the causal association between dietary habits and pancreatitis. Genetically driven alcohol drinking was associated with increased odds of AP, CP, AAP and ACP (all with $p \leq 0.05$). Genetic predisposition to higher dried fruit intake was associated with reduced risk of AP (OR = 0.280, $$p \leq 1.909$$ × 10−5) and CP (OR = 0.361, $$p \leq 0.009$$), while genetic predisposition to fresh fruit intake was associated with reduced risk of AP (OR = 0.448, $$p \leq 0.034$$) and ACP (OR = 0.262, $$p \leq 0.045$$). Genetically predicted higher consumption of pork (OR = 5.618, $$p \leq 0.022$$) or processed meat (OR = 2.771, $$p \leq 0.007$$) had a significant causal association with AP, and genetically predicted higher processed meat intake increased the risk of CP (OR = 2.463, $$p \leq 0.043$$). Our MR study showed that fruit intake may be protective against pancreatitis, whereas dietary intake of processed meat has potential adverse impacts. These findings may inform prevention strategies and interventions directed toward dietary habits and pancreatitis.
## 1. Introduction
Pancreatitis is a complex, progressive and destructive inflammatory disease of the pancreas with a high risk of morbidity and mortality. Acute pancreatitis (AP) has an estimated global incidence of 33.74 cases and 1.16 deaths per 100,000 person–years and ranks among the most common gastrointestinal cause of hospital admissions [1,2]. Approximately $20\%$ of patients with a first episode of AP develop recurrent acute pancreatitis (RAP), and 3–$35\%$ of patients will progress to chronic pancreatitis (CP) over 3–8 years [3,4,5]. CP is a serious condition that significantly deteriorates patients’ quality of life and decreases life expectancy, complications of which include pancreatic exocrine insufficiency, diabetes mellitus and pancreatic cancer.
Cholelithiasis, alcoholism, smoking and hyperlipidemia are common causes of pancreatitis, often in combination with other risk factors, including genetic factors or anatomic variants. Adding to these known risk factors, the role of dietary habits has received increasing attention as a potential risk factor for pancreatitis [6]. To date, several studies have been published to investigate the association between dietary factors and incidence of AP. A series of population-based prospective cohort studies conducted by Oskarsson et al. suggested associations between the incidence of non-gallstone-related AP and the consumption of vegetables, fish and high-glycemic load foods [7,8,9]. Another multiethnic cohort study showed that the dietary intake of food rich in saturated fat and cholesterol was associated with an increased risk of gallstone-related AP, whereas fiber intake protected against AP related and unrelated to gallstones [10]. Additionally, the association between vitamins and pancreatitis has received growing attention [11]. It has been a challenge to establish a link between diet and pancreatitis, and case-control studies are prone to recall bias. Currently, the relationship between food intake and the risk of pancreatitis, especially for CP, has not been fully elucidated yet.
Mendelian randomization (MR) is a popular approach that uses the unique properties of genotype to investigate causal associations between exposures and outcomes [12]. It uses measured genetic variants robustly related to an exposure of interest as instrumental variables (IVs), and these variants are randomly allocated across the population at meiosis and conception, mimicking a randomized controlled setting. The MR design can avoid the effects of the potential residual confounders and overcome the reverse causation bias [13]. To date, several studies using the Mendelian randomization to estimate the causal effects of multiple potential exposures on pancreatitis have been reported. Hansen et al. ’s study revealed that genetic variants associated with increased plasma levels of triglycerides increase the risk of AP [14]. Yuan et al. investigate the causal associations of gallstone disease, diabetes, serum calcium, triglycerides, smoking and alcohol in AP and CP [15]. More recently, Mi et al. reported that genetically elevated triglyceride levels and reduced degree of unsaturation in fatty acids were associated with the increased risk of pancreatitis [16]. These studies have provided new insights toward novel strategies for the prevention and treatment of pancreatitis.
Considering there was a lack of evidence pertaining to the relationship between dietary habits and pancreatitis, we used a two-sample MR approach to explore the causal effects of eighteen genetically proxied food intake patterns on the risks of AP, CP, alcohol-induced AP (AAP) and alcohol-induced CP (ACP), using publicly available summary statistics from genome-wide association studies (GWAS). Our study may elucidate the potential genetic mechanisms between dietary habits and pancreatitis and provide scientific evidence for disease primary prevention.
## 2.1. Study Design
A two-sample MR design was utilized to investigate the causal effect of dietary habits on different types of pancreatitis (Figure 1). Single nucleotide polymorphisms (SNPs) associated with these risk factors were selected as IVs. The MR design is based on three core assumptions: [1] genetic IVs must be closely related to the exposure; [2] the IVs are irrelevant to various confounders; [3] the selected IVs influence the outcome only via exposure. The datasets used in our study are retrieved from public databases and received ethical approval prior to implementation. This study, therefore, did not require additional ethical approval.
## 2.2. GWAS Summary-Level Data of Dietary Habits and Pancreatitis
The GWAS summary statistics of alcohol drinking were obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) [17]. We acquired GWAS summary data of 17 dietary patterns from the UK Biobank, which is a large prospective cohort including approximately 500,000 participants with genetic and various phenotypic information [18]. GWAS summary data for pancreatitis were obtained from the FinnGen consortium. The R7 release (June 2022) of the FinnGen consortium data was used (https://r7.finngen.fi/, accessed on 28 November 2022), which contains 4648 cases and 273, 442 controls for AP, 2659 cases and 273,442 controls for CP, 705 cases and 308,449 controls for AAP and 1425 cases and 307,729 controls for ACP.
## 2.3. Genetic Instrument Selection
To explore the causal association between genetically predicated dietary habits and pancreatitis, SNPs were used as IVs. We selected eligible genetic IVs from European-descent GWAS summary datasets and followed a series of quality control procedures. The SNPs highly related with each exposure ($p \leq 5$ × 108) were extracted. Second, we performed *Linkage disequilibrium* (LD)-based clumping procedure with r2 < 0.01 and a window size of 10,000 kb to ensure that each IV was independent. LD was estimated using the 1000 Genomes EUR reference panel. Third, the F statistic was used to assess the genetic instrument strength and avoid bias caused by weak IVs. The F statistic is a measure of instrument strength [19]. We evaluated the power of each single IV using the F statistics (F = beta2/se2). A general F statistic for each dietary habit was also calculated using the following equation:F=n−k−1R2k1−R2 where n is the sample size of the exposure dataset, k is the number of SNPs and R2 is the portion of exposure variance explained by the genetics. We calculated the R2 using the following formula [20]:R2=2×EAF×1−EAF×beta22×EAF×1−EAF×beta2+2×EAF×1−EAF×n×se2 where EAF is the effect allele frequency, beta is the estimated genetic effect and se is the standard error of the genetic effect.
An F statistic greater than 10 was considered a strong genetic variant [19]. In this study, all F statistics were higher than 10, indicating little chance of weak-instrument bias based on the summary statistics.
## 2.4. Univariate and Multivariate MR Analysis
The random-effect inverse-variance weighted (IVW) method was used as the primary methodology for the main analysis of MR. In addition, we used three different methods (weighted median, MR Egger and the MR-PRESSO-corrected approach) to enable valid estimation in the presence of horizontal pleiotropy. Horizontal pleiotropy occurs when the selected IV affects other traits outside of the pathway of the candidate exposure and has an impact on the target outcome or when the IV has a direct effect on the target outcome [21]. Violation of the ‘no horizontal pleiotropy’ assumption can lead to severe bias in MR. The weighted median method combines data on multiple genetic variants into a single causal estimate and provides unbiased causal effects if at least half of the chosen SNPs are valid [22]. The MR-Egger method does not force the regression line through the origin, allowing the included IVs to demonstrate unbalanced pleiotropy [23]. The MR-PRESSO approach was used to detect horizontal pleiotropic outliers, and causal effects were further analyzed with the IVW method after excluding the outliers [24]. The MR-PRESSO method was used to detect the existence of pleiotropy. Moreover, selected IVs are sometimes associated with multiple aspects of exposures. Such heterogeneity could undermine the ability to infer causality for particular dimensions of heterogeneous exposures [25]. Thus, the Cochran’s Q test were employed to evaluate the heterogeneity among IVs. To clarify whether significant causal dietary habits were directly associated with the risk of pancreatitis rather than being mediated by hub exposures, multivariable analysis was performed to adjust for known confounders.
## 2.5. Statistical Analyses
The univariable and multivariable MR analysis was performed using R software (R version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria) with the R packages “TwoSampleMR” (https://github.com/MRCIEU/TwoSampleMR accessed on 28 November 2022), and the MR-PRESSO was conducted using the R package “MR-PRESSO” (https://github.com/rondolab/MR-PRESSO, accessed on 28 November 2022). The data visualization was performed using R package “forestploter”. The results are reported as odds ratios (OR) with corresponding $95\%$ confidence intervals (CIs). Two-sided p-values < 0.05 were considered statistically significant.
## 3.1. Genetic Instruments for Eighteen Dietary Habits
The detailed information of each participating GWAS study are shown in Table 1. Overall, eighteen kinds of dietary patterns were included in the analyses. The number of SNPs for each dietary habit ranged from 9 to 124. Detailed information of IVs for eighteen dietary habits was listed in Supplementary Table S1. Across the dietary exposures that were examined, the F statistics of the obtained SNPs were all greater than the empirical threshold of 10, suggesting that the results are less likely to deviate owing to the influence of weak IVs.
## 3.2. Causal Effects of Dietary Habits on AP and CP
In the primary univariable MR analyses, four causal associations from eighteen dietary habits to AP were identified (Figure 2; Supplementary Table S2), while two causal associations were observed for CP (Figure 2; Supplementary Table S3). Genetically driven alcohol drinking increased the risk of AP (OR = 1.798; $95\%$ CI, 1.097–2.944; $$p \leq 0.020$$) and CP (OR = 3.546; $95\%$ CI, 1.813–6.935; $$p \leq 2.172$$ × 10−4). Genetically predicted dried fruit intake were strongly associated with a reduced risk of both AP (OR = 0.280; $95\%$ CI, 0.156–0.502; $$p \leq 1.909$$ × 10−5) and CP (OR = 0.361; $95\%$ CI, 0.167–0.776; $$p \leq 0.009$$). We also found evidence that genetic predisposition to increased consumption of fresh fruit was protective against AP (OR = 0.448; $95\%$ CI, 0.213–0.943; $$p \leq 0.034$$). On the contrary, genetically predicted processed meat intake levels were significantly associated with the risk of both AP (OR = 2.771; $95\%$ CI, 1.320–5.816; $$p \leq 0.007$$) and CP (OR = 2.463; $95\%$ CI, 1.029–5.895; $$p \leq 0.043$$). Genetic liability to pork intake was associated with a higher risk of AP (OR = 5.618; $95\%$ CI, 1.276–24.727; $$p \leq 0.022$$) but was not associated with CP (OR = 2.518; $95\%$ CI, 0.483–13.115; $$p \leq 0.273$$). There was no evidence for potential heterogeneity or pleiotropy biasing our findings based on the Cochran’s Q test and MR-PRESSO global test (all p values > 0.05; Supplementary Tables S2 and S3).
## 3.3. Causal Effects of Dietary Habits on AAP and ACP
Subsequently, we studied the causal associations between dietary habits and alcohol-induced pancreatitis (Figure 3; Supplementary Tables S4 and S5). Genetic liability to alcohol drinking was strongly associated with higher odds of AAP (OR = 10.806; $95\%$ CI, 2.739–42.626; $$p \leq 6.757$$ × 10−4) and ACP (OR = 8.760; $95\%$ CI, 2.714–28.268; $$p \leq 2.828$$ × 10−4). When we analyzed the association between genetically predicted alcohol drinking and ACP, we observed possible pleiotropy (Ppleiotropy = 0.006) and heterogeneity (Pheterogeneity = 0.004) (Supplementary Table S5). Thus, we performed a MR-PRESSO analysis to detect potentially pleiotropic outliers. After removing outlier SNPs, the relationship remained stable in the MR-PRESSO-corrected results (OR = 9.884; $95\%$ CI, 3.659–26.698; $$p \leq 6.213$$ × 10−6). Genetic predisposition to bread intake was significantly associated with a reduced risk of AAP (OR = 0.114; $95\%$ CI, 0.018–0.725; $$p \leq 0.021$$), while genetically predicted fresh fruit intake was associated with lower odds of ACP (OR = 0.262; $95\%$ CI, 0.071–0.971; $$p \leq 0.045$$). There was possible pleiotropy for bread intake (Pheterogeneity = 0.042; Supplementary Table S4). After removing potential outlier SNPs, the negative association between bread intake and AAP remained significant (OR = 0.148; $95\%$ CI, 0.026–0.861; $$p \leq 0.033$$). Genetic predisposition to higher coffee intake levels could be a protective factor against ACP (OR = 0.440; $$p \leq 0.057$$). It should be noted that genetic liability to dried fruit intake trended toward a decreased risk of AAP (OR = 0.278; $$p \leq 0.108$$) or ACP (OR = 0.378; $$p \leq 0.068$$) but did not achieve statistical significance.
## 3.4. Multivariable MR Analysis of Pancreatitis
Considering that gallstone disease is common among pancreatitis patients, we performed multivariable MR analyses to assess the association between dietary habits and AP or CP after adjusting for cholelithiasis (Figure 4). The association between genetic predisposition to alcohol drinking (adjusted OR = 1.825; $$p \leq 0.048$$), dried fruit intake (adjusted OR = 0.297; $$p \leq 1.382$$ × 10−4), fresh fruit intake (adjusted OR = 0.437; $$p \leq 0.044$$), pork intake (adjusted OR = 7.559; $$p \leq 0.004$$) or processed meat intake (adjusted OR = 3.036; $$p \leq 0.003$$) and AP remained significant in multivariable MR models. No significant association remained between pork intake and AP ($$p \leq 0.055$$; Supplementary Table S6) after adjusting for genetically predicted body mass index (BMI), suggesting that this association could be affected by BMI. Genetic predisposition to alcohol drinking (adjusted OR = 2.346; $$p \leq 0.046$$), dried fruit intake (adjusted OR = 0.352; $$p \leq 0.024$$) or processed meat intake (adjusted OR = 2.791; $$p \leq 0.043$$) also had similar significant causal effects on CP after adjusting for the genetic risk of cholelithiasis, which confirmed the robustness of the results. Finally, we performed multivariable analyses on alcohol-induced pancreatitis by adjusting for alcohol drinking (Figure 4). The protective effects of genetic-predicted bread intake ($$p \leq 0.178$$) and fresh fruit intake ($$p \leq 0.235$$) were no longer statistically significant.
## 4. Discussion
The human pancreas is a composite organ that serves two important functions: the production of enzymes for the digestion of food (exocrine function) and the secretion of hormones to regulate glucose metabolism (endocrine function). On the one hand, the pancreas releases three main groups of digestive enzymes, including amylase, trypsin and lipase, which can, respectively, digest carbohydrates, proteins and digest fats into their basic components so that they can be absorbed and utilized by the body [26]. On the other hand, animal studies demonstrated that dietary constituents affected the development of pancreatic functions and the secretion pattern of digestive enzymes [27,28,29]. It is generally accepted that the key event for the initiation of pancreatitis involves pathologic autodigestion triggered by prematurely activated pancreatic enzymes within the pancreas [30]. Additionally, unreasonable dietary habits may result in metabolic changes or disturbances, leading to the development of pancreatitis. However, there has been limited data about the influence of dietary habits on the risk of pancreatitis so far. Here, we illustrate the relationship between dietary habits and pancreatitis using MR analysis and large-scale GWAS data and identify specific food intake that might be causally associated with pancreatitis risk.
Alcohol drinking is a well-known lifestyle risk factor for both AP and CP, which accounts for $20\%$ of the aetiologies in AP patients and 40–$70\%$ of the aetiologies in CP patients [2,5]. Moreover, excessive alcohol consumption is an important risk factor for the recurrence of AP, as well as for progression to CP [2]. Our MR results confirmed the causal associations between genetically predicted alcohol drinking and all four types of pancreatitis. In line with the expectation, the odds ratio for alcohol use in AAP or ACP was higher than the odds ratio in AP or CP. The effects of alcohol drinking on AP and CP were partially attenuated after adjusting for cholelithiasis, suggesting that this association could be influenced by gallstone disease. The previous MR study by Yuan et al. did not support the positive association between alcohol consumption and AP [15]. It is possible that the limited sample size (1762 cases and 121,348 controls) might have affected the statistical power to identify significant associations in their results. In this present study, we take advantage of the latest available data for pancreatitis in the FinnGen consortium, which included a substantially higher number of cases and controls (4648 cases and 273, 442 controls).
We found that genetically predicted consumption of fruit (both fresh and dried) was inversely associated with the risk of AP, and genetic predisposition to dried fruit intake was suggestively protective of CP. Our results are consistent with findings by Setiawan et al., which showed that fruit intake was associated with a reduced risk of AP [10]. Notably, high intake of fruits has been shown to decrease the risks of gallstone diseases in previous studies [31], and the association between fruit intake and pancreatitis was no longer significant in non-gallstone-related AP cohorts [7,10]. Considering that gallstone disease is common among patients with pancreatitis, we performed multivariable MR analyses to adjust for the genetic risk of cholelithiasis. Inspiringly, the inverse association for genetically predicted higher fruit intake levels remained prominent in multivariable MR models, suggesting that fruit consumption might be the independent protective factor against AP and CP. The high content of antioxidants in fruits may prevent the onset of pancreatitis through reduction of the basal oxidative stress level. Moreover, dietary fibers from fruits have been reported to be protective against the occurrence of AP [32]. Genetic liability to fresh fruit intake significantly decreased the risk of ACP; however, the association did not persist after adjusting for cholelithiasis, suggesting that this association is not robust enough.
Setiawan et al. reported that diets rich in saturated fat and cholesterol, including red meat and eggs, were positively linked with a higher risk of gallstone-related AP [10]. Another cross-sectional study from China showed high meat consumption was associated with AP risk; however, the association was not significant after adjustment of confounding factors [33]. Our results provided evidence supporting the positive association between genetically predicted processed meat intakes and pancreatitis risk. Additionally, genetic liability to higher pork intake levels significantly increased the risk of AP. Long-term high-fat diet exposure and gallstones may work synergistically to promote the occurrence of AP [34], and high-fat and cholesterol diets have been reported as risk factors for gallstone in previous studies [35,36]. After adjustment for cholelithiasis, the positive relationship between processed meat intake and pork intake remained significant, suggesting that consumption of processed meat and pork may also affect the risk of pancreatitis via other mechanisms. We did not observe a positive association between genetically predicted beef intake and pancreatitis, which is consistent with a previous prospective case-control study [37].
Two Swedish population-based prospective studies by Oskarsson et al. showed a significant inverse association between consumption of vegetable or fish and the risk of non-gallstone-related AP [7,9]. We observed that genetic predisposition to the intake of cooked vegetables or raw vegetables appeared to decrease risks of both AP and CP, but this association did not reach statistical significance. We did not find evidence to support an association between fish consumption and pancreatitis. As proposed by Setiawan et al., the protective effects of fish intake against AP are likely to be ethnic-specific [10]. Currently, it is still controversial whether coffee drinking decreases risk of pancreatitis. Two studies have drawn differing conclusions regarding the relationship between coffee drinking and non-gallstone related AP [10,38]. The earlier cohort study in the United States showed coffee drinking is associated with reduced risk of alcohol-associated pancreatitis [39]. Our MR results did not support a significant association between coffee consumption and AP, CP or AAP. Genetically predicted higher coffee intake levels tended to reduce the risk of ACP, although only borderline statistically significant. Notably, we observed an inverse association of genetically predicted bread intake with AAP, whereas this relationship was no longer statistically significant after adjusting for alcohol consumption.
Our study has several significant strengths. First, the MR design is suitable for causal inference. As an alternative to randomized controlled trials, the MR design is less vulnerable to bias from reverse causation and unmeasured confounding, which are prevalent in conventional observational studies. Second, the present study systematically analyzed the relationship between pancreatitis and a wide range of dietary habits, some of which have never been reported in previous studies. The large sample size and the usage of strong instruments (all SNPs had F statistics > 10) guaranteed enough statistical power. Third, population stratification bias was minimized because all GWAS summary statistic data analyzed in this study were generated from individuals of European descent. Fourth, GWAS data for pancreatitis in this study were obtained from the FinnGen consortium, while data for food intakes were from the UK Biobank. The design avoided population overlap between exposures and outcomes, thereby decreasing the likelihood of type 1 error rate due to weak instrument bias [40].
Nevertheless, some limitations in this MR study should be noted. First, MR analyses can be potentially biased by pleiotropic effects. As with all MR studies, pleiotropy in the MR setting was challenging. In this present study, we conducted various sensitivity analyses under different assumptions about the underlying nature of pleiotropy, most of which showed stable results. We also used MR-Egger intercept tests and MR-PRESSO analyses to detect widespread horizontal pleiotropy [23,24]. After removing potential outlier SNPs, we observed robust MR-PRESSO-corrected results. Second, all the participants included in this study were of European descent, this may limit the generalizability of our findings to other populations. Further studies are required to verify our findings in individuals of non-European descent. Third, the strength of evidence in MR studies depends considerably on the plausibility of the instrumental variable assumptions for the genetic variants. Canalization or developmental compensation buffers against the effect of the genetic variation, which could make it difficult to evaluate the gene–disease association. Thus, randomized controlled trials remain the gold standard for estimating the direct causal effect of interventions on health outcomes.
## 5. Conclusions
In summary, we systematically evaluated the potential causal relationship between dietary habits and pancreatitis. This MR analysis showed that genetically predicted dried fruit intake is causally associated with a reduced risk of AP and CP, while fresh fruit intake has potential preventive value against AP. In addition, processed meat intake was found to increase the risk of AP and CP, and pork intake was associated with AP risk. Our study contributes to a more targeted prevention strategy for pancreatitis by providing a better understanding of the possible roles of dietary patterns in the development of pancreatitis.
## References
1. Xiao A.Y., Tan M.L., Wu L.M., Asrani V.M., Windsor J.A., Yadav D., Petrov M.S.. **Global incidence and mortality of pancreatic diseases: A systematic review, meta-analysis, and meta-regression of population-based cohort studies**. *Lancet Gastroenterol. Hepatol.* (2016) **1** 45-55. DOI: 10.1016/S2468-1253(16)30004-8
2. Boxhoorn L., Voermans R.P., Bouwense S.A., Bruno M.J., Verdonk R.C., Boermeester M.A., van Santvoort H.C., Besselink M.G.. **Acute pancreatitis**. *Lancet* (2020) **396** 726-734. DOI: 10.1016/S0140-6736(20)31310-6
3. Sankaran S.J., Xiao A.Y., Wu L.M., Windsor J.A., Forsmark C.E., Petrov M.S.. **Frequency of progression from acute to chronic pancreatitis and risk factors: A meta-analysis**. *Gastroenterology* (2015) **149** 1490-1500.e1. DOI: 10.1053/j.gastro.2015.07.066
4. Ahmed Ali U., Issa Y., Hagenaars J.C., Bakker O.J., van Goor H., Nieuwenhuijs V.B., Bollen T.L., van Ramshorst B., Witteman B.J., Brink M.A.. **Risk of Recurrent Pancreatitis and Progression to Chronic Pancreatitis After a First Episode of Acute Pancreatitis**. *Clin. Gastroenterol. Hepatol.* (2016) **14** 738-746. DOI: 10.1016/j.cgh.2015.12.040
5. Beyer G., Habtezion A., Werner J., Lerch M.M., Mayerle J.. **Chronic pancreatitis**. *Lancet* (2020) **396** 499-512. DOI: 10.1016/S0140-6736(20)31318-0
6. Yadav D., Lowenfels A.B.. **The epidemiology of pancreatitis and pancreatic cancer**. *Gastroenterology* (2013) **144** 1252-1261. DOI: 10.1053/j.gastro.2013.01.068
7. Oskarsson V., Sadr-Azodi O., Orsini N., Andrén-Sandberg Å., Wolk A.. **Vegetables, fruit and risk of non-gallstone-related acute pancreatitis: A population-based prospective cohort study**. *Gut* (2013) **62** 1187-1192. DOI: 10.1136/gutjnl-2012-302521
8. Oskarsson V., Sadr-Azodi O., Orsini N., Andrén-Sandberg Å., Wolk A.. **High dietary glycemic load increases the risk of non-gallstone-related acute pancreatitis: A prospective cohort study**. *Clin. Gastroenterol. Hepatol.* (2014) **12** 676-682. DOI: 10.1016/j.cgh.2013.09.058
9. Oskarsson V., Orsini N., Sadr-Azodi O., Wolk A.. **Fish consumption and risk of non-gallstone-related acute pancreatitis: A prospective cohort study**. *Am. J. Clin. Nutr.* (2015) **101** 72-78. DOI: 10.3945/ajcn.113.076174
10. Setiawan V.W., Pandol S.J., Porcel J., Wei P.C., Wilkens L.R., Le Marchand L., Pike M.C., Monroe K.R.. **Dietary Factors Reduce Risk of Acute Pancreatitis in a Large Multiethnic Cohort**. *Clin. Gastroenterol. Hepatol.* (2017) **15** 257-265.e3. DOI: 10.1016/j.cgh.2016.08.038
11. Cai F., Hu C., Chen C.J., Han Y.P., Lin Z.Q., Deng L.H., Xia Q.. **Vitamin D and Pancreatitis: A Narrative Review of Current Evidence**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14102113
12. Lawlor D.A., Harbord R.M., Sterne J.A., Timpson N., Davey Smith G.. **Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology**. *Stat. Med.* (2008) **27** 1133-1163. DOI: 10.1002/sim.3034
13. Skrivankova V.W., Richmond R.C., Woolf B.A.R., Davies N.M., Swanson S.A., VanderWeele T.J., Timpson N.J., Higgins J.P.T., Dimou N., Langenberg C.. **Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): Explanation and elaboration**. *BMJ* (2021) **375** n2233. DOI: 10.1136/bmj.n2233
14. Hansen S.E.J., Madsen C.M., Varbo A., Tybjærg-Hansen A., Nordestgaard B.G.. **Genetic variants associated with increased plasma levels of triglycerides, via effects on the lipoprotein lipase pathway, increase risk of acute pancreatitis**. *Clin. Gastroenterol. Hepatol.* (2021) **19** 1652-1660.e6. DOI: 10.1016/j.cgh.2020.08.016
15. Yuan S., Giovannucci E.L., Larsson S.C.. **Gallstone disease, diabetes, calcium, triglycerides, smoking and alcohol consumption and pancreatitis risk: Mendelian randomization study**. *NPJ Genom. Med.* (2021) **6** 27. DOI: 10.1038/s41525-021-00189-6
16. Mi J., Liu Z., Jiang L., Li M., Wu X., Zhao N., Wan Z., Bai X., Feng Y.. **Mendelian randomization in blood metabolites identifies triglycerides and fatty acids saturation level as associated traits linked to pancreatitis risk**. *Front. Nutr.* (2022) **9** 1021942. DOI: 10.3389/fnut.2022.1021942
17. Liu M., Jiang Y., Wedow R., Li Y., Brazel D.M., Chen F., Datta G., Davila-Velderrain J., McGuire D., Tian C.. **Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use**. *Nat. Genet.* (2019) **51** 237-244. DOI: 10.1038/s41588-018-0307-5
18. Sudlow C., Gallacher J., Allen N., Beral V., Burton P., Danesh J., Downey P., Elliott P., Green J., Landray M.. **UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age**. *PLoS Med.* (2015) **12**. DOI: 10.1371/journal.pmed.1001779
19. Burgess S., Thompson S.G.. **Avoiding bias from weak instruments in Mendelian randomization studies**. *Int. J. Epidemiol.* (2011) **40** 755-764. DOI: 10.1093/ije/dyr036
20. Papadimitriou N., Dimou N., Tsilidis K.K., Banbury B., Martin R.M., Lewis S.J., Kazmi N., Robinson T.M., Albanes D., Aleksandrova K.. **Physical activity and risks of breast and colorectal cancer: A Mendelian randomisation analysis**. *Nat. Commun.* (2020) **11** 597. DOI: 10.1038/s41467-020-14389-8
21. Solovieff N., Cotsapas C., Lee P.H., Purcell S.M., Smoller J.W.. **Pleiotropy in complex traits: Challenges and strategies**. *Nat. Rev. Genet.* (2013) **14** 483-495. DOI: 10.1038/nrg3461
22. Bowden J., Davey Smith G., Haycock P.C., Burgess S.. **Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator**. *Genet. Epidemiol.* (2016) **40** 304-314. DOI: 10.1002/gepi.21965
23. Bowden J., Davey Smith G., Burgess S.. **Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression**. *Int. J. Epidemiol.* (2015) **44** 512-525. DOI: 10.1093/ije/dyv080
24. Verbanck M., Chen C.-Y., Neale B., Do R.. **Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases**. *Nat. Genet.* (2018) **50** 693-698. DOI: 10.1038/s41588-018-0099-7
25. Zheng J., Baird D., Borges M.C., Bowden J., Hemani G., Haycock P., Evans D.M., Smith G.D.. **Recent Developments in Mendelian Randomization Studies**. *Curr. Epidemiol. Rep.* (2017) **4** 330-345. DOI: 10.1007/s40471-017-0128-6
26. Whitcomb D.C., Lowe M.E.. **Human pancreatic digestive enzymes**. *Dig. Dis. Sci.* (2007) **52** 1-17. DOI: 10.1007/s10620-006-9589-z
27. Kinouchi T., Koyama S., Harada E., Yajima T.. **Large molecule protein feeding during the suckling period is required for the development of pancreatic digestive functions in rats**. *Am. J. Physiol. Regul. Integr. Comp. Physiol.* (2012) **303** R1268-R1276. DOI: 10.1152/ajpregu.00064.2012
28. Swanson K.C., Kelly N., Salim H., Wang Y.J., Holligan S., Fan M.Z., McBride B.W.. **Pancreatic mass, cellularity, and alpha-amylase and trypsin activity in feedlot steers fed diets differing in crude protein concentration**. *J. Anim. Sci.* (2008) **86** 909-915. DOI: 10.2527/jas.2007-0514
29. Malfertheiner P., Glasbrenner B., Büchler M.. **Role of nutrients in pancreatic adaptation with implication of cholecystokinin release**. *Scand. J. Gastroenterol. Suppl.* (1988) **151** 108-113. DOI: 10.3109/00365528809095922
30. Hegyi E., Sahin-Tóth M.. **Genetic Risk in Chronic Pancreatitis: The Trypsin-Dependent Pathway**. *Dig. Dis. Sci.* (2017) **62** 1692-1701. DOI: 10.1007/s10620-017-4601-3
31. Wang Y., Lu J., Wen N., Nie G., Peng D., Xiong X., Cheng N., Li B.. **The role of diet and nutrition related indicators in biliary diseases: An umbrella review of systematic review and meta-analysis**. *Nutr. Metab.* (2022) **19** 51. DOI: 10.1186/s12986-022-00677-1
32. Ribichini E., Stigliano S., Rossi S., Zaccari P., Sacchi M.C., Bruno G., Badiali D., Severi C.. **Role of Fibre in Nutritional Management of Pancreatic Diseases**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11092219
33. Yang H., Wang L., Shi Y.H., Sui G.T., Wu Y.F., Lu X.Q., Li M.Y., Xia Q., Bian X.X., Li H.H.. **Risk factors of acute pancreatitis in the elderly Chinese population: A population-based cross-sectional study**. *J. Dig. Dis.* (2014) **15** 501-507. DOI: 10.1111/1751-2980.12168
34. Thomas T., Mah L., Barreto S.G.. **Systematic review of diet in the pathogenesis of acute pancreatitis: A tale of too much or too little?**. *Saudi. J. Gastroenterol.* (2012) **18** 310-315. DOI: 10.4103/1319-3767.101124
35. Méndez-Sánchez N., Zamora-Valdés D., Chávez-Tapia N.C., Uribe M.. **Role of diet in cholesterol gallstone formation**. *Clin. Chim. Acta.* (2007) **376** 1-8. DOI: 10.1016/j.cca.2006.08.036
36. Cuevas A., Miquel J.F., Reyes M.S., Zanlungo S., Nervi F.. **Diet as a risk factor for cholesterol gallstone disease**. *J. Am. Coll. Nutr.* (2004) **23** 187-196. DOI: 10.1080/07315724.2004.10719360
37. Mitta N., Barreto S.G., Rodrigues J.. **Dietary risk factors for acute pancreatitis: A case-control study**. *Surg. Chron.* (2011) **16** 186-187
38. Oskarsson V., Sadr-Azodi O., Orsini N., Wolk A.. **A prospective cohort study on the association between coffee drinking and risk of non-gallstone-related acute pancreatitis**. *Br. J. Nutr.* (2016) **115** 1830-1834. DOI: 10.1017/S0007114516000866
39. Morton C., Klatsky A.L., Udaltsova N.. **Smoking, coffee, and pancreatitis**. *Am. J. Gastroenterol.* (2004) **99** 731-738. DOI: 10.1111/j.1572-0241.2004.04143.x
40. Burgess S., Davies N.M., Thompson S.G.. **Bias due to participant overlap in two-sample Mendelian randomization**. *Genet. Epidemiol.* (2016) **40** 597-608. DOI: 10.1002/gepi.21998
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---
title: Effect of Inoculum Size and Age, and Sucrose Concentration on Cell Growth to
Promote Metabolites Production in Cultured Taraxacum officinale (Weber) Cells
authors:
- María Eugenia Martínez
- Lorena Jorquera
- Paola Poirrier
- Katy Díaz
- Rolando Chamy
journal: Plants
year: 2023
pmcid: PMC10004745
doi: 10.3390/plants12051116
license: CC BY 4.0
---
# Effect of Inoculum Size and Age, and Sucrose Concentration on Cell Growth to Promote Metabolites Production in Cultured Taraxacum officinale (Weber) Cells
## Abstract
Pentacyclic triterpenes, including lupeol, α- amyrin, and β-amyrin, present a large range of biological activities including anti-inflammatory, anti-cancer, and gastroprotective properties. The phytochemistry of dandelion (Taraxacum officinale) tissues has been widely described. Plant biotechnology offers an alternative for secondary metabolite production and several active plant ingredients are already synthesized through in vitro cultures. This study aimed to establish a suitable protocol for cell growth and to determine the accumulation of α-amyrin and lupeol in cell suspension cultures of T. officinale under different culture conditions. To this end, inoculum density ($0.2\%$ to $8\%$ (w/v)), inoculum age (2- to 10-week-old), and carbon source concentration ($1\%$, $2.3\%$, $3.2\%$, and $5.5\%$ (w/v)) were investigated. Hypocotyl explants of T. officinale were used for callus induction. Age, size, and sucrose concentrations were statistically significant in cell growth (fresh and dry weight), cell quality (aggregation, differentiation, viability), and triterpenes yield. The best conditions for establishing a suspension culture were achieved by using a 6-week-old callus at $4\%$ (w/v) and $1\%$ (w/v) of sucrose concentration. Results indicate that 0.04 (±0.02) α-amyrin and 0.03 (±0.01) mg/g lupeol can be obtained in suspension culture under these starting conditions at the 8th week of culture. The results of the present study provide a backdrop for future studies in which an elicitor could be incorporated to increase the large-scale production of α-amyrin and lupeol from T. officinale.
## 1. Introduction
Dandelion (Taraxacum sp.) has been used extensively as a traditional medicine for hundreds of years, primarily due to its medicinal properties, such as anti-inflammatory, anti-carcinogenic, and anti-rheumatic effects, among others. Medicinal plants typically contain several different chemical compounds that may act individually, additively, or in synergy to improve pharmacological properties. Several compounds have been identified from various organs of the dandelion plant to which medicinal activity has been attributed, primarily comprising various triterpenes (α-/β-amyrin, lupeol, taraxerol, taraxasterol, amidiol, faradiol), sterols, and phenolics (caffeic acid, phenylacetic acid, and chlorogenic acids) [1,2,3]. Specifically, pentacyclic triterpenes, including lupeol, α-amyrin, and β-amyrin, present a large range of biological activities including anti-inflammatory, antioxidant, anti-carcinogenic, and gastroprotective properties [4,5,6]; these triterpenes have been widely reported in Taraxacum’s tissues. T. officinale has great potential applicability as a food, cosmetic, and medicine due to its phytochemical characteristics supported by pharmacological research [7]. Plant biotechnology offers an alternative, and several active plant ingredients are already synthesized through in vitro cultures [8].
In vitro propagation methods are widely used for plants with cultivation difficulties and/or low yields and/or productivities, or if technical problems arise [9]. Among in vitro cultures, callus and plant cell suspension cultures provide rapid plant multiplication and allow manipulation of the environment, either to produce specific compounds or to increase their yields [10]. Specifically, the suspension culture is considered a stable production platform, ensuring consecutive production of natural products of uniform quality and yield, providing homogeneity and higher cell propagation efficiency compared to callus cultures grown on solidified medium in parallel [11]. From an engineering perspective, suspension culture has more immediate potential for industrial applications than callus or organ cultures [12]. In comparison with whole-plant systems, cells in suspension have a relatively shorter growth life cycle and remain undifferentiated, providing a continuous supply of experimental units grown under controlled environmental conditions [13]. Nevertheless, there are still unavoidable problems, including the instability of cell lines, low productivity, slow growth, and scale-up obstacles, that result in lower production of metabolites [11].
Many factors affect cell growth and secondary metabolites synthesis in the suspension culture, including nutrients and energy sources [14], plant growth regulators [15,16], conditioned medium [17], and ethylene/CO2 accumulation [18]. Specifically, for sucrose concentration in suspension cultures, differing outcomes have been reported regarding the effects of sucrose concentration on plant cell cultures. Sucrose not only acts as an external energy source but also contributes to the osmotic potential of the medium. allowing the absorption of mineral nutrients present in the medium, stimulating mitochondrial activity, and, hence, production of the energy required for metabolite synthesis [19,20]. Moreover, many researchers have pointed out the effects of sucrose concentration on secondary metabolite biosynthesis, during which development, chemical profile, and yields of in vitro cell cultures were highly dependent on the type and concentration of carbohydrates used in the medium [21,22,23].
For the Taraxacum genus, triterpene accumulation has been previously described for callus culture compared to wild plants [24,25]. Nonetheless, few studies on suspension cultures are available, and they are primarily focused on secondary metabolic studies. Recently, the establishment of an ex vivo laticifer cell suspension culture from T. brevicorniculatum as a production system for cis-isoprene was reported [26]. In other work, a hairy suspension culture of T. officinale was conducted to evaluate the yield increase of taraxasterol and taraxerol through elicitation by the addition of abiotic elicitors like methyl jasmonate and β-cyclodextrin [27]. This is the first work that characterizes the growth response of T. officinale suspension culture under different culture conditions to determine the quantification of lupeol and α-amyrin. Both molecules were selected in this study because they are metabolites of high pharmacological and commercial value and previous reports from the same research team reported that both secondary metabolites (lupeol and α-amyrin) are present in the leaves of wild plants in the Taraxacum species in a more abundant amount than other compounds of the triterpene group [28]. For this reason, this study aims to establish a suitable protocol for cell growth and to evaluate the effect of different inoculum conditions (age and size) and sucrose concentrations on the accumulation of triterpenes (α-amyrin and lupeol) in cell suspension cultures of T. officinale.
## 2.1.1. Cell Growth and Viability
A second-order polynomial model of dependent comprehensive responses on coded independent variables (Age and Size of inoculum) was established following a nonlinear regression technique for cell growth and viability. The estimation of dry weight, fresh weight, and viability under the proposed conditions could be modeled by Equation [1], Equation [2], and Equation [3], respectively; where A: Age and B: Size. ( See Section 4.4). [ 1]X g dwL=−3.15+3.40·B−0.11·A2−0.55·B2 [2]X g fwL=−115.64+110.58·B−4.23·A2−16.56·B2 [3]Viability %=62.9+4.31·A+12.31·B−0.31·A2−1.81·B2 Regression and variance analysis were applied to fit the model and to assess the statistical significance of the terms for cell growth and viability. The analysis of variance of the polynomial models for the response variables, along with the corresponding R-Squared, Adj R-Squared, Pred R-Squared, C.V., and Adeq Precision, is shown in Table 1, Table 2, and Table 3 for dry weight, fresh weight, and viability, respectively.
For cell dry weight, the Model F-value of 56.59 implies the model is significant. In this case, B, A2, B2 are significant model terms. The “Lack of Fit F-value” of 8.17 implies the Lack of *Fit is* significant relative to the pure error. There is a $0.67\%$ chance that a “Lack of Fit F-value” this large could occur due to noise. This demands another set of experiments for model validation in the future. The “Pred R-Squared” of 0.9044 is in reasonable agreement with the “Adj R-Squared” of 0.9392. “ Adeq Precision” measures the signal-to-noise ratio. Because a ratio greater than 4 is desirable, a ratio of 20.034 indicates an adequate signal. Therefore, this model can be used to navigate the design space.
For cell fresh weight, the Model F-value of 15.74 implies the model is significant. In this case, B, A2, B2 are significant model terms. The “Lack of Fit F-value” of 1.31 implies the Lack of *Fit is* not significant relative to the pure error. There is a $30.80\%$ chance that a “Lack of Fit F-value” this large could occur due to noise. The “Pred R-Squared” of 0.7217 is in reasonable agreement with the “Adj R-Squared” of 0.8037. “ Adeq Precision” measures the signal-to-noise ratio. Because a ratio greater than 4 is desirable, a ratio of 10.205 indicates an adequate signal. Therefore, this model can be used to navigate the design space.
The Model F-value of 23.59 implies the model is significant. In this case, A, B, A2, B2 are significant model terms. The “Lack of Fit F-value” of 2.69 implies the Lack of *Fit is* not significant relative to the pure error. There is an $11.20\%$ chance that a “Lack of Fit F-value” this large could occur due to noise. The “Pred R-Squared” of 0.7642 is in reasonable agreement with the “Adj R-Squared” of 0.8714. “ Adeq Precision” measures the signal-to-noise ratio. Because a ratio greater than 4 is desirable, a ratio of 13.496 indicates an adequate signal. Therefore, this model can be used to navigate the design space.
For the three parameters evaluated (dry and fresh weight and viability), statistical analysis showed that is only a $0.01\%$ chance ($p \leq 0.0001$) that a “Model F-Value” this large could occur due to noise, while values of “Prob > F” less than 0.0500 indicate model terms are significant. Also, R2 values, Adj R2 values, and Pred R2 values were satisfactory, implying that a high percentage of response variations were explained by the correspondent response surface equation. In Figure 1a,c, the comprehensive effect of cell growth (fresh and dry weight) and viability of the suspension cultures inoculated with callus at different ages (2- to 10-week-old) and sizes ($0.2\%$ to $8\%$ (w/v)) could be further represented with the response surface plots as presented. These variables were found significant in cell growth when fresh and dry weights were assessed (Table 1 and Table 2).
Fresh and dry weight was found to be higher when 20–$40\%$ w/v of 4–8-week-old calli were used for initiating the suspension culture. Results showed that the highest biomass accumulation in the suspension was 204.4 g fw/L (6.0 g dw/L with $94\%$ viability) when $20\%$ w/v of 6-week-old calli were used as inoculum to initiate the cell suspension culture, while the lowest values were obtained when a small amount (<$10\%$ (w/v)) of young cultures (2-week-old) was used.
*In* general, viability was considered adequate for the suspension cultures of T. officinale, especially at the end of the experimental period. However, when 2- and 10-week-old calli were used, viability was lower than $80\%$ when small-size inoculum (<$10\%$ (w/v)) was used, presenting a high amount of debris floating in the suspension medium, often forming clumps with the bigger cell clusters.
## 2.1.2. Cell Aggregation and Differentiation
General observations during the first month showed that Group 1 (small cluster, <5 cells/aggregate) was somehow higher in suspensions started with small inoculums while Group 3 (large cluster, >25 cells/aggregate) was preponderant when big cell masses were used as inoculums. However, this tendency derived from the initial cell disaggregation from the callus surface due to mechanical forces through flask agitation, and no statistical significance was observed ($p \leq 0.05$). Nonetheless, a weak tendency (R2 = 0.41) was observed for Group 2 (625 cells/aggregate) in a 2FI Model presenting a local maximum at the border conditions of low age and size (data not shown). After two months of the suspension culture initiation, aggregation in Group 2 was largest (near $60\%$) in cultures started with $0.2\%$ (w/v) of a 2-week-old inoculum. Throughout the entire experimental period, no organogenesis was observed in the suspension culture.
Regarding viability, a trypan blue test proved to be sufficient and complementary to morphological changes to indicate the viability of the culture during the experiments, which was higher than $75\%$ throughout. However, an important quantity of debris was observed, especially at the beginning of the experiment, in which cells were trapped (especially during the first subculture), probably derived directly from the callus surface during agitation during this first month. Even when debris was removed from the 2nd month, initial growth and cell adaptation could be affected by the presence of these residues.
Based on the best results obtained in experiment 1, the conditions for experiment 2 were established. Therefore, suspension cell culture was carried out using $4\%$ (w/v) of a 6-wk old friable callus as inoculum, that was maintained under CM medium. The culture was maintained for 2 months before the sucrose concentration was changed and evaluated. This suspension culture was able to maintain medium clusters (625 cell/aggregate) and viability above $60\%$ and $95\%$, respectively, during the entire period.
In Figure 2, suspension cultures of T. officinale are shown. For each of the treatments, cell suspension cultures initiated from calli at the previously selected conditions ($4\%$ (w/v) of 6-week-old calli) consisted initially of big pale-yellow/white cellular masses. After periodical filtrations (every week from the 4th week of culture), the biomass was finally composed of a mixture of fine cells and clusters of different sizes (Figure 2a–d). The microscopic observation indicated that cells changed their shape during the experimental period. Young cultures presented spherical shapes (Figure 2e–g) before elongation occured from the 4th–5th week of maintenance (Figure 2h,i). From the 8th week, cells were more elongated, showing a concatenated aspect, after which biomass primarily consisted of chains of different lengths in the cultures at 1, 2.3, and 3.2 (w/v) of sucrose (Figure 2j). At $5.5\%$ (w/v) of sucrose, cells turned highly compact in their cytoplasm and presented a dark brown color. However, viability remained above $75\%$ for all the conditions tested.
In Table 4, quality parameters of viability, differentiation, and aggregation during the experiment are presented. Excepting differentiation, these parameters were indeed affected by the carbon source concentration, showing less necrotic cells and smaller aggregates (especially for Group 2 or medium clusters, 6–25 cells/aggregate) at lower carbon source concentrations (1.0 and 2.3 (w/v)) ($p \leq 0.001$). Viability was similar for the suspension cultures maintained at $1.0\%$, $2.3\%$, and $3.2\%$ (w/v) of sucrose, with values of $87\%$, $91\%$, and $87\%$, respectively. Slightly lower water content was determined in the cultures maintained at $5.5\%$ (w/v) of sucrose, presenting an average value of $79\%$. No differentiation was observed during the experimental period.
Finally, the pH of the growth medium decreased slightly from 5.8 to 5.5 during the first months of the culture. When exponential growth initiated (approx. from the 5th week), pH variation between cycles became higher, decreasing to values in the range of 4.9–5.2 at the end of the subculturing period, especially for cultures at $1.0\%$ and $2.3\%$ (w/v) of sucrose (Figure S1, Supplementary material).
## 2.1.3. Triterpene Content
Results fitted weakly for α-amyrin (R2 = 0.53) and lupeol (R2 = 0.49) in a linear model related to the age factor when the size factor was $4\%$ (w/v) g (data not shown). Results indicated higher values when inoculum age was 6 weeks and size was between 4 and $8\%$ (w/v), with values of 0.09 mg/g for α-amyrin and 0.19 mg/g for lupeol. A ratio of lupeol/α-amyrin of 2.5 was observed for the conditions evaluated.
## 2.2.1. Effect of Carbon Source on Cell Growth
The cells were grown in culture media with different carbon sources, demonstrating that the maximum biomass production was higher in the 8th week of the medium containing $2.3\%$ (w/v) sucrose (Figure 3). It should be noted that the cell cultures that generated the lowest biomass were at $1.0\%$ and $5.5\%$ (w/v) sucrose. It was observed that there was a tendency for the cells to form clusters during the growth kinetics. The exponential growth phase was observed between the 5th and 9th week with a rapid decrease after this point, excepting the culture at $2.3\%$ sucrose. Moreover, the viability of the cells was considerably lower in this period.
In Figure 4, cell growth parameters (fresh and dry weight, biomass yield, and productivity) in suspension cultures of T. officinale maintained at different sucrose concentrations are presented. Sucrose concentration in the medium had a significant effect on cell growth ($p \leq 0.01$). Suspension cultures maintained at $2.3\%$ (w/v) of sucrose showed the highest value of fresh biomass (Figure 4a) with a value of 204.4 g/L, followed by the cultures maintained at $1.0\%$, $3.2\%$, and $5.5\%$ (w/v) of sucrose, with values of 136, 83, and 42 g/L, respectively. In terms of dry weight, it was largest in the suspension medium at $2.3\%$ (w/v) of sucrose, showing a value of 12 g dw/L (Figure 4b), followed by the cultures maintained at $1.0\%$, $3.2\%$, and $5.5\%$ (w/v) of sucrose, with values of 6.3, 5.6 and 3.3 g/L, respectively.
Biomass yield on the carbon source (YX/S) was similar for all the conditions tested. YX/S values of 223, 237, 236, and 257 mg/g were obtained for suspension cultures maintained at $1.0\%$, $2.3\%$, $3.2\%$, and $5.5\%$ (w/v), respectively (YX/S was calculated with Equation [4], see methods). Thus, carbon source concentration was not statistically significant ($$p \leq 0.68$$) on biomass yield, and it can be considered to have an average value of 238 mg/g. Cell productivity (Qx, in dw) values for cultures maintained at $1.0\%$, $2.3\%$, $3.2\%$, and $5.5\%$ (w/v) of sucrose had values of 0.8, 1.5, 0.7, and 0.4 g/L-week, respectively (Qx was calculated with Equation [5], see methods). Statistically, the analysis indicated that the effect of sucrose concentration was significant on cell productivity.
In Figure 5a,b, suspension cultures of T. officinale maintained at $1.0\%$ (w/v) and $5.5\%$ (w/v) of sucrose are shown, respectively. At the lower carbon source concentrations, a predominance of rounded cells fulfilled with the cellular content were observed, while at $5.5\%$ (w/v) most of the cells presented a condensed cellular content and were surrounded by a considerable amount of debris. However, contrary to what we expected based on medium osmolality, the humidity content of the cells was not significantly affected by sucrose content ($p \leq 0.05$), but a slight decrease in the average value as the sucrose concentration increased was observed.
## 2.2.2. Effect of Carbon Source on Triterpenes Content
*In* general, sucrose concentration in the growth medium was significant on lupeol yields in the callus (0.318 mg/g dw cell; $p \leq 0.01$) and suspension cultures (0.272 mg/g dw cell; $p \leq 0.05$). However, this parameter did not show a significant effect on α-amyrin accumulation ($p \leq 0.5$) in cell suspension (Figure 6a,b) (Figures S2 and S3, Supplementary material).
For the callus culture, after 8 weeks, the α-amyrin yield on cell (Yami/X) (Figure 6a) was similar with an average yield of 0.038 mg/g under the different sucrose conditions except for sucrose $3.2\%$ (w/v), that presented a yield of 0.067 mg/g. For the suspension culture, yields were similar to those obtained for the callus culture ($p \leq 0.05$) without being affected by the carbon source concentration in the liquid media. An average value of 0.041 mg/g was obtained. Regarding lupeol (Figure 6b), the highest content of this triterpene was obtained in the callus culture supplemented with sucrose $1.0\%$ and $2.3\%$ (w/v), presenting yields of 0.314 and 0.318 mg/g, respectively. At sucrose $3.2\%$ and $5.5\%$ (w/v), Y lup/X values were 0.215 and 0.136 mg/g, respectively. For the suspension cultures maintained in medium supplemented with $1.0\%$, $2.3\%$, $3.2\%$, and $5.5\%$ (w/v), Ylup/X values were 0.199, 0.272, 0.154, and 0.118 mg/g, respectively. In the suspension cultures, triterpenes yields were up to $40\%$ lower for lupeol and $50\%$ lower for α-amyrin compared to the callus culture.
In terms of the product yield on the carbon source (only measured for the suspension cultures) (Figure 7), sucrose concentration has a weak effect ($$p \leq 0.49$$) on lupeol yield (Y lup/S), for which values ranged from 28 to 44 mg/g, minimum at $2.3\%$ (w/v) and maximum at $1.0\%$ (w/v). However, an average value of 31 mg/g was calculated. Sucrose did not have a statistically significant difference on α-amyrin (Yami/S) ($p \leq 0.05$) when the medium was supplemented with $1.0\%$, $3.2\%$, and $5.5\%$ (w/v) sucrose, for which a yield of 7.0 mg/g was calculated. However, the lowest α-amyrin yield was at $2.3\%$ (w/v) sucrose.
## 3.1. Effect of Inoculum Age and Size on Cell Suspension Culture Establishment
The inoculum, that primarily derives from callus cultures, is also a determinant for a proper suspension culture initiation. Callus culture is maintained for extended periods by subculturing, representing a convenient way of long-term maintenance of a specific cell line [27]. In this sense, proper maintenance conditions and the quality of the callus used as the inoculum have an important effect on the performance of the suspension culture and may be critical for the success of its establishment [27,29]. Specifically, inoculum size and age are extremely important variables because it has been reported that some compounds are secreted from the callus to the medium stimulating their growth, needing a minimum amount of callus to resume the growth in a new culture. However, other authors report that a large inoculum may not be adequate because it produces “staling” compounds that affect growth [30] or that it might cause a fast nutrient depletion in the media [31]. The minimum number of cells needed to establish a suspension culture might be closely related to species and culturing conditions. However, having better growth at certain inoculum densities indicated that cell growth requires a certain initial density of cells up to an optimum concentration, and that a lower inoculum size might be inhibitory to the growth of suspension cultures [32]. It has been widely reported that low inoculum concentrations induce longer lag phases for plant cell cultures, and when a culture is transferred to a fresh medium, key growth factors diffuse out of the cells to the surrounding medium [29]. Low densities in the medium are detrimental to cell growth during this adaptation period, in which the cells cannot equal the rate of duplication to the rate of death during the initial adaptation period [33].
The inoculum’s age and size were found to be significant on cell suspension establishment and cell growth, in which suspension cultures initiated using a 6-week-old callus gave the highest growth rate (see Figure 1a,b). During cell growth, metabolism changes towards the adaptation to the environment, and the growth rate is determined by metabolism. A younger culture (growing in the lag phase) is still adapting to the culture conditions from the callus initiation, and cells present a lower growth rate. Therefore, these cells might not be capable of multiplying at a rate greater than cell death or not be suitable for a rapid response to the new environment. On the other hand, the accumulation of necrotic debris and toxic compounds from the callus culture can also be detrimental to suspension culture [34,35,36,37]. This might be why a higher percentage of the cells died within the first four weeks (or two subcultures). During this period, the callus was probably adapting to the new culture system, transitioning from a solid medium to being entirely immersed in liquid. This adaptation period also affected the viability of the cells. In the beginning, cells were mechanically detached from the callus clumps but they would not necessarily be viable and multiplicative. The lower growth achieved with cultures older than 6 weeks was probably due to the change of the cells to a new physiological state rather than nutrient or oxygen depletion. This environmental change could be also the reason why growth was observed as slower under suspension conditions than observed for callus culture, as previously reported for T. officinale [38]. It has been stated that cell growth is faster in suspension cultures because cells are in direct contact with the nutrients and gases, allowing rapid mass and gas transferences. However, in all cases, the suspension cultures of T. officinale grew slower than the respective callus culture.
To the best of our knowledge, the effect of callus age and size has not been studied for the *Taraxacum genus* on suspension culture establishment. The effect of inoculum age on the establishment has been studied for several species. For J. curcas cell suspensions, the use of 60-day-old calli had a negative effect on growth (as fresh and dry weight), as lower values were observed for cell biomass compared to the use of 35-day-old calli [13]. In another study, suspension cultures of C. canephora initiated using 28- and 35-day-old callus had the best growth performance in rate and morphology, while a decrease in growth occurred with increased calli age [36]. These results agree with those obtained in this work, in which 6-week-old (42 days-old) calli showed the best results for suspension culture initiation. From the 4th to the 8th week of culture (during the 2nd and 4th subculture), T. officinale callus was growing rapidly (presumably in its exponential phase), allowing for its use as an inoculum to maintain an accelerated growth. However, when using an inoculum in the sixth week (3rd subculture), higher viability was observed (see Figure 1d). In terms of inoculum size, authors have reported that the use of less than $1\%$ (w/v) of callus for *Vitis vinifera* suspension cultures could not initiate cell division and proliferation, while cell suspensions turned brown with a dramatic decrease of growth when initiated with $2\%$ (w/v) of callus [33]. In another study, authors reported that the growth of *Artemisia annua* cell cultures was inversely proportional to the initial inoculum, with maximum growth attained using an inoculum density of $1\%$ (w/v), and that the cultures with higher initial inoculums seemed to show limited growth when compared with cultures with lower initial inoculums. This is likely because exhaustion of oxygen or depletion of nutrients in the culture medium occurred at the beginning of the culture period due to the excess of inoculum [37]. This effect was also indicated for *Cyperus aromaticus* suspension cultures initiated using different inoculum sizes ($0.3\%$, $1\%$, $1.6\%$, $3.3\%$, and $5\%$ (w/v)) [39], in which the authors observed that using above $1\%$ w/v of inoculum size might cause nutrient depletion in the media. In another study on Pogostemon cablin cell suspension cultures, growth was higher in cultures initiated with $10\%$ (w/v) [31]. Therefore, a higher initial inoculum did not directly produce greater cell biomass. The best results on biomass growth found in the literature for suspension cultures were similar to those obtained in this work, using an inoculum size of $4\%$ (w/v).
As observed in our experiments (data not shown), debris has been also reported for suspension cultures of *Coleus forskohlii* and Catharanthus roseus, in which debris (such as pieces of the cell wall, clumped cytoplasmic materials, starch granules, etc.) could be due to inadequate inoculum densities [40,41]. However, periodical change into a new medium and the filtration of the suspension culture maintained a predominance of small (Group 1, <5 cells/aggregate) and medium clusters (Group 2, 6–25 cells/aggregate) during the experimental period. Specifically, small clusters (Group 1) were observed profusely in the suspension cultures, primarily at the beginning of the cultures. However, periodical filtering prevented the accumulation of larger aggregates. It has been stated that during the period of most active cell division, the suspensions normally show maximum aggregation but are not essential for high rates of growth and division in cell suspension cultures. The subculturing of the cultures maintained the cells in an actively dividing state, minimizing cell aggregation, probably because of the mechanical forces and continuous filtering. The importance of maintaining a homogeneous suspension culture is because the lack of uniformity in suspension cultures can affect cell growth. Aggregation occurs when daughter cells fail to separate after cell division, promoted by extracellular polysaccharides, and varying between cell lines, the age of the cells, and the growth conditions [42].
## 3.2. Effect of Inoculum Age and Size on Triterpene Content
Regarding triterpene content, both α-amyrin and lupeol were detected in the callus when inoculum age and size were above 2 weeks and $2\%$ (w/v), respectively. The identification of these two compounds agrees with a previous work, which used leaf extracts from the wild plant, in which lupeol acetate, lupeol, α-amyrin, β-sitosterol, and betulin (among others) were identified in hexane and ethyl acetate extracts. In 2018, Díaz et al. [ 28] indicated that lupeol content ($23.31\%$) was higher than α-amyrin ($4.78\%$) in wild plants as opposed to in vitro cultivated plants. Also, in this study the same trend was detected in dandelion cell suspension culture under in vitro conditions, with an average amount of lupeol (31 mg/g) higher than that of amyrin (6.5 mg/g), regardless of the concentration of the carbon source (sucrose) evaluated (Figure 7). The effect of inoculum characteristics has been studied previously in berberine production from *Tinospora cordifolia* Miers suspension cultures, in which cell aggregate size impacted mass transfer effects that might govern the metabolite synthesis. The authors reported that cell aggregates of 500 µm in diameter promoted the production of biomass, but larger cell aggregates up to 2000 µm in diameter favored berberine accumulation [43]. Maximum triterpene accumulation was obtained with an 8-week-old inoculum, while maximum growth was obtained with a 6-week-old inoculum. Interestingly, suspension cultures of *Ficus deltoidea* showed that the highest production of biomass was obtained from an initial inoculum size of $8\%$ (w/v), whereas the highest flavonoid was found when the inoculum was $2\%$ (w/v) of media [44]. Therefore, a decoupling of triterpene accumulation with cell growth can be supposed. Nevertheless, solasodine productivity was achieved using larger ($20\%$ (w/v)) rather than smaller ($10\%$ w/v) inoculum corresponding with higher dry cell weight [45]. Thus, a new set of experiments for studying α-amyrin and lupeol accumulation during cell growth to set the best compound production strategy should be proposed.
## 3.3. Effect of Sucrose Concentration on Cell Growth in Suspension Culture
Best growth in fresh and dry weight, and therefore cell productivity, was obtained when the cells were grown in media supplemented with sucrose $2.3\%$ (w/v) (see Figure 3 and Figure 4a,b,d). Biomass yield on sucrose consumption was almost constant despite the sucrose concentration in the medium slightly increasing at $5.5\%$ (w/v) (see Figure 4c). Humidity slightly decreased when the carbon source decreased in the medium. It has been stated that the accumulation of starch in the cells as a result of higher osmotic pressure can be present during suspension cultures [46]. Values obtained here were similar to those reported for other suspension cultures, reporting values of 0.18 g dw/g for *Elaeis guineensis* [32] and 0.4 g dw/g for *Dioscorea deltoidea* [47].
Differing outcomes have been reported regarding the effects of sucrose concentration on plant cell cultures. In cell suspension cultures of Melastoma malabathricum, $1.5\%$ (w/v) sucrose led to the highest fresh cell weight, while the highest dried cell weight was obtained from the cells cultured in the medium supplemented with 45 g/L of sucrose. However, when the sucrose level was increased to $6.0\%$ (w/v), there was a decreasing trend in cell growth [48]. In another work, sucrose at $6.0\%$ (w/v) allowed a higher dry matter accumulation when compared to lower sucrose concentrations, even when the lower level of fresh biomass was probably caused by an osmotic effect [49]. On Perilla frutescens cell cultures, growth rates increased with the increase in initial sucrose concentration from $1.5\%$ to $6.0\%$ w/v in the medium, suggesting that, in a medium in which all nutrients were present in excess, an increase in sugar concentration could result in a proportional increase in cell biomass [50]. Meanwhile, in suspension cultures of J. curcas, growth at different sucrose concentrations was higher when the culture medium was supplemented with $3.0\%$ and $4.0\%$ (w/v) of sucrose, and growth decreased when sucrose concentration was reduced to less than $2.0\%$ (w/v) [13]. On the contrary, sucrose at $3.0\%$ (w/v) and $5.0\%$ (w/v) resulted in a 3.9-fold and a 3.3-fold increase in growth during *Artemisia absinthium* L cultures, respectively [22], while stimulatory effect of sucrose concentration up to $5.0\%$ (w/v) was observed on biomass accumulation in cell cultures of *Taxus chinensis* [51], *Ocimum sanctum* [52], and Glycyrrhiza inflate [53]. On the contrary, inhibition in biomass accumulation in response to sucrose concentrations higher than 30 g/L was observed in suspension cultures of *Prunella vulgaris* [54]. These reports are somewhat in line with the results found in this study, where better growth and quality of the cells were obtained in culture media supplemented with $1.0\%$ and $2.3\%$ w/v of sucrose.
Sucrose concentration was significant on lupeol yields in the callus and suspension cultures, though this parameter did not show a significant effect on α-amyrin accumulation (see Figure 6). Moreover, triterpene accumulation in suspension cultures was lower (up to $30\%$) than in the callus culture at similar conditions, especially for lupeol. Results indicated that the accumulation of these compounds on suspension cultures was lower than reported for other cultures, and even lower in comparison with the respective callus cultures. For instance, the alkaloid content and yield were three times higher in the suspension culture of *Catharanthus roseus* L. when compared to a solid medium under similar treatments [55]. Overall, α-amyrin and lupeol concentrations and yields were considerably lower than those reported in the literature for other triterpenes recovered from cell cultures. α-Amyrin and lupeol were lower than 0.1 mg/g dw and 0.5 mg/g dw, respectively, for all the conditions tested. These values are considerably lower compared to those reported previously. For instance, T. officinale callus culture accumulated triterpene acids and triterpenol accounting for $0.11\%$, $0.07\%$, and $0.29\%$ dw for triterpenes, triterpenols, and triterpenes esters, respectively [24]. However, in this work, no triterpene acids and esters were measured that could be of great importance in future research to complement these results. Furuno et al. [ 23]. indicated that triterpenols were minor in callus cultures when compared to triterpenic acids. In callus cultures, the authors obtained around 0.5 mg/g dw of triterpenols (similar to that obtained here), while between 2.0 and 8.0 mg/g dw were contained in shoots and roots of plants. Moreover, even higher concentrations of triterpenes were reported in wild plants, with up to $25\%$ of β-amyrin and $4.0\%$ of sitosterol in the leaves and roots of T. officinale [56,57]. These results suggest that in vitro culture of undifferentiated cells of T. officinale should be useful to produce triterpenic acids rather than triterpenols.
Reports of higher values for similar triterpenoids include suspensions of *Lantana camara* L., in which $2.2\%$ (w/v) of betulinic, oleanic, and ursolic acids were achieved [58]. Another report indicated that ursolic and oleanolic acids accumulated up to 2.1 mg/g dw in cell cultures of *Uncaria tormentosa* [59]. However, lower triterpene accumulation was reported for the same species (up to 0.6 mg/g dw in cell cultures [60]). Compound yields and biomass production are extremely important in order to achieve the highest possible production of bioactive compounds. It appears, therefore, that screening cell lines for high and stable production of secondary metabolites in cultures is an important factor for commercial exploitation [61]. For instance, [62], screened hundreds of *Plumbago rosea* L. lines for plumbagin accumulation in suspension cultures solely to find a few highly promising lines.
In this study, lupeol concentration was up to three times higher than the α-amyrin concentration at every condition, including the leaves of the in vitro plants from which explants were derived. Authors reported higher concentrations of triterpene esters in wild shoots and roots than in callus cultures, while triterpene acids in wild shoots were not identified [24]. This means that the accumulation of the different kinds of triterpenoids is likely tissue-specific and that some degree of differentiation for the accumulation of certain types of compounds can be expected, in which undifferentiated tissue culture seems not completely suitable (in this case, probably α-amyrin).
No information was found on triterpenes yield on sucrose or any carbon source for the studied triterpenes. To the best of our knowledge, sucrose concentration has not been studied in relation to α-amyrin and lupeol synthesis for the Taraxacum genus. The effect of the carbon source was found to depend on the specific compound and the plant species. For instance, increasing sucrose from $2.0\%$ to $5.0\%$ (w/v) in *Uncaria tomentosa* cell suspension cultures enhanced ursolic acid and oleanolic acid production from 0.13 to 0.55 mg/g dw cell [60]. Rosmarinic acid accumulation was largest in cultures supplemented with $4.5\%$ (w/v), followed by the concentrations obtained at $6.0\%$ (w/v) [49]. Moreover, sucrose also enhanced the production of other secondary metabolites such as diosgenin in *Dioscorea deltoidea* [47] and anthraquinone in Galtium mollugo [63]. Moreover, an increase of initial sucrose concentration above the normally used level ($3.0\%$ w/v) enhanced the accumulation of flavonoids in cell cultures of *Glycyrrhiza inflata* and callus cultures of *Eryngium planum* L. [64], saponin and polysaccharide content in cell cultures of Panax ginseng [49], and taxol production in cell cultures of T. brevifolia [65]. In contrast, Baque et al. [ 66] have reported sucrose $1.0\%$ (w/v) as the optimum concentration for the maximum accumulation of anthraquinone, phenolics, and flavonoids in Morinda citrifolia. Sucrose at $3.0\%$ (w/v) was beneficial in the production of ajamalicine and catharanthine from immobilized cells of C. roseus using a conditioned medium [67]. However, sucrose ranging from $2.0\%$ to $6.0\%$ (w/v) increases the production of arbutin from suspension cultures of C. roseus by glucosylation of exogenous hydroquinone [68]. Maximum accumulation of betacyanin in the suspension culture of *Phytolacca americana* was enhanced by increasing the number of cells in the presence of 88 mM sucrose and by fresh weight in 175 mM sucrose-containing medium [69]. In another report, authors tested different sucrose concentrations to enhance cell growth and production of the phytomedicinal compound zerumbone in suspension cultures of Zingiber zerumbet Smith, showing that the production of this compound was not significantly affected by different concentrations of sucrose between $1.0\%$ and $3.0\%$ (w/v) [70]. These dissimilar results gathered from the literature suggest differing responses of cell lines of diverse species to the same culturing conditions, and therefore, the results must be taken as guidance. Nevertheless, it is somehow suggested that lower carbon source concentrations can improve biomass proliferation while secondary compounds accumulation can be improved by higher concentrations. Therefore, a two-stage strategy for growth and triterpene accumulation in suspension cultures of T. officinale can be considered for subsequent experiments.
Through the course of the experiments, it was observed that, at the beginning of the cultures, the cells presented spherical shapes, likely derived directly from the surface of the callus aggregates. Yellow callus showed predominantly small and isodiametric cells at the beginning of the experiment. In older cultures, cells were elongated and consist of chains of different lengths, likely due to cell division and differentiation. It has been stated that the embryogenic state is characterized by small densely cytoplasmic cells, while suspensor cells are highly vacuolated and elongated [71]. However, during the entire experimental period, suspension cultures were a mixture of different cell morphologies. No organogenesis or other structures appeared in the suspension culture during this experience, probably because potential embryos that could be developed were removed by periodic filtration. Moreover, the continuous supply of equal amounts of NAA and BAP (3.0 mg/L each, in combination) probably maintained low organogenesis in the callus culture of T. officinale, as seen in previous experiments [38].
## 4. Materials and Methods
Experiments were carried out at the Nucleus of Biotechnology of Curauma (NBC), the Pontifical Catholic University of Valparaiso (PUCV), and the Centre of Systems Biotechnology-Fraunhofer Chile Research (FCR-CSB) in Chile.
## 4.1. Callus Culture
Callus induction from T. officinale hypocotyl of 1 month-old, sterile, in vitro cultivated plants in 4.4 g/L Murashige and *Skoog medium* basal including vitamins (MS) (PhytoTech Labs, Lenexa, KS, USA) [72], $2.3\%$ (w/v) of sucrose and 7 g/L agar (Duchefa, The Netherlands), pH 5.6 and supplemented to 0.5 mg/L of NAA and 0.5 mg/L of BAP.
Cultures were maintained in complete darkness in the culture room at 22 ± 2 °C in 100 mm-diameter Petri dishes containing 20 mL of medium sealed with parafilm “M”®. For callus maintenance medium (CM), the same preparation mentioned above was used, but with the addition of 3.0 mg/L NAA and 3.0 mg/L BAP, as conditions that promote cell proliferation in good quality [38].
## 4.2. Cell Suspension Culture
Cell suspension culture was initially established by inoculating a white-friable callus into a 125 mL Erlenmeyer conical flask containing 10 mL CM medium as indicated in Section 4.2.1, without agar addition. Flasks were covered with cotton wool and aluminum foil and placed on an orbital shaker at 115 rpm under darkness at 22 (±2) °C. After four weeks of culture initiation, cells were harvested from the liquid medium by filtering through a sterilized stainless-steel sieve with 1.0 mm pores to separate small cell aggregates from bigger clusters. After the clusters were removed, the cell suspension was centrifuged at 1000× g rpm to collect the cells, and an aliquot (5.0 mL) of the cell-containing concentrated medium was transferred into 10 mL of fresh medium and subculturing every 2 weeks for a total period of 12 weeks.
To ensure the correct cell growth in the suspension culture, the filtrate was placed on a slide for microscopic observation, in which differentiation, aggregation, and viability were monitored at every subculture. Medium pH was also monitored every week.
## 4.2.1. Experiment 1—Effect of Inoculum Age and Size on Cell Growth in Suspension Culture Establishment
White-friable callus of different culture ages (2 to 10 weeks) and sizes (0.2–$8\%$ (w/v)) were used as inoculum and transferred into 125 mL Erlenmeyer flasks containing CM medium for suspension culture establishment by following the protocol mentioned in Section 4.2. A Response Surface Model (RSM) (see Section 4.4) was evaluated between the age and size as variables (Table 5) in a 2FI Model (a two-factor-interaction model). A central composite design was applied to optimize the response of each factor considered (Table 6). At the end of the culturing period, flasks of each treatment were taken and cell growth (fresh and dry weight) and triterpene (α-amyrin and lupeol) content were measured. Each treatment consisted of three replicates. In addition, cell viability, aggregation, and differentiation were determined.
An RSM was used as an experimental optimization design. Two response surface factors were selected, and the range of values X1 (Age, 2–10 weeks), and X2 (Size, 0.2–$8\%$ (w/v)) was determined based on preliminary experiments (Table 5). The coded values for the design are “1” for maximum value and “−1” for minimum value, covering a fractional factorial design and providing a total of 19 treatments.
Once inoculum conditions (callus age and size) were selected using RSM performed in experiment 1, the stock culture of callus was maintained in MS medium supplemented with $2.3\%$ (w/v) sucrose for experiment 2.
## 4.2.2. Experiment 2—Effect of Carbon Source on Triterpene Accumulation in Suspension Culture Establishment
From stock culture, media supplemented with different sucrose concentrations were inoculated for triterpene (α-amyrin and lupeol) content in suspension cultures. CM medium was modified in its sucrose concentration, for which $1.0\%$, $2.3\%$, $3.2\%$, and $5.5\%$ (w/v) of this sugar were evaluated.
Samples for determining cell growth and quantification of terpenes were taken every week during the 12 weeks of the experiment; three flasks of each treatment were taken and cell growth (fresh and dry weight) and triterpene (α-amyrin and lupeol) content were measured (see Section 4.3.2). In addition, cell viability, aggregation, and differentiation were determined.
For comparison of triterpene content, a callus culture on solid CM medium under the same operational conditions was assessed.
## Fresh Weight (fw), Dry Weight (dw)
These parameters were gravimetrically measured. For the callus culture, cell masses were collected and weight directly on the scale. For suspension cultures, samples collected were filtered through a Whatman®, grade N°1 paper filter. After the determination of the fresh weight, cells from solid or liquid cultures were dried at 50 °C for 24 h in an oven and weighed. Humidity was considered as the cell weight loss after oven drying. These data were used for growth kinetics and yield calculation.
## Carbon Source
The sucrose concentration of the growth medium was determined according to the method proposed by Dubois et al. [ 73], with some minor modifications. Sucrose remaining in the suspension culture was quantified during each subculturing after cells were harvested. These data were used for yield calculations.
## Viability, Differentiation, and Aggregation
At every subculture, a 20 μL aliquot of each suspension culture was observed under the microscope. Viability was determined as the percentage of living cells (with no evident cellular damage) to the total number of cells observed within each frame (a total of 20 frames) and by the trypan blue staining test [74]. Differentiation was monitored by observing cell wall structures and organ formation. Aggregation was monitored through microscopic observation (Microscope: Olympus CX31, Olympus Corporation, Japan) and evaluated by grouping the cell clusters as observed within each frame. Clusters were grouped as follows: Group 1 (small cluster) < 5 cells/aggregate; Group 2 (medium cluster) 6–25 cells/aggregate; and Group 3 (large cluster) > 25 cells/aggregate.
## Cell Extraction
For the callus culture, calli were taken directly from the plates and lyophilized at −40 °C and 560 psi for 8 h. Two grams of freeze-dried calli were ground in a porcelain mortar, placed in centrifuge tubes with 20 mL of methanol:chloroform (1:1), and stirred for 30 min. From the suspension culture, 50 mL was taken and centrifuged (2000 rpm× g for 5 min) for cell recovery. The culture was vacuum filtered on Whatman No. 1 paper. Cells were lyophilized (Virtis Company Benchtop Lyophilizer, New York, NY, USA) and extracted in Eppendorf tubes with 1.0 mL of methanol:chloroform (1:1) and stirred for 30 min. The residue of the extraction was removed by filtration using a syringe with 0.45 and 0.22 μm nylon filters (Millipore). The remaining solvent was evaporated under vacuum at 40 °C. The dried extracts were resuspended in 1.0 mL of methanol and stored frozen (−20 °C) until further analysis.
## α-Amyrin and Lupeol Reference Standards
α-amyrin (purity $99.3\%$) and lupeol (purity $99.7\%$) were purchased from Sigma-Aldrich Chemie GmbH (Aldrich Division, Steinbeim, Germany). Standard solutions of both compounds were prepared by adding 1.0 mg of the standard (α-amyrin or lupeol) to 1.0 mL methanol in a 1.5 mL Eppendorf tube. The tube was stirred for 2 min until the complete dissolution of the standard and stored frozen (−20 °C) until analysis was carried out (Figure S2a,b; supplementary material).
## α-Amyrin and Lupeol Quantification
Identification and quantification of α-amyrin and lupeol was performed using the methodology of Adhyapak and Dighe [75] with some modifications, using high-performance liquid chromatography with a diode-array detector (HPLC-DAD; DIONEX Ultimate 3000 RSCL Systems, Thermo Fischer Scientific Inc, Waltham, MA, USA), Zorbax® HPLC Column phase C18 (octadecyl), with a 5µm particle size, L × I.D. 25 cm× 4.6 mm. The protocol was set in an isocratic elution gradient of 30:70 v/v methanol/acetonitrile ($0.1\%$ TFA) at 1.0 mL/min for 20 min with an injection volume of 10 μL. The absorption of the compounds was carried out at 210 nm. Lupeol and α-amyrin were quantified by comparing the peak area, the spectral patterns, and the retention time obtained for the samples against the appropriate standard. Calibration curves of the two standards (α-amyrin and lupeol) were obtained by plotting the obtained areas against their corresponding concentration.
## 4.3.3. Growth Parameters
As the feeding regime was similar to a fed-batch process, kinetic parameters were calculated for each batch and adjusted subsequently through iteration of the entire process.
Cell mass yield coefficient (YX/S, g/g—Equation [4]), cell productivity (Qx, g/L·d—Equation [5]), α-amyrin and lupeol yield coefficients on biomass (Yami/X, Ylup/X, g/g—Equation [6]), triterpene yields on substrate (Yami/S, Ylup/S, g/g—Equation [7]), and volumetric productivities (Qami and Qlup, mg/L—Equation [8]) were calculated as follows:[4]YXS=X−X0∑inS−S0 [5]Qx=XV·t [6]YamiX=CamiX−X0; YlupX=ClupX−X0 [7]YamiS=Cami∑inS0−S; YlupS=Cami∑inS0−S [8]Qami=QX·Yami; Qlup=QX·Ylup where t0 and t are the initial and the total time (d) of the experiment, respectively; X0 and X are the cell concentration (g/L) at the beginning (at t0) and the end (at t) of the experiment, respectively; S0 and S are the sucrose (% (w/v)) at the beginning and the end of the experiment, respectively; i is the subculture number with $$n = 6$$ (3 months); and V is the suspension culture volume (L).
## 4.4. Data Analysis
Data were analyzed using SPSS software (version 16.0) in which ANOVA analysis was performed. The presented results represent the mean ± SE of three replicates per cycle. Means for groups in homogeneous subsets are displayed.
The response surface methodology (RSM) was selected as a statistical method for the design of the experiment [76] to optimize the number of experiments and determine the interactions between the variables.
Suspension culture responses were obtained by the design shown in Table 5, a mathematical model was developed, and optimal values were found. For RSM, the experimental data were fitted with a common second-order polynomial Equation [9]:[9]Y=β0+∑βiXi+∑βijXiXj+∑βiiXii2 where Y is the predicted response, β0 is a constant, βi is the linear coefficient, βii is the quadratic coefficient, and βij is the interaction coefficient. The DOE was performed using Design of Expert 7.0.0 software, using the quadratic design model.
The software provided coefficient of determination (R2) and lack of fit data to validate the model through analysis of variance (ANOVA). The suitability of the polynomial model equation’s fit was tested by the R2, adjusted R2 (Adj R2), predicted R2 (Pred R2), coefficient of variation (CV), and adequate precision (Adeq Pres). The non-significant terms were removed to obtain a reduced model ($p \leq 0.05$). The fitted polynomial Equation was further expressed as response surface plots (2D) to visualize the relation between the response and independent variables.
## 5. Conclusions
This is the first study on the establishment of cell suspension cultures and quantification of triterpenes of Taraxacum officinale under different inoculum and sucrose conditions in vitro. Because of cell growth, cell quality, and α-amyrin and lupeol yields, the conditions selected for the suspension cultures were its initiation with $4\%$ (w/v) of a 6-week-old inoculum. Among the sucrose concentrations, $2.3\%$ (w/v) supported maximum biomass (in quantity and quality) for at least 8 weeks of culture; however, the accumulation of triterpenes was favored at a sucrose concentration of $1.0\%$ (w/v). Results indicate that 0.045 (±0.017) and 0.19 (±0.02) mg/g of α-amyrin and lupeol can be obtained in suspension culture under these conditions, respectively, being $50\%$ and $40\%$ lower than that obtained in callus, respectively. Thus, the results of the present study form a background for future studies related to large-scale production of α-amyrin and lupeol T. officinale.
## References
1. Schütz K., Carle R., Schieber A.. *J. Ethnopharmacol.* (2006) **107** 313-323. DOI: 10.1016/j.jep.2006.07.021
2. González-Castejón M., Visioli F., Rodriguez-Casado A.. **Diverse biological activities of dandelion**. *Nutr. Rev.* (2012) **70** 534-547. DOI: 10.1111/j.1753-4887.2012.00509.x
3. Martínez M., Poirrier P., Chamy R., Prüfer D., Schulze-Gronover C., Jorquera L., Ruiz G.. *J. Ethnopharmacol.* (2015) **169** 244-262. DOI: 10.1016/j.jep.2015.03.067
4. Saleem M.. **Lupeol, a novel anti-inflammatory and anti-cancer dietary triterpene**. *Cancer Lett.* (2009) **285** 109-115. DOI: 10.1016/j.canlet.2009.04.033
5. Hernández L., Palazon J., Navarro-Oca A.. **The Pentacyclic Triterpenes, α-amyrins: A Review of Sources and Biological Activities**. *Phytochemicals—A Global Perspective of Their Role in Nutrition and Health* (2012). DOI: 10.5772/27253
6. Chen D., Xu F., Zhang P., Deng J., Sun H., Wen X., Liu J.. **Practical Synthesis of α-Amyrin, β-Amyrin, and Lupeol: The Potential Natural Inhibitors of Human Oxidosqualene Cyclase**. *Archiv. Der. Pharmazie.* (2017) **350** 1700178. DOI: 10.1002/ardp.201700178
7. Grauso L., Emrick S., de Falco B., Lanzotti V., Bonanomi G.. **Common dandelion: A review of its botanical, phytochemical and pharmacological profiles**. *Phytochem. Rev.* (2019) **18** 1115-1132. DOI: 10.1007/s11101-019-09622-2
8. Mulabagal V., Tsay H.. **Plant Cell Cultures—An Alternative and Efficient Source for the Production of Biologically Important Secondary Metabolites**. *Int. J. Appl. Sci. Eng.* (2004) **2** 29-48
9. Espinosa-Leal C.A., Puente-Garza C.A., García-Lara S.. **In Vitro plant tissue culture: Means for production of biological active compounds**. *Planta* (2018) **248** 1-18. DOI: 10.1007/s00425-018-2910-1
10. Hussain M.S., Ansari S., Ahmad I., Fareed S., Rahman M.A.. **Saeed Current approaches toward production of secondary plant metabolites**. *J. Pharm. Bioallied. Sci.* (2012) **4** 10. DOI: 10.4103/0975-7406.92725
11. Yue W., Ming Q.-L., Lin B., Rahman K., Zheng C.-J., Han T., Qin L.-P.. **Medicinal plant cell suspension cultures: Pharmaceutical applications and high-yielding strategies for the desired secondary metabolites**. *Crit. Rev. Biotechnol.* (2016) **36** 215-232. DOI: 10.3109/07388551.2014.923986
12. Kieran P.M., MacLoughlin P.F., Malone D.M.. **Plant cell suspension cultures: Some engineering considerations**. *J. Biotechnol.* (1997) **59** 39-52. DOI: 10.1016/S0168-1656(97)00163-6
13. Solís-Ramos L.Y., Carballo L.M., Valdez-Melara M.. **Establishment of cell suspension cultures of two Costa Rican Jatropha species (Euphorbiaceae)**. *Rev. Biol. Trop.* (2013) **61** 1095-1107. DOI: 10.15517/rbt.v61i3.11906
14. Chattopadhyay S., Farkya S., Srivastava A.K., Bisaria V.S.. **Bioprocess considerations for production of secondary metabolites by plant cell suspension cultures**. *Biotechnol. Bioprocess Eng.* (2002) **7** 138-149. DOI: 10.1007/BF02932911
15. Lian M.-L., Chakrabarty D., Paek K.-Y.. **Effect of plant growth regulators and medium composition on cell growth and saponin production during cell-suspension culture of mountain ginseng (**. *J. Plant Biol.* (2002) **45** 201-206. DOI: 10.1007/BF03030360
16. Thiruvengadam M., Rekha K., Rajakumar G., Lee T.-J., Kim S.-H., Chung I.-M.. **Enhanced Production of Anthraquinones and Phenolic Compounds and Biological Activities in the Cell Suspension Cultures of**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17111912
17. Trong T.T., Truong D.-H., Nguyen H.C., Tran D.-T., Nguyen Thi H.-T., Dang G.D., Huu H.N.. **Biomass accumulation of**. *Asian Pac. J. Trop. Med.* (2017) **10** 907-915. DOI: 10.1016/j.apjtm.2017.08.012
18. Hanus-Fajerska E., Czura A., Grabski K., Tukaj Z.. **The effect of conditioned medium obtained from Scenedesmus subspicatus on suspension culture of**. *Acta Physiol. Plant.* (2009) **31** 881-887. DOI: 10.1007/s11738-009-0301-8
19. Kumar P.P., Joy R.W., Thorpe T.A.. **Ethylene and Carbon Dioxide Accumulation, and Growth of Cell Suspension Cultures of**. *J. Plant Physiol.* (1990) **135** 592-596. DOI: 10.1016/S0176-1617(11)80642-5
20. Singh M., Chaturvedi R.. **Evaluation of nutrient uptake and physical parameters on cell biomass growth and production of spilanthol in suspension cultures of**. *Bioprocess Biosyst. Eng.* (2012) **35** 943-951. DOI: 10.1007/s00449-012-0679-3
21. Jayaraman S., Daud N.H., Halis R., Mohamed R.. **Effects of plant growth regulators, carbon sources and pH values on callus induction in**. *J. For. Res.* (2014) **25** 535-540. DOI: 10.1007/s11676-014-0492-8
22. Ali M., Abbasi B.H., Ahmad N., Ali S.S., Ali S., Ali G.S.. **Sucrose-enhanced biosynthesis of medicinally important antioxidant secondary metabolites in cell suspension cultures of**. *Bioprocess Biosyst. Eng.* (2016) **39** 1945-1954. DOI: 10.1007/s00449-016-1668-8
23. Furuno T., Kamiyama A., Akashi T., Usui M., Takahashi T., Ayabe S.. **Triterpenoid Constituents of Tissue Cultures and Regenerated Organs of**. *Plant Tissue Cult. Lett.* (1993) **10** 275-280. DOI: 10.5511/plantbiotechnology1984.10.275
24. Akashi T., Furuno T., Takahashi T., Ayabe S.-I.. **Biosynthesis of triterpenoids in cultured cells, and regenerated and wild plant organs of**. *Phytochemistry* (1994) **36** 303-308. DOI: 10.1016/S0031-9422(00)97065-1
25. Post J., Eisenreich W., Huber C., Twyman R.M., Prüfer D., Schulze Gronover C.. **Establishment of an**. *J. Mol. Catal. B Enzym.* (2014) **103** 85-93. DOI: 10.1016/j.molcatb.2013.07.013
26. Sharma K., Zafar R.. **Optimization of methyl jasmonate and β-cyclodextrin for enhanced production of taraxerol and taraxasterol in (**. *Plant Physiol Biochem.* (2016) **103** 24-30. DOI: 10.1016/j.plaphy.2016.02.029
27. Walker John M.. *Methods in Molecular Biology* (1997)
28. Díaz K., Espinoza L., Madrid A., Pizarro L., Chamy R.. **Isolation and Identification of Compounds from Bioactive Extracts of**. *Evid. Based. Complement. Altern. Med.* (2018) **2018** 2706417. DOI: 10.1155/2018/2706417
29. Carvalho E.B., Curtis W.R.. **The effect of inoculum size on the growth of cell and root cultures of**. *Biotechnol. Bioprocess Eng.* (1999) **4** 287-293. DOI: 10.1007/BF02933755
30. Syono K., Furuya T.. **Studies on plant tissue cultures I. Relationship between inocula sizes and growth of calluses in liquid culture**. *Plant Cell Physiol.* (1968) **9** 103-114
31. Che Saad N., Izzatie Mazlan F., Abd Karim K.. **Factors affecting the establishment and growth of**. *Int. J. Adv. Res. Sci. Eng. Technol.* (2016) **3** 2790-2796
32. Gorret N., bin Rosli S.K., Oppenheim S.F., Willis L.B., Lessard P.A., Rha C., Sinskey A.J.. **Bioreactor culture of oil palm (**. *J. Biotechnol.* (2004) **108** 253-263. DOI: 10.1016/j.jbiotec.2003.12.009
33. Ben Amar A., Cobanov P., Boonrod K., Krczal G., Bouzid S., Ghorbel A., Reustle G.M.. **Efficient procedure for grapevine embryogenic suspension establishment and plant regeneration: Role of conditioned medium for cell proliferation**. *Plant Cell Rep.* (2007) **26** 1439-1447. DOI: 10.1007/s00299-007-0341-8
34. Xing X.-H.H., Ono A., Miyanaga K., Tanji Y., Unno H.. **A kinetic model for growth of callus derived from**. *Math. Comput. Simul.* (2001) **56** 463-474. DOI: 10.1016/S0378-4754(01)00315-9
35. Karam N.S., Jawad F.M., Arikat N.A., Shibl R.A.. **Growth and rosmarinic acid accumulation in callus, cell suspension, and root cultures of wild**. *Plant Cell Tissue Organ Cult.* (2003) **73** 117-121. DOI: 10.1023/A:1022806420209
36. González-Vega M.E., Hernández-Espinosa M.M., Hernández-Rodríguez A.. **Influencia de la edad del callo en la inducción de suspenciones celulares de cafeto**. *Agron. Mesoam.* (2010) **21** 299-306. DOI: 10.15517/am.v21i2.4892
37. KiahYann L., Jelodar N.B., Lai-Keng C.. **Investigation on the effect of subculture frequency and inoculum size on the artemisinin content in a cell suspension culture of**. *Aust. J. Crop Sci.* (2012) **6** 801-807
38. Martinez M.E., Jorquera L., Poirrier P., Díaz K., Chamy R.. **Effect of the Carbon Source and Plant Growth Regulators (PGRs) in the Induction and Maintenance of an In Vitro Callus Culture of**. *Agronomy* (2021) **11**. DOI: 10.3390/agronomy11061181
39. Chan L.K., Lim P.S., Choo M.L., Boey P.L.. **Establishment of**. *Vitr. Cell. Dev. Biol.—Plant* (2010) **46** 8-12. DOI: 10.1007/s11627-009-9240-8
40. Mukherjee S., Ghosh B., Jha S.. **Establishment of forskolin yielding transformed cell suspension cultures of**. *J. Biotechnol.* (2000) **76** 73-81. DOI: 10.1016/S0168-1656(99)00181-9
41. Sakano K., Matsumoto M., Yazaki Y., Kiyota S., Okihara K.. **Inorganic phosphate as a negative conditioning factor in plant cell culture**. *Plant Sci.* (1995) **107** 117-124. DOI: 10.1016/0168-9452(95)04102-Z
42. Santos R.B., Abranches R., Fischer R., Sack M., Holland T.. **Putting the Spotlight Back on Plant Suspension Cultures**. *Front. Plant Sci.* (2016) **7** 297. DOI: 10.3389/fpls.2016.00297
43. Rao B.R., Kumar D.V., Amrutha R.N., Jalaja N., Vaidyanath K., Rao A.M., Rao S., Polavarapu R., Kishor P.B.. **Effect of growth regulators, carbon source and cell aggregate size on berberine production from cell cultures of**. *Curr. Trends Biotechnol. Pharm.* (2008) **2** 269-276
44. Haida Z., Syahida A., Ariff S.M., Maziah M., Hakiman M.. **Factors Affecting Cell Biomass and Flavonoid Production of**. *Sci. Rep.* (2019) **9** 9533. DOI: 10.1038/s41598-019-46042-w
45. Nigra H.M., Alvarez M.A., Giulietti A.M.. **Effect of carbon and nitrogen sources on growth and solasodine production in batch suspension cultures of**. *Plant Cell Tissue Organ Cult.* (1990) **21** 55-60. DOI: 10.1007/BF00034492
46. George E.F., Hall M.A., De Klerk G.-J.. **Plant Tissue Culture Procedure-Background**. *Plant Propagation by Tissue Culture* (2008) **Volume 1** 1-28. DOI: 10.1007/978-1-4020-5005-3_1
47. Tal B., Goldberg I.. **Growth and Diosgenin Production by**. *Planta Med.* (1982) **44** 107-110. DOI: 10.1055/s-2007-971413
48. Suan See K., Bhatt A., Lai Keng C., See K.S., Bhatt A., Keng C.L.. **Effect of sucrose and methyl jasmonate on biomass and anthocyanin production in cell suspension culture of**. *Rev. Biol. Trop.* (2011) **59** 597-606. PMID: 21717852
49. Sahraroo A., Mirjalili M.H., Corchete P., Babalar M., Fattahi-Moghadam M.R., Zarei A.. **Enhancement of rosmarinic acid production by**. *SABRAO J. Breed. Genet.* (2018) **50** 25-35
50. Zhong J.-J., Yoshida T.. **High-density cultivation of**. *Enzyme Microb. Technol.* (1995) **17** 1073-1079. DOI: 10.1016/0141-0229(95)00033-X
51. Wang H.Q., Yu J.T., Zhong J.J.. **Significant improvement of taxane production in suspension cultures of**. *Process Biochem.* (1999) **35** 479-483. DOI: 10.1016/S0032-9592(99)00094-1
52. Hakkim F.L., Kalyani S., Essa M.H., Girija S., Song H.. **Production of rosmarinic in**. *Rev. Int. De Investig. Médica Y Biológica* (2011) **2** 1070-1074
53. Yang Y., He F., Yu L., Ji J., Wang Y.. **Flavonoid accumulation in cell suspension cultures of**. *Z Nat. C J Biosci.* (2009) **64** 68-72. DOI: 10.1515/znc-2009-1-212
54. Fazal H., Abbasi B.H., Ahmad N., Ali M., Ali S.. **Sucrose induced osmotic stress and photoperiod regimes enhanced the biomass and production of antioxidant secondary metabolites in shake-flask suspension cultures of**. *Plant Cell Tissue Organ Cult.* (2016) **124** 573-581. DOI: 10.1007/s11240-015-0915-z
55. Mishra M.R., Srivastava R.K., Akhtar N.. **Enhanced Alkaloid Production from Cell Culture System of**. *J. Biotechnol. Biomed. Sci.* (2018) **1** 14-34. DOI: 10.14302/issn.2576-6694.jbbs-18-2475
56. Kristó T.S., Szoke É., Kéry Á., Terdy P.P., Selmeczi L.K., Simándi B.. **Production and characterisation of**. *Acta Hortic.* (2004) **597** 57-61. DOI: 10.17660/ActaHortic.2003.597.5
57. Simándi B., Kristo S.T., Kéry Á., Selmeczi L.K., Kmecz I., Kemény S.. **Supercritical fluid extraction of dandelion leaves**. *J. Supercrit. Fluids* (2002) **23** 135-142. DOI: 10.1016/S0896-8446(02)00012-8
58. Srivastava P., Sisodia V., Chaturvedi R.. **Effect of culture conditions on synthesis of triterpenoids in suspension cultures of**. *Bioprocess Biosyst. Eng.* (2011) **34** 75-80. DOI: 10.1007/s00449-010-0448-0
59. Flores-Sánchez I.J., Ortega-López J., Montes-Horcasitas M.d.C., Ramos-Valdivia A.C.. **Biosynthesis of Sterols and Triterpenes in Cell Suspension Cultures of**. *Plant Cell Physiol.* (2002) **43** 1502-1509. DOI: 10.1093/pcp/pcf181
60. Feria-Romero I., Lazo E., Ponce-Noyola T., Cerda-García-Rojas C.M., Ramos-Valdivia A.C.. **Induced accumulation of oleanolic acid and ursolic acid in cell suspension cultures of**. *Biotechnol. Lett.* (2005) **27** 839-843. DOI: 10.1007/s10529-005-6215-7
61. Ochoa-Villarreal M., Howat S., Hong S., Jang M.O., Jin Y.-W., Lee E.-K., Loake G.J.. **Plant cell culture strategies for the production of natural products**. *BMB Rep.* (2016) **49** 149-158. DOI: 10.5483/BMBRep.2016.49.3.264
62. Komaraiah P., Naga Amrutha R., Kavi Kishor P.B.L., Ramakrishna S.V.. **Elicitor enhanced production of plumbagin in suspension cultures of**. *Enzym. Microb. Technol.* (2002) **31** 634-639. DOI: 10.1016/S0141-0229(02)00159-X
63. Wilson G., Balague C.. **Biosynthesis of Anthraquinone by Cells of**. *J. Exp. Bot.* (1985) **36** 485-493. DOI: 10.1093/jxb/36.3.485
64. Kikowska M., Budzianowski J., Krawczyk A., Thiem B.. **Accumulation of rosmarinic, chlorogenic and caffeic acids in In Vitro cultures of**. *Acta Physiol. Plant.* (2012) **34** 2425-2433. DOI: 10.1007/s11738-012-1011-1
65. Kim J.H., Yun J.H., Hwang Y.S., Byun S.Y., Kim D.-I.. **Production of taxol and related taxanes in**. *Biotechnol. Lett.* (1995) **17** 101-106. DOI: 10.1007/BF00134204
66. Baque M.A., Elgirban A., Lee E.J., Paek K.Y.. **Sucrose regulated enhanced induction of anthraquinone, phenolics, flavonoids biosynthesis and activities of antioxidant enzymes in adventitious root suspension cultures of**. *Acta Physiol. Plant.* (2012) **34** 405-415. DOI: 10.1007/s11738-011-0837-2
67. Lee C.W., Shuler M.L.. **The effect of inoculum density and conditioned medium on the production of ajmalicine and catharanthine from immobilized Catharanthus roseus cells**. *Biotechnol. Bioeng.* (2000) **67** 61-71. DOI: 10.1002/(SICI)1097-0290(20000105)67:1<61::AID-BIT7>3.0.CO;2-J
68. Yokoyama M., Inomata S., Seto S., Yanagi M.. **Effects of Sugars on the Glucosylation of Exogenous Hydroquinone by**. *Plant Cell Physiol.* (1990) **31** 551-555. DOI: 10.1093/oxfordjournals.pcp.a077945
69. Sakuta M., Takagi T., Komamine A.. **Effects of sucrose on betacyanin accumulation and growth in suspension cultures of**. *Physiol. Plant.* (1987) **71** 455-458. DOI: 10.1111/j.1399-3054.1987.tb02883.x
70. Jalil M., Annuar M.S.M., Tan B.C., Khalid N.. **Effects of Selected Physicochemical Parameters on Zerumbone Production of**. *Evid. -Based Complement. Altern. Med.* (2015) **2015** 1-7. DOI: 10.1155/2015/757514
71. Santos A.L.W.d., Silveira V., Steiner N., Maraschin M., Guerra M.P.. **Biochemical and morphological changes during the growth kinetics of**. *Braz. Arch. Biol. Technol.* (2010) **53** 497-504. DOI: 10.1590/S1516-89132010000300001
72. Murashige T., Skoog F.. **A revised medium for rapid growth and bio assays with tobacco tissue cultures**. *Physiol. Plantarum.* (1962) **15** 473-497. DOI: 10.1111/j.1399-3054.1962.tb08052.x
73. DuBois M., Gilles K.A., Hamilton J.K., Rebers P.A., Smith F.. **Colorimetric Method for Determination of Sugars and Related Substances**. *Anal. Chem.* (1956) **28** 350-356. DOI: 10.1021/ac60111a017
74. Strober W.. **Trypan Blue Exclusion Test of Cell Viability**. *Current Protocols in Immunology* (2001). DOI: 10.1002/0471142735.ima03bs21
75. Adhyapak S., Dighe V.V.. **Investigation of microscopical and physicochemical characteristics of plants with anti-diabetic activity**. *Int. J. Pharm. Sci. Res.* (2014) **5** 882-888
76. Said K.A.M., Amin M.A.M.. **Overview on the Response Surface Methodology (RSM) in Extraction Processes**. *J. Appl. Sci. Process Eng.* (2016) **2** 8-17
|
---
title: Nutritional Intake Differences in Combinations of Carbohydrate-Rich Foods in
Pirapó, Republic of Paraguay
authors:
- Yuko Caballero
- Konomi Matakawa
- Ai Ushiwata
- Tomoko Akatsuka
- Noriko Sudo
journal: Nutrients
year: 2023
pmcid: PMC10004760
doi: 10.3390/nu15051299
license: CC BY 4.0
---
# Nutritional Intake Differences in Combinations of Carbohydrate-Rich Foods in Pirapó, Republic of Paraguay
## Abstract
A national strategy for obesity prevention has been promoted in Paraguay, reflecting the situation where half of adults and $23.4\%$ of children (under 5 years old) are overweight. However, the detailed nutritional intake of the population has not yet been studied, especially in rural areas. Therefore, this study aimed to identify obesity-causing factors in Pirapó by analyzing the results from a food frequency questionnaire (FFQ) and one-day weighed food records (WFRs). From June to October 2015, 433 volunteers (200 males and 233 females) completed the FFQ with 36 items and one-day WFRs. Body mass index (BMI) positively correlated with the consumption of sandwiches, hamburgers, and bread and with age and diastolic blood pressure, although pizza and fried bread (pireca) had a negative correlation in males ($p \leq 0.05$). BMI positively correlated with systolic blood pressure, whereas it negatively correlated with the consumption of cassava and rice in females ($p \leq 0.05$). The FFQ revealed that fried food with wheat flour was consumed once a day. WFRs showed that $40\%$ of meals consisted of two or more carbohydrate-rich dishes, significantly higher in energy, lipids, and sodium than those containing only one carbohydrate-rich dish. These results imply that less oily wheat dish consumption and healthy combinations of dishes should be considered for obesity prevention.
## 1. Introduction
Obesity is a known risk factor for many diseases, and recent studies have reported that it is caused by globalization and lifestyle urbanization, including eating habits [1,2]. Meanwhile, some studies have reported that a rising rural body mass index (BMI) is a major factor in the global obesity increase in adults [3]. In Latin America, more than half of adults are overweight, significantly higher than the global average of $39\%$ [2]. People in the Republic of Paraguay, located in the center of South America, are affected at the same prevalence and a BMI increase has been noted in both men and women [3,4]. The Instituto Nacional de Alimentación y Nutrición, Ministerio de Salud Pública y Bienestar Social in Paraguay (INAN) reported that $46.4\%$ of pregnant women were overweight, and obesity in females correlated with fewer formal education years [5,6]. Furthermore, a report from the Food and Agriculture Organization (FAO) and other collaborating organizations, including the International Fund for Agricultural Development (IFAD), United Nations International Children’s Emergency Fund (UNICEF), World Food Programme (WFP), and World Health Organization (WHO), noted that the percentage of overweight children (under 5 years of age) in Paraguay was $23.4\%$, which ranked high among South American countries [7]. Therefore, nutritional education is crucial, though the need for such education is only just beginning to be addressed at the governmental level [8].
The WHO reported that a cause of death from non-communicable diseases in Paraguay accounts for $75\%$ of deaths, higher than the world average of $70\%$, and that the top three non-communicable diseases that lead to death are ischemic heart disease, stroke, and diabetes mellitus [9]. Regarding non-communicable diseases, $10\%$ of the population has diabetes, and one in three people have hypertension [10,11]. To develop a strategy for preventing obesity and non-communicable diseases at the public level, obesity-related factors, especially eating styles, should be identified and reshaped.
The main staple foods in Paraguay were cassava (mandioca) and various types of corn meal until the 1490s, the years European settlement started, similar to other Central and South American countries. Since then, various other staple foods have been introduced. Regarding wheat flour, an Organization for Economic Cooperation and Development (OECD)-FAO Agricultural Outlook Report, and statistical information derived from the United States Department of Agriculture (USDA), reported that annual domestic wheat consumption in Paraguay has increased drastically, in fact, by nine times over 60 years (from 1960 to 2020), with the consumption per capita approximately 52 kg in 2020 [12,13]; however, cassava and corn are still the primary sources of carbohydrates in rural areas. Meanwhile, fruit and vegetable consumption is low, with $61.9\%$ of males and $71.0\%$ of females not consuming an adequate amount of fruits and vegetables (as the WHO recommends) [14,15].
Nutritional surveillance in Paraguay was first widely conducted in 2011, with limited foods surveyed [11]. At the regional level, there are only a few reports analyzing habitual food consumption and diet patterns in certain areas, especially rural areas [10,16,17,18]. Therefore, national policies for healthy dietary behavior have not yet been established [8,19]. Regarding medical welfare, the national health insurance coverage rate is only $3\%$, which causes delays in patients’ treatment and worsens their diseases, as was demonstrated during the coronavirus disease 2019 pandemic [4,20]. For geographical and economic reasons, people in rural areas have difficulty accessing medical treatment. Hence, healthy diet education and prevention of obesity and obesity-related diseases in rural areas are crucial. Finding direct causes of obesity and tackling them are ideally required. However, the diet is a complicated part of human lives and requires longitudinal study in a carefully monitored environment.
For these reasons, this study aimed to clarify habitual food consumption, analyze meal styles in detail, and identify obesity-related eating factors in Pirapó, a rural area, to compile nutritional information that will help promote obesity prevention.
## 2.1. One-Day Weighed Food Records
This is the second report of our research conducted, and the selection criteria and study design have been described elsewhere [21]. In short, 200 households in Pirapó completed a one-day weighed food records (WFR) at some point from June to October 2015. To determine the study site, we selected Pirapó as the rural area because, although most of its population comprises farmers, there are supermarkets in the area, spread out at 3–10 km distances, and food access is easy. In addition, it was safe to have people complete WFRs in the region when public security was considered.
We recruited volunteers who would participate in the survey by conducting house-to-house visits in both central and suburban areas of Pirapó, accompanied by acquaintances from city offices. The selection criteria were as follows: at least two adults (18–69 years old, any gender) living in the household, who did not restrict their diet or have any health problems requiring food restriction. The population of Pirapó is approximately 7000. Therefore, we recruited 200 households, which was judged to be a suitable sample size for the food survey.
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethical Committee of Ochanomizu University (no. 2015-14, June 2015). Informed consent was obtained from all participants involved in the study.
The day before the food survey, researchers or their assistants (local residents) explained the survey and acquired informed consent. Height and weight were measured using a portable stadiometer and a weight scale with light clothes and without socks in their home (stadiometer: seca213, Seca, Chiba, Japan, weight scale: HD-660, Tanita, Tokyo, Japan). The participants’ weight and height were measured to calculate their BMI (kg/m2). On the day of the WFRs, the researchers and their assistants visited the household in the early morning, within an hour of the participants’ waking time, and their blood pressure was measured three times while in a sitting position (after micturition and before breakfast). Blood pressure was measured using electronic sphygmomanometers (HEM-7200, Omron, Kyoto, Japan), with it taking one minute for each reading. The average of the second and third measurements was adopted as the pressure value. Hypertension was defined as systolic blood pressure >140 mmHg or diastolic blood pressure >90 mmHg [22]. The WFRs were completed as described in our previous studies [23,24]. All foods and dishes, including beverages consumed at each meal, were weighed using digital cooking scales (TL-280, KD-171, Tanita, Tokyo, Japan), and nutritional intakes were calculated in accordance with the food composition table. The survey was conducted by researchers and their assistants who were trained for three days before the study on how to explain the study protocol and demonstrate the correct use of the scale and calculation of the consumption of each food. Four groups, each consisting of two data collectors, comprising a researcher and their assistant, visited each household and stayed all day for the WFRs.
## 2.2. Food Frequency Questionnaire
Habitual food consumption was assessed using the food frequency questionnaire (FFQ). To develop the questionnaire, a year before the study, we conducted a pilot study of WFRs in nine households, created an FFQ list, and asked Paraguayan nutritionists to add items to cover all foods and dishes consumed in the region. Following a previous study, we developed a non-quantitative FFQ (without portion size (g) of the food or dish) because of the lack of validation and reproducibility of quantitative information [25]. Foods and dishes with 32 items were listed in the FFQ, which was pretested by some villagers, then revised to fit them. We confirmed that there were few seasonal variations in the region based on residents’ interviews and our previous survey. The participants in the FFQ were the same people as those who completed WFRs, who were asked to answer how often they consumed the listed foods or dishes on a daily basis. The questionnaires were paper-based and filled out by trained assistants. The question and answer were as per the following example: “How often do you eat bread?” and “I eat it once a day.” Then, the assistant circled “day” from the options of month/week/day, and wrote the number “1” as the frequency. The FFQ values were calculated as numeric numbers; if a participant’s egg consumption FFQ value was 1, it meant that the participant consumed it once a day.
## 2.3. Dish Combination Analysis
To clarify the types of dishes that were combined in a meal, dish combinations were scrutinized. Recipe data were obtained from the WFRs [21]. Energy and other nutritional intakes per dish were calculated based on the food composition tables provided online by the INAN, FAO, and governmental databases of neighboring countries (Argentine, Chile, and Brazil) [26]. Table S1 shows the dishes that appeared in the WFRs in the study and their nutritional characteristics, as calculated from the food composition tables. Some have already been reported in our previous study in Japanese [21].
Figure 1 shows the dish combination analysis procedure. These categorical criteria were based on Murakami and Yoshiike’s study and dietary guidelines for Japanese people because no studies on the combination of dishes were found in other Central and South American countries [27,28].
Among the 200 households, two were excluded because there were no adults aged between 18 and 69 years. So, we collected data on 1223 meals from 198 households. All dishes were categorized into four types: staple, main, mixed, and others. Then, all meals were categorized as follows: one carbohydrate-rich dish, two or more carbohydrate-rich dishes, and not containing any carbohydrate-rich dish.
## 2.4. Statistical Analysis
Multiple regression analysis was used to identify obesity factors and independent variables associated with BMI (the dependent variable) separately for males and females. These factors were selected via the backward stepwise selection method using $$p \leq 0.05$$ (both included and removed). In addition to investigating the relationship between blood pressure and the total FFQ values of fried flour dishes (fried tortilla from wheat flour, fried bread (pireca), fried dough with wheat flour (reviro), and fried dumpling with meat and boiled eggs (empanada)), multiple regression analysis was performed. Differences in energy, lipid, and sodium quantities between meal types were analyzed using a t-test. The level of significance was set at $p \leq 0.05.$ Statistical analyses were performed using SPSS (Statistical Package for the Social Sciences) version 27 for Windows (IBM, Armonk, NY, USA), JMP version 16.2 (SAS Inc., Cary, NC, USA), and free software R (version 4.2.1; 23 June 2022).
## 3.1. Participants’ Characteristics
A total of 433 volunteers participated in this survey (200 males and 233 females). Table 1 shows the participants’ physical characteristics, as referred to in our previous study [21]. Approximately $40\%$ of the male participants were farmers, and $70\%$ of the females were housewives. The mean BMI of the participants was 26.4 (standard deviation (SD) = 4.3) for males and 27.4 (SD = 5.2) for females. Overweight and obesity accounted for $61.5\%$ of males and $65.3\%$ of females, respectively.
## 3.2. FFQ
Table 2 shows the FFQ values and the ordered high-frequency intake of food or dishes per day. Any items in the gray color contained wheat flour, accounting for more than half of all food and dishes. The value of boiled cassava was 0.96, both in males and females, reflecting that it was consumed most frequently, approximately once a day. The second and fourth most abundant foods were bread and reviro, respectively; both of which contained wheat flour.
Galleta is a type of small and ball-shaped bread containing anise seed (herb), and reviro is a typical regional meal cooked like a scrambled egg with wheat flour, water, oil, and salt, but without eggs. Tortillas and pireca are made from wheat flour and fried in large amounts of oil. Pireca is fried dough like thin-crust pizza made from wheat flour, salt, and water, sometimes with egg, without fermentation.
The FFQ results and nutrient compositions of the dishes showed that the participants consumed cassava-based and oil-rich dishes with wheat flour (reviro, tortilla, pireca, or empanada) once a day.
## 3.3. BMI and Obesity-Related Factors
Table 3 shows the results of the multiple regression analysis to clarify the relationship between BMI and the independent variables selected via the stepwise method for males. The results showed that age, sandwiches, hamburgers, diastolic blood pressure, and bread (pan, galleta) were significantly positively correlated with BMI, with $p \leq 0.001$, 0.015, 0.006, 0.017, and 0.018, respectively. In contrast, pizza and fried bread (pireca) were negatively correlated with BMI ($$p \leq 0.015$$ and 0.036, respectively).
Table 4 shows the results for females when using the same multi-regression analysis method. Systolic blood pressure was significantly positively correlated with BMI, with $p \leq 0.001.$ However, the consumption of dishes with cassava, or rice was significantly negatively correlated with BMI, with $p \leq 0.001$ and 0.002, respectively.
Table 5 shows the results of the multi-regression analysis to clarify the relationship between systolic blood pressure and the total FFQ value of fried wheat flour dishes for males. Diastolic blood pressure, age, and total FFQ value of fried flour dishes were significantly positively correlated with systolic blood pressure, with $p \leq 0.001$, < 0.001, and 0.004, respectively.
For females, no association was found between blood pressure and the total FFQ value of fried flour dishes.
## 3.4. Food Combination Analysis
Table 6 shows the dish combinations observed frequently and their mean energy intake. The average energy intake during lunch was 980 or 780 kcal for males and females, respectively, making the highest contribution to the daily energy intake. However, combinations of reviro with cocido for breakfast and boiled cassava with tortilla for dinner also formed a high energy intake for both males and females. Cocido is a traditional Paraguayan beverage cooked from mate leaves caramelized with sugar and added to water and milk for serving, which is frequently consumed for both breakfast and dinner.
Most of the participants used soybean oil for cooking. Skipped meals were observed for all meals. As the main culprit, approximately $10\%$ of the participants skipped breakfast, 25 males and 24 females.
Table 7 shows the types of dish combinations and the differences in energy, lipid, and sodium intake between the dish types. Only 10 males and 14 females consumed meals without carbohydrate-rich dishes, and their energy and other nutritional intakes were lower than those of individuals who consumed other meal types. Among meal types, $43.8\%$ of males and $40.9\%$ of females consumed meals with two or more carbohydrate-rich dishes. When comparing meals with one carbohydrate-rich dish and those with two or more carbohydrate-rich dishes, energy, lipid, and sodium intakes were significantly higher for meals with two or more carbohydrate-rich dishes ($$p \leq 0.001$$ or < 0.001), except for lipid intake in males ($$p \leq 0.197$$).
Figure 2 shows the distribution of nutritional intake between the meal types. Males showed large ranges in all forms of nutritional intake, especially energy and lipids, for meals with one carbohydrate-rich ingredient, and sodium in meals with two or more carbohydrate-rich ingredients. An outlier in a meal without carbohydrate-rich ingredients in males came from heavy alcohol consumption.
## 4. Discussion
In this study, we scrutinized FFQ values and WFRs to identify obesity-related factors among villagers in Pirapó, Paraguay.
Most participants in Pirapó cultivated cassava in their fields. Therefore, boiled cassava was consumed more frequently than any other food on a daily basis (approximately once per day).
Bread and other staple dishes containing wheat flour were also frequently consumed. Moreover, six of the top ten most frequently consumed dishes contained wheat flour, and they were consumed as staple and main dishes. Among wheat flour dishes, reviro, tortilla, pireca, and empanada contained more than 20 g of lipid, and the sum of these FFQ values reached approximately 1.0 for both males and females. This indicates that they consumed oil-rich flour dishes once daily.
When obesity factors were compared between males and females, the FFQ values for sandwiches, hamburgers, and bread (pan and galleta) were positively correlated with BMI in males. In contrast, dishes with cassava and rice were negatively correlated with BMI in females. The total FFQ value of fried wheat flour dishes was positively associated with systolic blood pressure in males. However, this was not observed in females. Some studies have reported that males tend to be less aware of their body weight and less likely to go on a diet than females [29,30]. Our results could not reveal sex differences at the conscious level. However, considering a previous study conducted in Paraguay, there might have also been a different perspective on food between the sexes in this study [6]. These results imply that different approaches to nutritional education between sexes are needed.
In their review, Gadiraju et al. reported that more frequent consumption of fried foods (i.e., four or more times per week) was associated with a higher risk of developing type 2 diabetes, obesity, and hypertension [31]. Hypertension and obesity are strongly correlated [22,32]. Another cohort study showed that participants who consumed fried foods four or more times per week were approximately 1.2 times more likely to have hypertension than those who consumed fried foods less than twice per week (hazard ratio 1.21, $95\%$ confidence interval 1.04, 1.41) with a median follow-up of 6.3 years [33]. Furthermore, Soriguer et al. reported that a high-frequency intake of fried food, especially reused sunflower oil, contributed to hypertension [34]. At our study site, $38.0\%$ of males and $29.6\%$ of females had high blood pressure, and most participants used soybean oil. The fatty acid composition of soybean oil is similar to that of sunflower oil (native species), which contains a high level of linolenic acid. Therefore, considering the previous report and our results showing a positive correlation between high-frequency intake of fried food and systolic blood pressure [33,34], high-frequency consumption of fried food in this area might contribute to the participants’ obesity and hypertension.
Susceptibility to non-communicable diseases (NCDs), such as hypertension and diabetes, is known to depend on ethnicity and genotype. We did not study participants’ ethnicities. However, considering that approximately $85\%$ of Paraguayans are mestizo and the relationship between genotypes and NCDs has not been studied widely in South America, further study on the relationship between them is required. The average FFQ values of vegetables ranged from 0.44 to 0.45 and their consumption corresponded to 3.08–3.15 times per week, which was higher than the average consumption of vegetables nationally of 2.6 [11]. Nevertheless, vegetable consumption was still low compared with the WHO’s recommendation of five servings of fruits and vegetables on a daily basis, although only a few countries achieve that recommendation [15]. High-frequency consumption of vegetables and obesity are known to be inversely correlated [35]. Our finding of a few varieties of vegetable dishes from WFRs and low-frequency consumption of vegetables from FFQ implies that promoting cooking a variety of vegetable dishes will increase the high-frequency consumption of vegetables and will be effective for preventing obesity in the future. Considering food access, when supermarkets are situated at a distance of 3–10 km, growing vegetables at home could also be an effective measure to promote a greater quantity and frequency of vegetables’ consumption.
According to the WFRs results, meals with bread or dishes with wheat flour and mate milk tea were frequently observed to be consumed during breakfast and dinner. Boiled cassava and meat soup or pasta were observed more frequently during lunch. From a nutritional perspective, even though the participants’ energy intake was sufficient for breakfast and dinner, other nutrients, such as vitamins and minerals, were lacking, with lunch the main source of protein, vitamins, and minerals. Furthermore, a highly refined and small-particle starchy diet is known to induce both de novo lipogenesis and stearoyl-CoA desaturase [36,37]. Therefore, these eating styles of highly refined wheat flour dishes and sweetened mate tea with milk during breakfast and dinner might pose a risk of enlarging adipose cells.
Among the participants, $11\%$ and $6\%$ skipped breakfast and dinner, respectively. However, some participants exceeded the ideal energy intake per meal, particularly when combined with fried dishes, reviro, or tortilla. This might be compensatory behavior for skipping meals and is known to be an obesity factor [38]. Hence, eating meals without skipping could be crucial to covering nutritional necessities and preventing obesity, as the INAN recommends [39].
When categorizing meal types into the number of carbohydrate-rich dishes, meals consisting of two or more carbohydrate-rich dishes accounted for approximately $40\%$ of all meals. When comparing meal types, the energy, lipid, and sodium intakes were significantly higher when consuming two or more carbohydrate-rich meals. While many studies analyzing dietary patterns have been reported worldwide, there are few reports on carbohydrate-rich dish combinations in meals, except in Japan [40,41,42,43]. Some studies reported that they did not observe any cases of combining two or more staple dishes in a meal [27,44]. This might be because Japanese people are educated in school to eat by combining one type of staple food, a main dish, and some side dishes; school lunches are also served in the same manner. In contrast, meals with bread and pasta or meals with two types of carbohydrate-rich dishes are often seen in many countries, although the degree of the combination has not yet been studied. It seems such a combination of dishes depends on each country’s cultural background and nutritional education. A study of Hispanic older adults in the United States showed that BMI and waist circumference were related to diet type [42]. Diet patterns observed in our study implied that there were diet-type differences among participants, which might be related to BMI and other health outcomes, while the average daily energy intake and other macronutritional intakes of participants were normal [21]. Carbohydrates are important energy sources for daily activities. However, a combination of other macronutrients, total energy intake, and cooking style may be important for preventing over-intake of food. Further studies analyzing carbohydrate-rich dishes in the diet and the effects of obesity should be conducted.
Although dietary guides recommend eating well-balanced meals [38,45], a recent study showed that the scores for healthy diet styles in Paraguayans were the worst among 11 Latin American countries, and the prevalent diet styles seemed to be far from the heathy diet recommendation, as shown in a study conducted in Asunción, the capital of Paraguay [39,46]. This situation is presumed because the dietary guides advise on the frequency and quantity of food that people are recommended to consume, but not at the dish level [47].
This study had some limitations. First, we conducted only one-day WFRs, meaning day-to-day variations were not considered. Second, the FFQ was self-reported, and the accuracy depended on participants’ memories. Furthermore, the FFQ did not include portion sizes, meaning we could not calculate the habitual nutritional intake and could not conduct a direct comparison with the WFR. Third, we were unable to conduct a physical energy expenditure survey, though that is an obesity factor. Studies combining food intake and physical energy expenditure must be conducted in the future.
Nevertheless, this is the first report to reveal habitual food intake and dish combinations in detail and identify new routes for preventing obesity. The study’s findings lead us to make to some suggestions. First, people should consume fewer fried dishes, such as reviro, tortilla, and empanada, eating those only a few times per week at most. Second, the consumption of one carbohydrate-rich dish per meal is recommended. If two or more carbohydrate-rich dishes are combined in a meal, care must be taken to avoid over-intake of energy, lipids, and sodium. For example, the ideal eating style for one meal is as follows:A.Combination of staple dish, main dish, and one portion of vegetablesi.e., boiled cassava, meat soup without pasta, and saladB.One mixed food and one portion of vegetablesGuiso (a dish containing rice, meat, and vegetables) and salad However, these recommendations are limited to this area. A further consideration is that the situation may have changed after the study. Tracking the area and wider region, including city districts, may be helpful to propose effective dietary advice.
## 5. Conclusions
Oily and carbohydrate-rich dishes, particularly with wheat flour, were often consumed approximately once a day, and meals combining two or more carbohydrate-rich dishes accounted for approximately $40\%$ of all meals in Pirapó. Therefore, nutritional education on using less oil and healthier dish combinations should be promoted for obesity and non-communicable-disease prevention.
## References
1. Ford N.D., Patel S.A., Narayan K.M.V.. **Obesity in Low-and Middle-Income Countries: Burden, Drivers, and Emerging Challenges**. *Annu. Rev. Public Health* (2017.0) **7** 145-164. DOI: 10.1146/annurev-publhealth-031816-044604
2. Jiwani S.S., Carrillo-Larco R.M., Hernández-Vásquez A., Barrientos-Gutiérrez T., Basto-Abreu A., Gutierrez L., Irazola V., Nieto-Martínez R., Nunes B.P., Parra D.C.. **The Shift of Obesity Burden by Socioeconomic Status between 1998 and 2017 in Latin America and the Caribbean: A Cross-Sectional Series Study**. *Lancet. Glob. Health* (2019.0) **7** e1644-e1654. DOI: 10.1016/S2214-109X(19)30421-8
3. Bixby H., Bentham J., Zhou B., di Cesare M., Paciorek C.J., Bennett J.E., Taddei C., Stevens G.A., Rodriguez-Martinez A., Carrillo-Larco R.M.. **Rising Rural Body-Mass Index Is the Main Driver of the Global Obesity Epidemic in Adults**. *Nature* (2019.0) **569** 260-264. DOI: 10.1038/S41586-019-1171-X
4. Halpern B., Louzada M.L.D.C., Aschner P., Gerchman F., Brajkovich I., Faria-Neto J.R., Polanco F.E., Montero J., Juliá S.M.M., Lotufo P.A.. **Obesity and COVID-19 in Latin America: A Tragedy of Two Pandemics—Official Document of the Latin American Federation of Obesity Societies**. *Obes. Rev.* (2021.0) **22** e13165. DOI: 10.1111/obr.13165
5. **Situación Nutricional en la Población de Embarazadas en el Paraguay**
6. Cañete F., Fretes G., Sequera V.G., Turnes C., Santacruz E., Paiva T., Benitez G.. **Epidemiología de La Obesidad En El Paraguay**. *An. Fac. Cienc. Méd.* (2016.0) **49** 17-26. DOI: 10.18004/anales/2016.049(02)17-026
7. 7.
FAO
IFAD
UNICEF
WFP
WHO
The State of Food Security and Nutrition in the World 2022.Repurposing Food and Agricultural Policies to Make Healthy Diets More AffordableFAORome, Italy2022978-92-5-136499-4. *The State of Food Security and Nutrition in the World 2022.Repurposing Food and Agricultural Policies to Make Healthy Diets More Affordable* (2022.0)
8. Palacios C., Magnus M., Arrieta A., Gallardo H., Tapia R., Espinal C.. **Obesity in Latin America, a Scoping Review of Public Health Prevention Strategies and an Overview of Their Impact on Obesity Prevention**. *Public Health Nutr.* (2021.0) **24** 5142-5155. DOI: 10.1017/S1368980021001403
9. **Global Health Estimates: Leading Causes of Death**
10. Lee H., Kim J.E., Amarilla A., Kang Y., Boram S., Nam E.W.. **Social Determinants of Overweight and Obesity in Paraguayan Adults Using Quantile Regression**. *J. Public Health Res.* (2021.0) **10** 2196. DOI: 10.4081/jphr.2021.2196
11. **Primera Encuesta Nacional de Factores de Riesgo de Enfermedades No Transmisibles En Población General**
12. 12.
OECD/FAO
OECD-FAO Agricultural Outlook 2021–2030OECDParis, France202110.1787/19428846-en. *OECD-FAO Agricultural Outlook 2021–2030* (2021.0). DOI: 10.1787/19428846-en
13. **Paraguay Wheat Domestic Consumption by Year**
14. Vierci G.E., Ferro E.A.. **Capacidad Antioxidante Total Vinculada a la Ingesta de Frutas y Verduras en Adultos Jóvenes de Asunción, Paraguay**. *Nutr. Hosp.* (2019.0) **36** 118-124. PMID: 30834765
15. Hall J.N., Moore S., Harper S.B., Lynch J.W.. **Global Variability in Fruit and Vegetable Consumption**. *Am. J. Prev. Med.* (2009.0) **36** 402-409. DOI: 10.1016/j.amepre.2009.01.029
16. Fisberg M., Kovalskys I., Gómez G., Rigotti A., Cortés L.Y., Herrera-Cuenca M., Yépez M.C., Pareja R.G., Guajardo V., Zimberg I.Z.. **Latin American Study of Nutrition and Health (ELANS): Rationale and Study Design**. *BMC Public Health* (2016.0) **16**. DOI: 10.1186/s12889-016-2765-y
17. Pérez-Ferrer C., Auchincloss A.H., de Menezes M.C., Kroker-Lobos M.F., Cardoso L.D.O., Barrientos-Gutierrez T.. **The Food Environment in Latin America: A Systematic Review with a Focus on Environments Relevant to Obesity and Related Chronic Diseases**. *Public Health Nutr.* (2019.0) **22** 3447-3464. DOI: 10.1017/S1368980019002891
18. Paredes F.G., Ruiz Díaz L., González N.. **Hábitos Saludables y Estado Nutricional en el Entorno Laboral**. *Rev. Chil. Nutr.* (2018.0) **45** 119-127. DOI: 10.4067/S0717-75182018000300119
19. Galicia L., Grajeda R., de Romaña D.L.. **Nutrition Situation in Latin America and the Caribbean: Current Scenario, Past Trends, and Data Gaps**. *Rev. Panam. Salud Publica* (2016.0) **40** 104-113. PMID: 27982368
20. Báscolo E., Houghton N., del Riego A.. **Lógicas de Transformación de los Sistemas de Salud en América Latina y Resultados en Acceso y Cobertura de Salud [Types of Health Systems Reforms in Latin America and Results in Health Access and Coverage]**. *Pan Am. J. Public Health* (2018.0) **42** e126. DOI: 10.26633/RPSP.2018.126
21. Sudo N., Caballero Y.. **Nutritional Characteristics of Food with Wheat Flour Consumed in Pirapo, Paraguay**. *J. Japan Diet. Assoc.* (2017.0) **60** 213-219
22. Tiwari R., Srivastava D., Gour N.. **A Cross-Sectional Study to Determine Prevalence of Obesity in High Income Group Colonies of Gwalior City**. *Indian J. Community Med.* (2009.0) **34** 218-222. DOI: 10.4103/0970-0218.55287
23. Sudo N., Sekiyama M., Maharjan M., Ohtsuka R.. **Gender Differences in Dietary Intake among Adults of Hindu Communities in Lowland Nepal: Assessment of Portion Sizes and Food Consumption Frequencies**. *Eur. J. Clin. Nutr.* (2006.0) **60** 469-477. DOI: 10.1038/sj.ejcn.1602339
24. Yanagisawa A., Sudo N., Amitani Y., Caballero Y., Sekiyama M., Mukamugema C., Matsuoka T., Imanishi H., Sasaki T., Matsuda H.. **Development and Validation of a Data-Based Food Frequency Questionnaire for Adults in Eastern Rural Area of Rwanda**. *Nutr. Metab. Insights* (2016.0) **9** 31-42. DOI: 10.4137/NMI.S38374
25. McNutt S., Zimmerman T.P., Hull S.G.. **Development of Food Composition Databases for Food Frequency Questionnaires (FFQ)**. *J. Food Compos. Anal.* (2008.0) **21** S20-S26. DOI: 10.1016/j.jfca.2007.05.007
26. Blanco-Metzler A., de Pablo S., Samman N., de Ariza J.S., Masson L., de Núñez L.G., de Menezes E.W.. **Latinfoods Activities and Challenges during the Period of 2009–2012**. *Arch. Latinoam. Nutr.* (2014.0) **64** 206-214. PMID: 26137797
27. Murakami K., Livingstone M.B.E., Shinozaki N., Sugimoto M., Fujiwara A., Masayasu S., Sasaki S.. **Food Combinations in Relation to the Quality of Overall Diet and Individual Meals in Japanese Adults: A Nationwide Study**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12020327
28. Yoshiike N., Hayashi F., Takemi Y., Mizoguchi K., Seino F.. **A New Food Guide in Japan: The Japanese Food Guide Spinning Top**. *Nutr. Rev.* (2007.0) **65** 149-154. DOI: 10.1301/nr.2007.apr.149-154
29. Tsai S.A., Lv N., Xiao L., Ma J.. **Gender Differences in Weight-Related Attitudes and Behaviors Among Overweight and Obese Adults in the United States**. *Am. J. Mens. Health* (2016.0) **10** 389-398. DOI: 10.1177/1557988314567223
30. Wardle J., Haase A.M., Steptoe A., Nillapun M., Jonwutiwes K., Bellisle F.. **Gender Differences in Food Choice: The Contribution of Health Beliefs and Dieting**. *Ann. Behav. Med.* (2004.0) **27** 107-116. DOI: 10.1207/s15324796abm2702_5
31. Gadiraju T.V., Patel Y., Gaziano J.M., Djoussé L.. **Fried Food Consumption and Cardiovascular Health: A Review of Current Evidence**. *Nutrients* (2015.0) **7** 8424-8430. DOI: 10.3390/nu7105404
32. Hall J.E., do Carmo J.M., da Silva A.A., Wang Z., Hall M.E.. **Obesity-Induced Hypertension: Interaction of Neurohumoral and Renal Mechanisms**. *Circ. Res.* (2015.0) **116** 991-1006. DOI: 10.1161/CIRCRESAHA.116.305697
33. Sayon-Orea C., Bes-Rastrollo M., Gea A., Zazpe I., Basterra-Gortari F.J., Martinez-Gonzalez M.A.. **Reported Fried Food Consumption and the Incidence of Hypertension in a Mediterranean Cohort: The SUN (Seguimiento Universidad de Navarra) Project**. *Br. J. Nutr.* (2014.0) **112** 984-991. DOI: 10.1017/S0007114514001755
34. Soriguer F., Rojo-Martínez G., Dobarganes M.C., García Almeida J.M., Esteva I., Beltrán M., Ruiz de Adana M.S., Tinahones F., Gómez-Zumaquero J.M., García-Fuentes E.. **Hypertension Is Related to the Degradation of Dietary Frying Oils**. *Am. J. Clin. Nutr.* (2003.0) **78** 1092-1097. DOI: 10.1093/ajcn/78.6.1092
35. Nour M., Lutze S.A., Grech A., Allman-Farinelli M.. **The Relationship between Vegetable Intake and Weight Outcomes: A Systematic Review of Cohort Studies**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10111626
36. Chong M.F.F., Hodson L., Bickerton A.S., Roberts R., Neville M., Karpe F., Frayn K.N., Fielding B.A.. **Parallel Activation of de Novo Lipogenesis and Stearoyl-CoA Desaturase Activity after 3 d of High-Carbohydrate Feeding**. *Am. J. Clin. Nutr.* (2008.0) **87** 817-823. DOI: 10.1093/ajcn/87.4.817
37. Cai M., Dou B., Pugh J.E., Lett A.M., Frost G.S.. **The Impact of Starchy Food Structure on Postprandial Glycemic Response and Appetite: A Systematic Review with Meta-Analysis of Randomized Crossover Trials**. *Am. J. Clin. Nutr.* (2021.0) **114** 472-487. DOI: 10.1093/ajcn/nqab098
38. Berg C., Lappas G., Wolk A., Strandhagen E., Torén K., Rosengren A., Thelle D., Lissner L.. **Eating Patterns and Portion Size Associated with Obesity in a Swedish Population**. *Appetite* (2009.0) **52** 21-26. DOI: 10.1016/j.appet.2008.07.008
39. Morínigo Martínez M., Samudio M.. **Etapas del modelo conductual de los estilos de vida propuestos por las guías alimentarias del Paraguay en adultos**. *Mem. Inst. Investig. Cienc. Salud* (2015.0) **13** 41-48. DOI: 10.18004/Mem.iics/1812-9528/2015.013(01)41-048
40. Bermudez O.I., Tucker K.L.. **Trends in Dietary Patterns of Latin American Populations**. *Cad. Saude Publica* (2003.0) **19** S87-S99. DOI: 10.1590/S0102-311X2003000700010
41. Moreira P., Santos S., Padrão P., Cordeiro T., Bessa M., Valente H., Barros R., Teixeira V., Mitchell V., Lopes C.. **Food Patterns According to Sociodemographics, Physical Activity, Sleeping and Obesity in Portuguese Children**. *Int. J. Environ. Res. Public Health* (2010.0) **7** 1121-11138. DOI: 10.3390/ijerph7031121
42. Lin H., Bermudez O.I., Tucker K.L.. **Dietary Patterns of Hispanic Elders Are Associated with Acculturation and Obesity**. *J. Nutr.* (2003.0) **133** 3651-3657. DOI: 10.1093/jn/133.11.3651
43. Nakade M., Kibayashi E., Morooka A.. **The Relationship between Eating Behavior and a Japanese Well-Balanced Diet among Young Adults Aged 20–39 Years**. *Nippon. Eiyo Shokuryo Gakkaishi* (2021.0) **74** 265-271. DOI: 10.4327/jsnfs.74.265
44. Murakami K., Shinozaki N., Kimoto N., Masayasu S., Sasaki S.. **Relative Validity of Food Intake in Each Meal Type and Overall Food Intake Derived Using the Meal-Based Diet History Questionnaire against the 4-Day Weighed Dietary Record in Japanese Adults**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14153193
45. Oliveira M.S.D.S., Arceño M.A., Sato P.D.M., Scagliusi F.B.. **Comparison of Government Recommendations for Healthy Eating Habits in Visual Representations of Food-Based Dietary Guidelines in Latin America**. *Cad. De Saude Publica* (2019.0) **35** e00177418. DOI: 10.1590/0102-311x00177418
46. Durán-Agüero S., Ortiz A., Pérez-Armijo P., Vinueza-Veloz M.F., Ríos-Castillo I., Camacho-Lopez S., Cavagnari B.M., Nava-González E.J., Carpio-Arias V., Cordón-Arrivillaga K.. **Quality of the Diet during the COVID-19 Pandemic in 11 Latin-American Countries**. *J. Health Popul. Nutr.* (2022.0) **41** 33. DOI: 10.1186/s41043-022-00316-8
47. **Materiales Educativos—INAN**
|
---
title: Effects of a Synbiotic on Plasma Immune Activity Markers and Short-Chain Fatty
Acids in Children and Adults with ADHD—A Randomized Controlled Trial
authors:
- Liu L. Yang
- Miranda Stiernborg
- Elin Skott
- Jingjing Xu
- Yujiao Wu
- Rikard Landberg
- Samsul Arefin
- Karolina Kublickiene
- Vincent Millischer
- Ida A. K. Nilsson
- Martin Schalling
- MaiBritt Giacobini
- Catharina Lavebratt
journal: Nutrients
year: 2023
pmcid: PMC10004766
doi: 10.3390/nu15051293
license: CC BY 4.0
---
# Effects of a Synbiotic on Plasma Immune Activity Markers and Short-Chain Fatty Acids in Children and Adults with ADHD—A Randomized Controlled Trial
## Abstract
Synbiotic 2000, a pre + probiotic, reduced comorbid autistic traits and emotion dysregulation in attention deficit hyperactivity disorder (ADHD) patients. Immune activity and bacteria-derived short-chain fatty acids (SCFAs) are microbiota–gut–brain axis mediators. The aim was to investigate Synbiotic 2000 effects on plasma levels of immune activity markers and SCFAs in children and adults with ADHD. ADHD patients ($$n = 182$$) completed the 9-week intervention with Synbiotic 2000 or placebo and 156 provided blood samples. Healthy adult controls ($$n = 57$$) provided baseline samples. At baseline, adults with ADHD had higher pro-inflammatory sICAM-1 and sVCAM-1 and lower SCFA levels than controls. Children with ADHD had higher baseline sICAM-1, sVCAM-1, IL-12/IL-23p40, IL-2Rα, and lower formic, acetic, and propionic acid levels than adults with ADHD. sICAM-1, sVCAM-1, and propionic acid levels were more abnormal in children on medication. Synbiotic 2000, compared to placebo, reduced IL-12/IL-23p40 and sICAM-1 and increased propionic acid levels in children on medication. SCFAs correlated negatively with sICAM-1 and sVCAM-1. Preliminary human aortic smooth-muscle-cell experiments indicated that SCFAs protected against IL-1β-induced ICAM-1 expression. These findings suggest that treatment with Synbiotic 2000 reduces IL12/IL-23p40 and sICAM-1 and increases propionic acid levels in children with ADHD. Propionic acid, together with formic and acetic acid, may contribute to the lowering of the higher-than-normal sICAM-1 levels.
## 1. Introduction
Attention deficit hyperactivity disorder (ADHD) is a common childhood-onset neurodevelopmental psychiatric disorder with about $5\%$ worldwide prevalence among children and adolescents and $3\%$ in adults [1]. The core symptoms of the disorder are inattention and hyperactivity/impulsivity, which lead to functional impairments in life at school, work, home, and/or social activity [2]. ADHD is markedly heterogenic regarding clinical features and likely also in etiological and pathophysiological aspects. Around 75–$80\%$ of cases have a comorbid psychiatric condition (e.g., mood disorder, anxiety disorder, learning disorder, tic disorder, or autism spectrum disorder (ASD) [2,3]. The co-occurrence of ADHD with immune-mediated conditions, such as asthma and celiac disease, proposes that there is an altered immune response in ADHD [3,4]. Additionally, prenatal exposure to inflammation has been suggested to increase the risk for ADHD [3]. Current treatments available for ADHD, including medications and behavioral therapies, are to manage the symptoms. Micronutrients and vitamin D have been weakly supported for the treatment of ADHD [5].
Preclinical studies from germ-free and antibiotic drug-treated mouse models have shown that the absence or alteration of normal gut microbiota early in life has significant effects on immune activity [6], stress responsiveness, and behaviors resembling traits of hyperactivity, depression, anxiety, autism, and obsessive-compulsive behaviors [7,8,9,10,11,12,13]. The gut microbiome in ADHD has been reported to be different compared to that in healthy controls, although no specific ADHD-associated gut bacterial taxa have been confirmed [14,15,16,17,18,19,20,21,22]. Transfer of fecal microbiota from ADHD patients to mice reduced the murine brain structural integrity and functional connectivity and increased anxiety-like behavior. Thus, an altered microbiota state in ADHD may contribute to some behaviors in ADHD through the microbiota–gut–brain axis [23]. Likewise, fecal microbiota transfer from patients with depression, autism, or schizophrenia to rodent models induced corresponding disease-like behaviors [24,25,26]. We have shown that early life antibiotic exposure was associated with an increased risk of several psychiatric disorders, including ADHD [27]. Interventional strategies have provided treatment potential. Placebo-controlled clinical trials of probiotic interventions indicated positive effects on reducing symptoms of depression, anxiety, autism, and emotion-related behaviors [28,29,30,31]. Our randomized placebo-controlled trial of Synbiotic 2000, containing 3 lactic acid bacilli and 4 dietary fibers, also showed positive effects on autistic symptoms and emotion regulation in ADHD patients who, at baseline, had higher plasma levels of vascular inflammation markers [31]. However, the mechanisms behind the intervention effects have yet to be determined.
Short-chain fatty acids (SCFAs) have been proposed to be messengers for microbiota–gut–brain communication. They are fatty acids with less than six carbon atoms, which are mainly generated by anaerobic colonic bacteria via fermentation of dietary fibers or branched-chain amino acids [32]. The most abundant SCFAs in stool and body fluids are formic acids, acetic acid, propionic acid, and butyric acid (Human Metabolome Database, http://www.hmdb.ca, accessed on 15 September 2022). Succinic acid is an intermediate metabolite in the fermentation towards propionic acid. SCFAs are multifunctional molecules, being not only an essential energy source for local intestinal cells [33,34] but also influencing barrier function, neurotransmitter release, microglial maturation and activation, neural proliferation, mitochondrial function, immune-modulation, and anti-inflammatory processes [11,34,35,36,37,38,39,40,41]. These effects are likely mediated by the SCFA receptors (GPR41/GPR43/GPR109a) or by the histone deacetylase (HDAC) inhibitory activity epigenetically regulating gene expression [42]. Succinic acid also contributes to an adequate immune response and the regulation of blood pressure and thermogenesis [43].
Immune activity is also considered to be an important mediator in the microbiota–gut–brain axis [44,45,46]. In the last two decades, studies have revealed associations between immune activation and several neuropsychiatric disorders, especially by measuring circulating inflammatory markers [47]. The peripheral immune activity markers C-reactive protein (CRP), interleukin (IL)-1β, IL-6, IL-10, IL-18, transforming growth factor (TGF)-β1, tumor neurosis factor (TNF)-α, monocyte chemoattractant protein 1 (MCP-1), eotaxin-1, and sIL-2R were reported associated with mood disorders, schizophrenia (SZ) and ASD in meta-analyses [48,49,50] suggesting immune activation in the pathophysiology of these disorders [51,52,53]. However, only a few studies focused on ADHD, and the sample sizes were small [54,55]. A recent meta-analysis of children and adults with ADHD analyzed pro-inflammatory CRP, IL-1β, IL-6, IFN-α, TNF-α, and anti-inflammatory IL-10 and reported increased IL-6 and reduced TNF-α in children with ADHD compared to controls, while the other markers were not significantly different in ADHD patients [56]. Levels of IL-12/IL-23p40 in cerebrospinal fluid (CSF) were elevated in patients with SZ [57]. Intercellular adhesion molecule 1 (ICAM-1) has been recognized in psychiatric disorders because of its putative role in neuroinflammation and the blood–brain barrier (BBB) function [58]. Higher plasma levels of its soluble form, sICAM-1, were found in ADHD among children [59]. Elevated levels of soluble or membrane-bound ICAM-1 and VCAM-1 levels have been reported in the CSF or brains of individuals with schizophrenia, unipolar or bipolar depression [60,61,62]. Moreover, chronic oral exposure to methylphenidate, a commonly used ADHD medication, has at high clinically relevant doses been shown to cause microglia activation and neuroinflammation in the cerebral cortex, hippocampus, thalamus, and basal ganglia [63], and BBB hyperpermeability [64] in rodent brain. Likewise, the use of dexamphetamine has been reported to induce neuroinflammation in rodents [65,66]. Notably, children with psychostimulant medication for ADHD had higher plasma levels of sICAM-1 and sVCAM-1 than those without this medication [67]. A recent large epidemiological study found that ADHD is a risk factor for cardiovascular disease [68] in which ICAM-1 and VCAM-1 are known to often be upregulated [69].
The aim of this study was to explore the effects of Synbiotic 2000 on concentrations of plasma immune activity markers and SCFAs in ADHD. These analyte concentrations constitute secondary outcome measures in the placebo-controlled randomized trial ISRCTN57795429 (https://doi.org/10.1186/ISRCTN57795429).
## 2.1. Participants
All participants in this study, including ADHD patients and healthy controls, were recruited through a double-blind randomized controlled trial (ISRCTN57795429) of Synbiotic 2000 intervention performed between January 2016 and June 2018 at psychiatric clinics in Stockholm, Sweden, as previously described [31]. Patients included ($$n = 248$$) all had a prior ADHD diagnosis (based on criteria from ICD-10 or DSM-5), were 5–55 years old, and were, if treated, on a stable pharmacological treatment (the last four weeks before recruitment), were not on antibiotic treatment (the last six weeks) and did not have a gastrointestinal (GI)-diagnosis (except irritable bowel syndrome), diabetes or celiac disease. In parallel, adult healthy individuals without an ADHD diagnosis ($$n = 72$$) fulfilling the same criteria were recruited along with the patients at the same period. The healthy controls included were from two categories, healthy family members from the patients’ households and unrelated individuals. Patients were randomly allocated to one of the two treatments: Synbiotic 2000 or placebo. Each participant was assessed at baseline (the day before treatment start) and post-treatment (within 2 weeks after the 9-week intervention was completed) through an interview and questionnaires on psychiatric and GI symptoms and non-fasting blood sampling between 8 am and 4 pm. All participants, research nurses, and data analysts were blind to the allocation until all analyses were completed. Out of 248 patients, 182 completed the 9-week intervention, and 156 patients provided blood samples at both baseline and follow-up. Controls were assessed at only one time-point, and 61 out of them provided blood samples. The study was approved by the Regional Ethical Review Board in Stockholm ($\frac{2015}{884}$-$\frac{31}{1}$ and $\frac{2017}{91}$-31).
## 2.2. Interventions
Synbiotic 2000, the active treatment provided by Synbiotics AB Sweden for free, consisted of lyophilized 4 × 1011 CFU of three lactic acid bacteria, *Pediococcus pentosaceus* 5-33:$\frac{3}{16}$:1 (Strain deposit number: LMG P20608), Lactobacillus casei ssp paracasei F19 (LMG P-17806), *Lactobacillus plantarum* 2362 (LMG P-20606), and 2.5 g of each of the fermentable fibers beta-glucan, inulin, pectin and resistant starch per dose. The composition has been shown to have anti-infectious and anti-inflammatory effects in several randomized controlled trials exemplified by [70,71,72], in particular, preventing gut leakage [73]. Placebo was maltodextrin which is an oligosaccharide without a prebiotic effect. All sachets were stored at −20 °C until 14 days before use. Patients were asked to follow the treatment with one dose per day for 9 weeks. No patient missed treatment for more than 20 days and never more than 4 days in a row [31].
## 2.3. Analysis of Plasma Immune Activity Markers
Peripheral blood was collected in tubes containing EDTA. Immediately after collection, the tubes were centrifuged at 1700× g (3500 rpm) for 20 min, and plasma was directly aliquoted into sterile cryotubes and stored at −80 °C until analysis. In total, 24 predesigned markers were measured via the Meso Scale Discovery (MSD, Meso Scale Diagnostics, Rockville, MD, USA) platform. The levels of CRP, serum amyloid A (SAA), sICAM-1, and sVCAM-1 were measured using VPLEX Vascular Injury Panel 2 Human Kit (Cat. # K15198D). Eotaxin-1, fractalkine, growth-regulated oncogene α (GRO-α), interferon (IFN)-γ, IL-1β, IL-2, sIL-2Rα, IL-6, IL-10, IL-12/IL-23p40, IL-17A, IL-16, IL-18, MCP-1, TNF-α, TNF-related apoptosis-inducing ligand (TRAIL) and vascular endothelial growth factor A (VEGF-A) were measured using U-PLEX Biomarker Group1 Human Multiplex Assays (Cat. # K15067L), and TGF-β1, TGF-β2, and TGF-β3 were measured using U-PLEX TGF-β Combo Human kits (#K15241K), according to the manufacturer’s instructions. In each plate, standard curves were generated using the manufacturer-provided calibrators in duplicates, and all the curves had a robust correlation (R2 > 0.999). Two inter-plate controls were kept in each plate: manufacturer-provided Vascular Injury Control 1 and 2 for VPLEX, and two self-designed samples were a pool of patient samples for the UPLEX assays. Each plasma sample was run in a single well, and five 96-well plates in total were run per analyte. Samples from the same ADHD patient, i.e., from both baseline and follow-up, were run in the same plate, and patient and control samples were distributed evenly across all plates. The lower limit of detection (LLOD) per analyte and plate was set to 2.5* the standard deviation of the background signal (Table S1). More than $25\%$ of the detected values of IFN-γ, IL-1β, IL-2, IL-17A, and TNF-α were below LLOD; therefore, these five markers were excluded from the statistical analysis. Two analytes (IL-6 and IL-10) had a few data points with detected values below LLOD, and these values were replaced by the LLOD of the corresponding analyte and plate (Table S1). The plasma sample values obtained from the other analytes were all within the detection range. The median (range) of the within-plate coefficients of variation (CV) from the calibrators was $2.54\%$ (1.10–$5.28\%$), and the between-plate CV from inter-plate controls was $9.93\%$ (4.63–$16.9\%$) for the 19 analytes included in the statistical analysis (Table S1). All plasma samples had undergone two freeze/thaw cycles. To exclude major circadian rhythmicity of the analytes, we plotted their levels by day-time of sampling. No major change in level over time was detected for any analyte (Figure S1A), for sICAM-1 in agreement with Wipfler et al. [ 74].
## 2.4. Analysis of Plasma Short-Chain Fatty Acids (SCFAs)
SCFAs (formic, acetic, propionic, butyric, isobutyric, succinic, valeric, isovaleric, and caproic acid) were analyzed in EDTA plasma by liquid chromatography–mass spectrometry (LC-MS) according to a method described previously [75] with some modifications at Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg (details in Supplementary Materials). Forty-two samples in singlets from both ADHD patients (baseline and follow-up from the same person) and controls were run in each batch. In total, eleven batches were analyzed in two rounds (6 batches in the first round in March 2020 and 5 batches in the second round in July 2020). Twenty-two patient samples were analyzed in both rounds, selected to cover the range of the values in the first round. Three SCFAs (isobutyric, valeric, and caproic acid) were excluded from data analysis because of the poor correlation detected by the twenty-two rerun samples, leaving six SCFAs for statistical analyses (Figure S1B). All plasma samples for the analysis had undergone two freeze/thaw cycles. For each batch, two quality controls (QCs) for each analyte with levels in the range found in our patient samples were run in triplicates and were used to calculate the within-batch CV being $9\%$ (5–$11\%$) for the six SCFAs. The between-batch variation for the two rounds was controlled by normalizing the sample values with the same QCs kept in each batch. The normalization ratio for each analyte per batch was calculated as (mean of the QC values of the individual batch)/(mean of the total QC values from all batches run in the same round). All statistical analyses for SCFAs were performed on normalized data. Because plasma levels of acetate, propionate, and butyrate were reported to peak approximately 7 h after colonic administration of SCFAs [76], we tested if there was any apparent peak in plasma levels of these SCFAs, which would appear plausibly at 1–4 pm as a consequence of breakfast. We could not detect any indication of the major influence of breakfast (Figure S1A).
## 2.5. Cell Culture
Human aortic vascular smooth muscle cell line (hAVSMCs), developed from a 23 years old African American healthy female, was purchased from (ATCC, Manassas, VA, USA, https://www.atcc.org). The cells were cultured in DMEM (cat. # 12320032, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with $10\%$ FBS, $1\%$ Na Pyruvate, $2.5\%$ penicillin/streptomycin, $1\%$ FungiZone, $1\%$ L-glutamine and $1\%$ HEPES (cat. # A3840002, Thermo Fisher Scientific). All the cells used for experiments were from passages 9–13. Confluent cells from each of the three independent experiments were split into 21 wells (12-well plates). After seeding, cells were cultured overnight and then incubated with PBS (control), formic acid (500 μM and 50 mM), acetic acid (50 μM and 5 mM), or propionic acid (50 μM and 500 μM) for 24 h, whereafter IL-1β (2.5 ng/mL) was added. After 8 h, the cells were harvested in Trizol Reagent (cat. # 15596018, Invitrogen, Waltham, MA, USA) for investigating ICAM-1 expression determined using qRT-PCR (see Supplementary Materials).
## 2.6. Statistical Analysis
Analysis of differences in analyte levels between diagnosis groups (control versus ADHD patient), age groups (child versus adult), medication groups (yes versus no), and sexes (males versus females) was performed using Mann–Whitney U tests. Statistical relationships between two analytes were assessed by applying Spearman’s rank correlation tests. Descriptive statistics are presented with median levels and IQR. To correct for multiple testing, false discovery rate (FDR)-adjusted p values (q values according to the Benjamini–Hochberg method) are reported for immune activity markers, and statistical significance was set at αFDR = 0.05. As the SCFA concentrations formed only 3 non-correlated groups, FDR was not applied on SCFA data, but statistical significance was set at α = 0.017 (Bonferroni correction, $\frac{0.05}{3}$ = 0.017) (Figure S2). The levels of analytes in plasma were generally not normally distributed and were, therefore, naturally logarithm (ln) transformed for use in the parametric statistical analysis. A Synbiotic 2000 intervention effect, compared to placebo, was assessed separately for children and adults, applying analysis of covariance (ANCOVA) on ln-transformed analyte levels at follow-up, adjusting for sex and baseline levels of the analyte. Treatment effect estimates with $95\%$ and $99\%$ confidence intervals (CIs) of analyte levels are reported. Sensitivity analyses of intervention effects on analyte levels were performed in subgroups stratified by ADHD medication or plasma level at baseline of sVCAM-1 (high versus low). Statistical significance in the intervention effect models was set at α = 0.01, while suggestive statistical significance was set at α = 0.05. A list of both $95\%$ CIs and $99\%$ CIs for intervention effects is shown in Table S2. All statistical analyses were performed using R programming language version 3.6.3 (Posit, Boston, MA, USA). Graphs were made using the ggplot2 package from R [77].
## 3.1. Baseline Levels of Immune Activity Markers in ADHD Patients
Clinical characteristics of the study participants, being pediatric and adult ADHD patients and adult healthy controls, are summarized in Table 1 and Table S3. We measured the levels of 24 predesigned immune activity markers in plasma from the patients before (baseline) and after (follow-up) intervention. Nineteen markers out of the 24 had detectable levels and were included in the data analysis (Table S1). High correlations were observed between many of the 19 markers, especially within the pairs CRP/SAA, sICAM-1/sVCAM-1, and GRO-α/TGF-β1 in both healthy controls and patients (Figure S3). In adults, the baseline levels of pro-inflammatory sICAM-1 (FDR-adjusted p[pFDR] = 0.0022) and sVCAM-1 (pFDR = 2.9 × 10−5) were significantly higher in ADHD patients compared to healthy control (Figure 1).
The healthy controls included were from two categories, healthy family members and unrelated individuals. The baseline levels of eotaxin-1, fractalkine, MCP-1, TGF-β3, and TRAIL differed between these two groups of controls (Figure S4A). When comparing ADHD to the two categories of controls separately, we found that the baseline levels of MCP-1 (pFDR = 0.026), fractalkine (pFDR = 0.050), TGF-β3 (pFDR = 0.049), and TRAIL (pFDR = 0.039) were lower in ADHD as compared to family members only, and eotaxin-1 (pFDR = 0.042) were higher as compared to unrelated controls only (Figure S4B). Due to a small sample size of children controls ($$n = 4$$, age: 12–14 years), we could not determine any case-control difference for children. Comparing baseline levels between children and adults with ADHD revealed significant differences for 8 of the 19 markers (Figure 2). Among them, IL-12/IL-23p40 (pFDR = 2.4 × 10−4), IL-2Rα (pFDR = 1.2 × 10−5), sICAM-1 (pFDR = 1.6 × 10−7), sVCAM-1 (pFDR = 1.4 × 10−7), TGF-β2 (pFDR = 0.014) and TRAIL (pFDR = 7.6 × 10−4) levels were higher in children, while CRP (pFDR = 0.041) and eotaxin-1 (pFDR = 0.0013) levels were higher in adults. This suggests that children and adults with ADHD have different profiles of immune activity marker levels in plasma. In addition, sex-disaggregated statistical analyses showed that the levels of IL-10 were higher in boys vs. girls with ADHD, while eotaxin-1 (pFDR = 0.0037) and IL-16 (pFDR = 0.024) were higher and IL-12/IL-23p40 (pFDR = 0.0037) and SAA (pFDR = 0.00037) were lower in adult males vs. females with ADHD (Figure S4C). Patients were randomly allocated into either of the two treatment groups, and we could, at baseline, not detect any difference in the markers mentioned between the two treatment groups for neither children nor adults (Figure S4D).
## 3.2. Effects of Synbiotic 2000 on Immune Activity Markers
Children treated with Synbiotic 2000 showed a significant reduction of levels of IL-12/IL23p40, sICAM-1, and TGF-β3 from baseline to 9-week follow-up, while children treated with placebo did not show any level change of any immune activity marker over time (Figure S5A and Table S4). Among adults, participants from both the placebo and Synbiotic 2000 groups had reduced sICAM-1 and sVCAM-1 levels at 9 weeks, while only the placebo group showed reduced TGF-β2 and TGF-β3 levels over time (Figure S5A). The treatment effect of Synbiotic 2000, compared to placebo, on analyte levels was analyzed with ANCOVA adjusted by sex and baseline levels of the analyte (Figure 3A–D).
Among children with ADHD, pro-inflammatory cytokine IL-12/IL-23p40 was reduced by Synbiotic 2000 compared to by placebo, at α = 0.05, defined as the suggestive difference ($95\%$ CI: −0.158, −0.014, $$p \leq 0.020$$) (Figure 3A). Since our previous study showed that current (or last three months) ADHD medication in children is associated with elevated levels of the vascular inflammatory markers sICAM-1 and sVCAM-1 [67] (Yang et al., 2020a) (Figure S4E, pFDR-child = 0.050), we stratified the analysis by current ADHD medication [yes/no]. For children who were currently on ADHD medication, Synbiotic 2000 manifested a significant reduction of IL-12/IL-23p40 ($99\%$ CI: −0.180, −0.005, $$p \leq 0.0070$$) and a suggestive reduction in sICAM-1 levels ($95\%$ CI: −0.547, −0.030, $$p \leq 0.030$$) compared to placebo (Figure 3D). For children not currently on ADHD medication, sIL-2Rα was suggestively reduced ($95\%$ CI: −0.274, −0.001, $$p \leq 0.049$$). For children and adults who were not currently on ADHD medication, Synbiotic 2000 suggestively increased the levels of VEGF-A (children: $95\%$ CI: 0.054, 0.644, $$p \leq 0.024$$; and adults: $95\%$ CI: 0.007, 0.368, $$p \leq 0.043$$) (Figure 3C,E and Figure S5B). This suggestive VEGF-A increase in children without ADHD medication was, however, probably driven by a VEGF-A reduction in the placebo group ($$p \leq 0.21$$, $$n = 7$$, Figure S5B). As the effect of Synbiotic 2000 in children on ADHD medication may be because of elevated sVCAM-1 levels at baseline, we explored the effect on child and adult patients with baseline sVCAM-1 levels above the median (cut off = 519,519.7 pg/mL). As expected, for children, the effects of Synbiotic 2000 vs. placebo in the high sVCAM-1 group were similar to those in the ADHD medication group (data not shown). In adults, however, those with high sVCAM-1 level had a suggestive reduction of sVCAM-1 ($95\%$ CI: −0.245, −0.007, $$p \leq 0.039$$) and sIL-2Rα ($95\%$CI: −0.145, −0.017, $$p \leq 0.015$$) by Synbiotic 2000 compared to placebo (Figure S6A). Adults with low sVCAM-1 levels had a suggestive reduction of IL-6 ($95\%$ CI: −0.359, −0.011, $$p \leq 0.037$$) (Figure S6B), which was partially driven by the placebo effects (Figure S6C).
## 3.3. Baseline Levels of Short-Chain Fatty Acids (SCFAs) in ADHD Patients
Plasma concentrations of six SCFAs were analyzed in the ADHD patients and the healthy controls. We found that the shorter SCFAs (formic acid, acetic acid, propionic acid, and succinic acid) were significantly correlated with each other in both controls and patients (Figure S2). The statistical significance was set at α = 0.017, which was corrected for 3 independent tests of the 6 SCFAs. In adults, ADHD patients had significantly lower baseline concentrations of formic acid ($$p \leq 4.4$$ × 10−4) and propionic acid ($$p \leq 0.0064$$) as compared to healthy controls (Figure 4A). Furthermore, baseline acetic and propionic acids ($$p \leq 2.5$$ × 10−4, $$p \leq 0.0010$$ respectively) concentrations were significantly higher in family controls than in unrelated controls (Figure S7A). The baseline concentrations of formic ($$p \leq 0.00011$$), acetic ($$p \leq 0.016$$), propionic ($$p \leq 4.3$$ × 10−5), and butyric acids ($$p \leq 0.014$$) were all lower in adults with ADHD as compared to adult family controls, while acetic acid concentrations ($$p \leq 0.0043$$) were higher in ADHD as compared to adult unrelated controls (Figure S7B).
Comparing levels between pediatric and adult ADHD patients, concentrations of formic acid ($$p \leq 1.3$$ × 10−8), acetic acid ($$p \leq 3.5$$ × 10−5), and propionic acid ($$p \leq 0.017$$) were significantly lower in children than in adults (Figure 4B), which suggests that among those with ADHD children and adults have different SCFA profiles in plasma.
## 3.4. Effects of Synbiotic 2000 on SCFAs
No significant changes in SCFA concentrations from baseline to follow-up were found neither for those on placebo nor those treated with Synbiotic 2000 for neither children nor adults (Figure S8A). However, treatment effects on SCFA levels comparing the two interventions were analyzed using ANCOVA, applying similar models as those used for analyzing treatment effects on immune activity markers. We found that Synbiotic 2000, compared to the placebo, suggestively increased propionic acid concentrations in children with ADHD ($95\%$ CI: 0.006, 0.699, $$p \leq 0.046.$$) ( Figure 5).
As shown in Figure S7E, the concentrations of propionic acid were lower among the children with current ADHD medication than those without ADHD medication ($$p \leq 0.0057$$). In the sensitivity analysis where we stratified for ADHD medication, we found that Synbiotic 2000, compared to placebo, suggestively reduced formic acid levels in adults who were not on ADHD medication at sampling (Figure 5 and Figure S8B).
## 3.5. Associations between Immune Activity Markers and SCFAs
Immune activity markers and SCFAs are both important components in the microbiota–gut–brain axis, and a small number of cellular in vitro studies have reported the effects of butyrate on a few immune activity markers. We performed a correlation analysis between plasma levels of the immune activity markers and concentrations of the SCFAs, which we, in the aforementioned analyses, found to be different in ADHD compared to controls (all controls for immune activity markers and family controls for SCFAs). In children with ADHD, baseline acetic acid levels were significantly negatively correlated with pro-inflammatory sICAM-1 and sVCAM-1 (pFDR < 0.050), while formic and propionic acid levels were suggestively negatively correlated with sICAM-1 and sVCAM-1 ($p \leq 0.050$). Further, baseline acetic acid in children correlated positively with pro-inflammatory eotaxin-1 (pFDR < 0.050) and suggestively positively to IL-12/IL-23p40 and TRAIL ($p \leq 0.050$) (Figure 6A and Figure S9); however, the eotaxin-1 levels were lower than those seen in adult patients and controls (Figure 1). The significant negative correlation for acetic acid with sICAM-1 and sVCAM-1, and suggestive negative correlation between propionic acid and sICAM-1, in children, were also detected at follow-up (Figure 6B and Figure S9). The negative correlations of the SCFAs with sVCAM-1 and sICAM-1 were in part indicated also in adult patients at baseline (Figure 6D). Additionally, TGF- β2 levels correlated positively with propionic acid concentrations in adult patients but were within the range of the levels in the controls (Figure 1).
## 3.6. Effects of SCFAs on ICAM-1 Expression in Human Aortic Vascular Smooth Muscle Cells In Vitro
To further validate the negative correlations between SCFAs and sICAM-1, we did three independent in vitro experiments in human aortic vascular smooth muscle cells. Our results showed lower IL-1β-induced ICAM-1 expression when the cells were pre-incubated with formic acid, acetic acid, or propionic acid of the concentrations found in plasma (Figure S10).
## 4. Discussion
This study is the first to report the effects of a synbiotic intervention on plasma levels of immune activity markers and SCFAs in children and adults with ADHD. We previously reported that this intervention in an RCT design reduced autistic traits in children and improved emotion regulation in adults with ADHD [31]. Now, we report that there was no statistically significant overall effect of Synbiotic 2000 compared to placebo on any analyte analyzing all the pediatric and all adult participants as one group. However, age-group-stratified analyses are more appropriate as plasma levels of several of the analytes were at baseline different in the children compared to in the adults. Actually, in children the Synbiotic 2000 intervention, compared to the placebo, suggestively reduced pro-inflammatory IL-12/IL-23p40 levels. As children on ADHD medication have previously been reported to have higher levels of the pro-inflammatory adhesion molecules sICAM-1 and sVCAM-1 than children without ADHD medication and adults with ADHD [67], we analyzed this pediatric group on ADHD medication separately. In children on ADHD medication Synbiotic 2000, compared to placebo, reduced IL-12/IL-23p40 levels significantly and reduced sICAM-1 levels suggestively. In children without ADHD medication, Synbiotic 2000, compared to placebo, suggestively reduced IL-2Rα levels. We cannot determine if the children’s IL-12/IL-23p40, sICAM-1, or IL-2Rα levels at baseline were higher than that of healthy controls in this age group, although the controls’ levels were low (Figure S5A), as ncontrols is only 4. However, we show that children with ADHD at baseline have higher IL-12/IL-23p40 and IL-2Rα levels than adults with ADHD, and children on ADHD medication have higher sICAM-1 levels than ADHD children without medication and adults. A previous report has shown childhood IL-12/IL-23p40 levels to be lower than adulthood levels [78]. This suggests that children with ADHD do have abnormally high IL-12/IL-23p40 levels. However, we cannot exclude the possibility that higher baseline levels in the children of sICAM-1 [79] and IL-2Rα are normal. To explore a potential link for Synbiotic 2000 to IL-12/IL-23p40, sICAM-1, and IL-2Rα levels in pediatric ADHD patients, we assessed plasma levels of the bacterial fermentation metabolites SCFAs. Synbiotic 2000, compared to placebo, suggestively elevated plasma levels of propionic acid in the children, and the correlations between the shortest SCFAs: formic acid, acetic acid, and propionic acid, were very strong. Moreover, the levels of formic, acetic, and propionic acid correlated negatively with levels of sVCAM-1 and/or sICAM-1, and the latter two correlated strongly with each other. The levels of formic, acetic, and propionic acid in children were at baseline lower than in adults with and without ADHD (Figure 4A,B), and at least propionic acid levels appeared low compared to healthy control children (Figure S8A). Altogether, this proposes that elevating the highly correlated formic, acetic acid, and propionic acid might alleviate an sICAM-1-marked vascular inflammation in children with ADHD (Summarized in Figure 7). In support, our preliminary results from in vitro experiments with human aortic vascular smooth muscle cells showed that pre-incubation with formic, acetic, or propionic acid tended to reduce the expression of ICAM-1 induced by IL-1β (Figure S10). However, there was at baseline a suggestively significant positive correlation between levels of propionic acid and IL-12/IL-23p40 (Figure 6), indicating that the SCFAs did not mediate the Synbiotic 2000-induced reduction of IL-12/IL-23p40 levels.
IL-12 and IL-23 are heterodimers and share the p40 subunit called IL-12/IL-23p40. IL-12 and IL-23 promote Th1 and Th17 expansion, respectively, and are reported to be involved in the pathology of inflammatory bowel disease (IBD). The p40 subunit is a therapeutic target in IBD [80,81]. GI symptoms are overrepresented in ADHD [82], which is also the case in our cohort [67]. The adhesion molecules ICAM-1 and VCAM-1 are expressed predominantly by endothelial cells. ICAM-1 participates in binding leukocytes to the endothelial cell, and VCAM-1 participates in the subsequent leukocyte extravasation into the surrounding tissue. sICAM-1 and sVCAM-1 are the soluble isoforms of ICAM-1 and VCAM-1, respectively, found at plasma levels in proportion to endothelial cell membrane-bound levels [83]. They have key roles in regulating the immune homeostasis in the gut endothelium; both sICAM-1 and sVCAM-1 have been reported upregulated in IBD patients [84,85], and sICAM-1 levels were found to reduce the mucosal healing process in patients with Crohn’s disease [86]. Higher levels of ICAM-1 and VCAM-1 have also been associated with schizophrenia, depression, and bipolar disorder, and interestingly, higher ICAM-1 levels have been associated with BBB hyper-permeability [58,87]. IL-2Rα is like the other IL-2R subunits expressed by Treg cells and recently activated T cells, and elevated plasma levels of soluble IL-2Rα indicate ongoing pro-inflammatory immune activity and are reported in mood disorders, schizophrenia, and ASD [48,49,50]. Several RCTs of synbiotics or probiotics have previously been reported to reduce endothelial adhesion molecules and IL-12/IL23p40 in cardiometabolic disorders and IBD [88,89,90,91,92]. Moreover, an RCT conducted in patients with ulcerative colitis showed that butyrate enemas significantly increased the colonic IL-10/IL-12 ratio in mucosal biopsies, however not significantly when compared to the placebo group [93]. Butyrate was reported to suppress IL-12p40 mRNA accumulation and massively enhance IL-10 secretion in primary human monocytes [94]. Moreover, both butyrate and propionate were reported to inhibit the ICAM-1 and VCAM-1 expression in human endothelial cells in vitro [95,96]. However, the SCFA levels used in these models were higher than the physiological levels in human body fluids. Our preliminary results from cell culture experiments with human aortic vascular smooth muscle cells show supportive results of the anti-inflammatory potential of these SCFAs. Pre-incubation with formic, acetic, or propionic acid at the concentrations detected in human plasma tended to prevent the IL-1β-induced ICAM-1 expression (Figure S10). However, we did not analyze the cellular effects of butyrate, nor the effects on IL-12/IL-23 or IL-2Rα, in this study. IL-12 was previously shown to enhance IL-18-induced ICAM-1 expression in human monocytes [97]. Further studies using cell culture bioassays are needed to understand the complexity between physiological levels of SCFAs and inflammatory response.
Notably, the pattern of correlations between levels of immune activity markers and SCFAs detected in ADHD patients was not found in controls, suggesting that the associations between immune activity analytes and SCFAs are not generalizable beyond ADHD but depend on a complex regulation at physiological conditions (Figure 6 and Figure S11). However, in adults, there was no statistically significant or suggestive Synbiotic 2000 treatment effect in the whole group. Here, not only those treated with Synbiotic 2000 intervention but also those on placebo had a reduction of sICAM-1 and sVCAM-1 levels from baseline to follow-up (Figure S5A). Dietary change could not explain this placebo effect as there was dietary change for only beta-carotene between baseline and follow-up among the 57 nutrients [31]. However, $72.3\%$ of the adult patients and $42.9\%$ of the child patients were on dietary supplements, such as vitamins, omega-3, and probiotics, already at baseline and kept throughout the study (Table S3). Children on ADHD medication had higher sICAM-1 and sVCAM-1 levels [98]. An anti-inflammatory effect by Synbiotic 2000 may be more detectable when having limited group variation of baseline inflammatory state. That may partly explain why more suggestive effects of Synbiotic 2000 were seen in the children when stratifying for ADHD medication use. Accordingly, in adult patients with baseline sVCAM-1 levels above the median, Synbiotic 2000, compared to placebo, suggestively reduced IL-2Rα and sVCAM-1 levels. The detected reduction of IL-6 levels in those with baseline sVCAM-1 below the median may be explained by increases in the placebo group (Figure S6C). Thus, like in children with ADHD, our data suggest an effect of Synbiotic 2000 reducing certain markers involved in vascular inflammation in adult ADHD patients with elevated sVCAM-1 at baseline. In addition, Synbiotic 2000 suggestively reduced formic acid in the adults, not on ADHD medication; however, it was not supported by any effect on levels of immune marker that formic acid levels correlated with.
To our knowledge, we are the first to report plasma levels of fractalkine, GRO-α, IL-12/IL-23p40, IL-18, IL-2Rα, TGF-β1, TGF-β2, TGF-β3, TRAIL, and VEFG-A in individuals with ADHD. The adult ADHD patients displayed at baseline different levels only of pro-inflammatory sICAM-1 and sVCAM-1 compared to the whole control group. Compared to the adults with ADHD, the children with ADHD had different baseline levels of eight of the immune activity markers and were hence analyzed separately. Most of these differences in marker levels between children and adults are not previously reported, neither in ADHD patients nor healthy individuals. Levels at baseline of eotaxin-1, fractalkine, MCP-1, TGF-β3, and TRAIL were higher in healthy family members of the ADHD patients than among healthy unrelated controls. *Both* genetic and environmental underpinnings may explain this, although there is no report showing that these markers are higher in persons with ADHD. The plasma SCFA concentrations at baseline of the patients and family controls of this study were previously reported to show lower levels of plasma formic and propionic acid in adults with ADHD compared to family controls after controlling for antibiotic drug exposure and other potential influencing factors [98]. Accordingly, we now report that the baseline levels of these same SCFAs (formic and propionic acids) are in adults with ADHD lower compared to the whole control group but at similar levels as unrelated controls (Figure 4). Most studies on SCFAs in other neuropsychiatric disorders have analyzed SCFA levels in feces, which correlate poorly to SCFA levels in plasma, probably due to the significant uptake of certain SCFAs in the intestine [42]. A review on fecal SCFAs in children with autism showed poorly consistent findings between studies [99]. Additionally, most studies on SCFAs have focused on acetic, propionic, and butyric acid only [34,100]. An altered SCFA profile would indicate different dietary habits and/or different bacterial gut microbiomes, being established for autism [101] and proposed for ADHD [16].
The sample size of our RCT was relatively large, including 182 children and adults. An additional main strength is that the conducted in vitro experiments of SCFAs possible anti-inflammatory effect was at physiological SCFA levels. There are limitations to this study. First, there were only four healthy controls for children, and hence, we could not analyze this group. Therefore, we were unable to adequately relate the analyte levels in children with ADHD to reference values. Second, our data on medications with anti-inflammatory effects (melatonin, antidepressants, antipsychotics, anxiolytics, sleeping pills, proton-pump inhibitors, and statins) or medications with gut microbiome effects (melatonin, antipsychotics, or antidepressants) (Table S3) are not complete, as we lack information on the specific drug names and drugs specifically targeting inflammation; however, for the medication data that we do have, no associations with levels of the analytes were detected. Third, diet or diet supplements use during the intervention time could conceal the effects of Synbiotic 2000, although participants were asked not to change their diet from 4 weeks before baseline to follow-up, and we detected no relevant change over time in nutrients intake through a retrospective diet questionnaire [31]. Fourth, in the treatment effect analyses, we controlled only for sex.
## 5. Conclusions
This exploratory study revealed that persons with ADHD, especially children on ADHD medication, have higher-than-normal pro-inflammatory sICAM-1 and sVCAM-1 and lower SCFA levels in plasma and that children with ADHD also have higher levels of additional pro-inflammatory markers, e.g., IL-12/IL-23p40 and IL-2Rα. Treatment with Synbiotic 2000, compared to placebo, reduced IL-12/IL-23p40 levels and suggestively reduced sICAM-1 and IL-2Rα levels in children. Synbiotic 2000 also suggestively increased propionic acid levels, which, together with highly associated formic and acetic acid levels, in turn, correlated negatively with sICAM-1 and sVCAM-1 in the children and protected against IL-1β-induced sICAM-1 expression in vitro. This suggests that Synbiotic 2000, in children with ADHD, reduces markers of intestinal and vascular inflammation, the latter in part through increasing SCFA levels. The findings warrant further studies to determine if persons with ADHD would benefit inflammation-wise from dietary intake of Synbiotic 2000 or a similar synbiotic.
## References
1. Magnin E., Maurs C.. **Attention-deficit/hyperactivity disorder during adulthood**. *Rev. Neurol.* (2017) **173** 506-515. DOI: 10.1016/j.neurol.2017.07.008
2. Banaschewski T., Becker K., Döpfner M., Holtmann M., Rösler M., Romanos M.. **Attention-deficit/hyperactivity disorder**. *Dtsch. Arztebl. Int.* (2017) **114** 149-159. DOI: 10.3238/arztebl.2017.0149
3. Leffa D.T., Torres I.L., Rohde L.A.. **A Review on the role of inflammation in attention-deficit/hyperactivity disorder**. *Neuroimmunomodulation* (2018) **25** 328-333. DOI: 10.1159/000489635
4. Leffa D.T., Caye A., Santos I., Matijasevich A., Menezes A., Wehrmeister F.C., Oliveira I., Vitola E., Bau C.H.D., Grevet E.H.. **Attention-deficit/hyperactivity disorder has a state-dependent association with asthma: The role of systemic inflammation in a population-based birth cohort followed from childhood to adulthood**. *Brain Behav. Immun.* (2021) **97** 239-249. DOI: 10.1016/j.bbi.2021.08.004
5. Sarris J., Ravindran A., Yatham L.N., Marx W., Rucklidge J.J., McIntyre R.S., Akhondzadeh S., Benedetti F., Caneo C., Cramer H.. **Clinician guidelines for the treatment of psychiatric disorders with nutraceuticals and phytoceuticals: The World Federation of Societies of Biological Psychiatry (WFSBP) and Canadian Network for Mood and Anxiety Treatments (CANMAT) Taskforce**. *World J. Biol. Psychiatry* (2022) **23** 424-455. DOI: 10.1080/15622975.2021.2013041
6. Cryan J.F., Dinan T.. **More than a gut feeling: The microbiota regulates neurodevelopment and behavior**. *Neuropsychopharmacology* (2014) **40** 241-242. DOI: 10.1038/npp.2014.224
7. Luczynski P., McVey Neufeld K.-A., Oriach C.S., Clarke G., Dinan T.G., Cryan J.F.. **Growing up in a bubble: Using germ-free animals to assess the influence of the gut microbiota on brain and behavior**. *Int. J. Neuropsychopharmacol.* (2016) **19**. DOI: 10.1093/ijnp/pyw020
8. Desbonnet L., Clarke G., Traplin A., O’Sullivan O., Crispie F., Moloney R.D., Cotter P.D., Dinan T.G., Cryan J.F.. **Gut microbiota depletion from early adolescence in mice: Implications for brain and behaviour**. *Brain Behav. Immun.* (2015) **48** 165-173. DOI: 10.1016/j.bbi.2015.04.004
9. Heijtz R.D., Wang S., Anuar F., Qian Y., Björkholm B., Samuelsson A., Hibberd M.L., Forssberg H., Pettersson S.. **Normal gut microbiota modulates brain development and behavior**. *Proc. Natl. Acad. Sci. USA* (2011) **108** 3047-3052. DOI: 10.1073/pnas.1010529108
10. Hsiao E.Y., McBride S.W., Hsien S., Sharon G., Hyde E.R., McCue T., Codelli J.A., Chow J., Reisman S.E., Petrosino J.F.. **Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders**. *Cell* (2013) **155** 1451-1463. DOI: 10.1016/j.cell.2013.11.024
11. MacFabe D.F.. **Enteric short-chain fatty acids: Microbial messengers of metabolism, mitochondria, and mind: Implications in autism spectrum disorders**. *Microb. Ecol. Heal. Dis.* (2015) **26** 28177. DOI: 10.3402/mehd.v26.28177
12. Mora S., Martín-González E., Prados-Pardo A., Moreno J., López M.J., Pilar-Cuellar F., Castro E., Díaz Á., Flores P., Moreno M.. **Increased vulnerability to impulsive behavior after streptococcal antigen exposure and antibiotic treatment in rats**. *Brain Behav. Immun.* (2020) **89** 675-688. DOI: 10.1016/j.bbi.2020.08.010
13. Otten K., Keller L., Puiu A.A., Herpertz-Dahlmann B., Seitz J., Kohn N., Edgar J.C., Wagels L., Konrad K.. **Pre- and postnatal antibiotic exposure and risk of developing attention deficit hyperactivity disorder—A systematic review and meta-analysis combining evidence from human and animal studies**. *Neurosci. Biobehav. Rev.* (2022) **140** 104776. DOI: 10.1016/j.neubiorev.2022.104776
14. Cenit M.C., Nuevo I.C., Codoñer-Franch P., Dinan T.G., Sanz Y.. **Gut microbiota and attention deficit hyperactivity disorder: New perspectives for a challenging condition**. *Eur. Child Adolesc. Psychiatry* (2017) **26** 1081-1092. DOI: 10.1007/s00787-017-0969-z
15. Jiang H.-Y., Zhou Y.-Y., Zhou G.-L., Li Y.-C., Yuan J., Li X.-H., Ruan B.. **Gut microbiota profiles in treatment-naïve children with attention deficit hyperactivity disorder**. *Behav. Brain Res.* (2018) **347** 408-413. DOI: 10.1016/j.bbr.2018.03.036
16. Aarts E., Ederveen T.H.A., Naaijen J., Zwiers M.P., Boekhorst J., Timmerman H.M., Smeekens S.P., Netea M.G., Buitelaar J.K., Franke B.. **Gut microbiome in ADHD and its relation to neural reward anticipation**. *PLoS ONE* (2017) **12** e0183509. DOI: 10.1371/journal.pone.0183509
17. Richarte V., Rosales K., Corrales M., Bellina M., Fadeuilhe C., Calvo E., Ibanez P., Sanchez-Mora C., Ribases M., Ramos-Quiroga J.A.. **The gut-brain axis in attention deficit hyperactivity disorder: The role of the microbiota**. *Rev. Neurol.* (2018) **66** S109-S114. PMID: 29516462
18. Szopinska-Tokov J., Dam S., Naaijen J., Konstanti P., Rommelse N., Belzer C., Buitelaar J., Franke B., Bloemendaal M., Aarts E.. **Investigating the gut microbiota composition of individuals with attention-deficit/hyperactivity disorder and association with symptoms**. *Microorganisms* (2020) **8**. DOI: 10.3390/microorganisms8030406
19. Checa-Ros A., Jeréz-Calero A., Molina-Carballo A., Campoy C., Muñoz-Hoyos A.. **Current evidence on the role of the gut microbiome in ADHD pathophysiology and therapeutic implications**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13010249
20. Sukmajaya A.C., Lusida M.I., Setiawati Y.. **Systematic review of gut microbiota and attention-deficit hyperactivity disorder (ADHD)**. *Ann. Gen. Psychiatry* (2021) **20** 1-12. DOI: 10.1186/s12991-021-00330-w
21. Richarte V., Sánchez-Mora C., Corrales M., Fadeuilhe C., Vilar-Ribó L., Arribas L., Garcia E., Rosales-Ortiz S.K., Arias-Vasquez A., Soler-Artigas M.. **Gut microbiota signature in treatment-naïve attention-deficit/hyperactivity disorder**. *Transl. Psychiatry* (2021) **11** 1-7. DOI: 10.1038/s41398-021-01504-6
22. Wang L.-J., Yang C.-Y., Chou W.-J., Lee M.-J., Chou M.-C., Kuo H.-C., Yeh Y.-M., Lee S.-Y., Huang L.-H., Li S.-C.. **Gut microbiota and dietary patterns in children with attention-deficit/hyperactivity disorder**. *Eur. Child Adolesc. Psychiatry* (2019) **29** 287-297. DOI: 10.1007/s00787-019-01352-2
23. Tengeler A.C., Dam S.A., Wiesmann M., Naaijen J., van Bodegom M., Belzer C., Dederen P.J., Verweij V., Franke B., Kozicz T.. **Gut microbiota from persons with attention-deficit/hyperactivity disorder affects the brain in mice**. *Microbiome* (2020) **8** 1-14. DOI: 10.1186/s40168-020-00816-x
24. Kelly J.R., Borre Y., O’Brien C., Patterson E., El Aidy S., Deane J., Kennedy P.J., Beers S., Scott K., Moloney G.. **Transferring the blues: Depression-associated gut microbiota induces neurobehavioural changes in the rat**. *J. Psychiatr. Res.* (2016) **82** 109-118. DOI: 10.1016/j.jpsychires.2016.07.019
25. Sharon G., Cruz N.J., Kang D.-W., Gandal M.J., Wang B., Kim Y.-M., Zink E.M., Casey C.P., Taylor B.C., Lane C.J.. **Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice**. *Cell* (2019) **177** 1600-1618. DOI: 10.1016/j.cell.2019.05.004
26. Zhu F., Guo R., Wang W., Ju Y., Wang Q., Ma Q., Sun Q., Fan Y., Xie Y., Yang Z.. **Transplantation of microbiota from drug-free patients with schizophrenia causes schizophrenia-like abnormal behaviors and dysregulated kynurenine metabolism in mice**. *Mol. Psychiatry* (2019) **25** 2905-2918. DOI: 10.1038/s41380-019-0475-4
27. Lavebratt C., Yang L.L., Giacobini M., Forsell Y., Schalling M., Partonen T., Gissler M.. **Early exposure to antibiotic drugs and risk for psychiatric disorders: A population-based study**. *Transl. Psychiatry* (2019) **9** 1-12. DOI: 10.1038/s41398-019-0653-9
28. Kumperscak H.G., Gricar A., Ülen I., Micetic-Turk D.. **A pilot randomized control trial with the probiotic strain lactobacillus rhamnosus GG (LGG) in ADHD: Children and adolescents report better health-related quality of life**. *Front. Psychiatry* (2020) **11** 181. DOI: 10.3389/fpsyt.2020.00181
29. Liu Y.-W., Liong M.T., Chung Y.-C.E., Huang H.-Y., Peng W.-S., Cheng Y.-F., Lin Y.-S., Wu Y.-Y., Tsai Y.-C.. **Effects of**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11040820
30. Steenbergen L., Sellaro R., van Hemert S., Bosch J.A., Colzato L.S.. **A randomized controlled trial to test the effect of multispecies probiotics on cognitive reactivity to sad mood**. *Brain Behav. Immun.* (2015) **48** 258-264. DOI: 10.1016/j.bbi.2015.04.003
31. Skott E., Yang L.L., Stiernborg M., Söderström Å., Rȕegg J., Schalling M., Forsell Y., Giacobini M., Lavebratt C.. **Effects of a synbiotic on symptoms, and daily functioning in attention deficit hyperactivity disorder—A double-blind randomized controlled trial**. *Brain Behav. Immun.* (2020) **89** 9-19. DOI: 10.1016/j.bbi.2020.05.056
32. Kasubuchi M., Hasegawa S., Hiramatsu T., Ichimura A., Kimura I.. **Dietary Gut microbial metabolites, short-chain fatty acids, and host metabolic regulation**. *Nutrients* (2015) **7** 2839-2849. DOI: 10.3390/nu7042839
33. Hu J., Lin S., Zheng B., Cheung P.C.K.. **Short-chain fatty acids in control of energy metabolism**. *Crit. Rev. Food Sci. Nutr.* (2017) **58** 1243-1249. DOI: 10.1080/10408398.2016.1245650
34. Morris G., Berk M., Carvalho A., Caso J.R., Sanz Y., Walder K., Maes M.. **The role of the microbial metabolites including tryptophan catabolites and short chain fatty acids in the pathophysiology of immune-inflammatory and neuroimmune disease**. *Mol. Neurobiol.* (2016) **54** 4432-4451. DOI: 10.1007/s12035-016-0004-2
35. Arpaia N., Campbell C., Fan X., Dikiy S., Van Der Veeken J., DeRoos P., Liu H., Cross J.R., Pfeffer K., Coffer P.J.. **Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation**. *Nature* (2013) **504** 451-455. DOI: 10.1038/nature12726
36. DeCastro M., Nankova B.B., Shah P., Patel P., Mally P.V., Mishra R., La Gamma E.F.. **Short chain fatty acids regulate tyrosine hydroxylase gene expression through a cAMP-dependent signaling pathway**. *Mol. Brain Res.* (2005) **142** 28-38. DOI: 10.1016/j.molbrainres.2005.09.002
37. Duscha A., Gisevius B., Hirschberg S., Yissachar N., Stangl G.I., Eilers E., Bader V., Haase S., Kaisler J., David C.. **Propionic acid shapes the multiple sclerosis disease course by an immunomodulatory mechanism**. *Cell* (2020) **180** 1067-1080. DOI: 10.1016/j.cell.2020.02.035
38. Erny D., Hrabě de Angelis A.L., Jaitin D., Wieghofer P., Staszewski O., David E., Keren-Shaul H., Mahlakoiv T., Jakobshagen K., Buch T.. **Host microbiota constantly control maturation and function of microglia in the CNS**. *Nat. Neurosci.* (2015) **18** 965-977. DOI: 10.1038/nn.4030
39. Hoyles L., Snelling T., Umlai U.-K., Nicholson J.K., Carding S.R., Glen R.C., McArthur S.. **Microbiome–host systems interactions: Protective effects of propionate upon the blood–brain barrier**. *Microbiome* (2018) **6** 55. DOI: 10.1186/s40168-018-0439-y
40. Frye R.E., Melnyk S., MacFabe D.F.. **Unique acyl-carnitine profiles are potential biomarkers for acquired mitochondrial disease in autism spectrum disorder**. *Transl. Psychiatry* (2013) **3** e220. DOI: 10.1038/tp.2012.143
41. Yang L.L., Millischer V., Rodin S., MacFabe D.F., Villaescusa J.C., Lavebratt C.. **Enteric short-chain fatty acids promote proliferation of human neural progenitor cells**. *J. Neurochem.* (2019) **154** 635-646. DOI: 10.1111/jnc.14928
42. Martin-Gallausiaux C., Marinelli L., Blottiere H.M., Larraufie P., Lapaque N.. **SCFA: Mechanisms and functional importance in the gut**. *Proc. Nutr. Soc.* (2021) **80** 37-49. DOI: 10.1017/S0029665120006916
43. Martínez-Reyes I., Chandel N.S.. **Mitochondrial TCA cycle metabolites control physiology and disease**. *Nat. Commun.* (2020) **11** 102. DOI: 10.1038/s41467-019-13668-3
44. Mitrea L., Nemes S.A., Szabo K., Teleky B.E., Vodnar D.C.. **Guts imbalance imbalances the brain: A Review of gut microbiota association with neurological and psychiatric disorders**. *Front. Med.* (2022) **9**. DOI: 10.3389/fmed.2022.813204
45. Ratsika A., Pereira J.S.C., Lynch C.M., Clarke G., Cryan J.F.. **Microbiota-immune-brain interactions: A lifespan perspective**. *Curr. Opin. Neurobiol.* (2023) **78** 102652. DOI: 10.1016/j.conb.2022.102652
46. Fung T.C., Olson C.A., Hsiao E.Y.. **Interactions between the microbiota, immune and nervous systems in health and disease**. *Nat. Neurosci.* (2017) **20** 145-155. DOI: 10.1038/nn.4476
47. Bauer M.E., Teixeira A.L.. **Inflammation in psychiatric disorders: What comes first?**. *Ann. New York Acad. Sci.* (2018) **1437** 57-67. DOI: 10.1111/nyas.13712
48. Goldsmith D.R., Rapaport M.H., Miller B.J.. **A meta-analysis of blood cytokine network alterations in psychiatric patients: Comparisons between schizophrenia, bipolar disorder and depression**. *Mol. Psychiatry* (2016) **21** 1696-1709. DOI: 10.1038/mp.2016.3
49. Köhler C.A., Freitas T.H., Maes M., De Andrade N.Q., Liu C.S., Fernandes B.S., Stubbs B., Solmi M., Veronese N., Herrmann N.. **Peripheral cytokine and chemokine alterations in depression: A meta-analysis of 82 studies**. *Acta Psychiatr. Scand.* (2017) **135** 373-387. DOI: 10.1111/acps.12698
50. Masi A., Quintana D.S., Glozier N., Lloyd A.R., Hickie I.B., Guastella A.J.. **Cytokine aberrations in autism spectrum disorder: A systematic review and meta-analysis**. *Mol. Psychiatry* (2014) **20** 440-446. DOI: 10.1038/mp.2014.59
51. Dantzer R., O’Connor J.C., Freund G.G., Johnson R.W., Kelley K.W.. **From inflammation to sickness and depression: When the immune system subjugates the brain**. *Nat. Rev. Neurosci.* (2008) **9** 46-56. DOI: 10.1038/nrn2297
52. Khandaker G.M., Cousins L., Deakin J., Lennox B.R., Yolken R., Jones P.B.. **Inflammation and immunity in schizophrenia: Implications for pathophysiology and treatment**. *Lancet Psychiatry* (2014) **2** 258-270. DOI: 10.1016/S2215-0366(14)00122-9
53. Matta S.M., Hill-Yardin E.L., Crack P.J.. **The influence of neuroinflammation in Autism Spectrum Disorder**. *Brain Behav. Immun.* (2019) **79** 75-90. DOI: 10.1016/j.bbi.2019.04.037
54. Mitchell R.H., Goldstein B.I.. **Inflammation in children and adolescents with neuropsychiatric disorders: A systematic review**. *J. Am. Acad. Child Adolesc. Psychiatry* (2014) **53** 274-296. DOI: 10.1016/j.jaac.2013.11.013
55. Anand D., Colpo G.D., Zeni G., Zeni C.P., Teixeira A.L.. **Attention-deficit/hyperactivity disorder and inflammation: What does current knowledge tell us? A systematic review**. *Front. Psychiatry* (2017) **8** 228. DOI: 10.3389/fpsyt.2017.00228
56. Misiak B., Wójta-Kempa M., Samochowiec J., Schiweck C., Aichholzer M., Reif A., Samochowiec A., Stańczykiewicz B.. **Peripheral blood inflammatory markers in patients with attention deficit/hyperactivity disorder (ADHD): A systematic review and meta-analysis**. *Prog. Neuro-Psychopharmacology Biol. Psychiatry* (2022) **118** 110581. DOI: 10.1016/j.pnpbp.2022.110581
57. Bedrossian N., Haidar M., Fares J., Kobeissy F.H., Fares Y.. **Inflammation and Elevation of interleukin-12p40 in patients with schizophrenia**. *Front. Mol. Neurosci.* (2016) **9** 16. DOI: 10.3389/fnmol.2016.00016
58. Müller N.. **The role of intercellular adhesion molecule-1 in the pathogenesis of psychiatric disorders**. *Front. Pharmacol.* (2019) **10** 1251. DOI: 10.3389/fphar.2019.01251
59. Alaşehirli B., Oguz E., Gokcen C., Erbagcı A.B., Orkmez M., Demiryurek A.T.. **Relationship between soluble intercellular adhesion molecules and attention-deficit/hyperactivity disorder**. *Int. J. Psychiatry Med.* (2015) **50** 238-247. DOI: 10.1177/0091217415605040
60. Meixensberger S., Kuzior H., Fiebich B., Süß P., Runge K., Berger B., Nickel K., Denzel D., Schiele M., Michel M.. **Upregulation of sICAM-1 and sVCAM-1 levels in the cerebrospinal fluid of patients with schizophrenia spectrum disorders**. *Diagnostics* (2021) **11**. DOI: 10.3390/diagnostics11071134
61. Thomas A.J., Ferrier I.N., Kalaria R.N., Davis S., O’Brien J.T.. **Cell adhesion molecule expression in the dorsolateral prefrontal cortex and anterior cingulate cortex in major depression in the elderly**. *Br. J. Psychiatry* (2002) **181** 129-134. DOI: 10.1192/bjp.181.2.129
62. Thomas A.J., Davis S., Ferrier I., Kalaria R.N., O’Brien J.T.. **Elevation of cell adhesion molecule immunoreactivity in the anterior cingulate cortex in bipolar disorder**. *Biol. Psychiatry* (2004) **55** 652-655. DOI: 10.1016/j.biopsych.2003.10.015
63. Carias E., Hamilton J., Robison L.S., Delis F., Eiden R., Quattrin T., Hadjiargyrou M., Komatsu D., Thanos P.K.. **Chronic oral methylphenidate treatment increases microglial activation in rats**. *J. Neural Transm.* (2018) **125** 1867-1875. DOI: 10.1007/s00702-018-1931-z
64. Coelho-Santos V., Cardoso F.L., Magalhães A., Ferreira-Teixeira M., Leitão R.A., Gomes C., Rito M., Barbosa M., Fontes-Ribeiro C.A., Silva A.P.. **Effect of chronic methylphenidate treatment on hippocampal neurovascular unit and memory performance in late adolescent rats**. *Eur. Neuropsychopharmacol.* (2018) **29** 195-210. DOI: 10.1016/j.euroneuro.2018.12.007
65. Valvassori S.S., Dal-Pont G.C., Tonin P.T., Varela R.B., Ferreira C.L., Gava F.F., Andersen M.L., Soares J.C., Quevedo J.. **Coadministration of lithium and celecoxib attenuates the behavioral alterations and inflammatory processes induced by amphetamine in an animal model of mania**. *Pharmacol. Biochem. Behav.* (2019) **183** 56-63. DOI: 10.1016/j.pbb.2019.05.009
66. Thomas D.M., Kuhn D.M.. **Cyclooxygenase-2 is an obligatory factor in methamphetamine-induced neurotoxicity**. *Experiment* (2005) **313** 870-876. DOI: 10.1124/jpet.104.080242
67. Yang L.L., Stiernborg M., Skott E., Söderström A., Giacobini M., Lavebratt C.. **Proinflammatory mediators and their associations with medication and comorbid traits in children and adults with ADHD**. *Eur. Neuropsychopharmacol.* (2020) **41** 118-131. DOI: 10.1016/j.euroneuro.2020.10.005
68. Li L., Chang Z., Sun J., Garcia-Argibay M., Du Rietz E., Dobrosavljevic M., Brikell I., Jernberg T., Solmi M., Cortese S.. **Attention-deficit/hyperactivity disorder as a risk factor for cardiovascular diseases: A nationwide population-based cohort study**. *World Psychiatry* (2022) **21** 452-459. DOI: 10.1002/wps.21020
69. Schmidt C., Hulthe J., Fagerberg B.. **Baseline ICAM-1 and VCAM-1 are Increased in initially healthy middle-aged men who develop cardiovascular disease during 6.6 years of follow-up**. *Angiology* (2008) **60** 108-114. DOI: 10.1177/0003319708316899
70. Plaudis H., Pupelis G., Zeiza K., Boka V.. **Early low volume oral synbiotic/prebiotic supplemented enteral stimulation of the gut in patients with severe acute pancreatitis: A prospective feasibility study**. *Acta Chir. Belg.* (2012) **112** 131-138. DOI: 10.1080/00015458.2012.11680811
71. Giamarellos-Bourboulis E.J., Bengmark S., Kanellakopoulou K., Kotzampassi K.. **Pro- and synbiotics to control inflammation and infection in patients with multiple injuries**. *J. Trauma Inj. Infect. Crit. Care* (2009) **67** 815-821. DOI: 10.1097/TA.0b013e31819d979e
72. Vidot H., Cvejic E., Finegan L.J., Shores E.A., Bowen D.G., Strasser S.I., McCaughan G.W., Carey S., Allman-Farinelli M., Shackel N.A.. **Supplementation with synbiotics and/or branched chain amino acids in hepatic encephalopathy: A pilot randomised placebo-controlled clinical study**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11081810
73. Spindler-Vesel A., Bengmark S., Vovk I., Cerovic O., Kompan L.. **Synbiotics, Prebiotics, glutamine, or peptide in early enteral nutrition: A randomized study in trauma patients**. *J. Parenter. Enter. Nutr.* (2007) **31** 119-126. DOI: 10.1177/0148607107031002119
74. Wipfler P., Heikkinen A., Harrer A., Pilz G., Kunz A., Golaszewski S.M., Reuss R., Oschmann P., Kraus J.. **Circadian rhythmicity of inflammatory serum parameters: A neglected issue in the search of biomarkers in multiple sclerosis**. *J. Neurol.* (2012) **260** 221-227. DOI: 10.1007/s00415-012-6622-3
75. Han J., Lin K., Sequeira C., Borchers C.H.. **An isotope-labeled chemical derivatization method for the quantitation of short-chain fatty acids in human feces by liquid chromatography–tandem mass spectrometry**. *Anal. Chim. Acta* (2015) **854** 86-94. DOI: 10.1016/j.aca.2014.11.015
76. Boets E., Gomand S.V., Deroover L., Preston T., Vermeulen K., De Preter V., Hamer H.M., Van den Mooter G., De Vuyst L., Courtin C.M.. **Systemic availability and metabolism of colonic-derived short-chain fatty acids in healthy subjects: A stable isotope study**. *J. Physiol.* (2017) **595** 541-555. DOI: 10.1113/JP272613
77. Wickham H.. *ggplot2: Elegant Graphics for Data Analysis, 2nd ed* (2016)
78. Upham J.W., Lee P.T., Holt B.J., Heaton T., Prescott S.L., Sharp M.J., Sly P.D., Holt P.. **Development of interleukin-12-producing capacity throughout childhood**. *Infect. Immun.* (2002) **70** 6583-6588. DOI: 10.1128/IAI.70.12.6583-6588.2002
79. Enkhmaa B., Anuurad E., Zhang W., Kim K., Berglund L.. **Diverging trajectory patterns of systemic versus vascular inflammation over age in healthy Caucasians and African Americans**. *Atherosclerosis* (2015) **239** 509-515. DOI: 10.1016/j.atherosclerosis.2015.02.023
80. Moschen A.R., Tilg H., Raine T.. **IL-12, IL-23 and IL-17 in IBD: Immunobiology and therapeutic targeting**. *Nat. Rev. Gastroenterol. Hepatol.* (2018) **16** 185-196. DOI: 10.1038/s41575-018-0084-8
81. Neurath M.F.. **IL-23 in inflammatory bowel diseases and colon cancer**. *Cytokine Growth Factor Rev.* (2018) **45** 1-8. DOI: 10.1016/j.cytogfr.2018.12.002
82. McKeown C., Hisle-Gorman E., Eide M., Gorman G.H., Nylund C.M.. **Association of Constipation and fecal incontinence with attention-deficit/hyperactivity disorder**. *Pediatrics* (2013) **132** e1210-e1215. DOI: 10.1542/peds.2013-1580
83. Blankenberg S., Barbaux S., Tiret L.. **Adhesion molecules and atherosclerosis**. *Atherosclerosis* (2003) **170** 191-203. DOI: 10.1016/S0021-9150(03)00097-2
84. Thomas S., Baumgart D.C.. **Targeting leukocyte migration and adhesion in Crohn’s disease and ulcerative colitis**. *Inflammopharmacology* (2011) **20** 1-18. DOI: 10.1007/s10787-011-0104-6
85. Jones S.C., Banks R.E., Haidar A., Gearing A.J., Hemingway I.K., Ibbotson S.H., Dixon M.F., Axon A.T.. **Adhesion molecules in inflammatory bowel disease**. *Gut* (1995) **36** 724-730. DOI: 10.1136/gut.36.5.724
86. Yarur A.J., Quintero M.A., Jain A., Czul F., Barkin J.S., Abreu M.T.. **Serum amyloid a as a surrogate marker for mucosal and histologic inflammation in patients with Crohn’s disease**. *Inflamm. Bowel Dis.* (2017) **23** 158-164. DOI: 10.1097/MIB.0000000000000991
87. Varatharaj A., Galea I.. **The blood-brain barrier in systemic inflammation**. *Brain Behav. Immun.* (2017) **60** 1-12. DOI: 10.1016/j.bbi.2016.03.010
88. Rezazadeh L., Gargari B.P., Jafarabadi M.A., Alipour B.. **Effects of probiotic yogurt on glycemic indexes and endothelial dysfunction markers in patients with metabolic syndrome**. *Nutrition* (2019) **62** 162-168. DOI: 10.1016/j.nut.2018.12.011
89. Haghighat N., Mohammadshahi M., Shayanpour S., Haghighizadeh M.H.. **Effect of synbiotic and probiotic supplementation on serum levels of endothelial cell adhesion molecules in hemodialysis patients: A randomized control study**. *Probiotics Antimicrob. Proteins* (2018) **11** 1210-1218. DOI: 10.1007/s12602-018-9477-9
90. Liu W.-C., Yang M.-C., Wu Y.-Y., Chen P.-H., Hsu C.-M., Chen L.-W.. **Lactobacillus plantarum reverse diabetes-induced Fmo3 and ICAM expression in mice through enteric dysbiosis-related c-Jun NH2-terminal kinase pathways**. *PLoS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0196511
91. Cabrera S.M., Coren A.T., Pant T., Ciecko A.E., Jia S., Roethle M.F., Simpson P.M., Atkinson S.N., Salzman N.H., Chen Y.-G.. **Probiotic normalization of systemic inflammation in siblings of type 1 diabetes patients: An open-label pilot study**. *Sci. Rep.* (2022) **12** 1-15. DOI: 10.1038/s41598-022-07203-6
92. Simon E., Călinoiu L., Mitrea L., Vodnar D.. **Probiotics, prebiotics, and synbiotics: Implications and beneficial effects against irritable bowel syndrome**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13062112
93. Hamer H.M., Jonkers D.M.A.E., Vanhoutvin S.A.L.W., Troost F.J., Rijkers G., De Bruïne A., Bast A., Venema K., Brummer R.-J.M.. **Effect of butyrate enemas on inflammation and antioxidant status in the colonic mucosa of patients with ulcerative colitis in remission**. *Clin. Nutr.* (2010) **29** 738-744. DOI: 10.1016/j.clnu.2010.04.002
94. Säemann M.D., Böhmig G.A., Österreicher C.H., Burtscher H., Parolini O., Diakos C., Stöckl J., Hörl W.H., Zlabinger G.J.. **Anti-inflammatory effects of sodium butyrate on human monocytes: Potent inhibition of IL-12 and up-regulation of IL-10 production**. *FASEB J.* (2000) **14** 2380-2382. DOI: 10.1096/fj.00-0359fje
95. Zapolska-Downar D., Siennicka A., Kaczmarczyk M., Kołodziej B., Naruszewicz M.. **Butyrate inhibits cytokine-induced VCAM-1 and ICAM-1 expression in cultured endothelial cells: The role of NF-κB and PPARα**. *J. Nutr. Biochem.* (2004) **15** 220-228. DOI: 10.1016/j.jnutbio.2003.11.008
96. Zapolska-Downar D., Naruszewicz M.. **Propionate reduces the cytokine-induced VCAM-1 and ICAM-1 expression by inhibiting nuclear factor-kappa B (NF-kappaB) activation**. *J. Physiol. Pharmacol.* (2009) **60** 123-131. PMID: 19617655
97. Yoshida A., Takahashi H.K., Iwagaki H., Yoshino T., Morichika T., Yokoyama M., Itoh H., Mori S., Akagi T., Nishibori M.. **Essential role of ICAM-1/LFA-1 interaction in synergistic effect of IL-18 and IL-12 on IFN-γ production in human PBMC**. *Naunyn-Schmiedeberg’s Arch. Pharmacol.* (2002) **365** 181-186. DOI: 10.1007/s00210-001-0518-6
98. Yang L.L., Stiernborg M., Skott E., Gillberg T., Landberg R., Giacobini M., Lavebratt C.. **Lower plasma concentrations of short-chain fatty acids (SCFAs) in patients with ADHD**. *J. Psychiatr. Res.* (2022) **156** 36-43. DOI: 10.1016/j.jpsychires.2022.09.042
99. Liu S., Li E., Sun Z., Fu D., Duan G., Jiang M., Yu Y., Mei L., Yang P., Tang Y.. **Altered gut microbiota and short chain fatty acids in Chinese children with autism spectrum disorder**. *Sci. Rep.* (2019) **9** 287. DOI: 10.1038/s41598-018-36430-z
100. Tan J., McKenzie C., Potamitis M., Thorburn A.N., Mackay C.R., Macia L.. **The role of short-chain fatty acids in health and disease**. *Adv. Immunol.* (2014) **121** 91-119. DOI: 10.1016/b978-0-12-800100-4.00003-9
101. Yap C.X., Henders A.K., Alvares G.A., Wood D.L., Krause L., Tyson G.W., Restuadi R., Wallace L., McLaren T., Hansell N.K.. **Autism-related dietary preferences mediate autism-gut microbiome associations**. *Cell* (2021) **184** 5916-5931. DOI: 10.1016/j.cell.2021.10.015
|
---
title: 'The Fruit Intake–Adiposity Paradox: Findings from a Peruvian Cross-Sectional
Study'
authors:
- Jamee Guerra Valencia
- Willy Ramos
- Liliana Cruz-Ausejo
- Jenny Raquel Torres-Malca
- Joan A. Loayza-Castro
- Gianella Zulema Zeñas-Trujillo
- Norka Rocío Guillen Ponce
- Fiorella E. Zuzunaga-Montoya
- Mario J. Valladares-Garrido
- Víctor Juan Vera-Ponce
- Jhony A. De La Cruz-Vargas
journal: Nutrients
year: 2023
pmcid: PMC10004770
doi: 10.3390/nu15051183
license: CC BY 4.0
---
# The Fruit Intake–Adiposity Paradox: Findings from a Peruvian Cross-Sectional Study
## Abstract
Due to the increase in obesity worldwide, international organizations have promoted the adoption of a healthy lifestyle, as part of which fruit consumption stands out. However, there are controversies regarding the role of fruit consumption in mitigating this disease. The objective of the present study was to analyze the association between fruit intake and body mass index (BMI) and waist circumference (WC) in a representative sample of Peruvians. This is an analytical cross-sectional study. Secondary data analysis was conducted using information from the Demographic and Health Survey of Peru (2019–2021). The outcome variables were BMI and WC. The exploratory variable was fruit intake, which was expressed in three different presentations: portion, salad, and juice. A generalized linear model of the Gaussian family and identity link function were performed to obtain the crude and adjusted beta coefficients. A total of 98,741 subjects were included in the study. Females comprised $54.4\%$ of the sample. In the multivariate analysis, for each serving of fruit intake, the BMI decreased by 0.15 kg/m2 (β = −0.15; $95\%$ CI −0.24 to −0.07), while the WC was reduced by 0.40 cm (β = −0.40; $95\%$ CI −0.52 to −0.27). A negative association between fruit salad intake and WC was found (β = −0.28; $95\%$ CI −0.56 to −0.01). No statistically significant association between fruit salad intake and BMI was found. In the case of fruit juice, for each glass of juice consumed, the BMI increased by 0.27 kg/m2 (β = 0.27; $95\%$ CI 0.14 to 0.40), while the WC increased by 0.40 cm (β = 0.40; $95\%$ CI 0.20 to 0.60). Fruit intake per serving is negatively related to general body adiposity and central fat distribution, while fruit salad intake is negatively related to central distribution adiposity. However, the consumption of fruit in the form of juices is positively associated with a significant increase in BMI and WC.
## 1. Introduction
Obesity and its associated comorbidities have shown a marked global increase between 1980 and 2008 in some regions, such as Latin America [1]. In this context, international organizations such as the World Health Organization (WHO) have begun to promote the adoption of a healthy lifestyle, as part of which daily fruit and vegetable intake of five or more servings per day stands out for the prevention of non-communicable diseases [2]. Culinary and nutritional aspects of fruit consumption may differ in comparison to vegetable consumption [3,4]. For instance, fruits are usually consumed raw, as desserts, in juices, or in other presentations which potentially favor their intake relative to that of vegetables. In addition, the nutritional composition of fruits and vegetables differs since their fructose and water content can vary [3,4].Despite evidence points out a negative association between fruit intake and different health outcomes such as type 2 diabetes mellitus (DM2), hypertension, cancer and depression [5,6,7,8], fruit consumption has shown a downward trend in countries of the Latin American region [9]. In Peru, a country with high variability in dietary patterns among different areas, it has been reported that fruit and vegetable intake is insufficient to the extent that only 4.8 to $11.2\%$ of the population over 18 years old reaches the minimum recommendation of 5 servings per day [9,10].
Postulated mechanisms by which fruit intake exerts a regulatory role in body weight management include displacement of energy-dense foods; reduction in total energy intake; increase in satiety and satiation; and higher contribution of fiber, micronutrients and phytochemicals to the diet [3,11,12]. However, research analyzing the relationship between weight loss or obesity prevention and fruit consumption has shown great variability. For example, a systematic review that included randomized controlled trials reported that, in most of the studies, an increase in fruit intake, but not that of vegetables or that of fruits and vegetables, reduces the body weight, and/or waist circumference (WC) [3]. Furthermore, prospective and cross-sectional observational studies report an inverse association with weight gain or development of overweight-obesity for the consumption of fruits but not for the consumption of vegetables or fruits and vegetables together [3,13,14]. It should also be considered that the measurement of obesity is highly variable, as it can be assessed through different anthropometric markers such as the body mass index (BMI) and/or the WC [15].
To the best of our knowledge, few studies have analyzed the relationship between fruit intake and anthropometric markers of adiposity in the Latin American region. Furthermore, it has been reported that general obesity and central obesity prevalence are stepping increasing worldwide and particularly the South American countries are the most affected ones [16,17]. In addition, in the existing studies, methodological approaches such as representing the consumption of fruits and vegetables as a dichotomous variable, have failed to yield a significant association [18]. Since there is evidence of a dose-response relationship between fruit consumption and other health outcomes [6,7,8,9,19,20] and there is a knowledge gap on the topic, the current study aims to analyze the association between fruit intake and body mass index and waist circumference in a representative sample of Peruvians.
## 2.1. Study Design
This was a cross-sectional analytical study. Secondary data analysis was conducted using information from the Demographic and Health Survey of Peru (ENDES). For this manuscript, the data collected in the years 2019, 2020 and 2021 were analyzed. The STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines were followed for the present study [21].
## 2.2. Population and Sample
The ENDES is a nationally representative survey with a two-stage sampling design (National Institute of Statistics and Informatics, 2015). The sample was characterized as being probabilistic of a balanced, stratified, and independent type, at the departmental level and by urban and rural area.
## 2.3. Definition of Variables
The outcome variables were BMI and WC. BMI was calculated using the weight × height2 formula. Height was measured with a mobile, multipurpose wooden stadiometer with a precision of 1 mm and with the technical specifications of the National Food and Nutrition Center (CENAN by its acronym in Spanish). A SECA-878 brand scale was used to measure body weight with a precision of 50 g. For the waist circumference, a Lufkin brand retractable metal tape with a resolution of 0.1 cm was used. In addition, the anthropometric techniques recommended by the WHO were used to measure weight, height, and waist circumference. The latter was measured as the mean distance between the last costal margin and the upper edge of the iliac crest, as previously reported in the ENDES anthropometrist manuals [22].
The exploratory variable was fruit intake, which was self-reported and expressed in number of servings per day in three different presentations. The first form of intake was evaluated through the question: How many units, slices, or fruit bunches did you eat per day? The consumption of fruits as a garnish was included, and, for the case of dried fruits, only raisins were considered. The second form of intake was the consumption of fruit juice per day, which was assessed through the question: How many glasses of fruit juice did you drink per day? *In this* case, both fruit juices and their extracts were included. Finally, the consumption of fruit in salad per day was assessed through the question: How many servings of fruit salad did you eat per day? To assess the three forms of presentation, a laminar was used as graphic support [23] with portion sizes and standardized household measurements. The technical aspects of collecting this information have been previously published in manuals by the National Institute of Statistics and Informatics (INEI by its acronym in Spanish) [24].
The factors to be evaluated were sex (male vs. female); categorized age (15–34, 35–60, 61–69, and ≥70 years); educational level (no level, primary, secondary, and higher); wealth index (poorest, poor, medium, rich and richest); natural region (Metropolitan Lima, rest of the coast, mountains and jungle); daily tobacco use (yes vs. no); self-reported alcohol consumption in the previous 12 months (yes vs. no); history of type 2 diabetes mellitus (yes vs. no) and arterial hypertension (yes vs. no).
## 2.4. Statistical Analysis
The statistical software SPSS 26 was used. The median and interquartile range of each form of presentation of fruit intake were estimated. BMI and WC were presented with their respective $95\%$ confidence intervals ($95\%$CI). The descriptive variables were presented in absolute and relative frequencies. Student’s t test/one way ANOVA was used for the bivariate analysis, and Spearman’s correlation was used for the main variables. Finally, a generalized linear model of the Gaussian family and identity link function were performed to obtain the crude and adjusted beta coefficients. All the analyses were carried out considering that the samples were complex.
## 2.5. Ethical Aspect
This study was developed by analyzing survey data sets that are openly published and available online with all identifier information removed (http://iinei.inei.gob.pe/microdatos/, accessed on 16 November 2022). Furthermore, all survey data were coded to ensure anonymity to minimize potential harm.
## 3. Results
A total of 98,741 subjects were included in the study. The Females comprised $54.4\%$ of the study population. A total of $33.6\%$ of the subjects lived in Metropolitan Lima. A total of $20.2\%$ were over 60 years of age. The mean BMI was 27.3 ± 4.9 kg/m2, while for the WC, it was 58.7 ± 8.2 cm. Regarding fruit intake, the mean portions of whole fruit, juices, and salad, was 1.4 ± 1.3; 1.5 ± 0.8 and 1.2 ± 0.6, respectively. Table 1 shows the whole study sample characteristics, disaggregated by sex. Differences in BMI, WC, history of hypertension and DM2 were statistically significant between sexes.
In the bivariate analysis, a statistically significant association was found between each characteristic and BMI and WC (Table 2).
In the multivariate analysis (Table 3), for each serving unit of fruit intake, the BMI decreased by 0.15 kg/m2 (β= −0.15; $95\%$ CI −0.24 to −0.07), while the WC was reduced by 0.40 cm (β = −0.40; $95\%$ CI −0.52 to −0.27). In the case of fruit juice, for each glass of juice consumed, the BMI increased by 0.27 kg/m2 (β = 0.27; $95\%$ CI 0.14 to 0.40), while the WC increased by 0.40 cm (β = 0.40; $95\%$ CI 0.20 to 0.60). Finally, a negative relation between fruit salad intake and WC was found, with a 0.28 cm decrease for each serving (β = −0.28; $95\%$ CI −0.56 to −0.01). However, this was not the case for BMI (β= −0.10; $95\%$ CI −0.28 to 0.08).
Intake of three or more fruit portions was associated with a significant decrease in BMI and WC when compared to less than 3 portions intake (Table 4). A decline of 0.24 kg/m2 (β= −0.24; $95\%$ CI −0.32 to −0.17) and 0.60 cm (β = −0.60; $95\%$ CI −0.72 to −0.47), was found for BMI and WC, respectively
## 4.1. Main Findings
The present study found evidence of a relationship between fruit intake and anthropometric indicators of adiposity. However, the magnitude and direction of the relationship varied depending on the presentation of fruit consumption. Consumption of each portion of fruit resulted in a negative beta coefficient for BMI and waist circumference. A negative relation for fruit salad intake and WC was found; in contrast, the consumption of fruit juices and extracts was related to an increase in these indicators.
## 4.2. Comparison with Other Studies
Fruit intake in portions per day was lower than that reported by previous studies from Canada and European countries, with the average intake found to be between 1.8 and 2.4 portions per day [25,26]. In contrast, our findings are consistent with the ELANS study (Latin American Study of Nutrition and Health), in which the intake of fresh fruit in Latin American countries varied from less than one serving in Venezuela to 1.5 servings per day in Peru [9]. Our findings regarding fruit juice intake were similar to those of the ELANS study, although in that research, fruit juices and other homemade beverages were considered in the same category [9]. The comparison of fruit juice intake between studies is more complex due to the variable methods of reporting consumption (servings per day with different household measurements as standard measurements vs. grams per day). It is particularly noteworthy that fruit intake in Peru occurs not only in the form of whole fruits; rather, the intake of fruit salad is also a popular alternative, which is why there are businesses that are in charge of selling fruit and fruit salads. However, as there are no reports of consumption of this fruit presentation at the national level, it was not possible to compare the intake of fruits in this culinary presentation form with other studies.
Consuming more servings of fruit per day was inversely related to BMI and waist circumference. These findings are in line with previous cross-sectional [27,28] and prospective [3,13,27,28,29] studies. Although differences in the magnitude of the beta coefficients between fruit intake and anthropometric markers among studies exists, it should be considered that this variability may reflect the diverse nature of the population studied. For instance, while the PREDIMED-plus cohort study (PREDIMED: Prevención con Dieta Mediterránea) [26] exclusively analyzed patients with metabolic syndrome and reported a greater beta coefficient than that reported in the present study, the study by Yu et al. [ 25], included healthy individuals and those with chronic diseases as the present study did. After the corresponding adjustment for diseases was made, Yu et al. found beta coefficients similar to what we found. This suggests that the modulation that fruit intake has on adiposity could be greater in people with chronic diseases, compared to those who are healthy [4].
Since a specific fruit intake dose for excess adiposity accumulation protection is not currently suggested by any international organization or dietary guidelines, we decided further to analyze the relationship between a three-portion daily fruit intake against BMI and WC. This decision was made on the rational basis of existing evidence pointing out a protective effect of this dose against other cardiometabolic diseases [7,8]. Not surprisingly the three-portion daily fruit intake was significantly and negative associated with BMI and WC. Furthermore, the magnitude of WC reduction, this is −0.6 cm, may be considered as clinically relevant as a study revealed that each 1cm-increase of WC is associated with a $2\%$ increased risk of cardiovascular disease (CVD) [30]. Inference from that study could be made in a reverse way, such that the observed current reduction of 0.6 cm ($95\%$ CI −0.72 to −0.47) could be associated with a CVD risk reduction of $1\%$ (ranging 0.9–$1.5\%$). In this sense, our findings confirm the benefits that intake of three or more fruit portions per day have over cardiometabolic health and add evidence to previously published studies regarding the dose-dependent protective effects of fruit intake on DM2 and cardiovascular diseases prevention [7,8].
Fruit salad consumption was also negatively correlated with waist circumference but not with BMI. The reason for the lack of statistically significant correlation with BMI may lie in the fact that adding sugar-sweetened yogurts and energy-dense accompaniments is a frequent practice in the commercial preparation of fruit salads in Peru. Therefore, an increase in total energy intake may have neglected statistically significant association when analyzing total body adiposity with BMI. On the contrary, the potential presence of added sugar in fruit salad may have been overpowered by the fiber content when central adiposity was analyzed through waist circumference. However, as the presence of added sugar components was not assessed, this cannot be confirmed.
In contrast to what was found for fruit consumption per serving, the intake of fruit juices per day was positively related to an increase 0.27 kg/m2 and 0.40 cm of BMI and waist circumference, respectively, for each serving of juice consumed. This finding differs from that found in other works in which the intake of natural fruit juices showed an inverse and dose-dependent relationship with BMI and waist circumference [25,26,27,28,29,30,31,32,33]. Methodological differences in the assessment of fruit juice intake may partly explain this discrepancy. While some studies differentiated the consumption of natural fruit juice from packaged fruit juice [26,32,33], and others included both fruit and vegetable juices in the category of juices [24], the present work included both juices and fruit extracts, the latter being low in fiber content and more concentrated in sugars. Additionally, a core aspect to consider is that adding refined sugar is part of traditional fruit juice preparation in Peru, as well as in other Latin American countries [9], which increases caloric intake and reduces the protective effect against the development of adiposity. In line with the above, a systematic review analyzed the risk of developing type 2 diabetes mellitus for the consumption of fruit juices with and without added sugar and reported a higher risk for the former, but not for the latter [34].
The present study found that fruit intake, but not fruit juice consumption, is inversely related to adiposity. However, it is important to consider that this effect is part of a set of dietary patterns that include the intake of less energy-dense foods that are richer in micronutrients, phytochemicals, and fiber. In support of this, a prospective study with 5.5 years of follow-up found that the change in “waist circumference for a given BMI” was −0.04 cm/year for fruit intake, but the magnitude of the change rose to −1.1 cm/year when all other food groups that prevent the gain of central adiposity were included, which are characteristically higher in fiber and micronutrients and less energy-dense [29]. In this way, the evidence suggests that the mechanisms by which fruit consumption reduces body adiposity, particularly that of central distribution, are related to the more significant contribution of fiber and micronutrients that modulate satiety and intestinal microbiota and displace the consumption of foods with a high content of saturated fats, sugars and sodium [26,35].
Our research highlights the urgent need to strengthen public policies regarding fruit intake in low- to middle-income countries such as Peru in which fruit consumption, regardless of the culinary presentation, is well below what is needed for the population to benefit from its protective effect against cardiometabolic diseases. Furthermore, considering the high healthcare cost attributed to general and central obesity [36,37], its steepening trend [16,17], and the potential cost savings on its reduction [38,39], strengthening cost-effective public policies such as nutritional education targeted at vulnerable populations should be considered [40], especially when significant CVD risk reduction may be achieved with this kind of approaches.
## 4.3. Study Limitations
This study includes limitations that must be considered. First, the study’s cross-sectional nature prevents the establishment of a causal relationship for the outcomes obtained. Second, although the analysis was adjusted for different covariates that have been reported to be important for fruit intake [41], we could not adjust for total energy intake or physical activity, covariates that are significant in analyses of food consumption [27], as the information coming from ENDES does not assess caloric intake. Third, intake was assessed via self-report and was not carried out with the most frequently used methodology, that is, with the use of the Food Frequency Questionnaire (FFQ), which may limit the possibility for comparisons between studies. However, the intake assessment was carried out with open questions about the frequency per week and portions per day of consumption of fruits, fruit salads, and fruit juices. This was based on the proposal of the World Health Organization, the STEPwise approach to noncommunicable disease risk-factor surveillance [42].
Some strengths deserve to be highlighted. The study was developed with data from a national survey and with a sample size that guarantees representativeness and gives statistical power to the reported findings. Additionally, the analysis that was carried out differentiated the consumption of fruits in their whole form, in salad presentation, and in the form of juices. This is a relevant aspect since the forms of presentation of fruit consumption analyzed in the study are part of the food culture of Peru; the study allows for the differentiation of the possible effects of each of these culinary presentations.
## 5. Conclusions
Fruit intake per serving is negatively related to general body adiposity and central fat distribution, while fruit salad intake is negatively related to central distribution adiposity. However, fruit consumption of fruit in the form of juices is positively associated with a significant increase in BMI and WC. Due to the potential displacement mechanisms of more energy-dense foods and the increased satiety generated by fruits, it is recommended that future studies assess both energy intake and expenditure. Finally, since a dose-response analysis for each intake level of fruit and obesity protection is still lacking, it is suggested that future studies be carried out in this field.
## References
1. Malik V.S., Willet W.C., Hu F.B.. **Nearly a decade on—Trends, risk factors and policy implications in global obesity**. *Nat. Rev. Endocrinol.* (2020.0) **16** 615-616. DOI: 10.1038/s41574-020-00411-y
2. **Healthy Diet [Internet]**
3. Wilunda C., Sawada N., Goto A., Yamaji T., Takachi R., Ishihara J., Mori N., Kotemori A., Iwasaki M., Tsugane S.. **Associations between changes in fruit and vegetable consumption and weight change in Japanese adults**. *Eur. J. Nutr.* (2021.0) **60** 217-227. DOI: 10.1007/s00394-020-02236-x
4. Nour M., Lutze S.A., Grech A., Allman-Farinelli M.. **The Relationship between Vegetable Intake and Weight Outcomes: A Systematic Review of Cohort Studies**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10111626
5. Boeing H., Bechthold A., Bub A., Ellinger S., Haller D., Kroke A., Leschik-Bonnet E., Müller M.J., Oberritter H., Schulze M.. **Critical review: Vegetables and fruit in the prevention of chronic diseases**. *Eur. J. Nutr.* (2012.0) **51** 637-663. DOI: 10.1007/s00394-012-0380-y
6. Wolniczak I., Cáceres-DelAguila J.A., Maguiña J.L., Bernabe-Ortiz A.. **Fruits and vegetables consumption and depressive symptoms: A population-based study in Peru**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0186379
7. Aune D., Giovannucci E., Boffetta P., Fadnes L.T., Keum N.N., Norat T., Greenwood D.C., Riboli E., Vatten L.J., Tonstad S.. **Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality—A systematic review and dose-response meta-analysis of prospective studies**. *Int. J. Epidemiol.* (2017.0) **46** 1029-1056. DOI: 10.1093/ije/dyw319
8. Wu Y., Zhang D., Jiang X., Jiang W.. **Fruit and vegetable consumption and risk of type 2 diabetes mellitus: A dose-response meta-analysis of prospective cohort studies**. *Nutr. Metab. Cardiovasc. Dis.* (2015.0) **25** 140-147. DOI: 10.1016/j.numecd.2014.10.004
9. Kovalskys I., Rigotti A., Koletzko B., Fisberg M., Gómez G., Herrera-Cuenca M., Sanabria L.Y.C., García M.C.Y., Pareja R.G., Zimberg I.Z.. **Latin American consumption of major food groups: Results from the ELANS study**. *PLoS ONE* (2019.0) **14**. DOI: 10.1371/journal.pone.0225101
10. Bermudez O.I., Tucker K.L.. **Trends in dietary patterns of Latin American populations**. *Cad. Saúde Pública* (2003.0) **19** S87-S99. DOI: 10.1590/S0102-311X2003000700010
11. Kaiser K.A., Brown A.W., Brown M.M.B., Shikany J.M., Mattes R.D., Allison D.B.. **Increased fruit and vegetable intake has no discernible effect on weight loss: A systematic review and meta-analysis**. *Am. J. Clin. Nutr.* (2014.0) **100** 567-576. DOI: 10.3945/ajcn.114.090548
12. Mozaffarian D., Hao T., Rimm E.B., Willett W.C., Hu F.B.. **Changes in Diet and Lifestyle and Long-Term Weight Gain in Women and Men**. *N. Engl. J. Med.* (2011.0) **364** 2392-2404. DOI: 10.1056/NEJMoa1014296
13. Schwingshackl L., Hoffmann G., Kalle-Uhlmann T., Arregui M., Buijsse B., Boeing H.. **Fruit and Vegetable Consumption and Changes in Anthropometric Variables in Adult Populations: A Systematic Review and Meta-Analysis of Prospective Cohort Studies**. *PLoS ONE* (2015.0) **10**. DOI: 10.1371/journal.pone.0140846
14. Ledoux T.A., Hingle M.D., Baranowski T.. **Relationship of fruit and vegetable intake with adiposity: A systematic review**. *Obes. Rev.* (2011.0) **12** e143-e150. DOI: 10.1111/j.1467-789X.2010.00786.x
15. Apovian C.M.. **Obesity: Definition, comorbidities, causes, and burden**. *Am. J. Manag. Care* (2016.0) **22** s176-s185. PMID: 27356115
16. Wong M.C.S., Huang J., Wang J., Chan P.S.F., Lok V., Chen X., Leung C., Wang H.H.X., Lao X.Q., Zheng Z.-J.. **Global, regional and time-trend prevalence of central obesity: A systematic review and meta-analysis of 13.2 million subjects**. *Eur. J. Epidemiol.* (2020.0) **35** 673-683. DOI: 10.1007/s10654-020-00650-3
17. Wang Y., Beydoun M.A., Min J., Xue H., Kaminsky L.A., Cheskin L.J.. **Has the prevalence of overweight, obesity and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic**. *Int. J. Epidemiol.* (2020.0) **49** 810-823. DOI: 10.1093/ije/dyz273
18. Arribas-Harten C., Battistini-Urteaga T., Rodriguez-Teves M.G., Bernabe-Ortiz A.. **Asociación entre obesidad y consumo de frutas y verduras: Un estudio de base poblacional en Perú**. *Rev. Chil. Nutr.* (2015.0) **42** 241-247. DOI: 10.4067/S0717-75182015000300003
19. Wallace T.C., Bailey R.L., Blumberg J.B., Burton-Freeman B., Chen C.-Y.O., Crowe-White K.M., Drewnowski A., Hooshmand S., Johnson E., Lewis R.. **Fruits, vegetables, and health: A comprehensive narrative, umbrella review of the science and recommendations for enhanced public policy to improve intake**. *Crit. Rev. Food Sci. Nutr.* (2020.0) **60** 2174-2211. DOI: 10.1080/10408398.2019.1632258
20. Wang X., Ouyang Y., Liu J., Zhu M., Zhao G., Bao W., Hu F.B.. **Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: Systematic review and dose-response meta-analysis of prospective cohort studies**. *BMJ* (2014.0) **349** g4490. DOI: 10.1136/bmj.g4490
21. von Elm E., Altman D.G., Egger M., Pocock S.J., Gotzsche P.C., Vandenbroucke J.P.. **Declaración de la Iniciativa STROBE (Strengthening the Reporting of Observational studies in Epidemiology): Directrices para la comunicación de estudios observacionales. The Strengthening the Reporting of Observational Studies in Epidemiology [STROBE] statement: Guidelines for reporting observational studies**. *Gac. Sanit.* (2008.0) **22** 144-150. PMID: 18420014
22. 22.
Instituto Nacional de Estadística e Informática
Manual de la Antropometrista. Encuesta Demográfica y de Salud FamiliarInstituto Nacional de Estadística e InformáticaLima, Peru2019. *Manual de la Antropometrista. Encuesta Demográfica y de Salud Familiar* (2019.0)
23. 23.
PRISMA
Medidas Caseras LaminarioPRISMALima, Peru1997108p. *Medidas Caseras Laminario* (1997.0)
24. 24.
Instituto Nacional de Estadística e Informática
Manual de la Entrevistadora. Encuesta Demográfica y de Salud FamiliarDirección Nacional de Censos y EncuestasLima, Peru2021. *Manual de la Entrevistadora. Encuesta Demográfica y de Salud Familiar* (2021.0)
25. Yu Z.M., DeClercq V., Cui Y., Forbes C., Grandy S., Keats M., Parker L., Sweeney E., Dummer T.J.B.. **Fruit and vegetable intake and body adiposity among populations in Eastern Canada: The Atlantic Partnership for Tomorrow’s Health Study**. *BMJ Open* (2018.0) **8** e018060. DOI: 10.1136/bmjopen-2017-018060
26. Becerra-Tomás N., Paz-Graniel I., Tresserra-Rimbau A., Martínez-González M., Barrubés L., Corella D., Muñoz-Martínez J., Romaguera D., Vioque J., Alonso-Gómez M.. **Fruit consumption and cardiometabolic risk in the PREDIMED-plus study: A cross-sectional analysis**. *Nutr. Metab. Cardiovasc. Dis.* (2021.0) **31** 1702-1713. DOI: 10.1016/j.numecd.2021.02.007
27. Drapeau V., Harvey A.-A., Jacob R., Provencher V., Panahi S.. **The impact of a family web-based nutrition intervention to increase fruit, vegetable, and dairy intakes: A single-blinded randomized family clustered intervention**. *Nutr. J.* (2022.0) **21** 75. DOI: 10.1186/s12937-022-00825-6
28. Rautiainen S., Wang L., Lee I.-M., Manson J.E., Buring J.E., Sesso H.D.. **Higher Intake of Fruit, but Not Vegetables or Fiber, at Baseline Is Associated with Lower Risk of Becoming Overweight or Obese in Middle-Aged and Older Women of Normal BMI at Baseline**. *J. Nutr.* (2015.0) **145** 960-968. DOI: 10.3945/jn.114.199158
29. Romaguera D., Ängquist L., Du H., Jakobsen M.U., Forouhi N., Halkjær J., Feskens E., Van Der A.D.L., Masala G., Steffen A.. **Food Composition of the Diet in Relation to Changes in Waist Circumference Adjusted for Body Mass Index**. *PLoS ONE* (2011.0) **6**. DOI: 10.1371/journal.pone.0023384
30. De Koning L., Merchant A.T., Pogue J., Anand S.S.. **Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: Meta-regression analysis of prospective studies**. *Eur. Heart J.* (2007.0) **28** 850-856. DOI: 10.1093/eurheartj/ehm026
31. Saghafian F., Malmir H., Saneei P., Milajerdi A., Larijani B., Esmaillzadeh A.. **Fruit and vegetable consumption and risk of depression: Accumulative evidence from an updated systematic review and meta-analysis of epidemiological studies**. *Br. J. Nutr.* (2018.0) **119** 1087-1101. DOI: 10.1017/S0007114518000697
32. Akhtar-Danesh N., Dehghan M.. **Association between fruit juice consumption and self-reported body mass index among adult Canadians**. *J. Hum. Nutr. Diet* (2010.0) **23** 162-168. DOI: 10.1111/j.1365-277X.2009.01029.x
33. Pereira M.A., Fulgoni V.L.. **Consumption of 100% Fruit Juice and Risk of Obesity and Metabolic Syndrome: Findings from the National Health and Nutrition Examination Survey 1999–2004**. *J. Am. Coll. Nutr.* (2010.0) **29** 625-629. DOI: 10.1080/07315724.2010.10719901
34. Xi B., Li S., Liu Z., Tian H., Yin X., Huai P., Tang W., Zhou D., Steffen L.M.. **Intake of Fruit Juice and Incidence of Type 2 Diabetes: A Systematic Review and Meta-Analysis**. *PLoS ONE* (2014.0) **9**. DOI: 10.1371/journal.pone.0093471
35. Dreher M.L.. **Whole Fruits and Fruit Fiber Emerging Health Effects**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10121833
36. Bueno D.R., Marucci M.D.F.N., Gouveia L.A., Duarte Y.A.D.O., Lebrão M.L.. **Abdominal obesity and healthcare costs related to hypertension and diabetes in older adults**. *Rev. Nutr.* (2017.0) **30** 209-218. DOI: 10.1590/1678-98652017000200006
37. Li Q., Cai L., Cui W., Wang G., He J., Golden A.. **Economic burden of obesity and four obesity-related chronic diseases in rural Yunnan Province, China**. *Public Health* (2018.0) **164** 91-98. DOI: 10.1016/j.puhe.2018.07.024
38. Cawley J., Meyerhoefer C., Biener A., Hammer M., Wintfeld N.. **Savings in Medical Expenditures Associated with Reductions in Body Mass Index Among US Adults with Obesity, by Diabetes Status**. *Pharmacoeconomics* (2015.0) **33** 707-722. DOI: 10.1007/s40273-014-0230-2
39. Fallah-Fini S., Adam A., Cheskin L.J., Bartsch S.M., Lee B.Y.. **The Additional Costs and Health Effects of a Patient Having Overweight or Obesity: A Computational Model**. *Obesity* (2017.0) **25** 1809-1815. DOI: 10.1002/oby.21965
40. Graziose M.M., Koch P.A., Wang Y.C., Gray H.L., Contento I.R.. **Cost-effectiveness of a Nutrition Education Curriculum Intervention in Elementary Schools**. *J. Nutr. Educ. Behav.* (2017.0) **49** 684-691. DOI: 10.1016/j.jneb.2016.10.006
41. Heo M., Kim R.S., Wylie-Rosett J., Allison D., Heymsfield S.B., Faith M.S.. **Inverse association between fruit and vegetable intake and BMI even after controlling for demographic, socioeconomic and lifestyle factors**. *Obes. Facts* (2011.0) **4** 449-455. DOI: 10.1159/000335279
42. Riley L., Guthold R., Cowan M., Savin S., Bhatti L., Armstrong T., Bonita R.. **The World Health Organization STEPwise Approach to Noncommunicable Disease Risk-Factor Surveillance: Methods, Challenges, and Opportunities**. *Am. J. Public Health* (2016.0) **106** 74-78. DOI: 10.2105/AJPH.2015.302962
|
---
title: 'Association between Systemic Immunity-Inflammation Index and Hyperlipidemia:
A Population-Based Study from the NHANES (2015–2020)'
authors:
- Nayili Mahemuti
- Xiyue Jing
- Naijian Zhang
- Chuanlang Liu
- Changping Li
- Zhuang Cui
- Yuanyuan Liu
- Jiageng Chen
journal: Nutrients
year: 2023
pmcid: PMC10004774
doi: 10.3390/nu15051177
license: CC BY 4.0
---
# Association between Systemic Immunity-Inflammation Index and Hyperlipidemia: A Population-Based Study from the NHANES (2015–2020)
## Abstract
The systemic immunity-inflammation index (SII) is a novel inflammatory marker, and aberrant blood lipid levels are linked to inflammation. This study aimed to look at the probable link between SII and hyperlipidemia. The current cross-sectional investigation was carried out among people with complete SII and hyperlipidemia data from the 2015–2020 National Health and Nutrition Examination Survey (NHANES). SII was computed by dividing the platelet count × the neutrophil count/the lymphocyte count. The National Cholesterol Education Program standards were used to define hyperlipidemia. The nonlinear association between SII and hyperlipidemia was described using fitted smoothing curves and threshold effect analyses. A total of 6117 US adults were included in our study. A substantial positive correlation between SII and hyperlipidemia was found [1.03 (1.01, 1.05)] in a multivariate linear regression analysis. Age, sex, body mass index, smoking status, hypertension, and diabetes were not significantly correlated with this positive connection, according to subgroup analysis and interaction testing (p for interaction > 0.05). Additionally, we discovered a non-linear association between SII and hyperlipidemia with an inflection point of 479.15 using a two-segment linear regression model. Our findings suggest a significant association between SII levels and hyperlipidemia. More large-scale prospective studies are needed to investigate the role of SII in hyperlipidemia.
## 1. Introduction
Hyperlipidemia is a systemic metabolic illness defined by unusually high amounts of lipids in the blood, including cholesterol and triglycerides. Hyperlipidemia has been connected to a variety of health issues, including the combination of diabetes, obesity, and hypertension, known as metabolic syndrome, which poses major hazards to human health [1]. The consequences of hyperlipidemia on the vascular system are well established [2]. In populations in the United States, Europe, and emerging nations, hyperlipidemia is a key modifiable risk factor for developing atherosclerotic cardiovascular disease [3]. In the United States, 28 million people had total cholesterol levels higher than 240 mg/dL [4]. In addition, hyperlipidemia significantly increases the risk of cardiovascular and immune diseases and is a significant cause of stroke and death [5].
Systemic inflammation can be quantified using a variety of biochemical or hematological indicators that are regularly determined in normal blood tests or as ratios generated from these measures [6]. The systemic immunity-inflammation index (SII) is a stable new inflammatory biomarker computed from platelet count × neutrophil count/lymphocyte count [7,8]. SII could assess local systemic inflammation and the immunological response across the body [9,10]. SII is now employed as a prognostic factor in cancer investigations. The interaction between systemic inflammation and the local immune response has been identified as the seventh cancer hallmark, and it has been shown to be involved in the initiation, development, and progression of various forms of cancer [11,12]. Cervical cancer [13], esophageal cancer [14], and hepatocellular carcinoma [15] are examples. In addition to tumors, Ya et al. reported that SII has predictive value for coronary artery disease (CAD) [16].
Some studies have clarified that inflammation is associated with blood lipid levels. Ma et al. reported that higher plasma C-reaction protein (CRP) levels and higher urinary copper levels were associated with higher serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and lower high density lipoprotein cholesterol (HDL-C) concentrations. According to mediation analysis, CRP played a $6.27\%$ role in the association between urinary copper and TG. These findings imply that systemic inflammation plays a role in the association between copper exposure and abnormal lipids, which may contribute to the development of dyslipidemia [17]. Natalia et al. reported that postprandial hyperlipidemia (PPHL) is more common in rheumatoid arthritis (RA) patients than in healthy controls. In individuals with rheumatoid arthritis (RA), postprandial hyperlipidemia (PPHL) is linked to inflammation and subclinical atherosclerosis [18]. Melody et al. reported that blood lipid levels appear to have a pleiotropic connection with C-reactive protein (CRP) [19]. In addition, Kenneth et al. reported that inflammation could alter a variety of lipid metabolisms [20]. However, the association between the inflammatory level biomarker Systemic Immunity-Inflammation Index (SII) and hyperlipidemia is not well characterized.
Therefore, we conducted a population-based cross-sectional study to investigate the relationship between systemic immunity-inflammation indices (SII) and hyperlipidemia in National Health and Nutrition Examination Survey (NHANES) adult participants.
## 2.1. Study Population
The NHANES is an ongoing survey of the US national population that employs a complex, multistage, and probabilistic sampling technique to provide a plethora of information on the nutrition and health of the US population. More information is available at http://www.cdc.gov/nchs/nhanes/index.htm (accessed on 8 February 2023) detailing the NHANES survey’s continuous design. All study procedures were authorized by the National Center for Health Statistics’ ethical review board prior to data collection, and all participants gave their signed, informed consent.
In the investigation, we removed from the 25,531 eligible people 5264 participants with missing SII data, 12,969 participants with missing hyperlipidemia data, and 1181 participants younger than 20 years of age. The research included a total of 6117 individuals. Figure 1 depicts the sample selection.
## 2.2. Assessment of Hyperlipidemia
Adult Treatment Panel III (ATP 3) of the National Cholesterol Education Program (NCEP) classified hyperlipidemia as total cholesterol 200 mg/dL, triglycerides 150 mg/dL, HDL 40 mg/dL in males and 50 mg/dL in females, or low-density lipoprotein 130 mg/dL [21]. Alternately, persons who reported using cholesterol-lowering drugs were also classified as having hyperlipidemia.
## 2.3. SII and Covariates
The systemic immunity-inflammation index is the dependent variable in this investigation. SII was intended as an exposure variable in our research. Using automated hematology analysis equipment (a CoulterDxH 800 analyzer), the lymphocyte, neutrophil, and platelet counts were measured and reported as 103 cells/mL. The SII level was determined by multiplying the platelet count by the neutrophil count/lymphocyte count [7,22,23]. Based on prior studies, possible confounding factors linked with SII and hyperlipidemia were included in the final analysis [24]. Covariates included age, race, sex, education level, income-to-poverty ratio, marital status, drinking status, smoking status, BMI, hypertension, and diabetes. Among them, race was categorized as Mexican American, Non-Hispanic White, Non-Hispanic Black, other Hispanic, other race. The levels of education were designated as less than high school, high school, and more than high school. On a scale from 1.5 to >3.5, the income to poverty ratio was divided into three categories: 1.5, 1.5–3.5, and >3.5 [25]. Three categories of marital status were identified: married/living with a partner, widowed/divorced/separated, never married. Drinking status was categorized as excessive alcohol consumption, moderate alcohol consumption, or light alcohol consumption. Three drinks per day for women and four drinks per day for men were considered as excessive alcohol consumption. The definition of moderate alcohol consumption was two drinks per day for women and three drinks per day for men. Other alcohol consumption was deemed light [26]. Smoking status was categorized as either now smoking, formerly smoking, or never. Never smokers were defined as having smoked no more than 100 cigarettes in their lives, ex-smokers as having smoked more than 100 cigarettes but no longer smoking, and current smokers as having smoked more than 100 cigarettes but sometimes or consistently. <25 kg/m2, 25 to 30 kg/m2, >30 kg/m2 BMI categories were established [27]. Average blood pressure >140 mmHg systolic and/or 90 mmHg diastolic, as reported by a physician diagnosed with hypertension or using hypertensive medication, was used to characterize hypertension. Diabetes was defined as the reporting of a diabetic diagnosis and the use of diabetes medicine or insulin.
## 2.4. Statistical Analysis
SII was divided into quartiles from lowest (Q1) to highest (Q4); continuous variables were expressed as means with standard deviations (SDs) and categorical variables as proportions; the differences between participants grouped by SII quartiles and the differences between participants with or without hyperlipidemia were assessed using a weighted t-test (continuous variables) or a weighted chi-square test (categorical variables). To examine the association between SII and hyperlipidemia, multivariate logistic regression analysis between SII and hyperlipidemia was used to construct multivariate tests, using three models with no covariates in model 1; model 2 was adjusted for age, sex, and race; model 3 was adjusted for age, sex, race, marital status, income to poverty ratio, education level, drinking status, smoking status, BMI, hypertension, and diabetes; and SII and hyperlipidemia were evaluated using odds ratios (OR) and $95\%$ confidence interval (CI) in the models. Using three models, multivariate tests were constructed by controlling for variables and fitting a smooth curve. Using a threshold effects analysis model, the association and inflection points between SII and hyperlipidemia were investigated. Finally, the same statistical analysis procedures outlined before were used for the subgroup based on sex. The statistical analyses were conducted using R studio (Version 4.2.2) and EmpowerStats (version 2.0). A p-value < 0.05 was determined to be significant. We used a weighting strategy to lessen the substantial volatility of our dataset.
## 3.1. Baseline Characteristics of Participants
There were 6117 participants enrolled, of whom $48.08\%$ were male, with an average age of 50.70 ± 17.43 years. The mean SII ± SD concentrations were 459.54 ± 317.28. There were $69.72\%$ of participants have hyperlipidemia.
The clinical characteristics of the participants according to hyperlipidemia as a column-stratified variable are shown in Table 1. The presence or absence of hyperlipidemia was statistically significant with age, sex, race, education level, marital status, BMI, drinking status, smoking status, hypertension, diabetes, and SII ($p \leq 0.05$). Compared with non-hyperlipidemia, patients with hyperlipidemia tended to be older, female, non-Hispanic white, possess more high school education, married/living with partner, 0 < BMI < 25 kg/m2, light alcohol consumers, never smokers, and without diabetes or hypertension, as well as having higher levels of SII.
The clinical characteristics of the participants according to the quartiles of SII are shown in Table 2. There was statistically significant difference among the SII quartiles in terms of age, sex, race, marital status, BMI, drinking status, smoking status, hypertension, diabetes, and hyperlipidemia ($p \leq 0.05$). Participants who fell into the Quartile 4 group tended to be older, female, non-Hispanic white, married/living with a partner, a BMI > 30 kg/m2, light alcohol consumption, never smokers, with no diabetes, hypertension, and with hyperlipidemia.
## 3.2. Association between SII and Hyperlipidemia
Because the effect value is not apparent, SII/100 is used to amplify the effect value by 100 times. Table 3 showed the results of the multivariable regression analysis between SII/100 and hyperlipidemia. This association was significant both in model 1 (1.04 (1.02, 1.06)) and model 2 (1.03 (1.01, 1.05)). However, in model 3, the positive association between SII and hyperlipidemia became insignificant (1.02 (1.00, 1.04)). Sensitivity analysis was performed with SII quartiles, and the ORs for Q1, Q2, Q3, and Q4 in model 2 were 1.00, 1.05 (0.90, 1.23), 1.31 (1.12, 1.54), and 1.27 (1.08, 1.50), respectively, compared to Quartile 1, participants in Quartile 4 had an association with $27.04\%$ increased risk of hyperlipidemia (p for trend < 0.05).
Further subgroup analysis revealed that the association of SII with hyperlipidemia was not consistent, as shown in Figure 2. SII was shown to correlate significantly with hyperlipidemia in subgroups stratified by sex, BMI, and diabetes ($p \leq 0.05$). Interaction tests revealed that the relationship between SII and hyperlipidemia was not statistically different across strata, showing that age, sex, BMI, smoking status, hypertension, and diabetes did not significantly impact this positive correlation (p for interaction> 0.05).
The nonlinear association between SII and hyperlipidemia was then described using smoothed curve fitting (Figure 3 and Figure 4). Adjusted variables: age, sex, race, education level, marital status, BMI, drinking status, smoking status, hypertension, and diabetes. We discovered a nonlinear relationship between SII and hyperlipidemia using a two-stage linear regression model with an inflection point of 479.15. Adjusted variables: age, race, education level, marital status, BMI, drinking status, smoking status, hypertension, and diabetes. In women, an inverted U-shaped curve with an inflection point of 958.14 was detected after stratified analysis by sex, as shown in Table 4.
## 4. Discussion
In our cross-sectional study, we discovered that higher SII was associated with a higher risk of hyperlipidemia. The results of the subgroup analyses and interaction testing indicated that this connection was similar across populations. An inverted U-shape relationship between SII and hyperlipidemia was also discovered, with an inflection point of 479.15. The data mentioned above imply that when SII is below 479.15, SII is an independent risk factor for hyperlipidemia.
To our knowledge, this is the first investigation on the relationship between SII and hyperlipidemia. The relationship between SII levels and blood lipids has been observed in previous epidemiological studies. For example, According to Zhu et al., the observed connection between ethylene oxide(EO) exposure and serum lipid profiles is mediated by systemic inflammation. Inflammatory indicators substantially mediated the links between hemoglobin adducts of HbEO and HDL-C and TG at the highest mediated proportions of $21.40\%$ and $33.40\%$, respectively [28]. A study from rural northeast China shows that subjects with high LDL-C levels had higher levels of inflammatory markers overall. SII was also considerably higher in patients with low HDL-C [29]. Wei et al. discovered that lipid profiles were linked with neutrophils, lymphocytes, monocytes, and platelets, revealing a possible association between SII and abnormal blood lipid levels [30]. A cross-sectional study of 2631 participants in the East Coast city of Fujian Province showed that in male adults, five types of dyslipidemia increased circulation levels of IL-6, TNF-, and MCP-1 in male adults compared to the standard lipid group, and that dyslipidemia was associated with an altered inflammatory state [31]. According to several research studies, patients with inflammatory disorders have been shown to have aberrant blood lipid levels. Moreover, it has been discovered that individuals with Sjögren’s syndrome, inflammatory bowel disease, and ankylosing spondylitis have decreased HDL-C levels [32,33,34,35]. LDL-cholesterol (LDL-C) and triglyceride levels varied, although LDL-C levels tended to be lower and triglyceride levels tended to be higher. A case-control study from China discovered that very low LDL-cholesterol (VLDL-C), triglycerides (TG), the VLDL/LDL cholesterol ratio, the total/HDL cholesterol ratio, and the LDL/HDL cholesterol ratio were higher in polymyositis (PM) patients than in healthy individuals, indicating that dyslipidemia is a common feature in PM patients, characterized by high-density lipoprotein cholesterol (HDL-C) and elevated triglycerides (TG). The inflammatory condition of PM may be responsible for HDL-C metabolism [36]. Our study identified a positive linear correlation between SII levels and hyperlipidemia in models 1 and 2. An inverted U-shape association between SII levels and hyperlipidemia was also discovered, with a breakpoint of 479.15. There was a positive link on the left side of the breakpoint measurement. Still, no relationship was identified on the right side, indicating a substantial threshold impact of SII and hyperlipidemia. In summary, there have been several reports of an association between inflammation and blood lipid levels. Our findings confirm prior research suggesting high SII levels have an association with increasing the risk of hyperlipidemia.
The probable mechanisms behind this positive relationship between inflammation and abnormal blood lipid levels are not well elucidated. Wen et al. reported that STING signaling is important in mediating lipotoxicity-induced endothelial inflammation and injury, that IRE1-XBP1 signaling enhances STING signaling, that hyperlipidemia induces a pro-inflammatory response in retinal endothelial cells by activating expression of the STING pathway and signaling activation of IRE1-XBP1, and that other studies have confirmed STING’s pro-inflammatory function. Liu et al. described infantile-onset STING-associated vasculopathy caused by a systemic gain-of-function mutation in the TMEM173 gene and in patients characterized by systemic inflammation [37,38]. In addition, it has been reported that high-density lipoproteins play an important role in inflammation. Methionine sulfoxidation of apoA-I leads HDL to become pro-inflammatory via inducing pro-inflammatory cytokine production (TNF and IL-6) in mouse bone marrow-derived macrophages and mouse monocytes [39,40]. Many laboratory studies in a variety of human illness situations support the notion that statin therapy stimulates the synthesis of resolvins (SPMs), which can reduce and resolve inflammation [41,42]. SPMs work on PMNs and macrophages separately to drive resolution, making them multitarget agonists. Resolvins and all SPMs have stereochemically selective actions, which are supported by their capacity to activate receptors (G-protein-coupled receptors (GPCR)) that enhance and transmit their tissue response. This is a reflection of their production processes [43]. The ability of SPM to limit leukocyte infiltration and counter-regulate the production of pro-inflammatory mediators is one of its main biological functions [44]. The biological function of SPM in many chronic inflammatory diseases has also been demonstrated such as, periodontitis, a prevalent persistent inflammatory disorder that causes extensive periodontal damage. SPMs have been shown in several periodontal disease studies to play a significant role in controlling periodontal inflammation by restricting leukocyte trafficking to periodontal locations and decreasing the generation of pro-inflammatory mediators [45,46]. In recent studies, it has been discovered that the levels of lipoxin A4(LXA4), protectin D1(PD1), and maresin 1(MaR1) in the salivary tissues of people with periodontal inflammation are related to the progression of the illness. Interestingly, PD1 and MaR1 levels were shown to be positively connected, whereas LXA4 levels were found to be negatively associated with illness severity. Their results suggest that SPM biosynthesis pathways, or possibly their degradation routes, are controlled differently during illness, most likely as a host immune response to counteract ongoing inflammatory processes [47]. SPM biological processes have also been linked to allergy-related diseases such as allergic rhinitis and asthma. Recent studies have demonstrated that resolvin E3(RvE3) reduces the total amount of inflammatory cells and eosinophils recruited into the lungs of mice sensitized to and challenged with household dust mites. Moreover, this mediator reduced the amounts of IL-23 and IL-17 in lavage fluid and suppressed the expression of IL-23 and IL-17A mRNA in the lung and peribronchial lymph nodes. In a mouse model of allergic asthma, RvE1 decreased leukocyte recruitment into the lung and downregulated the production of pro-inflammatory cytokines in lavage fluids and macrophages [48]. Similar effects are described for metformin, which is used by diabetic patients. Metformin, a biguanide, is the most widely used diabetes medication. Metformin not only lowers chronic inflammation by improving metabolic parameters, but it also has direct anti-inflammatory action, according to recent research. A physiological dosage of metformin (100 M) was shown to inhibit Th17 inflammation in CD4 T cells from older individuals via an autophagy-dependent mechanism [49]. T cells from older individuals showed higher oxygen consumption rates (OCR), although metformin induced autophagy and reduced ROS in these cells. Previous studies in T cells from younger persons showed that autophagy suppression, driven by siRNA targeting the autophagy protein Atg3, recapitulated the respiratory and inflammatory characteristics of T cells from older individuals. Younger participants’ autophagy-deficient T cells produced no inflammatory cytokines, supporting the hypothesis that metformin lowers age-related inflammation by promoting autophagy [50].
Our investigation has several advantages. Firstly, our study’s reliability and representativeness were enhanced by a large sample size and suitable covariate correction. Sensitivity analysis reduces the possibility of false positives. However, this investigation also has limitations. Cross-sectional study designs do not allow us to identify causation, and high sample numbers of prospective studies are required to elucidate causality. Although we controlled for certain confounders, other confounding factors, such as a history of long-term use of medicines such as steroids, may still have an impact on the outcomes. Because these factors were not recorded in the NHANES, we were unable to use them in our analysis. However, the interaction between inflammation and illness is complex. Therefore, generalizing our findings may be improper.
## 5. Conclusions
Our findings suggest a significant association between SII levels and hyperlipidemia. However, the results could not establish a causal relationship, and further extensive prospective studies are needed.
## References
1. Su X., Peng H., Chen X., Wu X., Wang B.. **Hyperlipidemia and hypothyroidism**. *Clin. Chim. Acta* (2022) **527** 61-70. DOI: 10.1016/j.cca.2022.01.006
2. Chen Y.-Q., Zhao S.-P., Zhao Y.-H.. **Efficacy and tolerability of coenzyme A vs pantethine for the treatment of patients with hyperlipidemia: A randomized, double-blind, multicenter study**. *J. Clin. Lipidol.* (2015) **9** 692-697. DOI: 10.1016/j.jacl.2015.07.003
3. Yusuf S., Hawken S., Ôunpuu S., Dans T., Avezum A., Lanas F., McQueen M., Budaj A., Pais P., Varigos J.. **Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study**. *Lancet* (2004) **364** 937-952. DOI: 10.1016/S0140-6736(04)17018-9
4. Tsao C.W., Aday A.W., Almarzooq Z.I., Alonso A., Beaton A.Z., Bittencourt M.S., Boehme A.K., Buxton A.E., Carson A.P., Commodore-Mensah Y.. **Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association**. *Circulation* (2022) **145** e153-e639. DOI: 10.1161/cir.0000000000001052
5. Mozaffarian D., Benjamin E.J., Go A.S., Arnett D.K., Blaha M.J., Cushman M., de Ferranti S., Després J.-P., Fullerton H.J., Howard V.J.. **Heart disease and stroke statistics—2015 update: A report from the American Heart Association**. *Circulation* (2015) **131** e29-e322. DOI: 10.1161/CIR.0000000000000152
6. Sylman J.L., Mitrugno A., Atallah M., Tormoen G.W., Shatzel J.J., Yunga S.T., Wagner T., Leppert J.T., Mallick P., Mccarty O.J.T.. **The Predictive Value of Inflammation-Related Peripheral Blood Measurements in Cancer Staging and Prognosis**. *Front. Oncol.* (2018) **8** 78. DOI: 10.3389/fonc.2018.00078
7. Hu B., Yang X.-R., Xu Y., Sun Y.-F., Sun C., Guo W., Zhang X., Wang W.-M., Qiu S.-J., Zhou J.. **Systemic Immune-Inflammation Index Predicts Prognosis of Patients after Curative Resection for Hepatocellular Carcinoma**. *Clin. Cancer Res.* (2014) **20** 6212-6222. DOI: 10.1158/1078-0432.CCR-14-0442
8. Tong Y.-S., Tan J., Zhou X.-L., Song Y.-Q., Song Y.-J.. **Systemic immune-inflammation index predicting chemoradiation resistance and poor outcome in patients with stage III non-small cell lung cancer**. *J. Transl. Med.* (2017) **15** 221. DOI: 10.1186/s12967-017-1326-1
9. Zhang F., Niu M., Wang L., Liu Y., Shi L., Cao J., Mi W., Ma Y., Liu J.. **Systemic-Immune-Inflammation Index as a Promising Biomarker for Predicting Perioperative Ischemic Stroke in Older Patients Who Underwent Non-cardiac Surgery**. *Front. Aging Neurosci.* (2022) **14** 865244. DOI: 10.3389/fnagi.2022.865244
10. Wang J., Zhou D., Dai Z., Li X.. **Association Between Systemic Immune-Inflammation Index and Diabetic Depression**. *Clin. Interv. Aging* (2021) **16** 97-105. DOI: 10.2147/CIA.S285000
11. Diakos C.I., Charles K.A., McMillan D.C., Clarke S.J.. **Cancer-related inflammation and treatment effectiveness**. *Lancet Oncol.* (2014) **15** e493-e503. DOI: 10.1016/S1470-2045(14)70263-3
12. Crusz S.M., Balkwill F.R.. **Inflammation and cancer: Advances and new agents**. *Nat. Rev. Clin. Oncol.* (2015) **12** 584-596. DOI: 10.1038/nrclinonc.2015.105
13. Huang H., Liu Q., Zhu L., Zhang Y., Lu X., Wu Y., Liu L.. **Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Patients with Cervical Cancer**. *Sci. Rep.* (2019) **9** 3284. DOI: 10.1038/s41598-019-39150-0
14. Geng Y., Shao Y., Zhu D., Zheng X., Zhou Q., Zhou W., Ni X., Wu C., Jiang J.. **Systemic Immune-Inflammation Index Predicts Prognosis of Patients with Esophageal Squamous Cell Carcinoma: A Propensity Score-matched Analysis**. *Sci. Rep.* (2016) **6** 39482. DOI: 10.1038/srep39482
15. Fu H., Zheng J., Cai J., Zeng K., Yao J., Chen L., Li H., Zhang J., Zhang Y., Zhao H.. **Systemic Immune-Inflammation Index (SII) is Useful to Predict Survival Outcomes in Patients After Liver Transplantation for Hepatocellular Carcinoma within Hangzhou Criteria**. *Cell. Physiol. Biochem.* (2018) **47** 293-301. DOI: 10.1159/000489807
16. Yang Y.-L., Wu C.-H., Hsu P.-F., Chen S.-C., Huang S.-S., Chan W.L., Lin S.-J., Chou C.-Y., Chen J.-W., Pan J.-P.. **Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease**. *Eur. J. Clin. Investig.* (2020) **50** e13230. DOI: 10.1111/eci.13230
17. Ma J., Xie Y., Zhou Y., Wang D., Cao L., Zhou M., Wang X., Wang B., Chen W.. **Urinary copper, systemic inflammation, and blood lipid profiles: Wuhan-Zhuhai cohort study**. *Environ. Pollut.* (2020) **267** 115647. DOI: 10.1016/j.envpol.2020.115647
18. Mena-Vázquez N., Redondo-Rodríguez R., Rioja J., Jimenez-Nuñez F.G., Manrique-Arija S., Lisbona-Montañez J.M., Cano-García L., Rojas-Gimenez M., Ureña I., Valdivielso P.. **Postprandial Hyperlipidemia: Association with Inflammation and Subclinical Atherosclerosis in Patients with Rheumatoid Arthritis**. *Biomedicines* (2022) **10**. DOI: 10.3390/biomedicines10010133
19. Kasher M., Cherny S.S., Livshits G.. **Exploring potential shared genetic influences between rheumatoid arthritis and blood lipid levels**. *Atherosclerosis* (2022) **363** 48-56. DOI: 10.1016/j.atherosclerosis.2022.11.006
20. Feingold K.R., Anawalt B., Blackman M.R., Boyce A., Chrousos G., Corpas E., de Herder W.W., Dhatariya K., Hofland J., Dungan K.. *Endotext* (2000)
21. Grundy S.M.. **Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report**. *Circulation* (2002) **106** 3143-3421. DOI: 10.1161/circ.106.25.3143
22. Albany C.. **Systemic immune-inflammation index in germ-cell tumours: Search for a biological prognostic biomarker**. *Br. J. Cancer* (2018) **118** 761-762. DOI: 10.1038/bjc.2018.7
23. Xie R., Xiao M., Li L., Ma N., Liu M., Huang X., Liu Q., Zhang Y.. **Association between SII and hepatic steatosis and liver fibrosis: A population-based study**. *Front. Immunol.* (2022) **13** 925690. DOI: 10.3389/fimmu.2022.925690
24. Zhang Y., Liu W., Zhang W., Cheng R., Tan A., Shen S., Xiong Y., Zhao L., Lei X.. **Association between blood lead levels and hyperlipidemiais: Results from the NHANES (1999–2018)**. *Front. Public Health* (2022) **10** 981749. DOI: 10.3389/fpubh.2022.981749
25. Rahman H.H., Niemann D., Munson-McGee S.H.. **Association of albumin to creatinine ratio with urinary arsenic and metal exposure: Evidence from NHANES 2015–2016**. *Int. Urol. Nephrol.* (2021) **54** 1343-1353. DOI: 10.1007/s11255-021-03018-y
26. Phillips J.A.. **Dietary Guidelines for Americans, 2020–2025**. *Work. Health Saf.* (2021) **69** 395. DOI: 10.1177/21650799211026980
27. Seidell J.C., Deurenberg P., Hautvast J.A.. **Obesity and Fat Distribution in Relation to Health—Current Insights and Recommendations**. *World Rev. Nutr. Diet.* (1987) **50** 57-91. DOI: 10.1159/000414170
28. Zhu X., Kong X., Chen M., Shi S., Cheang I., Zhu Q., Lu X., Yue X., Tang Y., Liao S.. **Blood ethylene oxide, systemic inflammation, and serum lipid profiles: Results from NHANES 2013–2016**. *Chemosphere* (2022) **299** 134336. DOI: 10.1016/j.chemosphere.2022.134336
29. Wang P., Guo X., Zhou Y., Li Z., Yu S., Sun Y., Hua Y.. **Monocyte-to-high-density lipoprotein ratio and systemic inflammation response index are associated with the risk of metabolic disorders and cardiovascular diseases in general rural population**. *Front. Endocrinol.* (2022) **13** 944991. DOI: 10.3389/fendo.2022.944991
30. Wei Y., Wang T., Li G., Feng J., Deng L., Xu H., Yin L., Ma J., Chen D., Chen J.. **Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder**. *Front. Psychiatry* (2022) **13** 941728. DOI: 10.3389/fpsyt.2022.941728
31. Hong N., Lin Y., Ye Z., Yang C., Huang Y., Duan Q., Xie S.. **The relationship between dyslipidemia and inflammation among adults in east coast China: A cross-sectional study**. *Front. Immunol.* (2022) **13** 937201. DOI: 10.3389/fimmu.2022.937201
32. Piquer B.R., Nazih H., Bourreille A., Segain J.P., Huvelin J.M., Galmiche J.-P., Bard J.-M.. **Altered lipid, apolipoprotein, and lipoprotein profiles in inflammatory bowel disease: Consequences on the cholesterol efflux capacity of serum using Fu5AH cell system**. *Metabolism* (2006) **55** 980-988. DOI: 10.1016/j.metabol.2006.03.006
33. Biyyani R.S.R.S., Putka B.S., Mullen K.D.. **Dyslipidemia and lipoprotein profiles in patients with inflammatory bowel disease**. *J. Clin. Lipidol.* (2010) **4** 478-482. DOI: 10.1016/j.jacl.2010.08.021
34. Koutroumpakis E., Ramos-Rivers C., Regueiro M., Hashash J.G., Barrie A., Swoger J., Baidoo L., Schwartz M., Dunn M.A., Koutroubakis I.E.. **Association Between Long-Term Lipid Profiles and Disease Severity in a Large Cohort of Patients with Inflammatory Bowel Disease**. *Dig. Dis. Sci.* (2015) **61** 865-871. DOI: 10.1007/s10620-015-3932-1
35. Papagoras C., Markatseli T.E., Saougou I., Alamanos Y., Zikou A.K., Voulgari P.V., Kiortsis D.N., Drosos A.A.. **Cardiovascular risk profile in patients with spondyloarthritis**. *Jt. Bone Spine* (2014) **81** 57-63. DOI: 10.1016/j.jbspin.2013.03.019
36. Wang H., Cai Y., Cai L., Hu Y., Chen X., Deng J.. **Altered Lipid Levels in Untreated Patients with Early Polymyositis**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0089827
37. Wen Z., He X., Wang J., Wang H., Li T., Wen S., Ren Z., Cai N., Yang J., Li M.. **Hyperlipidemia induces proinflammatory responses by activating STING pathway through IRE1α-XBP1 in retinal endothelial cells**. *J. Nutr. Biochem.* (2023) **112** 109213. DOI: 10.1016/j.jnutbio.2022.109213
38. Liu Y., Jesus A.A., Marrero B., Yang D., Ramsey S.E., Montealegre Sanchez G.A., Tenbrock K., Wittkowski H., Jones O.Y., Kuehn H.S.. **Activated STING in a Vascular and Pulmonary Syndrome**. *N. Engl. J. Med.* (2014) **371** 507-518. DOI: 10.1056/NEJMoa1312625
39. Witkowski A., Carta S., Lu R., Yokoyama S., Rubartelli A., Cavigiolio G.. **Oxidation of methionine residues in human apolipoprotein A-I generates a potent pro-inflammatory molecule**. *J. Biol. Chem.* (2019) **294** 3634-3646. DOI: 10.1074/jbc.RA118.005663
40. Nazir S., Jankowski V., Bender G., Zewinger S., Rye K.-A., van der Vorst E.P.. **Interaction between high-density lipoproteins and inflammation: Function matters more than concentration!**. *Adv. Drug Deliv. Rev.* (2020) **159** 94-119. DOI: 10.1016/j.addr.2020.10.006
41. Dalli J., Chiang N., Serhan C.N.. **Elucidation of novel 13-series resolvins that increase with atorvastatin and clear infections**. *Nat. Med.* (2015) **21** 1071-1075. DOI: 10.1038/nm.3911
42. Planagumà A., Pfeffer M.A., Rubin G., Croze R., Uddin M., Serhan C.N., Levy B.D.. **Lovastatin decreases acute mucosal inflammation via 15-epi-lipoxin A4**. *Mucosal Immunol.* (2010) **3** 270-279. DOI: 10.1038/mi.2009.141
43. Serhan C.N., Chiang N., Dalli J., Levy B.D.. **Lipid Mediators in the Resolution of Inflammation**. *Cold Spring Harb. Perspect. Biol.* (2014) **7** a016311. DOI: 10.1101/cshperspect.a016311
44. Serhan C.N., Levy B.D.. **Resolvins in inflammation: Emergence of the pro-resolving superfamily of mediators**. *J. Clin. Investig.* (2018) **128** 2657-2669. DOI: 10.1172/JCI97943
45. Elabdeen H.R.Z., Mustafa M., Szklenar M., Rühl R., Ali R., Bolstad A.I.. **Ratio of Pro-Resolving and Pro-Inflammatory Lipid Mediator Precursors as Potential Markers for Aggressive Periodontitis**. *PLoS ONE* (2013) **8**. DOI: 10.1371/journal.pone.0070838
46. Wang C.-W., Colas R.A., Dalli J., Arnardottir H.H., Nguyen D., Hasturk H., Chiang N., Van Dyke T.E., Serhan C.N.. **Maresin 1 Biosynthesis and Proresolving Anti-infective Functions with Human-Localized Aggressive Periodontitis Leukocytes**. *Infect. Immun.* (2016) **84** 658-665. DOI: 10.1128/IAI.01131-15
47. Tobón-Arroyave S.I., Isaza-Guzmán D.M., Gómez-Ortega J., Flórez-Alzate A.A.. **Salivary levels of specialized pro-resolving lipid mediators as indicators of periodontal health/disease status**. *J. Clin. Periodontol.* (2019) **46** 978-990. DOI: 10.1111/jcpe.13173
48. Flesher R.P., Herbert C., Kumar R.K.. **Resolvin E1 promotes resolution of inflammation in a mouse model of an acute exacerbation of allergic asthma**. *Clin. Sci.* (2014) **126** 805-818. DOI: 10.1042/CS20130623
49. Bharath L.P., Agrawal M., McCambridge G., Nicholas D.A., Hasturk H., Liu J., Jiang K., Liu R., Guo Z., Deeney J.. **Metformin Enhances Autophagy and Normalizes Mitochondrial Function to Alleviate Aging-Associated Inflammation**. *Cell Metab.* (2020) **32** 44-55.e6. DOI: 10.1016/j.cmet.2020.04.015
50. Bharath L.P., Nikolajczyk B.S.. **The intersection of metformin and inflammation**. *Am. J. Physiol. Physiol.* (2021) **320** C873-C879. DOI: 10.1152/ajpcell.00604.2020
|
---
title: Moving Away from 12:12; the Effect of Different Photoperiods on Biomass Yield
and Cannabinoids in Medicinal Cannabis
authors:
- Tyson James Peterswald
- Jos Cornelis Mieog
- Razlin Azman Halimi
- Nelson Joel Magner
- Amy Trebilco
- Tobias Kretzschmar
- Sarah Jane Purdy
journal: Plants
year: 2023
pmcid: PMC10004775
doi: 10.3390/plants12051061
license: CC BY 4.0
---
# Moving Away from 12:12; the Effect of Different Photoperiods on Biomass Yield and Cannabinoids in Medicinal Cannabis
## Abstract
The standard practice to initiate flowering in medicinal cannabis involves reducing the photoperiod from a long-day period to an equal duration cycle of 12 h light (12L)/12 h dark (12D). This method reflects the short-day flowering dependence of many cannabis varieties but may not be optimal for all. We sought to identify the effect of nine different flowering photoperiod treatments on the biomass yield and cannabinoid concentration of three medicinal cannabis varieties. The first, “Cannatonic”, was a high cannabidiol (CBD)-accumulating line, whereas the other two, “Northern Lights” and “Hindu Kush”, were high Δ9-tetrahydrocannabinol (THC) accumulators. The nine treatments tested, following 18 days under 18 h light/6 h dark following cloning and propagation included a standard 12L:12D period, a shortened period of 10L:14D, and a lengthened period of 14L:10D. The other six treatments started in one of the aforementioned and then 28 days later (mid-way through flowering) were switched to one of the other treatments, thus causing either an increase of 2 or 4 h, or a decrease of 2 or 4 h. Measured parameters included the timing of reproductive development; the dry weight flower yield; and the % dry weight of the main target cannabinoids, CBD and THC, from which the total g cannabinoid per plant was calculated. Flower biomass yields were highest for all lines when treatments started with 14L:10D; however, in the two THC lines, a static 14L:10D photoperiod caused a significant decline in THC concentration. Conversely, in Cannatonic, all treatments starting with 14L:10D led to a significant increase in the CBD concentration, which led to a 50–$100\%$ increase in total CBD yield. The results show that the assumption that a 12L:12D photoperiod is optimal for all lines is incorrect as, in some lines, yields can be greatly increased by a lengthened light period during flowering.
## 1. Introduction
Cannabis sativa is an economically and socially significant plant species with uses ranging from producing fibre for clothing; seed for animal and human nutrition; and psychoactive compounds for medicinal, religious, and recreational use.
Medicinal cannabis produces large quantities of cannabinoids and other diverse secondary metabolites in the glandular trichomes predominantly found on the female reproductive organs [1,2]. At least 113 cannabinoids and over 120 terpenes have been identified from the resin produced by the trichomes [3]. However, the two predominant target cannabinoids from medicinal cannabis remain: Δ9-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) [2]. When the acid forms of these secondary metabolites are decarboxylated to their neutral forms Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), respectively, they can interact with the mammalian endocannabinoid system for the treatment of non-communicable illnesses including sleep disorders, multiple sclerosis, appetite stimulation, and epilepsy [4,5,6].
Cannabinoid composition and abundance are largely under genetic control [7]. However, it is also recognised that environmental conditions and plant management strategies, such as growth substrate, water restriction, light spectra, and plant architecture training, may influence cannabinoid accumulation [8,9,10,11]. Thus, growers can optimise these factors to push for increased yields [12]. Photoperiod-sensitive crops, such as cannabis, align their development with the amount and timing of light that is available, and in controlled environments require specific lighting schedules to maintain or switch developmental stages. To maintain the vegetative state, growers typically use daily photoperiods of ≥16 h of light (≤8 h dark), and when flowering is to be initiated, the photoperiod is abruptly switched to 12 h of light and 12 h of darkness [12,13,14]. The 12 h light/12 h dark (12L:12D) rule reflects the fact that most cannabis genotypes are photoperiod sensitive and exhibit short-day flowering dependence. However, the native distribution for *Cannabis is* thought to extend from Siberia to China and now extends worldwide, including through much of the southern hemisphere [15,16]. In a phylogeographic study of an extensive collection of hemp germplasm in China, it was observed that the accessions fell into three distinct haplogroups that were differentiated by adaption to a high, middle, or low latitudinal gradient [15]. The authors studied the climatic factors that best correlated with the haplotype distribution pattern and found the strongest relationship was with mean daylength, accounting for ≈$60\%$ of the observed variation [15].
Differences in the critical photoperiod between accessions were reported in a comprehensive study that included 15 medicinal (“essential oil”) varieties [17]. It was reported that 14 genotypes had a critical photoperiod requirement of ≥14 h, with 3 genotypes initiating flowering in photoperiods ≥15 h [17]. The initiation of flowering in response to photoperiod is also recognised to vary with cultivar and environment in industrial hemp [17,18]. A U.S. company, The Hemp Mine, provides the photoperiod required to initiate flowering for five genotype which range from 13 h:55 m to 13 h:18 m [19]. Although cannabis is unequivocally considered a short-day flowering plant, the evidence from these studies show that flowering is frequently initiated in a photoperiod in excess of 12 h, i.e., a slightly longer day than night. The differences in hemp flowering time have been linked to their origin with cultivars adapted to more northern climates (in the northern hemisphere), showing reduced photoperiod sensitivity (and thus earlier flowering) than southern adapted cultivars grown under the same photoperiod [18]. These previous observations support the question as to whether the blanket rule of 12:12, applied by commercial glasshouse/indoor growers, is actually optimal for maximum yields for all cultivars.
The cannabinoid profile has also been reported to change in response to extended photoperiods. In an experiment on hemp where the plants were exposed to an extra two hours of light, a significant increase in Δ9–THC was observed, nearly doubling it from its control level, while cannabidivarin (CBDV) levels were reduced by $50\%$ [20]. The timing of floral initiation is also important for the medicinal grower as yield is expressed as g cannabinoid m−2 day−1 [21]; therefore, any additional days to maturity lowers yield over time. In a study into the flowering time of cannabis explants, it was observed that photoperiods in excess of 13.2 h extended the number of days to flowering, with the shortest period of 12 h light causing the most rapid flowering [14]. Under natural field conditions, photoperiods progressively change with the seasons, whereas in controlled facilities, the changes are generally abrupt. Whether a progressively changing photoperiod, compared to a single sudden change, affects yield attributes in a glasshouse/indoor system remains to be determined.
*The* genetic control of short-day (SD) flowering is less characterised than in long-day flowering plants (e.g., Arabidopsis), and most of the knowledge in SD plants comes from rice (a monocot). However, the main mechanism for photoperiodism in long-day (LD) plants appears to be conserved in SD plants. The principal flowering-control gene in SD rice is HD3a, which is homologous to the main control gene, FT “Florigen”, in LD plants [22]. In LD flowering, FT is activated by the CONSTANS (CO) protein, which then initiates flowering [23]. A SD homolog of CO, Hd1, has been identified in rice, and it regulates the expression of HD3a [24]. Both CO and Hd1 are regulated by the circadian clock and show a diurnal pattern of oscillation. The periodicity of the expression pattern does not change with daylength, which means that the peak of expression occurs at different times of the day depending upon the season [25]. When the expression of Hd1 occurs in the dark the protein is stabilised and triggers the expression of HD3a [25]. As different cannabis lines were cultivated in new latitudes, variation in response to photoperiod would have also been selected for, as plants that flowered to late, or too early, would have been poor performers. Despite this likelihood, to our knowledge, the effect on yield of changes to the flowering photoperiod away from a static 12L:12D, e.g., 14L:10D, or a progressive shortening or lengthening, remains to be studied for medicinal lines.
We sought to test the effect of either lengthened or shortened photoperiod away from 12L:12D on flowering time, biomass flower yield, and target cannabinoid concentrations in three medicinal cannabis varieties. Nine treatments were tested that utilised either a static lengthened or shortened period, or treatments that lengthened or shortened over the flowering periods in two stages.
## 2.1. Reproductive Development
All plants in all three treatments had initiated flowering, as measured by the presence of pistils, on or before DAC 46 when the transfer between treatments took place (Table 1). While the high CBDA accumulating line, Cannatonic, was the latest to start developing pistils (in all treatments), it was the quickest to develop floral trichomes, being the first to reach $100\%$ of plants with visible trichomes in all three treatments. The shortened photoperiod of 10L:14D tended to be the slowest treatment in which pistils and trichomes were observed. Conversely, the lengthened period of 14L:10D was the only treatment in which some plants of one line had produced trichomes at DAC 34 (Table 1). Overall, these results showed that the initiation of flowering was delayed by reducing the photoperiod to 10L compared to 12L and 14L photoperiods for all three lines.
## 2.2. Plant Height
Plant height was measured weekly, and all plants in the 10L and 12L treatments had started to plateau in height by DAC 40, whereas the 14L plants kept increasing in height until ≈DAC 46 and were taller than the other treatments (Figure 1). Only minimal increases in height were observed in plants transferred from shorter to longer daylengths.
## 2.3. Flower Biomass Yield
The flower biomasses (Flower DW g Plant−1) were significantly higher for the 14L treatment (Figure 2) than for the 12L control for all three lines. In Cannatonic and Northern Lights, 14L > 12L and 14L > 10L were also significantly higher than 12L. These were the only significant differences, but in Cannatonic and Hindu Kush, the three treatments that resulted in the shortest photoperiods, 10L, 10L > 12L and 12L > 10L, also trended to result in the lowest yields.
## 2.4. Cannabinoid Concentration
In Cannatonic, the CBD concentration ranged from 7 to $11\%$, depending on the treatment (Figure 3). The highest levels were observed in the treatments that started at 14L (i.e 14L, 14 < 12L, 14 < 10). In treatments that started at 10L, the % CBD was significantly lower compared to 12L, even if they ended with 14L (Figure 3). This indicated that the CBD concentration was determined in the first 28 days in this genotype and that an extended photoperiod increased production. In contrast to Cannatonic, the 14L treatment significantly reduced the % THC by around $\frac{2}{5}$th from the standard 12L treatment in Northern Lights ($12\%$ down to $7\%$) and in Hindu Kush ($10\%$ down to $6\%$) (Figure 3). No significant differences between any of the other treatments were found for Northern Lights and Hindu Kush (Figure 3). The treatments that started at 14L and then dropped to 12L or 10L produced the same %THC as the 12L treatments. In Northern Lights, treatments that had the same total units of light but applied at different times such as 12L < 10L and 10L > 12L, and 14L < 10L and 10L > 14L had the same concentration of THC. This demonstrated that the timing of the shorter photoperiod, whether it was earlier or later in flowering, did not affect cannabinoid concentration. Only if the extended 14L period was maintained throughout the experiment was cannabinoid concentration decreased. The 12L photoperiod was optimal for THC production in both Northern Lights and Hindu Kush (Figure 3).
The relationship between biomass yield and cannabinoid concentration for the three static treatments (10L, 12L, and 14L) is shown in Figure 4. However, the critical point at which flower biomass started to decline was not identified by this study as, in all lines, the DW yield increased between 12 and 14L. These data also suggested that the optimal photoperiod for yield in Cannatonic may be longer than 14L as both the biomass and cannabinoid concentration continued to increase up to 14L (Figure 4).
## 2.5. Physical Appearance of Flowers
Photographs of the flowers from each line from the three static treatments (10L, 12L, and 14L) are shown in Figure 5. Only the 14L treatment was notably different from the other two treatments. In the 14L treatment in Cannatonic, there was less/no visible anthocyanin accumulation and instead of a single inflorescence at the top of the stem, there were multiple inflorescences. In both Northern Lights and Hindu Kush, the 14L inflorescence (“sugar”) leaves were longer than in the other treatments, and in Hindu Kush, also wider. The trichome density was visibly less in Northern Lights and Hindu Kush in the 14L treatment, and pistil senescence appeared delayed (Figure 5). In the replication of this experiment, all nine treatments were photographed weekly, and the final photographs taken at DAC 74 are shown in Figures S1–S3. The same trends in the static treatments were observed in the replication. In Cannatonic, plants that had started in 14L and then were moved to shorter photoperiods had larger, clustered flowers, as seen in the 14L treatment in Figure 5, but the pistils were more senesced. In Northern Lights and Hindu Kush, the plants that started in 14L and then moved were visibly less senesced, but the trichome density appeared higher than the 14L treatment (Figure 5, Figures S2 and S3).
## 2.6. Total Yield
In Cannatonic, the combination of increased biomass and increased CBD concentrations resulted in a doubling of yield in the 14L treatment compared to the 12L control (Figure 6). Yields of CBD were also nearly doubled in 14L > 12L and 14L > 10L.
In Northern Lights, the yield of THC was increased in the 14L > 12L compared to 12L, because of the increase in biomass coupled with no change in the THC% (Figure 6). No significant difference was observed in Hindu Kush (Figure 6). Although there was no statistically significant negative effect of the 10L treatments on total cannabinoid yields, there was a trend for reduced yield in the 10L treatment in both Cannatonic and Hindu Kush. A pair of two-way t-tests (assuming unequal variances) to compare 10L and 12L in Cannatonic and Hindu Kush showed a significant difference (p ≤ 0.05) between the two photoperiods for both lines (Figure 6).
## 3. Discussion
The practice of initiating flowering through a reduction in photoperiod to 12L:12D is long-held standard methodology in cannabis production, as demonstrated by its reference in early cannabis growing guides [26], as well as modern publications since legalisation [12,13,27,28]. The fact that this blanket rule is assumed to be optimal for all varieties is quite remarkable considering the diverse latitudinal origins of cannabis [16] and known variation in photoperiod-dependent and -independent (a.k.a autoflower trait) flowering time control in cannabis.
Our results showed differences between three varieties in their response to different light treatments with some significant yield increases in response to 14L. Most significantly, the high-CBD line (Cannatonic) showed cannabinoid yield increased to more than double when an early 14L photoperiod was applied compared to the standard 12L. In contrast, one of the two high-THC lines tested (Northern Lights) only showed a $50\%$ yield increase in the 14L < 12L treatment, whereas the second high-THC line tested (Hindu Kush) did not show any significant yield effects.
All three varieties showed a positive response to 14L in the early flowering phase with regard to height and flower biomass, which can be explained as a result of the extra energy available for photosynthesis. For two varieties (Cannatonic and Hindu Kush), the 14L treatment tended to lead to more flower biomass than 14 > 12 or 14 > 10, indicating that the reduction in energy later in the flowering phase negatively affected flower initiation and/or development. The absence of this effect for Northern *Lights is* possibly a result of earlier flower maturation in this variety, with the more-developed inflorescences less affected by the reduction in available light energy. Alternatively, stored carbohydrate reserves could have been remobilised closer to harvest, acting as a buffer against the reducing photoperiods in the second half of flowering.
In terms of cannabinoid concentration, Cannatonic responded very differently to the photoperiod treatments compared to the two high-THC varieties. The cannabinoid concentration of Cannatonic appeared to be positively correlated with the photoperiod length in the first flowering phase with no obvious differences resulting from the rotation of plants between treatments at DAC 46. Thus, for this variety, the cannabinoid concentration appears to be mostly determined in the early flowering phase, with more energy available leading to more cannabinoid accumulation. This may indicate that Cannatonic accumulates carbon reserves early in the flowering phase, which are mobilised for trichome production and/or cannabinoid biosynthesis later. As the floral tissues were still mostly forming at this stage, it is unlikely reserves were accumulated there. Instead, sugars could be remobilised from starch reserves accumulated in the stems and/or roots. In citrus, flowering is the most carbon-demanding developmental phase (greater than fruiting), and it costs ≈90 mg glucose per flower from flower development to anthesis [29,30]. The source of this carbon is largely from the remobilisation of starch reserves, of which $42\%$, equating to 230 g of non-structural carbohydrate (NSC), originated from the roots [30]. Citrus trees are perennial and so have a larger, woodier root system than an annual such as cannabis; however, in cotton, the remobilisation of carbohydrates from both roots and stems during flowering has also been documented. Furthermore, the efficiency of remobilisation was also greater from the cotton roots rather than the above-ground biomass, but the absolute units of carbohydrate remobilised were greater from the stems [31]. To our knowledge the carbohydrate dynamics of a flowering medicinal cannabis plant have not been studied, and thus the potential source of remobilised carbohydrate remains to be characterised.
In contrast, for the two THC varieties, not only was the above-mentioned positive correlation absent, a clear penalty for 14L was visible which was not apparent for Cannatonic. The inflorescences of the 14L treatments of Northern Lights and Hindu Kush showed sparser trichome distribution and elongated leaf shape, indicating that the reproductive phase may not have reached completion in this treatment, likely because of either a later transition from vegetative adult to reproductive or a slower ripening in the reproductive phase. It is known that the timing, density, and distribution of trichomes is related to developmental phase [32]. In maize, trichomes are confined to the leaf margins during the juvenile phase but also appear on the upper leaf-surface of adult leaves [32]. In juvenile Arabidopsis leaves, the trichomes are absent from the abaxial (lower leaf) surface but are present on both faces once the adult phase has been reached; furthermore, the leaf shape changes from flat and round/orbicular when juvenile to curled, serrated, and spatulate when mature [32,33]. The trichomes on Arabidopsis are different from those of cannabis because they are non-glandular, but transcription factors for trichome development from Arabidopsis have also been identified in cucumber plants, which possess both glandular and non-glandular trichomes, demonstrating conservation in the regulatory pathways [15]. Chien and Sussex [33] observed that both flowering and trichome development were photoperiod sensitive in Arabidopsis, but the timing of the sensitive phase differed, with trichome initiation being earlier than flowering. From this, it was concluded that although the photoperiod control of flowering and trichome development may be regulated by the same mechanisms, the timing of the events is separable [33]. This appears to be the case in our study where higher flower biomass yields were obtained in all lines under the 14L photoperiod, but trichome density and productivity were negatively affected in the two high-THC lines.
The significant gains in total CBD yield in treatments starting with 14L for Cannatonic resulted from increases in both flower biomass and flower CBD concentration (Figure 4), showing that this variety was able to utilise the extra energy available both in early and late flowering for growth and flower/trichome development.
Although the two high-THC lines showed similar increases in growth and flower biomass with increased photoperiod, a strong decrease in cannabinoid concentration eventuated once the photoperiod was extended beyond 12L. As a result, Hindu Kush showed no significant differences in total cannabinoid yield between treatments as the increase in flower biomass ($39\%$) almost perfectly matched the decline in THC concentration ($40\%$) when comparing 14L to 12L. For Northern Lights, gains were less than for Cannatonic but still significant, resulting from the 14L < 12L treatment not having a flower biomass reduction compared to 14L or a cannabinoid concentration reduction compared to 12L, leading to a net gain of around $50\%$. For Cannatonic, it is possible that extending the photoperiod even further may also further increase yield, although it is more likely that maturation issues similar to those seen in the high-THC varieties will limit further gain.
The different responses of the three lines to photoperiod duration probably reflects the geographical origin of their genetics. In a study of 654 cannabis genotypes, it was observed that those adapted to more northerly latitudes flowered earlier than southern adapted lines when grown in a more southerly location (with shorter daylengths), demonstrating greater photoperiodic sensitivity [15]. Flowering time in high-cannabinoid hemp cultivars was observed to show a pattern of variation that was consistent with control by several major-effect loci [34]. One of the major loci that was alluded to in this study was subsequently identified and named “Autoflower1” in a population produced from the cross between a photoperiod-sensitive and -insensitive (“autoflowering”) cultivar [34,35]. Autoflower1 is a recessive trait and homozygous plants flowered under continuous light, whereas those that were heterozygous would not flower under continuous light and flowered 2 weeks later under field conditions [35]. This study also identified a second locus “Early1” which conferred an earlier flowering time of 2–4 weeks in one cultivar [35]. A candidate gene for Early1 is HD16/Early flowering1, which is a major flowering time gene in rice [35,36]. Allelic variants of a related major flowering time gene, HD1, were shown to strongly correlate with flowering time in rice, with nine allele types (nucleotide polymorphisms) accounting for 50–$60\%$ of the variation. It is likely that the range in response to photoperiod observed in cannabis, including those used in this study, is the result of allelic variation of major flowering genes such as those underlying Autoflower1 and Early1.
Medicinal cannabis products are usually described by their % cannabinoid concentration, and there has been a trend for increasing levels since the 1980s [37]. In a study into potency and sales in Washington State, $90\%$ of sales were for flowers >$15\%$ THC, whereas sales for those <$10\%$ accounted for only $2\%$ in 2014–2016 [38]. Therefore, for inhalable products with high concentration, the photoperiod needs to be optimised to maximise cannabinoid concentration per inflorescence. However, pain relief is one of the main conditions for which medicinal *Cannabis is* prescribed, and a number of studies have demonstrated that a lower concentration of 5–$10\%$ THC is effective with minimal side effects [39,40,41]. If a lower-than-maximum level from a particular variety was desirable, our results indicate that this could be achieved by lengthening the photoperiod past 12L in order to achieve increased inflorescence yields with reduced cannabinoid concentration. Electricity is a major cultivation expense, as it estimated to require Australian growers 4kWh of energy to produce 1g of dried flower per m2 [42]. The environmental impacts for indoor cultivation are also considerable; in the USA, it is reported that the greenhouse gas emissions range from 2.283 to 5.184 kg CO2 equivalent to produce 1kg of flower [43]. Therefore, achieving gains without extending the duration (or better still, shortening) of the artificial lighting period would be preferred. Our results showed an almost doubling in yield for Cannatonic when the lighting was set to 14L for the first 28 days of flowering and then 10L for the remaining 29 days, which equates to the same total number of lighting hours as a 12L:12D cycle. This finding illustrates that a progressively reducing photoperiod can have substantial impacts on yield for certain specific genetic backgrounds with no additional inputs required.
In summary, our results showed that distinct varieties can exhibit markedly different responses to changes in photoperiod length, and the standard photoperiod for the flowering phase of 12L:12D is not optimal for all varieties. In particular, cannabinoid yields (g cannabinoid plant−1) can be more than doubled by increasing the photoperiod during the flowering phase from 12 h to 14 h, as demonstrated by the Cannatonic line, with the increase in cannabinoid yields driven by gains in both flower biomass and flower cannabinoid concentration. A 14L > 10L photoperiod also achieved a strong yield benefit which utilises the same number of light hours as 12L and therefore incurs no extra electricity costs. For one high-THC line, a 14L > 12L photoperiod increased THC yields by $49\%$, driven by a gain in biomass only (no change in % THC), whereas a second high-THC line did not show any significant differences. As this treatment also benefitted Cannatonic, this may be the best “all-round” treatment optimal for mixed cultivation and untested varieties.
## 4.1. Plant Material and Growing Conditions
Three medicinal cannabis genotypes were utilised that had been supplied from Cann Group Ltd (https://www.canngrouplimited.com/, accessed on 17 January 2023). They comprised of one high-CBDA line “Cannatonic” and two high-THCA lines, “Hindu Kush” and “Northern Lights” (previously referred to as “CBD1”, “THC6”, and “THC1”, respectively [21]).
All plants were cultivated in an Australian Government Department of Health and Aged Care Office of Drug Control (ODC) approved secure facility. All experiments were conducted under a Commonwealth license and associated permits. The temperature was maintained at 25 °C and humidity at $50\%$. Plants were grown in controlled environments (CE) throughout their life cycle (details below). Throughout the flowering period, plants were moved around within a CE, and three times over the course of the experiment, plants were transferred to a different CE and the conditions were re-set for that treatment. The purpose of these movements was to control for any environmental differences between the CEs. The entire experiment was repeated twice to ensure replicability of the findings.
The cloning and propagation method used has been previously described [21]. Experimental plants were cloned from donor mothers. New growth stems of approximately 15 cm were excised from the mother. All leaves up the sides of the stem were removed, leaving the top leaf bunch. The bottom of the stem was then cut diagonally across a node using a scalpel in order to form a clone approximately 12 cm in height. The top leaf bunch was trimmed to the height of the smallest emerging leaf to reduce water loss and prevent the clones from overlapping in the propagation dome. The bottom 1 cm of the stem, from which the roots would form, were lightly scraped with a scalpel and then dipped in hormone gel (Clonex Purple, Yates, DuluxGroup, Clayton, Australia) and placed in an organic propagation cube (Eazyplug CT12, Goirle, The Netherlands, eazyplug.nl). Once the propagation tray was full of new clones, it was placed in a propagation dome (Smart Garden heavy duty 3-piece propagation kit, Epping Hydroponics) for 18 days under an 18 h light/6 h dark (18L:6D) photoperiod in a growth cabinet (Conviron A2000, Conviron Asia Pacific Pty Ltd., Grovedale, Australia) at a light intensity of 100 µmol m2 s1 and a temperature of 25 °C. Humidity monitors were placed in a dome, and the humidity was progressively reduced over the fourteen-day propagation period. Plants with established roots were then potted into 1.8L pots containing a 30:$70\%$ blend of perlite and coco-coir, electrical conductivity (EC) = <0.5 mS/cm (Professors Nutrients, Truganina, Australia). Plants were then transferred to their flowering controlled environments under a PAR of 700 µmol m2 s (Viperspectra PAR 700, Viparspectra, Richmond, VA, USA) https://viparspectraled.com.au/ accessed on 17 January 2023). The temperature was maintained at 25 °C, and blackout curtains removed the risk of light leakage. Plants were watered and fed using a commercial fertigation recipe, E.C. = 2.2 mS/cm and pH = 6.
## 4.2. Treatments
Each treatment contained 15 replicates of each of the three genotypes in a fully randomised design. The three zones were programmed to one of each of the following photoperiods (hours light/hours dark): 10L:14D, 12L:12D, 14L:10D. All plants were maintained in these treatments for 28 days, which was the day after cloning (DAC) 46, the half-way point of the flowering treatments. At DAC 46, 10 plants from each of the three treatments (initially containing 15 plants) changed treatment so that 5 experienced a lengthening, and 5 a shortening, of the photoperiod, resulting in 9 combinations of 5 plants from each genotype. Plants then stayed in these treatments for the remaining 29 days of flowering before harvest on DAC 75. A description of the treatments is provided in Table 2. The direction of the symbol in the treatment name indicates whether the photoperiod increased (>) or decreased (<) in duration after DAC 46.
## 4.3. Measurements
The development of flowering was scored fortnightly until DAC 34 and then weekly thereafter for the presence or absence of pistils ($\frac{1}{0}$) and trichomes ($\frac{1}{0}$). Height was measured weekly.
Harvesting: The harvest took place on DAC 75. Plants were excised at the base, and then the whole plant was weighed (whole plant FW). The large fan leaves were removed, and the flowers were manually stripped from the stem and trimmed using a mechanical trimmer (TrimPro ROTOR, Saint-Jean-sur-Richelieu, QC, Canada). The trimmed flowers were re-weighed (flower fresh weight) and placed into a foil tray. The flowers were dried in a dedicated drying room at 21 °C and $50\%$ humidity until no further reduction in weight was observed (9 days). The samples were then re-weighed, and the total flower dry weight (g plant−1) was calculated.
## 4.4. Analytics
The method used for the quantification of cannabinoids has been previously described [44]. Five biological replicates for the three genotypes in each of the nine treatments and controls were analysed for THCA and THC (Hindu Kush and Norther Lights) and CBDA and CBD (Cannatonic). Total THC and CBD was then calculated with the following formulae: Total THC = THC + (THCA × 0.877) and Total CBD = CBD + (CBDA × 0.877).
Three florets were randomly removed from the dried subsample flower material from each individual plant and ground to fine powder in liquid nitrogen. A 0.1 g subsample was used for cannabinoid quantification in $100\%$ ethanol using sonication (SONICLEAN, Soniclean®, Dudley Park, Australia) at $\frac{50}{60}$ Hz for 30 min, followed by centrifuge at 10,000 rpm for 10 min. The ethanolic extracts were stored at −10 °C until use. The ethanolic extracts were diluted and analysed using high performance liquid chromatography–quadrupole time of flight mass spectrometry (UHPLC-QToFMS, Agilent Technologies, Santa Clara, CA, USA). Separation was achieved using a reversed-phase column (Agilent Infinity Poroshell 120, HPH-C18, 2.1 × 150 mm, 2.7 µm, narrow bore LC column, Agilent Technologies, Santa Clara, CA, USA) and methanol–water–acetonitrile and acetonitronitrile mobile phases, each containing $0.1\%$ formic acid (v/v). Analysis was performed using Quant Analysis Software 10.2 (Agilent Technologies, Santa Clara, CA, USA). Cannabinoid peaks were identified according to their m/z values and retention times by calibration against cannabinoid standards (Novachem, VIC, Australia).
The total yield (mg plant−1) of each cannabinoid was calculated as ((%Cannabinoid/100) × Total flower DW g) × 1000 = mg cannabinoid plant−1 [1]
## 4.5. Graphics and Statistics
All graphics and statistical analyses were performed in R 3.1 [45]. Multiple comparisons were performed with Dunnet’s tests [46] in order to identify significant differences between the 8 treatments and the control (12L). Individual t-tests were performed for pair-wise comparisons.
## References
1. Livingston S.J., Rensing K.H., Page J.E., Samuels A.L.. **A polarized supercell produces specialized metabolites in Cannabis trichomes**. *Curr. Biol.* (2022.0) **32** 4040-4047. DOI: 10.1016/j.cub.2022.07.014
2. Conneely L.J., Mauleon R., Mieog J., Barkla B.J., Kretzschmar T.. **Characterization of the**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0242633
3. Brousseau V.D., Wu B.S., MacPherson S., Morello V., Lefsrud M.. **Cannabinoids and Terpenes: How Production of Photo-Protectants Can Be Manipulated to Enhance**. *Front. Plant. Sci.* (2021.0) **12** 620021. DOI: 10.3389/fpls.2021.620021
4. Chandra S., Lata H., ElSohly M.A., Walker L.A., Potter D.. **Cannabis cultivation: Methodological issues for obtaining medical-grade product**. *Epilepsy Behav.* (2017.0) **70** 302-312. DOI: 10.1016/j.yebeh.2016.11.029
5. Mechoulam R., Ben-Shabat S.. **From gan-zi-gun-nu to anandamide and 2-arachidonoylglycerol: The ongoing story of cannabis**. *Nat. Prod. Rep.* (1999.0) **16** 131-143. DOI: 10.1039/a703973e
6. Blake A., Wan B.A., Malek L., DeAngelis C., Diaz P., Lao N., Chow E., O’Hearn S.. **A selective review of medical cannabis in cancer pain management**. *Ann. Palliat Med.* (2017.0) **6** S215-S222. DOI: 10.21037/apm.2017.08.05
7. Campbell L.G., Dufresne J., Sabatinos S.A.. **Cannabinoid Inheritance Relies on Complex Genetic Architecture**. *Cannabis Cannabinoid Res.* (2020.0) **5** 105-116. DOI: 10.1089/can.2018.0015
8. Caplan D., Dixon M., Zheng Y.B.. **Optimal Rate of Organic Fertilizer during the Flowering Stage for Cannabis Grown in Two Coir-based Substrates**. *Hortscience* (2017.0) **52** 1796-1803. DOI: 10.21273/HORTSCI12401-17
9. Caplan D., Dixon M., Zheng Y.B.. **Increasing Inflorescence Dry Weight and Cannabinoid Content in Medical Cannabis Using Controlled Drought Stress**. *Hortscience* (2019.0) **54** 964-969. DOI: 10.21273/HORTSCI13510-18
10. Danziger N., Bernstein N.. **Light matters: Effect of light spectra on cannabinoid profile and plant development of medical cannabis (**. *Ind. Crop. Prod.* (2021.0) **164** 113351. DOI: 10.1016/j.indcrop.2021.113351
11. Reichel P., Munz S., Hartung J., Kotiranta S., Graeff-Honninger S.. **Impacts of Different Light Spectra on CBD, CBDA and Terpene Concentrations in Relation to the Flower Positions of Different**. *Plants* (2022.0) **11**. DOI: 10.3390/plants11202695
12. Dang M.C.L., Arachchige N.M., Campbell L.G.. **Optimizing Photoperiod Switch to Maximize Floral Biomass and Cannabinoid Yield in**. *Front. Plant. Sci.* (2022.0) **12** 797425. DOI: 10.3389/fpls.2021.797425
13. Cervantes J.. *The Cannabis Encyclopedia: The Definitive Guide to Cultivation & Consumption of Medical Marijuana, Illustrated ed.* (2015.0)
14. Moher M., Jones M., Zheng Y.B.. **Photoperiodic Response of In Vitro**. *Hortscience* (2021.0) **56** 108-113. DOI: 10.21273/HORTSCI15452-20
15. Yang S., Cai Y., Liu X., Dong M., Zhang Y., Chen S., Zhang W., Li Y., Tang M., Zhai X.. **A CsMYB6-CsTRY module regulates fruit trichome initiation in cucumber**. *J. Exp. Bot.* (2018.0) **69** 1887-1902. DOI: 10.1093/jxb/ery047
16. Hillig K.W.. **Genetic evidence for speciation in Cannabis (Cannabaceae)**. *Genet. Resour. Crop. Ev.* (2005.0) **52** 161-180. DOI: 10.1007/s10722-003-4452-y
17. Zhang M., Anderson S.L., Brym Z.T., Pearson B.J.. **Photoperiodic Flowering Response of Essential Oil, Grain, and Fiber Hemp (**. *Front. Plant. Sci.* (2021.0) **12** 694153. DOI: 10.3389/fpls.2021.694153
18. Zhang Q.Y., Chen X., Guo H.Y., Trindade L.M., Salentijn E.M.J., Guo R., Guo M.B., Xu Y.P., Yang M.. **Latitudinal Adaptation and Genetic Insights Into the Origins of**. *Front. Plant. Sci.* (2018.0) **9** 1876. DOI: 10.3389/fpls.2018.01876
19. **Hemp Planting Map**
20. Sikora V., Berenji J., Latković D.. **Influence of agroclimatic conditions on content of main cannabinoids in industrial hemp (**. *Genetics* (2011.0) **43** 449-456. DOI: 10.2298/GENSR1103449S
21. Purdy S.J., Hewavitharana A.K., Halimi R.A., Magner N.J., Peterswald T.J., Trebilco A., Kretzschmar T., Hailstones D.. **A One-Step Grafting Methodology Can Adjust Stem Morphology and Increase THCA Yield in Medicinal Cannabis**. *Agronomy* (2022.0) **12**. DOI: 10.3390/agronomy12040852
22. Kojima S., Takahashi Y., Kobayashi Y., Monna L., Sasaki T., Araki T., Yano M.. **Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hd1 under short-day conditions**. *Plant Cell Physiol.* (2002.0) **43** 1096-1105. DOI: 10.1093/pcp/pcf156
23. Suarez-Lopez P., Wheatley K., Robson F., Onouchi H., Valverde F., Coupland G.. **CONSTANS mediates between the circadian clock and the control of flowering in Arabidopsis**. *Nature* (2001.0) **410** 1116-1120. DOI: 10.1038/35074138
24. Yano M., Kojima S., Takahashi Y., Lin H.X., Sasaki T.. **Genetic control of flowering time in rice, a short-day plant**. *Plant Physiol.* (2001.0) **127** 1425-1429. DOI: 10.1104/pp.010710
25. Hayama R., Coupland G.. **The molecular basis of diversity in the photoperiodic flowering responses of Arabidopsis and rice**. *Plant Physiol.* (2004.0) **135** 677-684. DOI: 10.1104/pp.104.042614
26. Frank M.. *The Marijuana Growers Guide Delux* (1978.0)
27. Park S.-H., Pauli C.S., Gostin E.L., Staples S.K., Seifried D., Kinney C., Vanden Heuvel B.D.. **Effects of short-term environmental stresses on the onset of cannabinoid production in young immature flowers of industrial hemp (**. *J. Cannabis Res.* (2022.0) **4** 1. DOI: 10.1186/s42238-021-00111-y
28. Naim-Feil E., Pembleton L.W., Spooner L.E., Malthouse A.L., Miner A., Quinn M., Polotnianka R.M., Baillie R.C., Spangenberg G.C., Cogan N.O.I.. **The characterization of key physiological traits of medicinal cannabis (**. *BMC Plant Biol.* (2021.0) **21**. DOI: 10.1186/s12870-021-03079-2
29. Bustan A., Goldschmidt E.E.. **Estimating the cost of flowering in a grapefruit tree**. *Plant Cell Environ.* (1998.0) **21** 217-224. DOI: 10.1046/j.1365-3040.1998.00267.x
30. Dovis V.L., Machado E.C., Ribeiro R.V., Magalhaes J.R., Marchiori P.E.R., Sales C.R.G.. **Roots are important sources of carbohydrates during flowering and fruiting in ‘Valencia’ sweet orange trees with varying fruit load**. *Sci. Hortic. Amsterdam* (2014.0) **174** 87-95. DOI: 10.1016/j.scienta.2014.05.011
31. Tang F.Y., Gao X., Peng J.J.. **The dynamics of carbohydrate and associated gene expression in the stems and roots of upland cotton (**. *Plant Physiol. Bioch.* (2022.0) **178** 125-136. DOI: 10.1016/j.plaphy.2022.02.022
32. Telfer A., Bollman K.M., Poethig R.S.. **Phase change and the regulation of trichome distribution in**. *Development* (1997.0) **124** 645-654. DOI: 10.1242/dev.124.3.645
33. Chien J.C., Sussex I.M.. **Differential regulation of trichome formation on the adaxial and abaxial leaf surfaces by Gibberellins and photoperiod in**. *Plant Physiol.* (1996.0) **111** 1321-1328. DOI: 10.1104/pp.111.4.1321
34. Stack G.M., Toth J.A., Carlson C.H., Cala A.R., Marrero-Gonzalez M.I., Wilk R.L., Gentner D.R., Crawford J.L., Philippe G., Rose J.K.C.. **Season-long characterization of high-cannabinoid hemp (**. *GCB Bioenergy* (2021.0) **13** 546-561. DOI: 10.1111/gcbb.12793
35. Toth J.A., Stack G.M., Carlson C.H., Smart L.B.. **Identification and mapping of major-effect flowering time loci Autoflower1 and Early1 in**. *Front. Plant. Sci.* (2022.0) **13** 991680. DOI: 10.3389/fpls.2022.991680
36. Hori K., Ogiso-Tanaka E., Matsubara K., Yamanouchi U., Ebana K., Yano M.. **Hd16, a gene for casein kinase I, is involved in the control of rice flowering time by modulating the day-length response**. *Plant. J.* (2013.0) **76** 36-46. DOI: 10.1111/tpj.12268
37. ElSohly M.A., Mehmedic Z., Foster S., Gon C., Chandra S., Church J.C.. **Changes in Cannabis Potency Over the Last 2 Decades (1995–2014): Analysis of Current Data in the United States**. *Biol. Psychiatry* (2016.0) **79** 613-619. DOI: 10.1016/j.biopsych.2016.01.004
38. Smart R., Caulkins J.P., Kilmer B., Davenport S., Midgette G.. **Variation in cannabis potency and prices in a newly legal market: Evidence from 30 million cannabis sales in Washington state**. *Addiction* (2017.0) **112** 2167-2177. DOI: 10.1111/add.13886
39. Wallace M.S., Marcotte T.D., Umlauf A., Gouaux B., Atkinson J.H.. **Efficacy of Inhaled Cannabis on Painful Diabetic Neuropathy**. *J. Pain* (2015.0) **16** 616-627. DOI: 10.1016/j.jpain.2015.03.008
40. Abrams D.I., Jay C.A., Shade S.B., Vizoso H., Reda H., Press S., Kelly M.E., Rowbotham M.C., Petersen K.L.. **Cannabis in painful HIV-associated sensory neuropathy—A randomized placebo-controlled trial**. *Neurology* (2007.0) **68** 515-521. DOI: 10.1212/01.wnl.0000253187.66183.9c
41. Cash M.C., Cunnane K., Fan C.Y., Romero-Sandoval E.A.. **Mapping cannabis potency in medical and recreational programs in the United States**. *PLoS ONE* (2020.0) **15**. DOI: 10.1371/journal.pone.0230167
42. **Australian Medicinal Cannabis Cultivation Indoors or Outdoors Debate**
43. Summers H.M., Sproul E., Quinn J.C.. **The greenhouse gas emissions of indoor cannabis production in the United States**. *Nat. Sustain.* (2021.0) **4** 644-650. DOI: 10.1038/s41893-021-00691-w
44. Hewavitharana A.K., Gloerfelt-Tarp F., Nolan M., Barkla B.J., Purdy S., Kretzschmar T.. **Simultaneous Quantification of 17 Cannabinoids in Cannabis Inflorescence by Liquid Chromatography-Mass Spectrometry**. *Separations* (2022.0) **9**. DOI: 10.3390/separations9040085
45. 45.
R Team
RStudio: Integrated Development for RRStudio, PBCBoston, MA, USA2020. *RStudio: Integrated Development for R* (2020.0)
46. Dunnett C.W.. **A multiple comparison procedure for comparing several treatments with a control**. *J. Am. Stat. Assoc.* (1955.0) **50** 1096-1121. DOI: 10.1080/01621459.1955.10501294
|
---
title: 'Ganoderic Acid A and Its Amide Derivatives as Potential Anti-Cancer Agents
by Regulating the p53-MDM2 Pathway: Synthesis and Biological Evaluation'
authors:
- Yi Jia
- Yan Li
- Hai Shang
- Yun Luo
- Yu Tian
journal: Molecules
year: 2023
pmcid: PMC10004777
doi: 10.3390/molecules28052374
license: CC BY 4.0
---
# Ganoderic Acid A and Its Amide Derivatives as Potential Anti-Cancer Agents by Regulating the p53-MDM2 Pathway: Synthesis and Biological Evaluation
## Abstract
The mechanisms of action of natural products and the identification of their targets have long been a research hotspot. Ganoderic acid A (GAA) is the earliest and most abundant triterpenoids discovered in Ganoderma lucidum. The multi-therapeutic potential of GAA, in particular its anti-tumor activity, has been extensively studied. However, the unknown targets and associated pathways of GAA, together with its low activity, limit in-depth research compared to other small molecule anti-cancer drugs. In this study, GAA was modified at the carboxyl group to synthesize a series of amide compounds, and the in vitro anti-tumor activities of the derivatives were investigated. Finally, compound A2 was selected to study its mechanism of action because of its high activity in three different types of tumor cell lines and low toxicity to normal cells. The results showed that A2 could induce apoptosis by regulating the p53 signaling pathway and may be involved in inhibiting the interaction of MDM2 and p53 by binding to MDM2 (KD = 1.68 µM). This study provides some inspiration for the research into the anti-tumor targets and mechanisms of GAA and its derivatives, as well as for the discovery of active candidates based on this series.
## 1. Introduction
Ganoderma lucidum is the dry fruiting body of *Ganoderma lucidum* Karst and Ganoderma sinensis, which belong to the genus Ganoderma of the family Polyporaceae. The chemical composition of *Ganoderma lucidum* is complex. There are currently about 400 known compounds, the majority of which are triterpenoids, polysaccharides, nucleosides, sterols, and other compounds, of which more than 300 are triterpenoids. Ganoderic acid A (GAA, Figure 1), one of the most prominent and highly concentrated triterpenes from Ganoderma lucidum, exhibits a variety of biological properties, including anti-tumor [1], anti-inflammatory [2,3,4], anti-depressant [5,6], neuroprotection [7,8], anti-fibrosis [9], liver protection [10,11], improvement of glucose and lipid metabolism and myocardial protection [12,13,14], etc., which can be used as a potential resource for drug development. The anti-tumor activity is one of the earliest discovered activities of GAA, which has received the most attention since then [1]. Many researchers have investigated the anti-tumor activities of *Ganoderma lucidum* triterpenoids and predicted their anti-tumor pathway. Studies have shown that GAA can inhibit tumor growth through a variety of signaling pathways. For example, GAA has good cytotoxicity on human glioblastoma by inducing apoptosis, autophagy and inhibiting PI3K/AKT signaling pathways [15]; it can inhibit the expression of KDR mRNA and protein, induce apoptosis of human glioma cell U251 cell, and inhibit its proliferation and invasion [16]. However, there is no relevant literature that clearly indicates the possible anti-tumor target of GAA.
Cancer and the MDM2-p53 signaling pathway are closely related. p53 is a tumor suppressor gene. When cells are damaged by a variety of causes such as DNA damage, ribosomal stress, the expression of the p53 protein is activated to repair damaged cells or to directly induce apoptosis if the DNA damage is already too severe. p53 is essential for a number of processes that occur throughout life, including DNA damage repair, cell cycle arrest, metabolism, senescence, and apoptosis [17]. If too much p53 protein is produced during certain physiological processes, cell function is impaired or the tendency to form tumors is increased. Therefore, the expression of murine double minute 2 (MDM2) protein in the downstream signaling pathway will increase when p53 protein accumulates in normal cells. To achieve the balance and stability of p53 protein levels in cells, MDM2 can interact with the transcriptional activation domain of the p53 to form the p53-MDM2 complex, which suppresses the transcriptional activity of p53. When a cell is stressed, MDM2 expression decreases, p53 expression increases, and the increase in p53 induces MDM2 expression at the transcriptional level, creating a negative feedback regulatory loop (Figure 2) [18]. MDM2 can also act as an E3 ubiquitin ligase, targeting p53 protein and inducing its ubiquitination and degradation to maintain low levels of p53 protein [19]. p53 has long been an intriguing cancer target [20]. Individuals carrying certain inherited loss-of-function mutations in p53 have a $50\%$ chance of developing cancer by the age of 30 and a $90\%$ chance of developing cancer by the age of 70 [21]. Mice knocked out of p53 quickly develop tumors. Up to $50\%$ of cancers have mutations in both copies of p53 [22]. Drugs that can reactivate the tumor suppressing ability of p53 may therefore have a powerful anti-cancer effect. However, it is more difficult to activate proteins than to inhibit them, so the interaction of MDM2 with p53 provides an opportunity to activate p53 by inhibiting the interaction of MDM2 to exert anti-tumor effects.
After summarizing the relevant literature, we discovered that GAA may interact with the p53-MDM2 pathway. For example, Xu Bin et al. found that GAA inhibited LNCaP in a concentration-dependent manner. Real-time experiments showed that GAA promoted the apoptosis-promoting genes bad and p53 [23]. Tang Wen et al. found that 95-D cells expressing wild-type p53 protein were 3.3 times more sensitive to ganoderic acid T than H1299 cells that did not express p53 protein [24]. Other studies suggest that GAA and sterols with similar structures may have some affinity for the MDM2. Froufe et al. found that some *Ganoderma lucidum* triterpenoids have potential affinities with MDM2 protein through virtual screening prediction, including ganoderic acid A (Ki = 147 nM) and ganoderic acid F (Ki = 212 nM) [25]. Staszczak et al. summarized the role of secondary metabolites in fungi on the ubiquitin–protesome system, in which sterols have certain interactions with MDM2, indicating that such structure has advantages in interactions with MDM2 [26]. All these results suggest that GAA is likely to be related to the p53-MDM2 pathway. However, considering the low anti-tumor effect of GAA and the size of the pocket of MDM2, and there is no relevant literature highlighting the anti-cancer activities of synthetic GAA derivatives on potential MDM2-p53 interaction inhibitions, we decided to simply modify the structure of GAA at the carboxyl group to improve its anti-tumor activity and reduce possible pharmacokinetic problems caused by the free carboxyl group, and investigated the effects of the different GAA amide derivatives on the MDM2-p53 pathway.
In this study, GAA was modified to determine the in vitro anti-tumor activity of these derivatives on different tumor cell lines, and compound A2 (Figure 1), which has good activity in different cell lines and low toxicity to normal cells, was selected to investigate the relevant mechanism. First, we investigated the effect of A2 on cell apoptosis and the expression of proteins related to the MDM2-p53 pathway by flow cytometry and Western blot experiment. Next, in silico target fishing and molecular docking was performed to investigate the binding potential of A2 and MDM2. We then used a surface plasmon resonance (SPR) experiment to show that A2 has a certain binding affinity with MDM2 in vitro. It was speculated that A2 might play a role in increasing p53 protein levels by binding to MDM2 to inhibit the interaction of MDM2 and p53. This work is valuable in further demonstrating the potential of GAA and its amide derivatives as MDM2-p53 binding inhibitors and in developing candidates with anti-tumor activity.
## 2.1. Chemistry
Based on the structure of GAA, we retained its core structure of tetracyclic triterpenoids, and introduced a series of amino groups to modify GAA at the carboxyl site. As shown in Scheme 1, GAA was treated with amino compounds, 2-(1H-benzotriazole-1-yl)-1,1,3,3-tetramethyluronium tetrafluoroborate (TBTU), and N,N-diisopropylethylamine (DIPEA) to obtain GAA derivatives [27,28]. A1–A12 refers to the amide derivatives formed with fatty amine, aniline, benzylamine, phenylethylamine and other different types of primary amine compound. A13–A15 refers to the derivatives formed with piperazine compounds.
Except for the low yield of substituted aniline, the yields of the other compounds are 70~$98\%$, which is easy to obtain. See Methods and Materials for the detailed synthesis and purification methods of all compounds. After being substituted by different amine fragments, the hydrogen signal of amide bond appears at 7–5 ppm. The methylene peak of amine fragments is mostly distributed at 4.5–3 ppm. The chemical shift of hydrogen signal in GAA itself does not change very much. All new compounds were identified by 1H-NMR, 13C-APT and HRMS spectroscopy. The corresponding spectra are presented in the Supplementary Materials.
## 2.2.1. The Anti-Proliferation Activity on MCF-7
MCF-7 is a commonly used tumor cell line. Previous studies have shown that GAA has some anti-tumor activity against MCF-7, and there is a high expression of MDM2-p53 in MCF-7. The anti-tumor activities of GAA derivatives on MCF-7 were tested for 48 h, and the results were shown in Table 1 and Figure 3. The results showed that compounds A2, A6, A7, A8, A9, A15 had significant anti-proliferation activities on MCF-7 cell line compared with GAA. Among all derivatives, A6 has the strongest anti-proliferation effect, and its inhibition rate of MCF-7 at 50 µM can reach $63.64\%$.
Overall, among aliphatic amines, anilines, benzylamines, phenylethylamines, and (hetero) cyclic amines, benzylamine derivatives (A6, A7, A8) were significantly more potent than other substituted compounds. Among the aliphatic amines (A1, A2), the chain length of six carbon atoms is better than that of four carbon atoms. Among benzylamine compounds, the activities of electron withdrawing groups on benzene ring (A6, A7, A8) (cell viability at 50 μM less than $50\%$) are better than that of electron donating group (A5), and 3,5-diCl double substitution is better than 4-Cl single substitution, indicating that the position and amount of electron withdrawing groups can affect the activities of GAA derivatives. Compared with anilines (A3, A4), benzylamines (A6, A7, A8) and phenylethylamines (A10, A11, A12) substituted compounds, the activities of benzylamines are better, which also indicated that the chain length of substituents may affect their activities. At the same time, the introduction of the common anti-tumor fragment indene can improve the anti-proliferative activity of GAA (A9). The introduction of N-methyl or N-ethyl piperazine with strong hydrophilicity can’t improve the anti-tumor activity of GAA, but N-phenyl piperazine can improve activity (A13, A14, A15), indicating that the anti-proliferation activities of GAA derivatives may have certain requirements for hydrophobicity.
## 2.2.2. The Anti-Proliferation Activity on SJSA-1, HepG2 and HK2
To investigate the selectivity of these derivatives towards different tumor cell lines, we also selected HepG2 and osteosarcoma cell line SJSA-1 cells to evaluate the anti-proliferation activity of the derivatives. The results were shown in Table 2 and Figure 3. The results showed that the inhibitory effect of this series of derivatives on HepG2 was overall better than that on MCF-7 on the whole. Except for compounds A2 and A11, the effects of other compounds on SJSA-1 were not strong. In HepG2 cell line, compounds A2, A7, A8 and A9 still have potent anti-proliferation activity, whereas A6 and A15, which were better in MCF-7, have weaker anti-proliferation effect on HepG2. However, A12 had strong selectivity on HepG2, and the inhibition rate of this cell below 50 µM can reach $74.37\%$. In SJSA-1 cell line, compound A2 still showed potent inhibition, whereas A11 showed some selectivity for SJSA-1, and it was found that GAA had better anti-tumor activity for SJSA-1 than for HepG2 and MCF-7.
## 2.3. A2 Induces Apoptosis in SJSA-1 Cells
We next examined the effect of A2 (24 h incubation, at concentrations of 12.5, 25, 50 µM) on the SJSA-1 which A2 showed the highest anti-proliferation potency among all the cell lines. Cells were stained with Annexin V-FITC and propidium iodide. The results are shown in the Figure 4. The results showed that different concentrations of A2 could induce different degrees of apoptosis in SJSA-1 cells. At low concentrations, the proportion of cells undergoing early apoptosis increased slightly from $11.6\%$ (12.5 µM) to $12.3\%$ (25 µM). However, the proportion of apoptosis cells increased significantly at 50 µM ($18.7\%$), while the proportion of late apoptosis remained essentially unchanged with increasing of concentration. The results indicated that A2 can induce cell apoptosis in a dose-dependent manner.
## 2.4.1. A2 Effects the Expression of p53 Protein, MDM2 and Bcl-2/Bax
In the introduction section, we introduced that the concept that the MDM2-p53 pathway can induce cell apoptosis by up-regulating the expression of p53 protein to inhibit the proliferation of tumor cells, and by blocking the interaction of MDM2 and p53, the activation of p53 results in transcription of MDM2 mRNA, leading to robust MDM2 protein accumulation [29,30,31]. In order to verify the effect of A2 on this pathway, we examined the effect of A2 on the protein level of MDM2, p53 protein and Bcl-2/Bax related to apoptosis, as shown in Figure 5. The results showed that after treatment of MCF-7 cells with A2 for 24 h, both MDM2 and p53 protein showed an increasing trend at 50 µM. The level of Bcl-2/Bax decreased which was consistent with the apoptosis of MCF-7 cells induced by A2. We also investigated the effect of A2 on the SJSA-1 cell line which overexpresses MDM2. Compared with MCF-7, the expression of MDM2 and p53 protein in this cell line increased in a dose-dependent manner which may be the reason for the best anti-proliferation effect on SJSA-1 among all three cell lines. This experiment demonstrated that A2 can affect the MDM2-p53 pathway to induces apoptosis.
## 2.4.2. GAA and A2 Have In Vitro Binding Affinity with MDM2
In order to speculate whether A2 effects the p53-MDM2 pathway by binding with MDM2 to inhibit the interaction between MDM2 and p53, we carried out in silico and in vitro binding experiments. First, we performed computer simulation to conduct target fishing of GAA and found that MDM2 interacts with GAA in silico (FitValue 0.79). We used the S-value to evaluate the binding degree of the compound and MDM2 in the molecular docking experiments. The higher the absolute value of this number, the stronger the binding force. Molecular docking (see Figure 6) revealed that the hydroxyl-H of GAA interacts with Met58 in MDM2 (S-value: −6.49). When A2 was docked to MDM2, it was found that, the core of A2 was in the opposite direction compared to GAA. In addition to the interaction with Met58 similar to GAA, the n-hexyl is well anchored in the hydrophobic pocket and the methylene has some hydrophobic interaction with His92 (S-value: −7.22). To verify whether GAA and A2 have a certain binding ability with MDM2 in vitro, we used surface plasmon resonance (SPR) experiment to investigate the interaction between GAA, A2 and MDM2 (see Figure 7). The KD of GAA and MDM2 is 12.73 µM, indicating that they do have some affinity. At the same time, A2 which has a stronger anti-proliferation activity has a stronger binding affinity with MDM2 than GAA, with a KD of 1.68 µM. These results demonstrated that A2 can affect the MDM2-p53 pathway to induces apoptosis probably by inhibiting the interaction of MDM2 and p53.
We then investigated the effects of compounds with significantly higher activity than GAA in three cell lines on HK2, which is a normal cell line used to assess cytotoxicity. The results are shown in Table 2 and Figure 3. The results showed that at high concentration, benzylamine compounds A6, A7 and A9 with anti-tumor fragments had some toxicity to HK2 cells, whereas the other compounds with stronger activity had lower cytotoxicity to HK2 cells. To sum up, this series of GAA derivatives showed some selectivity in different cell lines, and have the potential to be developed as various tumor inhibitors. Given the strong anti-proliferation effect of derivative A2 in various cell lines and its low effect on normal cells, A2 was selected to investigate its anti-proliferation mechanism.
## 3.1. Chemistry
Unless otherwise stated, all reagents and solvents were obtained from commercial sources were used without further purification. GAA were purchased from Biopurify (Chengdu, China). Flash column chromatography was performed on Biotage Isolera Four (Sweden). 1H NMR and 13C-APT spectra were recorded on a Bruker AvanceIII 600MHz spectrometer (Germany). HRMS was performed on a Thermo Fisher LTQ Orbitrap XL (United States).
## 3.1.1. Synthesis of (n-butyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A1)
To a solution of GAA (1eq., 50 mg, 0.01 mmol) in DCM (5 mL), n-butylamine (2 eq.), TBTU (1.5 eq.) and DIPEA (1.5 eq.) were added. The resulting reaction mixture was stirred at room temperature for 1 h and monitored by TLC. Upon completion, the reaction was then quenched with water and extracted with DCM. The organic layer was washed twice with water, dried over anhydrous sodium sulfate, filtered and concentrated under reduced pressure. The crude material was purified by column chromatography using dichloromethane and methanol (10:1, v/v) as mobile phase to obtain target molecule as white powder (yield $80.6\%$). mp: 120.7–121.5 °C. 1H-NMR (600 MHz, CDCl3) δ: 5.81 (t, $J = 5.6$ Hz, 1H, CONH), 4.80–4.77 (m, 1H, H-15), 4.63–4.60 (m, 1H, H-7), 4.16–4.15 (m, 1H, OH-15), 3.55–3.52 (m, 1H, OH-7), 3.22–3.19 (m, 2H, CONHCH2), 2.90–2.79 (m, 2H, H-24a, H-1b), 2.78–2.68 (m, 2H, H-12a, H-25), 2.54–2.45 (m, 3H, H-22a, H-12b, H-24b), 2.44–2.37 (m, 2H, H-2), 2.26–2.19 (m, 1H, H-22b), 2.07–2.01 (m, 1H, H-6a), 2.01–1.95 (m,1H, H-20), 1.83–1.78 (m, 3H, H-17, H-16), 1.74–1.66 (m, 2H, H-5, H-6b), 1.52–1.42 (m, 3H, H-1a, CH2), 1.37–1.30 (m, 2H, CH2), 1.27 (s, 3H, CH3), 1.25 (s, 3H, CH3), 1.14 (d, $J = 7.0$ Hz, 3H, CH3), 1.12 (s, 3H, CH3), 1.10 (s, 3H, CH3), 0.99 (s, 3H, CH3), 0.91 (t, $J = 7.3$ Hz, 3H, CH2CH3), 0.87 (d, $J = 6.4$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 217.3, 209.7, 199.6, 175.5, 159.3, 140.1, 72.3, 68.8, 53.9, 51.7, 49.8, 48.7, 48.1, 47.2, 46.7, 46.6, 39.3, 37.9, 36.3, 36.0, 35.5, 34.3, 32.7, 31.6, 28.9, 27.3, 20.7, 20.0, 19.6, 19.5, 19.4, 18.0, 17.3, 13.8. HRMS calculated for C34H53NO6Na [M + Na]+ m/z 594.3765, found 594.3746.
## 3.1.2. Synthesis of (n-hexyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A2)
The title compound was obtained from 1-hexanamine following similar synthesis procedure of A1 (white powder, yield $65.6\%$). mp: 129.9–131.3 °C. 1H-NMR (600 MHz, CDCl3) δ: 5.78 (t, $J = 5.6$ Hz, 1H, CONH), 4.79–4.77 (m, 1H, H-15), 4.64–4.60 (m, 1H, H-7), 4.06–3.96 (m, 1H, OH), 3.50–3.35 (m, 1H, OH), 3.21–3.17 (m, 2H, CONHCH2), 2.91–2.80 (m, 2H, H-24a, H-1b), 2.78–2.68 (m, 2H, H-12a, H-25), 2.54–2.45 (m, 3H, H-22a, H-12b, H-24b), 2.44–2.37 (m, 2H, H-2), 2.26–2.19 (m, 1H, H-22b), 2.09–2.02 (m, 1H, H-6a), 2.01–1.95 (m,1H, H-20), 1.84–1.76 (m, 3H, H-17, H-16), 1.74–1.64 (m, 2H, H-5, H-6b), 1.51–1.43 (m, 3H, H-1a, CH2), 1.35–1.23 (m, 12H, CH2 × 3, CH3 × 2), 1.25 (s, 3H, CH3), 1.14 (d, $J = 7.1$ Hz, 3H, CH3), 1.12 (s, 3H, CH3), 1.10 (s, 3H, CH3), 0.99 (s, 3H, CH3), 0.89−0.86 (m, 6H, 2×CH3). 13C-APT (150 MHz, CDCl3) δ: 217.2, 209.7, 199.6, 175.5, 159.3, 140.2, 72.3, 68.8, 54.0, 51.7, 49.9, 48.7, 48.1, 47.2, 46.8, 46.6, 39.6, 38.0, 36.4, 36.0, 35.5, 34.3, 32.8, 31.5, 29.5, 29.0, 27.4, 26.5, 22.6, 20.7, 19.6, 19.5, 19.4, 18.0, 17.3, 14.1. HRMS calculated for C36H57NO6Na [M + Na]+ m/z 622.4078, found 622.4070.
## 3.1.3. Synthesis of (4-Methylphenyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A3)
The title compound was obtained from p-toluidine following similar synthesis procedure of A1 (white powder, yield $30.3\%$). mp: 174.3–175.5 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.76 (s, 1H, CONH), 7.29 (d, $J = 8.2$ Hz, 2H, Ph-2, 6-H), 7.02 (d, $J = 8.2$ Hz, 2H, Ph-2, 6-H), 4.66–4.64 (m, 1H, H-15), 4.51–4.50 (m, 1H, H-7), 4.07–4.03 (m, 1H, OH), 3.46-3.32 (m, 1H, OH), 2.92–2.80 (m, 2H, H-24a, H-1b), 2.79–2.72 (m, 1H, H-25), 2.67–2.62 (d, $J = 16.1$ Hz, H-12a), 2.44–2.37 (m, 4H, H-22a, H-12b, H-24b, H-2a), 2.36–2.30 (m, 1H, H-2b), 2.23 (s, 3H, Ph-CH3), 2.21–2.14 (m, 1H, H-22b), 1.96–1.92 (m, 1H, H-6a), 1.92–1.87 (m,1H, H-20), 1.76-1.67 (m, 3H, H-17, H-16), 1.62–1.58 (m, 2H, H-5, H-6b), 1.42–1.34 (m, 1H, H-1a), 1.18 (s, 3H, CH3), 1.16 (d, $J = 6.7$ Hz, 3H, CH3), 1.15 (s, 3H, CH3), 1.03 (s, 3H, CH3), 1.01 (s, 3H, CH3), 0.89 (s, 3H, CH3), 0.78 (d, $J = 6.7$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.4, 209.2, 198.7, 172.9, 158.3, 139.0, 134.2, 133.0, 128.4, 119.0, 71.2, 67.7, 52.9, 50.6, 48.8, 47.6 47.0, 46.3, 45.7, 45.6, 36.9, 35.7, 35.1, 34.4, 33.2, 31.8, 27.8, 26.3, 19.9, 19.6, 18.6, 18.4, 18.4, 16.9, 16.2. HRMS calculated for C37H51NO6Na [M + Na]+ m/z 628.3609, found 628.3594.
## 3.1.4. Synthesis of (4-Chlorophenyl)-(7β,15α,25R)-7,15-Dhydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A4)
The title compound was obtained from p-chloroaniline following similar synthesis procedure of A1 (white powder, yield $26.3\%$). mp: 184.9–185.3 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.78 (s, 1H, CONH), 7.37 (d, $J = 8.6$ Hz, 2H, Ph-2, 6-H), 7.17 (d, $J = 8.7$ Hz, 2H, Ph-2, 6-H), 4.69–4.66 (m, 1H, H-15), 4.55–4.52 (m, 1H, H-7), 2.93–2.80 (m, 2H, H-24a, H-1b), 2.79–2.71 (m, 1H, H-25), 2.69–2.63 (d, $J = 16.1$ Hz, H-12a), 2.47–2.38 (m, 4H, H-22a, H-12b, H-24b, H-2a), 2.36–2.30 (m, 1H, H-2b), 2.21–2.14 (m, 1H, H-22b), 1.99–1.94 (m, 1H, H-6a), 1.94–1.88 (m,1H, H-20), 1.74–1.69 (m, 3H, H-17, H-16), 1.64–1.57(m, 2H, H-5, H-6b), 1.42–1.35(m, 1H, H-1a), 1.19–1.55 (m, 9H, 3 × CH3), 1.04 (s, 3H, CH3), 1.01 (s, 3H, CH3), 0.89 (s, 3H, CH3), 0.78 (d, $J = 6.2$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.3, 209.3, 198.5, 173.0, 158.1, 139.1, 135.5, 128.1, 127.9, 120.0, 71.3, 67.8, 52.8, 50.6, 48.7, 47.6, 47.0, 46.4, 45.7, 45.6, 36.9, 35.6, 35.2, 34.4, 33.2, 31.8, 27.9, 26.3, 19.6, 18.6, 18.4, 18.3, 16.8, 16.2. HRMS calculated for C36H48ClNO6Na [M + Na] + m/z 648.3062, found 648.3049.
## 3.1.5. Synthesis of (4-methylbenzyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A5)
The title compound was obtained from 4-methylphenyl following similar synthesis procedure of A1 (white powder, yield $91.9\%$). mp: 208.7–209.0 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.06 (m, 4H, Ph-H), 6.09 (t, $J = 7.2$ Hz, 1H, CONH), 4.70–4.67 (m, 1H, H-15), 4.53–4.51 (m, 1H, H-7), 4.29–4.28 (m, 2H, CONHCH2), 4.21–4.24 (m, 1H, OH), 3.64–3.45 (m, 1H, OH), 2.87–2.79 (m, 1H, H-1b), 2.78–2.73 (m, 1H, H-24a), 2.72-2.64 (m, 2H, H-25, H-12a), 2.45–2.30 (m, 5H, H-22a, H-12b, H-24b, H-2), 2.26 (s, 3H, Ph-CH3), 2.20–2.12 (m, 1H, H-22b), 1.97–1.93 (m, 1H, H-6a), 1.92–1.87 (m,1H, H-20), 1.76–1.69 (m, 3H, H-17, H-16), 1.64–1.56 (m, 2H, H-5, H-6b), 1.43–1.34 (m, 1H, H-1a), 1.19 (s, 3H, CH3), 1.18 (s, 3H, CH3), 1.10 (d, $J = 7.11$ Hz, 3H, CH3), 1.03 (s, 3H, CH3), 1.01 (s, 3H, CH3), 0.90 (s, 3H, CH3), 0.78 (d, $J = 6.4$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.4, 208.6, 198.7, 174.6, 158.5, 139.0, 136.1, 134.0, 128.3, 126.5, 71.2, 67.7, 52.9, 50.6, 48.8, 47.6, 47.0, 46.1, 45.7, 45.6, 42.3, 36.9, 35.2, 34.9, 34.5, 33.3, 31.7, 27.8, 26.3, 20.1, 19.6, 18.6, 18.5, 18.4, 17.0, 16.2. HRMS calculated for C38H53NO6Na [M + Na]+ m/z 642.3765, found 642.3752.
## 3.1.6. Synthesis of (4-Fluorobenzyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A6)
The title compound was obtained from p-fluorobenzylamine following similar synthesis procedure of A1 (white powder, yield $90.2\%$). mp: 178.4–179.3 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.16 (m, 2H, Ph-2, 6-H), 6.93 (m, 2H, Ph-3, 5-H), 6.18 (m, 1H, CONH), 4.70–4.67 (m, 1H, H-15), 4.53–4.52 (m, 1H, H-7), 4.34–4.26 (m, 2H, CONHCH2), 4.12–3.98 (m, 1H, OH), 3.56–3.37 (m, 1H, OH), 2.88–2.80 (m, 1H, H-1b), 2.78–2.64 (m, 3H, H-24a, H-12a, H-25), 2.47–2.30 (m, 5H, H-22a, H-12b, H-24b, H-2), 2.18–2.11 (m, 1H, H-22b), 1.99–1.93 (m, 1H, H-6a), 1.92–1.87 (m,1H, H-20), 1.75–1.69 (m, 3H, H-17, H-16), 1.64–1.58 (m, 2H, H-5, H-6b), 1.43–1.35 (m, 1H, H-1a), 1.19 (s, 3H, CH3), 1.17 (s, 3H, CH3), 1.10 (d, $J = 7.4$ Hz, 3H, CH3), 1.04 (s, 3H, CH3), 1.01 (s, 3H, CH3), 0.91 (s, 3H, CH3), 0.78 (d, $J = 6.4$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.3, 208.7, 198.6, 174.7, 161.9, 158.3, 139.1, 132.97, 132.95, 128.18, 128.13, 114.55, 114.41, 71.3, 67.8, 52.9, 50.6, 48.7, 47.6, 47.0, 46.1, 45.7, 45.6, 41.8, 36.9, 35.2, 34.9, 34.4, 33.2, 31.7, 28.7, 27.9, 26.3, 19.6, 18.6, 18.5, 18.4, 17.0, 16.2. HRMS calculated for C37H51FNO6 [M + H]+ m/z 624.3695, found 624.3686.
## 3.1.7. Synthesis of (4-Chlorobenzyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A7)
The title compound was obtained from p-chlorobenzylamine following similar synthesis procedure of A1 (white powder, yield $94.1\%$). mp: 188.6–189.4 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.21 (d, $J = 8.5$ Hz, 2H, Ph-2, 6-H), 7.11 (d, $J = 8.5$ Hz, 2H, Ph-3, 5-H), 6.28 (m, 1H, CONH), 4.67 (m, 1H, H-15), 4.52 (m, 1H, H-7), 4.34–4.25 (m, 2H, CONHCH2), 4.22–4.04 (m, 1H, OH), 3.74–3.38 (m, 1H, OH), 2.87–2.80 (m, 1H, H-1b), 2.78–2.64 (m, 3H, H-24a, H-12a, H-25), 2.46–2.28 (m, 5H, H-22a, H-12b, H-24b, H-2), 2.18-2.11 (m, 1H, H-22b), 2.00–1.93 (m, 1H, H-6a), 1.93–1.86 (m, 1H, H-20), 1.75–1.67 (m, 3H, H-17, H-16), 1.64–1.58 (m, 2H, H-5, H-6b), 1.44–1.33 (m, 1H, H-1a), 1.19 (s, 3H, CH3), 1.16 (s, 3H, CH3), 1.10 (d, $J = 7.0$ Hz, 3H, CH3), 1.04 (s, 3H, CH3), 1.01 (s, 3H, CH3), 0.90 (s, 3H, CH3), 0.78 (d, $J = 7.0$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.4, 208.7, 198.7, 174.8, 158.3, 139.0, 135.8, 132.1, 127.8, 127.7, 71.2, 67.7, 52.9, 50.6, 48.7, 47.6, 47.0, 46.1, 45.7, 45.6, 41.8, 36.9, 35.2, 34.7, 34.4, 33.2, 31.7, 27.8, 26.3, 19.6, 18.6, 18.5, 18.4, 17.0, 16.2. HRMS calculated for C37H50ClNO6Na [M + Na]+ m/z 662.3219, found 662.3206.
## 3.1.8. Synthesis of (3,5-Dichlorobenzyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A8)
The title compound was obtained from 3,5-dichlorobenzylamine following similar synthesis procedure of A1 (white powder, yield $96.5\%$). mp: 182.6–183.8 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.17 (m, 1H, Ph-4-H), 7.08 (m, 2H, Ph-2, 6-H), 6.49 (m, 1H, CONH), 4.67 (m, 1H, H-15), 4.53 (m, 1H, H-7), 4.40–4.19 (m, 2H, CONHCH2), 2.89–2.80 (m, 1H, H-1b), 2.79–2.64 (m, 3H, H-24a, H-12a, H-25), 2.46–2.36 (m, 4H, H-22a, H-12b, H-24b, H-2a), 2.34–2.27 (m, 1H, H-2b), 2.19–2.12 (m, 1H, H-22b), 1.97–1.89 (m, 2H, H-6a, H-20), 1.76–1.68 (m, 3H, H-17, H-16), 1.64–1.56 (m, 2H, H-5, H-6b), 1.42–1.33 (m, 1H, H-1a), 1.19 (s, 3H, CH3), 1.16 (s, 3H, CH3), 1.11 (d, $J = 6.9$ Hz, 3H, CH3), 1.04 (s, 3H, CH3), 1.01 (s, 3H, CH3), 0.90 (s, 3H, CH3), 0.77 (d, $J = 6.2$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 208.8, 198.8, 175.1, 158.5, 140.9, 139.0, 134.0, 126.4, 124.7, 71.2, 67.7, 52.9, 50.6, 48.5, 47.6, 47.0, 45.6, 41.3, 37.6, 36.9, 35.1, 34.9, 34.5, 33.2, 31.7, 27.8, 26.3, 19.6, 18.6, 18.5, 18.4, 17.0, 16.2. HRMS calculated for C37H49Cl2NO6Na [M + Na]+ m/z 696.2829, found 696.2818.
## 3.1.9. Synthesis of (2,3-Dihydro-1H-inden-2-yl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A9)
The title compound was obtained from 2-aminoindane HCl following similar synthesis procedure of A1 (white powder, yield $94.4\%$). mp: 199.7–200.3 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.23–7.22 (m, 2H, Ph-H), 7.19–7.17 (m, 2H, Ph-H), 6.10 (d, $J = 7.8$ Hz, 1H, CONH), 4.78 (m, 1H, H-15), 4.68–4.64 (m, 1H, CONHCH), 4.63–4.60 (m, 1H, H-7), 4.20–4.12 (m, 1H, OH), 3.62–3.52 (m, 1H, OH), 3.31–3.27 (m, 2H, CONHCHCH2), 2.88–2.82 (m, 2H, CONHCHCH2), 2.89–2.72 (m, 5H, H-1b, H-24a, H-12a, H-25, CONHCHCH2), 2.69–2.61 (m, 1H, CONHCHCH2), 2.53–2.35 (m, 5H, H-22a, H-12b, H-24b, H-2), 2.23–2.19 (m, 1H, H-22b), 2.07–2.01 (m, 1H, H-6a), 2.00–1.95 (m, 1H, H-20), 1.84–1.76 (m, 3H, H-17, H-16), 1.75–1.65 (m, 2H, H-5, H-6b), 1.50–1.42 (m, 1H, H-1a), 1.27 (s, 3H, CH3), 1.26 (s, 3H, CH3), 1.12–1.11 (d, $J = 7.3$ Hz, 6H, 2 × CH3), 1.09 (s, 3H, CH3), 0.99 (s, 3H, CH3), 0.89 (d, $J = 6.3$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 217.1, 209.5, 199.5, 175.3, 159.0, 140.8, 140.7, 140.2, 126.8, 126.7, 124.8, 124.7, 72.4, 68.8, 53.9, 51.7, 50.5, 49.8, 48.7, 48.1, 47.2, 46.8, 46.6, 43.4, 40.1, 40.0, 37.9, 36.4, 35.9, 35.5, 34.3, 32.7, 29.0, 27.3, 20.7, 19.6, 19.5, 19.4, 17.9, 17.3. HRMS calculated for C39H53NO6Na [M + Na]+ m/z 654.3765, found 654.3760.
## 3.1.10. Synthesis of (4-Methylphenethyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A10)
The title compound was obtained from 2-(4-methylphenyl) ethanamine following similar synthesis procedure of A1 (white powder, yield $85.6\%$). mp: 221.9–223.0 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.12 (d, $J = 8.0$ Hz, 2H, Ph-H), 7.09 (d, $J = 8.0$ Hz, 2H, Ph-H), 5.82–5.77 (m, 1H, CONH), 4.80–4.76 (m, 1H, H-15), 4.63–4.59 (m, 1H, H-7), 4.18–4.12 (m, 1H, OH-7), 3.55–3.39 (m, 3H, CONHCH2, OH-15), 2.88–2.80 (m, 2H, H-1b, H-24a), 2.78–2.64 (m, 4H, H-12a, H-25, CH2Ph), 2.52–2.46 (m, 3H, H-22a, H-12b, H-24b), 2.42–2.36 (m, 2H, H-2), 2.33 (s, 1H, Ph-CH3), 2.25–2.18 (m, 1H, H-22b), 2.07–2.00 (m, 1H, H-6a), 2.00–1.95 (m, 1H, H-20), 1.84–1.77 (m, 3H, H-17, H-16), 1.72–1.66 (m, 2H, H-5, H-6b), 1.50–1.42 (m, 1H, H-1a), 1.27 (s, 3H, CH3), 1.25 (s, 3H, CH3), 1.11–1.09 (m, 9H, 3 ×CH3), 0.99 (s, 3H, CH3), 0.87 (d, $J = 6.5$ Hz, 2H). 13C-APT (150 MHz, CDCl3) δ: 217.2, 209.5, 199.6, 175.6, 159.3, 140.2, 136.1, 135.6, 129.3, 128.7, 72.4, 68.8, 53.9, 51.7, 49.9, 48.7, 48.1, 47.0, 46.8, 46.6, 40.8, 38.0, 36.4, 36.0, 35.5, 35.2, 34.3, 32.8, 29.0, 27.4, 21.1, 20.7, 19.6, 19.5, 19.4, 18.0, 17.3. HRMS calculated for C39H55NO6Na [M + Na]+ m/z 656.3922, found 656.3910.
## 3.1.11. Synthesis of (4-Fluorophenethyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A11)
The title compound was obtained from 4-fluorophenethylamine hydrochloride following similar synthesis procedure of A1 (white powder, yield $97.2\%$). mp: 188.1–189.0 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.09 (m, 2H, Ph-H), 6.92 (m, 2H, Ph-H), 5.83 (m, 1H, CONH), 4.50 (m, 1H, H-15), 4.54 (m, 1H, H-7), 3.42–3.34 (m, 2H, CONHCH2), 2.81–2.73 (m, 2H, H-1b, H-24a), 2.72–2.56 (m, 4H, H-12a, H-25, CH2Ph), 2.45–2.37 (m, 3H, H-22a, H-12b, H-24b), 2.36–2.28 (m, 2H, H-2), 2.19–2.10(m, 1H, H-22b), 1.99–1.94 (m, 1H, H-6a), 1.93–1.87 (m, 1H, H-20), 1.77–1.70 (m, 3H, H-17, H-16), 1.67–1.59 (m, 2H, H-5, H-6b), 1.44–1.35(m, 1H, H-1a), 1.20 (s, 3H, CH3), 1.18 (s, 3H, CH3), 1.04–1.02 (m, 9H, 3 × CH3), 0.91 (s, 3H, CH3), 0.79 (d, $J = 6.4$ Hz, 2H). 13C-APT (150 MHz, CDCl3) δ: 216.4, 208.6, 198.6, 174.7, 161.4, 159.8, 158.4, 139.0, 133.39, 133.37, 129.2, 129.2, 114.4, 114.3, 71.2, 67.7, 52.9, 50.6, 48.8, 47.6, 47.0, 46.0, 45.7, 39.7, 36.9, 35.2, 34.9, 34.4, 33.8, 33.2, 31.7, 27.9, 26.3, 19.6, 18.6, 18.5, 18.4, 17.0, 16.2. HRMS calculated for C38H52FNO6Na [M + Na]+ m/z 660.3671, found 660.3650.
## 3.1.12. Synthesis of (4-Chlorophenethyl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A12)
The title compound was obtained from 4-chlorobenzeneethanamine following similar synthesis procedure of A1 (white powder, yield $95.5\%$). mp: 195.4–196.1 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.20 (m, 2H, Ph-H), 7.07 (m, 2H, Ph-H), 5.78 (m, 1H, CONH), 4.70 (m, 1H, H-15), 4.55 (m, 1H, H-7), 3.99 (s, 1H, OH), 3.67 (m, 3H, OH, CONHCH2), 2.81–2.73 (m, 2H, H-1b, H-24a), 2.72–2.55 (m, 4H, H-12a, H-25, CH2Ph), 2.45–2.37 (m, 3H, H-22a, H-12b, H-24b), 2.35–2.28 (m, 2H, H-2), 2.18–2.09(m, 1H, H-22b), 2.00–1.94 (m, 1H, H-6a), 1.93–1.87 (m, 1H, H-20), 1.78–1.70 (m, 3H, H-17, H-16), 1.66–1.59 (m, 2H, H-5, H-6b), 1.45–1.30 (m, 1H, H-1a), 1.19 (s, 3H, CH3), 1.18 (s, 3H, CH3), 1.04–1.02 (m, 9H, 3 × CH3), 0.91 (s, 3H, CH3), 0.79 (d, $J = 6.5$ Hz, 2H). 13C-APT (150 MHz, CDCl3) δ: 216.4, 208.6, 198.6, 174.7, 158.3, 139.1, 136.2, 131.3, 129.1, 127.7, 71.3, 67.7, 52.9, 50.6, 48.8, 47.6, 47.0, 46.0, 45.7, 45.6, 39.5, 36.9, 35.2, 34.9, 34.5, 34.0, 33.3, 31.7, 27.9, 26.3, 19.6, 18.6, 18.5, 18.4, 17.0, 16.2. HRMS calculated for C38H53ClNO6 [M + H]+ m/z 654.3556, found 654.3547.
## 3.1.13. Synthesis of (4-Methylpiperazin-1-yl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A13)
The title compound was obtained from methylpiperazine following similar synthesis procedure of A1 (white powder, yield $92.7\%$). mp: 173.2–174.9 °C. 1H-NMR (600 MHz, CDCl3) δ: 4.75–4.72 (m, 1H, H-15), 4.58–4.56 (m, 1H, H-7), 3.74–3.68 (m, 2H, CONCH2), 3.61−3.54 (m, 2H, CONCH2), 3.22–3.12 (1H, H-25), 3.01–2.95 (m, 1H, H-1b), 2.83–2.77 (m, 1H, H-24a), 2.72 (d, $J = 16.5$ Hz, 1H, H-12a), 2.64–2.51 (m, 2H, CH2N), 2.51–2.33 (m, 10H, H-24b, H-12b, H-22a, H-2, NCH3, CH2N), 2.25–2.16 (m, 1H, H-22b), 2.02–1.97 (m, 1H, H-6a), 1.97–1.91 (m, 1H, H-20), 1.82–1.73 (m, 3H, H-17, H-16), 1.71–1.62 (m, 2H, H-5, H-6b), 1.48–1.39 (m, 1H, H-1a), 1.24 (s, 3H, CH3), 1.22 (s, 3H, CH3), 1.09–1.06 (m, 9H, 3 × CH3), 0.98 (s, 3H, CH3), 0.84 (d, $J = 6.5$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.4, 208.8, 198.7, 173.1, 158.7, 138.9, 71.1, 67.6, 53.9, 53.5, 52.9, 50.7, 48.7, 47.6, 47.1, 46.2, 45.7, 45.6, 44.6, 44.1, 40.3, 36.9, 35.1, 34.5, 33.3, 31.8, 29.8, 27.8, 26.3, 19.7, 18.6, 18.5, 18.4, 16.5, 16.3. HRMS calculated for C35H55N2O6 [M + H]+ m/z 599.4055, found 599.4036.
## 3.1.14. Synthesis of (4-Ethylpiperazin-1-yl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A14)
The title compound was obtained from 1-ethylpiperazine following similar synthesis procedure of A1 (white powder, yield $98.1\%$). mp: 198.5–199.4 °C. 1H-NMR (600 MHz, CDCl3) δ: 4.72−4.70 (m, 1H, H-15), 4.56−4.54 (m, 1H, H-7), 3.82–3.72 (m, 2H, CONCH2), 3.66−3.50 (m, 2H, CONCH2), 3.18–3.09 (1H, H-25), 2.98–2.90 (m, 1H, H-1b), 2.81–2.72 (m, 1H, H-24a), 2.72–2.66 (m, 3H, H-12a, CH2), 2.66–2.31 (m, 9H, H-24b, H-12b, H-22a, H-2, CH2N), 2.22–2.12 (m, 1H, H-22b), 1.99–1.94 (m, 1H, H-6a), 1.94–1.86 (m, 1H, H-20), 1.77–1.70 (m, 3H, H-17, H-16), 1.49–1.60 (m, 2H, H-5, H-6b), 1.45–1.35 (m, 1H, H-1a), 1.20 (s, 3H, CH3), 1.18 (s, 3H, CH3), 1.09 (t, $J = 7.0$ Hz, 3H, NCH2CH3), 1.05–1.02 (m, 9H, 3 × CH3), 0.92 (s, 3H, CH3), 0.80 (d, $J = 6.2$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.4, 208.9, 198.6, 173.2, 158.4, 139.0, 71.2, 67.7, 52.9, 51.5, 51.2, 51.0, 50.7, 48.8, 47.6, 47.1, 46.2, 45.7, 45.6, 43.7, 40.0, 36.9, 35.2, 34.5, 33.3, 31.8, 29.7, 27.9, 26.4, 19.7, 18.6, 18.5, 18.4, 16.5, 16.3. HRMS calculated for C36H57N2O6 [M + H]+ m/z 613.4211, found 613.4205.
## 3.1.15. Synthesis of (4-Phenylpiperazin-1-yl)-(7β,15α,25R)-7,15-Dihydroxy-3,11,23-Trioxolanost-8-en-26-oic Amide (A15)
The title compound was obtained from 1-phenylpiperazine following similar synthesis procedure of A1 (white powder, yield $89.9\%$). mp: 229.4–229.9 °C. 1H-NMR (600 MHz, CDCl3) δ: 7.23−7.21 (m, 2H, Ph-H), 6.88−6.83 (m, 3H, Ph-H), 4.72−4.69 (m, 1H, H-15), 4.55−4.53 (m, 1H, H-7), 3.75−3.71 (m, 2H, CONCH2), 3.65−3.62 (m, 2H, CONCH2), 3.26–3.16 (m, 2H, CH2), 3.17–3.10(1H, H-25), 3.10–3.05 (m, 2H, CH2), 3.00–2.92 (m, 1H, H-1b), 2.80–2.74 (m, 1H, H-24a), 2.78 (d, $J = 16.5$ Hz, 1H, H-12a), 2.45–2.39 (m, 3H, H-22a, H-12b, H-24b), 2.38–2.31 (m, 2H, H-2), 2.22–2.14 (m, 1H, H-22b), 2.00–1.94 (m, 1H, H-6a), 1.94–1.88 (m, 1H, H-20), 1.78–1.71 (m, 3H, H-17, H-16), 1.66–1.58 (m, 2H, H-5, H-6b), 1.43–1.35 (m, 1H, H-1a), 1.20 (s, 3H, CH3), 1.18 (s, 3H, CH3), 1.06 (d, $J = 7.1$ Hz, 3H, CH3), 1.04 (s, 3H, CH3), 1.02 (s, 3H, CH3), 0.92 (s, 3H, CH3), 0.80 (d, $J = 6.4$ Hz, 3H, CH3). 13C-APT (150 MHz, CDCl3) δ: 216.3, 208.6, 198.6, 173.1, 158.2, 149.8, 139.1, 128.2, 119.5, 115.5, 71.3, 67.7, 52.9, 50.6, 48.8, 48.7, 48.4, 47.6, 47.1, 46.2, 45.7, 45.6, 44.6, 40.9, 37.6, 36.9, 35.3, 34.4, 33.2, 31.7, 29.9, 27.9, 26.3, 19.6, 18.6, 18.5, 18.4, 16.6, 16.2. HRMS calculated for C40H57N2O [M + H]+ m/z 661.4211, found 661.4203.
## 3.2. Cell Culture
The cell lines present in this study were obtained from Procell Life Science & Technology Co. Ltd. MCF-7, HepG2 and SJSA-1 cells were cultured in DMEM medium (DMEM, Gibco) supplemented with $10\%$ fetal bovine serum (FBS, Gibco), $1\%$ penicillin-streptomycin (Hyclone) at 37℃ in a humid environment with $5\%$ CO2. HK-2 cells were cultured in DMEM/F12 (1:1) medium and placed in incubators in the same environment.
## 3.3. Cell Viability Assay
Cell viability was determined by MTT assay. MCF-7, HepG2, SJSA-1 and HK-2 cells (6 × 103 cells /well) were seeded in 96-well plates with serum-free medium for 24 h. Then MCF-7, HepG2, SJSA-1 cells were treated with $0.1\%$ DMSO, 25, 50, 100 μM of GAA derivatives for 48 h (MCF-7, HepG2, HK2) or 72 h (SJSA-1). After 48 or 72 h, 10 μL MTT (5 mg/mL, Beyotime) was added and incubated at 37 °C for 4 h. Then 100 μL of lysate was added. After complete dissolution of the crystal, the absorbance was measured at 540 nm and expressed as the average percentage of absorbance between treated and control cells. The value for control cells was set at $100\%$. Cell survival rate was calculated as the ratio of the absorbance of the cells and negative control after minus the blank absorbance respectively.
## 3.4. Target Fishing and Molecular Docking by In Silico Approaches
The binding targets of GAA were predicted using Discovery Studio 2016 v16.1 (BIOVIA Software Inc., San Diego, CA, USA), a software suite for the computational analysis of data relevant to Life Sciences research. To predict the probable targets of GAA, we used Ligand Profiler protocol which maps a set of pharmacophores, including Pharma DB by default. The ligand GAA was prepared by the Specifying Ligands parameter protocol. After setting parameters, the job was run, and the results were gained for three days. To explore the potential binding mode of GAA and A2 with MDM2 protein (PDB code: 4j3e), a molecular modeling research was performed with docking program named Induced-Fit, a refinement method in another software MOE. To eliminate any bond length and bond angle biases, the ligands (GAA and A2) were subjected to the “energy minimize” prior to docking. The binding affinities (S-values) in MOE were used to evaluate the interactions between MDM2 and ligands. The scores (binding affinities) were obtained based on the virtual calculation of various interactions of ligands with the targeted receptor.
## 3.5. Surface Plasmon Resonance (SPR) Assay
GAA derivatives bound to MDM2 protein were assayed with a molecular interaction analyzer (PALL FORTEBIO, USA). MDM2 protein (5 mg/mL, Protintech) was immobilized on a PCH sensor chip (Octet) and preactivated with EDC/NHS mixture for 420 s at a flow rate of 10 μL/min. A2 was diluted to 100, 50, 25, 12.5, 6.25, 3.13, 0 μM with PBST buffer containing $1\%$ DMSO. The binding time was 600 s, and the flow rate was 20 μL/min. The dissociation time was 180 s, and the affinity constant KD value was obtained by computer fitting and steady-state analysis.
## 3.6. Flow Cytometric Analysis of the Apoptosis Rate with Annexin V-FITC/PI Staining
To determine the apoptosis rate, an Annexin V-FITC/PI double staining apoptosis assay kit (Beyotime) was used to detect apoptotic cells by flow cytometry (BD FACSALOBUR), according to the manufacturer’s instructions. Briefly, SJSA-1 cells were treated with $0.1\%$ DMSO, 12.5, 25, and 50 μM of A2 for 24 h. After harvesting, the cells were incubated with 5 μL Annexin V-FITC for 15 min and 10 μL PI for 5 min at 4 °C under dark conditions. Flow cytometry was then performed to analyze the apoptosis rate. Data were analyzed by using BD FACSDiva 8.0.1.
## 3.7. Western Blot Analysis of Protein Expression
For Western blot analysis, MCF-7 and SJSA-1 cells were treated with different concentrations of A2 for 24 h. The total cell protein was extracted, and proteins were isolated using $10\%$ SDS-PAGE gel system. The proteins on the gel were transferred to PVDF membrane, blocked in $5\%$ BSA at room temperature for 2 h, incubated in primary antibody dilution at 4 °C overnight, and washed with TBST for 3 times, 10 min each. Then, they were transferred to dilute release solution of secondary antibody and incubated at room temperature for 2 h. ECL chemiluminescence development solution (Beyotime, BeyoECL star) was added uniformly and detected on gel imaging system (Clinx ChemiScope, China). Antibodies for blotting were MDM2 (abcam, ab16895), P53 (Proteintech, 10442-1-AP), Bcl-2 (CST, 15071S), Bax (CST, 2772T) and β-actin (abcam, ab8226).
## 3.8. Statistical Analysis
The results are expressed as the means ± standard deviation. A one-way AVONA and t-test were used for comparison of differences between groups, and GraphPad Prism 8.0 software was used for graph and statistical analysis. Statistical significance was set at $p \leq 0.05.$
## 4. Conclusions
Natural products are rich in beneficial scaffolds that have been used in anti-tumor, anti-inflammatory, neuroprotective and other aspects. However, these natural products have the problem of unclear targets and weak activity. Therefore, if we can determine the relevant mechanism of the action of natural products and identify specific pathways and targets, we can improve their activity based on adding appropriate interaction with binding amino acid residues in the active pocket of the target. In this study, we modified *Ganoderma lucidum* triterpenoid compound GAA and evaluated anti-proliferative effects of these derivatives in different tumor cell lines. Finally, compound A2 was selected for further investigation of its mechanism. The results showed that A2 could induce apoptosis by interfering with the MDM2-p53 pathway. Target fishing and SPR experiments suggested that A2 might play a role by binding to MDM2 and blocking its inhibition of p53. Although these compounds may have weaker anti-tumor activity than other small molecule anti-tumor drugs, this study may provide insights into finding the target of GAA and developing new natural product anti-cancer compounds. If we can confirm the specific targets of GAA in different diseases, we can carry out target-based rational design of GAA to greatly improve its efficacy and provide an excellent scaffold for the development of new drugs.
## 5. Patents
In order to protect the structure and activity of compounds in a timely manner, the patent Preparation method of Ganoderic A amide derivatives useful as anti-tumor drugs, China CN112574272 A 2021-03-30, refers to the synthesis of the derivatives and simple in vitro cell anti-proliferation screening. In subsequent studies, the activity of the derivatives in other cell lines was found and the mechanism was investigated. The relevant experimental results are presented in this article.
## Figures, Scheme and Tables
**Figure 1:** *The structure of GAA and compound A2.* **Figure 2:** *MDM2-p53 negative feedback regulatory loop.* **Figure 3:** *The anti-proliferation effect of GAA derivatives on different cell lines. (A) The viability of MCF-7 cells treated for 48 h with 50 µM concentration of synthesized compounds; (B) The viability of HepG2 cells treated for 48 h with 50 µM concentration of synthesized compounds; (C) The viability of SJSA-1 cells treated for 72 h with 50 µM concentration of synthesized compounds; (D) The viability of HK2 cells treated for 48 h with 100 µM concentration of tested compounds. All data are presented as mean ± SD of three independent experiments. CON refers to cells treated with $0.1\%$ DMSO; ** $p \leq 0.01$, *** $p \leq 0.01$, **** $p \leq 0.0001$ vs GAA by ANOVA.* **Scheme 1:** **The synthesis* of GAA derivatives. Reagents and conditions: TBTU, DIPEA, DCM, rt, 0.5–2 h.* **Figure 4:** *Flow cytometry analysis of SJSA-1 cells after 24 h incubation with A2 at concentration of 12.5 µM, 25 µM, 50 µM and subsequent staining with Annexin V and propidium iodide. Q3 and the percentage means the proportion of the early phase apoptosis cells and Q2 and the percentage means the proportion of the late phase apoptosis cells.* **Figure 5:** *The effect of A2 on the relative protein expression. The protein expression in MCF-7 (A) and SJSA-1 (B) treated with A2 for 24 h with a concentration of 12.5, 25 and 50 µM. NC refers to $0.1\%$ DMSO.* **Figure 6:** *Molecular docking results of MDM2 (PBD ID: 4j3e) with GAA (A) and A2 (B). The pose of GAA in MDM2 is that the carboxyl term exposed to the solvent, the ring core is located in the pocket, and the hydroxyl-H has hydrogen bond interaction with Met58. The pose of A2 in MDM2 is opposite to that of GAA. The longer hydrophobic n-hexyl anchors into the pocket and has hydrophobic interaction with His92. The ring part faces out of the solvent and retains the interaction with Met58.* **Figure 7:** *The binding curve of MDM2 with GAA (A) and A2 (B).* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2
## References
1. Zhao R., He Y.. **Network pharmacology analysis of the anti-cancer pharmacological mechanisms of**. *J. Ethnopharmacol.* (2018) **210** 287-295. DOI: 10.1016/j.jep.2017.08.041
2. Zhang Y., Wang X., Yang X., Yang X., Xue J., Yang Y.. **Ganoderic Acid A to Alleviate Neuroinflammation of Alzheimer’s Disease in Mice by Regulating the Imbalance of the Th17/Tregs Axis**. *J. Agric. Food Chem.* (2021) **69** 14204-14214. DOI: 10.1021/acs.jafc.1c06304
3. Zheng S., Ma J., Zhao X., Yu X., Ma Y.. **Ganoderic Acid A Attenuates IL-1β-Induced Inflammation in Human Nucleus Pulposus Cells Through Inhibiting the NF-κB Pathway**. *Inflammation* (2021) **45** 851-862. DOI: 10.1007/s10753-021-01590-0
4. Jia Y., Zhang D., Li H., Luo S., Xiao Y., Zhou F., Wang C., Feng L., Wang G., Wu P.. **Activation of FXR by ganoderic acid A promotes remyelination in multiple sclerosis via anti-inflammation and regeneration mechanism**. *Biochem. Pharmacol.* (2021) **185** 114422. DOI: 10.1016/j.bcp.2021.114422
5. Zhang L., Zhang L., Sui R.. **Ganoderic Acid A-Mediated Modulation of Microglial Polarization is Involved in Depressive-Like Behaviors and Neuroinflammation in a Rat Model of Post-Stroke Depression**. *Neuropsych. Dis. Treat.* (2021) **17** 2671-2681. DOI: 10.2147/NDT.S317207
6. Bao H., Li H., Jia Y., Xiao Y., Luo S., Zhang D., Han L., Dai L., Xiao C., Feng L.. **Ganoderic acid A exerted antidepressant-like action through FXR modulated NLRP3 inflammasome and synaptic activity**. *Biochem. Pharmacol.* (2021) **188** 114561. DOI: 10.1016/j.bcp.2021.114561
7. Qi L., Liu S., Liu Y., Li P., Xu X.. **Ganoderic Acid A Promotes Amyloid-β Clearance (In Vitro) and Ameliorates Cognitive Deficiency in Alzheimer’s Disease (Mouse Model) through Autophagy Induced by Activating Axl**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22115559
8. Yu Z., Jia W., Liu C., Wang H., Yang H., He G., Chen R., Du G.. **Ganoderic acid A protects neural cells against NO stress injury in vitro via stimulating β adrenergic receptors**. *Acta Pharmacol. Sin.* (2020) **41** 516-522. DOI: 10.1038/s41401-020-0356-z
9. Ma J., Zhang Y., Tian Z.. **Anti-oxidant, anti-inflammatory and anti-fibrosis effects of ganoderic acid A on carbon tetrachloride induced nephrotoxicity by regulating the Trx/TrxR and JAK/ROCK pathway**. *Chem. Biol. Interact.* (2021) **344** 109529. DOI: 10.1016/j.cbi.2021.109529
10. Liu F., Shi K., Dong J., Jin Z., Wu Y., Cai Y., Lin T., Cai Q., Liu L., Zhang Y.. **Ganoderic acid A attenuates high-fat-diet-induced liver injury in rats by regulating the lipid oxidation and liver inflammation**. *Arch. Pharm. Res.* (2020) **43** 744-754. DOI: 10.1007/s12272-020-01256-9
11. Li X., Li Y., Song H.. **Ganoderic acid A against cyclophosphamide-induced hepatic toxicity in mice**. *J. Biochem. Mol. Toxic.* (2019) **33** e22271. DOI: 10.1002/jbt.22271
12. Guo W., Guo J., Liu B., Lu J., Chen M., Liu B., Bai W., Rao P., Ni L., Lv X.. **Ganoderic acid A from**. *Food Funct.* (2020) **11** 6818-6833. DOI: 10.1039/D0FO00436G
13. Zhu J., Jin J., Ding J., Li S., Cen P., Wang K., Wang H., Xia J.. **Ganoderic Acid A improves high fat diet-induced obesity, lipid accumulation and insulin sensitivity through regulating SREBP pathway**. *Chem. Biol. Interact.* (2018) **290** 77-87. DOI: 10.1016/j.cbi.2018.05.014
14. Zhang Y., Shi K., Lin T., Xia F., Cai Y., Ye Y., Liu L., Liu F.. **Ganoderic acid A alleviates myocardial ischemia-reperfusion injury in rats by regulating JAK2/STAT3/NF-κB pathway**. *Int. Immunopharmacol.* (2020) **84** 106543. DOI: 10.1016/j.intimp.2020.106543
15. Cheng Y., Xie P.. **Ganoderic acid A holds promising cytotoxicity on human glioblastoma mediated by incurring apoptosis and autophagy and inactivating PI3K/AKT signaling pathway**. *J. Biochem. Mol. Toxic.* (2019) **33** e22392. DOI: 10.1002/jbt.22392
16. Liu H., Zheng K., Shan X.. **Effects of Ganoderic Acid A on Proliferation, Apoptosis and Invasion of Human Glioma U251 Cells**. *Chin. J. Comp. Med.* (2016) **26** 64-69
17. Kanapathipillai M.. **Treating p53 Mutant Aggregation-Associated Cancer**. *Cancers* (2018) **10**. DOI: 10.3390/cancers10060154
18. Zhao Y., Yu H., Hu W.. **The regulation of MDM2 oncogene and its impact on human cancers**. *Acta. Biochim. Biophys. Sin.* (2014) **46** 180-189. DOI: 10.1093/abbs/gmt147
19. Li B., Cheng Q., Li Z., Chen J.. **p53 inactivation by MDM2 and MDMX negative feedback loops in testicular germ cell tumors**. *Cell Cycle* (2010) **9** 1411-1420. DOI: 10.4161/cc.9.7.11255
20. Nakamura M., Obata T., Daikoku T., Fujiwara H.. **The Association and Significance of p53 in Gynecologic Cancers: The Potential of Targeted Therapy**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20215482
21. Munisamy M., Mukherjee N., Thomas L., Pham A., Shakeri A., Zhao Y., Kolesar J., Rao P., Rangnekar V.M., Rao M.. **Therapeutic opportunities in cancer therapy: Targeting the p53-MDM2/MDMX interactions**. *Am. J. Cancer Res.* (2021) **11** 5762-5781. PMID: 35018225
22. Hassin O., Oren M.. **Drugging p53 in cancer: One protein, many targets**. *Nat. Rev. Drug Discov.* (2023) **22** 127-144. DOI: 10.1038/s41573-022-00571-8
23. Xu B., Jia W., Wang Z., Zhang H., Wu D., Tang C., Yang Y., Liu D., Zhang J., Wang W.. **Inhibitory effect and mechanism of ganoderic acid A on the growth of prostate cancer LNCaP cells**. *Mycosystema* (2019) **38** 717-727. DOI: 10.13346/j.mycosystema.190004
24. Tang W., Wu Y., Wang Y., Sun P., Ouyang J.. **P53 protein participates in the process of ganoderic acid inhibiting the proliferation of cancer cells**. *Sci. Technol. Food Ind.* (2015) **36** 193-196. DOI: 10.13386/j.issn1002-0306.2015.11.030
25. Froufe H., Abreu R., Ferreira I.. **Virtual screening of low molecular weight mushrooms compounds as potential Mdm2 inhibitors**. *J. Enzym. Inhib. Med. Chem.* (2013) **28** 569-575. DOI: 10.3109/14756366.2012.658787
26. Staszczak M.. **Fungal Secondary Metabolites as Inhibitors of the Ubiquitin-Proteasome System**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms222413309
27. Walczak J., Maksymilian D., Ziomkowska M., Śliwka-Kaszyńska M., Daśko M., Trzonkowski P., Cholewiński G.. **Novel amides of mycophenolic acid and some heterocyclic derivatives as immunosuppressive agents**. *J. Enzym. Inhib. Med. Chem.* (2022) **37** 2725-2741. DOI: 10.1080/14756366.2022.2127701
28. Nassim E., Wang M.. **Synthesis and biological evaluation of naloxone and naltrexone-derived hybrid opioids**. *Med. Chem.* (2012) **8** 683-689. DOI: 10.2174/157340612801216193
29. Li Y., Yang J., Aguilar A., McEachern D., Przybranowski S., Liu L., Yang C., Wang M., Han X., Wang S.. **Discovery of MD-224 as a First-in-Class, Highly Potent, and Efficacious Proteolysis Targeting Chimera Murine Double Minute 2 Degrader Capable of Achieving Complete and Durable Tumor Regression**. *J. Med. Chem.* (2019) **62** 448-466. DOI: 10.1021/acs.jmedchem.8b00909
30. Wurz R., Cee V.. **Targeted Degradation of MDM2 as a New Approach to Improve the Efficacy of MDM2-p53 Inhibitors**. *J. Med. Chem.* (2019) **62** 445-447. DOI: 10.1021/acs.jmedchem.8b01945
31. Lodi G., Gentili V., Casciano F., Romani A., Zauli G., Secchiero P., Zauli E., Simioni C., Beltrami S., Fernandez M.. **Cell cycle block by p53 activation reduces SARS-CoV-2 release in infected alveolar basal epithelial A549-hACE2 cells**. *Front. Pharmacol.* (2022) **13** 1018761. DOI: 10.3389/fphar.2022.1018761
|
---
title: Prospective Associations between Cumulative Average Intake of Flavonoids and
Hypertension Risk in the CArdioVascular Disease Association Study (CAVAS)
authors:
- Ji-Sook Kong
- Yu-Mi Kim
- Hye-Won Woo
- Min-Ho Shin
- Sang-Baek Koh
- Hyeon-Chang Kim
- Jin-Ho Shin
- Mi-Kyung Kim
journal: Nutrients
year: 2023
pmcid: PMC10004779
doi: 10.3390/nu15051186
license: CC BY 4.0
---
# Prospective Associations between Cumulative Average Intake of Flavonoids and Hypertension Risk in the CArdioVascular Disease Association Study (CAVAS)
## Abstract
In this study, we aimed to investigate the prospective associations and their shapes between the dietary intake of total flavonoids and their seven subclasses and hypertension risk in a prospective cohort, the KoGES_CArdioVascular disease Association Study (CAVAS), and to consider obesity status as an additional factor. A total of 10,325 adults aged 40 years and older were enrolled at baseline, and 2159 patients were newly diagnosed with hypertension during a median follow-up of 4.95 years. Cumulative dietary intake was estimated using a repeated food frequency questionnaire. Incidence rate ratios (IRRs) with $95\%$ confidence intervals (CIs) were estimated using modified Poisson models with a robust error estimator. We observed nonlinear inverse associations between total and seven subclasses of flavonoids and hypertension risk, although there was no significant association between total flavonoids and flavones with hypertension risk in the highest quartile. For men, these inverse associations tended to be pronounced in the high BMI group, particularly for anthocyanins and proanthocyanidins [IRR ($95\%$ CI) in overweight/obese men: 0.53 (0.42–0.67) for anthocyanins; 0.55 (0.42–0.71) for proanthocyanidins]. Our results suggested that consumption of dietary flavonoids may not be dose-responsive but is inversely associated with hypertension risk, particularly among overweight/obese men.
## 1. Introduction
Hypertension is one of the most important risk factors for all-cause mortality and cardiovascular diseases (CVD) [1,2]. High blood pressure (BP) was attributed to 10.8 million deaths worldwide [3]. Additionally, the prevalence and absolute burden of hypertension are rising globally. Specifically, the global age-standardized prevalence of hypertension was estimated to be $30.1\%$ and $31.9\%$ in women and men, respectively [4]. Therefore, maintaining an optimal BP is a key factor in reducing the burden of diseases related to hypertension. To maintain an optimal BP, certain lifestyle changes are necessary; these changes include adopting a healthy diet and exercising. Overall, these are the cheapest and most sustainable ways to achieve this aim.
Flavonoids are polyphenolic phytochemicals with potential health benefits arising from their antioxidant activities [5]. Additionally, core components of healthy dietary patterns, including the Dietary Approach to Stop Hypertension (DASH) and the Mediterranean diet, such as fruits, vegetables, and legumes, are rich sources of flavonoids [6]. Flavonoids have been intensively studied within the context of their role in the development of non-communicable diseases [6,7]. However, few studies have elucidated the relationship between flavonoids and hypertension; this is especially true when the longitudinal association of various subclasses of flavonoids and hypertension is concerned [8]. In addition to the lack of evidence, some issues should be taken into account in the study of the relationship between total flavonoids and their subclasses and hypertension risk: [1] Differences in the combinations of flavonoids consumed across the globe may result from differences in dietary cultures and their corresponding dietary patterns; this issue is exacerbated by the fact that most previous studies looking into this issue have been conducted in Western populations; [2] Dietary assessments may be confounded by measurement errors, and dietary preferences could be changed over the course of the study. However, only single dietary assessment data were used in these studies; [3] Furthermore, a systematic review of the relationship between flavonoids and vascular function pointed to the possibility that it does not follow a classical linear dose-response association [9]. Thus, the shape of the relationship (linearity or non-linearity) should be tested; [4] Finally, a few previous studies showed that the association of dietary factors with hypertension risk differed according to obesity status [10,11]. In addition, it was reported that flavonoids, such as quercetin, may lower BP in a male rat with diet-induced obesity [12]. However, there is no evidence of an association between flavonoids and hypertension, considering obesity status as a stratum.
We aimed to evaluate the prospective associations, as well as their shapes, between the cumulative average intake of the total flavonoids and its seven subclasses and hypertension risk among adults aged 40 years or older. Additionally, we evaluated whether there were differences in the associations between dietary flavonoids and hypertension risk according to obesity status.
## 2.1. Study Population
The Korean CArdioVascular disease Association Study (CAVAS) was a prospective multicenter cohort study that was part of the population-based Korean Genome and Epidemiology Study (KoGES) consortium [13]. CAVAS was established to investigate the associations of genetic and environmental risk factors with cardiometabolic diseases. Furthermore, this study was a combined cohort of three rural community cohorts: the Multi-Rural Communities Cohort (MRCohort), composed of Yangpyeong, Namwon, and Goryeong counties; the ARIRANG, composed of Wonju and Pyeongchang counties; and the Kangwha cohort. Using multistage cluster sampling, each cohort recruited community-dwelling residents aged ≥40 years. A total of 19,546 participants without CVD and/or cancer participated in the baseline survey between 2005 and 2011 (the MRCohort recruited 9759 participants, the ARIRANG recruited 5942 participants, and the Kangwha cohort recruited 3845 participants from Kangwha) and were followed up every 2–4 years from 2007 to 2017, with $78.2\%$ of them having more than one revisit (Supplementary Figure S1). Interviewers and examiners, who were trained by trainees from the quality control center, collected data in a manner that strictly adhered to standard protocols for questionnaire surveys and examinations. This was necessary to prevent limitations associated with multicenter studies from impacting the validity of the resultant findings.
We excluded participants with the following conditions in the baseline survey: prior physician-diagnosed hypertension and taking antihypertensive medications; systolic BP (SBP) ≥ 140 mmHg or diastolic BP (DBP) ≥ 90 mmHg ($$n = 8087$$); missing information on the baseline hypertension identification ($$n = 39$$); incomplete (≥10 items blank on the food frequency questionnaire (FFQ)) or implausible (≥ 99.5th or ≤ 0.5th percentile of total energy intake) dietary intake ($$n = 179$$); or missing data on covariates ($$n = 335$$) including education level, smoking status, regular exercise, alcohol consumption, body mass index (BMI), family history of hypertension, menopausal status (only women), serum creatinine, and diabetes status identification. Apart from these exclusions, participants with serum creatinine >2 mg/dL ($$n = 5$$) or prior physician-diagnosed diabetes and taking oral hypoglycemic agents or insulin ($$n = 578$$) were also excluded. The final analysis included 10,325 adults (3766 men and 6559 women) (Supplementary Figure S2).
The study was conducted following the principles of the Declaration of Helsinki, and the study protocol was approved by the Institutional Review Boards of Hanyang University, Chonnam University, Keimyung University, Yonsei Wonju College of Medicine, and Yonsei University. All participants provided written informed consent before participating in the study.
## 2.2. Assessments of Dietary Exposures and Covariates
Well-trained interviewers assessed food intake at baseline and revisited every 2–4 years using a validated FFQ comprising 106 food items [14]. Participants were asked to indicate how often 106 foods and beverages were consumed in the preceding year, as well as their average portion size. There were nine categories (“never or rarely” to “3 times/day”) and three portion sizes (mostly 0.5, 1.0, or 1.5 standard portion sizes).
Daily nutrient intake for nutrients other than flavonoids was calculated by weighting frequencies per day and portion sizes for each food item based on the nutrient database in the Seventh Edition of the Korean Food Composition Table [15]. Regarding dietary exposure (flavonoids), a total of 35 flavonoids (seven subclasses: flavonols, flavones, flavanones, flavan-3-ols, anthocyanins, isoflavones, and proanthocyanidins) were estimated based on three databases of the US Department of Agriculture (USDA) (flavonoids [16], isoflavones [17], and proanthocyanidins [18]) and the Phenol-Explorer 3.6 database of the French National Institute for Agricultural Research [19]. If flavonoid content was not available for a specific food, the value for a similar food (same family or similar species belonging to the same genus) was assigned. In the case of foods composed of multiple ingredients, each flavonoid value was calculated from the contents of each flavonoid-containing ingredient.
To reflect healthy dietary patterns, a modified DASH score, after excluding fruits and vegetables and excluding nuts from the nuts or legumes category, was calculated from the following six dietary components [20]: nuts, dairy products except for ice cream, whole grains, sodium, red and processed meats, and sugar-sweetened beverages (SSB). Since fruits, vegetables, and legumes were the main sources of flavonoids, we excluded them. Each component was ranked into quintiles (tertiles for nuts and SSB) and assigned scores between 1 and 5 (or 3). For healthy components, the highest quantile was assigned a score of 5 or 3; for less healthy components, the highest quantile was assigned a score of 1. Finally, we summed up each component score, and the possible overall scores ranged from 6 to 26.
To reduce measurement error in dietary assessment and reflect the long-term diet, we used the cumulative average intake of flavonoids and other nutrients calculated by averaging their intake at baseline and each follow-up survey before the diagnosis of hypertension or at the end of the follow-up [21].
## 2.3. Ascertainment of Outcome: BP Measures and Hypertension Incidence
According to a standard protocol, trained examiners measured BP in the right arm twice and consecutively, with a 5-minute resting period in between, using a standard mercury sphygmomanometer in the MRCohort (Baumanometer, Baumanometer Co., New York, NY, USA) and the ARIRIANG (Baumanometer, Baumanometer Co., USA, and CK-101; Spirit Medical Co., New Taipei City, Taiwan) and an automatic sphygmomanometer in the Kangwha cohort (Dinamap 1846 SX/P, General Electric Co., Boston, MA, USA). When the standard mercury sphygmomanometer was used, the first and fifth Korotkoff sounds were used to measure the SBP and DBP to the nearest 2 mmHg, respectively. If there was a difference of ≥5 mmHg between two consecutive readings, BP was measured again. The arithmetic mean was used for further analysis. However, in the ARIRANG, BP was measured only once before joining the CAVAS ($56.2\%$); thus, for participants in this situation, a single reading was used in the analysis.
At follow-up examinations, participants were asked whether they had been diagnosed with hypertension by a physician and if they had taken any antihypertensive medication between visits. We defined incident cases of hypertension as participants with the following conditions at follow-up: [1] newly diagnosed with hypertension by a physician and taking antihypertensive medications; or [2] measured BP higher than the criteria at follow-up examination (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg), based on the criteria of the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure [22].
## 2.4. Assessment of Non-Dietary Covariates
Data on demographics (sex, age, and education level) and previously established risk factors for hypertension, such as regular exercise, smoking status, drinking status, family history of hypertension [23,24,25,26], and menopausal status, were collected by trained interviewers using a structured questionnaire. Additionally, anthropometrics was measured during each health examination. Height was measured using a standard stadiometer to the nearest 0.1 cm, and weight was measured using a metric scale in light clothing without shoes to the nearest 0.1 kg. BMI was calculated using the ratio of weight (kg) to height squared (m2). Participants were divided into two groups based on their obesity status: normal weight (BMI < 23) and overweight/obese (BMI ≥ 23).
## 2.5. Statistics Analysis
All analyses were conducted separately for men and women. The cumulative average intake of dietary flavonoids and their subclasses were categorized into quartiles (Q). We presented means with standard deviations (SD) for continuous variables and frequencies with percentages for categorical variables to describe the characteristics of the study population. General linear models (GLM) were used to present both age-adjusted estimates and linear trends of covariates by the quartiles of dietary exposure. The mean differences across the quartiles of flavonoids and their subclasses were obtained using Tukey’s multiple comparison test.
We used a modified Poisson regression model with a robust error estimator to estimate incidence rate ratios (IRRs) and $95\%$ confidence intervals (CIs) of hypertension risk by the quartiles of total flavonoids and their seven subclasses (flavonols, flavones, flavanones, flavan-3ols, anthocyanins, isoflavones, and proanthocyanidins) using the lowest quartile (Q1) as reference category [27,28]. We considered three models to examine the risk of hypertension: [1] age-adjusted; [2] multivariable model 1: adjusting for age (years), a higher education level (≥12 years of schooling, yes or no), regular exercise (≥3 times/week and ≥30 min/session, yes or no), smoking (never/past/current smoker for men; non/current smoker for women), current drinkers (yes or no), BMI (kg/m2), total energy intake (kcal/day), family history of hypertension (yes or no in the first-degree relatives), menopausal status (yes or no for only women), and baseline BP (SBP and DBP, mmHg); and [3] multivariable model 2, adjusting for all covariates in multivariable model 1 and modified DASH score to consider a possible confounding of healthy dietary patterns [26]. Additionally, to evaluate associations between flavonoid intake from their major food sources and hypertension risk, we selected foods contributing at least $10\%$ of the intake of each flavonoid class and/or at least $10\%$ of the variation (R2) from multiple stepwise regression using total intake from all food items as the dependent variable and intake from each food item as the independent variable. Linearity was tested by treating the median cumulative average intake of total flavonoid and their subclasses in each quartile as a continuous variable. To consider a possible curvilinear association suggested by a systematic review of the relationship between flavonoids and vascular function [9], we accounted for the inherent nonlinearity of the fitted categorical model by comparing the deviance difference between the linearity model on 1 degree of freedom (d.f.) and ℓ ordered the categorical model on ℓ − 1 d.f. [ 29]. The associations between flavonoids and hypertension risk were reanalyzed in each stratum of obesity status (normal and overweight/obese groups). The presence of potential interactions was checked by adding the cross-product term of the flavonoid categories and obesity status group.
To test the robustness of the primary findings, we performed sensitivity analyses in several different ways: by testing the associations [1] after censoring participants who reported a diagnosis of CVD or cancer (533 participants) between visits to minimize the potential effect on the treatment of hypertension; [2] after excluding incident cases of hypertension within the first 2 years to account for the potential reverse causation ($$n = 9792$$; $$n = 3568$$ for men, $$n = 6224$$ for women); [3] using only non-users of antioxidant supplements (vitamin supplements and/or beta-carotene supplements) ($$n = 8026$$; $$n = 3112$$ for men, $$n = 4914$$ for women); and [4] for all 35 individual flavonoids. All statistical analyses were performed using SAS (version 9.4; Cary, NC, USA) and R (version 4.0.0; R Development Core Team, Vienna, Austria). Statistical significance was set at a p-value < 0.5, which was considered statistically significant.
## 3. Results
A total of 2159 cases of hypertension were identified during the 53,678 people’s follow-ups (Supplementary Table S1). The mean age at baseline was 56.9 years (SD, 9.73), and $36.5\%$ of the population were men ($$n = 3766$$). The cumulative average intake of total dietary flavonoids was 176 mg/day (SD, 164), and the highest proportion of seven subclasses of flavonoids was found in proanthocyanidins ($33.1\%$ for men and $37.3\%$ for women), followed by flavan-3-ols, isoflavones, flavanols, flavanones, anthocyanins, and flavones (Supplementary Table S2). The baseline characteristics and cumulative dietary intakes of the total and seven subclasses of flavonoids are presented by the quartiles (Q) of total flavonoid intake (Table 1). Men and women in the highest quartile (Q4) were younger, highly educated, exercised regularly, never smoked, had a higher BMI, and had a higher rate of self-reported family history of hypertension. They tended to have a higher intake of energy and all flavonoid subclasses and higher scores on the modified DASH.
Table 2 presents the association between the intake of flavonoids and hypertension risk. Flavonoids tended to be inversely associated with hypertension after adjusting for multiple covariates (multivariable model 1), although total flavonoids and flavones were not significant in Q4 in both men and women. The inverse association of six flavonoids, other than flavones, seemed to be non-linear L-shaped, although the associations of anthocyanins and proanthocyanidins for both men and women and flavanones for women were statistically linear (Plinearity < 0.05, but not significant at $\frac{0.05}{16}$ tests = 0.0031). The associations and their shapes, after additional adjustment for the modified DASH score (multivariable model 2), remained robust; therefore, the multivariable model without.
The associations between dietary flavonoid intake from their major food sources and hypertension risk are shown in Table 3. A few foods explained a large proportion of the total variation; for example, green tea explained about $68\%$ of the variation in total flavonoid intake and almost all variations in flavan-3-ols in both men and women. The inverse associations remained robust. Moreover, in both men and women, even for total flavonoids and flavones which were not associated with hypertension risk in Table 2, the associations of those from individual major food sources with hypertension risk were consistently inverse, except for flavones from pickled vegetables in salt (Baechu-kimchi). Flavones from pickled vegetables were not associated with hypertension risk in men and were positively associated with hypertension risk in women.
Figure 1 presents the associations of flavonoids with hypertension risk in each obesity status stratum. The beneficial associations for hypertension tended to be stronger in overweight/obese men (BMI, ≥23.0 kg/m2) [IRR ($95\%$ CI) in overweight/obese men and p-value for interaction by BMI groups: 0.67 (0.53–0.84) and Pinter = 0.0267 for flavan-3-ols; 0.53 (0.42–0.67) and Pinter = 0.0044 for anthocyanins; 0.55 (0.42–0.71) and Pinter = 0.0166 for proanthocyanidins]. In women, there were no differences in the association of flavonoids with hypertension risk according to obesity status.
These associations remained consistent in three sensitivity analyses: censoring participants who developed CVD and cancer during follow-up, excluding participants who reported hypertension within the first two years after baseline, and including only non-users of antioxidant supplements ($77.7\%$) (Supplementary Table S3). The associations between the 35 individual flavonoids and the risk of hypertension were similar to the findings of their corresponding subclasses (Supplementary Table S4).
## 4. Discussion
In this prospective cohort study with 10,325, we found an association between various subclasses of flavonoids and lowered hypertension risk. However, these associations were not significant in the highest quartile of total flavonoids and flavones (reverse J-shapes). The shapes of the associations for flavonoids other than total flavonoids and flavones appeared to be non-linear and L-shaped, with plateaus from moderate intakes. Inverse associations were more pronounced in men with high BMI.
The mean total dietary flavonoid intake was 176 mg/day (SD, 164 mg/day) and higher in men [161 mg/day (SD, 142) in men and 185 mg/day (SD, 175) in women]. Flavonoid intake values across studies may not be directly comparable because of the different flavonoid assessment tools, but the mean total flavonoid intake worldwide ranges between 150 and 600 mg/day [20]. In the present study, the highest contribution to total flavonoids in both men and women was proanthocyanidins, followed by flavan-3-ols, which is consistent with a previous report that they are the most abundant sources of flavonoids in East Asian countries such as China and South Korea [20]. This can be explained by the fact that flavonoids are ubiquitous in plants as secondary metabolites but are particularly abundant in fruits, vegetables, legumes, and beverages such as green tea [20].
In the present study, we found a reverse J-shaped association for total flavonoids (not significant in Q4) and L-shaped associations for most of its subclasses. Therefore, the inverse associations of the subclasses were stronger than those of the total flavonoids. To date, few prospective studies have examined the association between hypertension and total flavonoid intake and/or its subclasses. However, [1] in the previous literature review on the association between flavonoids and CVD risk [30], it was suggested that there might be a plateau in the trend; specifically, higher intakes might afford no added benefit, and our findings are consistent with this suggestion. In addition, [2] previous studies also found that the effects of some subclasses were similar to or exceeded those of the total flavonoids [31,32,33,34], like our findings: In middle-aged French women, the association of total flavonoids with hypertension risk showed a similar magnitude to the inverse associations of flavonols, anthocyanins, and proanthocyanidin polymers [32]. In pooled analyses of three prospective studies in middle-aged and older US adults, anthocyanin intake and flavan-3-ol compounds were inversely associated with the risk of hypertension [33], whereas total flavonoids were not associated with hypertension incidence. Among Australian women in two different life stages, individual flavonoid subclasses were different but inversely associated with a lower risk of hypertension [31]. They showed an inverse association of flavones, isoflavones, and flavanones with hypertension risk among middle-aged women and flavanols among reproductive-aged women [31], whereas there was no association between total flavonoids and hypertension incidence. These less beneficial findings for total flavonoids but strongly beneficial findings for different flavonoids may reflect various dietary cultures across populations in which harmful or beneficial substances other than flavonoids can be mixed. Flavones from Baechu-kimchi were found to have no or a positive association with hypertension risk in this study. Baechu-kimchi is the most commonly consumed side dish in Korea and is a sodium-rich food [35]. Increased sodium intake might lead to sodium-dependent oxidative stress by abolishing local nitric oxide activity and increasing microvascular reactive oxygen species (ROS) levels [36]. Although in our data, additionally adjusting for dietary sodium intake did not change the associations, it is necessary to consider sodium intake using a more accurate estimation method such as 24-h urine data [37].
Although there is no single mechanism or individual flavonoids to prevent hypertension and CVD [6], the main biological activities of flavonoids in the etiology of hypertension are enhanced endothelial function, antioxidant activity, anti-inflammatory properties, and antithrombotic activities [6,38]. In terms of individual flavonoid subclasses, the bioavailability of flavonoids depends on their composition, the total number of hydroxyl groups, and the substitution of functional groups [5]. Among flavonoids, anthocyanins, and proanthocyanidins, which are the most numerous and widely distributed pigments in plants, have been reported to have health benefits for BP owing to their potential antioxidant activities [39]. Flavanones are found in high concentrations in citrus fruit and mainly improve endothelial function by enhancing blood flow, increasing endothelial nitric oxide synthase activity, and inhibiting platelet function [38,40,41].
In addition, we found more pronounced beneficial associations with flavonoids in overweight and obese men but not in normal-weight men. There was no difference in obesity status among women. Previously, it has been demonstrated that the risk of obesity-associated hypertension has sex differences [42]. Our findings in obese men may be explained by the suppression of over-activation of the sympathetic nervous system by flavonoids. Previous studies have reported that sympathetic nerve activation (SNA), which is a significant contributing factor to hypertension, can be activated, particularly in men with high BMI [43], and the injection of antioxidants directly results in decreased SNA and BP [44]. Although some previous studies have suggested differences in age group [33] or sex [8], we did not find any other differences by covariates in the effect modification analyses after stratification of all covariates (data not shown).
This study had several limitations. Firstly, the present study used a prospective cohort design and considered the cumulative average dietary intake of flavonoids. However, we could not conclude the causal relationship between flavonoids and hypertension risk [1] because unmeasured confounding factors may still exist and [2] because there is a possibility that participants who had normal BP at baseline but developed prehypertension during the follow-up period may have changed their dietary habits to increase flavonoids intake. However, this possibility suggested that the association between flavonoid intake and hypertension in the present study may be underestimated. Furthermore, [3] to provide more robust evidence on the association between flavonoids and hypertension risk, further studies are needed using new emerging data analysis methods, including counterfactual models for causal inference and accumulating evidence from large-scale cohorts and randomized trials. Second, the flavonoid content in foods can be influenced by the cooking process, geographic area, seasonality, and food growth, but we could not consider these factors. Although this may attenuate the association, it is unlikely to cause a differential misclassification of exposure. Third, our FFQ did not include enough flavonoid-rich foods and seasonings, such as herbs [31], and some foods, such as berries and herbs, have recently gained popularity in Korea [14]. This may lead to an underestimation or misclassification of flavonoid intake. Fourth, BP measurement protocols in the three cohorts were different at the beginning of the cohort before joining CAVAS, as we mentioned above. Therefore, we conducted a pooled analysis with findings from separate analyses of each of the three cohorts and confirmed similar results. Fifth, although we considered comprehensive confounders such as baseline BP and DASH score, it is difficult to disentangle the unique effects of flavonoids from other dietary compounds such as vitamin C, potassium, folate, and magnesium, which are rich sources of flavonoids [6,8]. Lastly, dietary flavonoid intake does not reflect inter- and intra-individual variations such as absorption and metabolism; therefore, their association with health outcomes should be interpreted with caution [16]. Nevertheless, the present study had several strengths, such as its prospective design, large sample size, and repeated reliable assessments of individual diets.
In conclusion, our findings indicated that dietary flavonoid subclasses, as well as total dietary flavonoid intake, may be beneficial for hypertension risk in a non-linear reverse J- or L-shaped manner in both men and women. In men, these favorable associations may be predominant in the overweight/obese group.
## References
1. Forouzanfar M.H., Liu P., Roth G.A., Ng M., Biryukov S., Marczak L., Alexander L., Estep K., Abate K.H., Akinyemiju T.F.. **Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015**. *JAMA* (2017.0) **317** 165-182. DOI: 10.1001/jama.2016.19043
2. Kjeldsen S.E.. **Hypertension and cardiovascular risk: General aspects**. *Pharmacol. Res.* (2018.0) **129** 95-99. DOI: 10.1016/j.phrs.2017.11.003
3. Olsen M.H., Angell S.Y., Asma S., Boutouyrie P., Burger D., Chirinos J.A., Damasceno A., Delles C., Gimenez-Roqueplo A.-P., Hering D.. **A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: The Lancet Commission on hypertension**. *Lancet* (2016.0) **388** 2665-2712. DOI: 10.1016/S0140-6736(16)31134-5
4. Zhou B., Carrillo-Larco R.M., Danaei G., Riley L.M., Paciorek C.J., Stevens G.A., Gregg E.W., Bennett J.E., Solomon B., Singleton R.K.. **Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: A pooled analysis of 1201 population-representative studies with 104 million participants**. *Lancet* (2021.0) **398** 957-980. DOI: 10.1016/S0140-6736(21)01330-1
5. Mondal S., Rahaman S.. **Flavonoids: A vital resource in healthcare and medicine**. *Pharm. Pharmacol. Int. J.* (2020.0) **8** 91-104. DOI: 10.15406/ppij.2020.08.00285
6. Grosso G., Godos J., Currenti W., Micek A., Falzone L., Libra M., Giampieri F., Forbes-Hernández T.Y., Quiles J.L., Battino M.. **The effect of dietary polyphenols on vascular health and hypertension: Current evidence and mechanisms of action**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14030545
7. Zhang Y.-J., Gan R.-Y., Li S., Zhou Y., Li A.-N., Xu D.-P., Li H.-B.. **Antioxidant phytochemicals for the prevention and treatment of chronic diseases**. *Molecules* (2015.0) **20** 21138-21156. DOI: 10.3390/molecules201219753
8. Godos J., Vitale M., Micek A., Ray S., Martini D., Del Rio D., Riccardi G., Galvano F., Grosso G.. **Dietary polyphenol intake, blood pressure, and hypertension: A systematic review and meta-analysis of observational studies**. *Antioxidants* (2019.0) **8**. DOI: 10.3390/antiox8060152
9. Kay C.D., Hooper L., Kroon P.A., Rimm E.B., Cassidy A.. **Relative impact of flavonoid composition, dose and structure on vascular function: A systematic review of randomised controlled trials of flavonoid-rich food products**. *Mol. Nutr. Food Res.* (2012.0) **56** 1605-1616. DOI: 10.1002/mnfr.201200363
10. Livingstone K.M., McNaughton S.A.. **Diet quality is associated with obesity and hypertension in Australian adults: A cross sectional study**. *BMC Public Health* (2016.0) **16**. DOI: 10.1186/s12889-016-3714-5
11. Dominguez L.J., Gea A., Ruiz-Estigarribia L., Sayon-Orea C., Fresan U., Barbagallo M., Ruiz-Canela M., Martinez-Gonzalez M.A.. **Dietary magnesium and overweight/obesity in a mediterranean population: A detrimental synergy for the development of hypertension**. *Nutrients* (2020.0) **13**. DOI: 10.3390/nu13010125
12. Hosseini A., Razavi B.M., Banach M., Hosseinzadeh H.. **Quercetin and metabolic syndrome: A review**. *Phytother. Res.* (2021.0) **35** 5352-5364. DOI: 10.1002/ptr.7144
13. Kim Y., Han B.-G., Group K.. **Cohort profile: The Korean genome and epidemiology study (KoGES) consortium**. *Int. J. Epidemiol.* (2017.0) **46** e20. DOI: 10.1093/ije/dyv316
14. Ahn Y., Kwon E., Shim J., Park M., Joo Y., Kimm K., Park C., Kim D.. **Validation and reproducibility of food frequency questionnaire for Korean genome epidemiologic study**. *Eur. J. Clin. Nutr.* (2007.0) **61** 1435-1441. DOI: 10.1038/sj.ejcn.1602657
15. **Food Values. 2009**
16. Haytowitz D.B., Wu X., Bhagwat S.. **USDA Database for the Flavonoid Content of Selected Foods, Release 3.3**
17. Bhagwat S., Haytowitz D.B.. **USDA Database for the Isoflavone Content of Selected Foods Release 2.1**
18. Haytowitz D., Wu X., Bhagwat S.. **USDA Database for the Proanthocyanidin Content of Selected Foods Release 2.1**
19. **Phenol-Explorer 3.6. Database on polyphenol content in food**
20. Escobar-Cévoli R., Castro-Espín C., Béraud V., Buckland G., Zamora-Ros R., Béraud G.. *An Overview of Global Flavonoid Intake and Its Food Sources* (2017.0)
21. Hu F.B., Stampfer M.J., Rimm E., Ascherio A., Rosner B.A., Spiegelman D., Willett W.C.. **Dietary fat and coronary heart disease: A comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements**. *Am. J. Epidemiol.* (1999.0) **149** 531-540. DOI: 10.1093/oxfordjournals.aje.a009849
22. Chobanian A.V., Bakris G.L., Black H.R., Cushman W.C., Green L.A., Izzo Jr J.L., Jones D.W., Materson B.J., Oparil S., Wright Jr J.T.. **Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure**. *Hypertension* (2003.0) **42** 1206-1252. DOI: 10.1161/01.HYP.0000107251.49515.c2
23. Tsao C.W., Aday A.W., Almarzooq Z.I., Alonso A., Beaton A.Z., Bittencourt M.S., Boehme A.K., Buxton A.E., Carson A.P., Commodore-Mensah Y.. **Heart disease and stroke statistics—2022 update: A report from the American Heart Association**. *Circulation* (2022.0) **145** e153-e639. DOI: 10.1161/CIR.0000000000001052
24. Tiruneh S.A., Bukayaw Y.A., Yigizaw S.T., Angaw D.A.. **Prevalence of hypertension and its determinants in Ethiopia: A systematic review and meta-analysis**. *PLoS ONE* (2020.0) **15**. DOI: 10.1371/journal.pone.0244642
25. Lima R., Wofford M., Reckelhoff J.F.. **Hypertension in postmenopausal women**. *Curr. Hypertens. Rep.* (2012.0) **14** 254-260. DOI: 10.1007/s11906-012-0260-0
26. Filippou C.D., Tsioufis C.P., Thomopoulos C.G., Mihas C.C., Dimitriadis K.S., Sotiropoulou L.I., Chrysochoou C.A., Nihoyannopoulos P.I., Tousoulis D.M.. **Dietary approaches to stop hypertension (DASH) diet and blood pressure reduction in adults with and without hypertension: A systematic review and meta-analysis of randomized controlled trials**. *Adv. Nutr.* (2020.0) **11** 1150-1160. DOI: 10.1093/advances/nmaa041
27. Zou G.. **A modified Poisson regression approach to prospective studies with binary data**. *Am. J. Epidemiol.* (2004.0) **159** 702-706. DOI: 10.1093/aje/kwh090
28. Callas P.W., Pastides H., Hosmer D.W.. **Empirical comparisons of proportional hazards, poisson, and logistic regression modeling of occupational cohort data**. *Am. J. Ind. Med.* (1998.0) **33** 33-47. DOI: 10.1002/(SICI)1097-0274(199801)33:1<33::AID-AJIM5>3.0.CO;2-X
29. Woodward M.. *Epidemiology: Study Design and Data Analysis* (2013.0)
30. Parmenter B.H., Croft K.D., Hodgson J.M., Dalgaard F., Bondonno C.P., Lewis J.R., Cassidy A., Scalbert A., Bondonno N.P.. **An overview and update on the epidemiology of flavonoid intake and cardiovascular disease risk**. *Food Funct.* (2020.0) **11** 6777-6806. DOI: 10.1039/D0FO01118E
31. do Rosario V.A., Schoenaker D.A., Kent K., Weston-Green K., Charlton K.. **Association between flavonoid intake and risk of hypertension in two cohorts of Australian women: A longitudinal study**. *Eur. J. Nutr.* (2021.0) **60** 2507-2519. DOI: 10.1007/s00394-020-02424-9
32. Lajous M., Rossignol E., Fagherazzi G., Perquier F., Scalbert A., Clavel-Chapelon F., Boutron-Ruault M.-C.. **Flavonoid intake and incident hypertension in women**. *Am. J. Clin. Nutr.* (2016.0) **103** 1091-1098. DOI: 10.3945/ajcn.115.109249
33. Cassidy A., O’Reilly É.J., Kay C., Sampson L., Franz M., Forman J., Curhan G., Rimm E.B.. **Habitual intake of flavonoid subclasses and incident hypertension in adults**. *Am. J. Clin. Nutr.* (2011.0) **93** 338-347. DOI: 10.3945/ajcn.110.006783
34. Grosso G., Stepaniak U., Micek A., Kozela M., Stefler D., Bobak M., Pajak A.. **Dietary polyphenol intake and risk of hypertension in the Polish arm of the HAPIEE study**. *Eur. J. Nutr.* (2018.0) **57** 1535-1544. DOI: 10.1007/s00394-017-1438-7
35. Jeong Y., Kim E.S., Lee J., Kim Y.. **Trends in sodium intake and major contributing food groups and dishes in Korea: The Korea National Health and Nutrition Examination Survey 2013–2017**. *Nutr. Res. Pract.* (2021.0) **15** 382-395. DOI: 10.4162/nrp.2021.15.3.382
36. Lenda D.M., Boegehold M.A.. **Effect of a high-salt diet on oxidant enzyme activity in skeletal muscle microcirculation**. *Am. J. Physiol. Heart Circ. Physiol.* (2002.0) **282** H395-H402. DOI: 10.1152/ajpheart.0354.2001
37. McLean R.M., Farmer V.L., Nettleton A., Cameron C.M., Cook N.R., Campbell N.R., Consortium T.. **Assessment of dietary sodium intake using a food frequency questionnaire and 24-hour urinary sodium excretion: A systematic literature review**. *J. Clin. Hypertens.* (2017.0) **19** 1214-1230. DOI: 10.1111/jch.13148
38. Clark J.L., Zahradka P., Taylor C.G.. **Efficacy of flavonoids in the management of high blood pressure**. *Nutr. Rev.* (2015.0) **73** 799-822. DOI: 10.1093/nutrit/nuv048
39. Saigo T., Wang T., Watanabe M., Tohge T.. **Diversity of anthocyanin and proanthocyanin biosynthesis in land plants**. *Curr. Opin. Plant Biol.* (2020.0) **55** 93-99. DOI: 10.1016/j.pbi.2020.04.001
40. Jin Y.-R., Han X.-H., Zhang Y.-H., Lee J.-J., Lim Y., Chung J.-H., Yun Y.-P.. **Antiplatelet activity of hesperetin, a bioflavonoid, is mainly mediated by inhibition of PLC-γ2 phosphorylation and cyclooxygenase-1 activity**. *Atherosclerosis* (2007.0) **194** 144-152. DOI: 10.1016/j.atherosclerosis.2006.10.011
41. Cassidy A., Bertoia M., Chiuve S., Flint A., Forman J., Rimm E.B.. **Habitual intake of anthocyanins and flavanones and risk of cardiovascular disease in men**. *Am. J. Clin. Nutr.* (2016.0) **104** 587-594. DOI: 10.3945/ajcn.116.133132
42. Faulkner J.L., Belin de Chantemèle E.J.. **Sex differences in mechanisms of hypertension associated with obesity**. *Hypertension* (2018.0) **71** 15-21. DOI: 10.1161/HYPERTENSIONAHA.117.09980
43. Fu Q.. **Sex differences in sympathetic activity in obesity and its related hypertension**. *Ann. N. Y. Acad. Sci.* (2019.0) **1454** 31-41. DOI: 10.1111/nyas.14095
44. Bruno R.M., Ghiadoni L.. **Polyphenols, antioxidants and the sympathetic nervous system**. *Curr. Pharm. Des.* (2018.0) **24** 130-139. DOI: 10.2174/1381612823666171114170642
|
---
title: Metabolic Syndrome and Its Components Are Associated with New-Onset Hyperuricemia
in a Large Taiwanese Population Follow-Up Study
authors:
- Yen-Chieh Tu
- Yi-Hsueh Liu
- Szu-Chia Chen
- Ho-Ming Su
journal: Nutrients
year: 2023
pmcid: PMC10004782
doi: 10.3390/nu15051083
license: CC BY 4.0
---
# Metabolic Syndrome and Its Components Are Associated with New-Onset Hyperuricemia in a Large Taiwanese Population Follow-Up Study
## Abstract
The prevalence rate of hyperuricemia remains high in Taiwan, at $21.6\%$ in men and $9.57\%$ in women. Both metabolic syndrome (MetS) and hyperuricemia can cause many complications; however, few studies have evaluated the correlation between MetS and hyperuricemia. Therefore, in this observational cohort study, we explored associations between metabolic syndrome (MetS) and its components and new-onset hyperuricemia. Of 27,033 individuals in the Taiwan Biobank who had complete follow-up data, we excluded those with hyperuricemia at baseline ($$n = 4871$$), those with gout at baseline ($$n = 1043$$), those with no data on baseline uric acid ($$n = 18$$), and those with no data on follow-up uric acid ($$n = 71$$). The remaining 21,030 participants (mean age 50.8 ± 10.3 years) were enrolled. We found a significant association between new-onset hyperuricemia with MetS and the components of MetS (hypertriglyceridemia, abdominal obesity, low high-density lipoprotein cholesterol, hyperglycemia, and high blood pressure). Furthermore, compared to those without any MetS components, those with one MetS component (OR = 1.816), two MetS components (OR = 2.727), three MetS components (OR = 3.208), four MetS components (OR = 4.256), and five MetS components (OR = 5.282) were significantly associated with new-onset hyperuricemia (all $p \leq 0.001$). MetS and its five components were associated with new-onset hyperuricemia in the enrolled participants. Further, an increase in the number of MetS components was associated with an increase in the incidence rate of new-onset hyperuricemia.
## 1. Introduction
The prevalence of hyperuricemia not only differs geographically across the world but also according to the economic development of the country. It has been reported that around $20\%$ of men and women have hyperuricemia in the US [1] as well as up to $30\%$ of the elderly population in Taiwan [2]. Purine metabolism results in the production of urate, which is mainly synthesized in the liver [3]. Urate metabolism in tissues is usually negligible under normal physiological conditions, and most excretion takes place in the gut and kidneys. Hyperuricemia is defined as the overproduction of uric acid, and it can be the result of a diet rich in purine and an increase in the degradation and metabolism of purine. Other possible causes of hyperuricemia include excessive alcohol consumption, acute chronic kidney disease leading to a reduction in uric acid excretion, the use of diuretics, hyperparathyroidism, acidosis, hypothyroidism, and lead poisoning. Factors resulting in increased urate synthesis (glycogen storage diseases, excessive ethanol/seafood consumption) or the decreased clearance of urate (sarcoidosis, use of diuretics) contribute to the development of hyperuricemia [4]. Hyperuricemia has been associated with gout, chronic kidney disease, hypertension, atrial fibrillation [5], myocardial infarction, and stroke. Therefore, studies analyzing the factors potentially causing a high serum uric acid level are warranted.
Treatment with xanthine oxidase inhibitors has been shown to be a safe and effective strategy to lower levels of uric acid and manage chronic hyperuricemia; however, pharmacogenetics have been shown to strongly modify the efficacy of uricosuric agents [6]. Certain factors, including increased body mass index (BMI), hyperglycemia, high blood pressure, and kidney disease, have been associated with the development of hyperuricemia [7,8]. A previous study evaluated short-term interactions between uric acid, low-density lipoprotein (LDL) cholesterol, and incident hypertension, and found that the presence of suboptimal uric acid and LDL cholesterol levels were associated with an elevated risk of developing hypertension [9]. In addition, Cicero et al. investigated the association between uric acid and the prevalence and 4-year incidence of metabolic syndrome (MetS) in older, overall healthy subjects, and it was found that hyperuricemia appeared to be a highly prevalent component of MetS, especially in those with the most severe forms, as well as a risk factor for developing MetS [10].
The importance of MetS and its complications has also been increasingly recognized. The prevalence of MetS is similar for men ($24.0\%$) and women ($23.4\%$) in the United States [11], compared to a lower rate of $15.7\%$ in Taiwan [12]. MetS has been shown to increase the risk of developing type 2 diabetes mellitus (DM) [13], cardiovascular disease [14], chronic kidney disease [15], polycystic ovary syndrome [16], and obstructive sleep apnea [17], and a positive correlation between MetS and hyperuricemia has also been shown in previous studies [18,19]. Increased oxidative stress resulting from hyperuricemia in adipocytes may potentially play a role in the development of MetS [20]. However, large-scale studies investigating the association between MetS and hyperuricemia are lacking. To address this research gap, we conducted this longitudinal study with a large cohort of Taiwanese adults to explore correlations between MetS and its components and new-onset hyperuricemia.
## 2.1. Ethical Declaration
This study was conducted following the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20210058). Ethical approval for the Taiwan Biobank (TWB) was granted by the IRB on Biomedical Science Research, Academia Sinica, Taiwan, and the TWB Ethics and Governance Council.
## 2.2. TWB
The Taiwan government established the TWB to collect data on citizens aged 30–70 years who were enrolled around Taiwan for biomedical and epidemiological research purposes. Extensive genome and phenotype data were obtained during enrolment and follow-up. Fasting blood and urine samples were obtained to measure glucose, uric acid, hemoglobin, triglycerides, total/LDL, and high-density lipoprotein [HDL] cholesterol. In addition, physical examinations were performed to record data on body height/weight [BH/BW], waist circumference [WC], and hip circumference [HC]. Moreover, structured questionnaires were used to obtain information on histories of diabetes mellitus [DM], gout, hypertension, and smoking/alcohol habits, along with sex and age [21,22].
BMI was calculated as BW/BH2, and the 4-variable MDRD formula was used to calculate the estimated glomerular filtration rate (eGFR) [23]. Blood pressure (BP) was measured by researchers with an electronic device after the participants had abstained from exercising, consuming caffeine-related items, and smoking for a minimum of 30 min. Three BP measurements were recorded for each participant, separated by a 1–2 min break, and the average value was included in the analysis. The “Physical Fitness 333 Plan” criteria were used to define regular exercise as promoted by the Ministry of Education in Taiwan, which was defined as at least 30 min of exercise three times a week [24].
## 2.3. Definition of New-Onset Hyperuricemia
The participants were defined as having new-onset hyperuricemia if they were found to have an elevated serum uric acid level (>7.0 mg/dL in men; >6.0 mg/dL in women) during follow-up.
## 2.4. Definition of MetS
The NCEP-ATP III definition of MetS was adopted [25] with Asian-modified criteria [26] and required the presence of at least three of the following: [1] hyperglycemia, defined as fasting whole-blood glucose ≥ 110 mg/dL or a diagnosis of DM; [2] triglycerides ≥ 150 mg/dL; [3] HDL cholesterol < 50 mg/dL for women and <40 mg/dL for men; [4] systolic/diastolic BP ≥ $\frac{130}{85}$ mmHg, hypertension diagnosis, or prescription for anti-hypertensive drugs; [5] abdominal obesity, defined as a WC > $\frac{80}{90}$ cm in women/men.
## 2.5. Study Participants
Of 27,033 enrollees (males: 9555; females: 17,478) identified in the TWB (median follow-up period, 4 years), those with baseline hyperuricemia ($$n = 4871$$) or gout ($$n = 1043$$), and those who did not have data on baseline ($$n = 18$$) or follow-up uric acid ($$n = 71$$), were excluded. The remaining 21,030 participants provided written informed consent and were included in the study (Figure 1).
## 2.6. Study Design
This study was an observational cohort study.
## 2.7. Statistical Analysis
Categorical variables are expressed as numbers and percentages, and differences between them were analyzed using chi-square tests. Continuous variables are expressed as mean ± standard deviation, and differences between them were analyzed using independent t-tests. We have further performed multicollinearity analyses. The variance inflation factor (VIF) was used to detect multicollinearity in the regression model. Explanatory variables having a VIF of ≥5 indicated a multicollinearity problem. In our model, it was shown that the VIF of each variable was <5. Associations between MetS and its components and new-onset hyperuricemia were analyzed using multivariable logistic regression analyses and are presented as odds ratios (ORs) with $95\%$ confidence intervals (CIs). Comparisons among groups according to the number of MetS components were made using a one-way analysis of variance. A p-value < 0.05 was considered statistically significant. All statistical analyses were conducted using SPSS version 19.0 for Windows (IBM Corp., Armonk, NY, USA).
## 3. Results
The mean age of the 21,030 enrolled participants was 50.8 ± 10.3 years, and 6286 were male. At follow-up, 1804 ($8.6\%$) participants had developed new-onset hyperuricemia and 19,226 ($91.4\%$) did not.
## 3.1. Comparison of the Participants Who Did and Did Not Develop New-Onset Hyperuricemia
Compared to the participants who did not develop new-onset hyperuricemia, those who did develop new-onset hyperuricemia had higher rates of smoking and alcohol intake, hypertension, and DM; were older; were predominantly male; had higher systolic/diastolic BP, HC, BW, BH, WC, BMI, uric acid, fasting glucose, hemoglobin, triglycerides, total cholesterol, and LDL cholesterol; and lower HDL cholesterol and eGFR (Table 1). In addition, the new-onset hyperuricemia group had a higher prevalence of MetS and its components (high BP, low HDL cholesterol, hypertriglyceridemia, abdominal obesity, and hyperglycemia).
## 3.2. Association of MetS and New-Onset Hyperuricemia
Multivariable logistic regression analysis showed that, after adjusting for age, sex, smoking, alcohol consumption, uric acid, hemoglobin, total cholesterol, LDL cholesterol, and eGFR, there were significant associations among old age ($$p \leq 0.003$$), sex ($p \leq 0.001$), alcohol consumption ($p \leq 0.001$), high uric acid ($p \leq 0.001$), low total cholesterol ($$p \leq 0.015$$), high LDL-cholesterol ($$p \leq 0.021$$), low eGFR ($$p \leq 0.003$$), and MetS (OR = 1.493; $95\%$ CI = 1.312–1.700; $p \leq 0.001$) and new-onset hyperuricemia (Table 2).
## 3.3. Associations among MetS Components with New-Onset Hyperuricemia
The participants were classified according to the number of MetS components (0 to 5) into six groups. There were 8069, 6432, 3902, 1827, 660, and 140 participants in the six groups, respectively. The rates of new-onset hyperuricemia in these six groups were $4.6\%$, $8.4\%$, $12.4\%$, $14.\%$, $17.7\%$, and $22.1\%$, respectively (Figure 2). The highest prevalence of new-onset hyperuricemia was found among the participants with five MetS components. Compared to the participants with no components, those with 1–5 components had higher rates of new-onset hyperuricemia (all $p \leq 0.001$). Compared to the participants with 1 component, those with 2–5 components had higher new-onset hyperuricemia rates (all $p \leq 0.001$). Compared to the participants with 2 components, those with 4–5 components had higher new-onset hyperuricemia rates ($p \leq 0.001$, $$p \leq 0.001$$, respectively). Further, the participants with 5 components had a higher new-onset hyperuricemia rate than those with 3 components ($$p \leq 0.020$$).
Associations among the number of MetS components and new-onset hyperuricemia using multivariable logistic regression analysis are shown in Table 3. Compared to the participants with no components, those with 1 component (OR = 1.413; $95\%$ CI = 1.222–1.634; $p \leq 0.001$), 2 components (OR = 1.918; $95\%$ CI = 1.647–2.233; $p \leq 0.001$), 3 components (OR = 1.915; $95\%$ CI = 1.597–2.296; $p \leq 0.001$), 4 components (OR = 2.428; $95\%$ CI = 1.910–3.100; $p \leq 0.001$), and 5 components (OR = 3.593; $95\%$ CI = 2.281–5.658; $p \leq 0.001$) were significantly associated with new-onset hyperuricemia.
## 3.4. Associations among the MetS Components with New-Onset Hyperuricemia
Multivariable analysis showed that the participants with abdominal obesity (presented as ORs and $95\%$ CIs) (1.180; 1.134–1.229), hypertriglyceridemia (1.293; 1.233–1.355), low HDL cholesterol (1.185; 1.135–1.236), hyperglycemia (1.136; 1.075–1.201), and high BP (1.167; 1.118–1.217) were significantly associated with new-onset hyperuricemia (all p-values < 0.001; Table 4).
## 4. Discussion
In this follow-up study of a large Taiwanese cohort, we found that MetS and its five components were associated with new-onset hyperuricemia. Furthermore, we found that the incidence rate of new-onset hyperuricemia increased as the number of MetS components increased.
A positive correlation between MetS and hyperuricemia has been reported in previous studies [18,19]. One longitudinal cohort study with 3247 participants found that MetS and its components could increase the risk of hyperuricemia in Chinese adults aged 60 years or older [27]. In that study, hypertension was the most important risk factor, and subjects with hypertension in combination with DM and high triglycerides had the highest risk of developing hyperuricemia [27]. A four-year follow-up cohort study using cross-lagged panel analysis found a bidirectional relationship between MetS and new-onset hyperuricemia [28]. Moreover, two components of MetS, systolic BP, and triglycerides, were also found to share this bidirectional relationship with hyperuricemia [28]. The authors suggested that the excessive fat storage in MetS upregulated the activity of xanthine oxidoreductase, which in turn increased the secretion of uric acid. This phenomenon has been reported to be most pronounced in individuals with obesity, low HDL cholesterol, hyperglycemia, or elevated triglycerides [29].
We also found that abdominal obesity was associated with new-onset hyperuricemia. A causal relationship was found between BMI and the risk of hyperuricemia in a large cohort study using mendelian randomization analysis, in which the risk of hyperuricemia increased by $7.5\%$ ($3.9\%$ to $11.1\%$) with one standard deviation increase in BMI [30]. One study in China also found a positive correlation between newly diagnosed hyperuricemia and abdominal obesity regardless of sex [8]. However, the prevalence of hyperuricemia has been reported to be higher in obese men than women, possibly due to the effect of estrogen and progesterone on increasing urate excretion and reducing reabsorption [31]. In addition, abdominal obesity (defined as WC ≥ 85.0 cm for males and ≥ 80.0 cm for females) showed an OR of 2.26 (1.88, 2.73) for men and 1.96 (1.61, 2.39) for women to develop new-onset hyperuricemia [31]. The overproduction and impaired excretion of uric acid may serve as the link between obesity and hyperuricemia, and it has been hypothesized that the location of fat accumulation may also affect the development of hyperuricemia [32]. Increased leptin secretion, which is one of the cytokines produced by adipose tissue, may also result in hyperuricemia by decreasing renal uric acid excretion [33].
Another important finding of this study is the association between hypertriglyceridemia and new-onset hyperuricemia. A prospective study with 6 years of follow-up data also found that hypertriglyceridemia was a strong and independent risk factor for developing hyperuricemia [34]. Moreover, the results did not differ regardless of whether the triglyceride level at 6 years or the change in triglyceride level was used for analysis. Various previous studies also support the significant correlation between hypertriglyceridemia and hyperuricemia [35]. However, the mechanisms underlying the association between hypertriglyceridemia and hyperuricemia are not fully understood. Decreased glyceraldehyde 3-phosphate dehydrogenase activity has been observed in individuals with hyperlipidemia, resulting in enhanced uric acid synthesis [36]. Triglycerides can also cause stenosis of small renal vessels under long-term conditions of dyslipidemia, ultimately impairing the ability to excrete urate [37].
The fourth important finding of this study is the association between low HDL cholesterol and new-onset hyperuricemia. A prospective study of 1508 participants found that rather than decreasing the level of HDL, hyperuricemia impaired the antioxidative/ anti-inflammatory effect of HDL itself [38]. While hyperuricemia and low serum HDL both have a proinflammatory effect and contribute to the formation of atherosclerosis, the exact mechanism underlying their association remains to be elucidated. Previous studies have shown an inverse association between serum uric acid concentration and HDL cholesterol level [39]. In one study, hyperuricemia was associated with not only low HDL cholesterol but also the presence of denser, smaller particles of HDL [40]. While uric acid has an antioxidant effect, smaller HDL particles have been closely linked to high oxidative stress and reduced paraoxonase activity, reflecting a decreased antioxidative effect [41]. It has, therefore, been hypothesized that hyperuricemia may act as a compensatory response to the elevated oxidative stress resulting from a low HDL cholesterol level and smaller HDL particle size [40].
We also found that hyperglycemia was associated with new-onset hyperuricemia. This is consistent with the findings of Yoo et al., who found that hyperglycemia was a risk factor for the development of hyperuricemia [42]. Hyperuricemia has been shown to cause insulin resistance and the dysregulation of glucose metabolism through β-cell injury and dysfunction [15]. However, conflicting data have been reported regarding the association between hyperglycemia and hyperuricemia. While it has been observed that hyperglycemia remains a risk factor for the development of hyperuricemia [42], a study with 2374 participants in China did not find a statistically significant correlation between the two [43], suggesting future studies are needed to further clarify their interaction. In that study, among the group with a normal serum glucose level, the uric acid level increased as the fasting glucose concentration increased. Andrade et al. reported an increase in the prevalence of hyperuricemia from controls ($3.9\%$) to those with euglycemic hypertension ($7.6\%$) to those with prediabetes ($14.0\%$), with a significant difference between the patients with prediabetes and the controls. In addition, the prevalence of hyperuricemia in diabetic patients was $11.4\%$, which was also significantly different compared to the control group [44]. The mechanism underlying the association between hyperglycemia and hyperuricemia may be related to proximal tubules. Nephron hypertrophy occurs during the early stage of dysglycemia, possibly to prevent the loss of glucose [45]. However, increased proximal glucose reabsorption may affect the level of serum glucose and worsen the retention of urate. In addition, a high level of insulin is common in patients with insulin resistance, and this may play a role in sodium and urate retention at more distant sites along nephrons [46].
Finally, we found that high BP was associated with new-onset hyperuricemia. It has become increasingly recognized that hypertension is strongly correlated with hyperuricemia; however, the direction of the causal effect still remains under debate [4,47]. There is increasing recognition that hyperuricemia is strongly correlated with hypertension. Hyperuricemia has not only been observed to be associated with the risk of hypertension and metabolic syndrome but also increased carotid intima-media thickness [48]. The PAMELA study, a large epidemiological study conducted in Italy, found that the risks of home and ambulatory hypertension increased by $34\%$ and $29\%$, respectively, for each 1 mg/dL increase in serum uric acid concentration [49]. Animal studies have shed light on the possible mechanism behind the correlation between hyperuricemia and hypertension. In the acute phase, hyperuricemia impairs the release of endothelium-derived nitric oxide [50] and increases oxidative stress in macula densa cells, which in turn leads to renal vaso-constriction and ischemia [51]. Prolonged changes in inflammatory microvascular results in glomerular afferent arteriolopathy and interstitial inflammation, further leading to the development of hypertension [52]. Using cross-lagged path analysis, Han et al. reported that insulin resistance resulting from hyperuricemia may play a role in the development of hypertension [47]. A bidirectional relationship between systolic BP and serum uric acid was found in a longitudinal study in China [28]. One large, population-based cohort study of 15,792 individuals in the US identified several risk factors for developing hyperuricemia, of which, hypertension carried a 1.65-fold increased risk [53]. Hypertension results in elevated systemic and renal vascular resistance, which in turn decreases renal blood flow and subsequently increases urate reabsorption [27,35,54]. Renal microvascular injury resulting from hypertension has also been reported to lead to impaired urate excretion and increased urate synthesis [54].
This study is strengthened by the large-scale investigation and follow-up. Nevertheless, there are also some limitations. First, information on drugs that may influence the presence of hyperuricemia is lacking in the TWB. Consequently, the association between MetS and hyperuricemia may have been underestimated. Second, all enrollees were ethnically Chinese, and hence, our findings may not be generalizable to other groups. In addition, because it is an observational cohort study, this study cannot determine a causal relationship between MetS and hyperuricemia. Finally, not all TWB enrollees returned for follow-up assessments, and this may have led to sample bias.
In conclusion, new-onset hyperuricemia was associated with MetS and its five components in a large Taiwanese population. Further, the incidence of new-onset hyperuricemia increased with the increase in the number of MetS components.
## References
1. Li L., Zhang Y., Zeng C.. **Update on the epidemiology, genetics, and therapeutic options of hyperuricemia**. *Am. J. Transl. Res.* (2020.0) **12** 3167-3181. PMID: 32774692
2. Yu Q., Shen H.-C., Hu Y.-C., Chen Y.-F., Tung T.-H.. **Prevalence and Metabolic Factors of Hyperuricemia in an Elderly Agricultural and Fishing Population in Taiwan**. *Arch. Rheumatol.* (2017.0) **32** 149-157. DOI: 10.5606/ArchRheumatol.2017.6075
3. Maiuolo J., Oppedisano F., Gratteri S., Muscoli C., Mollace V.. **Regulation of uric acid metabolism and excretion**. *Int. J. Cardiol.* (2015.0) **213** 8-14. DOI: 10.1016/j.ijcard.2015.08.109
4. Stewart D.J., Langlois V., Noone D.. **Hyperuricemia and Hypertension: Links and Risks**. *Integr. Blood Press. Control* (2019.0) **12** 43-62. DOI: 10.2147/IBPC.S184685
5. Kuwabara M., Niwa K., Nishihara S., Nishi Y., Takahashi O., Kario K., Yamamoto K., Yamashita T., Hisatome I.. **Hyperuricemia is an independent competing risk factor for atrial fibrillation**. *Int. J. Cardiol.* (2016.0) **231** 137-142. DOI: 10.1016/j.ijcard.2016.11.268
6. Day R.O., Graham G.G., Hicks M., McLachlan A.J., Stocker S., Williams K.M.. **Clinical Pharmacokinetics and Pharmacodynamics of Allopurinol and Oxypurinol**. *Clin. Pharmacokinet.* (2007.0) **46** 623-644. DOI: 10.2165/00003088-200746080-00001
7. Kuwabara M., Kuwabara R., Niwa K., Hisatome I., Smits G., Roncal-Jimenez C.A., MacLean P.S., Yracheta J.M., Ohno M., Lanaspa M.A.. **Different Risk for Hypertension, Diabetes, Dyslipidemia, and Hyperuricemia According to Level of Body Mass Index in Japanese and American Subjects**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10081011
8. Wang Y.-Y., Li L., Cui J., Yin F., Yang F., Yuan D.-M., Xin H.-L., Zhang L., Gao W.-G., Sun J.-P.. **Associations between anthropometric parameters (body mass index, waist circumference and waist to hip ratio) and newly diagnosed hyperuricemia in adults in Qingdao, China: A cross-sectional study**. *Asia Pac. J. Clin. Nutr.* (2020.0) **29** 763-770. PMID: 33377370
9. Cicero A.F.G., Fogacci F., Giovannini M., Grandi E., D’Addato S., Borghi C.. **Interaction between low-density lipoprotein-cholesterolaemia, serum uric level and incident hypertension: Data from the Brisighella Heart Study**. *J. Hypertens.* (2019.0) **37** 728-731. DOI: 10.1097/HJH.0000000000001927
10. Cicero A.F.G., Fogacci F., Giovannini M., Grandi E., Rosticci M., D’addato S., Borghi C.. **Serum uric acid predicts incident metabolic syndrome in the elderly in an analysis of the Brisighella Heart Study**. *Sci. Rep.* (2018.0) **8** 11529. DOI: 10.1038/s41598-018-29955-w
11. Ford E.S., Giles W.H., Dietz W.H.. **Prevalence of the Metabolic Syndrome among US Adults: Findings from the third National Health and Nutrition Examination Survey**. *JAMA* (2002.0) **287** 356-359. DOI: 10.1001/jama.287.3.356
12. Hwang L.-C., Bai C.-H., Chen C.-J.. **Prevalence of Obesity and Metabolic Syndrome in Taiwan**. *J. Formos. Med. Assoc.* (2006.0) **105** 626-635. DOI: 10.1016/S0929-6646(09)60161-3
13. Klein B.E., Klein R., Lee K.E.. **Components of the Metabolic Syndrome and Risk of Cardiovascular Disease and Diabetes in Beaver Dam**. *Diabetes Care* (2002.0) **25** 1790-1794. DOI: 10.2337/diacare.25.10.1790
14. Gami A.S., Witt B.J., Howard D.E., Erwin P.J., Gami L.A., Somers V.K., Montori V.M.. **Metabolic Syndrome and Risk of Incident Cardiovascular Events and Death: A Systematic Review and Meta-Analysis of Longitudinal Studies**. *J. Am. Coll. Cardiol.* (2007.0) **49** 403-414. DOI: 10.1016/j.jacc.2006.09.032
15. Chen J., Muntner P., Hamm L.L., Jones D.W., Batuman V., Fonseca V., Whelton P.K., He J.. **The Metabolic Syndrome and Chronic Kidney Disease in U.S. Adults**. *Ann. Intern. Med.* (2004.0) **140** 167-174. DOI: 10.7326/0003-4819-140-3-200402030-00007
16. Pasquali R., Gambineri A., Anconetani B., Vicennati V., Colitta D., Caramelli E., Casimirri F., Morselli/labate A.M.. **The natural history of the metabolic syndrome in young women with the polycystic ovary syndrome and the effect of long-term oestrogen-progestagen treatment**. *Clin. Endocrinol.* (1999.0) **50** 517-527. DOI: 10.1046/j.1365-2265.1999.00701.x
17. Ip M.S.M., Lam B., Ng M.M.T., Lam W.K., Tsang K.W.T., Lam K.S.L.. **Obstructive Sleep Apnea Is Independently Associated with Insulin Resistance**. *Am. J. Respir. Crit. Care Med.* (2002.0) **165** 670-676. DOI: 10.1164/ajrccm.165.5.2103001
18. Huang G., Xu J., Zhang T., Cai L., Liu H., Yu X., Wu J.. **Hyperuricemia is associated with metabolic syndrome in the community very elderly in Chengdu**. *Sci. Rep.* (2020.0) **10** 8678. DOI: 10.1038/s41598-020-65605-w
19. Ali N., Miah R., Hasan M., Barman Z., Mou A.D., Hafsa J.M., Das Trisha A., Hasan A., Islam F.. **Association between serum uric acid and metabolic syndrome: A cross-sectional study in Bangladeshi adults**. *Sci. Rep.* (2020.0) **10** 1-7. DOI: 10.1038/s41598-020-64884-7
20. Sautin Y., Nakagawa T., Zharikov S., Johnson R.J.. **Adverse effects of the classic antioxidant uric acid in adipocytes: NADPH oxidase-mediated oxidative/nitrosative stress**. *Am. J. Physiol. Physiol.* (2007.0) **293** C584-C596. DOI: 10.1152/ajpcell.00600.2006
21. Chen C.-H., Yang J.-H., Chiang C.W.K., Hsiung C.-N., Wu P.-E., Chang L.-C., Chu H.-W., Chang J., Song I.W., Yang S.-L.. **Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project**. *Hum. Mol. Genet.* (2016.0) **25** 5321-5331. DOI: 10.1093/hmg/ddw346
22. Fan C.-T., Hung T.-H., Yeh C.-K.. **Taiwan Regulation of Biobanks**. *J. Law Med. Ethics A J. Am. Soc. Law Med. Ethics* (2015.0) **43** 816-826. DOI: 10.1111/jlme.12322
23. Levey A.S., Bosch J.P., Lewis J.B., Greene T., Rogers N., Roth D.. **A More Accurate Method to Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation. Modification of Diet in Renal Disease Study Group**. *Ann. Intern. Med.* (1999.0) **130** 461-470. DOI: 10.7326/0003-4819-130-6-199903160-00002
24. **Physical Fitness 333 Plan**. 1999
25. Isomaa B., Henricsson M., Almgren P., Tuomi T., Taskinen M.-R., Groop L.. **The metabolic syndrome influences the risk of chronic complications in patients with Type II diabetes**. *Diabetologia* (2001.0) **44** 1148-1154. DOI: 10.1007/s001250100615
26. Tan C.-E., Ma S., Wai D., Chew S.-K., Tai E.-S.. **Can We Apply the National Cholesterol Education Program Adult Treatment Panel Definition of the Metabolic Syndrome to Asians?**. *Diabetes Care* (2004.0) **27** 1182-1186. DOI: 10.2337/diacare.27.5.1182
27. Liu J.-H., Ma Q.-H., Xu Y., Chen X., Pan C.-W.. **Metabolic Syndrome and 5-Year Incident Hyperuricemia among Older Chinese Adults: A Community-Based Cohort Study**. *Diabetes Metab. Syndr. Obes. Targets Ther.* (2020.0) **13** 4191-4200. DOI: 10.2147/DMSO.S278542
28. Chen W.-Y., Fu Y.-P., Zhou M.. **The bidirectional relationship between metabolic syndrome and hyperuricemia in China: A longitudinal study from CHARLS**. *Endocrine* (2022.0) **76** 62-69. DOI: 10.1007/s12020-022-02979-z
29. Battelli M.G., Bortolotti M., Polito L., Bolognesi A.. **The role of xanthine oxidoreductase and uric acid in metabolic syndrome**. *Biochim. Biophys. Acta (BBA)-Mol. Basis Dis.* (2018.0) **1864** 2557-2565. DOI: 10.1016/j.bbadis.2018.05.003
30. Palmer T.M., Nordestgaard B.G., Benn M., Tybjaerg-Hansen A., Smith G.D., Lawlor D.A., Timpson N.J.. **Association of plasma uric acid with ischaemic heart disease and blood pressure: Mendelian randomisation analysis of two large cohorts**. *BMJ* (2013.0) **347** f4262. DOI: 10.1136/bmj.f4262
31. Takiue Y., Hosoyamada M., Kimura M., Saito H.. **The Effect of Female Hormones Upon Urate Transport Systems in the Mouse Kidney**. *Nucleosides Nucleotides Nucleic Acids* (2011.0) **30** 113-119. DOI: 10.1080/15257770.2010.551645
32. Matsuura F., Yamashita S., Nakamura T., Nishida M., Nozaki S., Funahashi T., Matsuzawa Y.. **Effect of visceral fat accumulation on uric acid metabolism in male obese subjects: Visceral fat obesity is linked more closely to overproduction of uric acid than subcutaneous fat obesity**. *Metabolism* (1998.0) **47** 929-933. DOI: 10.1016/S0026-0495(98)90346-8
33. Fruehwald-Schultes B., Peters A., Kern W., Beyer J., Pfützner A.. **Serum leptin is associated with serum uric acid concentrations in humans**. *Metabolism* (1999.0) **48** 677-680. DOI: 10.1016/S0026-0495(99)90163-4
34. Zhang Y., Wei F., Chen C., Cai C., Zhang K., Sun N., Tian J., Shi W., Zhang M., Zang Y.. **Higher triglyceride level predicts hyperuricemia: A prospective study of 6-year follow-up**. *J. Clin. Lipidol.* (2017.0) **12** 185-192. DOI: 10.1016/j.jacl.2017.10.009
35. Ryu S., Chang Y., Zhang Y., Kim S.-G., Cho J., Son H.J., Shin H., Guallar E.. **A Cohort Study of Hyperuricemia in Middle-aged South Korean Men**. *Am. J. Epidemiol.* (2011.0) **175** 133-143. DOI: 10.1093/aje/kwr291
36. Gao Z., Zuo M., Han F., Yuan X., Sun M., Li X., Liu R., Jiang W., Zhang L., Chang B.. **Renal impairment markers in type 2 diabetes patients with different types of hyperuricemia**. *J. Diabetes Investig.* (2018.0) **10** 118-123. DOI: 10.1111/jdi.12850
37. Li Q., Shao X., Zhou S., Cui Z., Liu H., Wang T., Fan X., Yu P.. **Triglyceride-glucose index is significantly associated with the risk of hyperuricemia in patients with diabetic kidney disease**. *Sci. Rep.* (2022.0) **12** 19988. DOI: 10.1038/s41598-022-23478-1
38. Onat A., Can G., Örnek E., Altay S., Yüksel M., Ademoğlu E.. **Elevated serum uric acid in nondiabetic people mark pro-inflammatory state and HDL dysfunction and independently predicts coronary disease**. *Clin. Rheumatol.* (2013.0) **32** 1767-1775. DOI: 10.1007/s10067-013-2339-7
39. Peng T.-C., Wang C.-C., Kao T.-W., Chan J.Y.-H., Yang Y.-H., Chang Y.-W., Chen W.-L.. **Relationship between Hyperuricemia and Lipid Profiles in US Adults**. *BioMed Res. Int.* (2015.0) **2015** 127596. DOI: 10.1155/2015/127596
40. Vekic J., Jelic-Ivanovic Z., Spasojevic-Kalimanovska V., Memon L., Zeljkovic A., Bogavac-Stanojevic N., Spasic S.. **High serum uric acid and low-grade inflammation are associated with smaller LDL and HDL particles**. *Atherosclerosis* (2009.0) **203** 236-242. DOI: 10.1016/j.atherosclerosis.2008.05.047
41. Vekic J., Kotur-Stevuljevic J., Jelić-Ivanović Z., Spasic S., Spasojević-Kalimanovska V., Topić A., Zeljkovic A., Stefanovic A., Žunić G.. **Association of oxidative stress and PON1 with LDL and HDL particle size in middle-aged subjects**. *Eur. J. Clin. Investig.* (2007.0) **37** 715-723. DOI: 10.1111/j.1365-2362.2007.01849.x
42. Yoo T.W., Sung K.C., Shin H.S., Kim B.J., Kim B.S., Kang J.H., Lee M.H., Park J.R., Kim H., Rhee E.J.. **Relationship between Serum Uric Acid Concentration and Insulin Resistance and Metabolic Syndrome**. *Circ. J.* (2005.0) **69** 928-933. DOI: 10.1253/circj.69.928
43. Chen L.-Y., Zhu W.-H., Chen Z.-W., Dai H.-L., Ren J.-J., Chen J.-H., Chen L.-Q., Fang L.-Z.. **Relationship between hyperuricemia and metabolic syndrome**. *J. Zhejiang Univ. B* (2007.0) **8** 593-598. DOI: 10.1631/jzus.2007.B0593
44. Andrade J., Kang H., Greffin S., Rosa M.G., Lugon J.. **Serum uric acid and disorders of glucose metabolism: The role of glycosuria**. *Braz. J. Med. Biol. Res.* (2014.0) **47** 917-923. DOI: 10.1590/1414-431X20143878
45. Vallon V., Thomson S.C.. **Renal Function in Diabetic Disease Models: The Tubular System in the Pathophysiology of the Diabetic Kidney**. *Annu. Rev. Physiol.* (2012.0) **74** 351-375. DOI: 10.1146/annurev-physiol-020911-153333
46. Ito O., Kondo Y., Takahashi N., Omata K., Abe K.. **Role of calcium in insulin-stimulated NaC1 transport in medullary thick ascending limb**. *Am. J. Physiol. Physiol.* (1995.0) **269** F236-F241. DOI: 10.1152/ajprenal.1995.269.2.F236
47. Han T., Lan L., Qu R., Xu Q., Jiang R., Na L., Sun C.. **Temporal Relationship Between Hyperuricemia and Insulin Resistance and Its Impact on Future Risk of Hypertension**. *Hypertension* (2017.0) **70** 703-711. DOI: 10.1161/HYPERTENSIONAHA.117.09508
48. Cicero A.F., Salvi P., D’Addato S., Rosticci M., Borghi C.. **Association between serum uric acid, hypertension, vascular stiffness and subclinical atherosclerosis: Data from the Brisighella Heart Study**. *J. Hypertens.* (2014.0) **32** 57-64. DOI: 10.1097/HJH.0b013e328365b916
49. Bombelli M., Ronchi I., Volpe M., Facchetti R., Carugo S., Dell’oro R., Cuspidi C., Grassi G., Mancia G.. **Prognostic value of serum uric acid: New-onset in and out-of-office hypertension and long-term mortality**. *J. Hypertens.* (2014.0) **32** 1237-1244. DOI: 10.1097/HJH.0000000000000161
50. Mishima M., Hamada T., Maharani N., Ikeda N., Onohara T., Notsu T., Ninomiya H., Miyazaki S., Mizuta E., Sugihara S.. **Effects of Uric Acid on the NO Production of HUVECs and its Restoration by Urate Lowering Agents**. *Drug Res.* (2016.0) **66** 270-274. DOI: 10.1055/s-0035-1569405
51. Sánchez-Lozada L.G., Soto V., Tapia E., Avila-Casado C., Sautin Y.Y., Nakagawa T., Franco M., Rodríguez-Iturbe B., Johnson R.J.. **Role of oxidative stress in the renal abnormalities induced by experimental hyperuricemia**. *Am. J. Physiol.-Ren. Physiol.* (2008.0) **295** F1134-F1141. DOI: 10.1152/ajprenal.00104.2008
52. Kanellis J., Watanabe S., Li J.H., Kang D.H., Li P., Nakagawa T., Wamsley A., Sheikh-Hamad D., Lan H.Y., Feng L.. **Uric Acid Stimulates Monocyte Chemoattractant Protein-1 Production in Vascular Smooth Muscle Cells Via Mitogen-Activated Protein Kinase and Cyclooxygenase-2**. *Hypertension* (2003.0) **41** 1287-1293. DOI: 10.1161/01.HYP.0000072820.07472.3B
53. McAdams-DeMarco M.A., Law A., Maynard J.W., Coresh J., Baer A.N.. **Risk factors for incident hyperuricemia during mid-adulthood in African American and White men and women enrolled in the ARIC cohort study**. *BMC Musculoskelet. Disord.* (2013.0) **14** 347. DOI: 10.1186/1471-2474-14-347
54. Messerli F.H., Frohlich E.D., Dreslinski G.R., Suarez D.H., Aristimuno G.G.. **Serum Uric Acid in Essential Hypertension: An Indicator of Renal Vascular Involvement**. *Ann. Intern. Med.* (1980.0) **93** 817-821. DOI: 10.7326/0003-4819-93-6-817
|
---
title: 'Metabolically Healthy Overweight and Obesity, Transition to Metabolically
Unhealthy Status and Cognitive Function: Results from the Framingham Offspring Study'
authors:
- Matina Kouvari
- Nathan M. D’Cunha
- Thomas Tsiampalis
- Manja Zec
- Domenico Sergi
- Nikolaj Travica
- Wolfgang Marx
- Andrew J. McKune
- Demosthenes B. Panagiotakos
- Nenad Naumovski
journal: Nutrients
year: 2023
pmcid: PMC10004783
doi: 10.3390/nu15051289
license: CC BY 4.0
---
# Metabolically Healthy Overweight and Obesity, Transition to Metabolically Unhealthy Status and Cognitive Function: Results from the Framingham Offspring Study
## Abstract
Aims: To evaluate the association between metabolically healthy overweight/obesity (MHO) status and longitudinal cognitive function while also considering the stability of the condition. Methods: In total, 2892 participants (mean age 60.7 (9.4) years) from Framingham Offspring Study completed health assessments every four years since 1971. Neuropsychological testing was repeated every four years starting from 1999 (Exam 7) to 2014 (Exam 9) (mean follow-up: 12.9 (3.5) years). Standardized neuropsychological tests were constructed into three factor scores (general cognitive performance, memory, processing speed/executive function). Healthy metabolic status was defined as the absence of all NCEP ATP III [2005] criteria (excluding waist circumference). MHO participants who scored positively for one or more of NCEP ATPIII parameters in the follow-up period were defined as unresilient MHO. Results: No significant difference on the change in cognitive function over time was observed between MHO and metabolically healthy normal weight (MHN) individuals (all $p \leq 0.05$). However, a lower processing speed/executive functioning scale score was observed in unresilient MHO participants compared to resilient MHO participants (β = −0.76; $95\%$ CI = −1.44, −0.08; $$p \leq 0.030$$). Conclusions: *Retaining a* healthy metabolic status over time represents a more important discriminant in shaping cognitive function compared to body weight alone.
## 1. Introduction
Global obesity prevalence ranges from $11\%$ to $15\%$ and the results of the Non-Communicable Disease Collaboration analyses indicate the prevalence of obesity in the world doubled between 1975 and 2016 [1]. Excess fat accumulation, especially visceral adiposity, is linked to several chronic diseases, disability and reduced life expectancy and quality of life with a large body of research showing that obesity increases the risk of developing individual cardiometabolic diseases, as well as cardiometabolic multi-morbidity [2]. Recent estimates forecast a threefold increase in the number of dementia cases globally by 2050, underscoring the need for public health planning efforts and policy to address the needs of groups at higher risk for dementia [3].
Metabolically healthy overweight/obesity (MHO) affects approximately 10–$15\%$ of overweight or obese individuals. Recent findings from a vascular health perspective corroborate that metabolically benign obesity may not be an innocuous low-risk condition as previously believed [4,5,6]. In particular, two major issues have recently arisen. First, the definition of MHO status is still debated. The majority of prospective studies define MHO as a condition that does not meet the diagnostic criteria of metabolic syndrome. Hence, in many cases, individuals with obesity and even two metabolic abnormalities could be misclassified as being “healthy” [4,5,6]. In an effort to standardize the definition of MHO, a study by Lavie and colleagues [7] proposed a harmonized definition of MHO, moving from the more flexible concept of “metabolic syndrome absence” to a more comprehensive rationale that demands the absence of all metabolic syndrome features excluding waist circumference. Second, the stability of MHO status over time (i.e., no transition from metabolically healthy to metabolically unhealthy status) and its significance in defining future health outcomes remains to be fully elucidated. These points have recently been the subject of intense debate in the CVD research [4,5,6,7,8,9,10].
Evidence on the relationship between increased body weight, cognitive function, and dementia from prospective studies remain controversial, reporting neutral, adverse and age-dependent associations [11,12]. Several studies have observed an association between MHO and impaired cognitive function, as well as dementia [13,14,15]. However, to the best of our knowledge, no previous study has investigated the relationship between cognitive function, the aforementioned strictest definition of MHO [7] and the stability of this condition longitudinally. Using data from the Framingham Offspring Study [16], we aimed to determine how a priori defined MHO status using the latest criteria [7] is associated with cognitive function in this well-characterized cohort of community-dwelling adults. We posed two a priori research hypotheses: a. MHO status is not associated with poorer cognitive function over time compared to MHN status; b. The transition from MHO to metabolically unhealthy status (non-persistent MHO participants) is associated with poorer cognitive function compared to their resilient MHO counterparts.
## 2.1. Sample
The Framingham Heart Study (FHS) is a community-based prospective cohort study established in 1948 with the aim to identify risk factors that contribute to cardiovascular disease. More details on FHS can be found elsewhere [17]. In the current study, participants were members of the Offspring Cohort, which includes biological children of the original FHS cohort and spouses of offspring ($$n = 5124$$) who have undergone over nine health examination cycles approximately every 4 years since 1971. The present sample is based on the 2893 offspring participants who also underwent neuropsychological assessments starting on the seventh assessment in 1999. Follow-up data up to 2014 (Exam 9) were included in these analyses. Mean follow-up time was 12.9 (3.5) years. The protocol was approved by the Institutional Review Board of Boston University Medical Center, and all participants provided written informed consent. Access to the database was also approved by the University of Canberra Human Research Ethics Committee (UCHREC-2021–9271).
## 2.2. Neuropsychological Assessment
A standardized neuropsychological battery of tests was administered in three separate waves of testing. We constructed factor scores using the data from the battery to represent general cognitive performance, processing speed/executive function and memory in the same way as previously described by Bangen et al. [ 2019] [17]. In summary, factor scores were estimated from a 2-parameter logistic graded response item theory model of the neuropsychological test battery for each domain. The neuropsychological measures used to construct the memory, processing speed/executive function and general cognition factors are previously described [17]. In particular, for the memory factor, the Wechsler Memory Scale (WMS) Logical Memory immediate recall, delayed recall and recognition were used [18]. Trail Making Tests A and B, WMS Digit Span Backward, Wechsler Adult Intelligence Scale (WAIS) Similarities subtest, Controlled Word Association Test (FAS) and Category Fluency (Animals) were used to construct processing speed/executive function factor [19]. All the above variables combined with WMS Paired Associates total learning, learning of easy pairs, learning of hard pairs and delayed recall of hard pairs were used for the general cognition factor [20]. Each composite factor was scaled to have a mean of 50 and standard deviation (SD) of 10.
## 2.3. Combined Weight and Metabolic Status Definition
Normal weight was defined as body mass index (BMI) between 18.5 and 25 kg/m2, overweight as BMI from 25 to 29.9 kg/m2 and obesity as BMI ≥ 30 kg/m2. Underweight was defined as BMI < 18.5 kg/m2. Metabolic status was defined using the criteria suggested by Lavie and colleagues [7]. In particular, healthy metabolic status was defined as the absence of all of the following metabolic syndrome features such as hypertension, dyslipidemia and glycemic abnormalities. Hypertension was defined as systolic blood pressure (SBP) ≥ 130 mmHg and/or diastolic blood pressure (DBP) ≥ 85 mmHg [7]. Dyslipidemia was defined as triglyceride levels ≥ 150 mg/dL and/or high-density lipoprotein cholesterol (HDL-cholesterol) levels < 40 mg/dL in men and <50 mg/dL in women [7]. Glycemic abnormalities were defined as fasting glucose ≥ 100 mg/dL [7]. Medication treatments for the aforementioned conditions were set as alternative indicators of metabolic abnormalities. For the scope of the present work, participants were divided into four groups as follows; a. MHN defined as BMI < 25 kg/m2 and healthy metabolic status; b. MHO defined as BMI ≥ 25 kg/m2 and healthy metabolic status; c. MUN defined as BMI < 25 kg/m2 and unhealthy metabolic status; d. MUO defined as BMI ≥ 25 kg/m2 and unhealthy metabolic status.
## 2.4. Statistical Analysis
Baseline participant characteristics are presented in terms of mean (SD) values for continuous variables, and absolute (N) and relative (%) frequencies for categorical variables. Comparison among the different categories of obesity and metabolic health status was based on the one-way Analysis of Variance (ANOVA) for continuous characteristics and on the Pearson Chi-square test for categorical characteristics. An independent samples Student’s t-test was used to examine the difference in participant baseline characteristics (continuous) between those who remained resilient at the metabolically healthy obese status and those who transitioned to the metabolically unhealthy obese status. In addition, mixed effects linear regression analysis (both unadjusted and adjusted for participants characteristics) was implemented to examine the effect of the baseline obesity and metabolic health status on cognitive time trajectories. The same statistical methodology was also used to investigate the association of the transition to metabolically unhealthy status with cognitive time trajectories. All statistical analyses were performed using STATA software (version 14.0) and the statistical significance was set at a p-value < 0.05.
## 3.1. Participants Characteristics Based on Their Obesity and Metabolic Health Status at Baseline
Table 1 presents participant demographic and baseline characteristics, both for the total sample, as well as separately according to their obesity and metabolic health status. Only participants with available responses on both metabolic and psychological assessment were included—excluding participants who were classified as MHN at the recruitment phase, yet they transitioned to other BMI or metabolic categories within the decade, as well as participants initially classified as MHO who changed BMI category within the follow-up period—for a final sample size of $$n = 1990$.$ The sample was on average 60.7 (9.4) years old, and over half were women ($54.2\%$). In total, $13.6\%$ of the sample were metabolically healthy normal-weight, $16.7\%$ were metabolically unhealthy normal-weight, $11.8\%$ were metabolically healthy overweight/obese while the majority ($57.9\%$) of the sample were metabolically unhealthy overweight/obese. Regarding cognitive factor scores, MHN participants seemed to have the best scoring in all metrics followed by their MHO counterparts while the unhealthy categories scored significantly lower (all p-values < 0.05).
Table 2 presents participant demographic and baseline characteristics for the MHO participants, both overall and separately for those who retained or lost their healthy metabolic status over the observation period. No significant differences between the two categories were observed other than the metabolic syndrome components (all p-values < 0.001).
Based on the results from the multivariable mixed effects linear regression analysis (Table 3), after adjusting for participants’ sex, baseline age, waist circumference, LDL-C levels, educational level and smoking status, when compared to MHN participants, a significant decrease in general cognitive performance scale score was observed among the MUN (β = −1.23; $95\%$ CI = −2.00, −0.46; $$p \leq 0.002$$). There was no significant difference between MHN and MHO participants ($$p \leq 0.725$$). In the MUO participants, from Model 1 to Model 3, there was a significantly lower general cognitive performance scale score compared with MHO participants; however, this was not significant after adjusting for lipidemic and visceral adiposity factors ($$p \leq 0.283$$). Regarding the change observed in the memory scale score, when compared to the MHN reference group, lower scores were observed, both among the MUO (β = −1.48; $95\%$ CI = −2.76, −0.19; $$p \leq 0.025$$), as well as among the MUN subjects (β = −2.89; $95\%$ CI = −4.14, −1.64; $p \leq 0.001$), while there was no significant difference with the MHO participants ($$p \leq 0.813$$). Finally, regarding the change in the processing speed/executive functioning scale score, no significant difference was observed among all subsamples (all p-values > 0.05).
Based on the results from the multivariable mixed effects linear regression analysis (Table 4), after adjusting for participants’ sex, baseline age, waist circumference, LDL-C levels, educational level and smoking status, when compared to the participants who remained resilient in the MHO, a lower processing speed/executive functioning scale score was observed among those who transitioned to a metabolically unhealthy status (non-resilient MHO participants) (β = −0.76; $95\%$ CI = −1.44, −0.08; $$p \leq 0.030$$). However, regarding the change observed in the memory score ($$p \leq 0.181$$), as well as in the general cognitive performance scale score ($$p \leq 0.722$$), there was no significant difference between the two categories.
The present work revealed that MHO status may not result in poorer cognitive function over time compared with MHN individuals. However, MHO status is a transient condition. Based on the findings of this study, overweight and obesity status resulted in poorer general cognitive performance. MHO participants who lost their metabolically healthy status seemed to have lower processing speed/executive functioning scores compared with their resilient MHO counterparts while no significant trends were observed in the general cognitive performance and memory scale scores. To the best of our knowledge, this is one of the first studies that examined the role of MHO status on cognitive trajectories using a stricter definition for metabolic status and considering the stability of this condition.
The paradoxical association between weight status and cognitive function is highly discussed in the literature. Previous studies suggest that being overweight or obese in older age is protective against the development of cognitive impairment or dementia [21,22,23,24,25]. In contrast, other studies have revealed that abnormal weight status in midlife is associated with poorer cognitive function over time resulting in twice as high risk of dementia compared with their normal weight counterparts [26,27]. In line with the aforementioned contradictions, a recent meta-analysis of longitudinal studies suggests a positive association between obesity in midlife and later dementia. Nevertheless, the opposite happens in case of obesity in older age, suggesting a potential protective effect of the maintenance of body weight [12].
The vast majority of previous works that examined the effect of weight status on cognitive function [28] did not stratify the sample according to their metabolic profile. Considering that unhealthy metabolic status and its specific features have been independently associated with cognitive disorders, the effect of overweight and obesity should be examined independently from and in the absence of metabolic abnormalities [28]. Here, we show that being overweight or obese per se may not be linked with cognitive decline especially in the context of no metabolic abnormalities. Several studies have reported that the presence of metabolic syndrome is a risk factor for mild cognitive impairment [29], AD [30] and vascular dementia [31]. In addition, another analysis of the Framingham Offspring Cohort revealed that metabolic syndrome (yet not defined with the strict definition used here) was associated with a lower level of cognitive function, implying higher rates of dementia [17]. However, all aforementioned studies did not stratify the sample according to their weight status. Nevertheless, a combined analysis of cohorts from Europe, the US and Asian countries ($$n = 1$$,349,857) found a harmful effect of higher BMI over twenty years before a dementia diagnosis, but lower BMI was predictive of dementia when BMI was assessed less than ten years before diagnosis [32]. Together, these findings imply that weight beyond the normal range may support cognitive function in older age.
As previously mentioned, a limited number of studies have examined the combined effect of weight and metabolic status on cognitive function, reporting controversial results with regard to the relationship between MHO and cognitive function. In line with the outcomes presented here, a longitudinal nationwide study using data from South Korea revealed that after a median follow-up time of 5.5 years, MHO individuals had the lowest incidence of overall dementia and AD compared to all other categories except for vascular dementia [33]. Similarly, results from the Worldwide Alzheimer’s Disease Neuroimaging Initiative revealed that MHO participants in older age had a lower risk for AD during the follow-up period [13]. In contrast, a cross-sectional analysis, again in a South Korean population revealed no association between MHO status and cognitive disorders [34].
The potential advantage of MHO status compared with MHN group has been ascribed to a variety of factors. Firstly, lower weight in older age is frequently associated with other comorbidities like cardiometabolic disorders, and an accelerated decline in BMI during older age often precedes cognitive impairment [35]. Secondly, adipokines secreted from the adipose tissue may also mediate this association [36]. In particular, a higher circulating leptin level results in a higher cerebral brain volume, which is inversely correlated with cognitive impairment [36]. Third, decreased serum IGF-1—observed in individuals with lower weight—was identified as an independent risk factor for AD and vascular dementia [37].
Recently, it has been suggested that MHO status may be transient in nature. Prospective population-based studies have revealed that a considerable proportion, ranging between 33 and $52\%$, of MHO middle-aged individuals lose this status over time [38,39,40]. This comes in line with the present work suggesting an even worse condition in case of older adults (i.e., about two to three MHO individuals) lost their metabolically healthy status after a 12-year observation period. Such evidence implies that there are resilient and non-resilient individuals with MHO who may have differences in their health status over time. *This* generates the hypothesis that metabolic abnormalities may indicate a threshold of cumulative obesity exposure translated to health risk. Non-resilient MHO status has been evaluated in relation to cardiometabolic health, revealing either positive [9,39] or neutral [38] associations with CVD onset. Hence, another novelty is that this is the first study examining the connection between the stability of MHO status and cognitive function. Our analysis suggested that MHO individuals who lose their metabolically healthy status present worse cognitive function over time compared with their resilient MHO counterparts, yet not in all cognitive domains.
## 3.2. Strengths and Limitations
The main strength of the present work is that this is the first study that evaluated the transition of MHO to MUO status and their longitudinal associations with cognitive function. Additionally, we examined these associations using a strict definition regarding metabolic status. Other strengths include a large, well characterized, community-based sample with a prospective study design and an ongoing follow-up for over four decades. The implementation of a comprehensive neuropsychological assessment at multiple time points is another strength that increases the validity of the examined outcome. This study also has limitations. First, the principal hypothesis examined here was related with an intermediate condition. Most of intermediate forms of the disease do not strictly correspond to a well-defined phenotype. To this issue, even if the bias attributed to the transition to other BMI or metabolic status categories was partially avoided, misclassification of transitions cannot be precluded due to the extended interim periods between follow-up assessments. Second, data on other factors co-existing with abnormal weight status and simultaneously affecting cognition such as sleep quality, medication and so on were not available for the current analysis; however, we believe that considering these conditions or factors would make the effect of the non-resilient MHO status even stronger. Lastly, the study sample included predominantly white subjects, who were generally healthy and well-educated, which may affect generalizability.
## 4. Conclusions
An increased weight status in an older population has been suggested as a protective factor against cognitive decline. However, the fact that MHO individuals did not have a clear benefit compared with their normal weight counterparts along with the proven instability of this condition implies the need for modifications with the aim to retain a healthy metabolic status during aging. At the same time, body weight alone does not seem to be a key driver of cognitive impairment, which instead is driven by an impairment in metabolic health. Considering the multi-comorbidity in older age being common, as well as the intercorrelation between cardiometabolic and brain health, retention of healthy metabolic status should be prioritized irrespective of weight status.
## References
1. Bentham J., Di Cesare M., Bilano V., Bixby H., Zhou B., Stevens G.A., Riley L.M., Taddei C., Hajifathalian K., Lu Y.. **Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults**. *Lancet* (2017) **390** 2627-2642. DOI: 10.1016/S0140-6736(17)32129-3
2. Kivimäki M., Kuosma E., Ferrie J.E., Luukkonen R., Nyberg S.T., Alfredsson L., Batty G.D., Brunner E.J., Fransson E., Goldberg M.. **Overweight, obesity, and risk of cardiometabolic multimorbidity: Pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe**. *Lancet Public Health* (2017) **2** e277-e285. DOI: 10.1016/S2468-2667(17)30074-9
3. **Dementia Forecasting Collaborators Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019**. *Lancet Public Health* (2022) **7** e105-e125. DOI: 10.1016/S2468-2667(21)00249-8
4. Eckel N., Meidtner K., Kalle-Uhlmann T., Stefan N., Schulze M.B.. **Metabolically healthy obesity and cardiovascular events: A systematic review and meta-analysis**. *Eur. J. Prev. Cardiol.* (2016) **23** 956-966. DOI: 10.1177/2047487315623884
5. Fan J., Song Y., Chen Y., Hui R., Zhang W.. **Combined effect of obesity and cardio-metabolic abnormality on the risk of cardiovascular disease: A meta-analysis of prospective cohort studies**. *Int. J. Cardiol.* (2013) **168** 4761-4768. DOI: 10.1016/j.ijcard.2013.07.230
6. Kramer C.K., Zinman B., Retnakaran R.. **Are metabolically healthy overweight and obesity benign conditions?: A systematic review and meta-analysis**. *Ann. Intern. Med.* (2013) **159** 758-769. DOI: 10.7326/0003-4819-159-11-201312030-00008
7. Lavie C.J., Laddu D., Arena R., Ortega F.B., Alpert M.A., Kushner R.F.. **Healthy Weight and Obesity Prevention: JACC Health Promotion Series**. *J. Am. Coll. Cardiol.* (2018) **72** 1506-1531. DOI: 10.1016/j.jacc.2018.08.1037
8. Zheng R., Zhou D., Zhu Y.. **The long-term prognosis of cardiovascular disease and all-cause mortality for metabolically healthy obesity: A systematic review and meta-analysis**. *J. Epidemiol. Community Health* (2016) **70** 1024-1031. DOI: 10.1136/jech-2015-206948
9. Kouvari M., Panagiotakos D.B., Yannakoulia M., Georgousopoulou E., Critselis E., Chrysohoou C., Tousoulis D., Pitsavos C., Investigators A.S.. **Transition from metabolically benign to metabolically unhealthy obesity and 10-year cardiovascular disease incidence: The ATTICA cohort study**. *Metabolism* (2019) **93** 18-24. DOI: 10.1016/j.metabol.2019.01.003
10. Kouvari M., Chrysohoou C., Skoumas J., Pitsavos C., Panagiotakos D.B., Mantzoros C.S.. **ATTICA study Investigators The presence of NAFLD influences the transition of metabolically healthy to metabolically unhealthy obesity and the ten-year cardiovascular disease risk: A population-based cohort study**. *Metabolism* (2022) **128** 154893. DOI: 10.1016/j.metabol.2021.154893
11. Dahl A.K., Löppönen M., Isoaho R., Berg S., Kivelä S.-L.. **Overweight and obesity in old age are not associated with greater dementia risk**. *J. Am. Geriatr. Soc.* (2008) **56** 2261-2266. DOI: 10.1111/j.1532-5415.2008.01958.x
12. Pedditzi E., Peters R., Beckett N.. **The risk of overweight/obesity in mid-life and late life for the development of dementia: A systematic review and meta-analysis of longitudinal studies**. *Age Ageing* (2016) **45** 14-21. DOI: 10.1093/ageing/afv151
13. Ma L.-Z., Huang Y.-Y., Wang Z.-T., Li J.-Q., Hou X.-H., Shen X.-N., Ou Y.-N., Dong Q., Tan L., Yu J.-T.. **Metabolically healthy obesity reduces the risk of Alzheimer’s disease in elders: A longitudinal study**. *Aging* (2019) **11** 10939-10951. DOI: 10.18632/aging.102496
14. Cho Y.K., Lee J., Kim H.S., Park J.-Y., Lee W.J., Kim Y.-J., Jung C.H.. **The risk of Alzheimer’s disease according to dynamic changes in metabolic health and obesity: A nationwide population-based cohort study**. *Aging* (2021) **13** 16974-16989. DOI: 10.18632/aging.203255
15. Farruggia M.C., Small D.M.. **Effects of adiposity and metabolic dysfunction on cognition: A review**. *Physiol. Behav.* (2019) **208** 112578. DOI: 10.1016/j.physbeh.2019.112578
16. Feinleib M., Kannel W.B., Garrison R.J., McNamara P.M., Castelli W.P.. **The Framingham Offspring Study. Design and preliminary data**. *Prev. Med.* (1975) **4** 518-525. DOI: 10.1016/0091-7435(75)90037-7
17. Bangen K.J., Armstrong N.M., Au R., Gross A.L.. **Metabolic Syndrome and Cognitive Trajectories in the Framingham Offspring Study**. *J. Alzheimers Dis.* (2019) **71** 931-943. DOI: 10.3233/JAD-190261
18. Wechsler D.. **A Standardized Memory Scale for Clinical Use**. *J. Psychol.* (1945) **19** 87-95. DOI: 10.1080/00223980.1945.9917223
19. Wechsler D.. *Wechsler-Belllevue Intelligence Scale* (1939)
20. Kaplan E., Goodglass H., Weintraub S.. *The Boston Naming Test* (1983)
21. Atti A.R., Palmer K., Volpato S., Winblad B., De Ronchi D., Fratiglioni L.. **Late-life body mass index and dementia incidence: Nine-year follow-up data from the Kungsholmen Project**. *J. Am. Geriatr. Soc.* (2008) **56** 111-116. DOI: 10.1111/j.1532-5415.2007.01458.x
22. Fitzpatrick A.L., Kuller L.H., Lopez O.L., Diehr P., O’Meara E.S., Longstreth W.T., Luchsinger J.A.. **Midlife and late-life obesity and the risk of dementia: Cardiovascular health study**. *Arch. Neurol.* (2009) **66** 336-342. DOI: 10.1001/archneurol.2008.582
23. Hughes T.F., Borenstein A.R., Schofield E., Wu Y., Larson E.B.. **Association between late-life body mass index and dementia: The Kame Project**. *Neurology* (2009) **72** 1741-1746. DOI: 10.1212/WNL.0b013e3181a60a58
24. Buchman A.S., Schneider J.A., Wilson R.S., Bienias J.L., Bennett D.A.. **Body mass index in older persons is associated with Alzheimer disease pathology**. *Neurology* (2006) **67** 1949-1954. DOI: 10.1212/01.wnl.0000247046.90574.0f
25. Ye B.S., Jang E.Y., Kim S.Y., Kim E.-J., Park S.A., Lee Y., Hong C.H., Choi S.H., Yoon B., Yoon S.J.. **Unstable Body Mass Index and Progression to Probable Alzheimer’s Disease Dementia in Patients with Amnestic Mild Cognitive Impairment**. *J. Alzheimers Dis.* (2016) **49** 483-491. DOI: 10.3233/JAD-150556
26. Kivipelto M., Ngandu T., Fratiglioni L., Viitanen M., Kåreholt I., Winblad B., Helkala E.-L., Tuomilehto J., Soininen H., Nissinen A.. **Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease**. *Arch. Neurol.* (2005) **62** 1556-1560. DOI: 10.1001/archneur.62.10.1556
27. Whitmer R.A., Gunderson E.P., Barrett-Connor E., Quesenberry C.P., Yaffe K.. **Obesity in middle age and future risk of dementia: A 27 year longitudinal population based study**. *BMJ* (2005) **330** 1360. DOI: 10.1136/bmj.38446.466238.E0
28. Cook R., Cheng H.L., O’Dwyer N., Raymond J., Rooney K., Steinbeck K., O’Connor H.. **The relationship between overweight, obesity and cognitive function in adults: A systematic review and meta-analysis**. *Obes. Res. Clin. Pract.* (2014) **8** 21. DOI: 10.1016/j.orcp.2014.10.037
29. Ng T.P., Feng L., Nyunt M.S.Z., Feng L., Gao Q., Lim M.L., Collinson S.L., Chong M.S., Lim W.S., Lee T.S.. **Metabolic Syndrome and the Risk of Mild Cognitive Impairment and Progression to Dementia: Follow-up of the Singapore Longitudinal Ageing Study Cohort**. *JAMA Neurol.* (2016) **73** 456-463. DOI: 10.1001/jamaneurol.2015.4899
30. Vanhanen M., Koivisto K., Moilanen L., Helkala E.L., Hänninen T., Soininen H., Kervinen K., Kesäniemi Y.A., Laakso M., Kuusisto J.. **Association of metabolic syndrome with Alzheimer disease: A population-based study**. *Neurology* (2006) **67** 843-847. DOI: 10.1212/01.wnl.0000234037.91185.99
31. Solfrizzi V., Scafato E., Capurso C., D’Introno A., Colacicco A.M., Frisardi V., Vendemiale G., Baldereschi M., Crepaldi G., Di Carlo A.. **Metabolic syndrome and the risk of vascular dementia: The Italian Longitudinal Study on Ageing**. *J. Neurol. Neurosurg. Psychiatry* (2010) **81** 433-440. DOI: 10.1136/jnnp.2009.181743
32. Kivimäki M., Luukkonen R., Batty G.D., Ferrie J.E., Pentti J., Nyberg S.T., Shipley M.J., Alfredsson L., Fransson E.I., Goldberg M.. **Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals**. *Alzheimers Dement.* (2018) **14** 601-609. DOI: 10.1016/j.jalz.2017.09.016
33. Lee J.-Y., Han K., Han E., Kim G., Cho H., Kim K.J., Lee B.W., Kang E.S., Cha B.-S., Brayne C.. **Risk of Incident Dementia According to Metabolic Health and Obesity Status in Late Life: A Population-Based Cohort Study**. *J. Clin. Endocrinol. Metab.* (2019) **104** 2942-2952. DOI: 10.1210/jc.2018-01491
34. Kim S.-R., Kim H.-N., Song S.-W.. **Associations Between Mental Health, Quality of Life, and Obesity/Metabolic Risk Phenotypes**. *Metab. Syndr. Relat. Disord.* (2020) **18** 347-352. DOI: 10.1089/met.2020.0028
35. Dixon J.B., Lambert G.W.. **The obesity paradox--a reality that requires explanation and clinical interpretation**. *Atherosclerosis* (2013) **226** 47-48. DOI: 10.1016/j.atherosclerosis.2012.11.005
36. Beccano-Kelly D., Harvey J.. **Leptin: A novel therapeutic target in Alzheimer’s disease?**. *Int. J. Alzheimers Dis.* (2012) **2012** 594137. DOI: 10.1155/2012/594137
37. Westwood A.J., Beiser A., Decarli C., Harris T.B., Chen T.C., He X.-M., Roubenoff R., Pikula A., Au R., Braverman L.E.. **Insulin-like growth factor-1 and risk of Alzheimer dementia and brain atrophy**. *Neurology* (2014) **82** 1613-1619. DOI: 10.1212/WNL.0000000000000382
38. Appleton S.L., Seaborn C.J., Visvanathan R., Hill C.L., Gill T.K., Taylor A.W., Adams R.J.. **North West Adelaide Health Study Team Diabetes and cardiovascular disease outcomes in the metabolically healthy obese phenotype: A cohort study**. *Diabetes Care* (2013) **36** 2388-2394. DOI: 10.2337/dc12-1971
39. Mongraw-Chaffin M., Foster M.C., Anderson C.A.M., Burke G.L., Haq N., Kalyani R.R., Ouyang P., Sibley C.T., Tracy R., Woodward M.. **Metabolically Healthy Obesity, Transition to Metabolic Syndrome, and Cardiovascular Risk**. *J. Am. Coll. Cardiol.* (2018) **71** 1857-1865. DOI: 10.1016/j.jacc.2018.02.055
40. Fingeret M., Marques-Vidal P., Vollenweider P.. **Incidence of type 2 diabetes, hypertension, and dyslipidemia in metabolically healthy obese and non-obese**. *Nutr. Metab. Cardiovasc. Dis.* (2018) **28** 1036-1044. DOI: 10.1016/j.numecd.2018.06.011
|
---
title: Association between Serum Vitamin D and Metabolic Syndrome in a Sample of Adults
in Lebanon
authors:
- Myriam Abboud
- Rana Rizk
- Suzan Haidar
- Nadine Mahboub
- Dimitrios Papandreou
journal: Nutrients
year: 2023
pmcid: PMC10004784
doi: 10.3390/nu15051129
license: CC BY 4.0
---
# Association between Serum Vitamin D and Metabolic Syndrome in a Sample of Adults in Lebanon
## Abstract
The evidence on the association between vitamin D and metabolic syndrome (MetS) is inconclusive. This was a cross-sectional study to explore the relationship between vitamin D serum levels and MetS in a sample of Lebanese adults ($$n = 230$$), free of diseases that affect vitamin D metabolism, recruited from an urban large university and neighboring community. MetS was diagnosed according to the International Diabetes Federation criteria. A logistic regression analysis was performed taking MetS as the dependent variable, and vitamin D was forced into the model as an independent variable. The covariates included sociodemographic, dietary, and lifestyle variables. The mean (SD) serum vitamin D was 17.53 (12.40) ng/mL, and the prevalence of MetS was $44.3\%$. Serum vitamin D was not associated with MetS (OR = 0.99 ($95\%$ CI: 0.96, 1.02), $p \leq 0.757$), whereas the male sex, compared with the female sex and older age, was associated with higher odds of having MetS (OR = 5.92 ($95\%$ CI: 2.44, 14.33), $p \leq 0.001$ and OR = 1.08 ($95\%$ CI: 1.04, 1.11), $p \leq 0.001$, respectively). This result adds to the controversy in this field. Future interventional studies are warranted to better understand the relationship between vitamin D and MetS and metabolic abnormalities.
## 1. Introduction
Metabolic syndrome (MetS) is a constellation of metabolic derangements which includes abdominal obesity, increased fasting blood glucose (FBG), elevated blood pressure (BP), elevated triglycerides, and low high-density lipoprotein cholesterol (HDL-C). MetS is strongly associated with increased morbidity, mortality [1], and healthcare costs [2]. Globally, MetS prevalence ranges between 10 and $84\%$, depending on the definition used, sex, race, and geographical distribution of the population under study [2,3]. The primary approach in its management is to modify underlying environmental risk factors, including excessive body weight, sedentary lifestyle, atherogenic diet, smoking, and alcohol consumption [4,5,6].
Vitamin D is a fat-soluble prohormone, which was previously associated with bone mineral metabolism [7]. Over time, extra skeletal functions of vitamin D have been suggested, and recently, there has been a growing number of studies linking vitamin D insufficiency to MetS and its components. This association was suggested due to largely overlapping risk factors, such as inadequate exercise and lack of sun exposure [8]. Studies have shown that a lack of vitamin D lowers intracellular calcium levels, preventing cells from releasing insulin, thus decreasing glucose tolerance. Furthermore, vitamin D increases the number of insulin receptors, which are crucial for insulin responsiveness and glucose metabolism. Moreover, vitamin D possesses hormonal, anti-inflammatory, anti-apoptotic, and anti-fibrotic properties, suggestive of its MetS-preventive properties [7].
Vitamin D is typically measured through serum levels of 25-hydroxyvitamin D (25(OH)D). Observational data have revealed that a rise in blood 25(OH)D levels by just 1 ng/mL was associated with a $54\%$ lower risk of MetS, whereby a serum 25(OH)D level was associated with atherogenic dyslipidemia [9]. However, some studies found no association between vitamin D serum levels and MetS in adults [10,11,12]. In a recent systematic review, observational data showed a strong correlation between vitamin D and components of MetS, i.e., obesity, dyslipidemia, blood pressure, insulin, and glucose metabolism, and experimental data indicated a positive effect of vitamin D supplementation on blood pressure, abdominal obesity, insulin, and glucose metabolism [13]. Another recent systematic review and dose-response meta-analysis showed that, in cross-sectional studies and cohort studies, respectively, a 10 ng/mL increase in vitamin D concentration was linked to 20 and $15\%$ decreased chances of MetS [14].
Despite being a sun-rich country, vitamin D deficiency is frequently observed in Lebanon. Around $50\%$ of older persons and $72.8\%$ of adults have serum vitamin D levels below 10 and 12 ng/mL, respectively [15,16]. Moreover, MetS is also widespread in the country, with estimates ranging between 23.5 and $31.2\%$ [17,18]. Two previous studies examined the association between vitamin D and MetS among specific populations in Lebanon. Ghadieh et al. [ 18] showed that among employees of one private university, those with vitamin D < 20 ng/mL had 2.5 higher odds of having MetS than those with adequate vitamin D. Moreover, among the components of MetS, only hypertriglyceridemia and low HDL were associated with inadequate vitamin D. Gannge-Yared et al. [ 19] explored the association between vitamin D and each of the components of MetS among non-obese students with adequate vitamin D status recruited from one private university and reported a significant association with FBG only. Due to the limited evidence on the association between MetS and vitamin D, the aim of the present study is to examine this association among Lebanese adults.
## 2.1. Design
This study of cross-sectional design was conducted in a sample of Lebanese adults during May 2022. Subjects were recruited from a large university and neighboring community via community announcements.
## 2.2. Subjects
Participants were requested to come to fast for more than 8 h on the day of data collection and were only included in the study if they were between 18 and 65 years of age, Lebanese, neither pregnant nor lactating, not using medications affecting vitamin D metabolism, such as those taking seizure and antituberculosis drugs, and free of diseases affecting vitamin D metabolism, as in the case of severe renal or liver disease, and free of active infections such as COVID-19 [20].
## 2.3. Ethical Considerations
Trained research assistants confirmed eligibility and informed participants about the study protocol. All participants consented before data collection. The study was ethically approved by the Lebanese International University’s Institutional Review Board (IRB) (case number: LIUIRB-220201-SH-111).
## 2.4.1. Blood Sample
After taking a seated resting position for at least five minutes, a 5-mL blood sample was taken from the participants by a phlebotomist into a sterile serum separator tube with a clot activator. Samples were transported to the laboratory using a thermally insulated box, where they were centrifuged at 4000 revolutions/minute for ten minutes and analyzed for total cholesterol (mg/dL), HDL-C (mg/dL), triglycerides (mg/dL), FBG (mg/dL), and 25(OH)D (ng/mL) using an automated chemiluminescence micro-particle immunoassay (CMIA) kit (ARCHITECT; Abbott Laboratories, Abbott Park, IL, USA). For the present study, the vitamin D deficiency cut-off was 20 ng/mL [21].
## 2.4.2. Blood Pressure
A nurse measured BP using a standardized mercury sphygmomanometer following best practice. Two consecutive readings on the same arm were recorded, and their average was used for analysis [22].
## 2.4.3. Anthropometry
Trained dietitians collected the anthropometric data from the participants using standardized techniques and calibrated equipment. Height (cm) and weight (kg) were measured using a portable stadiometer (ADE, Germany) and a beam scale, respectively. Participants took their shoes and heavy clothes off. Height was taken to the nearest 0.1 cm, and weight to the nearest 100 g while the head was positioned in the Frankfort plane. Body mass index (BMI) was calculated as the ratio of weight (kg) and height squared (m2). Waist circumference (to the nearest 0.1 cm) was taken via a measuring tape at the midpoint between the right iliac crest and the lower costal region [23].
## 2.4.4. Diagnosis of Metabolic Syndrome
The International Diabetes Federation (IDF) criteria [24] were used to diagnose MetS. Participants were considered to be suffering from MetS if they had central obesity (≥94 cm in males and ≥80 cm in females; or BMI > 30 kg/m2, thus assuming central obesity) and two of the following factors: elevated triglycerides (≥150 mg/dL) or being treated for it; low HDL-C (<40 mg/dL in males and <50 mg/dL in females) or being treated for it; raised BP (systolic BP ≥ 130 or diastolic BP ≥ 85 mmHg) or treatment for hypertension; and FBG ≥ 100 mg/dL or diagnosed type 2 diabetes.
## 2.4.5. Questionnaires
The following questionnaires were used and filled out by trained research assistants to limit ambiguity: Demographic and medical history questionnaire: includes questions about the age, sex, education, employment and socioeconomic status, smoking status, and history of chronic diseases.
Mediterranean Diet Adherence Screener (MEDAS): Taken from the Prevencion con Dietamediterranea (PREDIMED) and translated into Arabic [25]. The screener includes 14 questions pertaining to food intake and frequency of food/food ingredients. When an answer to a question is in favor of the Mediterranean diet pattern, one point is scored, whereas unfavorable responses are assigned a 0 score. The final value, calculated by adding all question scores, ranges between 0 and 14, whereby higher values denote a greater adherence to the Mediterranean diet.
The International Physical Activity Questionnaire (IPAQ)—Short Form [26]: Includes seven questions aimed to identify both duration and frequency of physical activity performed in the past week. Metabolic equivalent of tasks (METs) are calculated by multiplying the total minutes expended in a certain activity by the frequency (days) by the constants of 3.3 for light, 4.0 for moderate, and 8.0 for vigorous activity. Total METs are the sum of the respective MET values for activities performed for more than 10 min. The Arabic version of the IPAQ—Short Form was used [27].
The Pittsburgh Sleep Quality Index (PSQI): Includes nine questions addressing sleep quantity and quality. The total score is calculated, with higher scores (≥5) indicating poor sleep [28]. The Arabic version, which was culturally adapted by Haidar et al. [ 29], was used.
The 10-item Cohen Perceived Stress Scale (PSS-10): A ten-item questionnaire assessing the levels of stress in the last month [30]. PSS uses a 5-point Likert scale ranging between never [0] and very often [4]. Final scores range between 0 and 40, with higher scores indicating higher levels of perceived stress. The Arabic version, which was validated by Chaaya et al. [ 31], was used.
The modified Yale Food Addiction Scale (mYFAS-Ar-Leb): *This is* the Arabic version to diagnose food addiction [32]. This is a nine-item questionnaire, including one item from each of the symptom groups that compose the seven diagnostic criteria for substance use disorders of the DSM-4 Text Revision [33], plus two individual items assessing clinical impairment and distress. Food addiction is diagnosed when a person endorses at least three dependence symptoms and meets the criterion for clinical significance. The mYFAS-Ar-*Leb is* a validated tool in the Lebanese population.
Before data collection, the survey was pilot-tested on ten adults, based on which the final version was produced.
## 2.5. Sample Size Calculation
We used Epi-info version 7.2 to calculate the minimum sample size, with a 1 − β = 0.8 and a $95\%$ confidence level. The outcome percentage was retrieved from a Lebanese study evaluating the association between vitamin D and MetS, where $52.3\%$ of those with inadequate vitamin D level had MetS, and an odds ratio of being diagnosed with MetS of 2.5 among those with inadequate vitamin D was reported [18]. It was determined that 184 participants were required, for which we added $10\%$ ($$n = 19$$) to account for missing data, leading to a target of 204 participants.
## 2.6. Statistical Analysis
SPSS (version 25) was used to analyze the data. Counts and percentages were used to summarize categorical variables and mean and standard deviation for continuous measures. The bivariate analysis included chi-square and Fisher exact tests to compare categorical variables and the Student t-test to compare means of two groups. A logistic regression analysis was then conducted using the Enter method, taking MetS as the dependent variable and vitamin D serum level and lifestyle variables as the independent variables adjusted over the sociodemographic variables. All covariates showing a $p \leq 0.2$ in the bivariate analysis were entered in the model, while vitamin D was forced. Statistical significance was determined with a p-value < 0.05.
## 3.1. Demographics and Medical Characteristics
The mean age of the sample was 43.36 ± 16.05 years. The majority of study participants were females ($62.9\%$) and married ($55.6\%$). More than half of study participants ($53.8\%$) had a high school education or below, and $53\%$ were unemployed. Just over a quarter were cigarette smokers ($28.5\%$), and $41.6\%$ were water-pipe smokers. More than half of the sample had a family history of diabetes ($54.8\%$) or hypertension ($57.3\%$), and $38.6\%$ had a history of dyslipidemia. The majority had no food addiction ($81\%$). The total mean IPAQ (log10) score among the study population was 3.15 ± 0.49, indicating moderate physical activity. The mean total PSQI was 6.99 ± 3.63; a score ≥ 5 indicates poor sleep quality. The average PSS was 19.84 ± 7.32, denoting moderate stress levels in the previous month. The mean MEDAS score was 5.98 ± 2.17, suggesting low adherence to the Mediterranean diet. The mean 25(OH)D level was 17.53 ± 12.40 ng/mL, denoting vitamin D deficiency. As for the prevalence of MetS, $44.3\%$ ($$n = 98$$) of the sample were diagnosed with it.
The association of sociodemographic and lifestyle factors with MetS is shown in Table 1. Significant differences were observed in sociodemographic characteristics between study participants who had MetS and those who did not, including gender, age, education level, and marital status ($p \leq 0.05$). The association between MetS and cigarette smoking showed borderline significance ($$p \leq 0.05$$), whereby $68.8\%$ of smokers and $51.1\%$ of current smokers had MetS compared with $39.9\%$ of nonsmokers. With regards to lifestyle characteristics and vitamin D serum levels, none were found to be significantly different by MetS status ($p \leq 0.05$).
## 3.2. Association between Vitamin D and Metabolic Syndrome
Table 2 presents the logistic regression for MetS and vitamin D status, adjusting for numerous sociodemographic and lifestyle factors. The results showed that vitamin D was not associated with MetS (OR = 0.99 ($95\%$ CI: 0.96, 1.02), $p \leq 0.757$). In contrast, the odds of having MetS, were approximately six times greater among males compared with females (OR = 5.92 ($95\%$ CI: 2.44, 14.33), $p \leq 0.001$). In addition, older age was associated with higher odds of MetS (OR = 1.08 ($95\%$ CI: 1.04, 1.11), $p \leq 0.001$).
## 4. Discussion
This study explored the relationship between vitamin D and MetS. The logistic regression models revealed that the participants’ vitamin D level was not associated with MetS. Furthermore, the present study found alarming prevalence rates of MetS ($44.3\%$), which were positively associated with older age and the male gender.
## Association between Vitamin D and MetS in Study Population
In this study, serum vitamin D levels were not statistically associated with MetS after adjusting for age, sex, and lifestyle factors (physical activity, sleep, stress, food addiction, and smoking). Participants with MetS had slightly higher serum levels of vitamin D in comparison with those without MetS; however, this association was not significant.
The evidence surrounding vitamin D and MetS association is inconsistent. Some studies revealed an inverse relationship [18,34,35], while others did not report such an association [19,35,36,37,38,39]. Additionally, it is uncertain which components of MetS might be involved in this association, with some studies suggesting obesity, while others suggest glucose homeostasis.
The contradictory findings from this study may be due to several factors. First, differences in the general study characteristics, including the study design, definition of MetS, unit and method of measuring serum vitamin D, and the representativeness, size, and health status of the sample, in addition to adjustments for confounders, might impact the findings of these studies. Second, factors such as age, sex, socioeconomic status, pregnancy, clothing style, sun exposure, seasons, latitude, pollution, BMI, and skin pigmentation might affect serum vitamin D levels [40,41]. Genetics might also affect vitamin D status [7]; for instance, the difference in gene expression in vitamin D-metabolizing enzymes and impaired hepatic 25-hydroxylation could result in vitamin D deficiency. Much of these factors were assessed in our study; however, other factors may be implicated in the relationship between vitamin D levels and MetS that could not have possibly been accounted for, such as sun exposure, clothing style, and genetic predisposition, etc. Likewise, Gannagé-Yared et al. [ 19] found that vitamin D levels are similar between subjects with and without MetS (28.65 ± 15 vs. 31.1 ± 12.34 ng/mL, respectively, $$p \leq 0.38$$). In addition, the correlation between vitamin D levels and the number of MetS risk factors was not significant ($$p \leq 0.09$$).
The present study population is relatively small and considered a low-risk group, especially as $63\%$ of our sample are females, $71\%$ are non-smokers, $58\%$ are non-waterpipe smokers, $58\%$ are physically active, the median age is 43 years, and the participants had average stress levels. This might have hindered us from observing a significant association between vitamin D and MetS.
This study revealed that vitamin D deficiency is highly prevalent among Lebanese adults. Our results align with those reported by Chakhtoura et al. [ 41], whereby the prevalence of hypovitaminosis D, i.e., 25(OH)D levels below 20 ng/mL, ranged between 44 and $96\%$, and the mean 25(OH)D was between 11 and 20 ng/mL. The possible reasons for the widespread vitamin D deficiency in Lebanon can be related to increased obesity rates in Lebanese adults, the dietary transition to Westernized dietary patterns, and diets high in total fat, saturated fat, and sugar but low in micronutrients such as vitamin A, vitamin D, and folic acid as well as iron, calcium, and zinc, despite having long sunny days throughout the year. Moreover, Arabi et al. [ 40] found a high prevalence of vitamin D deficiency ($39.1\%$ using the Institute of Medicine threshold) in rural and urban areas of Lebanon in the year 2000. Likewise, Gannagé-Yared et al. [ 19] reported high vitamin D deficiency in a similar age group (30 to 50 years), whereby $75\%$ of the study population were vitamin D-deficient using the same threshold. It is thus necessary to tackle vitamin D deficiency in Lebanon through evidence-based approaches.
In this study, MetS was diagnosed in $44.3\%$ of participants; this is higher than other studies among national samples of Lebanese adults of 36 [42], 31.2 [17], and $34.6\%$ [43]. In addition, MetS was more common in males than females. Although some studies did not report sex differences in MetS prevalence [44,45], others reported a higher prevalence of MetS among males, and others found a higher prevalence in females [46]. These conflicting results may be attributed to differences associated with physiological factors, socio-economic status, and lifestyle factors.
In our study, advanced age and being male independently increased the odds of MetS. Comparable with our results, age was positively associated with MetS across sexes; this is in line with previous studies [17,18]. Age is a risk factor for MetS. A plausible explanation may be related to the fact that with age, blood vessels progressively lose elasticity and increase their resistance, which slows blood flow. Additionally, poor circulation makes it more likely for fat to build up in the abdomen and release free fatty acids into the blood, thus increasing insulin resistance and triglyceride levels in the blood and, ultimately, the risk of MetS [47]. In addition, this could be attributed to age-related decline in numerous physiological variables and the unhealthy lifestyles adopted throughout life, which greatly increase metabolic risk factors.
This is one of the few studies exploring the relationship between MetS and vitamin D among Lebanese adults while accounting for several confounders. A biochemical analysis was performed in a certified laboratory in Lebanon, hence ensuring high quality and validity. Moreover, validated assessment tools were used. However, the results of the study should be interpreted in the light of some limitations. First, the present study included a convenient sample of Lebanese adults, and the results may not be generalized. Nevertheless, the study sample included participants from different socioeconomic characteristics (e.g., education levels and employment). Second, as for the Lebanese population [40], the majority of the sample was vitamin D-deficient. This could potentially underestimate or confound the association with MetS. Third, this was a cross-sectional study, hence disabling causal inferences [48].
## 5. Conclusions
The study did not find an association between vitamin D status and MetS in a sample of Lebanese adults. Vitamin D deficiency was widespread in the study sample. MetS was also widespread, with higher age and the male gender being main determinants. Further studies should examine the relationship between vitamin D and MetS and its components, adjusting for a wide range of socioeconomic (income, area of residence), lifestyle (e.g., diet, alcohol), and metabolic factors (e.g., insulin resistance, body fat percentage) among Lebanese adults. Future interventional studies are warranted to confirm the causal relationship between vitamin D and MetS and metabolic abnormalities. Finally, given the widespread prevalence of vitamin D deficiency, we suggest that urgent action be taken at a national level to address the problem and prevent associated complications.
## References
1. Mazloomzadeh S., Karami Zarandi F., Shoghli A., Dinmohammadi H.. **Metabolic syndrome, its components and mortality: A population-based study**. *Med. J. Islam. Repub. Iran* (2019.0) **33** 11. DOI: 10.47176/mjiri.33.11
2. Saklayen M.G.. **The Global Epidemic of the Metabolic Syndrome**. *Curr. Hypertens. Rep.* (2018.0) **20** 12. DOI: 10.1007/s11906-018-0812-z
3. Krishnamoorthy Y., Rajaa S., Murali S., Sahoo J., Kar S.S.. **Association Between Anthropometric Risk Factors and Metabolic Syndrome Among Adults in India: A Systematic Review and Meta-Analysis of Observational Studies**. *Prev. Chronic Dis.* (2022.0) **19** E24. DOI: 10.5888/pcd19.210231
4. Dobrowolski P., Prejbisz A., Kuryłowicz A., Baska A., Burchardt P., Chlebus K., Dzida G., Jankowski P., Jaroszewicz J., Jaworski P.. **Metabolic syndrome—A new definition and management guidelines: A joint position paper by the Polish Society of Hypertension, Polish Society for the Treatment of Obesity, Polish Lipid Association, Polish Association for Study of Liver, Polish Society of Family Medicine, Polish Society of Lifestyle Medicine, Division of Prevention and Epidemiology Polish Cardiac Society, “Club 30” Polish Cardiac Society, and Division of Metabolic and Bariatric Surgery Society of Polish Surgeons**. *Arch. Med. Sci. AMS* (2022.0) **18** 1133-1156. DOI: 10.5114/aoms/152921
5. Aryannejad A., Eghtesad S., Rahimi Z., Mohammadi Z., Malihi R., Danehchin L., Paridar Y., Abolnezhadian F., Cheraghian B., Mard A.. **Metabolic syndrome and lifestyle-associated factors in the ethnically diverse population of Khuzestan, Iran: A cross-sectional study**. *J. Diabetes Metab. Disord.* (2021.0) **20** 747-756. DOI: 10.1007/s40200-021-00811-5
6. Kim S.K., Hong S.H., Chung J.H., Cho K.B.. **Association Between Alcohol Consumption and Metabolic Syndrome in a Community-Based Cohort of Korean Adults**. *Med. Sci. Monit. Int. Med. J. Exp. Clin. Res.* (2017.0) **23** 2104-2110. DOI: 10.12659/MSM.901309
7. Melguizo-Rodríguez L., Costela-Ruiz V.J., García-Recio E., De Luna-Bertos E., Ruiz C., Illescas-Montes R.. **Role of Vitamin D in the Metabolic Syndrome**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13030830
8. Kauser H., Palakeel J.J., Ali M., Chaduvula P., Chhabra S., Lamsal Lamichhane S., Ramesh V., Opara C.O., Khan F.Y., Kabiraj G.. **Factors Showing the Growing Relation Between Vitamin D, Metabolic Syndrome, and Obesity in the Adult Population: A Systematic Review**. *Cureus* (2022.0) **14** e27335. DOI: 10.7759/cureus.27335
9. Zhu W., Heil D.P.. **Associations of vitamin D status with markers of metabolic health: A community-based study in Shanghai, China**. *Diabetes Metab. Syndr.* (2018.0) **12** 727-732. DOI: 10.1016/j.dsx.2018.04.010
10. Mansouri M., Abasi R., Nasiri M., Sharifi F., Vesaly S., Sadeghi O., Rahimi N., Sharif N.A.. **Association of vitamin D status with metabolic syndrome and its components: A cross-sectional study in a population of high educated Iranian adults**. *Diabetes Metab. Syndr.* (2018.0) **12** 393-398. DOI: 10.1016/j.dsx.2018.01.007
11. Ganji V., Tangpricha V., Zhang X.. **Serum Vitamin D Concentration ≥75 nmol/L Is Related to Decreased Cardiometabolic and Inflammatory Biomarkers, Metabolic Syndrome, and Diabetes; and Increased Cardiorespiratory Fitness in US Adults**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12030730
12. Wang H.H., Lee D.K., Liu M., Portincasa P., Wang D.Q.. **Novel Insights into the Pathogenesis and Management of the Metabolic Syndrome**. *Pediatr. Gastroenterol. Hepatol. Nutr.* (2020.0) **23** 189-230. DOI: 10.5223/pghn.2020.23.3.189
13. Theik N.W., Raji O.E., Shenwai P., Shah R., Kalluri S.R., Bhutta T.H., Hannoodee H., Al Khalili M., Khan S.. **Relationship and effects of vitamin D on metabolic syndrome: A systematic review**. *Cureus* (2021.0) **13** e17419. DOI: 10.7759/cureus.17419
14. Lee K., Kim J.. **Serum vitamin D status and metabolic syndrome: A systematic review and dose-response meta-analysis**. *Nutr. Res. Pract.* (2021.0) **15** 329-345. DOI: 10.4162/nrp.2021.15.3.329
15. Gannagé-Yared M.H., Chemali R., Yaacoub N., Halaby G.. **Hypovitaminosis D in a sunny country: Relation to lifestyle and bone markers**. *J. Bone Miner. Res. Off. J. Am. Soc. Bone Miner. Res.* (2000.0) **15** 1856-1862. DOI: 10.1359/jbmr.2000.15.9.1856
16. Gannagé-Yared M.H., Brax H., Asmar A., Tohmé A.. **[Vitamin D status in aged subjects. Study of a Lebanese population]**. *Presse Med.* (1998.0) **27** 900-904. PMID: 9767849
17. Sibai A.-M., Obeid O., Batal M., Adra N., El Khoury D., Hwalla N.. **Prevalence and correlates of metabolic syndrome in an adult Lebanese population**. *CVD Prev. Control* (2008.0) **3** 83-90. DOI: 10.1016/j.precon.2007.06.002
18. Ghadieh R., Mattar Bou Mosleh J., Al Hayek S., Merhi S., El Hayek Fares J.. **The relationship between hypovitaminosis D and metabolic syndrome: A cross sectional study among employees of a private university in Lebanon**. *BMC Nutr.* (2018.0) **4**. DOI: 10.1186/s40795-018-0243-x
19. Gannagé-Yared M.H., Maalouf G., Khalife S., Challita S., Yaghi Y., Ziade N., Chalfoun A., Norquist J., Chandler J.. **Prevalence and predictors of vitamin D inadequacy amongst Lebanese osteoporotic women**. *Br. J. Nutr.* (2009.0) **101** 487-491. DOI: 10.1017/S0007114508023404
20. Bookshelf N.C.. **A service of the National Library of Medicine, National Institutes of Health**. *Stat Pearls* (2020.0)
21. 21.
IOM (Institute of Medicine)
Dietary Reference Intakes for Calcium and Vitamin DThe National Academies PressWashington, DC, USA2011. *Dietary Reference Intakes for Calcium and Vitamin D* (2011.0)
22. Pickering T.G., Hall J.E., Appel L.J., Falkner B.E., Graves J., Hill M.N., Jones D.W., Kurtz T., Sheps S.G., Roccella E.J.. **Recommendations for blood pressure measurement in humans and experimental animals: Part 1: Blood pressure measurement in humans: A statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research**. *Circulation* (2005.0) **111** 697-716. DOI: 10.1161/01.cir.0000154900.76284.f6
23. van der Kooy K., Seidell J.C.. **Techniques for the measurement of visceral fat: A practical guide**. *Int. J. Obes. Relat. Metab. Disord. J. Int. Assoc. Study Obes.* (1993.0) **17** 187-196
24. Alberti K.G., Zimmet P., Shaw J.. **Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation**. *Diabet. Med. A J. Br. Diabet. Assoc.* (2006.0) **23** 469-480. DOI: 10.1111/j.1464-5491.2006.01858.x
25. Martínez-González M.Á., Corella D., Salas-Salvadó J., Ros E., Covas M.I., Fiol M., Wärnberg J., Arós F., Ruíz-Gutiérrez V., Lamuela-Raventós R.M.. **Cohort profile: Design and methods of the PREDIMED study**. *Int. J. Epidemiol.* (2012.0) **41** 377-385. DOI: 10.1093/ije/dyq250
26. Craig C.L., Marshall A.L., Sjöström M., Bauman A.E., Booth M.L., Ainsworth B.E., Pratt M., Ekelund U., Yngve A., Sallis J.F.. **International physical activity questionnaire: 12-country reliability and validity**. *Med. Sci. Sport. Exerc.* (2003.0) **35** 1381-1395. DOI: 10.1249/01.MSS.0000078924.61453.FB
27. **Download the IPAQ**
28. Buysse D.J., Reynolds C.F., Monk T.H., Berman S.R., Kupfer D.J.. **The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research**. *Psychiatry Res.* (1989.0) **28** 193-213. DOI: 10.1016/0165-1781(89)90047-4
29. Haidar S.A., de Vries N.K., Papandreou D., Rizk R., Karavetian M.. **The Freshman Weight Gain Phenomenon: Does It Apply To**. *Open Access Maced. J. Med. Sci.* (2018.0) **6** 2214-2220. DOI: 10.3889/oamjms.2018.431
30. Cohen S., Williamson G., Spacapam S., Oskamp S.. **Perceived stress in a probability sample of the US**. *The Social Psychology of Health: Claremont Symposium on Applied Social Psychology* (1998.0)
31. Chaaya M., Osman H., Naassan G., Mahfoud Z.. **Validation of the Arabic version of the Cohen Perceived Stress Scale (PSS-10) among pregnant and postpartum women**. *BMC Psychiatry* (2010.0) **10**. DOI: 10.1186/1471-244X-10-111
32. Hallit S., Brytek-Matera A., Malaeb D., Obeid S.. **Validation of the Arabic version of the modified Yale Food Addiction Scale in the general population in Lebanon**. *J. Eat. Disord.* (2022.0) **10** 112. DOI: 10.1186/s40337-022-00638-2
33. Pursey K.M., Stanwell P., Gearhardt A.N., Collins C.E., Burrows T.L.. **The prevalence of food addiction as assessed by the Yale Food Addiction Scale: A systematic review**. *Nutrients* (2014.0) **6** 4552-4590. DOI: 10.3390/nu6104552
34. Hajhashemy Z., Shahdadian F., Moslemi E., Mirenayat F.S., Saneei P.. **Serum vitamin D levels in relation to metabolic syndrome: A systematic review and dose-response meta-analysis of epidemiologic studies**. *Obes. Rev. Off. J. Int. Assoc. Study Obes.* (2021.0) **22** e13223. DOI: 10.1111/obr.13223
35. Al-Dabhani K., Tsilidis K.K., Murphy N., Ward H.A., Elliott P., Riboli E., Gunter M., Tzoulaki I.. **Prevalence of vitamin D deficiency and association with metabolic syndrome in a Qatari population**. *Nutr. Diabetes* (2017.0) **7** e263. DOI: 10.1038/nutd.2017.14
36. Amirbaigloo A., Hosseinpanah F., Sarvghadi F., Tohidi M., Eskandary P.S., Azizi F.. **Absence of association between vitamin D deficiency and incident metabolic syndrome: Tehran Lipid and Glucose Study**. *Metab. Syndr. Relat. Disord.* (2013.0) **11** 236-242. DOI: 10.1089/met.2012.0121
37. Chon S.J., Yun B.H., Jung Y.S., Cho S.H., Choi Y.S., Kim S.Y., Lee B.S., Seo S.K.. **Association between vitamin D status and risk of metabolic syndrome among Korean postmenopausal women**. *PLoS ONE* (2014.0) **9**. DOI: 10.1371/journal.pone.0089721
38. Rueda S., Fernández-Fernández C., Romero F., Martínez de Osaba J., Vidal J.. **Vitamin D, PTH, and the metabolic syndrome in severely obese subjects**. *Obes. Surg.* (2008.0) **18** 151-154. DOI: 10.1007/s11695-007-9352-3
39. Hjelmesaeth J., Røislien J., Hofsø D., Bollerslev J.. **Plasma 25-hydroxyvitamin d concentration and metabolic syndrome among middle-aged and elderly chinese individuals: Response to Lu et al**. *Diabetes Care* (2010.0) **33** e13. DOI: 10.2337/dc09-1568
40. Arabi A., Chamoun N., Nasrallah M.P., Tamim H.M.. **Vitamin D Deficiency in Lebanese Adults: Prevalence and Predictors from a Cross-Sectional Community-Based Study**. *Int. J. Endocrinol.* (2021.0) **2021** 3170129. DOI: 10.1155/2021/3170129
41. Chakhtoura M., Rahme M., Chamoun N., El-Hajj Fuleihan G.. **Vitamin D in the Middle East and North Africa**. *Bone Rep.* (2018.0) **8** 135-146. DOI: 10.1016/j.bonr.2018.03.004
42. Aoun R., Chokor F.A.Z., Taktouk M., Nasrallah M., Ismaeel H., Tamim H., Nasreddine L.. **Dietary fructose and its association with the metabolic syndrome in Lebanese healthy adults: A cross-sectional study**. *Diabetol. Metab. Syndr.* (2022.0) **14** 29. DOI: 10.1186/s13098-022-00800-5
43. Naja F., Nasreddine L., Itani L., Adra N., Sibai A.M., Hwalla N.. **Association between dietary patterns and the risk of metabolic syndrome among Lebanese adults**. *Eur. J. Nutr.* (2013.0) **52** 97-105. DOI: 10.1007/s00394-011-0291-3
44. Delavari A., Forouzanfar M.H., Alikhani S., Sharifian A., Kelishadi R.. **First nationwide study of the prevalence of the metabolic syndrome and optimal cutoff points of waist circumference in the Middle East: The national survey of risk factors for noncommunicable diseases of Iran**. *Diabetes Care* (2009.0) **32** 1092-1097. DOI: 10.2337/dc08-1800
45. Dutra E.S., de Carvalho K.M., Miyazaki E., Hamann E.M., Ito M.K.. **Metabolic syndrome in central Brazil: Prevalence and correlates in the adult population**. *Diabetol. Metab. Syndr.* (2012.0) **4** 20. DOI: 10.1186/1758-5996-4-20
46. Erem C., Hacihasanoglu A., Deger O., Topbaş M., Hosver I., Ersoz H.O., Can G.. **Prevalence of metabolic syndrome and associated risk factors among Turkish adults: Trabzon MetS study**. *Endocrine* (2008.0) **33** 9-20. DOI: 10.1007/s12020-008-9044-3
47. Tsai T.Y., Cheng J.F., Lai Y.M.. **Prevalence of metabolic syndrome and related factors in Taiwanese high-tech industry workers**. *Clinics* (2011.0) **66** 1531-1535. DOI: 10.1590/S1807-59322011000900004
48. Levin K.A.. **Study design III: Cross-sectional studies**. *Evid.-Based Dent.* (2006.0) **7** 24-25. DOI: 10.1038/sj.ebd.6400375
|
---
title: Hypericum foliosum Quality Botanical and Chemical Markers and In Vitro Antioxidant
and Anticancer Activities
authors:
- Gonçalo Infante Caldeira
- Guanghong Zhang
- Luís Pleno Gouveia
- Mafalda Videira
- Rita Serrano
- Olga Silva
journal: Plants
year: 2023
pmcid: PMC10004786
doi: 10.3390/plants12051087
license: CC BY 4.0
---
# Hypericum foliosum Quality Botanical and Chemical Markers and In Vitro Antioxidant and Anticancer Activities
## Abstract
Hypericum foliosum *Aiton is* an endemic Azorean Hypericum species. Even though the aerial parts of *Hypericum foliosum* are not described in any official pharmacopoeia, they are utilized in local traditional medicine due to their diuretic, hepatoprotective, and antihypertensive properties. This plant has previously been the subject of phytochemical characterization and has been studied for its antidepressant activity, showing significant results in animal models. The lack of a description of the main characteristics of the aerial parts, which would be necessary to properly identify this medicinal plant species, contributes to the possibility of misidentification events. We performed macroscopic and microscopic analyses that identified specific differential characteristics, such as the absence of dark glands, the dimensions of the secretory pockets in the leaf, and the presence of translucent glands in the powder. To continue our previous work on the biological activity of Hypericum foliosum, ethanol, dichloromethane/ethanol, and water extracts were prepared and studied for their antioxidant and cytotoxic activity. Extracts showed in vitro selective cytotoxic activity in human lung cancer cell line A549, colon cancer cell line HCT 8, and breast cancer cell line MDA-MB-231, with dichloromethane/ethanol showing higher activity against all cell lines (IC50 values of 71.49, 27.31, and 9.51 µg/mL, respectively). All extracts also showed significant antioxidant activity.
## 1. Introduction
Hypericum foliosum *Aiton is* a low shrub with large yellow flowers that belongs to the Hypericaceae family. This species is endemic to the Azores and belongs to the same section (Androsaemum) as Hypericum androsaemum, which is also used in Portuguese traditional medicine due to its diuretic, hepatoprotective, and antihypertensive properties [1]. Locally, H. foliosum is known as malfurada or furalha [2].
Currently, the Hypericaceae family comprises nine genera: Cratoxylum Blume, Eliea Cambess, Harungana Lamarck, Hypericum L., Lianthus N. Robson, Santomasia N. Robson, Thornea Breedlove & McClintock, Triadenum Rafinesque, and Vismia Vand. Most of the biodiversity within this family is included in the *Hypericum genus* (roughly $80\%$) [3,4]. However, this taxonomic classification has not been updated in the Flora Europea or in the Nova Flora de Portugal, in which the *Hypericum genus* is still part of the Clusiaceae (Guttiferae) [5].
The *Hypericum genus* comprises 484 species spread across all continents except for Antarctica. These species may exist as herbaceous or bushy species—or, rarely, as trees—and they are grouped into 36 taxonomic sections constructed according to specific combinations of morphological characteristics and biogeographic distribution. They are distributed across various habitats, from temperate regions to high mountains in the tropics, avoiding areas with extreme temperature, aridity, or salinity [6].
Many Hypericum species have long been used in traditional medicine and had their biological activities demonstrated through pharmacological studies. However, only the flowering part of H. perforatum is recognized as an herbal drug and described in the Portuguese and European Pharmacopoeias, and it was accepted as such by the European Medicines Agency (EMA) in 2009 [7,8]. In the last few years, a vast range of biological activities, such as anti-inflammatory, anticancer, antidiabetic, and antioxidant activities, related to plants from the *Hypericum genus* have been described in the published literature [9,10,11,12].
H. foliosum has long been a subject of study by our team. We previously conducted botanical, chemical, pharmacological, and toxicological studies using dried extracts of the flowering aerial parts of H. foliosum. The results from our preliminary botanical analysis led to the description of the first anatomical features necessary for identifying this medicinal plant as an herbal drug. Other specimens harvested in different areas and seasons should also be observed to confirm the obtained data. In our chemical and pharmacological work on H. foliosum, we established the main secondary metabolites’ fingerprint profile with a methanolic extract. Phenolic compound derivatives, including biapigenin, catechin, chlorogenic acid, miquelianin, quercetin, and quinic acid, were identified. Antidepressant activity studies showed that the antidepressant activity of the H. foliosum methanolic extract was not inferior to those of H. perforatum and H. androsaemum in animal models. In addition, evaluation of chronic toxicity in vivo showed no significant impact on the liver, pancreas, kidneys, or lipid profile [1,7]. H. foliosum methanolic extract also showed strong radical scavenging and acetylcholinesterase-inhibitory activities [13,14].
Previous work by other authors on H. foliosum led to the identification of an acylphloroglucinol metabolite that demonstrated antimicrobial in vitro activity against *Staphylococcus aureus* [15]. The essential oil composition is known to mainly comprise n-nonane, limonene, terpinolene, and other terpene derivatives [2,7,13]. Conservation of the species was also a focus of study, with micropropagation methodologies showing good results [16].
To legitimize the use of H. foliosum as an herbal drug, it is essential to describe its botanical characteristics and establish a clear and universal quality control methodology through a quality herbal drug monograph, as well as deepen knowledge regarding its biological activity. Several secondary metabolites with diverse biological activities have previously been identified in *Hypericum genus* extracts. These compounds generally belong to classes such as acylphloroglucinols, flavonoids, phloroglucinols, and xanthones and exhibit in vitro anticancer, cell protection, anti-inflammatory, antimicrobial, and antidepressant activities [17]. Since the selective cytotoxicity and antioxidant activities related, respectively, to anticancer and cell protection mechanisms were among the most frequent biological activities described in studies focusing on this botanical genus, we decided to study these activities in H. foliosum ethanolic, dichloromethane:ethanol, and water extracts using in vitro models. We also aimed to continue our previous work and fully describe the distinctive macroscopic and microscopic characteristics of the raw material of H. foliosum aerial parts.
## 2.1. Macroscopic Analysis
The macroscopic characterization of the aerial parts of H. foliosum (Table 1, Figure 1) revealed the presence of a yellowish-brown, branched, bare, and crenate stem (Figure 1a) with an average diameter of 3.5 ± 0.4 mm, prominent longitudinal ridges and dark pits scattered along the light brown surface (Figure 1b), and an internode distance of 2.0 ± 0.7 cm.
Opposite leaves were observed, showing ovate-oblong to lanceolate shapes, an entire margin, an acute apex, a length of 4.0 ± 0.6 mm and width 1.9 ± 0.4 mm, a short or almost inexistent petiole at the base, and yellowish-brown stipules (Figure 1c). The flowers were regular and the corymbs grouped (Figure 1d), with five green sepals with a brown base and lanceolate form, five orange-yellow petals, and long styles inserted at the base of the carpels (Figure 1e). Many pale-yellow stamens surrounded the three dark red carpels with lighter-colored stigmas (Figure 1e). Brown stilettos and ovaries were observed.
## 2.2.1. Stem
The main microscopic characteristics of the H. foliosum stem are presented in Table 2 and Figure 2. From the periphery to the interior, the transverse section of the H. foliosum adult stem (Figure 2a–e) showed four concentric rings with an uneven distribution. The outer ring consisted of a thick, red-brownish cuticle of phellem cells (cork cells) with striated walls (Figure 2a,b) followed by the external cortex, which had two to eight layers of laminar collenchyma consisting of rectangular and flattened cells. These layers were similar to the layer adjacent to the phellem, alternating with parenchyma cells of thinner walls (Figure 2c). In the inner cortex, there were a variety of roughly polyhedral parenchymatous cells. Dispersed by the cortical parenchyma, there were several types of secretory canals (or channels) composed of four degenerated secretory cells and an evident lumen with a diameter of 29.1 ± 8.7 μm (18.6 to 65.9 μm), forming a rosette-like structure (Figure 2c). More interiorly, vascular bundles radially occupying the major portion of the stem transverse section formed a shape like a circle. Phloem parenchymatous cells were neatly arranged in rows with a width of 48.0 ± 14.9 μm (17.1 to 95.4 μm). The secondary xylem was ring-porous, uniseriate, or multiseriate, occasionally containing distinctly heterogeneous xylem with large calibers, with an area of 639.4 ± 252.1 μm2 (210.5 to 1419.4 μm2). The parenchymatous medullary rays were depressed into the form of an elongated shape consisting of one to three rows of cells (Figure 2d). These pith rays were generally uniseriate, occasionally bi- or triseriate. In the inner ring, the pith was formed of round, thin-walled cells containing oval starch grains (Figure 2a). Compared to the adult stem, observation of the juvenile stem did not reveal significant morphological differences except for the presence of a lower degree of differentiation; a smaller lumen size and four companion cells, which constituted the initial formation of the narrow secretory canals, were observed. Observation of the epidermal strips of the H. foliosum stem (Table 2, Figure 2f) showed polyhedral and rectangular phellem cells from the surface view, with an average cell area of 639.8 ± 165.0 μm2. Specifically, a few paracytic stomata accompanied by two equal subsidiary cells and several surface glands showed brownish stains, and translucent interiors were also observed (Figure 2e). No calcium oxalate crystals were detected. With regard to the epidermal detachment of the stem and leaf, a significant difference was observed in the stem phellem cells, which were more rectangular and larger in size.
## 2.2.2. Leaf
The main microscopic characteristics of the H. foliosum leaf are presented in Table 3 and Figure 3. The top view of the H. foliosum leaf showed reticulated venation and translucent oil glands dispersed throughout the bifacial surface (Figure 3a,b). A midrib with a thickness of 719.3 ± 148.1 μm (294.0 to 882.1 μm) originated at angles of 45–60° from the secondary veins (Figure 3c). Secondary veins forming an angle of about 90° with the midrib (Figure 3a) were also observed. In Table 3, the main microscopic characteristics of the H. foliosum leaf are summarized. Microscopic analysis of the adaxial epidermis of the H. foliosum leaf clearly showed the presence of juxtaposed and polygonal cells, with an area of 317.4 ± 168.8 μm2 (79.3 to 802.3 μm2). No stomata were observed (Figure 3a,c). At the abaxial epidermis, a cell area of 382.4 ± 154.4 μm2 (187.1 to 650.9 μm2) was observed. When highlighted, this epidermis was revealed to be composed of cells with corrugated walls. Most of the observed stomata were anomocytic and anisocytic types and associated with two stomatal guard cells. Paracytic stomata were also found, but less frequently (Figure 3d). The abaxial stomatal index was 11.5 ± $3.6\%$. The microscopic analysis of the transverse section of the H. foliosum leaf (Figure 3e,f, Table 3) revealed several distinguishable characteristics; namely, a bifacial mesophyll (Figure 3e) formed by a single palisade layer of long cylindrical cells, sometimes regimented into two or three strata with similar lengths, and spongy parenchyma comprising several layers of branched cells that were not tightly connected. The mesophyll thickness was 126.9 ± 18.9 μm (94.3 to 177.9 μm), with a corresponding palisade parenchyma–spongy parenchyma ratio of 0.4 ± 0.1; the adaxial and abaxial epidermis consisted of one elongated layer of cells, covered by a smooth cuticle. The thickness of the adaxial cuticle was 4.0 ± 1.7 μm, similar to the observed abaxial cuticle value (3.6 ± 1.4 μm); several translucent glands with a diameter of 44.7 ± 11.2 μm (18.1 to 66.4 μm) were observed, mostly dispersed just between the abaxial epidermis and the spongy cells, showing a translucent oil content when observed from the top view (Figure 3a,b). The midrib had an elliptical shape with a thickness of 719.3 ± 148.1 μm (294.0 to 882.1 μm) and protruded from the underside. The abaxial view (Figure 3f,g) showed a large collenchyma area occupied by numerous circularly shaped cells with thick walls. The cambium was obviously distinguishable between the xylem and phloem, which contributed to a characteristic opened collateral vascular bundle. The thickness of the phloem was 63.8 ± 15.6 μm (40.5 to 92.5 μm). Some type B secretory canals were prominently scattered into the phloem, with a diameter of 32.2 ± 12.2 μm (11.2 to 59.7 μm) and a visible lumen surrounded by four flattened cells. The xylem, which had a thickness of 80.2 ± 8.8 μm (65.4 to 97.3 μm), was lying on the adaxial side of the vascular bundle and formed of lignified vessels of medium caliber. No calcium oxalate crystals were observed.
Despite the few paracytic stomata in the stem epidermis, the presence of stomata in the leaf, predominantly anomocytic and anisocytic, was crucial to distinguish between the adaxial and abaxial epidermis. However, there were no statistical differences between the adaxial and abaxial epidermis regarding single-cell dimensions. The presence of translucent oil glands dispersed throughout the leaf’s bifacial surface was easily observed in the powdered fragments, which was also an important characteristic for this medical raw-material identification.
## 2.2.3. Powder
The powdered drug produced from the H. foliosum aerial parts (Figure 4) presented a yellowish-green color and characteristic odor. Microscopically, it was identified by the presence of most of the characteristic elements of the leaf, stem, and flower; namely, fragments of the stem with yellowish-brown phellem cells; fragments of the central parenchyma of the stem with lignified and pitted rectangular cells; fragments of leaf bifacial mesophyll containing translucent glands inserted in palisade and spongy parenchyma; fragments of xylem with clusters of tracheids and punctuated vessels; fragments of the fibrous staminal filament with continuous thin-walled epidermal cells and striated cuticle fragments of anthers with countless yellow-brownish striated cuticle cells dotted in a stereo pod-shaped plane; fragments of obovate pollen grains with three germinal pores and a smooth exine, occurring singly or in dense groups; fragments of the petal epidermis with an elongated shape and straight or wavy anticlinal walls, distributed frequently with bright yellow oil glands, sometimes associated with small vessels; fragments of the black-red pistil strips covered by wrinkled and thick cuticles and with pollen tube-transmitting tracts in the center of the stigma (transverse section), associated with vessels; and fragments of the white ovary with a thick cuticle and filled with circular or rectangle parenchymatous cells.
In the microscopic analysis of the powder, we not only completed the previous observations of the leaf and stem with the most frequent elements of the raw material but also found flower fragments with specific and identical characteristics, such as fragments of fibrous staminal filament, fragments of stereo pod-shaped anthers formed of striated cuticle cells, and fragments of the petal epidermis containing plenty of oil glands. We highlight the observation of fragments of pistils related to pollen tube-transmitting tracts in the center of the stigma (transverse section). On the other hand, our results revealed the presence of translucent glands, not only dispersed in the leaf’s spongy mesophyll but also in the palisade parenchyma. Furthermore, in the aerial parts of H. foliosum, pollen glands with three germinal pores and a smooth exine were identified, which were distinct from the H. androsaemum pollen grains (coarse exine) [18] but similar to those observed in the aerial parts of H. perforatum [19] (EDQM 2019). Additionally, when observed under a scanning electron microscope (SEM), the H. foliosum pollen grains were larger than those observed in H. androsaemum [18].
## 2.3. Antioxidant Activity
The results of the antioxidant activity evaluation of the aerial parts of H. foliosum using 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and phosphomolybdic acid (PA) colorimetric assays are presented in Table 4. Hydroethanolic (Hf. E), dichloromethane/methanol (Hf. DM), and water (Hf. W) extracts were used for analysis.
All extracts showed antioxidant activity. However, the hydroethanolic extract (Hf. E) was the most active in terms of free radical scavenging capacity, and the dichloromethane extract (Hf. DM) was the extract with the higher total antioxidant capacity. A similar reducing power was observed with all the extracts. The Hf. W extract showed statistically (two-way ANOVA, alpha = 0.05) lower antioxidant activity than the Hf. E and DM extracts when assessed with the DPPH and PA methods. No discrimination between the extracts could be achieved when using the FRAP method (Table 4).
## 2.4. In Vitro Anticancer Activity
The results of the assessment of the anticancer activity of the aerial parts of H. foliosum against different cell lines are presented in Table 5.
Both Hf. E and Hf. DM showed selective anticancer activity against all human tumor cell lines tested, as seen in Table 5. Hf. W showed no effect under the tested conditions. The Hf. DM extract showed a statistically (two-way ANOVA, alpha = 0.05) lower cytotoxic activity than the Hf. E extract when assessed with any of the three methods used.
## 2.5. Chromatographic Profile
The obtained results can be seen in Figure 5 and Figure 6, where the UV spectra for the characteristic *Hypericum foliosum* compounds identified in the extracts under analysis are presented. A comparison with data generated in previous work by Machado with a different H. foliosum aerial part extract was undertaken [20]. Our analysis showed the presence of chlorogenic acid (Hf-A), catechin (Hf-B), miquelianin (Hf-C), and quercetin (Hf-D), which had already been isolated and identified in an H. foliosum aerial part extract. The chemical structures of these major compounds are presented in Figure 7. The presence of these marker compounds allowed for the confirmation of the phytochemical profile of H. foliosum.
## 3. Discussion
In the present study, we describe the first complete and comprehensive macroscopic and microscopic characterization of herbal medicine produced from H. foliosum aerial part.
Our macroscopic characterization of dried, fragmented raw material from H. foliosum aerial parts was consistent with the morphological botanical description of the species in the Nova Flora de Portugal by Franco et al. related to the species’ taxonomic identification [5]. The crenated stem with longitudinal ridges and dark pits; reticulated venation; stipules in the leaf’s basis; the number and color of sepals, petals, and carpels; and the absence of dark glands were the main features used for the species’ initial identification.
Our microscopic analysis revealed distinct and useful characteristics for accurately identifying herbal medicine produced from H. foliosum aerial parts. For instance, in the transverse section view, we could see the stem with a thick cuticle; the number of collenchymatous layers, type A secretory canal, circularly shaped vascular bundles, and uniseriate medullary rays also contributed to a clear identification of H. foliosum. With regard to the leaf, the bifacial mesophyll with translucent glands, the open collateral vascular bundle, and the type B secretory canals in the protruded midrib were the most useful characteristics. As anticipated, our results shared several similarities with Serrano et al. ’s findings [21].
To the best of our knowledge, this is the first full description of the stem, leaf, and flower of H. foliosum. No calcium oxalate crystals were observed. The outlines and proportions of vascular bundles in the stem and leaf are also important characteristics in distinguishing some Hypericum species. For instance, the xylem of the *Hipericum thymopsis* Boiss. fills the major part of the stem, whereas the pith is in the small region of the transverse section’s center [22], which can be observed easily. Our analysis of H. foliosum—specifically, the observation of circularly shaped vascular bundles in the stem and an open collateral vascular bundle type in the midrib of the leaf—can be used as important microscopic characteristics to differentiate Hypericum species and thus to identify H. foliosum aerial parts properly.
A growing body of literature is focused on the secretory structure of Hypericum species [19,23,24,25,26]. Secretory canals seem to be a common secretory structure in the Hypericum sp. and play an important role in its anatomy and distinction. Generally, each species is characterized by the presence of different types of secretory structures, including translucent glands, black nodules, and secretory canals with different shapes, ontogenesis routes, and localizations [25]. In H. foliosum, the translucent glands were present within the lamina of the leaf, close to the abaxial surface; type A and type B secretory canals were found in the cortical parenchyma of the stem and associated with phloem in the midrib, respectively. These secretory structures and their localization have previously been described in other Hypericum sp., such as Hypericum elodes L. [20], *Hypericum perforatum* L. [16], *Hypericum inodorum* Mill., *Hypericum olympicum* L., and *Hypericum forrestii* (Chitt.) N. Robson, as well as in other families of Angiosperms [27].
In addition, even though the representative metabolic naphthodianthrones (such as hypericin) are only present in black nodules [22], histochemical tests have been used to establish a correlation between the function of secretory structures and the localization of some secondary metabolites [21]. From a pharmacological point of view, translucent glands and type B canals produce biologically secondary metabolites with potential pharmacological activity (such as alkaloids, lipids, and resins), which may protect the plant against herbivores and parasites. As tannins accumulate in stems [25], type A canals can act as storage cavities.
Macroscopic analysis of new plant material harvested in 2021 confirmed the botanical characteristics previously observed, validating their use as monographic identification elements in a future quality monograph on H. foliosum aerial parts as an herbal drug.
Previous H. foliosum studies conducted by our team led to the identification of compounds such as quinic acid, catechin, chlorogenic acid, quercetin, miquelianin, and biapigenin in the methanolic extract and the establishment of a correlation between the antioxidant activity and flavonoid composition of the plant extract [20]. The higher the flavonoid content was, the stronger the antioxidant activity observed. Therefore, considering the antioxidant activity shown in our results, we can infer that the H. foliosum extracts under analysis in this study had different flavonoid contents, the ethanolic extract being the extract with the higher content, followed by dichloromethane/methanol 1:1.
In this work, Hf. E and HF. DM showed significant anticancer activity against all cancer cell lines tested. Selectively, these extracts showed different levels of activity against MDA-MB-231, A549, and HCT8 cells, which are, respectively, related to breast, lung, and colon cancers. These results suggest a possible efflux of the main Hf. E secondary metabolites (phenol acid derivatives) by P-glycoprotein.
The antioxidant and anticancer activities exhibited by H. foliosum in this study were in line with the major biological activities exhibited by natural compounds isolated from plants and subject to analysis, such as quercetin and catechin [17].
Our results are currently being investigated to achieve a better understanding of the cytotoxic activity exhibited by H. foliosum in this study. By establishing a correlation between class compounds and selective anticancer activities, we can contribute to the discovery of new compounds with potential therapeutic applications in oncology.
## 4.1. Plant Material
Aerial parts of H. foliosum were collected during the flowering and fruiting season from the Flores and Corvo Islands, Azores, Portugal, between April and June 2016. The parts were collected in protected natural parks under the supervision of competent legal authorities since it is a protected species. The plant material was identified and dried by Eng. José Maria de Freitas from the Azorean Agricultural Development Services Island, currently named the Environment and Climate Change Service of S. Jorge. A voucher species was deposited in this institution. The plant material was stored in the Pharmacognosy Laboratory of the Faculty of Pharmacy, Universidade de Lisboa. All samples were dried at room temperature and protected from direct sunlight and moisture.
Forty samples of the aerial part of H. foliosum were randomly selected from 250 g of the collected raw material according to the standard methods of sampling described in the European Pharmacopoeia.
In 2021, we obtained new plant material harvested under the same conditions and performed the same macroscopic analyses.
## 4.2. Macroscopic Analysis (MA)
The 40 selected samples were examined macroscopically according to the standard methods described in the European Pharmacopoeia. The shape, size, color, and surface texture of the leaf, stem, and flower were the main characteristics observed. Samples were directly examined with the naked eye and then by using an Olympus SZ61 stereo microscope (Switzerland) coupled with a Leica MC170 HD digital camera controlled with the Leica Application Suite (LAS) Version 4.8.0 software (Switzerland). Images were acquired using this software.
## 4.3. Light Microscopy (LM)
Transverse sections of leaf lamina and midrib regions and epidermal strips of the leaves and stems from 20 representative samples of the selected plant material were prepared using conventional microscope techniques. Sections of the prepared samples were mounted in $60\%$ aqueous chloral hydrate solution and examined using an Olympus CX31 microscope coupled with a Leica MC170 HD digital camera controlled with the LAS Version 4.8.0 software (Switzerland). The powdered plant material for the 20 samples was obtained using a water-cooled laboratory Analytical Mill A-10 (IKA) and observed using the same equipment under the same conditions as for the sample sections. Images were also acquired using this software and edited using Lightroom software.
## 4.4. Quantitative and Statistical Analysis
Quantification of selected morphological and anatomical characteristics was performed using LAS software (Switzerland). Statistical values were calculated using Microsoft Excel 2016 software. All macro- and microscopic results were expressed as means ± SD (Min (minimum) to Max (maximum)), except for the determination of the spongy parenchyma–palisade parenchyma ratio and the stomatal index. The stomatal index (SI) was determined using the formula SI = (S × 100)/(S + E), where (S) represents the number of stomata in a given area of a leaf and (E) the number of epidermal cells (including trichomes) in the same area of the leaf [19] (EDQM, 2019).
## 4.5. Extract Preparation
Aerial parts of H. foliosum were ground into a coarse powder in accordance with the European Pharmacopoeia 9 [2016]. The powdered drug used in the botanical identification protocol was obtained via pulverization in a porcelain mortar using a pestle until a degree of fineness equating to coarse powder was reached. About 324 g of powder was obtained and uniformly divided into two portions of 162 g:The first portion was used for the whole-extract preparation (Hf. E), adding ten times the amount of cold ethanol ($70\%$) and soaking it for 24 h. This procedure was repeated until the total raw material was exhausted. The obtained solution was filtered and evaporated under reduced pressure (T < 40 °C) in a rotary evaporator. The Hf. E total extract was kept in a desiccator at room temperature and protected from light;The second portion was used for the dichloromethane/methanol 1:1 (Hf. DM) and water (Hf. W) extracts. The raw material was completely covered with 1:1 DCM/methanol for 24 h. The Hf. DM extract was obtained after evaporating both solutions under pressure with a rotary vacuum flask evaporator;After the extraction with DCM/methanol and methanol, the final obtained residue was re-extracted with ultrapure water for 24 h. The obtained solution was freeze dried for 2~3 days and lyophilized (Hf. W).
## 4.6.1. DPPH (2,2-Diphenyl-1-picrylhydrazyl) Assay for Free Radical Scavenging Activity
This test was performed according to the method described by Blois [1958] with the slight modifications suggested by Brand-Williams et al. [ 1995]. Diluted extracts (100 µL) of H. foliosum (Hf. E, Hf. DM, Hf. W) in different concentrations were added to 3.9 mL of DPPH solution (6 × 10–5 M in methanol). After 30 min of incubation in the dark at room temperature, the absorbance was measured at 517 nm. The reference standard was ascorbic acid. The free radical scavenging activity (% antiradical activity) was calculated using the following equation: % antiradical activity = (Acontrol − Asample)/Acontrol × 100, where Acontrol was the absorbance of the control test (containing all reagents except the sample) and Asample was the absorbance of the extract. Each experiment was carried out in triplicate, and the results were expressed as the mean percentage antiradical activity ± SD ($$n = 3$$) and presented as the inhibitory concentration (IC50 value), which represents the concentration of the sample required to scavenge $50\%$ of the DPPH free radicals.
## 4.6.2. Ferric Reducing Antioxidant Power (FRAP) Assay for Reducing Power
For the ferric reducing antioxidant power (FRAP) assay, diluted extracts of H. foliosum (Hf. E, Hf. DM, and Hf. W) (100 µL) in different concentrations were mixed with 3 mL of freshly prepared FRAP reagent (0.3 M acetate buffer (pH = 3.6), 10 mM 2,4,6-tripyridyl-s-triazine (TPTZ) in 40 mM HCl, and 20 mM FeCl3·6H2O were mixed in a ratio of 10:1:1 (v/v/v) and warmed to 37 °C before use). After 4 min of incubation at 37 °C, the absorbance was measured at 593 nm. Ascorbic acid was used as a standard. The results were expressed as ascorbic acid equivalents (mg AAE/g dE) and calculated as the mean value ± SD ($$n = 3$$).
## 4.6.3. Phosphomolybdic Acid (PA) Assay for Total Antioxidant Activity
Diluted extracts of H. foliosum (Hf. E, Hf. DM, and Hf. W) (300 µL) in different concentrations were mixed with 3 mL of reagent solution containing 0.6 M sulfuric acid, 28 mM sodium phosphate, and 4 mM ammonium molybdate. After 90 min of incubation at 95 °C, the absorbance was measured at 695 nm with a Hitachi U-2000 UV–Vis spectrophotometer (Tokyo, Japan). Ascorbic acid was used as a standard, and the results were expressed as the inhibitory concentration (IC50 value).
## 4.7.1. Tumor Cell Lines
Anticancer activity tests were performed using three cancer cell lines: the human lung cancer cell line A549, the colon cancer cell line HCT 8, and the breast cancer cell line MDA-MB-436. All were obtained from the American Type Culture Collection (Manassas, VA, USA) and maintained under appropriate conditions.
## 4.7.2. MTT Assay
For the cell viability assays, 10,000 cells/well were seeded in a 96-well plate. After 24 h, the medium was removed and washed with 100 μL of 1× PBS (VWR; Portugal). Cells were then incubated without (negative control) or with the extracts (Hf. E, Hf. DM, and Hf. W) in concentrations ranging from 25 to 200 µg/mL for 24 h. At the end of this time, the medium was removed and each well was washed with 100 μL of 1× PBS. Cell viability was characterized using the MTT assay. Briefly, cells were incubated for 3 h with 200 μL of MTT (0.5 mg/mL non-supplemented medium/well) (VWR; Portugal) and then lysed with 100 μL of DMSO (VWR; Portugal). Absorbance (570 nm) was measured with a FLUOstar® Omega MicroPlate Reader. Cell viability was determined using the formula: cell viability % = [−(OD(experimental) −OD(blank))/(OD(control) − OD(blank)) × 100] (OD: optical density). Cytotoxicity was expressed as the concentration of the extract inhibiting cell growth by $50\%$ (IC50). All experiments were undertaken in triplicate.
## 4.8. LC-UV/DAD Chromatographic Profile
To conduct our experimental work, samples of the extracts were reconstituted in an acetonitrile/water mixture to a concentration of 18.2 mg/mL. After that, samples were filtered with a 0.22 μm filter and submitted to chromatographic analysis at 28 °C using a Waters Symmetry (3.9 × 150 mm, 5 μm) protected with a Waters Symmetry pre-column (3.9 × 20 mm, 5 μm). The gradient elution was performed with a 0.8 mL/min flow rate using solvent A ($0.05\%$ v/v trifluoroacetic acid (TFA) in water (H2O)), solvent B (acetonitrile (MeCN)), and solvent C (MeOH) and the gradient program presented in Table 6. Peaks were detected at the maximum intensity and maxplot chromatograms were generated using Waters Millenium 32 software. HPLC-UV/DAD analysis was performed using a Waters Alliance 2690 Separations Module (Waters Corporation, Milford, MA, USA) coupled with a Waters 996 photodiode array detector (UV/DAD) (Waters Corporation, MA).
## 5. Conclusions
Our botanical characterization provides valuable information to be included in identification protocols for H. foliosum as a raw material for industrial use and offers additional support for further biochemical studies concerning this medicinal plant. Since the botanical specificities of H. foliosum are now well-described, quality control of the plant can be standardized. Our results regarding the antioxidant and selective anticancer activities of H. foliosum extracts are currently the subject of research intended to provide a better characterization of the class compounds responsible for the exhibited biological activities.
## References
1. Ramalhete N., Machado A., Serrano R., Gomes E.T., Mota-Filipe H., Silva O.. **Comparative study on the in vivo antidepressant activities of the Portuguese Hypericum foliosum, Hypericum androsaemum and Hypericum perforatum medicinal plants**. *Ind. Crops Prod.* (2016) **82** 29-36. DOI: 10.1016/j.indcrop.2015.12.014
2. Santos P.A., Figueiredo A.C., Barroso J.G., Pedro L.G., Scheffer J.J.. **Composition of the essential oil of Hypericum foliosum Aiton from five Azorean islands**. *Flavour Fragr. J.* (1999) **14** 283-286. DOI: 10.1002/(SICI)1099-1026(199909/10)14:5<283::AID-FFJ826>3.0.CO;2-2
3. Ruhfel B.R., Bittrich V., Bove C.P., Gustafsson M.H., Philbrick C.T., Rutishauser R., Xi Z., Davis C.C.. **Phylogeny of the clusioid clade (**. *Am. J. Bot.* (2011) **98** 306-325. DOI: 10.3732/ajb.1000354
4. Nürk N.M., Crockett S.L.. **Morphological and Phytochemical Diversity among**. *Med. Aromat. Plant Sci. Biotechnol.* (2011) **5** 14-28. PMID: 22662020
5. do Amaral Franco J.. *Nova Flora de Portugal* (1971) **Volume 1**
6. Robson S.L.C.N.K.B.. **Taxonomy and Chemotaxonomy of the Genus Hypericum**. *Med. Aromat. Plant Sci. Biotechnol.* (2012) **5** 1-13
7. Rainha N., Lima E., Baptista J.. **Comparison of the endemic Azorean Hypericum foliosum with other Hypericum species: Antioxidant activity and phenolic profile**. *Nat. Prod. Res.* (2011) **25** 123-135. DOI: 10.1080/14786419.2010.512560
8. 8.
EMA
Community Herbal Monograph Hypericum perforatum L., Herba (Well-Established Medicinal Use)European Medicines Agency—Committee on Herbal Medicinal Products (HPMC)Lisboa, Portugal2009. *Community Herbal Monograph Hypericum perforatum L., Herba (Well-Established Medicinal Use)* (2009)
9. Cardile A., Zanre V., Campagnari R., Asson F., Addo S.S., Orlandi E., Menegazzi M.. **Hyperforin Elicits Cytostatic/Cytotoxic Activity in Human Melanoma Cell Lines, Inhibiting Pro-Survival NF-kappaB, STAT3, AP1 Transcription Factors and the Expression of Functional Proteins Involved in Mitochondrial and Cytosolic Metabolism**. *Int. J. Mol. Sci.* (2023) **24**. DOI: 10.3390/ijms24021263
10. Deng X., Xia J., Hu B., Hou X.C., Pu X.D., Wu L.. **Hyjapones A-D, trimethylated acyphloroglucinol meroterpenoids from Hypericum japonicum thunb. With anti-inflammatory activity**. *Phytochemistry* (2022) **202** 113308. DOI: 10.1016/j.phytochem.2022.113308
11. Zhai X., Chen Y., Han X., Zhu Y., Li X., Zhang Y., Lu Y.. **The protective effect of hypericin on postpartum depression rat model by inhibiting the NLRP3 inflammasome activation and regulating glucocorticoid metabolism**. *Int. Immunopharmacol.* (2022) **105** 108560. DOI: 10.1016/j.intimp.2022.108560
12. Rafailovska E., Tushevski O., Shijakova K., Simic S.G., Kjovkarovska S.D., Miova B.. **Hypericum perforatum L. extract exerts insulinotropic effects and inhibits gluconeogenesis in diabetic rats by regulating AMPK expression and PKCepsilon concentration**. *J. Ethnopharmacol.* (2023) **302** 115899. DOI: 10.1016/j.jep.2022.115899
13. Rainha N., Lima E., Baptista J., Rodrigues C.. **Antioxidant properties, total phenolic, total carotenoid and chlorophyll content of anatomical parts of**. *J. Med. Plants Res.* (2011) **5** 1930-1940
14. Arruda M., Rainha N., Barreto M., Lima E., Baptista J.. **Acetylcholinesterase inhibition properties of**. *Planta Med.* (2010) **76** P073. DOI: 10.1055/s-0030-1264371
15. Gibbons S., Moser E., Hausmann S., Stavri M., Smith E., Clennett C.. **An anti-staphylococcal acylphloroglucinol from Hypericum foliosum**. *Phytochemistry* (2005) **66** 1472-1475. DOI: 10.1016/j.phytochem.2005.04.024
16. Moura M.. **Conservation ofHypericum foliosum aiton, an endemic azorean species, by micropropagation**. *Vitr. Cell. Dev. Biol.* (1998) **34** 244-248. DOI: 10.1007/BF02822715
17. Caldeira G.I., Gouveia L.P., Serrano R., Silva O.D.. **Hypericum Genus as a Natural Source for Biologically Active Compounds**. *Plants* (2022) **11**. DOI: 10.3390/plants11192509
18. Bottega S., Garbar F., Pagni A.M.. *Secretory Structures in Hypericum elodes L. (Hypericaceae). I. Preliminary Observations* (1999)
19. Perrone R., De Rosa P., De Castro O., Colombo P.. **A further analysis of secretory structures of some taxa belonging to the genus Hypericum (Clusiaceae) in relation to the leaf vascular pattern**. *Turk. J. Bot.* (2013) **37** 847-858. DOI: 10.3906/bot-1206-22
20. Machado A.M.P.. **Contribuição para o Conhecimento do Perfil Metabolómico e das Potencialidades Farmacológicas de**. *Master’s Thesis* (2012)
21. Serrano R., Ferreira P., Gomes E.T., Silva O.. **The Use of SEM and Light Microscopy for the Characterization of Hypericum foliosum Aerial Part as a Medicinal Plant**. *Microsc. Microanal.* (2008) **14** 107-108. DOI: 10.1017/S1431927608089551
22. Tekin M.. **Pharmacobotanical study of Hypericum thymopsis**. *Rev. Bras. Farmacogn.* (2017) **27** 143-152. DOI: 10.1016/j.bjp.2016.09.004
23. Fornasiero R.B., Bianchi A., Pinetti A.. **Anatomical and Ultrastuctural Observations inHypericum perforatumL. Leaves**. *J. Herbs Spices Med. Plants* (1998) **5** 21-33. DOI: 10.1300/J044v05n04_04
24. Ciccarelli D., Andreucci A.C., Pagni A.M.. **The “black nodules” of Hypericum perforatum L. subsp. perforatum: Morphological,anatomical, and histochemical studies during the course of ontogenesis**. *Isr. J. Plant Sci.* (2001) **49** 33-40
25. Ciccarelli D.. **Translucent Glands and Secretory Canals in Hypericum perforatum L. (Hypericaceae): Morphological, Anatomical and Histochemical Studies During the Course of Ontogenesis**. *Ann. Bot.* (2001) **88** 637-644. DOI: 10.1006/anbo.2001.1514
26. Perrone R., Rosa P., Castro O., Colombo P.. **Leaf and stem anatomy in eight Hypericum species (Clusiaceae)**. *Acta Bot. Croat.* (2013) **72** 269-286. DOI: 10.2478/botcro-2013-0008
27. Osinska B.L.E.. **Shoot anatomy and secretory structures in**. *Bot. J. Linn. Soc.* (2010) **163** 70-86
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---
title: The Phenolic Compounds’ Role in Beer from Various Adjuncts
authors:
- Irina N. Gribkova
- Mikhail N. Eliseev
- Irina V. Lazareva
- Varvara A. Zakharova
- Dmitrii A. Sviridov
- Olesya S. Egorova
- Valery I. Kozlov
journal: Molecules
year: 2023
pmcid: PMC10004787
doi: 10.3390/molecules28052295
license: CC BY 4.0
---
# The Phenolic Compounds’ Role in Beer from Various Adjuncts
## Abstract
Background: The present article considers the influence of malt with various adjuncts on beer organic compounds and taste profile composition, with more attention paid to the phenol complex change. The topic under consideration is relevant since it studies the interactions of phenolic compounds with other biomolecules, and expands the understanding of the adjuncts organic compounds contribution and their joint effect on beer quality. Methods: Samples of beer were analyzed at a pilot brewery using barley and wheat malts, barley, rice, corn and wheat, and then fermented. The beer samples were assessed by industry-accepted methods and using instrumental analysis methods (high-performance liquid chromatography methods—HPLC). The obtained statistical data were processed by the Statistics program (Microsoft Corporation, Redmond, WA, USA, 2006). Results: The study showed that at the stage of hopped wort organic compounds structure formation, there is a clear correlation between the content of organic compounds and dry substances, including phenolic compounds (quercetin, catechins), as well as isomerized hop bitter resines. It is shown that the riboflavin content increases in all adjunct wort samples, and mostly with the use of rice—up to 4.33 mg/L, which is 9.4 times higher than the vitamin levels in malt wort. The melanoidin content in the samples was in the range of 125–225 mg/L and its levels in the wort with additives exceeded the malt wort. Changes in β-glucan and nitrogen with thiol groups during fermentation occurred with different dynamics and depending on the adjunct’s proteome. The greatest decrease in non-starch polysaccharide content was observed in wheat beer and nitrogen with thiol groups content—in all other beer samples. The change in iso-α-humulone in all samples at the beginning of fermentation correlated with a decrease in original extract, and in the finished beer there was no correlation. The behavior of catechins, quercetin, and iso-α-humulone has been shown to correlate with nitrogen with thiol groups during fermentation. A strong correlation was shown between the change in iso-α-humulone and catechins, as well as riboflavin and quercetin. It was established that various phenolic compounds were involved in the formation of taste, structure, and antioxidant properties of beer in accordance with the structure of various grains, depending on the structure of its proteome. Conclusions: The obtained experimental and mathematical dependences make it possible to expand the understanding of intermolecular interactions of beer organic compounds and take a step toward predicting the quality of beer at the stage of using adjuncts.
## 1. Introduction
The structure of food products, including beverages, is formed on the basis of the interaction of organic compounds, which are contained in raw materials and can be extracted as a result of the conditions of the technological cycle. In this regard, the beer colloidal structure consists of primary, biomodified, and newly formed organic structures of plant raw materials: Cereal, hop-based, as well as obtained through the microorganism’s cultures [1,2].
The focus of our research interest is on the phenolic compounds of plant raw materials, which are still the most studied in terms of the sources of turbidity formation as well as nutraceutical properties [3,4,5,6].
Among the phenolic compounds, simple phenolic acids and aldehydes, catechins, proanthocyanins, prenylated flavonoids α- and iso-α-acids, etc., have been described in beer [6]. The listed classes of compounds correlated with the styles of beer: Prenylated flavonoids were most abundant in dark stout beer (3.10 mg/L) and dry-hopped beer (3.68 mg/L), while alkylresorcinols were more abundant in stout beer (4.52 μg/L) [6]. Alkylresorcinols belong to a group of phenolic lipids of cereals (barley, wheat, rye, oats, rice, and other cereal grains), and contain resorcinol [7,8]. Other phenolic compounds, such as tyrosol and hydroxytyrosol, are yeast products and derived from tyrosine [9]. In addition, their concentration in beer was 0.2–44.4 and 0.0–0.1 mg/L.
Therefore, there are phenolic compounds of cereal sources (malt or unmodified grain raw material), hop origin, as well as those resulting from the microorganism’s activity.
Non-fermented grains and malt in beer are sources of flavan-3-ols, proanthocyanidins, hydroxycinnamic acid derivatives, and small amounts of flavanols [10] in bound and free forms. It is widely believed that catechin and ferulic acid [11] are the most common flavonoid compounds. Moreover, it has been shown that hydrolytic processes of dissolution, fermentation, and drying of malt endosperm lead to a decrease in the content of catechin, prodelfinidin B3, procyanidin B3, and ferulic acid in the grain [12,13] and an increase in the content of water-soluble esterified fractions of phenolic compounds [13,14]. Elevated temperatures in the final stage of commercial malt production lead to a decrease in the amount of ferulic acid included in the structure of melanoidins, which indicates the esterification of acids and nitrogenous compounds of the cereal matrix [15,16]. Furthermore, malt is a source of mono-O-glucosides (myricetin and myricetin-O-glucoside) [17].
Phenolic compounds of native cereals are rarely detected in a free form since they are esterified or glycosylated with organic compounds of cereals (lignin, β-glucans, arabinoxylans, nitrogenous compounds, and other organic compounds of the plant matrix) [18]. Table 1 shows the composition of cereal phenolic compounds and their possible presence in beer.
The main hop polyphenols are flavonols (quercetin and kaempferol), flavan-3-ols (catechins, epicatechin, proanthocyanidins), phenolic acids, and prenylated chalcones. Prenylated flavonoids combine the flavonoid skeleton with a lipophilic prenyl side chain, increasing the lipophilicity of flavonoids [41,42]. Isoxanthohumol is the main prenylated chalcone in lupulin glands (0.1–$1\%$ in dry weight); however, during the brewing process, it isomerizes the 2’-hydroxy group and is converted to isoxanthohumol (ranging from 0.04 to 3.44 mg/L), which also contributes to the bitterness of beer [43,44].
As a result of temperature, pressure, oxygen, and other conditions, some phenolic compounds are oxidized, biotransformed, and precipitated, which affects the structure and sensory perception of the beer. The effect of phenolic compounds can be direct or indirect. Therefore, hop products introduced after fermentation provided an extraction of humulon and lupulon, while oxidation of those in the container provided a “smoky” tone, which is an off-flavor [45]. The indirect effect is due to biotransformation or decarboxylation of phenolic compounds by yeast enzymes to 4-vinylguaiacol, which is responsible for the clove aroma [46]. In addition, the taste and aroma tones depend on the concentration of the compound [47]. It has been shown that isoferulic acid causes fruit tones [48]. The direct effect of phenolic compounds is associated with their antioxidant properties that counteract the oxidation of other organic compounds [49].
Table 2 shows the beer phenolic compounds from various cereal raw materials (adjuncts) [50,51,52].
As shown in Table 2, many classes of phenolic compounds are lost during technological processes and are not present in the beer, which is also confirmed by other authors [53].
Table 3 shows the flavor attributes according to the adjuncts used.
Therefore, the role of phenolic compounds and their influence on beer quality is extensive and depends on the raw materials used, technological parameters of production, storage conditions, etc. As a result, the purpose of the study was to establish implicit relationships between beer organic compounds produced from different adjunct types and assess their impact on beer quality.
## 2.1. The Determination and Mathematical Analysis of Different Adjuncts on Wort Sample Composition
The wort samples with different adjuncts studied in this work were selected in order to reveal the difference in the influence of various phenolic and other compounds of plant raw materials on the hopped wort flavor descriptors.
Table 4 and Table 5 show the composition of the wort samples.
In Table 4, the data indicate that the addition of different adjuncts affects the quantitative expression of the compounds responsible for the formation of the beer’s structure. The β-glucan content increased in relation to the malt wort level by a factor of 1.3, 1.6, 1.5, 1.8, and 2.9 in barley malt + barley, barley malt + rice, barley malt + corn, wheat malt + wheat wort samples, accordingly. Moreover, the content of soluble nitrogen was 1.1, 1.3, 1.2, and 1.25 times higher in the wort with the adjuncts’ addition when barley, rice, corn, and wheat were added, respectively. The wort obtained from wheat malt and wheat contains the least soluble nitrogen, which correlates with the wort original extract (10.2 °P). The content of nitrogen (peptones) with thiol groups does not correlate with the wort content of soluble nitrogen and original extract. Note that the content of iso-α-humulone in the wort correlates with the content of the wort original extract, since a similar amount of the same hop species was applied in all cases under consideration.
In Table 5, the data indicate the influence of the composition of grain raw materials on the hopped wort matrix structure. In fact, the quantitative composition of the matrix structure of organic compounds is determined by the adjuncts’ composition.
The higher content of catechins was observed in wheat wort (81.7 mg/L) and the lowest in barley malt wort (18.6 mg/L). The melanoidin’s content was naturally higher in the wort with adjuncts, except for wort with barley malt + barley. Here, there is an increase in free amino nitrogen content, which occurs when the protein matrix undergoes proteolysis during mashing and the thermal reaction of sugars and amino acids during temperature pauses. Moreover, the riboflavin content is increased in the wort with adjuncts and mostly in barley malt + rice wort samples (4.33 mg/L), which is 9.4 times higher than the barley malt wort.
In Table 5, the data were subjected to mathematical processing and obtained from pairwise correlation coefficients, and thus describe the strength of the relationship between the compounds, as shown in Table 6.
Pairwise correlation coefficients (Table 6) showed that catechins have a direct effect on color to the greatest extent. Melanoidins along with riboflavin have a strong influence on color (pairwise correlation coefficient of 0.87), as well as catechins along with riboflavin are associated with and determine the color characteristics formation (pairwise correlation coefficient of 0.91). The overall system correlation coefficient under consideration is $R = 0.96$, and the coefficient of determination is R2 = 0.94, which indicates that an additional effect on color formation of $6\%$ was unaccounted for in the compounds.
## 2.2. The Variation of Different Adjuncts on Wort Sample Composition during Brewing
To identify implicit connections of the colloidal structure of organic compounds, which are formed during wort fermentation with different adjuncts, we monitored the dynamics of changes in the significant compounds content, which is reflected in Figure 1a–d.
The change in the content of β-glucan (Figure 1a) occurs most significantly for a total of three times in fermented wort from wheat malt + wheat, since the formation of colloidal particles consisting of nitrogenous, phenolic, and non-starch carbohydrate compounds occurs, which are sufficiently high in molecular weight to remain in equilibrium in the dispersed structure [66].
The decrease in the concentration of non-starch polysaccharide is more linear in the other cases with a decrease in β-glucan content observed by 1.1–2 times. Moreover, we can arrange the options of cereal composition in the following order by a decrease in β-glucan content during fermentation: Wheat malt + wheat→ barley malt + rice→ barley malt + barley → barley malt + corn→ barley malt fermented wort samples. On the one hand, a lower β-glucan concentration indicates a more dissolved structure of the cereal components and a lower molecular weight of the non-starch polysaccharide inherent in the fermented wort; on the other hand, this is indicated by the type of structure [67,68].
Changes in soluble nitrogen (peptones) with thiol groups (Figure 1b) show interesting dynamics and divisions of samples into groups. Based on the changes in nitrogen compounds, natural barley malt + corn, barley malt + barley, barley malt + rice beer samples can be combined into the first group, and wheat malt + wheat, barley malt + wheat beer samples can be combined into the second group. The first group of samples is characterized by a high intensity of nitrogen reduction in the first 7 days of fermentation and a smooth decrease in the following days, whereas the second group is characterized by a systematic reduction in nitrogen with thiol groups during the whole period of fermentation. If beer samples are ranked quantitatively with respect to nitrogen compounds with thiol groups at the beginning of fermentation, then the series would be as follows: Barley malt + corn→ barley malt + barley→ barley malt→ wheat malt + wheat→ barley malt + rice→ barley malt + wheat beer samples. By the end of fermentation, a different trend was observed: Wheat malt + wheat→ barley malt + corn→ barley malt + barley→ barley malt + wheat→ barley malt→ barley malt + rice beer samples. Therefore, the use of malted and non-malted wheat contributes to a smaller decrease in thiol-containing nitrogen compounds in the beer: In the case of wheat malt + wheat beer samples, the thiol-containing nitrogen compounds content decreased by 4 times (up to 70.2 μM/L), and in the case of barley malt + wheat beer samples, it decreased by 7.3 times (up to 29.8 μM/L), which is explained by the presence of nitrogenous fraction in the beer structure from modified and non-modified wheat raw materials with thiol groups of molecular mass 2.1–40 kDa, which are responsible for the foam quality [69].
The content of catechins (Figure 1c) changed typically in all samples, except for wheat malt + wheat beer samples, which contained the greatest catechins amount (81.68 mg/L) and in 7 days, it decreased to 10.4 mg/L, i.e., 8 times, which distinguished this sample from all others, in which the catechins content decreased equally. It should be noted that the catechins content in the wort was in the range of 18.56–81.68 mg/L, while during the fermentation this indicator decreased by 1.7–12.8 times. The highest decrease in these phenolic compounds was shown by the sample obtained from barley malt and barley, and the lowest by the sample from barley malt and corn. This corresponds to an inversely proportional relationship with the quantitative characteristic of the catechins content in non-malted adjuncts, with the highest amount contained in barley (46.9 μg/g), followed by rice (13.7 μg/g), then corn (1.7 μg/g), and wheat (0.008 μg/g) [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,70]. Moreover, it is necessary to take into account the structure, the structural relationships in the plant matrix of individual cereals, and the conditions of malt and wort production [71,72].
Regarding the iso-α-humulone content (Figure 1d), we can conclude that the characteristics of changes in the isomerized resin amount are identical in all samples, namely, it does not depend on the adjunct’s type, but more on the original extract content. The highest amount of iso-α-humulone is in wheat malt + wheat fermented wort samples with an original extract content of 10.2 °P, while in other samples of fermented wort, the iso-α-humulone amount is 11.0 °P and above. It was noted that according to the data obtained, the content of iso-α-humulone correlates with the content of dissolved substances in the wort, as in the case of wheat malt + wheat wort samples. However, there is no correlation in the fermented wort. Apparently, this is due to implicit connections with other organic compounds in the beer’s structure.
The quercetin content in the wort samples correlates with its content in the cereal [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40], but this correlation is not observed in the fermented wort. The highest quercetin content is in fermented wheat malt + wheat beer samples (0.66 mg/L), followed in descending order by the beer samples with barley malt and barley→ wheat→ rice→ $100\%$ malt beer→ corn.
The mathematical correlation multivariate analysis method was applied to identify implicit relationships between the change in nitrogen content with thiol groups (Y), catechins (X1), quercetin (X2), and iso-α-humulone (X3) based on data from Table 4 and Table 5 and Figure 1, as shown in Table 7 and Table 8.
Barley malt beer sample (Table 7) was characterized by high values of pairwise correlation coefficients, indicating all organic compounds under consideration ($R = 0.86$–0.99). The highest correlation coefficient (strong connection) was characterized by the quercetin and nitrogen compounds with thiol groups connection. The same pattern was observed in barley malt + barley and barley malt + rice beer samples, as well as in barley malt beer, and the pairwise correlation coefficient between quercetin and thiol nitrogen compounds was $R = 0.98.$ Barley malt + corn beer samples had lower values of pairwise correlation coefficients ($R = 0.59$–0.75) compared to the above samples, and barley malt + wheat and wheat malt + wheat beer samples had values of $R = 0.92$–0.99 and 0.65–0.95, respectively, indicating the participation of unaccounted organic compounds in the relationship between quercetin, catechins, iso-α-humulone, and nitrogen compounds with thiol groups.
Pairwise correlation coefficients (Table 8) showed a strong mutual influence of phenolic compounds on thiol nitrogen compounds in all beer samples regardless of adjuncts type. The elasticity coefficient characterizing the degree of influence of one parameter on the other showed the significance of iso-α-humulone and catechin with respect to the connection with thiol nitrogen compounds. Therefore, it was interesting to assess the presence or absence of pairwise correlation coefficients between these indicators in different beer samples.
In Figure 1, the data were mathematically analyzed for a linear relationship between iso-α-humulone and catechins, and the data are presented in Table 9.
It is possible to describe the equations in Table 9 mathematically by the coefficient before the variable X, denoting the tangent of the slope angle: The greater the tangent of the angle, the more vigorous the binding reaction of catechins and iso-α-humulone [73]. The highest coefficient is characterized by barley malt + corn beer samples, and then follows in the degree of rate reduction: Barley malt→ barley malt + rice→ barley malt + barley→ barley malt + wheat→ wheat malt + wheat beer samples. Note that the free coefficient characterizes the minimum value of iso-α-humulone, at which its equilibrium reacts with catechin coupling. Moreover, the lower the free coefficient, the higher the association reaction rate.
Table 6 shows the beer’s color formation, in which catechin and riboflavin molecules are significantly associated with beer color. However, it was interesting to observe whether there was a connection between riboflavin and quercetin. For this purpose, the data obtained during fermentation were mathematically processed, and the results are presented in Table 10.
In Table 10, the data indicate the highest coefficient before the variable is characterized by barley malt + corn beer samples, followed by: Barley malt + barley→ barley malt→ barley malt + wheat→ wheat malt + wheat→ barley malt + rice beer samples.
Based on the data obtained (Table 9 and Table 10), we plotted the dependences of changes in iso-α-humulone content on catechins (Figure 2a) and riboflavin content on quercetin (Figure 2b).
The graphs and dependences of Figure 2 and Table 10 are linear in nature, and the strength of the relationship between the variables, the change in which occurs according to these patterns, is close to functional in nature.
## 2.3. The Matrix Organic Compounds Influence on Beer’s Descriptor Flavor Formation
In Table 11, the data indicate the beer’s characteristics using different types of adjuncts, and Figure 3 shows the taste profiles of beer samples.
It is interesting to note that, regarding the data in Table 11, the highest β-glucan content was in the beer sample obtained from $95\%$ barley malt and $5\%$ wheat—the content of non-starch polysaccharide exceeded the content of barley malt beer sample by 2.3 times. Moreover, there was an increase of $25\%$, $78\%$, $7\%$, and $43\%$ in β-glucan content in barley malt and barley, rice, corn, and wheat beer samples, respectively. The soluble nitrogen compound content was in range of 495–695.6 mg/L, with the highest content observed in barley malt + barley beer samples, and the lowest in wheat malt + wheat. The latter case is associated with a low original extract content in the wort sample, as confirmed by other authors [74].
The high content of soluble nitrogen compounds with thiol groups was observed in wheat malt + wheat beer samples (70.2 μM/L), while in other compounds, it varied in the range of 12.3–33.1 μM/L. This agrees with the data of other authors reporting on the structure of protein compounds responsible for foam quality [75,76,77].
When comparing the amounts of iso-α-humulone in different beer samples according to Table 11, it can be observed that wheat malt + wheat beer samples exceed the barley malt sample by $80\%$ according to this indicator, which is due to the difference in the original extract content in the wort. Considering the same hop dosage and different iso-α-humulone levels in beer samples (17.9–25.3 mg/L), we can assume the influence of organic compounds of different adjuncts and fermentation conditions.
The isoxanthohumol content, a hop product prenylflavonoid, also differs in the beer samples. The highest content of this substance refers to wheat malt + wheat beer samples (1.39 mg/L). The level of isoxanthohumol in barley malt, barley malt + barley, barley malt + rice and barley malt + wheat beer samples varies in a narrow range of 0.94–1.04 mg/L, and in the barley malt + corn beer samples, its content is the lowest—0.44 mg/L, which is also the reason for studying the influence of the cereal organic molecules structure on the isoxanthohumol content.
The content of catechins, quercetin, and riboflavin in beer samples varies in the range of 3.0–12.36 mg/L, 0.11–0.66 mg/L, and 0.28–5.26 mg/L, respectively, and is determined by the type of adjuncts with $5\%$ replacement of malt.
Melanoidin content and color in beer samples do not correlate directly with each other, since other organic compounds are also involved in the development of beer color [78].
Based on the data in Table 11, the correlation between iso-α-humulone and organic substances, as well as between isoxanthohumol and beer substances was investigated.
Pairwise correlation coefficients showed that there was a strong relationship between iso-α-humulone and soluble nitrogen compounds (R = −0.85). Figure 3a shows the relationships between organic compounds and iso-α-humulone in beer samples with adjuncts ($R = 0.80$–0.95).
Mathematical correlation analysis revealed a pairwise correlation between isoxanthohumol and quercetin ($R = 0.84$), riboflavin ($R = 0.84$), and melanoidins (0.87). Figure 3b shows the relationships between organic compounds and isoxanthohumol in beer samples with adjuncts ($R = 0.81$–0.98).
Figure 3 shows the complex interactions of hop substances and the plant matrix of beer, but the relationship with a variety of raw materials, in our opinion, is most responsible for soluble nitrogen compounds, since this indicator refers to the species characteristic of the beer’s proteome.
Figure 4 shows the taste characteristics of beer samples from different adjuncts.
According to Figure 4, the beer samples mostly differed by the descriptor “Smoky tone” and “Floral tone”, which is formed by phenolic compounds, the sources of which are amino acids of raw materials, bioconverted by yeast, and converted into phenolic compounds [79]. The malty tone was the least intense in barley malt + rice beer samples, which is explained by the decrease in amino acid content according to other authors [80].
In Table 11 and Figure 4, the data were subjected to mathematical analysis with respect to the beer flavor descriptors and organic compounds relationship, and the data are shown in Table 12 and Table 13.
In Table 12, the data indicate that a direct strong relationship between the flavor fullness and foam stability descriptors is not found; however, the «smoky» tone correlates with the thiol nitrogen, iso-α-humulone, and quercetin content (R > 0.8). Pairwise correlation coefficients (Table 13) indicate a close relationship between thiol nitrogen compounds, iso-α-humulone, β-glucan, riboflavin, and quercetin on the completeness of taste and stability of beer foam, which is confirmed in part by other authors [75,81,82]. However, no data on the effect of riboflavin and quercetin were found. The only assumption is that since thioredoxins, including thiol groups [83], are responsible for the stability of the foam, respectively, then antioxidants (riboflavin and quercetin) are responsible for the stability of the thiol S-S groups. On the other hand, it was reported that kaempferol, quercetin, and myricetin interacted with nitrogenous compounds via hydroxyl substituents in the B and C rings (positions 3, 3′, 4′, 5′) [84].
With regard to the descriptor “smoke tone”, in addition to the influence of phenolic compounds, the influence of soluble nitrogen and β-glucan was noted, which suggests the importance in the perception of the density of the “body” of beer by the tasters’ receptors, which affects the degree of brightness or perceptibility of the descriptor [82].
## 3. Discussion
By evaluating the contribution of organic compounds and their mutual influence on each other at the kettle hopping stage, it can be observed that there is a relationship between the type of adjuncts introduced to replace part of the malt ($5\%$ in a particular case).
Considering the change in bittering and phenolic components, it can be seen that iso-α-humulone correlates with the amount of extractive substances, namely, the extractable and isomerizable hop resin is bound by covalent bonds with organic compounds of different molecular weights that comprise the wort proteome, primarily with LTP and Z peptones [85].
The catechins and quercetin content correlated with the type of adjuncts, i.e., showed the ability of barley malt enzymes to hydrolyze the plant matrix, which differs in free and conjugated phenolic compounds (phenolic acids, residual sugars, etc.) in its structure [86].
A direct effect of catechins on the wort color characteristics has been shown. Meanwhile, melanoidins and riboflavin indirectly influence the wort color index through catechins, which is confirmed in the works of other authors [78].
The decrease in dynamics of the organic compounds content during fermentation was not only due to the biocatalytic processes, but also to the difference in composition of nitrogenous and other compounds. Wheat beer, consisting of wheat malt and $5\%$ wheat, showed the most significant dynamic decrease in β-glucan content. It is known that the β-D-glucan of cereals consist of a linear polymer composed of D-glucopyranosyl units connected by isolated ß-D-(1→3) glycosidic bonds and sets of ß-D-(1→4) glycosidic bonds [67]. Wheat contains less β-glucan in the endosperm wall (up to $25\%$) compared to barley (up to $80\%$); however, the structure of wheat has a complex relationship with the arabinoxylan complex, which includes ferulic acid and peptide residues [74]. Malting and mashing conditions promote the extraction of more complex non-starch polysaccharides during the technological process, as confirmed by us and other authors [74].
The usage of barley malt with another adjunct affects the process of clarification of the fermented wort, and the decrease in dynamics follows the chain toward a decrease in the intensity of colloid formation from the cereal type: Wheat→ rice→ barley→ corn, which correlates with the structure and localization of β-glucan [74,87,88].
The dynamics of the change in thiol-containing nitrogen compounds is important in terms of predicting foam stability and the influence of various factors on foam structure. It is interesting to note the grouping of samples according to the rate and nature of the reduction in thiol-containing nitrogen concentration. It was shown that the replacement of $5\%$ barley malt with corn, barley, and rice promotes a strong reduction in thiol-containing nitrogen during the main fermentation (during the first 7 days of fermentation). The difference in the dynamics of thiol nitrogen changes in barley malt beer with adjuncts and wheat beers indicates the special reactivity, i.e., the structure and location of the active sites of amino acid sequences and groups of wheat raw materials (malted and whole wheat cereals) [77]. In terms of the comparative characterization of specific foam structures with thiol-containing amino acids in their composition (LTP1, Z4, Z7-proteins), the corn, barley, and rice proteins are similar in their affinity for binding to organic compounds and differ slightly in their affinity for agglomeration with lipids based on the size of active cavities in the structure, which distinguishes them from the structural and functional properties of LTP1-proteins of wheat [89].
The organic compounds’ influence on colloid formation in accordance with the cereal type helped in clarifying the mathematical analysis. It is shown that in samples with $5\%$ of adjuncts, along with barley and wheat malts, there is a functional correlation between thiol nitrogen and quercetin. In barley malt beer, the correlation coefficient is (0.99), barley malt and barley/rice is (0.99), barley malt and corn is (−0.59), barley malt and wheat is (0.92), and wheat malt and wheat is (0.92). The beer made of $100\%$ malt had correlation coefficients at the level of ($R = 0.86$–0.99) between nitrogen with thiol groups and the organic compounds in question, while the malt wheat + wheat beer samples had correlation coefficients at the level of ($R = 0.92$–0.99) and ($R = 0.65$–0.95), respectively, which suggests the participation of uncounted organic compounds in the connection between quercetin, catechins, iso-α-humulone, and nitrogen compounds with thiol groups.
The study revealed the mutual influence of changes in phenolic compounds on thiol nitrogen in all beer samples, and in particular, the influence of iso-α-humulone and catechin on thiol nitrogen content, which served the purpose of studying the pairwise correlation between these indices in different beer samples.
Iso-α-humolone, an isomerization product of hop α-humol, causes beer bitterness and stabilizes the foam structure [85,90]. The relationship between changes in the content of isomerized resin and catechin can be attributed to a single source of origin (hops) [91]. In addition, catechins are known to inhibit the activity of metalloproteinase enzymes, i.e., they actively bind to the protein molecules [92] present in the structure of beer. This may be due to preferential binding to beer foam proteins at iso-α-humulone binding sites.
The source of riboflavin in beer is malt, which exists in the structure of an aporiboflavin-binding protein and contributes to the removal of riboflavin from beer [93]. Riboflavin’s chemical composition is 7,8-dimethyl-10-ribityl-isoalloxazine, which consists of a flavin-isoalloxazine ring associated with a sugar side chain, ribitol [94]. Riboflavin is resistant to temperature, but not to light. Riboflavin in the form of flavinmonophosphate plays a key role as a cofactor in oxidation and reduction reactions [95] and is active in terms of oxygen absorption. It has been shown that in the presence of quercetin, the antioxidant function of riboflavin increases, i.e., quercetin acts as a synergistic compound [96]. Therefore, to activate riboflavin into flavin monophosphate, it is necessary to influence riboflavin kinase, the content of which is stated by the authors in all cereals [97], and this content decreases due to technological actions (milling). For example, milling wheat flour reduces riboflavin levels by $64\%$, rice flour by $67\%$, and corn flour by $56\%$ [98]. It can be assumed that the presented sequence of the decrease in the rate of association of quercetin and riboflavin depends on the concentration of riboflavin or riboflavin kinase in beer from a particular cereal. Moreover, quercetin can bind to protein compounds differently depending on the nature of the protein. Therefore, it was shown that the rice protein matrix had a higher affinity for quercetin compared to the almond protein matrix [99]. It has been established that the reactivity of phenolic compounds with respect to grain proteins correlates with the number of hydroxyl groups and their position in the structure of phenolic compounds, namely, the distribution of the external electronic charge [100].
On the one hand, changes in the bitterness of hop products revealed a lack of correlation with beer organic compounds, and on the other hand, revealed the influence of soluble nitrogen on adjuncts. It is known that the beer’s soluble nitrogen includes the concept of foam peptones (LTP1, Z4,7), peptides and amino acids of raw materials, and sugars formed during fermentation [79]. The connection between the nitrogenous compounds of foam and the bittering resins of hop products has already been discussed [85,90]. The interactions of isomerized resins with amino acids and peptides are confirmed by other authors [101], who describe the occurrence of ionic, ion-dipole, and hydrophobic interactions.
Since phenolic compounds interact with many classes of organic compounds that comprise the beer’s “body”, they consequently affect flavor descriptors. The study confirmed that different phenolic compounds indirectly affect the fullness of flavor, foam quality, and some tones. As already mentioned, the structure of the plant matrix of cereals not only determines the configuration and properties of the nitrogenous compounds, but also the non-starch polysaccharides. Therefore, the degree of influence of thiol nitrogen in combination with iso-α-humolone, β-glucan, riboflavin, and quercetin on flavor descriptors significantly affects tasters’ evaluation of beer quality.
## 4.1. The Research Materials
Barley malt (Russia), wheat malt (Germany), and adjuncts (barley, rice, corn, wheat) were mixed in a ratio of 95:5 by weight and mixed with water in a ratio of 1:4, and then mashed using the infusion method. Hopping was carried out with one hop type variety “Tettnanger” (Germany), and the kettle boiling wort duration was 60 min. Fermentation was carried out with brewer’s yeast S. cerevisiae. General stage of fermentation was at a temperature of (9 ± 2) °C for 7 days, and post-fermentation stage was at (3 ± 2) °C for 14 days. Beer production was carried out at the «Easy Drew» pilot brewery (Russia), filtered and stored at temperature (4 ± 2) °C and air humidity W ≤ (75 ± 2)% before the study. The analyzed beers included six samples, in which the characteristics are represented in Table 14.
## 4.2.1. Chemicals
All reagents and standards were of analytical grade. Quercetin, isoxanthohumol, riboflavin, and catechin standards were from Sigma-Aldrich (St. Louis, MO, USA) with a purity ≥ of $99\%$. Potassium dihydrogen phosphate (KH2PO4), acetonitrile, acetic acid, orthophosphoric acid (H3PO4), and ammonium dihydro-phosphate (NH4H2PO4) were purchased from Galachem (Moscow, Russia).
Sulfuric acid, boric acid, hydrochloric acid (HCl), ethanol, isooctane, 5,5′-dithiobis [2-nitrobenzoic] acid, and sodium bicarbonate (Na2CO3) were purchased from Limited liability company “Reatorg” (Moscow, Russia).
Chemicals for determination of β-glucan content were purchased from Megazyme Int. ( Lansing, MI, USA).
Bidistilled prepared water was used in the determinations.
## 4.2.2. Determination of Original Extract and Alcohol Content
To determine the original extract and alcohol content, the 2.13.16.1 standard MEBAK® method was used [102].
## 4.2.3. Determination of Nitrogen Compounds
To determine the common amount of soluble nitrogen, the Kjeldahl method (EBC Method 4.9.3) was used [103].
## 4.2.4. Determination of Soluble Nitrogen with Thiol Groups Mass Concentration
We employed Ellman’s method for determining the mass concentration of nitrogen with thiol groups. A total of 3 mL of protein solution from the sample, 2 mL of 0.2 M phosphate buffer (pH 8), and 5 mL of distilled water (sample A) were added to a 20 mL test tube (sample A). Then, 10 mM of Ellman’s reagent was prepared as follows: 37 mg of 5,5′-dithiobis [2-nitrobenzoic] acid was dissolved in 10 mL of 0.1 M potassium phosphate buffer, with pH 7.0, and then stirred. Thereafter, 15 mg of sodium bicarbonate was added to the resulting solution and mixed again. Next, 3 mL of sample A was mixed with 0.02 mL of Ellman’s reagent, with using a micropipette. The sample optical density was measured on a spectrophotometer DR 3900 (HACH-LANGE, GmBH, Berlin, Germany) at a wavelength of 412 nm after 3 min of exposure [104].
## 4.2.5. Determination of Iso-α-Humulone Mass Concentration
We employed the EBC Method 9.47 for the determination of the mass concentration of iso-α-humulone [105].
## 4.2.6. Determination of Catechin Mass Concentration
The determination of the catechin mass concentration was carried out using the high-performance liquid chromatography method, with an “Agilent Technologies 1200” LC system (“Agilent Technologies”, Santa Clara, CA, USA) equipped with a diode array detector. HPLC system was equipped with a fitted column Supelco C18 150 × 4.6 mm 5 μm (Thermo, Waltham, MA, USA), with wavelength of 280 nm. The samples and all standard solutions were injected at a volume of 10 μL in a reversed-phase column at 25 °C. HPLC mobile phase was prepared as follows: Solution A: 50 mM of NH4H2PO4 + 1.0 mL of orthophosphoric acid dissolved in 900 mL of HPLC grade water. The volume was comprised of 1000 mL with water and the solution was filtered through 0.45 μm membrane filter and degassed in a sonicator for 3 min. Solution B: Acetonitrile mobile phase was carried out using gradient elution at 1 min, $5\%$ B; at 10 min, $15\%$ B; at 10 to 45 min, $40\%$ B; at 45 to 55 min, $98\%$ B, and at 55 to 60 min, $5\%$ B. The mobile phase flow rate was 1.2 mL/min and the injection volume was 10 μL [106].
## 4.2.7. Determination of Quercetin Mass Concentration
The determination of the quercetin and rutin mass concentration was carried out using the high-performance liquid chromatography method, with an “Agilent Technologies 1200” LC system (“Agilent Technologies”, Santa Clara, CA, USA) equipped with a diode array detector. HPLC system was equipped with fitted Luna 5 u C18 [2] 250 × 4.6 mm 5 μm (Phenomenex, Torrance, CA, USA) column with wavelength of 290 nm. The samples and all standard solutions at a volume of 20 μL were injected into a reversed-phase column at 25 °C. The mobile phase was $2\%$ acetic acid solution (A) and acetonitrile solution (B) with the ratio (A:B—70:30). The eluent flow rate was 1.5 mL/min [107].
## 4.2.8. Determination of Isoxanthohumol Mass Concentration
A high-performance liquid chromatography method using “Agilent Technologies 1200” LS system (“Agilent Technologies”, Santa Clara, CA, USA) equipped with a diode array detector was applied to determine the isoxanthohumol mass concentration. HPLC system was equipped with fitted Kromasil C18 150 × 4.6 mm 5 μm (Supelco, Bellefonte, PA, USA) column with wavelength of 290 nm. The samples and all standard solutions at a volume of 10 μL were injected into a reversed-phase column at 25 °C. The mobile phase was acetonitrile solution (A), water (B), and orthophosphoric acid solution (C) with the ratio (A:B:C—40:60:0.1). The eluent flow rate was 1 mL/min [108].
## 4.2.9. Determination of the Mass Concentration of β-Glucan
To quantify the mass concentration of β-glucan, the standard fermentation method was used (8.13.1) [109].
## 4.2.10. Determination of the Beer’s Color
To determine the color of beer, the EBC method (EBC Method 9.6) was used [110].
## 4.2.11. Determination of the Mass Concentration of Riboflavin
The determination of the riboflavin mass concentration was carried out using the high-performance liquid chromatography method, with Agilent Technologies 1200 LC system (Agilent Technologies, Santa Clara, CA, USA) equipped with a diode array detector. HPLC system was equipped with fitted column NanoSpher WSVitamins HILIC 250 × 4.6 mm, 5 μm (Rokland, ON, Canada) with wavelength of 260 nm. The samples and all standard solutions were injected at a volume of 10 μL in a reversed-phase column at 25 °C. The HPLC mobile phase was prepared as follows: Acetonitrile of 50 mM of NH4H2PO4-H3PO4 was mixed in the ratio of 70:30:0.3. The resulting solution was filtered through a 0.45 μm membrane filter and degassed in an ultrasonic apparatus for 10 min. The mobile phase was carried out using the isocratic elution. The mobile phase flow rate was 1.1 mL/min [93].
## 4.2.12. Determination of the Mass Concentration of Melanoidins
Wort (beer) melanoidins were extracted using the ethanol dissolution method. A total of 30 mL of wort (beer) was mixed with 100 mL of $10\%$ ethanol (v/v), left for 20 h (overnight) to extract melanoidins at 4 °C, then the solution was centrifuged at 5000 rpm for 10 min on centrifuge Sigma 2-16KHL (Sigma, Darmstadt, Germany), after which ethanol was added to the supernatant to a $65\%$ (v/v) final concentration and left for 12 h at 4 °C to dissolve the melanoidins. Next, ethanol was evaporated on a rotary evaporator Advantage G3 ML (Heidolf, Schwabach, Germany) before centrifugation. Then, the supernatant was defatted by mixing with acetone to precipitate melanoidins and the latter was isolated by centrifugation in a centrifuge Sigma 2-16KHL (Sigma, Darmstadt, Germany). Next, melanoidins were resuspended in distilled water (0.6 mg/ml). The optical density of solutions was determined at 420 nm on a spectrophotometer DR 3900 (HACH-LANGE, GmBH, Berlin, Germany) [111].
## 4.2.13. Organoleptic Evaluation of Beer Samples by Descriptors
The organoleptic analysis was carried out by a professional group of researchers, consisting of 10 people on a 5-point scale according to the characteristic taste descriptors selected. Five points indicate a strong descriptor shade, 4 points indicate a well-developed descriptor shade, 2 points indicate a slightly visible descriptor shade, and 1 point indicates a subtle descriptor shade. The results obtained were summarized and the average score was recorded.
## 4.2.14. Statistical Analysis
Statistical analysis was performed in five replicates. Descriptive statistics were performed and values are expressed as mean ± standard deviation (SD). In the studies, the Student-Fisher method was used, as a result of which multivariate models of the correlation-regression dependence of the studied parameters were obtained. The reliability limit of the obtained data (p ≥ 0.95) was considered to assess various factors affecting the content of polyphenols in all studies. Statistical data were processed by the Statistics program (Microsoft Corporation, Redmond, WA, USA, 2006).
## 5. Conclusions
The article examined the effect of the adjuncts in organic compounds, including phenolic compounds, on the main indicators of hop wort, fermented wort (beer) during fermentation. It was shown that the change in the wort’s phenolic complex correlates with extractive compounds, and during fermentation, it is determined by the adjunct’s proteome. Mathematical analysis showed the effect of quercetin, catechin, and iso-α-humulone on the dynamics and magnitude of the decrease in nitrogen compound content with thiol groups. The effect of quercetin on the dynamics of the reduction in riboflavin levels in beer samples, and the effect of the adjuncts in plant matrix on this process were shown. The reason for the different levels of iso-α-humulone in beer samples with adjuncts has been established, which lies in the specific structure of the nitrogen compound fractions. It was found that the formation of the descriptors “flavor fullness” and “foam stability” through pairwise correlation coefficients are related by thiol nitrogen, iso-α-humulone, β-glucan, riboflavin, and quercetin, while the descriptor “smoky tone” is formed through the relationship between thiol nitrogen compounds, iso-α-humulone, and quercetin. Therefore, the behavior of phenolic compounds in the final stages of beer production is determined by the cereal grain proteome and affects the beer taste and color characteristics, as well as its persistence.
## References
1. Kerr E., Caboche C., Pegg C., Phung T., Gonzalez Viejo C., Fuentes S., Howes M., Howell K., Schulz B.. **The post-translational modification landscape of commercial beers**. *Sci. Rep.* (2021.0) **11** 15890. DOI: 10.1038/s41598-021-95036-0
2. He Y., Dong J., Yin H., Zhao Y., Chen R., Wan X., Chen P., Hou X., Liu J., Chen L.. **Wort Composition and Its Impact on the Flavour-Active Higher Alcohol and Ester Formation of Beer—A Review**. *J. Inst. Brew.* (2014.0) **120** 157-163. DOI: 10.1002/jib.145
3. Gerhäuser C.. **Beer constituents as potential cancer chemopreventive agents**. *Eur. J. Cancer* (2015.0) **41** 1941-1954. DOI: 10.1016/j.ejca.2005.04.012
4. Mikyška A., Hrabák M., Hašková D., Šrogl J.. **The Role of Malt and Hop Polyphenols in Beer Quality, Flavour and Haze Stability**. *J. Inst. Brew.* (2002.0) **108** 78-85. DOI: 10.1002/j.2050-0416.2002.tb00128.x
5. Moura-Nunes N., Cazaroti Brito T., Dias da Fonseca N., Fernandes de Aguiar P., Monteiro M., Perrone D., Guedes Torres A.. **Phenolic compounds of Brazilian beers from different types and styles and application of chemometrics for modeling antioxidant capacity**. *Food Chem.* (2016.0) **199** 105-113. DOI: 10.1016/j.foodchem.2015.11.133
6. Boronat A., Soldevila-Domenech N., Rodríguez-Morató J., Martínez-Huélamo M., Lamuela-Raventós R.M., de la Torre R.. **Beer Phenolic Composition of Simple Phenols, Prenylated Flavonoids and Alkylresorcinols**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25112582
7. Landberg R., Marklund M., Kamal-Eldin A., Åman P.. **An update on alkylresorcinols–Occurrence, bioavailability, bioactivity and utility as biomarkers**. *J. Funct. Foods* (2014.0) **7** 77-89. DOI: 10.1016/j.jff.2013.09.004
8. Rodríguez-Morató J., Jayawardene S., Huang N.K., Dolnikowski G.G., Galluccio J., Lichtenstein A.H.. **Simplified method for the measurement of plasma alkylresorcinols: Biomarkers of whole-grain intake**. *Rapid Commun. Mass Spectrom.* (2020.0) **34** e8805. DOI: 10.1002/rcm.8805
9. Hazelwood L.A., Daran J.M., Van Maris A.J.A., Pronk J.T., Dickinson J.R.. **The Ehrlich pathway for fusel alcohol production: A century of research on Saccharomyces cerevisiae metabolism**. *Appl. Environ. Microbiol.* (2008.0) **74** 2259-2266. DOI: 10.1128/AEM.02625-07
10. Shopska V., Denkova-Kostova R., Dzhivoderova-Zarcheva M., Teneva D., Denev P., Kostov G.. **Comparative Study on Phenolic Content and Antioxidant Activity of Different Malt Types**. *Antioxidants* (2021.0) **10**. DOI: 10.3390/antiox10071124
11. Magalhães P.J., Almeida S.M., Carvalho A.M., Gonçalves L.M., Pacheco J.G., Cruz J.M., Guido L.F., Barros A.A.. **Influence of malt on the xanthohumol and isoxanthohumol behavior in pale and dark beers: A micro-scale approach**. *Food Res. Int.* (2011.0) **44** 351-359. DOI: 10.1016/j.foodres.2010.10.008
12. Leitao C., Marchioni E., Bergaentzle M., Zhao M., Didierjean L., Miesch L., Holder E., Miesch M., Ennahar S.. **Fate of polyphenols and antioxidant activity of barley throughout malting and brewing**. *J. Cereal Sci.* (2012.0) **55** 318-322. DOI: 10.1016/j.jcs.2012.01.002
13. Samaras T.S., Camburn P.A., Chandra S.X., Gordon M.H., Ames J.M.. **Antioxidant Properties of Kilned and Roasted Malts**. *J. Agric. Food Chem.* (2005.0) **53** 8068-8074. DOI: 10.1021/jf051410f
14. Dvoráková M., Guido L.F., Dostalek P., Skulilová Z., Moreira M.M., Barros A.A.. **Antioxidant Properties of Free, Soluble Ester and Insoluble-Bound Phenolic Compounds in Different Barley Varieties and Corresponding Malts**. *J. Inst. Brew.* (2008.0) **114** 27-33. DOI: 10.1002/j.2050-0416.2008.tb00302.x
15. Carvalho D.O., Correia E., Lopes L., Guido L.F.. **Further insights into the role of melanoidins on the antioxidant potential of barley malt**. *Food Chem.* (2014.0) **160** 127-133. DOI: 10.1016/j.foodchem.2014.03.074
16. Inns E.L., Buggey L.A., Booer C., Nursten H.E., Ames J.M.. **Effect of Modification of the Kilning Regimen on Levels of Free Ferulic Acid and Antioxidant Activity in Malt**. *J. Agric. Food Chem.* (2011.0) **59** 9335-9343. DOI: 10.1021/jf201253t
17. Mikyška A., Dušek M.. **How wort boiling process affects flavonoid polyphenols in beer**. *Kvas. Prum.* (2019.0) **65** 192-200. DOI: 10.18832/kp2019.65.192
18. Socha R., Pająk P., Fortuna T., Buksa K.. **Antioxidant activity and the most abundant phenolics in commercial dark beers**. *Int. J. Food Prop.* (2017.0) **20** S595-S609. DOI: 10.1080/10942912.2017.1306550
19. Dang B., Zhang W.-G., Zhang J., Yang X.-J., Xu H.-D.. **Evaluation of Nutritional Components, Phenolic Composition, and Antioxidant Capacity of Highland Barley with Different Grain Colors on the Qinghai Tibet Plateau**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11142025
20. Ikram S., Zhang H., Ming H., Wang J.. **Recovery of major phenolic acids and antioxidant activity of highland barley brewer’s spent grains extracts**. *J. Food Process Preserv.* (2020.0) **44** e14308. DOI: 10.1111/jfpp.14308
21. Carvalho D.O., Curto A.F., Guido L.F.. **Determination of Phenolic Content in Different Barley Varieties and Corresponding Malts by Liquid Chromatography-diode Array Detection-Electrospray Ionization Tandem Mass Spectrometry**. *Antioxidants* (2015.0) **4**. DOI: 10.3390/antiox4030563
22. Setyaningsih W., Saputro I.E., Palma M., Barroso C.G.. **Profile of individual phenolic compounds in rice (**. *AIP Conf. Proc.* (2016.0) **1755** 130012. DOI: 10.1063/1.4958556
23. Li S., Xu H., Sui Y., Mei X., Shi J., Cai S., Xiong T., Carrillo C., Castagnini J.M., Zhu Z.. **Comparing the LC-MS Phenolic Acids Profiles of Seven Different Varieties of Brown Rice (**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11111552
24. Goufo P., Trindade H.. **Rice antioxidants: Phenolic acids, flavonoids, anthocyanins, proanthocyanidins, tocopherols, tocotrienols, γ-oryzanol, and phytic acid**. *Food Sci. Nutr.* (2014.0) **2** 75-104. DOI: 10.1002/fsn3.86
25. Yhang F., Yang L., Huang W.. **Flavonoid Metabolic Profiles and Gene Mapping of Rice (**. *Rice* (2022.0) **15** 43. DOI: 10.1186/s12284-022-00589-x
26. Yamuangmorn S., Prom-U-Thai C.. **The Potential of High-Anthocyanin Purple Rice as a Functional Ingredient in Human Health**. *Antioxidants* (2021.0) **10**. DOI: 10.3390/antiox10060833
27. Phimphilai S., Koonyosying P., Hutachok N., Kampoun T., Daw R., Chaiyasut C., Prasartthong-osoth V., Srichairatanakool S.. **Identifying Chemical Composition, Safety and Bioactivity of Thai Rice Grass Extract Drink in Cells and Animals**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26226887
28. Méndez-Lagunas L.L., Cruz-Gracida M., Barriada-Bernal L.G.. **Profile of phenolic acids, antioxidant activity and total phenolic compounds during blue corn tortilla processing and its bioaccessibility**. *J. Food Sci. Technol.* (2020.0) **57** 4688-4696. DOI: 10.1007/s13197-020-04505-3
29. Nikolić N., Mitrović J., Karabegović I., Savić S., Petrović S., Lazić M.G.. **A comparison between wheat and different kinds of corn flour based on minerals, free phenolic acid composition and antioxidant activity**. *Qual. Assur. Saf. Crops Foods* (2018.0) **11** 341-349. DOI: 10.3920/QAS2018.1411
30. Horvat D., Šimić G., Drezner G., Lalić A., Ledenčan T., Tucak M., Plavšić H., Andrić L., Zdunić Z.. **Phenolic Acid Profiles and Antioxidant Activity of Major Cereal Crops**. *Antioxidants* (2020.0) **9**. DOI: 10.3390/antiox9060527
31. Ndolo V., Beta T.. **Comparative Studies on Composition and Distribution of Phenolic Acids in Cereal Grain Botanical Fractions**. *Cereal Chem.* (2014.0) **91** 522-530. DOI: 10.1094/CCHEM-10-13-0225-R
32. Zondo S., Mahlambi P.. **Correlation between Chemical Composition, Water Holding Capacity and Flavonoids Content of Maize Verities Harvested at Buxedeni Village of KwaNongoma in KwaZulu Natal, South Africa**. *J. Food Nutr. Res.* (2020.0) **8** 675-681. DOI: 10.12691/jfnr-8-11-8
33. Casas M.I., Duarte S., Doseff A.I., Grotewold E.. **Flavone-rich maize: An opportunity to improve the nutritional value of an important commodity crop**. *Front. Plant Sci.* (2014.0) **5** 440. DOI: 10.3389/fpls.2014.00440
34. Moreno Y.S., Sánchez G.S., Hernández D.R., Lobato N.R.. **Characterization of anthocyanin extracts from maize kernels**. *J. Chromatogr. Sci.* (2005.0) **43** 483-487. DOI: 10.1093/chromsci/43.9.483
35. Kim J.-T., Yi G., Chung I.-M., Son B.-Y., Bae H.-H., Go Y.S., Ha J.Y., Baek S.-B., Kim S.-L.. **Timing and Pattern of Anthocyanin Accumulation during Grain Filling in Purple Waxy Corn (**. *ACS Omega* (2020.0) **5** 15702-15708. DOI: 10.1021/acsomega.0c02099
36. Gammoh S., Alu’datt M.H., Alhamad M.N., Rababah T., Ereifej K., Almajwal A., Ammari Z.A., Al Khateeb W., Hussein N.M.. **Characterization of phenolic compounds extracted from wheat protein fractions using highperformance liquid chromatography/liquid chromatography mass spectrometry in relation to antiallergenic, anti-oxidant, anti-hypertension, and anti-diabetic properties**. *Int. J. Food Prop.* (2017.0) **20** 2383-2395. DOI: 10.1080/10942912.2016.1238832
37. Zeng Z., Liu C., Luo S., Chen J., Gong E.. **The Profile and Bioaccessibility of Phenolic Compounds in Cereals Influenced by Improved Extrusion Cooking Treatment**. *PLoS ONE* (2016.0) **11**. DOI: 10.1371/journal.pone.0161086
38. Eliášová M., Kotíková Z., Lachman J., Orsák M., Martinek P.. **Influence of baking on anthocyanin content in coloured-grain wheat bread**. *Plant Soil Environ.* (2020.0) **66** 381-386. DOI: 10.17221/210/2020-PSE
39. Sharma N., Tiwari V., Vats S., Kumari A., Chunduri V., Kaur S., Kapoor P., Garg M.. **Evaluation of Anthocyanin Content, Antioxidant Potential and Antimicrobial Activity of Black, Purple and Blue Colored Wheat Flour and Wheat-Grass Juice against Common Human Pathogens**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25245785
40. Buśko M., Goral T., Ostrowska A., Matysiak A., Walentyn-Góral D., Perkowski J.. **The Effect of Fusarium Inoculation and Fungicide Application on Concentrations of Flavonoids (Apigenin, Kaempferol, Luteolin, Naringenin, Quercetin, Rutin, Vitexin) in Winter Wheat Cultivars**. *Am. J. Plant Sci.* (2014.0) **5** 3727-3736. DOI: 10.4236/ajps.2014.525389
41. Yang X., Jiang Y., Yang J., He J., Sun J., Chen F., Zhang M., Yang B.. **Prenylated Flavonoids, Promising Nutraceuticals with Impressive Biological Activities**. *Trends Food Sci. Technol.* (2015.0) **44** 93-104. DOI: 10.1016/j.tifs.2015.03.007
42. Zugravu C.-A., Bohiltea R.-E., Salmen T., Pogurschi E., Otelea M.R.. **Antioxidants in Hops: Bioavailability, Health Effects and Perspectives for New Products**. *Antioxidants* (2022.0) **11**. DOI: 10.3390/antiox11020241
43. Magalhaes P.J., Carvalho D.O., Cruz J.M., Guido L.F., Barros A.A.. **Fundamentals and Health Benefits of Xan-Thohumol, a Natural Product Derived from Hops and Beer**. *Nat. Prod. Commun.* (2009.0) **4** 591-610. DOI: 10.1177/1934578X0900400501
44. Vazquez-Cervantes G.I., Ortega D.R., Blanco Ayala T., Pérez de la Cruz V., Esquivel D.F.G., Salazar A., Pineda B.. **Redox and Anti-Inflammatory Properties from Hop Components in Beer-Related to Neuroprotection**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13062000
45. Obata Y., Koshika M.. **Studies on the Sunlight Flavor of Beer**. *Bull. Agric. Chem. Soc. Jpn.* (1960.0) **24** 644-646
46. Vanbeneden N., Van Roey T., Willems F., Delvaux F., Delvaux F.R.. **Release of phenolic flavour precursors during wort production: Influence of process parameters and grist composition on ferulic acid release during brewing**. *Food Chem.* (2008.0) **111** 83-91. DOI: 10.1016/j.foodchem.2008.03.029
47. Sterckx F.L., Missiaen J., Saison D., Delvaux F.R.. **Contribution of monophenols to beer flavour based on flavour thresholds, interactions and recombination experiments**. *Food Chem.* (2011.0) **126** 1679-1685. DOI: 10.1016/j.foodchem.2010.12.055
48. Bettenhausen H.M., Barr L., Broeckling C.D., Chaparro J.M., Holbrook C., Sedin D., Heuberger A.L.. **Influence of malt source on beer chemistry, flavor, and flavor stability**. *Food Res. Int.* (2018.0) **113** 487-504. DOI: 10.1016/j.foodres.2018.07.024
49. Langos D., Granvogl M., Schieberle P.. **Characterization of the key aroma compounds in two Bavarian wheat beers by means of the sensomics approach**. *J. Agric. Food Chem.* (2013.0) **61** 11303-11311. DOI: 10.1021/jf403912j
50. Deng Y., Lim J., Lee G.H., Nguyen T.T.H., Xiao Y., Piao M., Kim D.. **Brewing Rutin-Enriched Lager Beer with Buckwheat Malt as Adjuncts**. *J. Microbiol. Biotechnol.* (2019.0) **29** 877-886. DOI: 10.4014/jmb.1904.04041
51. Yorke J., Cook D., Ford R.. **Brewing with Unmalted Cereal Adjuncts: Sensory and Analytical Impacts on Beer Quality**. *Beverages* (2021.0) **7**. DOI: 10.3390/beverages7010004
52. Gu L., Kelm M.A., Hammerstone J.F., Beecher G., Holden J., Haytowitz D., Gebhardt S., Prior R.L.. **Concentrations of proanthocyanidins in common foods and estimations of normal consumption**. *J. Nutr.* (2004.0) **134** 613-617. DOI: 10.1093/jn/134.3.613
53. Nardini M., Foddai M.S.. **Phenolics Profile and Antioxidant Activity of Special Beers**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25112466
54. Han H., Kim J., Choi E., Ahn H., Kim W.J.. **Characteristics of Beer Produced from Korean Six-Row Barley with the Addition of Adjuncts**. *J. Inst. Brew.* (2016.0) **122** 500-507. DOI: 10.1002/jib.350
55. Steiner E., Auer A., Becker T., Gastl M.. **Comparison of Beer Quality Attributes between Beers Brewed with 100% Barley Malt and 100% Barley Raw Material**. *J. Sci. Food Agric.* (2012.0) **92** 803-813. DOI: 10.1002/jsfa.4651
56. Kunz T., Müller C., Mato-Gonzales D., Methner F.J.. **The Influence of Unmalted Barley on the Oxidative Stability of Wort and Beer**. *J. Inst. Brew.* (2012.0) **118** 32-39. DOI: 10.1002/jib.6
57. Evans D.E., Redd K., Haraysmow S.E., Elvig N., Metz N., Koutoulis A.. **The Influence of Malt Quality on Malt Brewing and Barley Quality on Barley Brewing with Ondea Pro, Compared by Small-Scale Analysis**. *J. Am. Soc. Brew. Chem.* (2014.0) **72** 192-207. DOI: 10.1094/ASBCJ-2014-0630-01
58. Fumi M.D., Galli R., Lambri M., Donadini G., De Faveri D.M.. **Effect of Full-Scale Brewing Process on Polyphenols in Italian All-Malt and Maize Adjunct Lager Beers**. *J. Food Compos. Anal.* (2011.0) **24** 568-573. DOI: 10.1016/j.jfca.2010.12.006
59. Poreda A., Czarnik A., Zdaniewicz M., Jakubowski M., Antkiewicz P.. **Corn Grist Adjunct—Application and Influence on the Brewing Process and Beer Quality**. *J. Inst. Brew.* (2014.0) **120** 77-81. DOI: 10.1002/jib.115
60. Taylor J.R.N., Dlamini B.C., Kruger J.. **125th Anniversary Review: The Science of the Tropical Cereals Sorghum, Maize and Rice in Relation to Lager Beer Brewing**. *J. Inst. Brew.* (2013.0) **119** 1-14. DOI: 10.1002/jib.68
61. Diakabana P., Mvoula-Tsieri M., Dhellot J., Kobawila S.C., Louembe D.. **Physico-Chemical Characterization of Brew during the Brewing Corn Malt in the Production of Maize Beer in Congo**. *Adv. J. Food Sci. Technol.* (2013.0) **5** 671-677. DOI: 10.19026/ajfst.5.3147
62. Agu R.C.. **A Comparison of Maize, Sorghum and Barley as Brewing Adjuncts**. *J. Inst. Brew.* (2002.0) **108** 19-22. DOI: 10.1002/j.2050-0416.2002.tb00115.x
63. Marconi O., Sileoni V., Ceccaroni D., Perretti G.. **The Use of Rice in Brewing**. *Adv. Int. Rice Res.* (2017.0) 49-66. DOI: 10.5772/66450
64. Jaeger A., Zannini E., Sahin A.W., Arendt E.K.. **Barley Protein Properties, Extraction and Applications, with a Focus on Brewers’ Spent Grain Protein**. *Foods* (2021.0) **10**. DOI: 10.3390/foods10061389
65. Taylor P., Bechtel D.B.. **Properties of Whole and Undigested Fraction of Protein Bodies of Milled Rice Preparations Had Higher Fat Content than Whole PB Preparation. Endosperm Protein of Rice Exists Mainly as That the Protein Fraction Rendered Undigested**. *Agric. Biol. Chem.* (2015.0) **42** 2015-2023. DOI: 10.1271/bbb1961.42.2015
66. Wang Y., Ye L.. **Haze in Beer: Its Formation and Alleviating Strategies, from a Protein–Polyphenol Complex Angle**. *Foods* (2021.0) **10**. DOI: 10.3390/foods10123114
67. Du B., Meenu M., Liu H., Xu B.. **A Concise Review on the Molecular Structure and Function Relationship of β-Glucan**. *Int. J. Mol. Sci.* (2019.0) **20**. DOI: 10.3390/ijms20164032
68. Zielke C., Stradner A., Nilsson L.. **Characterization of cereal β-glucan extracts: Conformation and structural aspects**. *Food Hydrocoll.* (2018.0) **79** 218-227. DOI: 10.1016/j.foodhyd.2017.12.036
69. Wu X., Du J., Zhang K., Ju Y., Jin Y.. **Changes in protein molecular weight during cloudy wheat beer brewing**. *J. Inst. Brew.* (2015.0) **121** 137-144. DOI: 10.1002/jib.198
70. Johansson E., Lange S., Oshalim M.. **Anti-Inflammatory Substances in Wheat Malt Inducing Antisecretory Factor**. *Plant Foods Hum. Nutr.* (2019.0) **74** 489-494. DOI: 10.1007/s11130-019-00767-1
71. Udeh H.O., Duodu K.G., Jideani A.I.O.. **Malting Period Effect on the Phenolic Composition and Antioxidant Activity of Finger Millet (**. *Flour Mol.* (2018.0) **23**. DOI: 10.3390/molecules23092091
72. Koren D., Kun S., Hegyesné Vecseri B.. **Study of antioxidant activity during the malting and brewing process**. *J. Food Sci. Technol.* (2019.0) **56** 3801-3809. DOI: 10.1007/s13197-019-03851-1
73. Zakharova V.G., Kavardina V.A.. **Kinetic equation of elementary reaction**. *Proceedings of the 10th International Youth Scientific Conference “Future Generation: Young Scientists Perspective 2021”*
74. Li M., Du J., Zheng Y.. **Non-Starch Polysaccharides in Wheat Beers and Barley Malt beers: A Comparative Study**. *Foods* (2020.0) **9**. DOI: 10.3390/foods9020131
75. Kosiv R.. **Comparison of the hydrocolloids application efficiency for stabilizing the foam of beer**. *ScienceRise* (2021.0) **6** 25-30. DOI: 10.21303/2313-8416.2021.002232
76. Bamforth C.. **The physics and chemistry of beer foam: A review**. *Eur. Food Res. Technol.* (2022.0) **249** 3-11. DOI: 10.1007/s00217-022-04134-4
77. Hu X., Jin Y., Du J.. **Differences in protein content and foaming properties of cloudy beers based on wheat malt content**. *J. Inst. Brew.* (2019.0) **125** 235-241. DOI: 10.1002/jib.550
78. Lukinac J., Mastanjević K., Mastanjević K., Nakov G., Jukić M.. **Computer Vision Method in Beer Quality Evaluation—A Review**. *Beverages* (2019.0) **5**. DOI: 10.3390/beverages5020038
79. Ambra R., Pastore G., Lucchetti S.. **The Role of Bioactive Phenolic Compounds on the Impact of Beer on Health**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26020486
80. Hill A.E., Stewart G.G.. **Free Amino Nitrogen in Brewing**. *Fermentation* (2019.0) **5**. DOI: 10.3390/fermentation5010022
81. Guerra P.V., Yaylyan V.A.. **Interaction of Flavanols with Amino Acids: Postoxidative Reactivity of the β-Ring of Catechin with Glycine**. *J. Agric. Food Chem.* (2014.0) **62** 3831-3836. DOI: 10.1021/jf5005989
82. Gernet M., Gribkova I., Zakharov M., Kobelev K.. **Research of hop polyphenols impact on malt hopped wort aroma formation model experiments**. *Potravin. Slovak J. Food Sci.* (2021.0) **15** 262-273. DOI: 10.5219/1518
83. Iimure T., Takoi K., Kaneko T., Makoto K., Hayashi K., Ito K., Sato K., Takeda K.. **Novel Prediction Method of Beer Foam Stability Using Protein Z, Barley Dimeric α-Amylase Inhibitor-1 (BDAI-1) and Yeast Thioredoxin**. *J. Agric. Food Chem.* (2008.0) **56** 8664-8671. DOI: 10.1021/jf801184k
84. Kroll J., Rawel H.M., Rohm S.. **Reactions of Plant Phenolics with Food Proteins and Enzymes under Special Consideration of Covalent Bonds**. *Food Sci. Technol. Res.* (2003.0) **9** 205-218. DOI: 10.3136/fstr.9.205
85. Lu Y., Bergenståhl B.. **Interfacial properties and interaction between beer wort protein fractions and iso-humulone**. *Food Hydrocoll.* (2020.0) **103** 105648. DOI: 10.1016/j.foodhyd.2020.105648
86. Shabbir U., Rubab M., Daliri E.B., Chelliah R., Javed A., Oh D.H.. **Curcumin, Quercetin, Catechins and Metabolic Diseases: The Role of Gut Microbiota**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13010206
87. Phuwadolpaisarn P.. **Comparison of β-Glucan Content in Milled Rice, Rice Husk and Rice Bran from Rice Cultivars Grown in Different Locations of Thailand and the Relationship between β-Glucan and Amylose Contents**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26216368
88. Sampson G.O., Tetteh A.Y., Oldham J.H.. **Beta-Glucan Profile in Tropical Maize Genotypes: Effect of Isolation Method**. *J. Glob. Biosci.* (2015.0) **4** 1339-1349
89. Salminen T.A., Blomqvist K., Edqvist J.. **Lipid transfer proteins: Classification, nomenclature, structure, and function**. *Planta* (2016.0) **244** 971-997. DOI: 10.1007/s00425-016-2585-4
90. Merinas-Amo T., Merinas-Amo R., Font R., del Río Celestino M., Alonso-Moraga Á.. **Toxicological and Epigenetic Studies of Two Types of Ale Beer, Tyrosol and Iso-Alpha Humulone**. *Processes* (2021.0) **9**. DOI: 10.3390/pr9030485
91. Khatib A.. **Studies of iso-Alpha-Acids: Analysis, Purification, and Stability. 10 October 2006**
92. Arct J., Bielenda B., Oborska A., Pytkowska K.. **The tea and its cosmetic application**. *J. Appl. Cosmetol.* (2003.0) **21** 117-127
93. Duyvis M.G., Hilhorst R., Laane C., Evans D.J., Schmedding D.J.. **Role of riboflavin in beer flavor instability: Determination of levels of riboflavin and its origin in beer by fluorometric apoprotein titration**. *J. Agric. Food Chem.* (2002.0) **50** 1548-1552. DOI: 10.1021/jf010511k
94. Dym O., Eisenberg D.. **Sequence-structure analysis of FAD-containing proteins**. *Prot. Sci.* (2001.0) **10** 1712-1728. DOI: 10.1110/ps.12801
95. Suwannasom N., Kao I., Pruß A., Georgieva R., Bäumler H.. **Riboflavin: The Health Benefits of a Forgotten Natural Vitamin**. *Int. J. Mol. Sci.* (2020.0) **21**. DOI: 10.3390/ijms21030950
96. Qureshi A.A., Reis J.C., Qureshi N.. **δ-Tocotrienol and quercetin reduce serum levels of nitric oxide and lipid parameters in female chickens**. *Lipids Health Dis.* (2011.0) **10** 39. DOI: 10.1186/1476-511X-10-39
97. Garg M., Sharma A., Vats S., Tiwari V., Kumari A., Mishra V., Krishania M.. **Vitamins in Cereals: A Critical Review of Content, Health Effects, Processing Losses, Bioaccessibility, Fortification, and Biofortification Strategies for Their Improvement**. *Front. Nutr.* (2021.0) **8** 586815. DOI: 10.3389/fnut.2021.586815
98. Acosta-Estrada B.A., Gutiérrez-Uribe J.A., Serna-Saldivar S.O., Serna-Saldivar O.. *Minor Constituents and Phytochemicals of the Kernel* (2019.0) 369-403. DOI: 10.1016/B978-0-12-811971-6.00014-0
99. Kopjar M., Buljeta I., Ćorković I., Pichler A., Šimunović J.. **Adsorption of Quercetin on Brown Rice and Almond Protein Matrices: Effect of Quercetin Concentration**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11060793
100. Rawel H.M., Czajka D., Rohn S., Kroll J.. **Interactions of different phenolic acids and flavonoids with soy proteins**. *Int. J. Biolog. Macromol.* (2002.0) **30** 137-150. DOI: 10.1016/S0141-8130(02)00016-8
101. Gänz N., Becher T., Drusch S., Titze J.. **Interaction of proteins and amino acids with iso-α-acids during wort preparation in the brewhouse**. *Eur. Food Res. Technol.* (2022.0) **248** 741-750. DOI: 10.1007/s00217-021-03926-4
102. 102.
Middle European Brewing Analysis Commission
Brautechnische Analysenmethoden3rd ed.Band I Selbstverlag der MEBAK; Freising-WeihenstephanNüremberg, Germany1997. *Brautechnische Analysenmethoden* (1997.0)
103. **Institute of Brewing (IOB) Recommended methods of analysis. EBC Method 4.9.3**. *J. Inst. Brew.* (2007.0) **7** 54-76
104. Aitken A., Learmonth M., Walker J.M.. **Estimation of Disulfide Bonds Using Ellman’s Reagent**. *The Protein Protocols Handbook. Springer Protocols Handbooks* (2009.0). DOI: 10.1007/978-1-59745-198-7_114
105. Gutova S., Novoseltseva M., Kagan E.. **Mathematical Modelling of Isohumulone Extraction Process in Beer Wort Hopping**. *Proceedings of the 2019 International Russian Automation Conference* 1-5. DOI: 10.1109/RUSAUTOCON.2019.8867778
106. Gottumukkala R.V., Nadimpalli N., Sukala K., Subbaraju G.V.. **Determination of Catechin and Epicatechin Content in Chocolates by High-Performance Liquid Chromatography**. *Int. Sch. Res. Not.* (2014.0) **2014** 5. DOI: 10.1155/2014/628196
107. Silva M.C., dos Anjos J.P., Guarieiro L.L.N., Machado B.A.S.. **A Simple Method for Evaluating the Bioactive Phenolic Compounds’ Presence in Brazilian Craft Beers**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26164716
108. Dvorakova M., Hulin P., Karabin M., Dostálek P.. **Determination of polyphenols in beer by an effective method based on solid-phase extraction and high-performance liquid chromatography with diode-array detection**. *Czech J. Food Sci.* (2007.0) **25** 182-188. DOI: 10.17221/690-CJFS
109. **Institute of Brewing (IOB) Recommended methods of analysis. EBC Method 8.13.1**. *J. Inst. Brew.* (2007.0) **7** 156-185
110. Koren D., Vecseri B.H., Kun-Farkas G., Urbin Á., Nyitrai Á., Sipos L.. **How to objectively determine the color of beer?**. *J. Food Sci. Technol.* (2020.0) **57** 1183-1189. DOI: 10.1007/s13197-020-04237-4
111. Patrignani M., González-Forte L.d.S.. **Characterisation of melanoidins derived from Brewers’ spent grain: New insights into their structure and antioxidant activity**. *Int. J. Food Sci. Technol.* (2021.0) **56** 384-391. DOI: 10.1111/ijfs.14653
|
---
title: 'Nutrient Patterns and Body Mass Index: A Comparative Longitudinal Analysis
in Urban Black South African Adolescents and Adults'
authors:
- Gudani Mukoma
- Shane A. Norris
- Tinashe Chikowore
journal: Nutrients
year: 2023
pmcid: PMC10004796
doi: 10.3390/nu15051075
license: CC BY 4.0
---
# Nutrient Patterns and Body Mass Index: A Comparative Longitudinal Analysis in Urban Black South African Adolescents and Adults
## Abstract
Objective: We set out to evaluate the association between nutrient patterns and general adiposity in black South African adolescents and adults and to determine whether the interactions are longitudinally sustained over 24 months. Methods: Principal Component Analysis (PCA) was used to derive the nutrient patterns of 750 participants (250 adolescents between 13 and 17 years old and 500 adults who were 27 years or 45+ years old). PCA was applied to 25 nutrients, computed from the quantified food frequency questionnaire (QFFQ) over a 24 months period. Results: The nutrient patterns between adolescents and adults were similar over time; however, their associations with BMI were different. Among the adolescents, only the “plant-driven nutrients pattern” was significantly associated with a $0.56\%$ ($95\%$ CI (0.33; 0.78); $p \leq 0.001$) increase in BMI. Among the adults, the “plant-driven nutrient pattern” ($0.43\%$ ($95\%$ CI (0.03; 0.85); $p \leq 0.001$) and the “fat-driven nutrients pattern” ($0.18\%$ ($95\%$ CI (0.06; 0.29); $p \leq 0.001$) were significantly associated with a BMI increase. Furthermore, the “plant-driven nutrient pattern”, “fat-driven nutrient pattern” and the animal-driven nutrient pattern revealed sex differences in their association with BMI. Conclusion: Urban adolescents and adults had consistent nutrient patterns, but their BMI relationships changed with age and gender, an important finding for future nutrition interventions.
## 1. Introduction
Obesity is a global epidemic [1,2]. Obese people have a greater likelihood of dying from NCDs such as diabetes and heart disease [3,4,5]. In 2016, $13\%$ of adults (18 years and older) worldwide were obese ($11\%$ men, $15\%$ women) [6]. Recent studies suggest that over $70\%$ of the world’s obese population live in developing countries [7]. South Africa now leads Sub-Saharan Africa in obesity prevalence [2,4]. Women ($68\%$) are more likely to be overweight or obese than men ($31\%$) [8]. South African adolescents are also becoming overweight or obese ($9\%$ of boys and $27\%$ of girls), similar to several high-income countries [8,9]. This combined prevalence has increased from age 11 to $46.5\%$ in the 21+ age group in South African urban settings, indicating that adolescent NCD risk is rising rapidly [10]. Thus, more research is needed to understand the alarming rise in obesity rates and why more women than men are affected [11].
Obesity is linked to a sedentary lifestyle, poor diet, low physical activity and insufficient sleep [12]. South Africans of all ages exercise less due to economic changes and urbanisation [13,14,15]. Adults with higher SES have lower PA and a higher BMI [16,17]. The ongoing nutritional transition has led to more people eating westernised foods (meat, fats and oils, sauces, dressings, condiments, sweets and soft drinks) [5]. This diet increases the weight and BMI of children and adults [4,8]. In women, obesity reduces female fertility and increases offspring obesity [18]; a high maternal BMI may increase the birth and childhood weight due to the elevated maternal glucose and insulin concentrations, which drive foetal growth and adiposity [19]. Although a mother’s diet affects her offspring’s long-term health, little is known about a father’s diet [20]. However, father’s lifestyle, sperm quality and offspring health are linked [21]. Male sperm counts decrease when men eat a western-style diet, which is high in sugar, fat and processed foods [22]. Furthermore, high-fat diet-induced paternal obesity damages the sperm DNA, reduces blastocyst development and implantation rates and causes subfertility in male and female offspring for up to two generations [23]. Thus, the preconception risk factors for both men and women—healthy body composition, physical activity and diet—must be addressed [24].
Research shows that poor eating habits start in the early stages of life [12]. However, nutrient patterns in adolescents are rarely studied [13]. In addition, many of the studies conducted [25,26,27] have only examined how single nutrients and foods affect obesity. Nutrient patterns across all age groups are important because humans eat different foods [28,29,30,31]. Several South African studies have examined nutrient patterns and adiposity in different age groups and settings [28,30,31,32]. Only one rural South African adolescent study found that animal-driven nutrient patterns increase BMI [28]. Urban adolescents have no data on this association. In other studies of urban middle-aged South African adults, animal-driven nutrients [32], also called animal and fat-driven nutrients [30], were associated with an increased BMI. The association was stronger among men than women [30]. It is unknown whether there are also sex differences between boys and girls among urban adolescents. Only one study of middle-aged women showed that dietary patterns remained the same over time [31]. However, data exploring this in men is not available. In addition, in a publication [33] that forms part of this papers series, we found that young women of low socioeconomic status (SES) who ate a mixed diet (meat, vegetables, fruit, dairy, starch, cakes and biscuits) and did moderate-vigorous physical activity (MVPA) had a lower BMI than women of high SES. Despite being overweight or obese, young women with low household SES had a lower risk of NCDs than those with high SES [14]. Even with these results, it was unclear whether eating patterns change over time, if they are the same for teens and adults and if they relate to BMI differently for men and women. As most of the previous studies were cross-sectional, conducted in different age groups and settings [28,30,32] and only adult women were studied longitudinally [31], this paper aims is to build on our previous findings and other research [28,34,35] in South Africa by using nutrient pattern analysis to compare the unique eating patterns of adolescents and adults and assess their longitudinal (24 month period) effects on BMI.
## 2.1. Study Population and Design
Our study was conducted at the South African Medical Research Council (SAMRC)/Wits Developmental Pathways for Health Research Unit (DPHRU) at Chris Hani Baragwanath Academic Hospital (CHBAH) in Soweto. CHBAH is a public tertiary care institution that provides medical services to the low-income community of Greater Soweto, located in the southwestern area of Johannesburg, South Africa. It is one of the largest hospitals in the world. The peri-urban neighbourhood of *Soweto is* well known for its established communities, as well as the socioeconomic and cultural diversity that can be found there. We adopted a longitudinal design for our research. The following were the selection criteria for the participants who were part of the study: (i) adolescent boys ($$n = 125$$) and girls ($$n = 125$$) aged 13–17 years, all of whom needed to be accompanied by their parent/caregiver and who resided in Soweto; (ii) young adult males ($$n = 125$$) and females ($$n = 125$$) aged 27 years old; and (iii) middle-aged men ($$n = 125$$) and women ($$n = 125$$) aged 45+ years. A random sampling of households in Soweto was conducted to recruit 250 adolescent participants. In addition, the purposeful selection of 250 young adults who participated in the “Birth to Twenty” cohort study and 250 middle-aged adults from the “Determinants for Type 2 Diabetes Mellitus (T2D)” study was used to obtain 500 adult participants. In total, 750 self-identified black South Africans agreed to take part in the study after being recruited. Before taking part in the study, each participant first provided his or her informed consent in written form. The Human Research Ethics Committee (HREC) of Witwatersrand University, with ethics numbers M170663 and M160604, granted ethical approval (Figure 1).
Following the recruiting and enrolling of the adults and adolescents in the study, all of the data were collected at the SAMRC/Wits DPHRU site using the same methodology. May 2017 marked the beginning of the collection of the baseline data. After 12 months, and then again after 24 months, the participants returned to the DPHRU for follow-up visits.
## 2.2. Dietary Intake Assessment
Dietary intake was estimated using a seven-day quantitative food frequency questionnaire (QFFQ), with 214 commonly consumed foods taken from the analyses of eleven dietary surveys conducted in rural and urban South Africa since 1983 [36,37]. Furthermore, this tool has also been piloted and utilised extensively in Soweto, as described elsewhere [38,39,40]. To complete the QFFQ, trained research assistants used high-quality photographs of food items to trigger participants’ memories of all foods and beverages consumed during the previous seven days. The participants were asked to arrange the cards into three piles: foods eaten in the last seven days; foods eaten occasionally; or foods never consumed, and this was recorded. The QFFQ was then administered and took approximately 40–50 min to complete. In the case of food items consumed in the past seven days, additional data on the frequency and quantity of consumption was recorded. Portion sizes were estimated using a combination of high-quality two-dimensional drawings of foods, household utensils and three-dimensional food models, which have been described and validated by Steyn et al. [ 37]. The estimated portion sizes were converted to grams to allow for the calculation of the participant’s average intake over the previous seven days. The QFFQ was captured and managed online using the REDCap electronic data capture tools hosted at The University of the Witwatersrand [41]. The nutrient composition (energy and macronutrients) was calculated from the conversion of the single food item intakes by the SAMRC using the South African Food Composition Tables. Over- and under-reporting of dietary intake was corrected by removing the participants with total energy intake <3000 and >30,000 kJ, as described by Vorster et al. [ 42].
## 2.3. Demographic Questionnaires
The Demographic and Health Surveys household questionnaire (available at: www.measuredhs.com; accessed on 1 May 2017), which has been extensively utilised in this setting, was used to collect the participant socio-demographic variables [43,44]. An asset index was used to score each participant according to the number of assets they possessed out of a possible 12 (electricity, radio, television, refrigerator, mobile phone, personal computer, bicycle, motorcycle/scooter, car, agricultural land and farm animals). This was conducted so that the socio-economic status of the household could be determined.
## 2.4. Anthropometry
A Holtain, UK, stadiometer was used to measure their height in millimeters (mm), and those readings were then converted to meters (m). Utilizing a portable electronic bathroom scale, their exact weight was determined down to the nearest 0.1 kg (kg) (Seca Gmbh & Co. KG, Hamburg, Germany). All of the participants were asked to remove their shoes and wear light clothing for the measurement. The body mass index (BMI) was determined by dividing a person’s weight in kilograms by their height in meters squared (kg/m2).
## 2.5. Data Analysis
Both Stata SE version 17 and the statistical package for social scientists (SPSS) version 26 were utilised in the compilation and interpretation of the statistical findings. Q-Q plots were utilised in the process of conducting normality tests on the continuous variables. The daily macronutrient intakes of the study participants, both adolescents and adults, were characterised through the application of descriptive statistics. Twenty-five nutrients were used to derive the nutrient patterns via principal component analysis (PCA), as described by Pisa et al. [ 28]. Additionally, from the 25 nutrients, the total protein was split into animal protein and plant protein; the total carbohydrates were divided into total sugar, starch and total dietary fibre. The total fat was categorised into saturated fat, monounsaturated fat and polyunsaturated fat [29]. The total dietary fibre comprised of soluble and insoluble dietary fibre. To remove bias due to variance caused by the different measures of scale used to quantify the nutrients, we log-transformed the nutrient intake variables from the QFFQ [29]. The nutrient density method was used to adjust the total energy intake [35]. PCA was performed with the variance based on the correlation matrix and varimax rotation. We used the scree plot (Figure 2) to determine the number of PCs to retain (Figure 2 and Figure 3), which we indicated as the nutrient patterns. The nutrients with loadings greater than ±0.47 on the PCs were used to name the nutrient patterns [29]. In order to determine the significance of the extracted PCs, the total variances that were explained by the retained PCs were also analysed and evaluated. Both the Kaiser–Meyer–Olkin measure of sampling adequacy, which was 0.911, and Bartlett’s test of sphericity, which was significant at $p \leq 0.001$, indicated that the principal component analysis (PCA) was an appropriate method for the data reduction approach used for the nutrient data in this study.
Generalised estimating equations (GEE) regression models were computed separately for the adults and adolescents to assess the association between BMI as the dependent variable. In contrast, the nutrient patterns, household socioeconomic status (SES), gender, age, the estimated energy intake and estimated energy requirements (EI/EER) ratio (EI/EER), which is an indicator of the plausibility of dietary energy intake reporting, and the general energy efficiency index were used as predictors. The EI/EER ratio [45] was determined using the following Institute of Medicine (IOM) energy expenditure equations (EER).
Girls (9–18 years); EER = 88.5 − (61.9 × age [y]) + PA × (26.7 × weight [kg] + 903 × height [m]) + 25 kcal) Boys (9–18 years); EER = 135.3 − (30.8 × age [y]) + PA × (10.0 × weight [kg] + 934 × height [m]) + 25 kcal) Men (19 years and older); EER = 662 − (9.53 × age [y]) + PA × (15.91 × weight [kg] + 539.6 × height [m]) Women (19 years and older); EER = 354 − (6.91 × age [y]) + PA × (9.36 × weight [kg] + 726 × height [m])
To catergorise the plausible reporters (0.7–1.42), under-reporters (<0.7) and over-reporters (>1.42), the EI/EER ratio was used [46].
## 3.1. Descriptive Characteristics of the Study Population
The median BMI level was 20.1 (18.2; 22.8) kg/m2 for the adolescents and 26.6 (22.2; 32.7) kg/m2 for the adults. The adult women’s median BMI of 29.1 (23.7; 36.1) kg/m2 signified that a considerable proportion of the women were overweight. Overall, the adolescent boys had the highest total energy intake, with 11,305 (8709; 14,153) KJ, and consumed higher amounts of plant protein 35.9 (27.6; 48.3) and carbohydrates 366.0 (282.4; 436.7) g/day than the other groups. The boys and girls differed significantly ($p \leq 0.05$) in terms of their BMI, height, fat, carbohydrate and EI/EER ratio. Furthermore, the weight, height, BMI, total energy, total protein, total fat, saturated fat, mono and poly unsaturated fat and added sugar were significantly different between the men and women ($p \leq 0.05$). With that said, the adolescent girls had higher intakes of total fat, 100.8 (73.9; 144.6) g/day, while the adults had higher intakes of animal protein, 34.6 (24.5; 48.5) g/d, compared to other groups, as indicated in (Table 1).
## 3.2. Nutrient Patterns
Four similar nutrient patterns were extracted at the baseline visit, 12 month follow-up and 24 month follow-up from the principal component analysis among the peri-urban adolescents and adults; these patterns were named according to the nutrients with the highest factor loadings, as indicated in Figure 3. The four nutrient patterns cumulatively explained $71.50\%$ and $69.04\%$ at baseline, $66.03\%$ and $64.62\%$ at the 12 month follow-up and $61.15\%$ and $64.90\%$ at the 24 month follow-up of the total variance in the adolescents and among the adults, respectively.
For adolescents and adults, the first PC retained had higher loadings of plant protein, starch, dietary fibre, iron, magnesium, zinc, vitamin B6, riboflavin, thiamine and folate over the 24 months. It was named “Plant-driven nutrients”. The second PC was named “animal-driven nutrients” because it had high positive loadings of vitamin B12, vitamin D, cholesterol, phosphorus, riboflavin, animal protein, phosphorus, calcium, retinol, saturated fat, monounsaturated fat and folate over the 24 months. The third PC was named “fat-driven nutrients”. This nutrient pattern had high positive loadings of saturated fat, monounsaturated fat, polyunsaturated fat, cholesterol, retinol, animal protein and vitamin E. The fourth extracted PC had high loadings of animal protein, calcium, potassium, phosphorus, monounsaturated fat, polyunsaturated fat, vitamin B12, vitamin E, vitamin D and vitamin C. As a result of these positive loadings, this nutrient pattern was named “Plant and Dairy-driven nutrients” (Figure 3). Factor loadings ≥ 0.47 were used for naming the nutrient patterns.
## 3.3. Nutrient Pattern Associations with BMI
The association results of the extracted nutrient patterns and BMI are shown in Figure 3 and Supplementary File (Tables S1–S6) for the peri-urban adolescents and adults. A standard deviation change in the “Plant-driven nutrients pattern” was significantly associated with a 0.56 kg/m−2 ($95\%$ CI (0.33; 0.78); $$p \leq 0.000$$ increase in BMI among the adolescents, as shown in Figure 3A. There was a significant sex*plant-driven nutrient pattern interaction for BMI, with the association being significant in adolescent girls only (0.81 kg/m−2 ($95\%$ CI: 0.46; 1.15) $$p \leq 0.054$$) as illustrated in Figure 3C. Among the adults, the “plant-driven nutrient pattern” and the “fat-driven nutrient pattern” were associated with an increase in BMI, as illustrated in Figure 3B. A standard deviation change in the “plant-driven nutrient pattern” was significantly associated with 0.43 kgm−2 ($95\%$ CI (0.03; 0.85); $$p \leq 0.000$$), and the “fat-driven nutrient pattern” was significantly associated with a 0.18 kgm−2 ($95\%$ CI (0.06; 0.29); $$p \leq 0.000$$) increase in BMI for all of the adults. Further analysis revealed that there was a significant sex*plant-driven nutrient pattern interaction for BMI, with the association being significant in the women (0.34 kg/m−2 ($95\%$ CI: 0.03; 0.69) $$p \leq 0.011$$), and a considerable sex*animal-driven nutrient pattern interaction and sex*fat-driven nutrient pattern interaction for BMI, with the association being significant in the men (0.88 kgm−2 ($95\%$ CI (0.51; 1.25)) $$p \leq 0.000$$) and (0.45 kgm−2 ($95\%$ CI (0.09; 0.80)); $$p \leq 0.013$$), as shown in Figure 3D, respectively.
## 4. Discussion
We set out to identify and compare the nutrient patterns changes in black South African adolescents and adults over a period of 24 months, and to assess how these changes are associated with the participants’ body mass index. At baseline, the extracted nutrient patterns explained $71.50\%$ and $69.04\%$ of the total variance in the adolescent and adult nutrient intakes, respectively; at the 12 month follow-up, they explained $66.03\%$ and $64.62\%$; and at the 24 month follow-up, they explained $61.15\%$ and $64.90\%$. While the nutrient patterns of the adolescents and adults were comparable over time, their associations with BMI were distinct. Only the “plant-driven nutrients pattern” was significantly and positively associated to an increase in BMI of $0.56\%$ ($95\%$ CI (0.33; 0.78); $p \leq 0.001$) in the adolescents. Among the adults, both the “plant-driven nutrient pattern” ($0.43\%$ ($95\%$ CI (0.03; 0.85); $p \leq 0.001$) and the “fat-driven nutrients pattern” ($0.18\%$ ($95\%$ CI (0.06; 0.29); $p \leq 0.001$) were significantly and positively associated with an increase in BMI. Additionally, sex differences in the associations of the “plant-driven nutrient pattern”, “fat-driven nutrient pattern” and “animal-driven nutrient pattern” with BMI were observed. Notably, the “plant-driven nutrient pattern” and BMI had a positive and significant relationship in the adolescent girls and women, but a negative and non-significant relationship in the adolescent boys and men. In contrast, only in the men, compared to the women, were the animal- and fat-driven nutrient patterns more positively and significantly associated with BMI.
To the author’s knowledge, no studies have evaluated the longitudinal association between nutrient patterns and BMI in black South African adolescents and adults. According to the findings of our study, urban adolescents of both sexes consume a diet that is predominantly plant-based in terms of its nutrient composition. On the contrary, Pisa et al. [ 2015] [28] found that adolescents in rural areas consumed “animal-derived nutrients” the most. This implies that the dietary habits of adolescents are affected by their location and socioeconomic status (urban versus rural), and interventions designed to promote healthier dietary choices should consider this before they are put into action. Findings from other research studies shows that rural areas are undergoing a rapid transition in nutrition [28,47,48], which is accompanied by lower levels of physical activity [48,49], especially in teenage girls. As a result, higher intakes of animal protein, fat and added sugar have been observed [28,50,51], showing a shift to a more “western” diet [49], which further supports the disparity between the results of the current study and those of Pisa et al. [ 28] among adolescents. Despite this, the adult nutrient patterns presented in this study, which show that plant-based nutrients are consumed the most, are consistent with those reported by Ratshikombo et al. [ 23] among middle-aged men and women and by Makura-Kankwende et al. [ 31,32] among middle-aged women residing in urban South Africa. The implication that the patterns presented in this study are similar to those presented in earlier studies on adults [30,31,32] is due to the reason that all of these studies were conducted on urban-dwelling adults, which suggests that the nutrient patterns among adults who live in cities are applicable to a broad range of communities. Furthermore, the differences in nutrient patterns intake between rural and urban adolescents, and the similarities between urban adolescents and adults, reveals that city dwellers have different nutrient intake patterns than rural residents. Consequently, this also implies that the nutrient patterns of urban and rural dwellers will vary in their relation to body mass index. This variation highlights the need to factor in the impact of context on individuals’ nutrient patterns when designing interventions.
A comparison of the nutrient patterns of adults and adolescents was carried out, and our findings revealed that the patterns remained constant over the course of the two years that the research was carried out, with the plant-based nutrient pattern being the one that was consumed the most overall. This consistency and similarity demonstrates that urban households consume diets that contain foods that are comparable to one another, and it lends support to the finding that the plant-based nutrient pattern is the one that is most consumed in urban South Africa [23]. However, as a result of the adolescents’ low BMI, which made it difficult to detect associations at a young age, we also observed that the relationship between nutrient patterns and BMI was more pronounced in the men and women than in the boys and girls. However, given the similarities of the nutrient patterns between the adults and adolescents in this study over time, the nutrient patterns of urban dwellers can be generalised regardless of age. Furthermore, because of their consistency and homogeneity over time, interventions to promote healthy dietary intake can begin in adolescence and continue into adulthood. The results of this study lend credence to the idea that adolescence is an important period in which to encourage health-friendly changes in behaviour in order to enhance both short-term and long-term health outcomes and ease the burden to our health system [51].
Noteworthy gender differences were observed in the association between nutrient patterns and BMI. The plant-driven pattern’s association with BMI varied by sex, with women showing a stronger association than men. This is consistent with previous cross-sectional findings in urban men and women [30]. Interestingly, our findings show that this relationship emerges during adolescence, with the adolescent girls in the present study also showing a significant association between the plant-driven pattern and BMI, a previously unknown finding. On the contrary, the association between animal-driven and fat-driven nutrient patterns and BMI was found to be stronger in men than in women A finding that is similar to that reported in previous cross-sectional research [30]. This is also in line with reports on black households in South Africa, which found that male dietary needs are given higher priority than those of women and children, especially when it comes to meals that contain high amounts of protein, such as meat-based meals [44]. Most importantly, these associations point to a shift toward westernised diets that are high in energy-dense foods [43,44]. In turn, this means that men have a higher risk of developing non-communicable diseases than women.
The current study’s strengths are its longitudinal design, the use of a validated and comprehensive QFFQ and the assessment of nutrient patterns in both adolescents and adults. Longitudinal nutrient evaluation has several benefits, including the ability to identify stability and compare similarities between adolescents and adults. Despite the subjective nature of the pattern interpretation and labelling, the PCA technique is well-established for capturing real-life dietary behaviours [28,29]. One limitation of our study was that we did not collect data on the participants’ levels of physical activity beyond what was captured by the QFFQ. However, the EI/EER ratio was used to adjust the participants’ self-reported energy intake in order to reduce dietary reporting bias. Furthermore, it is not possible to generalise or extrapolate the relationships shown between nutrient patterns and BMI to people living in rural areas because the data presented are restricted to urban areas only. As a consequence of this, investigations are required consisting of data gathered from both urban and rural areas.
## 5. Conclusions
To conclude, the nutrient patterns of urban adolescents and adults are comparable, and while there are age and gender differences in their association with BMI, their consistency over time suggests that nutrition interventions aimed at improving health outcomes can start earlier, in adolescence. The current findings highlight the significance of taking gender differences into account and the significance of addressing both girls’ and boys’ health-related behaviours, which has implications for future efforts to improve preconception health through nutrition. They can serve as a starting point for developing nutrition interventions that aim to address the health needs of adolescents and, in turn, address the issue of maternal and child mortality, as well as prevent the risk of chronic diseases in adulthood and future generations. Moreover, our findings provide insightful data that can be used monitor progress and help revise the current South African Strategy for the Prevention and Control of Obesity (2015–2020). Our recommendation is that future longitudinal studies examine the relationship between the nutrient patterns and body mass index of rural residents in order to gain a deeper understanding of their dietary behaviour and ensure no one is left behind, as the current study only examined urban residents. By doing so, South Africa would make significant progress in battling obesity and chronic NCDs, as it will aid the development and implementation of national level interventions.
## References
1. **Health effects of overweight and obesity in 195 countries over 25 years**. *N. Engl. J. Med.* (2017) **377** 13-27. DOI: 10.1056/NEJMoa1614362
2. Cois A., Day C.. **Obesity trends and risk factors in the South African adult population**. *BMC Obes.* (2015) **2**. DOI: 10.1186/s40608-015-0072-2
3. Maluleke T., Shisana O., Shisana O.. *South African National Health and Nutrition Examination Survey (SANHANES-1)* (2013)
4. Ajayi I.O., Adebamowo C., Adami H.O., Dalal S., Diamond M.B., Bajunirwe F., Guwatudde D., Njelekela M., Nankya-Mutyoba J., Chiwanga F.S.. **Urban-rural and geographic differences in overweight and obesity in four sub-Saharan African adult populations: A multi-country cross-sectional study**. *BMC Public Health* (2016) **16**. DOI: 10.1186/s12889-016-3789-z
5. Lim S.S., Vos T., Flaxman A.D., Danaei G., Shibuya K., Adair-Rohani H., AlMazroa M.A., Amann M., Anderson H.R., Andrews K.G.. **A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010**. *Lancet* (2012) **380** 2224-2260. DOI: 10.1016/S0140-6736(12)61766-8
6. **Obesity and Overweight Facts Sheet**. (2021)
7. Popkin B.M., Du S., Green W.D., Beck M.A., Algaith T., Herbst C.H., Alsukait R.F., Alluhidan M., Alazemi N., Shekar M.. **Individuals with obesity and COVID-19: A global perspective on the epidemiology and biological relationships**. *Obes. Rev.* (2020) **21** e13128. DOI: 10.1111/obr.13128
8. 8.
National Department of Health (NDoH)
Statistics South Africa
South African Medical Research Council
ICF
South Africa Demographic and Health Survey 2016Statistics South AfricaCape Town, South Africa2019. *South Africa Demographic and Health Survey 2016* (2019)
9. Ng M., Fleming T., Robinson M., Thomson B., Graetz N., Margono C., Mullany E.C., Biryukov S., Abbafati C., Abera S.F.. **Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: A systematic analysis for the Global Burden of Disease Study 2013**. *Lancet* (2014) **384** 766-781. DOI: 10.1016/S0140-6736(14)60460-8
10. Nyati L.H., Pettifor J.M., Norris S.A.. **The prevalence of malnutrition and growth percentiles for urban South African children**. *BMC Public Health* (2019) **19**. DOI: 10.1186/s12889-019-6794-1
11. Micklesfield L.K., Kagura J., Munthali R., Crowther N.J., Jaff N., Gradidge P., Ramsay M., Norris S.A.. **Demographic, socio-economic and behavioural correlates of BMI in middle-aged black men and women from urban Johannesburg, South Africa**. *Glob. Health Action* (2018) **11** 1448250. DOI: 10.1080/16549716.2018.1448250
12. Phaswana-Mafuya N., Peltzer K., Chirinda W., Musekiwa A., Kose Z.. **Sociodemographic predictors of multiple non-communicable disease risk factors among older adults in South Africa**. *Glob. Health Action* (2013) **6** 20680. DOI: 10.3402/gha.v6i0.20680
13. Micklesfield L.K., Pedro T.M., Kahn K., Kinsman J., Pettifor J.M., Tollman S., A Norris S.. **Physical activity and sedentary behavior among adolescents in rural South Africa: Levels, patterns and correlates**. *BMC Public Health* (2014) **14**. DOI: 10.1186/1471-2458-14-40
14. Mukoma G., Wrottesley S.V., Kagura J., Oni T., Micklesfield L., Norris S.A.. **The relationships between socioeconomic status, dietary knowledge and patterns, and physical activity with adiposity in urban South African women**. *S. Afr. J. Clin. Nutr.* (2022) 1-7. DOI: 10.1080/16070658.2022.2076374
15. Malambo P., Kengne A.P., Lambert E.V., De Villiers A., Puoane T.. **Prevalence and socio-demographic correlates of physical activity levels among South African adults in Cape Town and Mount Frere communities in 2008–2009**. *Arch. Public Health* (2016) **74** 54. DOI: 10.1186/s13690-016-0167-3
16. Micklesfield L.K., Munthali R.J., Prioreschi A., Said-Mohamed R., Van Heerden A., Tollman S., Kahn K., Dunger D., Norris S.A.. **Understanding the Relationship between Socio-Economic Status, Physical Activity and Sedentary Behaviour, and Adiposity in Young Adult South African Women Using Structural Equation Modelling**. *Int. J. Environ. Res. Public Health* (2017) **14**. DOI: 10.3390/ijerph14101271
17. Gradidge P.J.-L., Norris S.A., Munthali R., Crowther N.J.. **Influence of socioeconomic status on changes in body size and physical activity in ageing black South African women**. *Eur. Rev. Aging Phys. Act.* (2018) **15** 6. DOI: 10.1186/s11556-018-0196-8
18. Chandrasekaran S., Neal-Perry G.. **Long-term consequences of obesity on female fertility and the health of the offspring**. *Curr. Opin. Obstet. Gynecol.* (2017) **29** 180-187. DOI: 10.1097/GCO.0000000000000364
19. Nicholas L.M., Morrison J.L., Rattanatray L., Zhang S., Ozanne S.E., McMillen I.C.. **The early origins of obesity and insulin resistance: Timing, programming and mechanisms**. *Int. J. Obes.* (2016) **40** 229-238. DOI: 10.1038/ijo.2015.178
20. Fleming T.P., Watkins A.J., Velazquez M.A., Mathers J.C., Prentice A.M., Stephenson J., Barker M., Saffery R., Yajnik C.S., Eckert J.J.. **Origins of lifetime health around the time of conception: Causes and consequences**. *Lancet* (2018) **391** 1842-1852. DOI: 10.1016/S0140-6736(18)30312-X
21. Sinclair K.D., Watkins A.J.. **Parental diet, pregnancy outcomes and offspring health: Metabolic determinants in developing oocytes and embryos**. *Reprod. Fertil. Dev.* (2013) **26** 99-114. DOI: 10.1071/RD13290
22. Gaskins A.J., Colaci D.S., Mendiola J., Swan S.H., Chavarro J.E.. **Dietary patterns and semen quality in young men**. *Hum. Reprod.* (2012) **27** 2899-2907. DOI: 10.1093/humrep/des298
23. Agbaje I., Rogers D., McVicar C., McClure N., Atkinson A., Mallidis C., Lewis S.. **Insulin dependant diabetes mellitus: Implications for male reproductive function**. *Hum. Reprod.* (2007) **22** 1871-1877. DOI: 10.1093/humrep/dem077
24. Mullins E., Davies S.. **Annual Report of the Chief Medical Officer, 2014 The Health of the 51%: Women**. (2015)
25. Berdanier C.D., Dwyer J.T., Feldman E.B.. *Handbook of Nutrition and Food* (2007)
26. Kelly O.J., Gilman J.C., Kim Y., Ilich J.Z.. **Micronutrient intake in the etiology, prevention and treatment of osteosarcopenic obesity**. *Curr. Aging Sci.* (2016) **9** 260-278. DOI: 10.2174/1874609809666160509122001
27. Hammad S.S., Eck P., Sihag J., Chen X., Connelly P.W., Lamarche B., Couture P., Guay V., Maltais-Giguère J., West S.G.. **Common Variants in Lipid Metabolism–Related Genes Associate with Fat Mass Changes in Response to Dietary Monounsaturated Fatty Acids in Adults with Abdominal Obesity**. *J. Nutr.* (2019) **149** 1749-1756. DOI: 10.1093/jn/nxz136
28. Pisa P.T., Pedro T.M., Kahn K., Tollman S.M., Pettifor J.M., Norris S.A.. **Nutrient patterns and their association with socio-demographic, lifestyle factors and obesity risk in rural South African adolescents**. *Nutrients* (2015) **7** 3464-3482. DOI: 10.3390/nu7053464
29. Chikowore T., Pisa P.T., Van Zyl T., Feskens E.J.M., Wentzel-Viljoen E., Conradie K.R.. **Nutrient Patterns Associated with Fasting Glucose and Glycated Haemoglobin Levels in a Black South African Population**. *Nutrients* (2017) **9**. DOI: 10.3390/nu9010009
30. Ratshikombo T., Goedecke J.H., Soboyisi M., Kufe C., Makura-Kankwende C.B.T., Masemola M., Micklesfield L.K., Chikowore T.. **Sex Differences in the Associations of Nutrient Patterns with Total and Regional Adiposity: A Study of Middle-Aged Black South African Men and Women**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13124558
31. Makura-Kankwende C.B., Gradidge P.J., Crowther N.J., Ratshikombo T., Goedecke J.H., Micklesfield L.K., Norris S.A., Chikowore T.. **Association of Longitudinal Nutrient Patterns with Body Composition in Black Middle-Aged South African Women: A Five-Year Follow-Up Study**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph191912792
32. Makura-Kankwende C.B.T., Gradidge P.J., Crowther N.J., Norris S.A., Chikowore T.. **Nutrient Patterns and Body Composition Parameters of Black South African Women**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13010006
33. **Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016**. *Lancet* (2017) **390** 1345-1422. DOI: 10.1016/S0140-6736(17)32366-8
34. Newby P.K., Tucker K.L.. **Empirically derived eating patterns using factor or cluster analysis: A review**. *Nutr. Rev.* (2004) **62** 177-203. DOI: 10.1111/j.1753-4887.2004.tb00040.x
35. Hu F.B.. **Dietary pattern analysis: A new direction in nutritional epidemiology**. *Curr. Opin. Lipidol.* (2002) **13** 3-9. DOI: 10.1097/00041433-200202000-00002
36. Zingoni C., Norris S.A., Griffiths P.L., Cameron N.. **Studying a population undergoing nutrition transition: A practical case study of dietary assessment in urban South African adolescents**. *Ecol. Food Nutr.* (2009) **48** 178-198. DOI: 10.1080/03670240902794713
37. Steyn N.P., Senekal M., Norris S.A., Whati L., MacKeown J.M., Nel J.H.. **How well do adolescents determine portion sizes of foods and beverages? Asia Pac**. *J. Clin. Nutr.* (2006) **15** 35-42
38. Wrottesley S.V., Pisa P.T., Norris S.A.. **The Influence of Maternal Dietary Patterns on Body Mass Index and Gestational Weight Gain in Urban Black South African Women**. *Nutrients* (2017) **9**. DOI: 10.3390/nu9070732
39. Feeley A.B., Musenge E., Pettifor J.M., Norris S.A.. **Investigation into longitudinal dietary behaviours and household socio-economic indicators and their association with BMI Z-score and fat mass in South African adolescents: The Birth to Twenty (Bt20) cohort**. *Public Health Nutr.* (2013) **16** 693-703. DOI: 10.1017/S1368980012003308
40. Jean-Luc Gradidge P., Norris S.A., Jaff N.G., Crowther N.J.. **Metabolic and Body Composition Risk Factors Associated with Metabolic Syndrome in a Cohort of Women with a High Prevalence of Cardiometabolic Disease**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0162247
41. Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G.. **Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support**. *J. Biomed. Inform.* (2009) **42** 377-381. DOI: 10.1016/j.jbi.2008.08.010
42. Vorster H.H., Kruger A., Wentzel-Viljoen E., Kruger H.S., Margetts B.M.. **Added sugar intake in South Africa: Findings from the Adult Prospective Urban and Rural Epidemiology cohort study**. *Am. J. Clin. Nutr.* (2014) **99** 1479-1486. DOI: 10.3945/ajcn.113.069005
43. Griffiths P.L., Johnson W., Cameron N., Pettifor J.M., Norris S.A.. **In urban South Africa, 16 year old adolescents experience greater health equality than children**. *Econ. Hum. Biol.* (2013) **11** 502-514. DOI: 10.1016/j.ehb.2013.05.004
44. Kagura J., Adair L.S., Pisa P.T., Griffiths P.L., Pettifor J.M., Norris S.A.. **Association of socioeconomic status change between infancy and adolescence, and blood pressure, in South African young adults: Birth to Twenty Cohort**. *BMJ Open* (2016) **6** e008805. DOI: 10.1136/bmjopen-2015-008805
45. Trumbo P., Schlicker S., Yates A.A., Poos M.. **Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids**. *J. Am. Diet. Assoc.* (2002) **102** 1621-1630. DOI: 10.1016/S0002-8223(02)90346-9
46. Steyn N.P., Mchiza Z.J.. **Obesity and the nutrition transition in Sub-Saharan Africa**. *Ann. N. Y. Acad. Sci.* (2014) **1311** 88-101. DOI: 10.1111/nyas.12433
47. Wrottesley S.V., Pedro T.M., Fall C.H., Norris S.A.. **A review of adolescent nutrition in South Africa: Transforming adolescent lives through nutrition initiative**. *S. Afr. J. Clin. Nutr.* (2020) **33** 94-132. DOI: 10.1080/16070658.2019.1607481
48. Prioreschi A., Wrottesley S.V., Cohen E., Reddy A., Said-Mohamed R., Twine R., Tollman S.M., Kahn K., Dunger D.B., Norris S.A.. **Examining the relationships between body image, eating attitudes, BMI, and physical activity in rural and urban South African young adult females using structural equation modeling**. *PLoS ONE* (2017) **12**. DOI: 10.1371/journal.pone.0187508
49. Wentzel-Viljoen E., Lee S., Laubscher R., Vorster H.H.. **Accelerated nutrition transition in the North West Province of South Africa: Results from the Prospective Urban and Rural Epidemiology (PURE-NWP-SA) cohort study, 2005 to 2010**. *Public Health Nutr.* (2018) **21** 2630-2641. DOI: 10.1017/S1368980018001118
50. Tydeman-Edwards R., Van Rooyen F.C., Walsh C.M.J.H.. **Obesity, undernutrition and the double burden of malnutrition in the urban and rural southern Free State, South Africa**. *Heliyon* (2018) **4** e00983. DOI: 10.1016/j.heliyon.2018.e00983
51. Pringle J., Doi L., Jindal-Snape D., Jepson R., McAteer J.. **Adolescents and health-related behaviour: Using a framework to develop interventions to support positive behaviours**. *Pilot Feasibility Stud.* (2018) **4** 69. DOI: 10.1186/s40814-018-0259-7
|
---
title: Development and Characterization of Electrospun Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)
Biopapers Containing Cerium Oxide Nanoparticles for Active Food Packaging Applications
authors:
- Kelly J. Figueroa-Lopez
- Cristina Prieto
- Maria Pardo-Figuerez
- Luis Cabedo
- Jose M. Lagaron
journal: Nanomaterials
year: 2023
pmcid: PMC10004799
doi: 10.3390/nano13050823
license: CC BY 4.0
---
# Development and Characterization of Electrospun Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Biopapers Containing Cerium Oxide Nanoparticles for Active Food Packaging Applications
## Abstract
Food quality is mainly affected by oxygen through oxidative reactions and the proliferation of microorganisms, generating changes in its taste, odor, and color. The work presented here describes the generation and further characterization of films with active oxygen scavenging properties made of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) loaded with cerium oxide nanoparticles (CeO2NPs) obtained by electrospinning coupled to a subsequent annealing process, which could be used as coating or interlayer in a multilayer concept for food packaging applications. The aim of this work is to explore the capacities of these novel biopolymeric composites in terms of O2 scavenging capacity, as well as antioxidant, antimicrobial, barrier, thermal, and mechanical properties. To obtain such biopapers, different ratios of CeO2NPs were incorporated into a PHBV solution with hexadecyltrimethylammonium bromide (CTAB) as a surfactant. The produced films were analyzed in terms of antioxidant, thermal, antioxidant, antimicrobial, optical, morphological and barrier properties, and oxygen scavenging activity. According to the results, the nanofiller showed some reduction of the thermal stability of the biopolyester but exhibited antimicrobial and antioxidant properties. In terms of passive barrier properties, the CeO2NPs decreased the permeability to water vapor but increased the limonene and oxygen permeability of the biopolymer matrix slightly. Nevertheless, the oxygen scavenging activity of the nanocomposites showed significant results and improved further by incorporating the surfactant CTAB. The PHBV nanocomposite biopapers developed in this study appear as very interesting constituents for the potential design of new active organic recyclable packaging materials.
## 1. Introduction
Food products are subjected to different conditions (temperature, water vapor, oxygen, ultraviolet light, microorganisms, mechanical stress, etc.) throughout the supply chain, which could alter food quality. In this sense, conventional food packaging technology was originally developed to contain and protect food products from these external factors [1]. Nevertheless, recently food packaging has evolved, incorporating novel active, passive, and intelligent constituents to prolong expiration dates and to preserve or increase quality, safety, and integrity [2,3]. In addition, taking into account the current environmental concern, there is an increased attention in renewable biopolymer-based packaging materials as eco-friendly and sustainable alternatives to substitute traditional non-biodegradable petroleum-based plastics [4,5].
Polyhydroxyalkanoates have increasingly become an attractive alternative to non-renewable polymers for food packaging applications following the circular economy (PHAs) principles [6,7] due to their biocompatibility and wide range of physical properties [8]. PHAs can be defined as homo-, co-, and terpolymers [9] and classified in relation to the number of carbon atoms in the monomer into: short-chain-length PHAs (scl-PHAs) with 3 to 5 carbons, medium-chain-length PHAs (mcl-PHAs) with 6 to 14 carbons, and long-chain-length PHAs (lcl-PHAs) with more than 14 carbons [10,11]. Among them, the copolymer PHBV has a strong potential for food packaging applications since it has considerably lower crystallinity and melting temperature (Tm), which diminish as the percentage of HV fraction in the polymer increases [12], exhibits enhanced flexibility, ductility, and elongation at break, and augmented tensile strength as a consequence of a reduction in the Young’s modulus as the fraction of HV increases [13].
The novel generation of packaging has an active part in the conservation of food quality throughout the supply chain. According to Regulation (CE) No. $\frac{450}{2009}$ ($\frac{29}{05}$/2009), active packaging consists of a material that incorporates intentionally active compounds that release and absorb substances into or from either the environment or the packaged food [14]. Active compounds in active packaging are specially catalogued as either scavengers, which can remove unwanted substances from the food environment, or active-releasing (emitters), which provide substances to packaged food or into headspace, conferring long-term antioxidant and/or antimicrobial properties [15]. Thus, oxygen scavengers have the ability to capture the oxygen present inside the packaging material, creating an oxygen-free atmosphere, which can reduce the oxidative, enzymatic, and microbial reactions in food [16]. Oxygen scavengers are normally iron-based, in which the mechanism of action is principally due to the oxidation of iron when moisture is present. Thus, ferrous oxide (Fe2+) is transformed to ferric oxide (Fe3+). Another widely known oxygen scavenger is palladium, which reacts based on the adsorption of both hydrogen and oxygen on palladium surface (by chemisorption), followed by a chemical reaction at the surface and desorption of water into gas phase [17,18]. These materials are classified as moisture-activated oxygen scavengers, and consequently they highly depend on the relative humidity of the environment and are capable of reducing oxygen concentration within the food package to percentages lower than $0.01\%$ [19].
An alternative to common oxygen scavengers is cerium oxide (CeO2), which has attracted much attention due to its capability to transition from Ce3+ to Ce4+ states reversibly, which is responsible for the catalytic, antioxidant, anti-inflammatory, and anti-bacterial activity characteristics of these nanoparticles [20]. CeO2NPs has numerous applications that include catalysis, biomedicine, energy, electrochemistry, photoelectronics, novel materials with enhanced antimicrobial, superhydrobophic, anticorrosive or mechanical properties or sensors, among others due to their special structure and atomic characteristics in comparison to other systems [21,22,23,24,25,26,27,28,29,30,31].
One of the most promising methods to incorporate these nanoparticles into biopolymeric matrixes is electrospinning [23]. This simple, cost-effective, and versatile technique generates ultrathin polymeric fibers with control of fiber diameters and porosity [24]. This technique offers advantages; the process is carried out at room temperature and provides optimum encapsulation efficiency, which maintains the bioactivity of the encapsulated substances and offers their sustained and controlled release. These advantages have permitted the development of active and high-barrier food packaging by means of electrospinning [32]. In this sense, this technology has proven very valuable for the production of active packaging based on poly(3-hydroxybutyrate) (PHB) and polycaprolactone (PCL) loaded with palladium nanoparticles (PdNPs) for the development of monolayer and multilayer materials with oxygen scavenging capacity [33,34].
In this context, this research aimed at assessing the capacity of the electrospinning technology to produce active and passive barrier biopaper films from annealed electrospun PHBV comprising CeO2NPs and CTAB as a surfactant. This film could be potentially of use as an interlayer or coating in a multilayer concept for food packaging applications. Firstly, the electrospun PHBV fibers and resultant biopapers after annealing were characterized based on their optical and thermal properties, morphology, crystallinity, and oxygen scavenging capacity. Secondly, the films with the best oxygen scavenging performance were chosen and characterized according to their mechanical, barrier, antimicrobial, and antioxidant properties.
## 2.1. Materials
PHBV (ENMATTM Y1000P) was purchased from Tianan Biologic Materials (Ningbo, China) and provided in the form of pellets by NaturePlast (Ifs, France). As stated by the producer, the material is characterized by a density of 1.23 g/cm³, a melt flow index (MFI) of 5–10 g/10 min (190 °C, 2.16 kg), and a 3HV fraction of 2 mol.-%. The cerium (IV) oxide (CeO2) nanoparticles, with particle size < 25 nm calculated by means of the Brunauer, Emmett, and Teller (BET) method, were provided by Sigma Aldrich (Madrid, Spain). According to the literature, the cytotoxicity (LC50) of CeO2 was 1000 µg/mL measured in MCF7 cells [35], and the oral acute toxicity was (LD50) >5000 mg/kg measured in rats [36]. Hexadecyltrimethylammonium bromide (CTAB) ($99\%$); 2,2,2-trifluoroethanol (TFE) (≥$99\%$); and D-limonene ($98\%$) were provided by Sigma Aldrich (Madrid, Spain).
## 2.2. Electrospinning Process
First, PHBV was mixed with the solvent TFE at a concentration of 10 wt$.\%$. Nanoparticles of CeO2 were added (0.5, 1.0, 1.5, 2.0, 5.0, and 10 wt$.\%$) in relation to the PHBV. Hexadecyltrimethylammonium bromide (CTAB) is a cationic surfactant, not yet approved for use in foods but acceptable for pharmaceutical and food contact applications [37], which has been studied for the development of novel materials for food packaging applications [38,39,40]. It was added at a concentration of 0.5 wt$.\%$ to the PHBV mixture to enhance the dispersion of the nanoparticles.
The PHBV-TFE solutions containing the nanoparticles were electrospun in a Fluidnatek® LE-10 from Bioinicia S.L. (Valencia, Spain). The device operated under a steady flow rate using a multineedle injector, with horizontal scanning movement towards a metallic collector. The process was carried out using a flow rate of 6 mL/h, a needle-to-collector distance of 15 cm, and a voltage of 15 kV. The processing time was 2 h under controlled environmental conditions of 25 °C and $40\%$ RH. The obtained electrospun fibers were maintained at 25 °C and $0\%$ RH until further analysis.
## 2.3. Electrospun Films
The thermal post-treatment was carried out with a 4122 hot-plates press (Carver Inc., Wabash, IN, USA). The post-treatment was applied to the electrospun mats below the melting temperature (Tm) of the biopolymer, at 160 °C for 10 s without pressure. This low temperature also avoids the thermal degradation of the polymer. The final samples showed a mean thickness between 70 and 80 µm.
## 2.4.1. Morphology
The PHBV electrospun fibers and their films loaded with CeO2NPs were analyzed by scanning electron microscopy (SEM) in a Hitachi S-4800 (Tokyo, Japan). Previous to SEM observation, samples were coated with gold–palladium for 3 min under vacuum conditions. SEM images were acquired at 10 kV. The cross section of the films was also performed. For that, the obtained films were immersed in liquid nitrogen and cryo-fractured.
Particle morphology as well as size of CeO2NPs loaded in electrospun PHBV fibers were characterized directly by TEM Hitachi HT7700 (Tokyo, Japan). Previously, samples were deposited onto clamping holders. TEM images were acquired at 100 kV. Image J Launcher v1.41 software (National Institutes of Health, Bethesda, USA) was used to determine the size of the nanoparticles along with the average fiber diameter using at least 20 images.
## 2.4.2. Transparency
A UV4000 spectrophotometer (Dinko Instruments, Barcelona, Spain) was used to evaluate the light transmission of the films loaded with the nanoparticles. Samples of 50 mm × 30 mm were used to quantify the light absorption at wavelengths from 200 to 700 nm. The transparency value (T) was determined according to Equation [1], and the opacity value (O) was calculated according to Equation [2] [41]:[1] T=A600L [2]O=A500 L A500 is the absorbance of the sample at 500 nm, whereas A600 is the absorbance of the sample at 600 nm. L corresponds to the thickness of the film (mm).
## 2.4.3. Color
A Chroma Meter CR-400 (Konica Minolta, Tokyo, Japan) was employed to characterize the color of the films. The illuminant D65 was used. The color difference (∆E) was determined according to Equation [3]:[3]ΔE=[(ΔL*)2+(Δa*)2+(Δb*)2]0.5 where L* denotes the luminance (black to white), a* designates the change between green and red, and b* is the change from blue to yellow; ΔL*, Δa*, and Δb* corresponded to the differences between the brightness and color parameters of the PHBV films containing CeO2NPs and the values of the reference film (neat PHBV) (a* = 0.74, b* = −0.41, L* = 90.44) [42]. The color difference is considered unnoticeable if ΔE < 1. Color differences for ΔE ≥ 1 and < 2 are only detected by experienced personnel. Color differences for values of ΔE* ≥ 2 and < 3.5 can be detected by an inexperienced observer. Color difference for ΔE ≥ 3.5 and < 5 are clearly noticeable, and different colors are detected when ΔE ≥ 5 [43]. Tests were carried out in triplicate.
## 2.4.4. X-ray Diffraction Analysis
An AXS D4 Endeavour diffractometer (Bruker Corporation, Billerica, MA, USA) was used to analyze the CeO2NPs and fiber samples by wide angle X-ray diffraction (WAXD). The analyses were performed at room temperature, in reflection mode with an incident CuKα radiation ($k = 1.5406$ Å); the generator was set to 40 kV, the filament current to 40 mA, and scattering angles (2θ) between 2 and 90° were used. Peak analysis was performed with the Igor Pro software using a Gaussian function to fit the data.
## 2.4.5. Attenuated Total Reflection—Fourier Transform Infrared Spectroscopy (ATR-FTIR)
ATR-FTIR spectra were acquired by using the ATR sampling accessory Golden Gate (Specac Ltd., Orpington, UK) coupled to the Tensor 37 FTIR device (Bruker, Ettlingen, Germany). Spectra were obtained within the wavenumber range 4000–600 cm−1 by averaging 20 scans at 4 cm−1 resolution. Analysis of spectral data was performed using the OPUs 4.0 data collection software program (Bruker, Ettlingen, Germany).
## 2.4.6. Thermal Analysis
The thermal transitions were studied with a DSC-8000 analyzer from PerkinElmer, Inc. (Waltham, MA, USA), coupled to a cooling accessory Intracooler 2 also from PerkinElmer, Inc. Samples followed a thermal sequence as follows: a first ramp from −30 °C to 190 °C, then a cooling step to −30 °C with heating and cooling rates of 10 °C/min. The measurements were performed under a nitrogen atmosphere using a flow rate of 20 mL/min. Sample weight was around 3.0 mg, using an empty aluminum pan as reference. Calibration was performed using an indium sample. Measurements were performed, at least, in duplicate. Thermograms were analyzed using the Pyris Manager software (PerkinElmer, Inc., Waltham, MA, USA).
A 550-TA Instruments Thermogravimetric Analyzer (New Castle, DE, USA) was used to perform the thermogravimetric analysis (TGA) between 25 °C to 700 °C, at a heating rate of 10 °C/min under a nitrogen atmosphere. The obtained data were analyzed by means of the TA analysis software. Measurements were carried out in triplicate.
## 2.4.7. Oxygen Scavenging Capacity
The activity of the fibers and films containing CeO2NPs as oxygen scavengers were characterized using an OXY-4 mini device (PreSens Precision Sensing GmbH, Regensburg, Germany). For the measurements, 50 cm3 Schleck round-bottom flasks (VidraFoc S.A., Barcelona, Spain) with a polytetrafluoroethylene (PTFE) stopcock were used. An O2-sensitive sensor spot (PSt3, detection limit 15 ppb, 0–$100\%$ oxygen, PreSens) was glued onto the inner wall of the flasks. Then, 5 × 5 cm2 samples were put inside the flasks, flushed for three minutes with 100 vol$.\%$ N2, and then the gas mixture containing 4 vol. % oxygen, 2 vol. % hydrogen, and 94 vol. % nitrogen (Abelló Linde, S.A. Barcelona, Spain) was injected for 1 min at 1 bar. The oxygen concentration analysis inside the flask in function of time was performed via the fluorescence decay method by means of the OXY-4 mini (PreSens). Measurements were carried out at a temperature of 23 °C and $100\%$ RH.
## 2.4.8. Mechanical Test
Mechanical properties were determined with a universal mechanical testing device (AGS-X 500N, Shimadzu Corp. Kyoto, Japan) at room temperature. The load cell was 1 kN, and the cross-head speed was 10 mm/min. Tests were performed following the ASTM D638 (Type IV) standard. Samples were shaped into dumbbell specimens. At least six specimens were analyzed for each sample. Tensile modulus (E), tensile strength at yield (σy), elongation at break (εb), and toughness (T) were determined from the stress–strain curves calculated from the force–distance data.
## 2.4.9. Barrier Properties
The water vapor permeability (WVP) of the film samples was obtained with a gravimetric method ASTM E96-95. For this, 5 mL of distilled water was placed inside a Payne permeability cup (diameter of 3.5 cm) (Elcometer Sprl, Hermallesous-Argenteau, Belgium). The films were exposed to $100\%$ RH on one side. The samples were kept inside a desiccator at $0\%$ RH and 25 °C. Aluminum films were employed as control samples to evaluate the solvent loss through the sealing. An analytical balance (±0.0001 g) was used to determine the weight loss of the cups. WVP was determined from the regression analysis of weight loss data versus time, and the weight loss was corrected taking into account the small losses through the sealing. The permeability was obtained taking into account the permeance and the average film thickness.
Following a similar methodology, limonene permeability (LP) was evaluated with Payne permeability cups with 5 mL of D-limonene (25 °C and $40\%$ RH). The permeation rate of limonene vapor (LPRT) was calculated from the steady-state permeation slopes, and the weight loss was corrected with the loss through the sealing. In addition, the average film thickness was taken into consideration for the LP determination. The analyses were performed in triplicate.
The oxygen permeability coefficient was calculated from oxygen transmission rate results obtained by means of an Oxygen Permeation Analyzer M8001 from Systech (Illinois, UK) and according to the ASTM D3985-05 standard. Experiments were performed at 23 °C and $60\%$ RH. Samples were flushed with nitrogen before being exposed to an oxygen flow of 10 mL min−1; 5 cm2 was the exposure area for each sample during the test. Film thickness and gas partial pressure were taken into consideration to calculate the oxygen permeability. The analyses were conducted in triplicate.
## 2.5. Antimicrobial Activity
The antimicrobial properties of the PHBV films loaded with CeO2NPs were evaluated following the Japanese Industrial Standard (JIS) Z 2801:2010. Common food bacteria, specifically S. aureus CECT240 (ATCC 6538P) and E. coli CECT434 (ATCC 25922) strains, were provided by the Spanish Type Culture Collection (CECT) (Valencia, Spain). They were reconstituted and kept in phosphate-buffered saline (PBS) with 10 wt$.\%$ tryptic soy broth (TSB) and 10 wt$.\%$ glycerol at −80 °C. A loopful of bacteria was relocated to 10 mL of TSB and incubated at 37 °C for 24 h. A 100 μL aliquot from the bacterial culture was again relocated to TSB and grown at 37 °C to the mid-exponential phase of growth, when approximately 5 × 105 colony-forming units (CFU)/mL of culture were obtained.
Film samples containing CeO2NPs were cut in squares 1.5 cm × 1.5 cm. Additionally, a polyethylene film was taken as the control film because it shows no antimicrobial activity. To determine the antimicrobial activity, a suspension of S. aureus and E. coli was deposited on the film samples and incubated for 24 h at 24 °C in at least, $95\%$ RH. The bacteria were then recovered with PBS, 10-fold serially diluted, and incubated at 37 °C for 24 h to quantify the number of viable bacteria by plate count. The antimicrobial reduction (R) was determined by means of Equation [4]:[4]R=[Log(BA)−(CA)]=Log(BC) where A is the average of the number of bacterial counts for the control sample immediately after inoculation. Alternatively, B is known as the average of the number of bacterial counts for the control sample after 24 h, whilst C is the average of the number of bacterial counts for the film sample after 24 h. Antimicrobial activity was assessed taking into account that values of R < 0.5 are considered non-significant, values of R ≥ 0.5 and < 1 are considered slight, values of R ≥ 1 and < 3 are significant, and values of R ≥ 3 mean strong reduction.
## 2.6. Antioxidant Activity
The DPPH method was run to determine the antioxidant effect of the CeO2NPs, CTAB, fibers, and films. Approximately 10 mg of the sample was used and then 3 mL of the DPPH stock solution (0.04 g/L in aqueous methanol) wase added. DPPH solution was used as a control. Methanol was used as blank. Samples were kept at room temperature for 24 h in the dark. Subsequently, the absorbance of the samples was evaluated in a UV 4000 spectrophotometer (Dinko Instruments, Barcelona, Spain) at a wavelength of 517 nm. Equation [5] was used to calculate the percentage of DPPH inhibition [44]. [ 5]DPPH Inhibition (%)=AControl−(Asample−Ablank)Acontrol*100 where *Acontrol is* the absorbance of the DPPH solution, *Ablank is* the absorbance of the methanol, and *Asample is* the absorbance of the test sample.
## 2.7. Statistical Analysis
Statistically significant results obtained for the different samples were assessed via the analysis of variance (ANOVA) with a $95\%$ significance level (p ≤ 0.05) and a multiple comparison test (Tukey) using the software OriginPro8 (OriginLab Corporation, Northampton, MA, USA).
## 3.1. Morphological Characterization
First, the morphological characterization of the CeO2NPs was performed by TEM. Figure 1a illustrates that the morphology of the nanoparticles is made of cubes of approximately 20 × 20 nm2. The vast majority of CeO2NPs had sizes ranging from 15–25 nm. The observed morphology was in agreement with observations by Salarizadeh et al. [ 45], who reported CeO2 nanoparticles with average sizes of ca. 25 nm.
Figure 2 and Figure 3 show the morphology of the electrospun PHBV fibers containing CeO2NPs and CeO2NPs + CTAB, respectively. The incorporation of CTAB enhanced the distribution of CeO2NPs inside the PHBV fibers when comparing Figure 2 and Figure 3. The PHBV fibers containing CeO2NPs without CTAB (Figure 2) presented beads and aggregates. It can be seen that the CeO2NPs resulted in larger fiber diameter in the 0.58–0.65 μm range and also led to the formation of spindle-type beads. This phenomenon is seen in Figure 2d–f, which corresponds to 2 wt$.\%$, 5 wt$.\%$, and 10 wt$.\%$ CeO2NPs within the PHBV matrix, respectively. This observation suggests that some agglomeration of CeO2NPs may occur in some fiber regions. On the other hand, the electrospun PHBV fibers containing CeO2NPs + CTAB gathered in Figure 3 showed a smooth, homogeneous, and bead-free morphology, demonstrating the advantage of surfactants in enhancing nanoparticle dispersion. Analysis on fiber diameter revealed a 0.50–0.60 μm size, being the thinnest fibers containing the highest concentration of CeO2NPs (Figure 3d–f). It has been previously reported that the diameter of nanofibers diminishes when adding surfactants and nanoparticles in a polymer solution due to an effect on solution parameters (i.e., surface tension or conductivity) that in turn affects the stretching forces of the jet, generating fibers with smaller diameters [46]. For instance, the conductivity of solutions containing 1.5 wt$.\%$ and 5.0 wt$.\%$ CeO2NPs increased significantly with the addition of CTAB from 7.07 to 61.98 µS/cm and from 8.57 to 62.84 µS/cm, correspondingly. All the electrospun fibers presented here decreased in diameter when CeO2NPs were added compared to neat PHBV fibers, which showed diameters of ~0.78 µm according to our previous studies [47,48]. This phenomenon could be ascribed to the change in charge density and conductivity as the concentration of CeO2NPs increased [49]. Cherpinski et al. [ 33] observed that the incorporation of surfactants, i.e., CTAB and TEOS, successfully enhanced the distribution of PdNPs in the PHB fibers, similar to the observation reported in this work.
Figure 4 and Figure 5 showed the TEM images of the electrospun fibers in order to assess the dispersion and distribution of the CeO2NPs inside the PHBV fibers, without and with CTAB, respectively. The CeO2NPs were successfully incorporated within the PHBV fibers by means of the electrospinning process. However, as the nanoparticle concentration increased, so did their agglomeration. Figure 4a–f show the PHBV fibers containing 0.5, 1, 1.5, 2, 5, and 10 wt$.\%$ of CeO2NPs, which present clear agglomeration features, whereas the fibers containing 0.5, 1, 1.5, 2, 5, and 10 wt$.\%$ CeO2NPs + CTAB (Figure 5a–f) present a better CeO2NPs dispersion due to the surface activity of CTAB. Despite the fact that CTAB significantly improved the nanoparticles dispersion, these were seen heterogeneously distributed within the PHBV matrix of the fibers. This morphology further confirms the observations made by SEM in the PHBV fibers, demonstrating the successful enhancement in dispersion and distribution of the CeO2NPs within the PHBV matrix as a result of adding the CTAB surfactant.
A thermal post-treatment at 160 °C was applied to the electrospun fiber mats to be turned into films. To analyze the internal morphology of these films, they were cryo-fractured with liquid N2 and observed by SEM. Figure 6 and Figure 7 show SEM images of the cross sections of the PHBV films loaded with CeO2NPs and with CTAB, correspondingly. The pure PHBV film presented a thickness of ~80 μm, as in our previous works [47,48]. The CeO2NPs-containing PHVB films (Figure 6) showed a uniform and non-porous structure with thicknesses in the 75–85 μm range; only a few pores were observed in the PHBV film containing 10 wt$.\%$ CeO2NPs in its cross section, probably due to the most likely detachment of filler agglomerates. The PHBV films loaded with CeO2NPs + CTAB showed similar morphologies as shown in Figure 7. These films showed more uniform, smooth, and homogeneous surfaces compared to the films without CTAB with thicknesses in the 70–81 μm range, which is in agreement with the fibers studied by SEM and TEM, in which the good dispersion of the nanoparticles due to CTAB could be inferred. The incorporation of CeO2NPS also slightly augmented the film thicknesses up to ~85 μm in all film samples, which may be related to the restrictions of the fibers for its reorganization due to the presence of the nanoparticles whilst annealing. Several research studies have reported increased film thickness and the appearance of cracks and pores since the nanoparticles tend to disrupt the homogeneous matrix within the polymer fibers [34,41,47].
## 3.2. Optical Characterization of the Electrospun Films
The electrospun PHBV films containing CeO2NPs are shown in Figure 8. A macroscopic inspection was used to evaluate their contact transparency, and Table 1 gathers the variations on the color coordinates (L*, a*, b*) and on the values of ΔE, T, and O due to the incorporation of CeO2NPs. The optical characterization of the pure PHBV film was also presented for the purpose of comparison. All PHBV films showed contact transparency, although films developed some yellow colorification when the CeO2NPs were added, reducing brightness (L*) and increasing opacity (O), which was corroborated by the rise in the b* coordinate. The consequences of the presence of the nanoparticles on color modification were related to nanoparticle concentration; for instance, concentrations of 5 and 10 wt$.\%$ CeO2NPs showed values of ΔE* ≥ 5, meaning that an observer can distinguish different colors [43], whereas films with lower concentrations of nanoparticles showed a slight color change. Thus, films containing 1.5 and 2 wt$.\%$ CeO2NPs exhibited a major color difference with ΔE* ≥ 3.5 and < 5, and the films containing 1 wt$.\%$ CeO2NPs a exhibited a color change with ΔE* ≥ 2 and < 3.5, where an inexperienced observer can notice the color difference. For films containing 0.5 wt$.\%$, the color difference was unnoticeable with ΔE* < 1, similar to the neat PHBV.
Neat PHBV showed transparency and opacity values similar to the values reported by Figueroa et al. for the same biopolymer [41]. However, other polymeric films made of electrospun PCL showed reduced transparency in comparison to neat PHBV [50]. The presence of the CeO2NPs caused light scattering, reducing the ability of transmission among visible and UV light of the films. Particularly, the films containing 5 and 10 wt$.\%$ CeO2NPs reduced the transparency properties and augmented the opacity of the films. This phenomenon was also observed for other metallic nanofillers such as ZnO [41]. However, this property may also be a preferred characteristic for some food packaging solutions to avoid oxidative reactions of lipids, carbohydrates, and proteins due to the action of ultraviolet light. These results agree with previous studies indicating that the incorporation of nanofillers increased the opacity, a*, and b* values, and decreased L* and the transparency results of biodegradable films [51,52].
## 3.3. X-ray-Diffraction (XRD) of the Electrospun Fibers
Diffractograms of CeO2NPs and electrospun PHBV fibers containing CeO2NPs and CTAB are displayed in Figure 9. The peaks of the neat CeO2NPs sample were ascribed to the diffraction planes (Miller indices) of [111], [200], [220], [311], [222], [400], [331], [420], and [422] at 2θ values of 28.70°, 33.14°, 47.59°, 56.48°, 59.11°, 69.63°, 76.79°, 79.21°, and 88.61°, respectively. These planes belong to the cubic phase of CeO2; the orderly peaks arrangement highlights a high crystallinity of the CeO2NPs. The same peaks for CeO2NPs were observed by Wang et al. [ 53]. Moreover, Youn et al. [ 54] reported the predominant peaks of CeO2NPs assigned to the [111], [200], [220], [311], [222], [400], and [311] planes, indicating that CeO2NPs were ascribed to the pure fluorite cubic structures of CeO2. The neat PHBV showed the characteristic peaks at 2θ values of 13.55°, 16.99°, 25.64°, and 26.86°. These peaks are ascribed to the diffraction planes of [020], [110], [121], and [040], respectively [41]. The crystalline lattice parameters are in concordance with the parameters previously described for PHBV$3\%$, PHBV$12\%$, and PHBV$18\%$ 3HV [55]. It is well-known that PHBV copolymers with fractions of 3HV lower than 40 mol% crystallize within the PHB crystalline lattice and exhibit the same PHB homopolymer diffractograms [56,57,58]. In this regard, the PHBV here used with 2 mol% 3HV content presented the same orthorhombic crystal structure as the homopolymers PHB [59]. The CeO2NPs-loaded PHBV samples showed peaks that confirmed the presence of the nanoparticles in the PHBV fibers with a lower relative intensity because of the dilution effect.
## 3.4. Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) of the Electrospun Fibers
ATR-FTIR absorption spectra for CeO2NPs (Figure 10) as well as electrospun PHBV fibers containing CeO2NPs and CTAB (Figure 11) were scanned between 4000 and 600 cm−1. The ATR-FTIR spectra of pure CeO2NPs suggested a pronounced peak at 3344 cm−1 ascribed to the O–H vibration of sorbed water on the CeO2 surface [60]. The band below 700 cm−1 is assigned to the Ce–O stretching mode vibration of nCeO2 [61]. Figure 11 shows the typical vibrational bands arising from the functional groups of the PHBV. The bands from 2987 to 2932 cm−1 are attributed to the asymmetric stretching mode of the methyl (–CH3) and antisymmetric stretching mode of methylene (–CH2), respectively [62]. The band at 1720 cm−1 belongs to the carbonyl stretching band (C=O). The stretching vibration of the C–O groups is ascribed to the peak from 1447 and 1000 cm−1. The strong vibration band at 1275 cm−1 is assigned to the C–C group and at 1053 cm−1 to the C–O group. The absorption bands at 977, 891, and 820 cm−1 belonged to the C–C groups [63]. Moreover, the absorbance peaks at 1093 and 1184 cm−1 were ascribed to the stretching vibration of ether (C–O–C) [64,65,66]. The spectra of the PHBV containing CeO2NPs also showed similar characteristic peaks as for pure PHBV, demonstrating that the incorporation of CeO2NPs + CTAB did not affect the PHBV spectra. As reported by other authors before, changes in the PHBV spectra occur primarily in the most prominent bands between 1720 and 1740 cm−1 region (C=O); band shifts in this region have been attributed to changes in crystallinity [67], as well as to nanoparticle–matrix interactions [68,69]. Nevertheless, for the PHBV prepared here, the nCeO2-specific bands were not observed because of the low concentration of CeO2NPs within the PHBV matrix, and band shifts in the above-cited regions were not observed, suggesting no measurable interactions between the filler and the biopolymer [70].
## 3.5.1. Differential Scanning Calorimetry (DSC) of the Electrospun Fibers
DSC was performed to discern the main thermal events of electrospun PHBV fibers containing CeO2NPs and CTAB. Results are shown in Table 2. In the first heating, the pure PHBV showed a single melting phenomenon at 169.46 °C. The melting point for all PHBV fibers containing CeO2NPs slightly increased up to 172.66 °C for the sample with a 5 wt$.\%$ CeO2NPs. Nevertheless, the Tm1 values for samples containing CeO2NPs + CTAB slightly decreased up to 162.85 °C for the sample with a 10 wt$.\%$ CeO2NPs. On the other hand, the enthalpy of melting (ΔHm1) of PHBV was reduced by CeO2NPs + CTAB with values in the 65.10–77.98 J/g range. All samples showed a unique crystallization peak during cooling. For instance, the crystallization temperature (Tc) of the pure PHBV was 117.35 °C. Samples containing CeO2NPs slightly augmented the crystallization temperature of PHBV up to 119.60 °C for the sample containing a 10 wt$.\%$ CeO2NPs, indicating a nucleating effect of the PHBV matrix, whereas the samples with CeO2NPs and CTAB slightly decreased the Tc up to a value of 116.05 °C for samples with a concentration of 10 wt$.\%$ CeO2NPs. This slight change in crystallization temperature can be attributed to the dispersing effect of the surfactant, suggesting that a better dispersion of the CeO2NPs can impair to some extent the packing of the PHBV chains during cooling [33]. During the second heating, the melting point (Tm2) for all samples shifted slightly towards a higher temperature with values in the 172.53–176.80 °C range. The enthalpy of melting (ΔHm2) in the second heating showed somewhat lower values compared to the first heating with values in the 63.32–73.49 J/g range. This thermal behavior is in agreement with Augustine et al. [ 70] who communicated that CeO2 loadings did not lead to a considerable change in the thermal properties of PHBV, such as its melting point or its crystallization temperature, but did cause a slight variation in the enthalpies of fusion and crystallization of the developed materials. Similar observations were also made before for other nanofillers, such as ZnO [71], Ag [72], CuO [73], and mesoporous silica [47].
## 3.5.2. Thermogravimetric Characterization of the Electrospun Fibers
Thermogravimetric analysis was run from 25 °C to 700 °C. Table 3 gathers the temperature for $5\%$ weight loss (T$5\%$), the degradation temperature (Tdeg), and the residual mass at 700 °C obtained by TGA. Data shown in this table shows that CeO2NPs were thermally stable up to 700 °C, displaying a discrete weight loss (∼$3.56\%$) below 500 °C attributed to the evaporation of sorbed water. This thermal stability is characteristic of metal oxide nanoparticles [74]. The pure PHBV showed a Tdeg of 278.7 °C and a residual mass of $1.13\%$. Similar results were reported for PHBV-based materials with values between 270 and 290 °C [47,48,64,75]. The presence of the CeO2NPs reduced the temperature for $5\%$ weight loss and the Tdeg compared to pure PHBV. This decrease in onset degradation temperatures may be attributed to the CTAB decomposition and to the high thermal conductivity properties of the CeO2NPs. It has been reported that the addition of metallic nanoparticles to polymeric films can lead to a decrease of the degradation temperature; however, the effect of the filler depends on the type, content, interfacial interaction, and the degree of dispersion and distribution of the particles in the polymer matrix [34]. Additionally, the obtained results prove that CeO2 nanoparticles are able to scavenge O2 at room temperature under the presence of humidity and hydrogen in the headspace gas mixture.
Gofman et al. also observed a decrease in the degradation temperature when preparing bacterial cellulose films containing CeO2 nanoparticles. Additionally, the degradation temperature decreased as the content of CeO2 nanoparticles increased [76]. Brito et al. also observed that the addition of metallic fillers decreased the thermal stability of the polymeric matrix [77].
Residual mass was between 1.03 and $17.22\%$ for all samples, which increased with the CeO2NPs’ concentration due to their non-degradable nature. Moreover, the samples with CTAB showed a general trend of somewhat higher residual mass compared to the samples without CTAB, possibly attributed to a potential catalytic effect that would be promoting the formation of higher char residues during the degradation stage [78]. Castro-Mayorga et al. [ 75] also observed a reduction of Tdeg for PHBV containing ZnO.
## 3.6. Oxygen Scavenging Capacity of Electrospun Fibers and Films
The oxygen scavenger profile of the electrospun PHBV fibers and selected films containing CeO2NPs + CTAB was analyzed by determining the oxygen scavenging rate (OSR) with an initial oxygen concentration of $4.0\%$ in the headspace of the Schleck flasks. The mechanism of action of oxygen scavengers is mainly associated with their ability to catalyze the oxidation of hydrogen, which can then remove residual oxygen in the packaging headspace [16]. Figure 12 and Figure 13 show the oxygen concentration depletion over time for a timeframe of 1400 min at 23 °C and $100\%$ RH. Figure 12 shows that the neat PHBV fibers were not able to scavenge oxygen, while PHBV fibers containing CeO2NPs in the 0.5–10 wt$.\%$ concentrations range showed a reduction of headspace oxygen from $6\%$ up to $60\%$. From Figure 13, it can be seen that free CeO2NPs in a powder form in similar quantities as used in the samples of 1.5 wt$.\%$ and 5 wt$.\%$ were able to reduce up to $17.8\%$ and $31.6\%$, respectively, of the headspace oxygen. The PHBV fibers containing 1.5 wt$.\%$ and 5 wt$.\%$ CeO2NPs presented a depletion of $20.7\%$ and $44.8\%$, respectively. The CTAB-containing PHBV/CeO2NPs fibers improved the oxygen scavenging activity, which further confirmed the surfactant-induced enhanced dispersion and distribution of the nanoparticles, as observed above by TEM, achieving a depletion of $27.4\%$ (1.5 wt$.\%$ CeO2NPs) and $52.3\%$ (5 wt$.\%$ CeO2NPs). However, when the selected fibers turned into films after the annealing process, the oxygen depletion decreased to $16.1\%$ and $34.1\%$ for PHBV films loaded with 1.5 wt$.\%$ and 5 wt$.\%$ CeO2NPs, correspondingly. This decrease in the OSR is associated with the lower surface-to-volume ratio of the films compared to the fibers because the annealing treatment reduced the interfiber porosity by a coalescence process. Regardless of this, the PHBV film containing 5 wt$.\%$ CeO2NPs presented significant oxygen scavenging capacity. Additionally, the obtained results prove that CeO2 nanoparticles are able to scavenge O2 at room temperature under the presence of humidity and some hydrogen in the headspace gas composition. These observations are in concordance with Cherpinski et al. [ 33,34] who studied the oxygen scavenging capacity of poly(3-hydroxybutyrate) (PHB) and polycaprolactone (PCL) biopolymers containing palladium nanoparticles (PdNPs) and surfactants prepared by electrospinning, also concluding that the fibers provided better oxygen scavenging performance than the annealed films.
## 3.7. Mechanical Properties of the Electrospun Films
Table 4 shows the mechanical characterization of the selected electrospun PHBV films loaded with CeO2NPs and CTAB. The pure PHBV film showed an E of 2394 MPa, a σy of 14.1 MPa, an εb of $1.01\%$, and a T of 0.09 mJ/m3. Similar mechanical values were observed by Melendez-Rodriguez et al. [ 47] for electrospun films of pure PHBV, where E was 1252 MPa, σy was 18.1 MPa, and εb was $2.4\%$. The addition of CeO2NPs into the PHBV matrix increased the mechanical values; at 1.5 wt$.\%$ CeO2NPs, the E value was 3309 MPa, σy was 26.9 MPa, εb was $1.22\%$, and T was 0.18 mJ/m3. At 5 wt$.\%$ CeO2NPs, the E value was 3546 MPa, σy was 27.52 MPa, εb was $1.19\%$, and T was 0.18 mJ/m3. This increase in mechanical properties can be ascribed to the nanofillers’ reinforcement effect, which may also be related to aspects, such as nanoparticle dispersion, nanofiller concentration, changes in polymer crystallinity, and the interfacial adhesion between the nanoparticles and the biopolymer matrix [79]. Similar reinforcing effects on the PHBV matrix were observed by Figueroa-Lopez et al. [ 41] when ZnONPs were incorporated. Ashori et al. [ 80] also observed that the incorporation of nanofillers, such as cellulose nanocrystals and aluminum oxide nanoparticles within the PHBV matrix, significantly increased the mechanical properties of PHBV composites.
## 3.8. Barrier Properties of the Electrospun Films
Table 5 gathers the permeability to water (WVP) and limonene (LP) vapors of the neat PHBV and the selected PHBV films loaded with CeO2NPs and CTAB. The water and limonene permeability values for neat PHBV were 5.34 × 10−14 and 26.8 × 10−15 kg·m·m−2·s−1·Pa−1, respectively [47]. The incorporation of CeO2NPs into PHBV diminished the permeability of both vapors. The PHBV films containing 1.5 wt$.\%$ CeO2NPs presented WPV and LP results of 1.58 × 10−14 and 6.71 × 10−15 kg·m·m−2·s−1·Pa−1, respectively. Regarding samples containing 5.0 wt$.\%$ CeO2NPs, the WVP and LP results were 2.68 × 10−14 and 8.23 × 10−15 kg·m·m−2·s−1·Pa−1, respectively. The reduction in WVP can be due to the presence of CeO2NPs into PHBV, which potentially sorbed water over the surface of the nanoparticles and further blocked water transport through the polymer matrix, due to a more tortuous path generated by the homogenized distribution of the nanoparticles [42,73]. The decrease in limonene permeability may be associated with a reduction in the sorption of limonene molecules by the PHBV film, where solubility plays an important role in permeability due to the strong plasticizing effect of organic vapors onto the PHBV film [48]. Similar water barrier enhancements were observed by Díez-Pascual et al. [ 81] who tested the water vapor permeability and the water uptake of PHBV nanocomposites containing ZnO, concluding that both parameters dropped gradually with increasing ZnO concentration in comparison to the neat biopolymer. Castro-Mayorga et al. [ 72] observed that the presence of a small amount of AgNPs to PHBV3/PHBV18 films could reduce water vapor permeability, reaching values close to the neat PHBV3 film. Melendez-Rodriguez et al. [ 47] reported that WVP and LP were also enhanced for concentrations over 7.5 wt$.\%$ of MCM-41 + eugenol into PHBV films.
The oxygen barrier enhancement mechanism created in nanocomposites is mainly attributed to the increased pathway (tortuosity) of the non-interacting permeant molecules to pass through the films. This mechanism is associated with factors, such as the effect of the microstructure in terms of dispersion, distribution, and aspect ratio of the impermeable nanofiller, its hygroscopic nature of it, the crystallization behavior of the polymer matrix, and the interfacial interaction across the nanofiller–polymer matrix [82]. The oxygen permeability (OP) of the pure PHBV and selected PHBV films containing CeO2NPs are found in Table 5. The neat PHBV film presented an OP value of 3.65 × 10−19 m3·m·m−2·Pa−1·s−1. After incorporation of 1.5 wt$.\%$ and 5.0 wt$.\%$ of CeO2NPs, the OP values increased to 6.92 × 10−19 m3·m·m−2·Pa−1·s−1 and 8.35 × 10−19 m3·m·m−2·Pa−1·s−1, respectively. The lower OP value in the film containing 1.5 wt$.\%$ CeO2NPs could be attributed to the better dispersion observed at low nanoparticle loading. In any case, the DSC results show that the enthalpy of fusion related to crystallinity decreased for the concentrations of nanofiller selected. As it is well-known, diffusion of non-interacting gas molecules occurs through the free volume in the amorphous phase of a semi-crystalline polymer matrix [83]. It is also known that the higher the nanofiller dispersion, distribution, and aspect ratio, the better the tortuosity factor is [84]. Thus, the interpretation could be that since the crystallinity of the nanocomposites is slightly lower, the oxygen molecule is very small and non-interactive, and some agglomeration was seen and expected at higher filler contents, the presence of the water-soluble cationic surfactant CTAB and the fact that the nanofiller has a square morphology, and no platelets could explain the somewhat higher oxygen permeability for the nanocomposites. Xu et al. [ 82] evaluated the oxygen barrier of platelets based on PHA and 5 wt$.\%$ GO-g-LAQ at 23 °C and $63.5\%$ RH, obtaining an OP enhancement. However, in the case of PHA containing 5.0 wt$.\%$ GO-KH570 platelets, the OP was increased. Öner et al. [ 85] measured the OP at 23 °C and $0\%$ HR of composites based on PHBV and boron nitride (BN) processed by melt blending. The oxygen permeability was reduced by $26.4\%$ with 1 wt$.\%$ BN and $36.4\%$ with 2 wt$.\%$ of BN, but it did not improve with a further increase in BN content, e.g., 3 wt$.\%$ BN. Similar observations were reported by Castro-Mayorga et al. [ 73] who concluded that the addition of 0.05 wt$.\%$ CuONPs to PHBV reduced the OP by $34.2\%$ measured at 23 °C and $80\%$ RH. However, an increase in the CuONPs concentration did not enhance the oxygen barrier properties of the PHBV. Considering the abovementioned studies, it becomes clear that the OP is extremely dependent on nanofiller concentration. Thus, a higher permeability was seen with an increase in the concentration, an effect often associated with nanofiller agglomeration and the formation of preferential paths for gas diffusion. The films developed here presented an intermediate oxygen barrier according to the ASTM D3985-05 standard [3]. In any case, the main aim for the use of this additive in this study was to exploit its active oxygen scavenging properties, for which higher concentrations demand to be used. Thus, from the overall results, it appears that a balance between active and passive oxygen barrier properties has to be accepted when using the material to design the most adequate food packaging.
## 3.9. Antimicrobial Activity of the Electrospun Films
Table 6 highlights the results of the antimicrobial activity of selected PHBV films containing CeO2NPs against S. aureus and E. coli strains. It can be observed that the PHBV films containing 1.5 wt$.\%$ CeO2NPs presented a significant reduction (R ≥ 1 and <3) of S. aureus and a slight reduction (R ≥ 0.5 and <1) of E. coli, whereas in PHBV films with 5.0 wt$.\%$ CeO2NPs, the reduction was significant (R ≥ 1 and <3) for both bacteria. The antimicrobial proficiency of CeO2NPs generally depends on their chemical and physical properties, i.e., specific surface area, size, morphology, and polar surface [86]. CeO2NPs can cause irreversible damage to bacteria membranes by different mechanisms of action, such as membrane dysfunction, nanoparticles penetration, interruption, blockage of transmembrane electron transport, ion release, and reactive oxygen species (ROS), such as the superoxide anion radical (O2−˙) and the hydroxyl radical (OH˙) [86,87,88]. The S. aureus showed slightly higher R values than E. coli because the cell wall morphology of E. coli is mainly formed of lipopolysaccharides and peptidoglycans that obstruct the diffusion of negatively charged reactive oxygen species created by the CeO2NPs [41]. Kızılkonca et al. [ 89] developed antibacterial films with CeO2NPs, chitosan, hydroxyethyl cellulose, and polyethylene glycol that reduced the growth of E. coli and S. aureus around 1.2 and 1.4 CFU/mL, respectively, after 12 h exposure. The films developed here containing CeO2NPs can also be used as packaging materials to avoid the growth of microorganisms.
## 3.10. Antioxidant Assay of the Electrospun Fibers and Films
The antioxidant activity of the CeO2NPs, CTAB, fibers, and selected films was determined by the DPPH free radical method. Figure 14 shows the percent inhibition of the free radical DPPH of the CTAB, CeO2NPs, neat PHBV fibers, and PHBV loaded with 1.5 wt$.\%$ and 5.0 wt$.\%$ CeO2NPs fibers and their corresponding annealed films. The CTAB did not show DPPH inhibition (~$3.52\%$), while the CeO2NPs presented a DPPH inhibition of ~$38\%$ and the neat PHBV fibers of ~$37.03\%$. The antioxidant activity increased in the electrospun PHBV fibers loaded with 1.5 wt$.\%$ and 5.0 wt$.\%$ CeO2NPs. In both cases, an increase in the antioxidant activity between $20\%$ and $25\%$ was observed in comparison to the neat PHBV fibers. When the films were formed, the antioxidant activity decreased, obtaining a DPPH inhibition of ~$36.67\%$ and $37.79\%$ for films loaded with 1.5 wt$.\%$ and 5.0 wt$.\%$ CeO2NPs, respectively. Salevic et al. measured the antioxidant activity of PCL electrospun films containing sage extract, an essential oil with antioxidant activity. They also observed that the pure polymer did not possess antioxidant activity, which increased by increasing the content of essential oil until $80\%$ [50].
Naidi et al. [ 86] measured the antioxidant activity of CeO2NPs by DPPH. A higher antioxidant activity was obtained with the increase of CeO2NPs, and the maximum scavenging activity of $55\%$ was detected for 10 mg. Mohamed et al. [ 90] determined the total antioxidant capacity of CeO2NPs. The total antioxidant capacity was dependent on the nanoparticles concentration. The highest DPPH free-radical scavenging activity of ca. $36.07\%$ was achieved at 400 μg mL−1. The selected films presented what could be considered a relevant antioxidant performance, which added to their oxygen scavenging and antibacterial properties, making them of interest for packaging applications, especially for extending shelf-life of products [91].
## 4. Conclusions
PHBV fibers loaded with different quantities of CeO2NPs were developed by electrospinning. The electrospinning technique allowed us to ensure the homogeneous distribution of the nanoparticles within the fibers. Additionally, this technique can generate ultrathin interlayers or coatings with bioadhesive properties, increasing the biobased content in the formulation and/or reducing the amount of raw materials required and potentially the cost. The electrospun fibers were transformed to biopapers of ~85 μm by means of an annealing treatment. The obtained PHBV films showed a uniform and continuous surface due to a thermally induced interfiber coalescence below the melting point and the degradation temperature of the biopolymer. The films showed contact transparency and a slight yellow color when loaded with the CeO2NPs. The thermal stability profile of the generated films was somewhat reduced by CeO2NPs, but all the PHBV films remained stable beyond 200 °C. The best morphological, barrier, mechanical, antimicrobial, antioxidant, and oxygen scavenging performance was attained for PHBV films containing 1.5 wt$.\%$ and 5 wt$.\%$ CeO2NPs + CTAB, which decreased the water vapor permeability but increased the limonene and oxygen permeability slightly. These films showed significant inhibition up to 15 days of evaluation against foodborne bacteria and a DPPH inhibition of over ~$30\%$. The films became better oxygen by adding CTAB, achieving a significant headspace oxygen volume depletion even in film form. This is a preliminary work exploring the capacities of novel biopolymeric nanocomposites. Despite the raw materials being expensive at the lab scale, the active properties of the obtained materials could compensate for the potential higher cost. Therefore, the obtained electrospun biopapers could be used as a coating or an interlayer system for organic recyclable active packaging applications, which could extend shelf life and maintain the quality and safety of oxygen sensitive food items, such as such as chilled meat, hard cheese, dry mixes, coffee, snacks products, and fresh products, such as pasta and other food products that are packaged in for instance vacuum packaging and bag-in-box applications. In future works, the biodegradability of the full multilayer packaging concepts and the nanoparticles migration will be studied.
## References
1. Zhang C., Li Y., Wang P., Zhang H.. **Electrospinning of nanofibers: Potentials and perspectives for active food packaging**. *Compr. Rev. Food Sci. Food Saf.* (2020) **19** 479-502. DOI: 10.1111/1541-4337.12536
2. Byun Y., Darby D., Cooksey K., Dawson P., Whiteside S.. **Development of oxygen scavenging system containing a natural free radical scavenger and a transition metal**. *Food Chem.* (2011) **124** 615-619. DOI: 10.1016/j.foodchem.2010.06.084
3. Gaikwad K.K., Singh S., Lee Y.S.. **Oxygen scavenging films in food packaging**. *Environ. Chem. Lett.* (2018) **16** 523-538. DOI: 10.1007/s10311-018-0705-z
4. Wang C., Gong C., Qin Y., Hu Y., Jiao A., Jin Z., Qiu C., Wang J.. **Bioactive and functional biodegradable packaging films reinforced with nanoparticles**. *J. Food Eng.* (2022) **312** 110752. DOI: 10.1016/j.jfoodeng.2021.110752
5. Wang J., Euring M., Ostendorf K., Zhang K.. **Biobased materials for food packaging**. *J. Bioresour. Bioprod.* (2022) **7** 1-13. DOI: 10.1016/j.jobab.2021.11.004
6. Dutt Tripathi A., Paul V., Agarwal A., Sharma R., Hashempour-Baltork F., Rashidi L., Khosravi Darani K.. **Production of polyhydroxyalkanoates using dairy processing waste—A review**. *Bioresour. Technol.* (2021) **326** 124735. DOI: 10.1016/j.biortech.2021.124735
7. Torres-Giner S., Montanes N., Fombuena V., Boronat T., Sanchez-Nacher L.. **Preparation and characterization of compression-molded green composite sheets made of poly(3-hydroxybutyrate) reinforced with long pita fibers**. *Adv. Polym. Technol.* (2018) **37** 1305-1315. DOI: 10.1002/adv.21789
8. Mutlu G., Calamak S., Ulubayram K., Guven E.. **Curcumin-loaded electrospun PHBV nanofibers as potential wound-dressing material**. *J. Drug Deliv. Sci. Technol.* (2018) **43** 185-193. DOI: 10.1016/j.jddst.2017.09.017
9. Park S.J., Kim T.W., Kim M.K., Lee S.Y., Lim S.-C.. **Advanced bacterial polyhydroxyalkanoates: Towards a versatile and sustainable platform for unnatural tailor-made polyesters**. *Biotechnol. Adv.* (2012) **30** 1196-1206. DOI: 10.1016/j.biotechadv.2011.11.007
10. Raza Z.A., Abid S., Banat I.M.. **Polyhydroxyalkanoates: Characteristics, production, recent developments and applications**. *Int. Biodeterior. Biodegrad.* (2018) **126** 45-56. DOI: 10.1016/j.ibiod.2017.10.001
11. Reddy C.S.K., Ghai R., Rashmi V.C.. **Polyhydroxyalkanoates: An overview**. *Bioresour. Technol.* (2003) **87** 137-146. DOI: 10.1016/S0960-8524(02)00212-2
12. McChalicher C.W.J., Srienc F.. **Investigating the structure–property relationship of bacterial PHA block copolymers**. *J. Biotechnol.* (2007) **132** 296-302. DOI: 10.1016/j.jbiotec.2007.04.014
13. Zhuikov V.A., Zhuikova Y.V., Makhina T.K., Myshkina V.L., Rusakov A., Useinov A., Voinova V.V., Bonartseva G.A., Berlin A.A., Bonartsev A.P.. **Comparative Structure-Property Characterization of Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate)s Films under Hydrolytic and Enzymatic Degradation: Finding a Transition Point in 3-Hydroxyvalerate Content**. *Polymers* (2020) **12**. DOI: 10.3390/polym12030728
14. Ozdemir M., Floros J.D.. **Active Food Packaging Technologies**. *Crit. Rev. Food Sci. Nutr.* (2004) **44** 185-193. DOI: 10.1080/10408690490441578
15. Torres-Giner S., Prieto C., Lagaron J.M.. **Nanomaterials to Enhance Food Quality, Safety, and Health Impact**. *Nanomaterials* (2020) **10**. DOI: 10.3390/nano10050941
16. Yildirim S., Röcker B., Rüegg N., Lohwasser W.. **Development of Palladium-based Oxygen Scavenger: Optimization of Substrate and Palladium Layer Thickness**. *Packag. Technol. Sci.* (2015) **28** 710-718. DOI: 10.1002/pts.2134
17. Sängerlaub S., Witzgall S., Müller K., Wiegert T., Pecyna M.J.. **Palladium-based oxygen scavenger for food packaging: Choosing optimal hydrogen partial pressure**. *Food Packag. Shelf Life* (2021) **28** 100666. DOI: 10.1016/j.fpsl.2021.100666
18. Dey A., Neogi S.. **Oxygen scavengers for food packaging applications: A review**. *Trends Food Sci. Technol.* (2019) **90** 26-34. DOI: 10.1016/j.tifs.2019.05.013
19. Calderon V. S., Gomes B., Ferreira P.J., Carvalho S.. **Zinc nanostructures for oxygen scavenging**. *Nanoscale* (2017) **9** 5254-5262. DOI: 10.1039/C7NR01367A
20. Kalaycıoğlu Z., Kahya N., Adımcılar V., Kaygusuz H., Torlak E., Akın-Evingür G., Erim F.B.. **Antibacterial nano cerium oxide/chitosan/cellulose acetate composite films as potential wound dressing**. *Eur. Polym. J.* (2020) **133** 109777. DOI: 10.1016/j.eurpolymj.2020.109777
21. Xu C., Qu X.. **Cerium oxide nanoparticle: A remarkably versatile rare earth nanomaterial for biological applications**. *NPG Asia Mater.* (2014) **6** e90. DOI: 10.1038/am.2013.88
22. Prasanna S.R.V.S., Balaji K., Pandey S., Rana S., Karak N.. **Chapter 4—Metal Oxide Based Nanomaterials and Their Polymer Nanocomposites**. *Nanomaterials and Polymer Nanocomposites* (2019) 123-144. DOI: 10.1016/B978-0-12-814615-6.00004-7
23. Singh K.R.B., Nayak V., Sarkar T., Singh R.P.. **Cerium oxide nanoparticles: Properties, biosynthesis and biomedical application**. *RSC Adv.* (2020) **10** 27194-27214. DOI: 10.1039/D0RA04736H
24. Sarif M., Jegel O., Gazanis A., Hartmann J., Plana-Ruiz S., Hilgert J., Frerichs H., Viel M., Panthöfer M., Kolb U.. **High-throughput synthesis of CeO**. *Sci. Rep.* (2022) **12** 3935. DOI: 10.1038/s41598-022-07833-w
25. Nallappan M., Gopalan M.. **Fabrication of CeO**. *Mater. Res. Bull.* (2018) **106** 357-364. DOI: 10.1016/j.materresbull.2018.05.025
26. Mogensen M., Sammes N.M., Tompsett G.A.. **Physical, chemical and electrochemical properties of pure and doped ceria**. *Solid State Ion.* (2000) **129** 63-94. DOI: 10.1016/S0167-2738(99)00318-5
27. Rossinyol E., Arbiol J., Peiró F., Cornet A., Morante J.R., Tian B., Bo T., Zhao D.. **Nanostructured metal oxides synthesized by hard template method for gas sensing applications**. *Sens. Actuators B Chem.* (2005) **109** 57-63. DOI: 10.1016/j.snb.2005.03.016
28. Feng X., Sayle D., Wang Z., Paras M., Santora B., Sutorik A., Sayle T., Yang Y., Ding Y., Wang X.. **Converting Ceria Polyhedral Nanoparticles into Single-Crystal Nanospheres**. *Science* (2006) **312** 1504-1508. DOI: 10.1126/science.1125767
29. Reid D.L., Russo A.E., Carro R.V., Stephens M.A., LePage A.R., Spalding T.C., Petersen E.L., Seal S.. **Nanoscale Additives Tailor Energetic Materials**. *Nano Lett.* (2007) **7** 2157-2161. DOI: 10.1021/nl0625372
30. Atisme T.B., Yu C.-Y., Tseng E.N., Chen Y.-C., Shu P.-K., Chen S.-Y.. **Interface Interactions in Conjugated Polymer Composite with Metal Oxide Nanoparticles**. *Nanomaterials* (2019) **9**. DOI: 10.3390/nano9111534
31. Saleh S.M., Alminderej F.M., Mohamed A.M.A.. **Superhydrophobic and Corrosion Behaviour of PVDF-CeO**. *Materials* (2022) **15**. DOI: 10.3390/ma15238674
32. Cerqueira M.A., Fabra M.J., Castro-Mayorga J.L., Bourbon A.I., Pastrana L.M., Vicente A.A., Lagaron J.M.. **Use of Electrospinning to Develop Antimicrobial Biodegradable Multilayer Systems: Encapsulation of Cinnamaldehyde and Their Physicochemical Characterization**. *Food Bioprocess Technol.* (2016) **9** 1874-1884. DOI: 10.1007/s11947-016-1772-4
33. Cherpinski A., Gozutok M., Sasmazel H.T., Torres-Giner S., Lagaron J.M.. **Electrospun Oxygen Scavenging Films of Poly(3-hydroxybutyrate) Containing Palladium Nanoparticles for Active Packaging Applications**. *Nanomaterials* (2018) **8**. DOI: 10.3390/nano8070469
34. Cherpinski A., Szewczyk P.K., Gruszczyński A., Stachewicz U., Lagaron J.M.. **Oxygen-Scavenging Multilayered Biopapers Containing Palladium Nanoparticles Obtained by the Electrospinning Coating Technique**. *Nanomaterials* (2019) **9**. DOI: 10.3390/nano9020262
35. Javadi F., Taghavizadeh Yazdi M.E., Baghani M., Es-haghi A.. **Biosynthesis, characterization of cerium oxide nanoparticles using Ceratonia siliqua and evaluation of antioxidant and cytotoxicity activities**. *Mater. Res. Express* (2019) **6** 065408. DOI: 10.1088/2053-1591/ab08ff
36. 36.
Integradated Laboratory Systems Inc.
Chemical Information Profile for Ceric Oxide. Supporting Nomination for Toxicological Evaluation by the National Toxicology ProgramNational Institute of Environmental Health SciencesResearch Triangle Park, NC, USA2006. *Chemical Information Profile for Ceric Oxide. Supporting Nomination for Toxicological Evaluation by the National Toxicology Program* (2006)
37. Ismail R., Baaity Z., Csóka I.. **Regulatory status quo and prospects for biosurfactants in pharmaceutical applications**. *Drug Discov. Today* (2021) **26** 1929-1935. DOI: 10.1016/j.drudis.2021.03.029
38. Abou-Zeid R.E., Diab M.A., Mohamed S.A.A., Salama A., Aljohani H.A., Shoueir K.R.. **Surfactant-Assisted Poly(lactic acid)/Cellulose Nanocrystal Bionanocomposite for Potential Application in Paper Coating**. *J. Renew. Mater.* (2018) **6** 394. DOI: 10.7569/JRM.2017.634156
39. Rittirong K., Uasopon S., Prachayawasin P., Euaphantasate N., Aiempanakit K., Ummartyotin S.. **CTAB as a soft template for modified clay as filler in active packaging**. *Data Brief* (2015) **3** 47-50. DOI: 10.1016/j.dib.2015.02.002
40. Bezerra Lima E.M., Middea A., Marconcini J.M., Corrêa A.C., Fernandes Pereira J., Vieira Guimarães A., Firmino de Lima J., Ramos dos Anjos M., Miranda de Castro I., Nunes Oliveira R.. **Biodegradable PLA based nanocomposites for packaging applications: The effects of organo-modified bentonite concentration**. *J. Appl. Polym. Sci.* (2021) **138** 50907. DOI: 10.1002/app.50907
41. Figueroa-Lopez K.J., Torres-Giner S., Enescu D., Cabedo L., Cerqueira M.A., Pastrana L.M., Lagaron J.M.. **Electrospun Active Biopapers of Food Waste Derived Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) with Short-Term and Long-Term Antimicrobial Performance**. *Nanomaterials* (2020) **10**. DOI: 10.3390/nano10030506
42. Figueroa-Lopez K.J., Torres-Giner S., Angulo I., Pardo-Figuerez M., Escuin J.M., Bourbon A.I., Cabedo L., Nevo Y., Cerqueira M.A., Lagaron J.M.. **Development of Active Barrier Multilayer Films Based on Electrospun Antimicrobial Hot-Tack Food Waste Derived Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) and Cellulose Nanocrystal Interlayers**. *Nanomaterials* (2020) **10**. DOI: 10.3390/nano10122356
43. Agüero A., Morcillo M.d.C., Quiles-Carrillo L., Balart R., Boronat T., Lascano D., Torres-Giner S., Fenollar O.. **Study of the Influence of the Reprocessing Cycles on the Final Properties of Polylactide Pieces Obtained by Injection Molding**. *Polymers* (2019) **11**. DOI: 10.3390/polym11121908
44. Figueroa-Lopez K.J., Vicente A.A., Reis M.A.M., Torres-Giner S., Lagaron J.M.. **Antimicrobial and Antioxidant Performance of Various Essential Oils and Natural Extracts and Their Incorporation into Biowaste Derived Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Layers Made from Electrospun Ultrathin Fibers**. *Nanomaterials* (2019) **9**. DOI: 10.3390/nano9020144
45. Salarizadeh P., Askari M.B., Beydaghi H., Rastgoo-Deylami M., Rozati S.M.. **Hybrid of cerium dioxide nanoparticles/reduced graphene oxide as an electrode material for supercapacitor applications**. *J. Phys. Chem. Solids* (2021) **159** 110284. DOI: 10.1016/j.jpcs.2021.110284
46. Sainudeen S.S., Asok L.B., Varghese A., Nair A.S., Krishnan G.. **Surfactant-driven direct synthesis of a hierarchical hollow MgO nanofiber–nanoparticle composite by electrospinning**. *RSC Adv.* (2017) **7** 35160-35168. DOI: 10.1039/C7RA05812H
47. Melendez-Rodriguez B., Figueroa-Lopez K.J., Bernardos A., Martínez-Máñez R., Cabedo L., Torres-Giner S., Lagaron J.M.. **Electrospun Antimicrobial Films of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Containing Eugenol Essential Oil Encapsulated in Mesoporous Silica Nanoparticles**. *Nanomaterials* (2019) **9**. DOI: 10.3390/nano9020227
48. Figueroa-Lopez K.J., Cabedo L., Lagaron J.M., Torres-Giner S.. **Development of Electrospun Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Monolayers Containing Eugenol and Their Application in Multilayer Antimicrobial Food Packaging**. *Front. Nutr.* (2020) **7** 140. DOI: 10.3389/fnut.2020.00140
49. Castro-Mayorga J.L., Fabra M.J., Cabedo L., Lagaron J.M.. **On the Use of the Electrospinning Coating Technique to Produce Antimicrobial Polyhydroxyalkanoate Materials Containing In Situ-Stabilized Silver Nanoparticles**. *Nanomaterials* (2017) **7**. DOI: 10.3390/nano7010004
50. Salević A., Prieto C., Cabedo L., Nedović V., Lagaron J.M.. **Physicochemical, Antioxidant and Antimicrobial Properties of Electrospun Poly(ε-caprolactone) Films Containing a Solid Dispersion of Sage (**. *Nanomaterials* (2019) **9**. DOI: 10.3390/nano9020270
51. Sooch B.S., Mann M.K.. **Nanoreinforced biodegradable gelatin based active food packaging film for the enhancement of shelf life of tomatoes (**. *Food Control* (2021) **130** 108322. DOI: 10.1016/j.foodcont.2021.108322
52. Moustafa H., Karmalawi A.M., Youssef A.M.. **Development of dapsone-capped TiO**. *Environ. Nanotechnol. Monit. Manag.* (2021) **16** 100482. DOI: 10.1016/j.enmm.2021.100482
53. Wang M., Shen M., Jin X., Tian J., Shao Y., Zhang L., Li Y., Shi J.. **Exploring the enhancement effects of hetero-metal doping in CeO**. *Chem. Eng. J.* (2022) **427** 130987. DOI: 10.1016/j.cej.2021.130987
54. Youn D.H., Tran N.M., Kim B.J., Kim Y., Jeon J.P., Yoo H.. **Shape effect of cerium oxide nanoparticles on mild traumatic brain injury**. *Sci. Rep.* (2021) **11** 15571. DOI: 10.1038/s41598-021-95057-9
55. Bossu J., Le Moigne N., Dieudonné-George P., Dumazert L., Guillard V., Angellier-Coussy H.. **Impact of the processing temperature on the crystallization behavior and mechanical properties of poly[R-3-hydroxybutyrate-co-(R-3-hydroxyvalerate)]**. *Polymer* (2021) **229** 123987. DOI: 10.1016/j.polymer.2021.123987
56. Škrbić Z., Divjaković V.. **Temperature influence on changes of parameters of the unit cell of biopolymer PHB**. *Polymer* (1996) **37** 505-507. DOI: 10.1016/0032-3861(96)82922-3
57. Ten E., Jiang L., Wolcott M.P.. **Crystallization kinetics of poly(3-hydroxybutyrate-co-3-hydroxyvalerate)/cellulose nanowhiskers composites**. *Carbohydr. Polym.* (2012) **90** 541-550. DOI: 10.1016/j.carbpol.2012.05.076
58. Wu C.-N., Woo E.M., Nagarajan S.. **Periodic crystal assembly of Poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid): From surface to interior microstructure**. *Polymer* (2021) **228** 123866. DOI: 10.1016/j.polymer.2021.123866
59. Zhou Y., Katsou E., Fan M.. **Interfacial structure and property of eco-friendly carboxymethyl cellulose/poly(3-hydroxybutyrate-co-3-hydroxyvalerate) biocomposites**. *Int. J. Biol. Macromol.* (2021) **179** 550-556. DOI: 10.1016/j.ijbiomac.2021.03.009
60. Zheng X., Huang S., Yang D., Zhai H., You Y., Fu X., Yuan J., Zhou X., Wen J., Liu Y.. **Synthesis of X-architecture CeO2 for the photodegradation of methylene blue under UV-light irradiation**. *J. Alloys Compd.* (2017) **705** 131-137. DOI: 10.1016/j.jallcom.2017.02.110
61. Subashini A., Varun Prasath P., Sagadevan S., Anita Lett J., Fatimah I., Mohammad F., Al-Lohedan H.A., Alshahateet S.F., Chun Oh W.. **Enhanced photocatalytic degradation efficiency of graphitic carbon nitride-loaded CeO**. *Chem. Phys. Lett.* (2021) **769** 138441. DOI: 10.1016/j.cplett.2021.138441
62. Sinaei N., Zare D., Azin M.. **Production and characterization of poly 3-hydroxybutyrate-co-3-hydroxyvalerate in wheat starch wastewater and its potential for nanoparticle synthesis**. *Braz. J. Microbiol.* (2021) **52** 561-573. DOI: 10.1007/s42770-021-00430-5
63. Tanikkul P., Sullivan G.L., Sarp S., Pisutpaisal N.. **Biosynthesis of medium chain length polyhydroxyalkanoates (mcl-PHAs) from palm oil**. *Case Stud. Chem. Environ. Eng.* (2020) **2** 100045. DOI: 10.1016/j.cscee.2020.100045
64. Melendez-Rodriguez B., Reis M.A.M., Carvalheira M., Sammon C., Cabedo L., Torres-Giner S., Lagaron J.M.. **Development and Characterization of Electrospun Biopapers of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Derived from Cheese Whey with Varying 3-Hydroxyvalerate Contents**. *Biomacromolecules* (2021) **22** 2935-2953. DOI: 10.1021/acs.biomac.1c00353
65. Shi L., Hu W., He Y., Ke Y., Wu G., Xiao M., Huang L., Tan S.. **Preparation and Characterization of Poly(ethylene glycol)-block-Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)-block-Poly(ethylene glycol) Triblock Copolymers**. *Macromol. Res.* (2020) **28** 310-318. DOI: 10.1007/s13233-020-8005-4
66. Chotchindakun K., Pathom-Aree W., Dumri K., Ruangsuriya J., Pumas C., Pekkoh J.. **Low Crystallinity of Poly(3-Hydroxybutyrate-co-3-Hydroxyvalerate) Bioproduction by Hot Spring Cyanobacterium**. *Plants* (2021) **10**. DOI: 10.3390/plants10030503
67. Bayarı S., Severcan F.. **FTIR study of biodegradable biopolymers: P(3HB), P(3HB-co-4HB) and P(3HB-co-3HV)**. *J. Mol. Struct.* (2005) **744–747** 529-534. DOI: 10.1016/j.molstruc.2004.12.029
68. Ojha N., Das N.. **Fabrication and characterization of biodegradable PHBV/SiO**. *IET Nanobiotechnol.* (2020) **14** 785-795. DOI: 10.1049/iet-nbt.2020.0066
69. Chotchindakun K., Pekkoh J., Ruangsuriya J., Zheng K., Unalan I., Boccaccini A.R.. **Fabrication and Characterization of Cinnamaldehyde-Loaded Mesoporous Bioactive Glass Nanoparticles/PHBV-Based Microspheres for Preventing Bacterial Infection and Promoting Bone Tissue Regeneration**. *Polymers* (2021) **13**. DOI: 10.3390/polym13111794
70. Augustine R., Hasan A., Patan N.K., Dalvi Y.B., Varghese R., Antony A., Unni R.N., Sandhyarani N., Moustafa A.-E.A.. **Cerium Oxide Nanoparticle Incorporated Electrospun Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Membranes for Diabetic Wound Healing Applications**. *ACS Biomater. Sci. Eng.* (2020) **6** 58-70. DOI: 10.1021/acsbiomaterials.8b01352
71. Mahamuni-Badiger P.P., Patil P.M., Patel P.R., Dhanavade M.J., Badiger M.V., Marathe Y.N., Bohara R.A.. **Electrospun poly(3-hydroxybutyrate-co-3-hydroxyvalerate)/polyethylene oxide (PEO) microfibers reinforced with ZnO nanocrystals for antibacterial and antibiofilm wound dressing applications**. *New J. Chem.* (2020) **44** 9754-9766. DOI: 10.1039/D0NJ01384F
72. Castro-Mayorga J.L., Fabra M.J., Lagaron J.M.. **Stabilized nanosilver based antimicrobial poly(3-hydroxybutyrate-co-3-hydroxyvalerate) nanocomposites of interest in active food packaging**. *Innov. Food Sci. Emerg. Technol.* (2016) **33** 524-533. DOI: 10.1016/j.ifset.2015.10.019
73. Castro Mayorga J.L., Fabra Rovira M.J., Cabedo Mas L., Sánchez Moragas G., Lagarón Cabello J.M.. **Antimicrobial nanocomposites and electrospun coatings based on poly(3-hydroxybutyrate-co-3-hydroxyvalerate) and copper oxide nanoparticles for active packaging and coating applications**. *J. Appl. Polym. Sci.* (2018) **135** 45673. DOI: 10.1002/app.45673
74. Calvache-Muñoz J., Prado F.A., Rodríguez-Páez J.E.. **Cerium oxide nanoparticles: Synthesis, characterization and tentative mechanism of particle formation**. *Colloids Surf. A Physicochem. Eng. Asp.* (2017) **529** 146-159. DOI: 10.1016/j.colsurfa.2017.05.059
75. Castro-Mayorga J.L., Fabra M.J., Pourrahimi A.M., Olsson R.T., Lagaron J.M.. **The impact of zinc oxide particle morphology as an antimicrobial and when incorporated in poly(3-hydroxybutyrate-co-3-hydroxyvalerate) films for food packaging and food contact surfaces applications**. *Food Bioprod. Process.* (2017) **101** 32-44. DOI: 10.1016/j.fbp.2016.10.007
76. Gofman I.V., Nikolaeva A.L., Khripunov A.K., Ivan’kova E.M., Shabunin A.S., Yakimansky A.V., Romanov D.P., Popov A.L., Ermakov A.M., Solomevich S.O.. **Bacterial Cellulose-Based Nanocomposites Containing Ceria and Their Use in the Process of Stem Cell Proliferation**. *Polymers* (2021) **13**. DOI: 10.3390/polym13121999
77. Brito Z., Sánchez G.. **Influence of metallic fillers on the thermal and mechanical behaviour in composites of epoxy matrix**. *Compos. Struct.* (2000) **48** 79-81. DOI: 10.1016/S0263-8223(99)00076-8
78. Chhetri S., Adak N.C., Samanta P., Murmu N.C., Kuila T.. **Exploration of Mechanical and Thermal Properties of CTAB-Modified MoS2/LLDPE Composites Prepared by Melt Mixing**. *J. Compos. Sci.* (2018) **2**. DOI: 10.3390/jcs2030037
79. Hossain K.M.Z., Felfel R.M., Rudd C.D., Thielemans W., Ahmed I.. **The effect of cellulose nanowhiskers on the flexural properties of self-reinforced polylactic acid composites**. *React. Funct. Polym.* (2014) **85** 193-200. DOI: 10.1016/j.reactfunctpolym.2014.09.012
80. Ashori A., Jonoobi M., Ayrilmis N., Shahreki A., Fashapoyeh M.A.. **Preparation and characterization of polyhydroxybutyrate-co-valerate (PHBV) as green composites using nano reinforcements**. *Int. J. Biol. Macromol.* (2019) **136** 1119-1124. DOI: 10.1016/j.ijbiomac.2019.06.181
81. Díez-Pascual A.M., Díez-Vicente A.L.. **ZnO-Reinforced Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Bionanocomposites with Antimicrobial Function for Food Packaging**. *ACS Appl. Mater. Interfaces* (2014) **6** 9822-9834. DOI: 10.1021/am502261e
82. Xu P., Yang W., Niu D., Yu M., Du M., Dong W., Chen M., Jan Lemstra P., Ma P.. **Multifunctional and robust polyhydroxyalkanoate nanocomposites with superior gas barrier, heat resistant and inherent antibacterial performances**. *Chem. Eng. J.* (2020) **382** 122864. DOI: 10.1016/j.cej.2019.122864
83. Khan I., Saeed K., Khan I.. **Nanoparticles: Properties, applications and toxicities**. *Arab. J. Chem.* (2019) **12** 908-931. DOI: 10.1016/j.arabjc.2017.05.011
84. Lagarón J.M., Lagarón J.-M.. **1—Multifunctional and nanoreinforced polymers for food packaging**. *Multifunctional and Nanoreinforced Polymers for Food Packaging* (2011) 1-28. DOI: 10.1533/9780857092786.1
85. Öner M., Keskin G., Kızıl G., Pochat-Bohatier C., Bechelany M.. **Development of poly(3-hydroxybutyrate-co-3-hydroxyvalerate)/boron nitride bionanocomposites with enhanced barrier properties**. *Polym. Compos.* (2019) **40** 78-90. DOI: 10.1002/pc.24603
86. Naidi S.N., Harunsani M.H., Tan A.L., Khan M.M.. **Green-synthesized CeO**. *J. Mater. Chem. B* (2021) **9** 5599-5620. DOI: 10.1039/D1TB00248A
87. Sadidi H., Hooshmand S., Ahmadabadi A., Javad Hoseini S., Baino F., Vatanpour M., Kargozar S.. **Cerium Oxide Nanoparticles (Nanoceria): Hopes in Soft Tissue Engineering**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25194559
88. Zhang M., Zhang C., Zhai X., Luo F., Du Y., Yan C.. **Antibacterial mechanism and activity of cerium oxide nanoparticles**. *Sci. China Mater.* (2019) **62** 1727-1739. DOI: 10.1007/s40843-019-9471-7
89. Kızılkonca E., Torlak E., Erim F.B.. **Preparation and characterization of antibacterial nano cerium oxide/chitosan/hydroxyethylcellulose/polyethylene glycol composite films**. *Int. J. Biol. Macromol.* (2021) **177** 351-359. DOI: 10.1016/j.ijbiomac.2021.02.139
90. Mohamed H.E.A., Afridi S., Khalil A.T., Ali M., Zohra T., Akhtar R., Ikram A., Shinwari Z.K., Maaza M.. **Promising antiviral, antimicrobial and therapeutic properties of green nanoceria**. *Nanomedicine* (2020) **15** 467-488. DOI: 10.2217/nnm-2019-0368
91. Vidal O.L., Barros Santos M.C., Batista A.P., Andrigo F.F., Baréa B., Lecomte J., Figueroa-Espinoza M.C., Gontard N., Villeneuve P., Guillard V.. **Active packaging films containing antioxidant extracts from green coffee oil by-products to prevent lipid oxidation**. *J. Food Eng.* (2022) **312** 110744. DOI: 10.1016/j.jfoodeng.2021.110744
|
---
title: 'Packed School Lunch Food Consumption: A Childhood Plate Waste Nutrient Analysis'
authors:
- Jack R. Thomas
- Derek Hanson
- Ashley Chinnan-Pothen
- Christine Freaney
- Jill Silverman
journal: Nutrients
year: 2023
pmcid: PMC10004809
doi: 10.3390/nu15051116
license: CC BY 4.0
---
# Packed School Lunch Food Consumption: A Childhood Plate Waste Nutrient Analysis
## Abstract
Packed school lunch consumption remains a sparsely studied aspect of childhood nutrition. Most American research focuses on in-school meals provided through the National School Lunch Program (NSLP). The wide variety of available in-home packed lunches are usually nutritionally inferior compared to the highly regulated in-school meals. The purpose of this study was to examine the consumption of home-packed lunches in a sample of elementary-grade children. Through weighing packed school lunches in a 3rd grade class, mean caloric intake was recorded at $67.3\%$ ($32.7\%$ plate waste) of solid foods, while sugar-sweetened beverage intake reported a $94.6\%$ intake. This study reported no significant consumption change in the macronutrient ratio. Intake showed significantly reduced levels of calories, sodium, cholesterol, and fiber from the home-packed lunches ($p \leq 0.05$). The packed school lunch consumption rates for this class were similar to those reported for the regulated in-school (hot) lunches. Calories, sodium, and cholesterol intake are within childhood meal recommendations. What is encouraging is that the children were not “filling up” on more processed foods at the expense of nutrient dense foods. Of concern is that these meals still fall short on several parameters, especially low fruit/vegetable intake and high simple sugar consumption. Overall, intake moved in a healthier direction compared to the meals packed from home.
## 1. Introduction
Health and nutrition studies on packed school lunches typically split their focus on either preschool, elementary, or high school intake, each requiring its own research focus. The purpose of this study was to examine the consumption of home-packed lunches in a group of 3rd grade elementary children. Few studies have empirically examined home-packed school lunches for nutritional content. However, a multitude of researchers have examined the caloric/nutritional content of hot meals provided in schools [1,2,3]. The National School Lunch Program (NSLP) dominates most research on school lunches. NSLP focuses on delivering nutritious food at reduced or no cost to families in need [4]. Over $90\%$ of schools accept this federal program, which typically provides an estimated 30 million meals a day in the United States [5]. That leaves tens of millions of meals provided to children by their families every school day. Estimates place the number of home-packed lunches at U.S. schools at around $40\%$ of all school meals, with their packed content found to have a higher caloric value and a lesser nutritional value than in-school lunches [6]. All NSLP-participating schools are obligated to make all their lunch offerings meet minimum nutrition/caloric standards and be healthy in nature [7]. Because schools meet these strict NSLP nutrition parameters, parent-packed lunches have been called inferior on many levels [8,9,10].
The selection and preparation of these home meals are complex issues. With no guiding regulations on what to pack, home meals provide all the food and calories a child consumes during a typical lunch period, which should result in 25 to 30 percent of their daily food intake [11]. Multiple food choices exist for packed school lunch preparation but are absent from the in-school meals because that food is provided by the school on a cycle menu. Prominent variables influencing the packing of a child’s lunch include parent/child communication [12], geographic region, economic status of the school/region/family, ethnic makeup of the school, and school administrative policies [13].
Because parent-packed lunches are tailored towards a student’s likes, consumption is thought to be greater than in school lunches [14]. One area of concern with home-packed meals is their high energy density, especially sugar-sweetened beverages, chips, and baked goods [15]. Childhood obesity research occasionally discusses using both in-school and packed school lunches to help understand/control body weight. Historically, lunchtime dietary intake has become secondary to physical activity [16]. A 2018 review found 18 weight-focused studies used physical activity interventions, and only 3 contained dietary means to control childhood obesity [17].
## 1.1. Weighing Lunch Plate Waste (New Sub Section)
It is a challenge to assess dietary intake among children since information for school food intake is often derived from self-reported questionnaires, collected by memory, dietary recall, or from food photograph/visual analysis [18]; none of these are direct measurements. The recall error in some dietary intake methods has been estimated at more than 30 percent [19]. Therefore, most articles on intake rely on large subject numbers to normalize the data and stress the training of data collection personnel to help reduce this large potential error. Visual interpretation of school plate waste, though validated, was found inferior to the more laborious scale weighing of leftovers [20]. Plate waste measurement is the direct weighing of food. It is considered the most accurate method of nutrient intake because no memory or portion estimates are used [21]. While there are advantages to memory recalls, questionnaires, and the visual interpretation of school lunches, weighing provides the most specific information by overcoming estimations that depend on student memory or observation [22].
## 1.2. Parenting
The primary responsibility for home-packed lunch content is with the parent/adult. The amount of time and effort put into meal preparation is often limited, resulting in processed and prepackaged food items being the easy choice. This is especially true for working families that have both parents employed and those with multiple children. The primary role of in-school NSLP lunches is to meet minimum nutritional standards. The main reason for packing lunches, according to parents, is not nutritional but to meet the child’s taste preferences [23].
Parents may well be concerned about nutrition, but they do not have to abide by federal or school guidelines. Most often, the main concerns are for their children to maintain a healthy body weight and to eat what they pack [24]. Estimates place the number of home-packed lunches at U.S. schools at around $40\%$ of all school meals, and they have determined their total content, as packed, to have a higher caloric value and lesser nutritional value than in-school lunches [25,26]. However, what is packed and what is eaten are two very different things. Because of their wide variety, home meals present extra challenges to research. The lack of rigorous examination of consumption leaves the actual nutritional intake of packed lunches in doubt [27].
Preparation of school lunches must fit into the family schedule and work with family dynamics both inside and outside the home. Some parent food preparation behavior is best described as unconcerned when it comes to constructing school lunches. This minimal effort is associated with higher calorie, sodium, and fat content [28]. Other parents show coercive control practices by pressuring their children to eat healthy at both home and school. Some parents address their meal preparation efforts as simply providing access to healthy foods at home and in school lunches [29].
In 2017, preschooler home-packed lunches were reported not to provide consistently adequate nutrients [30]. After nursery school and the first couple of years of elementary school, parents become accustomed to the general eating likes and dislikes of their children [31], and they buy food accordingly. Regardless of the age of the child, lunch leftovers/waste returning home is viewed as a negative reinforcing factor for parents buying food and packing meals [32]. It influences future lunch preparation. Finding food returned from school sends the message not to buy more of that food in the future. It is noteworthy that one study on children and eating habits said that up to $30\%$ of children throw away food to avoid conflict with their parents [33].
## 1.3. School Policies and Plate Waste
Intrusive school policy and poor food quality have been shown to be the two biggest contributors to plate waste for in-school lunches [34]. These same policies also influence plate waste for home-packed lunches. Poor communication among administrators, food service staff, health educators, and teachers reduces healthy eating behavior for all school children [35]. A longer scheduled lunchtime at school increases the amount of food consumed [36]. The later in the afternoon schools serve lunch, the greater the consumption of both in-school and packed lunches because the kids are hungrier [37,38]. Rules such as having lunch before recess (exercise), not having enough time to eat, not sharing unopen foods, and not talking among peers all lead to increased food waste [38,39,40,41].
## 2. Materials and Methods
This research followed a group of 3rd grade children from a suburban Long Island, New York, parochial school for 5 consecutive days ($$n = 118$$). Recruitment was complete and consistent for all student-packed lunches brought from home. Lunches were collected as the children entered the classroom over a one-week period. These meals were taken, labeled with individual identification stickers, and weighed in grams for each individual food item (pre-weight). All packed lunches were then returned to the classroom before lunch. During lunch, children were observed from a distance to ensure no sharing of foods occurred. Children were informed to throw nothing out after eating lunch but to take everything left over to the nearby research table. A lunchroom table was established, with trash cans placed behind the researchers. All home-packing third graders handed the entire lunch bag, including all wrapping and food waste, to the researchers. Using the identification numbers, the leftover items were reweighed (post-weight), and the lunch containers were returned to the classroom. This direct weighing avoided misreporting eaten food. Weights outside of three standard deviations were considered outliers; no outliers were detected in the sample weights.
Water and calorie-free liquids were weighed but categorized individually from caloric liquids for separate statistical analysis (non-caloric). Any child who purchased prepackaged food from the school cafeteria, such as prepackaged chips, snacks, candy, or cookies, had that food included in the study by weighing individual leftovers against an unused representative package weight. Children with both a packed lunch and supplemented in-school food entrees were not used in this study with the goal of avoiding in-school lunch influence. A Nutritionist Pro (Axxya systems) dietary analysis module software was used to convert individual food pre/post weights into nutrient intake information.
## Data Analysis
Percentages of consumption were calculated from the total food items pre-weighed minus the weight of each leftover food item, including all wrapping material. Sandwiches and other combination items were reported as single-entry items for analysis. All weights were obtained directly by the authors of this study. Descriptive statistics of frequency, mean, standard deviation, and confidence intervals were calculated for all variables. A one-tailed paired t-test was performed to compare consumption within home-packed lunches. Pre/post mean differences were considered to be statistically significant at p ≤ 0.05. The mean decrease in food weight was determined for energy, macronutrients, water, sugar-sweetened beverages, sodium, fiber, and cholesterol.
## 3. Results
A total of 118 meals were weighed. Not all students were present or consistently packed a daily lunch, resulting in a mean of 23.6 meals/day analyzed. Baseline characteristics of the sample were male students that represented $39.1\%$, with $60.9\%$ being female. Ages ranged from 8 to 9 years old. Measurements were calculated for the percentage of foods eaten and various nutritional content consumed. The average packed meal arrived with 639.6 kcal; when consumed, the meal averaged 209.1 kcal less, a $32.7\%$ decrease, at 430.6 kcal ($$p \leq 0.00001$$) (Table 1). Using a 1550 kcal daily intake as a reference, the children averaged $27.8\%$ of their daily energy requirement with lunch [42].
## 3.1. Fluid Intake and Sugar Sweetened Beverages
A fluid intake analysis showed bottled water was packed with $44\%$ of the lunches, and $92\%$ of that water was consumed with lunch. Sugar-sweetened beverages (usually fruit-based juices) were present in $61\%$ of the lunches ($94.6\%$ consumption) (Table 1).
## 3.2. Macronutrient Intake
The macronutrients of carbohydrates, lipids, and proteins all showed no significant percentage difference between what was packed and what was eaten. Carbohydrates changed from $58.1\%$ to $59.5\%$, lipids from $28.9\%$ to $27.0\%$, and proteins from $13.3\%$ to $13.4\%$ (Table 2). Packed versus consumed lunches showed an overall $1.4\%$ increase in carbohydrates, a $1.9\%$ decrease in lipids, and a $0.1\%$ increase in proteins.
## 3.3. Sodium Intake
Sodium in packed lunches recorded a mean of 1112.3 ± 1480.3 mg per lunch and was significantly reduced through packed lunch consumption to 635.8 ± 284.2 mg (−$57.2\%$) ($p \leq 0.05$). Using the Institute of Medicine’s current guideline of 1200 mg sodium intake (aged 4–8) from lunches, students met $53.0\%$ of their USDA daily recommendation from lunch intake (Table 3).
## 3.4. Fiber Intake
The fiber in packed lunches averaged 3.32 ± 1.79 g. Actual fiber consumption was 2.16 ± 0.89 g, with a $65.1\%$ consumption rate ($p \leq 0.05$). Based on an averaged fiber recommendation for male and female children aged 6–11 of 12.85 g of fiber/day [43], these children ate an average of $16.8\%$ of their daily fiber recommendation at lunch.
## 3.5. Cholesterol Intake
Cholesterol in packed lunches averaged 31.7 ± 27.4 mg cholesterol. The actual consumed cholesterol in lunches was 23.7 ± 22.9 mg. This $83.4\%$ consumption rate was a significant decrease in cholesterol ($p \leq 0.05$). Conway et al. reported a similar middle school cholesterol intake average of 32.6 mg of cholesterol and a significant difference with seventh graders eating more than 6th graders [44].
## 3.6. Sugar Sweetened Beverage Intake
Daily recommendations call for no more than $10\%$ of total calories from sugar-sweetened beverages, such as fruit drinks [45]. Based on the previous 1550 kcal per day energy recommendation, the lunches’ weighed intake represented a mean of $13.2\%$ of daily total simple sugar calories across the entire sample of students (Table 4). A better representation of intake from sugary fruit-based juice liquids was to exclude the students that only consumed water from this calculation. The $66\%$ of sugary fruit-juice beverage drinkers increased their calorie intake from simple sugar drinks to $19.7\%$ of their total lunch calories.
## 4.1. Plate Waste Weighing and Calories
Approximately 186,000 children in New York attend private schools outside of New York City [46]. This research examined home-packed lunches at one of those private schools in a third-grade elementary class on Long Island. It did not estimate intake; it weighed plate waste to better understand childhood dietary intake and to make comparisons to national childhood dietary references on select parameters. There are important nutritional disparities in intake recorded between estimated and weighed childhood lunches. One study revealed that when estimating food intake, the children reported eating $17\%$ of non-observed food items but only reported $67\%$ of the observed items [47]. All forms of estimated dietary assessment have been criticized with respect to accuracy [48,49]. The direct weighing of the food ensures accurate reporting of all consumed foods and serves as an important methodological consideration for future research on school lunch food intake. Through weighing, an average of $67.3\%$ of the packed lunch food was eaten by the children. This rate of consumption is comparable to that found for in-school hot lunches, at approximately $70\%$ [50,51,52]. Regardless of whether packed or purchased, school lunch intake has implications for total daily energy intake and childhood obesity [53]. Using age-specific USDA calorie recommendation ranges, an appropriate caloric intake of 1150 kcal was selected for a reference daily energy intake [54]. Packed meals as they arrived at school met $41.3\%$ of the daily energy intake and would be considered high in calories. The actual consumption of these lunches recorded a lower mean of $27.8\%$ daily energy intake. Packed lunches, as eaten, presented a moderate range of calories for a typical child’s lunch.
## 4.2. Macronutrient Distribution
Although the caloric reduction of packed meals was evident, nutrient distribution remained constant. Parent-packed meals arrived at school with $58\%$ carbohydrate, $29\%$ lipid, and $13\%$ protein. Lunches as eaten by the students demonstrated a similar distribution of $60\%$, $27\%$, and $13\%$ (carbohydrate, lipid, and protein). These results represent a typical energy distribution of nutrients for a healthy child’s diet. A meal analysis on preschool daycare children by Romo-Palafox et al. found similar child macronutrient intakes in this study at 56, 31, and $15\%$ carbohydrates, lipids, and proteins, respectively [30].
This packed lunch macronutrient distribution falls within basic childhood nutrition recommendations [55]. Importantly, no significant macronutrient composition difference was detected, indicating the children were eating a portion of each food item packed instead of consuming all of select foods and nothing of others. A scenario that did not happen was that the children entirely ate the higher-calorie processed foods and left large amounts of the nutrient-dense foods as waste. This study showed the food waste was evenly distributed among all packed food items, excluding liquids, which were consistently consumed in the 90th plus percentile.
Previous behavioral research on parental food choices in preschool lunch packing supports this recorded “grazing” consumption of food items [56]. It has its basis in the parent/child communication of a home-packed meal [57]. The parental role in preparing a packed lunch for an elementary school child cannot be underestimated. Parent/child communication will always be an important factor in the contents of a child’s packed lunch [58,59]. As food waste returns home each school day, that negative feedback continually informs the parents of the child’s food likes and dislikes. In-school or “hot” lunch children have limited food choices and throw away their food leftovers before reaching home. In packed school lunches, independent of nutrition content, if the child has shown a past tendency to eat specific foods, that behavior will continue in the future [59]. If the child likes everything in the lunch, they avoid nothing. It also explains the observation of very few fruits and vegetables in the packed lunches. Other than prepackaged, single serving containers of apple sauce and mixed-type fruit cups, no fresh fruit other than an occasional banana was recorded. Reasons for low fruit and vegetable intake are beyond the scope of this study but would likely include parent/child communication of likes and dislikes as well as parents seeing uneaten foods returned home, resulting in a negative reinforcement to exclude these types of foods.
## 4.3. Fluid Intake and Sugar Sweetened Beverages
The liquid portion of the packed school lunches revealed relatively higher consumption rates than for solid foods. Bottled water was present in $44\%$ of the packed lunches. The same children brought water consistently throughout the study. Packed lunch bottled water consumption averaged $92\%$. Bottled water size varied from 8 to 16 ounces, and regardless of size, it was usually completely consumed by the children. For the in-school lunches, the federal government requires access to water at lunchtime for all students and not to restrict the sale of milk to children [60]. No children with packed lunches took water or milk provided by the school in jugs/cups/cartons on the front tables. Because water lacks calories, its weight was excluded from calorie and macronutrient calculations. The presence of bottled water in a packed school lunch was considered a healthy eating choice.
Sugar-sweetened beverages have been a negative factor in school lunches and related to weight gain in children [61]. The added simple sugar intake recommendation is set by the Dietary Guidelines for Americans at less than $10\%$ of total calories [54]. Although simple sugars were not analyzed for all individual food items, liquid simple sugar content in the form of sugary sweetened beverages was recorded as part of the packed lunch pre- and post-weighing. These sugar-sweetened beverages were recorded in $61\%$ of the packed lunches, with a $94.6\%$ consumption rate (Table 2). Normally, carbonated sweetened sodas are included as a simple sugar source in the analysis of a packed school lunch. However, a school policy prevented families from packing carbonated sodas for lunch, and none were recorded for this home-packed lunch sample group. Overall, average sugar-sweetened beverages contributed $13.2\%$ of total packed lunch calories and slightly exceeded the recommendation of less than $10\%$. For a more applicable analysis, water drinkers were removed from the analysis. For the results of only those children consuming sugar-sweetened beverages, $19.7\%$ of calories came from those liquids (Table 3). The sugar-sweetened beverage lunches were found to contain almost double the amount of recommended simple sugar upon the removal of the water-containing lunches. This simple sugar intake is a minimum representation, as only liquids were analyzed for sugar content. The solid foods were not individualized for simple sugar calories and data should be interpreted only as sugar-sweetened beverages and sugar calories. This $94.6\%$ consumption rate for $61\%$ of the lunches relates this sample’s high sugar-sweetened beverage intake to the risk variable of childhood obesity [62].
## 4.4. Sodium Intake
This study examined children ages 8 to 9. The current transitional regulation standard for in-school lunch sodium intake, as issued by the National School Lunch Program, for the 2023 to 2024 school year is 1225 mg for 6- to 8-year-olds [63]. The recorded average sodium intake from packed school lunches for this group of children was below the “final rule” target amount of sodium intake. The 635.8 ± 284.2 mg average sodium intake of these children meets the strictest federal standards and would be described as a healthy lunchtime intake of sodium.
The sodium targets require in-school lunch programs to meet ever stricter sodium limitations and give schools several years to meet those standards. The most restrictive sodium school lunch reduction mandate for lunches is less than 640 mg [64]. The recorded average sodium intake from the packed school lunch for this group of children was below the amount of sodium intake imposed on the NSLP in-school lunches. Cohen, Richardson, Roberto, and Rim suggest that a lower sodium intake, as seen in their research on elementary and middle school lunches, may have a negative relationship with simple sugar intake [65]. Although complete simple sugar content was not calculated for this study, the sugar-sweetened beverage calories were excessive and would support such a relationship.
A State of Washington parent-packed school lunch study in 2014 used photography with plate waste weight validation and reported school lunch sodium content was 931.8 mg, with consumed sodium at 746.4 mg [66]. This study’s packed lunch sodium intake of 635.8 mg was similar. One caveat of the Washington State study was that these schools were all participating in the Healthier U.S. School Challenge, which provided and encouraged healthy food choices for all the children. This presents a potential confounding variable for their packed lunch intake.
## 4.5. Fiber Intake
Most research on fiber intake focuses on adults and not children. National intake data show most U.S. children do not meet fiber recommendations [67]. This lack of child-specific fiber research is demonstrated by the existence of several different fiber recommendations for children. The FDA’s label guide for childhood fiber is 12 g/1000 kcal consumed. Current research shows children and adolescents only consume approximately half of the suggested 25 g of fiber per day [68]. Consistent with earlier school lunch studies, the fiber intake for this study was low at 2.16 ± 0.89 g. These findings provided $8.6\%$ toward meeting that daily fiber recommendation. Although there was a significant numerical reduction in fiber intake of −1.6 g ($p \leq 0.05$), the amount of food containing this amount of fiber was small. The starting fiber content of these meals met $13.3\%$ of the fiber recommendations. This is not unexpected because some of the best fiber sources, such as fruits and vegetables, were very low in the packed lunches. A Canadian study on packed lunch content for 7–10 year-old students reported 9.5 g fiber intake through visual food intake observation [69].
## 4.6. Cholesterol Intake
Cholesterol intake from the packed lunches is in agreement with several previous childhood lunch studies, as reported at 23.7 mg of cholesterol [70,71]. The 2015–2020 Dietary Guidelines for Americans removed the cholesterol recommendation of 300 mg/day and replaced it with the guidance of eating as little as possible. For comparison, the old 300 mg/day level was used in percentage calculations. Home-packed lunches were within this recommendation [72]. The “as packed” lunch value for cholesterol was 31.7 mg, and the eaten value was 23.7 mg of cholesterol, with a $74.8\%$ consumption rate, meeting $10.6\%$ of the old daily cholesterol recommendation.
## 5. Summary
This research on packed school lunches, conducted on Long Island in New York, counters several common misrepresentations that home-packed school lunch construction and consumption are of a consistently lesser nutritional value than their in-school alternatives. Regardless of in-school or packed meals, the direct intake of food needs to be considered. Through direct plate waste measurement, the children who ate packed school lunches fell well within calorie recommendations and presented a healthy macronutrient distribution of carbohydrate, lipid, and protein consumption. The macronutrient pre-post consistency showed the children evenly consumed each item packed and did not eat one or two select foods, leaving others untouched. These lunches were reported as being within range for sodium and cholesterol but high in simple sugar and low in fiber. Plate waste weight measurement remains an integral component for determining actual dietary intake. More research on the American and global nutritional quality of home-packed school foods at various ages is important for gaining a better understanding of how to improve childhood nutrition.
## 6. Limitations
Data from this study were obtained from a small sample of 3rd grade children enrolled in a northeastern (New York) suburban parochial school on Long Island and should not be generalized beyond that specific environment to different demographic, socioeconomic, or ethnic population compositions in the United States or globally. The itemized weighing of the after-lunch plate waste required immediate measurement. Any weight recording error or missed plate waste would alter the dataset. The food collection logistics at the end of the lunch period (signal) prevented researchers from individually observing each student’s complete handling of the waste collection. Some paper or food plate waste may have inadvertently avoided researcher collection through outside trash receptacles.
## References
1. Rees G.A., Richards C.J., Gregory J.. **Food and nutrient intakes of primary school children: A comparison of school meals and packed lunches**. *J. Hum. Nutr. Diet.* (2008.0) **21** 420-427. DOI: 10.1111/j.1365-277X.2008.00885.x
2. Farris A.R., Misyak S., Duffey K.J., Davis G.C., Hosig K., Atzaba-Poria N., McFerren M.M., Serrano E.L.. **Nutritional Comparison of Packed and School Lunches in Pre-Kindergarten and Kindergarten Children Following the Implementation of the 2012–2013 National School Lunch Program Standards**. *J. Nutr. Educ. Behav.* (2014.0) **46** 621-626. DOI: 10.1016/j.jneb.2014.07.007
3. Joyce J.M., Rosenkranz R.R., Rosenkranz S.K.. **Evaluation of Variability in Dietary Quality of School Lunches Meeting National School Lunch Program Guidelines by Socioeconomic Status and Rurality**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17218012
4. Golino G., Ralston K., Guthrie J.. **Participation Trends for Full Price Meals in the National School Lunch Program**. *Appl. Econ. Perspect. Policy* (2021.0) **43** 1161-1175. DOI: 10.1002/aepp.13071
5. **United States Department of Agriculture**. (2023.0)
6. Johnston C.A., Moreno J.P., El-Mubasher A., Woehler D.. **School Lunches and Lunches Brought from Home: A Comparative Analysis**. *Child. Obes.* (2012.0) **8** 364-368. DOI: 10.1089/chi.2012.0012
7. Andreyeva T., Tripp A.S., Schwartz M.B.. **Dietary Quality of Americans by Supplemental Nutrition Assistance Program Participation Status**. *Am. J. Prev. Med.* (2015.0) **49** 594-604. DOI: 10.1016/j.amepre.2015.04.035
8. Hur I., Burgess-Champoux T., Reicks M.. **Higher Quality Intake From School Lunch Meals Compared with Bagged Lunches**. *ICAN Infant Child Adolesc. Nutr.* (2011.0) **3** 70-75. DOI: 10.1177/1941406411399124
9. Crepinsek M.K., Gordon A.R., McKinney P.M., Condon E.M., Wilson A.. **Meals Offered and Served in US Public Schools: Do They Meet Nutrient Standards?**. *J. Am. Diet. Assoc.* (2009.0) **109** S31-S43. DOI: 10.1016/j.jada.2008.10.061
10. Peckham J.G., Kropp J.D., Mroz T.A., Haley-Zitlin V., Granberg E.M.. **Selection and consumption of lunches by National School Lunch Program participants**. *Appetite* (2019.0) **133** 191-198. DOI: 10.1016/j.appet.2018.10.033
11. Gola A.A.H., Figueroa H., Bardin S., Gearan E.. **Reaching a Vulnerable Population: Why Adolescents Participate in the National School Lunch Program**. *J. Adolesc. Health* (2021.0) **69** 489-494. DOI: 10.1016/j.jadohealth.2021.01.034
12. Lindquist K., Mann G., Farris A., Gordon K., Misyak S.. **Parent Perspectives of Packing School Lunches**. *J. Hunger Environ. Nutr.* (2021.0) 1-18. DOI: 10.1080/19320248.2021.1994081
13. Bardin S., Washburn L., Gearan E.. **Disparities in the Healthfulness of School Food Environments and the Nutritional Quality of School Lunches**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12082375
14. Hubbard K.L., Must A., Eliasziw M., Folta S.C., Goldberg J.. **What’s in Children’s Backpacks: Foods Brought from Home**. *J. Acad. Nutr. Diet.* (2014.0) **114** 1424-1431. DOI: 10.1016/j.jand.2014.05.010
15. Taylor J.C., Sutter C., Ontai L.L., Nishina A., Zidenberg-Cherr S.. **Comparisons of school and home-packed lunches for fruit and vegetable dietary behaviours among school-aged youths**. *Public Health Nutr.* (2019.0) **22** 1850-1857. DOI: 10.1017/S136898001900017X
16. Kim Y., Son K., Kim J., Lee M., Park K.H., Lim H.. **Associations between School Lunch and Obesity in Korean Children and Adolescents Based on the Korea National Health and Nutrition Examination Survey 2017–2019 Data: A Cross-Sectional Study**. *Nutrients* (2023.0) **15**. DOI: 10.3390/nu15030698
17. Martin A., Booth J.N., Laird Y., Sproule J., Reilly J.J., Saunders D.H.. **Physical Activity, Diet and Other Behavioural Interventions for Improving Cognition and School Achievement in Children and Adolescents with Obesity or Overweight**. *Cochrane Database Syst. Rev.* (2018.0) **1** CD009728. PMID: 29376563
18. Baxter S.D., Guinn C.H., Smith A.F., Hitchcock D.B., Royer J.A., Puryear M.P., Collins K.L., Smith A.L.. **Children’s school-breakfast reports and school-lunch reports (in 24-h dietary recalls): Conventional and reporting-error-sensitive measures show inconsistent accuracy results for retention interval and breakfast location**. *Br. J. Nutr.* (2016.0) **115** 1301-1315. DOI: 10.1017/S0007114515005413
19. Warren J.M., Henry C.J.K., Livingstone M.B.E., Lightowler H.J., Bradshaw S.M., Perwaiz S.. **How well do children aged 5–7 years recall food eaten at school lunch?**. *Public Health Nutr.* (2003.0) **6** 41-47. DOI: 10.1079/PHN2002346
20. Liz Martins M., Cunha L.M., Rodrigues S.S.P., Rocha A.. **Determination of plate waste in primary school lunches by weighing and visual estimation methods: A validation study**. *Waste Manag.* (2014.0) **34** 1362-1368. DOI: 10.1016/j.wasman.2014.03.020
21. Kubena K.S.. **Accuracy in dietary assessment: On the road to good science**. *J. Am. Diet. Assoc.* (2000.0) **100** 775-776. DOI: 10.1016/S0002-8223(00)00226-1
22. Lindke A.R., Smith T.A., Cotwright C.J., Morris D., Cox G.O.. **Plate Waste Evaluation of Plant-Based Protein Entrees in National School Lunch Program**. *J. Nutr. Educ. Behav.* (2022.0) **54** 12-19. DOI: 10.1016/j.jneb.2021.06.002
23. Vollmer R.L.. **Parental feeding style changes the relationships between children’s food preferences and food parenting practices: The case for comprehensive food parenting interventions by pediatric healthcare professionals: VOLLMER**. *J. Spec. Pediatr. Nurs.* (2019.0) **24** e12230. DOI: 10.1111/jspn.12230
24. Hawthorne D.L., Neilson L.J., Macaskill L.A., Luk J.M.H., Horner E.J., Parks C.A., Salvadori M.I., Seabrook J.A., Dworatzek P.D.. **Parental Reports of Lunch-Packing Behaviours Lack Accuracy: Reported Barriers and Facilitators to Packing School Lunches**. *Can. J. Diet. Pract. Res.* (2018.0) **79** 99-105. DOI: 10.3148/cjdpr-2018-011
25. Tugault-Lafleur C.N., Black J.L., Barr S.I.. **A Systematic Review of Methods to Assess Children’s Diets in the School Context**. *Adv. Nutr.* (2017.0) **8** 63-79. DOI: 10.3945/an.116.013144
26. Sweitzer S.J., Briley M.E., Roberts-Gray C., Hoelscher D.M., Staskel D.M., Almansour F.D.. **How to Help Parents Pack Better Preschool Sack Lunches: Advice from Parents for Educators**. *J. Nutr. Educ. Behav.* (2011.0) **43** 194-198. DOI: 10.1016/j.jneb.2010.09.002
27. Minaya S., Rainville A.J.. **How Nutritious are Children’s Packed School Lunches? A Comparison of Lunches Brought from Home and School Lunches**. *J. Child Nutr. Manag.* (2016.0) **40** n2
28. Chen B., Kattelmann K., Comstock C., McCormack L., Wey H., Bowne M., Meendering J.. **Identifying Food Parenting Practices From Comprehensive Home Environment Survey**. *J. Nutr. Educ. Behav.* (2022.0) **54** 557-564. DOI: 10.1016/j.jneb.2022.02.010
29. Roberts-Gray C., Briley M.E., Ranjit N., Byrd-Williams C.E., Sweitzer S.J., Sharma S.V., Palafox M.R., Hoelscher D.M.. **Efficacy of the Lunch is in the Bag intervention to increase parents’ packing of healthy bag lunches for young children: A cluster-randomized trial in early care and education centers**. *Int. J. Behav. Nutr. Phys. Act.* (2016.0) **13** 3. DOI: 10.1186/s12966-015-0326-x
30. Romo-Palafox M.J., Ranjit N., Sweitzer S.J., Roberts-Gray C., Byrd-Williams C.E., Briley M.E., Hoelscher D.M.. **Adequacy of Parent-Packed Lunches and Preschooler’s Consumption Compared to Dietary Reference Intake Recommendations**. *J. Am. Coll. Nutr.* (2017.0) **36** 169-176. DOI: 10.1080/07315724.2016.1240634
31. Sabinsky M.S., Toft U., Sommer H.M., Tetens I.. **Effect of implementing school meals compared with packed lunches on quality of dietary intake among children aged 7–13 years**. *J. Nutr. Sci.* (2019.0) **8** e3. DOI: 10.1017/jns.2018.29
32. Johnson C.M., Bednar C., Kwon J., Gustof A.. **Comparison of Nutrient Content and Cost of Home-Packed Lunches to Reimbursable School Lunch Nutrient Standards and Prices**. *J. Child Nutr. Manag.* (2009.0) **33** 1-8
33. Zhao C., Panizza C., Fox K., Boushey C.J., Byker Shanks C., Ahmed S., Chen S., Serrano E.L., Zee J., Fialkowski M.K.. **Plate Waste in School Lunch: Barriers, Motivators, and Perspectives of SNAP-Eligible Early Adolescents in the US**. *J. Nutr. Educ. Behav.* (2019.0) **51** 967-975. DOI: 10.1016/j.jneb.2019.05.590
34. Chriqui J.F., Pickel M., Story M.. **Influence of School Competitive Food and Beverage Policies on Obesity, Consumption, and Availability: A Systematic Review**. *JAMA Pediatr.* (2014.0) **168** 279. DOI: 10.1001/jamapediatrics.2013.4457
35. Cho H., Nadow M.Z.. **Understanding Barriers to Implementing Quality Lunch and Nutrition Education**. *J. Community Health* (2004.0) **29** 421-435. DOI: 10.1023/B:JOHE.0000038656.32950.45
36. Burg X., Metcalfe J.J., Ellison B., Prescott M.P.. **Effects of Longer Seated Lunch Time on Food Consumption and Waste in Elementary and Middle School–age Children: A Randomized Clinical Trial**. *JAMA Netw. Open* (2021.0) **4** e2114148. DOI: 10.1001/jamanetworkopen.2021.14148
37. McLoughlin G.M., Edwards C.G., Jones A., Chojnacki M.R., Baumgartner N.W., Walk A.D., Woods A.M., Graber K.C., Khan N.A.. **School Lunch Timing and Children’s Physical Activity During Recess: An Exploratory Study**. *J. Nutr. Educ. Behav.* (2019.0) **51** 616-622. DOI: 10.1016/j.jneb.2019.01.006
38. Hunsberger M., McGinnis P., Smith J., Beamer B.A., O’Malley J.. **Elementary school children’s recess schedule and dietary intake at lunch: A community-based participatory research partnership pilot study**. *BMC Public Health* (2014.0) **14**. DOI: 10.1186/1471-2458-14-156
39. Getlinger M.J., Laughlin C.V.T., Bell E., Akre C., Arjmandi B.H.. **Food Waste is Reduced when Elementary-School Children Have Recess before Lunch**. *J. Am. Diet. Assoc.* (1996.0) **96** 906-908. DOI: 10.1016/S0002-8223(96)00245-3
40. Grao-Cruces A., Velázquez-Romero M.J., Rodríguez-Rodríguez F.. **Levels of Physical Activity during School Hours in Children and Adolescents: A Systematic Review**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17134773
41. Price J., Just D.R.. **Lunch, recess and nutrition: Responding to time incentives in the cafeteria**. *Prev. Med.* (2015.0) **71** 27-30. DOI: 10.1016/j.ypmed.2014.11.016
42. Griffen A.S.. **Height and calories in early childhood**. *Econ. Hum. Biol.* (2016.0) **20** 55-69. DOI: 10.1016/j.ehb.2015.10.004
43. Kranz S., Brauchla M., Slavin J.L., Miller K.B.. **What Do We Know about Dietary Fiber Intake in Children and Health? The Effects of Fiber Intake on Constipation, Obesity, and Diabetes in Children**. *Adv. Nutr.* (2012.0) **3** 47-53. DOI: 10.3945/an.111.001362
44. Conway T.L., Sallis J.F., Pelletier R.L., Powers H.S., Marshall S.J., Zive M.M., Elder J.P.. **What Do Middle School Children Bring in Their Bag Lunches?**. *Prev. Med.* (2002.0) **34** 422-427. DOI: 10.1006/pmed.2001.0987
45. Nguyen M., Jarvis S.E., Tinajero M.G., Yu J., Chiavaroli L., Mejia S.B., Khan T.A., Tobias D.K., Willett W.C., Hu F.B.. **JSugar-sweetened beverage consumption and weight gain in children and adults: A systematic review and meta-analysis of prospective cohort studies and randomized controlled trials**. *Am. J. Clin. Nutr.* (2023.0) **117** 160-174. DOI: 10.1016/j.ajcnut.2022.11.008
46. Domanico R.. **A Statistical Profile of New York’s K-12 Educational Sector: Race, Income, and Religion**. (2020.0)
47. Baxter S.D., Thompson W.O., Litaker M.S., Guinn C.H., Frye F.H.A., Baglio M.L., Shaffer N.M.. **Accuracy of Fourth-Graders’ Dietary Recalls of School Breakfast and School Lunch Validated with Observations: In-Person versus Telephone Interviews**. *J. Nutr. Educ. Behav.* (2003.0) **35** 124-134. DOI: 10.1016/S1499-4046(06)60196-6
48. Scott A.R., Reed D.B., Kubena K.S., McIntosh W.. **Evaluation of a Group Administered 24-Hour Recall Method for Dietary Assessment**. *J. Ext.* (2007.0) **45** 1RIB3
49. Beasley J., Riley W.T., Jean-Mary J.. **Accuracy of a PDA-based dietary assessment program**. *Nutrition* (2005.0) **21** 672-677. DOI: 10.1016/j.nut.2004.11.006
50. Byker Shanks C., Banna J., Serrano E.L.. **Food Waste in the National School Lunch Program 1978–2015: A Systematic Review**. *J. Acad. Nutr. Diet.* (2017.0) **117** 1792-1807. DOI: 10.1016/j.jand.2017.06.008
51. Smith S.L., Cunningham-Sabo L.. **Food choice, plate waste and nutrient intake of elementary- and middle-school students participating in the US National School Lunch Program**. *Public Health Nutr.* (2014.0) **17** 1255-1263. DOI: 10.1017/S1368980013001894
52. Paxton A., Baxter S.D., Fleming P., Ammerman A.. **Validation of the School Lunch Recall Questionnaire to Capture School Lunch Intake of Third- to Fifth-Grade Students**. *J. Am. Diet. Assoc.* (2011.0) **111** 419-424. DOI: 10.1016/j.jada.2010.11.017
53. Miyawaki A., Lee J.S., Kobayashi Y.. **Impact of the school lunch program on overweight and obesity among junior high school students: A nationwide study in Japan**. *J. Public Health* (2019.0) **41** 362-370. DOI: 10.1093/pubmed/fdy095
54. 54.Dietary Guidelines for Americans, 2020–20259th ed.U.S. Department of Agriculture and U.S. Department of Health and Human ServicesWashington, DC, USA2020Available online: https://medicine.uams.edu/familymedicine/wp-content/uploads/sites/7/2021/05/USDA-Nutrition-Guide-for-Breastfeeding.pdf(accessed on 29 November 2022). *Dietary Guidelines for Americans, 2020–2025* (2020.0)
55. **National Standards in the National School Lunch and School Breakfast Programs: Final Rule. Federal Register: 2012**
56. Chen B., Kattelmann K., Comstock C., McCormack L., Wey H., Meendering J.. **Parenting Styles, Food Parenting Practices and Dietary Intakes of Preschoolers**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13103630
57. Skinner J.D., Carruth B.R., Bounds W., Ziegler P.J.. **Children’s Food Preferences**. *J. Am. Diet. Assoc.* (2002.0) **102** 1638-1647. DOI: 10.1016/S0002-8223(02)90349-4
58. Sharma S.V., Rashid T., Ranjit N., Byrd-Williams C., Chuang R.J., Roberts-Gray C., Briley M., Sweitzer S., Hoelscher D.M.. **Effectiveness of the Lunch is in the Bag program on communication between the parent, child and child-care provider around fruits, vegetables and whole grain foods: A group-randomized controlled trial**. *Prev. Med.* (2015.0) **81** 1-8. DOI: 10.1016/j.ypmed.2015.07.005
59. Aydin G., Margerison C., Worsley A., Booth A.. **Parents’ Communication with Teachers about Food and Nutrition Issues of Primary School Students**. *Children* (2022.0) **9**. DOI: 10.3390/children9040510
60. Kenney E.L., Cradock A.L., Long M.W., Barrett J.L., Giles C.M., Ward Z.J., Gortmaker S.L.. **Cost-Effectiveness of Water Promotion Strategies in Schools for Preventing Childhood Obesity and Increasing Water Intake**. *Obesity* (2019.0) **27** 2037-2045. DOI: 10.1002/oby.22615
61. Malik V.S., Pan A., Willett W.C., Hu F.B.. **Sugar-sweetened beverages and weight gain in children and adults: A systematic review and meta-analysis**. *Am. J. Clin. Nutr.* (2013.0) **98** 1084-1102. DOI: 10.3945/ajcn.113.058362
62. Vinke P.C., Blijleven K.A., Luitjens M.H.H.S., Corpeleijn E.. **Young Children’s Sugar-Sweetened Beverage Consumption and 5-Year Change in BMI: Lessons Learned from the Timing of Consumption**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12082486
63. **Lowering Sodium in School Lunches. American Heart Association**
64. Connelly J., Palacios C., Campa A., Gonzalez N., De La Torre K., Panchana X.. **Sodium and Sugar Intake Comparison among Participants of the USDA National School Lunch Program Versus Non-Participants Based on 2015–2016 NHANES Survey**. *Curr. Dev. Nutr.* (2020.0) **4** 4140172. DOI: 10.1093/cdn/nzaa043_023
65. Cohen J.F.W., Richardson S., Rimm E.B.. **Impact of the Updated USDA School Meal Standards, Chef-Enhanced Meals, and the Removal of Flavored Milk on School Meal Selection and Consumption**. *J. Acad. Nutr. Diet.* (2019.0) **119** 1511-1515. DOI: 10.1016/j.jand.2019.04.003
66. Bergman E.A., Saade C., Shaw E., Englund T., Cashman L., Taylor K.W., Watkins T., Rushing K.. **Lunches Selected and Consumed from the National School Lunch Program in Schools Designated as Healthier US School Challenge Schools are More Nutritions than Lunches Brought from Home**. *J. Child Nutr. Manag.* (2014.0) **38** n2
67. 67.
Panel on Macronutrients
Panel on the Definition of Dietary Fiber
Subcommittee on Upper Reference Levels of Nutrients
Subcommittee on Interpretation and Uses of Dietary Reference Intakes
Standing Committee on the Scientific Evaluation of Dietary Reference Intakes
Food and Nutrition Board
Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino AcidsNational Academies PressWashington, DC, USA2005Available online: https://www.nap.edu/catalog/10490(accessed on 12 July 2022). *Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids* (2005.0)
68. Condon E.M., Crepinsek M.K., Fox M.K.. **School Meals: Types of Foods Offered to and Consumed by Children at Lunch and Breakfast**. *J. Am. Diet. Assoc.* (2009.0) **109** S67-S78. DOI: 10.1016/j.jada.2008.10.062
69. Williams C.L.. **Importance of Dietary Fiber in Childhood**. *J. Am. Diet. Assoc.* (1995.0) **95** 1140-1149. DOI: 10.1016/S0002-8223(95)00307-X
70. Gan K., Tithecott C., Neilson L., Seabrook J.A., Dworatzek P.. **Picky Eating Is Associated with Lower Nutrient Intakes from Children’s Home-Packed School Lunches**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13061759
71. Whitaker R.C., Wright J.A., Finch A.J., Deyo R.A., Psaty B.M.. **School lunch: A comparison of the fat and cholesterol content with dietary guidelines**. *J. Pediatr.* (1993.0) **123** 857-862. DOI: 10.1016/S0022-3476(05)80380-4
72. 72.Dietary Guidelines for Americans. 2015–20208th ed.U.S. Department of Agriculture and U.S. Department of Health and Human ServicesWashington, DC, USA2015Available online: https://health.gov/our-work/nutrition-physical-activity/dietary-guidelines/previous-dietary-guidelines/2015(accessed on 12 July 2022). *Dietary Guidelines for Americans. 2015–2020* (2015.0)
|
---
title: 'What Has Changed over Years on Complementary Feeding in Italy: An Update'
authors:
- Marco Congiu
- Valeria Cimador
- Irene Bettini
- Teresa Rongai
- Flavio Labriola
- Francesca Sbravati
- Caterina Marcato
- Patrizia Alvisi
journal: Nutrients
year: 2023
pmcid: PMC10004811
doi: 10.3390/nu15051280
license: CC BY 4.0
---
# What Has Changed over Years on Complementary Feeding in Italy: An Update
## Abstract
Current practice regarding complementary feeding (CF) is influenced by socio-cultural background. Our group already investigated the Italian approach to CF in the years 2015–2017. Our aim was to update those data by finding out: if the habits have changed nationwide, how the trends changed in each area, and if the differences between regions still exist. We devised and submitted to Italian primary care paediatricians (PCP) a questionnaire consisting of four items regarding the suggestions they gave to families about CF and compared the results to the ones from our previous survey. We collected 595 responses. Traditional weaning was the most recommended method, with a significant reduction compared to the period of 2015–2017 ($41\%$ vs. $60\%$); conversely, the proportion of PCP endorsing baby-led weaning (BLW) or traditional spoon-feeding with adult food tastings has increased, while the endorsement of commercial baby foods dropped. BLW is still more popular in the North and Centre compared to the South ($24.9\%$, $22.3\%$, and $16.7\%$, respectively). The age to start CF and the habit of giving written information have not changed over time. Our results highlighted that Italian paediatricians encourage BLW and traditional CF with adult tastings more than in the past, at the expense of traditional spoon-feeding.
## 1. Introduction
The definition of complementary feeding (CF) given by the World Health Organization (WHO, Geneva, Switzerland) in 2002 is “the process starting when breast milk alone is no longer sufficient to meet the nutritional requirements of infants” so that “other foods and liquids are needed, along with breast milk” [1]. CF represents an essential milestone for a child’s growth but also a delicate and worrisome phase for parents. It is important not only for the child’s thriving but also for his or her neurological development. CF plays a key role in developing a taste for different foods [2] and represents an irreplaceable occasion to educate the taste for the following years, as the child wants to experience foods other than milk: a recent study pointed out that providing vegetables as first foods may be an effective strategy for improving vegetable consumption in infancy [3].
Whereas it is widely accepted that breast milk is the most adequate food in children up to approximately six months of age, there is much debate on the modalities to implement CF. These are indeed heavily influenced by socio-cultural context, paediatricians’ preferences and the families’ inclinations and are, therefore, various and diverse. We report the definitions of the main modalities to implement CF; however, these are not universal and are prone to interpretation. Therefore, they may assume different characteristics in different parts of the world:“Traditional spoon-feeding”: the parent spoon-feeds the child, which remains mostly passive in the process; the diet comprises homemade liquid or semi-liquid foods or commercial baby foods. Italian families usually feed their children, as a first food, a meal made of vegetable broth with semolina or rice flour, meat, olive oil and parmesan cheese. “Traditional spoon-feeding with adult food tastings”: while following a traditional CF style, in this case, parents occasionally mash and mince the food they are eating and feed it to their child. Baby-Led Weaning (BLW): parents are responsible for what, when and where the infant is fed by providing chunks of food but allowing the infant to bring the food to the mouth and deciding on how much food to consume [4]. ○“Self-weaning”: an on-demand approach that can be considered the Italian take on BLW; the food offered to the infant is partly the same as that of the parents; the main difference is that here the food is minced, mashed and spoon-fed.
Among these different modalities, BLW has quickly gained popularity in the past few years in Western countries [4]. In a recent Italian study, the frequency of a BLW approach was positively related to breastfeeding, later exposure to complementary foods, earlier exposure to both finger and family foods, and higher interest in family food and shared family meals [5].
Due to its pivotal role in the infant’s present and future well-being, there has been much interest recently in investigating the most adequate way to implement CF. The most recent recommendation update was recently produced by the Italian Society for Preventive and Social Pediatrics (SIPPS), the Italian Society for Developmental Origins of Health and Disease (SIDOHaD), the Italian Federation of Pediatricians (FIMP), and the Italian Society of Pediatric Nutrition (SINUPE) Joint Working Group: their indications are strictly evidence-based and cover several aspects of CF [6]. It is apparent how old beliefs are being brought into question, e.g., no longer is it recommended to postpone or bring forward the introduction of allergenic foods to reduce the risk of allergy [6,7].
The Italian National Healthcare System provides all families with a primary care paediatrician (PCP) who has an established role in caring for the pediatric population during infancy, childhood, and adolescence, with particular attention to their growth and overall health. This includes, among other things, counselling the families about nutrition and the proper way to implement weaning. In order to better understand the current Italian situation regarding CF, in our previous study, we described CF practices in Italy and the differences existing among different geographical areas, considering the timespan from January 2015 to December 2017 [8]. Since many aspects of CF are quickly changing, we submitted the same questionnaire to primary care paediatricians during the year 2022. Therefore, the objective of this study is to compare how CF has evolved over this period, investigating: [1] if the habits have changed on a national basis, [2] how the trends could have changed in each area; [3] if the differences between regions already pointed out still exist.
## 2. Materials and Methods
In order to investigate the paediatricians’ opinions on CF, we designed a specific questionnaire, which was administered through Pediatotem® (Pediatotemweb v. 2.22.0), software developed by Lviiier srl. Pediatotem® is used by almost 1600 PCP all over Italy and is therefore extremely useful for epidemiologic research regarding primary care paediatrics and assisted families. Based on estimates regarding the number of PCP working in Italy during the year 2022 (extracted from national reports from previous years), the total number of active PCP working in Italy during the timespan of the study was considered to be around 7300. Therefore, the Pediatotem® software enabled us to reach almost ¼ of them. The minimum number of respondents was calculated to be 365 PCP in order to achieve a representative sample with a $5\%$ margin of error and $95\%$ confidence interval. The questionnaire was administered during a timespan ranging from January to October 2022 to all the PCP using the Pediatotem software and was filled out on a voluntary basis. The questionnaire was specifically intended to clarify the attitude of PCP towards CF in full-term and healthy babies. It was a slightly modified version of the one designed for the previous inquiry, consisting of four items regarding [1] the method of proposed CF; [2] the suggested age for introduction of CF; [3] the habit of providing parents written information; and [4] the paediatrician’s opinion about the use of baby foods (Table 1).
We divided Italy into three geographical areas based on socio-cultural similarities, as shown in Figure 1.
Categorical variables were tabled, indicating the total number (n) and frequency (%). Categorical variables were analysed by 2-way tables, and an χ² test was performed to detect statistically significant differences between the groups. A difference of proportions was performed to compare the results we obtained with the ones from the previous study. Statistical analysis was performed with JASP software (v0.16.2), and p-values < 0.01 were considered significant.
## 3. Results
We collected a total of $\frac{595}{1600}$ ($37\%$) responses: 225 from Northern, 148 from Central and 222 from Southern Italy. Considering the entirety of PCP working in Italy during the examined timespan, roughly $8\%$ of them filled out the questionnaire ($7\%$ of the PCPs from Northern regions, $9\%$ from the Centre and $8.5\%$ from the South of Italy) [9]. The collection of ancillary data, such as the PCP age and the specific district in which they work, was optional, and therefore we were able to get only partial data.
## 3.1. Style of CF
We report an overall rate of $41\%$ ($\frac{244}{595}$) of paediatricians recommending the traditional spoon-feeding approach, mainly in the South ($53.6\%$ of Southern paediatricians). This practice is less common in the North of Italy ($25.8\%$), and Central regions lay in the middle ($45.3\%$). On the other hand, 244 paediatricians ($37.6\%$) suggest spoon-feeding with adult food tastings ($49.3\%$ of Northern paediatricians, $32.4\%$ of Central and $29.3\%$ of Southern ones). A considerable share of doctors follows the self-weaning approach ($\frac{121}{595}$, namely $21.2\%$), with a prevalence of this habit among the Northern paediatricians ($24.9\%$ compared to $22.3\%$ and $16.7\%$ of Central and Southern ones).
## 3.2. Age of CF Introduction
Overall, $\frac{544}{595}$ ($91.5\%$) of the interviewed paediatricians suggest starting CF between the 5th and 6th month of life, with a slight predilection for the 5th month ($\frac{275}{595}$, $46.2\%$). Only 45 ($7.6\%$) of them propose the introduction of CF at four months of age and six ($1\%$) beyond six months.
## 3.3. Written Information
Of the sample, $\frac{512}{595}$ ($86.1\%$) provide families with written information about CF, with no statistical difference between the three areas.
## 3.4. Baby Foods
Some $\frac{305}{595}$ ($51.3\%$) of the interviewed paediatricians endorse the use of BFs, mainly in Southern and Central regions.
The results are summarised in Table 2.
## 3.5. Changing Trend
Comparing the results with the ones from the previous study (taking place during the period 2015–2017), we found a nationwide tendency to suggest less traditional feeding and more self-weaning and traditional CF with adult food tastings. The differences showed statistical significance (Table 3). This is also true on a regional basis: the endorsement of traditional spoon-feeding has reduced across the country. On the contrary, the application of self-weaning has increased significantly only in the Centre of the country, and the traditional CF complemented with adult food tastings only in the South; the increase is evident (even if not significant) in the other regions too (Table 4).
The use of baby foods showed a significant reduction, both from a national and regional perspective. The only regions that seem to continue using baby foods as during the previous period are those from Central Italy (Table 3 and Table 4).
Regarding the other items of the questionnaire (the age to start CF and the habit of giving written information to families), the analysis did not point out any significant difference from the previous report.
## 4. Discussion
CF is still a controversial subject in clinical practice. Its importance is widely acknowledged, as it is essential to guarantee adequate growth to children, as well as to favour healthy eating habits in the future, thus preventing several non-communicable diseases in adult life [2]: this becomes even more important considering that in the Italian population, roughly $20\%$ of children between eight and nine years are overweight, almost $10\%$ are obese, and a considerable share do not consume an adequate quantity of fruit and vegetables [10]. Yet, conclusive evidence on the best modality to implement CF is lacking [6]. This can be confusing for families, who can be influenced by the new trends that are gaining popularity (e.g., vegetarian or vegan diets), which may pose a threat to the child’s growth and neurological development if the paediatrician is not involved [11].
From the paediatricians’ perspective, the situation is not much clear either. Even for a widely used method of weaning, such as BLW, there is no agreement on its formal definition. Some authors focus on food consistency (pureed vs. whole pieces) [12], others on the modality of the feeding (parent-led vs baby-led) [13], and some others value the sharing of meals with the rest of the family, which stimulates the child to eat the same food as they do [5,14]. Also, “BLW” is a generic term which encompasses various practices, all sharing the central role of the infant in leading the process of CF. It was first introduced to the international scientific community by Gill Rapley [15], but other modified versions of BLW have been described [16,17], fuelling the uncertainty about what is to be considered BLW and what is not. In this paper, we refer generically to it as the alternative to parent-led weaning (or traditional spoon feeding), mainly intended as its Italian variation, namely “self-weaning” (as illustrated by Lucio Piermarini even before the label of “baby-led weaning” was coined) [17].
The role of paediatricians in supporting the process of CF has already been pointed out [8]. As society evolves, the needs and expectations of parents about this topic change, and recommendations given to families must somehow meet these new demands. Following the growing interest in this topic, we wanted to update the results from our previous study with a new survey sent to PCP. From the data we were able to collect, it emerged that indeed in Italy, the approach of paediatricians to CF has changed over the past few years.
## 4.1. CF Style
Compared to the period 2015–2017, we observed a nationwide reduction in traditional spoon feeding (from $60\%$ to $41\%$, $p \leq 0.01$), although it is still the most recommended method across the country. The reduction is more evident in the Northern regions, where it is almost halved (from $48\%$ to $25.8\%$), where its use is significantly lower compared to the other two regions. On the contrary, there exists a tendency to maintain this habit in the Centre and South of Italy, where it is still by far the most endorsed way of implementing CF.
Conversely, BLW styles became more popular. The increase in self-weaning is more evident in the Central regions (where it has risen from $11\%$ to $22.3\%$), but also in the North and South, it strengthened its position as an effective and feasible way to wean infants—even if in the South it is still the least chosen method. Now self-weaning appears to be evenly distributed across the country, without significant differences between the regions.
Finally, traditional spoon-feeding with adult food tastings, which represents somehow a middle ground between the other two, obtained a substantial increase (passing from $28\%$ to $37.6\%$): now it appears to be the most endorsed method in the North of Italy (where it is recommended by almost half of the interviewed paediatricians); also in the other regions the use of this style has increased considerably. This approach seems to be especially appreciated due to the fact that it appropriately balances all macronutrients while at the same time providing a vast arrangement of different foods (and therefore tastes and consistencies). CF with adult food tastings has the advantage of respecting the traditions of every family, which is of capital importance in a multicultural environment as the one modern society is evolving into. Furthermore, knowing that the child might be involved during the family meal may encourage the whole family to adopt a more wholesome diet. In this respect, it is essential for the paediatrician to educate the parents not to add salt to the foods and give their children only foods made with fresh and seasonal ingredients.
Current literature shows little data about how CF is implemented in other countries. For what concerns Europe, a Spanish survey taking place in 2018 highlighted how PCP mostly recommended traditional spoon-feeding and showed a scarce tendency to the routine use of the BLW approach [18]. A more recent study confirmed that in Spain, BLW is not a common choice for weaning: the prevalence of use is low, and more than half of the interviewed mothers had no knowledge of this practice [19]. BLW is also quite uncommon in New Zealand, wherein only between $8\%$ and $18\%$ of parents indicated it as the preferred practice, while the traditional spoon-feeding method was implemented by $70\%$ of parents [13,20]. On the contrary, in the UK, between $30\%$ and $60\%$ of parents strictly follow BLW practices [21,22].
BLW is substantially different from traditional CF, as discussed above. However, the two styles appear to be similar as regards short-term outcomes such as energy intake and risk of choking [4]. It is of capital importance to understand that, based on the currently available scientific evidence, it is not possible to recommend BLW over the traditional approach with the objective of preventing longer-term outcomes such as obesity, or improving children’s growth, as it is not proven to be more effective in achieving these goals either [6,23]. Nevertheless, it is considered to be a safe approach which meets the nutritional needs of the weaning infant, with a few postulated advantages such as lower food fussiness and higher satiety responsiveness. Interestingly, as picky eating during early years has been related to an increased risk of subsequently developing eating disorders such as anorexia nervosa [24], a feeding style which is more baby-led may be of help in reducing this kind of burden. BLW has also proven to be positively associated with language production (eating foods of different textures helps in developing oral and motor skills also required for language production) and comprehension (the BLW approach entails sharing the meal with the rest of the family, which exposes the child to specific interactions and language use) [25] which potentially represents another incentive to resort to this method.
## 4.2. CF Age
The advised age to start weaning has not changed since the 2015–2017 period. Most paediatricians (roughly $90\%$ of the total) still recommend beginning CF between five and six months of age. A few paediatricians recommend an earlier beginning, at four months of age; interestingly enough, they appear to be located mostly in the South (where they represent $12.2\%$, a significantly higher proportion compared to the other regions).
The suggested age to begin CF is one of the few clear recommendations available about this topic [6,7]. This is directly related to the maturation of both the gastrointestinal tract and the kidney, and, more importantly, to the neurodevelopmental milestones that the child needs to achieve before starting this process safely (the ability to sit unsupported, reach for food, chew, etc.). Consistently with these statements, the age at which CF is generally started has not significantly changed over time in Italian practice. Of note, the most recent recommendations state that CF should be started between 17 and 26 weeks of age (namely between five and six months), but the optimal goal for breastfed infants should be to start at six months of age, considering the adequacy of milk as nourishment until this age and the non-nutritional benefits of mother milk. Human milk is, in fact, rich in compounds that help the immunologic function of the baby (IgA, lactoferrin, HMO), hormones that modulate the metabolic function (such as insulin and leptin), as well as carrying beneficial bacteria contributing to establishing a healthy microbiota, and stem cells [26].
As for the practices carried out in other regions of the world, a survey investigating the habits of physicians towards CF in Middle-East and North Africa showed a high rate of introduction of foods out of the optimal period, namely before four months of age ($2\%$) or after six months ($36\%$) [27]. On the contrary, in Spain, $10.7\%$ of paediatricians suggest starting CF at four months of age, showing similar results to the one obtained in the Southern regions of Italy [18].
From our results, it emerged that Italian paediatricians generally seem to adhere to the WHO’s advice, suggesting beginning CF at six months of age (or during the sixth month of life). There are still a few exceptions, mostly in the South of the country: the explanations could be various, but in our opinion, they could be mainly related to the socio-economic discrepancies between the North and the South. This is bound to the different rates of BLW found in these regions: in previous reports, BLW was shown to be associated with a later introduction of solid foods [28,29]. BLW is also related to a higher rate of breastfeeding [29], and in turn, breastfeeding is (at least in high-income countries) positively associated with the economic condition and educational level of mothers [30]. In fact, from our previous study, it emerged that the breastfeeding rate is lower in the South compared to the North of Italy [8]. In taking all this into account, the current evidence draws attention to the common thread linking the families’ socio-economic condition with BLW, breastfeeding, and compliance with CF recommendations. It is considered that the cited studies focus primarily on the family perspective, while our goal is to assess the conduct of paediatricians; however, it is likely that the recommendations given by doctors are adjusted to the target population they are directed at, supporting the population preferences instead of contrasting them.
## 4.3. CF Written Information
Written information is an easy and convenient way to deliver the mainstays of CF to families. It has a few noteworthy advantages: it is clearer, less open to misinterpretation and always available for parents to consult whenever needed. Nevertheless, it could represent a cause of distance between the paediatrician and the family, as it can be perceived as a manner to standardise a practice that, if anything, every family inevitably must decline into their own personal reality. We attested to a slight reduction in giving written information to families—which nevertheless continues to be the most common habit (about $85\%$ of paediatricians still use this approach nationwide). This could be due to the fact that paediatricians are becoming more aware of the necessity to personalise the practice of CF, in respect of family preferences and multiethnicity, among other things. It is also in line with the increasing endorsement of BLW practices (which are by definition tailored to the child they are addressed to), as stated above.
Among other countries, the practices around this topic are varied. In Spain, paediatricians seem to adopt measures similar to Italy, giving written information to families in $95.3\%$ of cases [18]. On the contrary, in France, the preferred method is to give oral information, with only about $5\%$ of families receiving written information from their paediatrician [31]. This could lead parents to search for advice from other sources, possibly increasing the uncertainty about this topic.
It is fundamental, from the paediatrician’s perspective, to teach families the main nutritional concepts to guarantee an adequate intake of nutrients and healthy eating habits, but it is also of capital importance to empower the parents and encourage them to adopt a responsible approach, which has shown an association with positive outcomes, such as increased satiety responsiveness and lower childhood body mass index (BMI) [23]. Responsible feeding per se does not prevent the development of medical conditions (such as hypertension and type 2 diabetes mellitus) later in life. Even so, it is advisable to promote a responsible approach from the very first months of life and to reinforce it during CF, as it likely contributes to achieving adequate weight during the first years of life [6]. We also believe that a responsible feeding style could help children to become more confident in their approach to food and to foster a bond with their parents.
## 4.4. CF Industrial Baby Foods
The type of food suggested to begin CF is gradually changing. In the period of 2015–2017, we found a tendency to endorse the use of industrial baby foods (roughly two out of every three paediatricians), while now the proportion of paediatricians recommending the use of ready-made products almost equals that of those who do not ($51.3\%$ vs. $48.7\%$, respectively). These differences are more evident if seen from a regional perspective. In the North of Italy, the trend seems to have completely inverted, with $57.8\%$ not recommending commercial baby foods (vs. $40\%$ of the 2015–2017 period). In the South, the paediatricians recommending the use of industrial baby foods are still the majority, but with a significant drop from $81\%$ to $58.1\%$. For comparison purposes, the survey conducted in the Middle East and North Africa reports $44\%$ of physicians recommending using only homemade foods [27].
Our data seem to confirm the aforementioned trends: in a setting where BLW is becoming more and more popular, paediatricians have to support families by encouraging them to cook healthier foods which can be eaten by infants, thus decreasing the consumption of ready-made baby foods accordingly.
## 5. Conclusions
The most recent data show a general change regarding CF in Italy, at least from the paediatricians’ perspective. BLW—or slight variations of this approach—and traditional spoon feeding with adult food tastings are becoming more and more popular, and this may be due to the increasing evidence supporting some advantages over the traditional weaning method. The use of industrial baby foods varied accordingly over time: in an environment where infants share the foods with the rest of the family, the consumption of ready-made commercial products is supposed to drop, as pointed out by our data. It is unlikely that our study will bring immediate consequences to the current practice regarding CF. Nevertheless, we find that traditional spoon-feeding with adult food tastings shares the benefit of both traditional CF and self-weaning, as it guarantees the appropriate food intake and variety and respects the cultural preferences of families (which represents a key aspect as multiethnicity is becoming one of the most relevant features of our society). We, therefore, deem it the most suitable option for weaning. In future symposiums, we are going to present our results to PCP and our hope is that this “middle way” of weaning will become even more used in the forthcoming years.
Our study has a few limitations. Firstly, the questionnaire was filled out on a voluntary basis, thus creating a selection bias. Secondly, in this study, we did not investigate if the environment in which the paediatricians work (e.g., bigger cities vs smaller towns) is related to different behaviours in CF advice, taking into consideration that weaning habits are influenced by the socio-economic status of the family. Further studies are needed to clarify this point. Third, ancillary data such as PCP’s gender and age were not available for all the participants. Thus, it was not possible to draw conclusions about whether they could influence the habits of PCP or not.
The strong point of this study is that the data come from the real-life practice of the paediatricians interviewed, which permits us to portray an accurate picture of the current state of the art on CF in Italy. The data were collected at a short distance from the last report, giving insight into the changes taking place in this short time span. Moreover, the paediatricians who were included in the study are more evenly distributed around Italy compared to our last report, reducing the biases of the analysis and giving a more accurate depiction of the practice in our country.
From the paediatricians’ perspective, assisting families during CF is a pivotal opportunity to educate them on healthy feeding habits, which could potentially influence long-term health conditions for all family members at all ages. From what is stated above, it appears clear that the habits about CF among Italian paediatricians are radically changing. Interestingly enough, the differences between regions seem to be less evident compared to our previous report. But more importantly, these changes point towards a more responsible approach to CF, consistently with the indications from WHO [32,33] and in line with the latest recommendations published by the main scientific organizations involved. Our hope is that this trend will be maintained in the forthcoming years, with paediatricians being able to support and guide the families in the critical moment of CF and cooperating to ensure that children and their family experience a healthy approach to food. In this regard, the Italian Society of Pediatrics (SIP) and the Italian Society of Pediatric Gastroenterology and Nutrition (SIGENP) promoted cultural campaigns in order to further raise awareness of the importance of CF for the child’s health and well-being.
## References
1. 1.
World Health Organization
Complementary Feeding: Report of the Global Consultation, and Summary of Guiding Principles for Complementary Feeding of the Breastfed ChildWorld Health OrganizationGeneva, Switzerland2003Available online: https://apps.who.int/iris/handle/10665/42739(accessed on 8 January 2023). *Complementary Feeding: Report of the Global Consultation, and Summary of Guiding Principles for Complementary Feeding of the Breastfed Child* (2003)
2. Agostoni C., Decsi T., Fewtrell M., Goulet O., Kolacek S., Koletzko B., Michaelsen K.F., Moreno L., Puntis J., Rigo J.. **Complementary feeding: A commentary by the ESPGHAN Committee on Nutrition**. *J. Pediatr. Gastroenterol. Nutr.* (2008) **46** 99-110. DOI: 10.1097/01.mpg.0000304464.60788.bd
3. Rapson J.P., von Hurst P.R., Hetherington M.M., Mazahery H., Conlon C.A.. **Starting complementary feeding with vegetables only increases vegetable acceptance at 9 months: A randomized controlled trial**. *Am. J. Clin. Nutr.* (2022) **116** 111-121. DOI: 10.1093/ajcn/nqac080
4. Boswell N.. **Complementary Feeding Methods—A Review of the Benefits and Risks**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph18137165
5. Addessi E., Galloway A.T., Wingrove T., Brochu H., Pierantozzi A., Bellagamba F., Farrow C.V.. **Baby-led weaning in Italy and potential implications for infant development**. *Appetite* (2021) **164** 105286. DOI: 10.1016/j.appet.2021.105286
6. Caroli M., Vania A., Verga M.C., Di Mauro G., Bergamini M., Cuomo B., D’Anna R., D’Antonio G., Dello Iacono I., Dessì A.. **Recommendations on Complementary Feeding as a Tool for Prevention of Non-Communicable Diseases (NCDs)-Paper Co-Drafted by the SIPPS, FIMP, SIDOHaD, and SINUPE Joint Working Group**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14020257
7. Fewtrell M., Bronsky J., Campoy C., Domellöf M., Embleton N., Mis N.F., Hojsak I., Hulst J.M., Indrio F., Lapillonne A.. **Complementary Feeding: A Position Paper by the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN) Committee on Nutrition**. *J. Pediatr. Gastroenterol. Nutr.* (2017) **64** 119-132. DOI: 10.1097/MPG.0000000000001454
8. Alvisi P., Congiu M., Ficara M., De Gregorio P., Ghio R., Spisni E., Di Saverio P., Labriola F., Lacorte D., Lionetti P.. **Complementary Feeding in Italy: From Tradition to Innovation**. *Children* (2021) **8**. DOI: 10.3390/children8080638
9. **Annuario Statistico del Servizio Sanitario Nazionale**. (2020)
10. **OKKIO alla Salute**. (2019)
11. Nuzzi G., Gerini C., Comberiati P., Peroni D.G.. **The weaning practices: A new challenge for pediatricians?**. *Pediatr. Allergy Immunol.* (2022) **33** 44-46. DOI: 10.1111/pai.13627
12. Brown A., Lee M.. **A descriptive study investigating the use and nature of baby-led weaning in a UK sample of mothers**. *Matern. Child Nutr.* (2011) **7** 34-47. DOI: 10.1111/j.1740-8709.2010.00243.x
13. Cameron S.L., Taylor R.W., Heath A.L.M.. **Parent-led or baby-led? Associations between complementary feeding practices and health-related behaviours in a survey of New Zealand families**. *BMJ Open* (2013) **3** e003946. DOI: 10.1136/bmjopen-2013-003946
14. Piermarini L.. **Svezzamento Libero, Svezzamento Felice?**. *Med. e Bambino* (2020) **39** 16-19
15. Rapley G., Hall Moran V., Dykes F.. **Baby-led weaning**. *Maternal and Infant Nutrition and Nurture: Controversies and Challenges* (2005) 275-298
16. Daniels L., Heath A.L.M., Williams S.M., Cameron S.L., Fleming E.A., Taylor B.J., Wheeler B.J., Gibson R.S., Taylor R.W.. **Baby-Led Introduction to SolidS (BLISS) study: A randomised controlled trial of a baby-led approach to complementary feeding**. *BMC Pediatr.* (2015) **15**. DOI: 10.1186/s12887-015-0491-8
17. Piermarini L.. **Autosvezzamento**. *Med. e Bambino* (2002) **21** 468-471
18. Martínez Rubio A., Cantarero Vallejo D., Espín Jaime B.. **¿Cómo orientan los pediatras de Atención Primaria la alimentación complementaria?**. *Pediatr. Atención Primaria* (2018) **20** 35-44
19. Pérez-Ríos M., Santiago-Pérez M.I., Butler H., Mourino N., Malvar A., Hervada X.. **Baby-led weaning: Prevalence and associated factors in Spain**. *Eur. J. Pediatr.* (2020) **179** 849-853. DOI: 10.1007/s00431-020-03579-7
20. Fu X., Conlon C.A., Haszard J.J., Beck K.L., von Hurst P.R., Taylor R.W., Heath A.-L.M.. **Food fussiness and early feeding characteristics of infants following Baby-Led Weaning and traditional spoon-feeding in New Zealand: An internet survey**. *Appetite* (2018) **130** 110-116. DOI: 10.1016/j.appet.2018.07.033
21. Rowan H., Lee M., Brown A.. **Differences in dietary composition between infants introduced to complementary foods using Baby-led weaning and traditional spoon feeding**. *J. Hum. Nutr. Diet.* (2018) **32** 11-20. DOI: 10.1111/jhn.12616
22. Brown A.. **Differences in eating behaviour, well-being and personality between mothers following baby-led vs. traditional weaning styles**. *Matern. Child Nutr.* (2016) **12** 826-837. DOI: 10.1111/mcn.12172
23. Bergamini M., Simeone G., Verga M.C., Doria M., Cuomo B., D’Antonio G., Dello Iacono I., Di Mauro G., Leonardi L., Miniello V.L.. **Complementary Feeding Caregivers’ Practices and Growth, Risk of Overweight/Obesity, and Other Non-Communicable Diseases: A Systematic Review and Meta-Analysis**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14132646
24. Herle M., Smith A.D., Kininmonth A., Llewellyn C.. **The Role of Eating Behaviours in Genetic Susceptibility to Obesity**. *Curr. Obes. Rep.* (2020) **9** 512-521. DOI: 10.1007/s13679-020-00402-0
25. Webber C., Blissett J., Addessi E., Galloway A.T., Shapiro L., Farrow C.. **An infant-led approach to complementary feeding is positively associated with language development**. *Matern. Child Nutr.* (2021) **17** e13206. DOI: 10.1111/mcn.13206
26. Mosca F., Giannì M.L.. **Human milk: Composition and health benefits**. *Pediatr. Med. Chir.* (2017) **39** 155. DOI: 10.4081/pmc.2017.155
27. Lifschitz C.H., Miqdady M., Indrio F., Haddad J., Tawfik E., AbdelHak A., Mouane N., Salah M., Khatami K., Olang B.. **Practices of introduction of complementary feeding and iron deficiency prevention in the middle East and North Africa**. *J. Pediatr. Gastroenterol. Nutr.* (2018) **67** 538-542. DOI: 10.1097/MPG.0000000000002059
28. Brown A., Jones S.W., Rowan H.. **Baby-Led Weaning: The Evidence to Date**. *Curr. Nutr. Rep.* (2017) **6** 148-156. DOI: 10.1007/s13668-017-0201-2
29. Morison B.J., Taylor R.W., Haszard J.J., Schramm C.J., Erickson L.W., Fangupo L.J., Fleming E.A., Luciano A., Heath A.L.M.. **How different are baby-led weaning and conventional complementary feeding? A cross-sectional study of infants aged 6–8 months**. *BMJ Open* (2016) **6** e010665. DOI: 10.1136/bmjopen-2015-010665
30. Victora C.G., Bahl R., Barros A.J., França G.V., Horton S., Krasevec J., Murch S., Sankar M.J., Walker N., Rollins N.C.. **Breastfeeding in the 21st century: Epidemiology, mechanisms, and lifelong effect**. *Lancet* (2016) **387** 475-490. DOI: 10.1016/S0140-6736(15)01024-7
31. Marduel Boulanger A., Vernet M.. **Introduction of new food textures during complementary feeding: Observations in France**. *Arch. Pediatr.* (2018) **25** 6-12. DOI: 10.1016/j.arcped.2017.10.025
32. 32.Guidelines for an Integrated Approach to the Nutritional Care of HIV-Infected Children (6 Months–14 Years)World Health OrganizationGeneva, Switzerland2009Available online: http://www.ncbi.nlm.nih.gov/books/NBK143685/(accessed on 9 January 2023). *Guidelines for an Integrated Approach to the Nutritional Care of HIV-Infected Children (6 Months–14 Years)* (2009)
33. Dewey K.. **Who PAHO. Guiding Principles for Complementary Feeding of the Breastfed Child**. (2003)
|
---
title: 'Effect of Immuno-Nutrition on Malnutrition, Inflammatory Response and Clinical
Course of Semi-Critically Ill COVID-19 Patients: A Pilot Perspective Study'
authors:
- Marialaura Scarcella
- Emidio Scarpellini
- Sara Piergallini
- Emanuele Rinninella
- Karen Routhiaux
- Carlo Rasetti
- Ludovico Abenavoli
- Edoardo De Robertis
- Pietro Manzi
- Rita Commissari
- Riccardo Monti
- Michela Zanetti
journal: Nutrients
year: 2023
pmcid: PMC10004815
doi: 10.3390/nu15051250
license: CC BY 4.0
---
# Effect of Immuno-Nutrition on Malnutrition, Inflammatory Response and Clinical Course of Semi-Critically Ill COVID-19 Patients: A Pilot Perspective Study
## Abstract
Background: The SARS-COV 2 pandemic has hit on our lives since early 2020. During different contagion waves, both malnutrition and overweight significantly correlated with patient mortality. Immune-nutrition (IN) has shown promising results in the clinical course of pediatric inflammatory bowel disease (IBD) and in both the rate of extubation and mortality of patients admitted to an intensive care unit (ICU). Thus, we wanted to assess the effects of IN on a clinical course of patients admitted to a semi-intensive COVID-19 Unit during the fourth wave of contagion that occurred at the end of 2021. Methods: we prospectively enrolled patients admitted to the semi-intensive COVID-19 Unit of San Benedetto General hospital. All patients had a biochemical, anthropometric, high-resolution tomography chest scan (HRCT) and complete nutritional assessments at the time of admission, after oral administration of immune-nutrition (IN) formula, and at 15 days interval follow-up. Results: we enrolled 34 consecutive patients (age 70.3 ± 5.4 years, 6 F, BMI 27.0 ± 0.5 kg/m2). Main comorbidities were diabetes ($20\%$, type 2 90 %), hyperuricemia ($15\%$), hypertension ($38\%$), chronic ischemic heart disease (8 %), COPD ($8\%$), anxiety syndrome ($5\%$), and depression ($5\%$). $58\%$ of patients were affected as moderately-to-severely overweight; mini nutritional assessment (MNA) score (4.8 ± 0.7) and phase angle (PA) values (3.8 ± 0.5) suggestive of malnutrition were present in $15\%$ of patients, mainly with a history of cancer. After 15 days upon admission, we recorded 3 deaths (mean age 75.7 ± 5.1 years, BMI 26.3 ± 0.7 kg/m2) and 4 patients were admitted to the ICU. Following IN formula administration, inflammatory markers significantly decreased ($p \leq 0.05$) while BMI and PA did not worsen. These latter findings were not observed in a historical control group that did not receive IN. Only one patient needed protein-rich formula administration. Conclusions: in this overweight COVID-19 population immune-nutrition prevented malnutrition development with a significant decrease of inflammatory markers.
## 1. Introduction
From January 2020, the novel corona virus (SARS-CoV2) disease, firstly believed to be characterized by pneumonia only (namely, COVID-19), spread around the world. A terrible pandemic has caused healthcare systems to crash with a high damage in terms of lives and morbidity to humanity. Wave after wave of COVID-19 pandemic helped researchers to understand and assemble a clearer and clearer knowledge of this hyper-inflammatory syndrome. In fact, it was true that a significant percentage of patients developed a serious bilateral pneumonia, resembling severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), but other symptoms and clinical manifestations were prevalent also. For example, several patients were developing pulmonary thrombo-embolism, encephalitis, and hemorrhage [1]. Interestingly, COVID-19 pneumonia is often associated with gastrointestinal disorders that do not allow the patient to be adequately fed, especially in the pre-Intensive Care Unit (ICU) stages [1,2].
Although the severity of the clinical condition has been reported to be milder than SARS with a mortality rate ranging from 4.3 to $11\%$, the latter has been a huge wound for our societies [2]. In detail, there is a direct correlation between inflammatory status, incidence of comorbidities, and mortality in COVID-19 patients [2]. Looking at physiology, main defensive mechanisms in the human body against viruses, in general, and SARS-CoV2, specifically, are the physical barriers (namely, skin, and mucosal membranes): stomach acid content and digestive enzymes, gut microbiome, and innate and acquired immunity [2]. Many of the reactions maintaining these mechanisms need vitamins A, D, B, iron, and zinc as coenzymes. This is one of the main reasons why setting an appropriate nutritional strategy as a part of the treatment is crucial for survival of these patients [2].
There is a clear correlation between obesity and mortality in COVID-19 patients. In fact, sarcopenia as feature of malnutrition, typical of obesity, is significantly associated with micro-inflammation and major allowance of SARS-CoV 2 entrance into target cells [3].
To date, there are limited therapeutic remedies available for the treatment of COVID-19. Thus, nutritional modulation of the immune system function has been investigated both as a preventive and curative option [4,5]. In fact, there is a growing number of recently published key studies suggesting promising effects of immuno-nutrition on acute respiratory infections [6,7]. Indeed, immuno-nutrition can be defined as “modulation of either the activity of the immune system or modulation of the consequences of activation of the immune system by nutrients or specific food items fed in amounts above those normally encountered in the diet” [8]. Recently, specific immuno-nutrients have been proposed as effective items for both or add-on treatments of COVID-19 vs. evidence-based standard therapy, promising results for the reduction of innate and adaptive immune response responsible of “cytokines’ storm” typical of COVID-19 patients [9].
In ICU patients, the inflammatory status is associated with an increased mortality [3,4]. For example, the higher is the Nutritional Risk Score (NRS), the higher the incidence of acquired healthcare-associated infections and the mortality risk index [3,5].
The ICU COVID-19 patient is a frail one with multiple comorbidities, affected by hypoxia, inflammation, high body temperature, increased oxygen demand, and often prone to malnutrition. In the early stages of COVID-19, patients experience anorexia exacerbated by severe coughing, fever, dyspnea, anosmia, hypoxia, and fatigue, causing difficulties to maintain an appropriate nutritional oral intake. Moreover, before ICU admission, the patients are often treated with Continuous Positive Airway Pressure (CPAP) or Non-invasive Ventilation (NIV) that do not allow oral feeding in a large percentage.
Previous data from our study group demonstrated the efficacy of whey-protein-rich enteral feeding formula in ICU ventilated COVID-19 patients with earlier extubation time and improved nutritional status [10].
Thus, this prospective observational exploratory single-center study aims to evaluate the nutritional and anti-inflammatory effects of an outlined nutritional protocol based on immuno-nutrition (IN) in COVID-19 patients admitted to the semi-intensive COVID-19 unit.
## 2.1. Study Protocol
In this single-center perspective exploratory study, we consecutively enrolled COVID-19 adult patients admitted at the semi-intensive Unit of “Madonna del Soccorso“ General Hospital, San Benedetto del Tronto, Italy between 1 September and 31 December 2021. We respected regional Ethical Committee rules for patients’ enrollment (Ethical Committee Marche, Italy). Inclusion criteria were: age > 18 years, confirmed diagnosis of SARS-CoV2 infection, and need for non-invasive mechanical ventilation for at least 48 h. Patients were treated according to the updated guidelines for COVID-19 [11].
All patients had a biochemical, anthropometric, high-resolution tomography chest scan (HRCT) and complete nutritional assessments (MNA test and bioimpedance vector analysis (BIVA)) at the time of admission and at 15 days interval follow-up, namely after daily oral administration of immune-nutrition (IN) formula.
The study group was compared with historical COVID-19 patients not administered with IN formula.
## 2.2. Inclusion and Exclusion Criteria
We included consecutive patients admitted to the semi-intensive Unit of San Benedetto General Hospital in need of NIV because of SARS-CoV 2 infection.
Exclusion criteria were: pregnancy, artificial nutrition in the previous 15 days upon admission, allergy to the immuno-nutrition components, major GI tract surgery, malapsorption syndromes, inflammatory bowel disease, GI motility disorders, acute or chronic pancreatitis, immudepression (e.g., acquired immunodepression syndrome (HIV)), hematologic disease, and cognitive status impairment.
## 2.3. Immune-Nutrition Administration Scheme
The immune-unutrition (IN) formula used in the study is a powdered oral nutritional supplement designed for patients affected by inflammatory bowel disease. In fact, there is much evidence confirming its anti-inflammatory effect, especially in inflammatory bowel disease in children [12].
Its composition consists of: proteins 3.5 g/100 mL (consisting exclusively of casein naturally rich in TGF-ß2); fats 4.6 g/100 mL (milk fat, MCT, corn oil, soy lecithin. MCT: $25\%$ of total lipids, in order to facilitate rapid replenishment; essential fatty acids equivalent to $4.6\%$ of total calories; limited content of linoleic acid (n-6)); carbohydrates 11 g/100 mL (maltodextrin ($61\%$) and sucrose ($39\%$).
The powder is reconstituted at $20\%$—1 Kcal/mL: 200 g of powder in 850 mL of water, to re-constitute 1 L of IN formula (1000 Kcal). Later, it is possible to increase the concentration up to $30\%$-1.5 Kcal/mL: 300 g of powder in 750 mL of water to reconstitute 1 L (1500 Kcal) [12].
The formula was administered once daily (in detail, 300 g of powder in 750 mL of water to reconstitute 1 L (1500 Kcal)) together with the diet of the patient, delivering on average 30–$40\%$ of the total calories of the daily diet.
## 2.4.1. Mini Nutritional Assessment (MNA) Test
The Mini Nutritional *Assessment is* a multidimensional screening tool, validated in many clinical settings. More specifically, it is an integrated nutrition index that evaluates different nutritional parameters in order “to obtain a synthetic information and a more accurate nutritional diagnosis” [13]. MNA has $96\%$ sensitivity, $98\%$ specificity, and $97\%$ predictive value to describe nutritional status of patients [14]. Moreover, MNA is easily repeatable and can be used also by non-trained nutritionists [2].
MNA can be used both as a first-level screening and for follow-ups in elderly patients [15]. Interestingly, in hospitalized elderly patients, MNA scores can help predict healthcare costs, length of stay, and short-term and long-term mortality. In fact, MNA test shows an inverse correlation with these variables [16,17].
MNA test is a reliable index of muscle disability and motility and, also a complementary tool for nutritional status assessment in patients [18].
The MNA test is composed by 18 items divided into three sections: one for anthropometry and weight changes; one that evaluates quality and quantity of food intake; one measuring disabilities and cognitive status [19].
There are two steps:-Screening (maximum score of 14 out of six variables): story of weight loss in the previous three months, food intake, motility, acute stress, cognitive status, and Body Mass Index (BMI) assessment. In particular, score of 0–7 is predictive of malnutrition, a score of 8–11 suggests that patients are at risk of malnutrition, and a score of 12–14 indicates that the person is well nourished and needs no further investigation. If the score is less than 11 it is strongly recommended to continue with the remaining test items. A MNA score higher than 24 indicates the patient is well-nourished, a score between 17–23.5 suggests a risk of malnutrition and scores lower than 17 clearly highlight malnutrition.-Self-Global Assessment (history of drugs assumption, food habits, fluid intake, residence place, and patient’s considerations on personal health status and on nutritional status).
## 2.4.2. Bioimpedance Vector Analysis
Bioelectrical impedance analysis (BIA) is a non-invasive tool to assess human body composition (i.e., analysis of fat, bone, water, and muscle content). BIA delivers a low frequency electrical current and is based on the principle that fluid and cellular structures present different levels of resistance to an electrical current when it passes through a living system [20]. In particular, BIA measures: Resistance (R-Ohms), assessing cellular hydration; Reactance (Xc—Ohms), assessing tissue integrity and Phase Angle (PA—degrees), representing the arc tangent between R and Xc. Thus, BIA serves to evaluate hydration and nutrition in humans [21].
Bioelectrical impedance vector analysis (BIVA) assesses body composition in advanced illness such as intensive care admitted patients. In fact, statistical vector analysis of BIA data leads to human body composition measurements in this particular subset of patients [22]. Bioelectrical impedance vector analysis (BIVA) is made with graphical vectors to analyze BIA data. Thus, impedance (Z) is plotted as a vector from its components R (X-axis) and Xc (Y-axis), after being standardized by height (H). The RXc graph represents the sex- and race-specific tolerance intervals of a comparative reference population. Tolerance ellipses are plotted on the RXc graph to represent the $50\%$, $75\%$, and $95\%$ centiles (i.e., confidence intervals) for the population in study. This method allows a simultaneous assessment of changes in tissue hydration or soft tissue mass, independent of regression equations, or body weight. For these reasons, BIVA can be interpreted accurately also in critically ill ICU patients that are at extremes of weight or volume distribution.
## 2.5. Data Collection
We prospectively collected antropometric, clinical and laboratory tests’ data from the patient’s medical file. In detail, we collected general and demographic variables on the day of semi-intensive unit admission. All the other data and parameters measured were recorded daily for the entire patients’ stays, starting from admission to discharge/death. In particular, we recorded inflammation and infection markers (CRP, IL-6, white blood cells count and formula, procalcitonin, and erythrocyte sedimentation rate), renal and hepatic function indices, and blood gas analysis variables. The collected data were filled in a database guaranteeing the anonymity of the patients.
## 2.6. Statistical Analysis
Statistical analysis was performed with SPSS Software 21 (IBM, New York, NY, USA). Preliminarily, quantitative variables’ distribution was assessed with the Kolmogorov-Smirnov normality test. All data are presented as mean ± standard deviation (SD) or median [interquartile range, IQR] according to the normal or not normal distribution. Parametric (Student’s t-test) and non-parametric tests (Mann-Whitney U test) were applied to describe the differences between groups for the variables of interest, when appropriate. The alpha level of significance was set at 0.05 [23].
## 3. Results
From 1 September and 31 December 2021, we consecutively enrolled 34 COVID-19 adult patients admitted at the semi-intensive Unit of “Madonna del Soccorso” General Hospital, San Benedetto del Tronto, Italy.
Mean age of the she study population (namely, COVID-19 IN) was 70.3 ± 5.4 years, 5 females, BMI 27.0 ± 0.5 kg/m2.
Main comorbidities were diabetes ($20\%$, type 2 90 %), hyperuricemia ($15\%$), hypertension ($38\%$), chronic ischemic heart disease ($8\%$), chronic obstructive pulmonary disease (COPD) ($8\%$), anxiety ($5\%$), and depression ($5\%$).
Considering inflammatory markers at enrollment, median CRP was 19 [5.6–31] mg/L; IL-6 101 pg/mL; white blood cells count 8070 (6263–11,000).
HRCT scan results were as following: mild pneumonitis ($30\%$), moderate pulmonary parenchima involvement ($45\%$), and severe involvement ($25\%$).
Control group ($$n = 20$$) (COVID-19 patients not giving informed consent to IN) characteristics shown in Table 1.
Comorbidities prevalence (data not shown) and other antropometric, nutritional, and inflammatory characteristics were comparable except for female sex representation and BMI ($$p \leq 0.05$$). In addition, MNA test results and BIVA confirmed a statistical difference for overweight representation between study and control group (both, $p \leq 0.05$) (Figure 1).
During semi-intensive unit stay all IN and control group patients were treated with guidelines-guided treatments (namely, remdesevir, metilprednisolone, piperacillin/tazobactam, and levofloxacin). There was no statistical difference among groups for medications used (p = NS). There was no difference on non-invasive mechanic ventilation type duration used among groups (p = NS).
Figure 2 shows inflammatory markers values in IN group according to their nutritional status at T0. Control group showed a similar behavior (data not shown) at T0. In both groups, malnutrition and overweight were significantly associated with higher CRP and IL-6 values (both, $p \leq 0.05$).
After 15 days of semi-intensive unit stay (namely T1), we observed 3 deaths (mean age 75.7 ± 5.1, 1F, BMI 26.3 kg/m2) and two patients were moved to ICU care in the IN group because of respiratory performance worsening. The latter was associated with worsened HRCT pneumonitis findings.
In the control group, at T1 we observed 2 deaths (mean age 70.1 ± 3.1, 1F, BMI 23.5 kg/m2) and two patients were moved to ICU care because of respiratory performance worsening. The latter was associated with worsened HRCT pneumonitis findings.
After 2 weeks of IN formula administration, we observed a significant reduction of inflammatory markers (PCR, IL-6), for both *, **, *** $p \leq 0.05$ in the IN group (Figure 3). In the control group, a similar trend was observed, without reaching statistical significance (p = NS) (data not shown). Glycemic assessment was not affected by IN nutrition (data not shown, p = NS).
Figure 4 describes nutritional status change in the IN and control group at T1. Immuno-nutrition administration was able to prevent nutritional status worsening in the treatment vs. control group (* $p \leq 0.05$).
Semi-intensive unit days of stay were not affected by IN use (p = NS).
## 4. Discussion
In this single-center perspective pilot study, COVID-19 patients admitted to a semi-intensive unit of our hospital were evaluated for the impact of immuno-nutrition on nutritional status and inflammatory response vs. a historical control group of COVID-19 patients not administered with IN formula.
We have shown, for the first time, that immuno-nutrition is able to prevent worsening of nutritional status in COVID-19 patients with a consensual inflammatory response reduction. A similar trend was observed for inflammatory markers only, as well as in the control group.
These findings are in line with the previous report from our study group, although in those investigation, patients were administered with whey protein-rich formula and treated in the ICU ward [10]. The finding of prevention of malnutrition development observed in the present study can be explained by an accurate nutritional assessment operated in these semi-intensive patients in our secondary care center. In fact, COVID-19 patients are difficult to assess because of difficulties related to individual protective disposables use. Only a fine organization allows health care operators to prevent malnutrition development in SARS-CoV2 patients, improving their respiratory performance and reducing their morbidity and mortality [10].
In the present study there was not a significant correlation between prevention of malnutrition development and improved mortality or prevention of worsened clinical course (namely, need for ICU admission). This finding can be explained by the small sample size and short follow-up time of the population in study that do not allow further speculation on the impact of IN administration on prognosis of COVID-19 patients.
There is solid evidence showing the positive impact of nutritional assessment and use of specific protein-rich food nutrient supplement on COVID-19 patients’ morbidity and mortality [24]. In detail, in the literature there are reports that evaluated the impact of nutrition in ICU COVID-19 patients with early manifestations of malnutrition and, sarcopenia [25]. Both of these conditions are significantly associated with morbidity and mortality rate of critical and rehabilitation patients [20,26].
In this study we explored the impact of a formula rich in casein used with success in pediatric IBD populations [12]. We hypothesized that this formula with IN properties was able to reproduce effects described in gastrointestinal tract inflammatory conditions such as Chron disease and ulcerative colitis. In fact, COVID-19 is characterized by hyper-inflammatory state. Thus, we went over the study of the impact of adequate nutritional status care in COVID-19 patients.
In fact, several reports from literature have shown how the use of pre-, pro-, and postbiotics is able to be efficient as add-on treatment for steroids, antibiotics, and antivirals against the entrance of SARS-CoV2 into our body cells [27,28]. Moreover, these remedies can help reducing the cytokines’ storm typical of COVID-19 [29]. However, some data are available on immuno-nutrition in ICU and non-ICU patients, respectively.
To date, we found only one report from Brazil evaluating the impact of hyper-proteic normo-caloric diet with or without IN formula add-on on the inflammatory response and related lymphopenia in non-ventilated COVID-19 patients [30]. On the other hand, our investigation evaluated patients under non-invasive ventilation in the semi-intensive ward. Thus, these patients had a higher inflammatory response and more severe pulmonary involvement than the study by Pimentel et al. Moreover, the other investigation did not report significant effects of IN formula administration on nutritional status.
Although in the control group a similar trend was observed for the reduction of inflammatory markers, only the group of patients treated with IN demonstrated a statistically significant reduction of IL-6 and CRP. Thus, these findings support an anti-inflammatory effect of IN. In particular, omega-3 fatty acids are essential in the prevention and treatment of cardiovascular and auto-inflammatory disease [31,32].
For example, both diabetic and septic patients showed a reduction of CRP and other inflammatory cytokines after ω -3 fatty acids administration (another IN formula). Specifically, in a 2020 trial performed in Iran, 128 COVID-19 ICU patients were randomized to standard diet with/without add-on of ω -3 fatty acids. Compared with those who did not receive omega-3 fatty acids, treated patients presented a significant improvement of renal function and a reduction of systemic inflammatory response. This anti-inflammatory effect can be explained by the “competition” between fatty acids and SARS-CoV-2 for cell entrance. In fact, ω -3 fatty acids can bind viral spike protein and modify its spatial conformation, resulting in a lower viral load for the infected host [33].
In another study from France, 26 COVID-19 patients admitted to the ICU ward showed a significantly higher myeloid-derived suppressor (MDSC) cell activation, associated with the typical COVID-19-driven lymphopenia. After administration of arginine, these patients showed a significant reversal of lymphopenia. However, the sample size of study was too small to drive definitive conclusions [34].
More in detail, arginine seems to be able to reduce SARS-CoV-2 infectivity. Further evidence on this capability is derived from molecular biology data: isoleucine replacement with arginine in the 407 position of spike protein worsen its interaction with the human Angiotensin-Converting Enzyme 2 (ACE2). The latter is crucial for the virus cell infection.
The IN formula used in this study has particular characteristics. It has been successfully used in pediatric inflammatory bowel disease (IBD) patients [12]. In fact, the add-on use of bioactive peptides to the industrial diet may favor mucosal healing in Crohn disease (CD) patients because of their anti-inflammatory effect [12,35]. In detail, bioactive peptides are specific growth factors such as transforming growth factor-β (TGF-β). The latter belongs to the group of multifunctional regulatory peptides produced by various cell types. Particularly, TGF-β controls the processes of lymphocytes, macrophages, and dendritic cells differentiation, proliferation, and activation. Thus, TGF-β has a strong anti-inflammatory effect and can prevent the development of autoimmune diseases [34]. In CD, in particular, and IBD patients, in general, inflammation reduction obtained through this IN formula administration has been assessed endoscopically and by fecal calprotectin (FC) dosage [12,34]. Thus, local immunomodulation has been confirmed in this subset of patients. In our study, systemic hyper-inflammation state is present and IN administration can help in down-regulating this process (e.g., as expressed by the significant decrease of CRP and IL-6). However, more data are needed to confirm this preliminary finding, perhaps with the dosage of FC also.
Our study has several limitations. First, the sample size was small, according to the pilot design of the study. Second, our study took into consideration the third wave of the SARS-CoV 2 pandemic where virus strain, vaccine, and antivirals use had changed the clinical and laboratory characteristics of the disease. Third, our cohort in treatment had a low representation of female sex and high representation of obese people. The latter could have conditioned the prevalence of sarcopenia, and therefore have conditioned the results.
## 5. Conclusions
In conclusion, data from this pilot single-center perspective study showed that immuno-nutrition is able to prevent malnutrition development in COVID-19 patients admitted in semi-intensive unit, together with a significant reduction of pro-inflammatory cytokines’ storm. The potential relationship between risk of malnutrition reduction and fall of hyper-inflammatory response in these patients needs to be further investigated. However, these promising results are conditioned by the small sample size of patients enrolled in a single-center secondary hospital. Thus, larger sample size and multi-centric randomized placebo-controlled studies are needed to confirm these results.
## References
1. Lai C., Hsu C., Hsueh S., Yen M., Ko W., Hsueh P.. **Multisystem inflammatory syndrome in adults: Characteristics, treatment, and outcomes**. *J. Med. Virol.* (2022) **95** 28426. DOI: 10.1002/jmv.28426
2. Li M., Guo L., Feng L.. **Interplay between swine enteric coronaviruses and host innate immune**. *Front. Veter Sci.* (2022) **9** 1083605. DOI: 10.3389/fvets.2022.1083605
3. Di Renzo L., Gualtieri P., Pivari F., Soldati L., Attinà A., Leggeri C., Cinelli G., Tarsitano M.G., Caparello G., Carrano E.. **COVID-19: Is there a role for immuno-nutrition in obese patient?**. *J. Transl. Med.* (2020) **18** 415. DOI: 10.1186/s12967-020-02594-4
4. Subedi L., Tchen S., Gaire B.P., Hu B., Hu K.. **Adjunctive Nutraceutical Therapies for COVID-19**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22041963
5. Shakoor H., Feehan J., Al Dhaheri A.S., Ali H.I., Platat C., Ismail L.C., Apostolopoulos V., Stojanovska L.. **Immune-boosting role of vitamins D; C; E; zinc; selenium and omega-3 fatty acids: Could they help against COVID-19?**. *Maturitas* (2021) **143** 1-9. DOI: 10.1016/j.maturitas.2020.08.003
6. Derbyshire E., Delange J.. **COVID-19: Is there a role for immuno-nutrition; particularly in the over 65s?**. *BMJ Nutr. Prev. Health* (2020) **3** 100-105. DOI: 10.1136/bmjnph-2020-000071
7. Jovic T.H., Ali S.R., Ibrahim N., Jessop Z.M., Tarassoli S.P., Dobbs T.D., Holford P., Thornton C.A., Whitaker I.S.. **Could Vitamins Help in the Fight Against COVID-19?**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12092550
8. Grimble R.F.. **Basics in clinical nutrition: Immuno-nutrition—Nutrients which influence immunity: Effect and mechanism of action**. *Eur. E-J. Clin. Nutr. Metab.* (2009) **4** e10-e13. DOI: 10.1016/j.eclnm.2008.07.015
9. Bae M., Kim H.. **Mini-Review on the Roles of Vitamin C, Vitamin D, and Selenium in the Immune System against COVID-19**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25225346
10. Scarcella M., Scarpellini E., Ascani A., Commissari R., Scorcella C., Zanetti M., Parisi A., Monti R., Milic N., Donati A.. **Effect of Whey Proteins on Malnutrition and Extubating Time of Critically Ill COVID-19 Patients**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14030437
11. 11.Coronavirus Disease 2019 (COVID-19) Treatment GuidelinesNational Institutes of Health (US)Bethesda, MD, USA2021. *Coronavirus Disease 2019 (COVID-19) Treatment Guidelines* (2021)
12. Matuszczyk M., Meglicka M., Landowski P., Czkwianianc E., Sordyl B., Szymańska E., Kierkuś J.. **Oral exclusive enteral nutrition for induction of clinical remission, mucosal healing, and improvement of nutritional status and growth velocity in children with active Crohn’s disease—A prospective multicentre trial**. *Prz. Gastroenterol.* (2021) **16** 346-351. DOI: 10.5114/pg.2021.111483
13. Cereda E.. **Mini nutritional assessment**. *Curr. Opin. Clin. Nutr. Metab. Care* (2012) **15** 29-41. DOI: 10.1097/MCO.0b013e32834d7647
14. Lundin H., Sääf M., Strender L.E., Mollasaraie H.A., Salminen H.. **Mini nutritional assessment and 10-year mortality in free-living elderly women: A prospective cohort study with 10-year follow-up**. *Eur. J. Clin. Nutr.* (2012) **66** 1050-1053. DOI: 10.1038/ejcn.2012.100
15. Cederholm T., Barazzoni R., Austin P., Ballmer P., Biolo G., Bischoff S.C., Compher C., Correia I., Higashiguchi T., Holst M.. **ESPEN guidelines on definitions and terminology of clinical nutrition**. *Clin. Nutr.* (2017) **36** 49-64. DOI: 10.1016/j.clnu.2016.09.004
16. Chima C.S., Barco K., Dewitt M.L., Maeda M., Teran J., Mullen K.D.. **Relationship of Nutritional Status to Length of Stay, Hospital Costs, and Discharge Status of Patients Hospitalized in the Medicine Service**. *J. Am. Diet. Assoc.* (1997) **97** 975-978. DOI: 10.1016/S0002-8223(97)00235-6
17. Gentile S., Lacroix O., Durand A.C., Cretel E., Alazia M., Sambuc R., Bonin-Guillaume S.. **Malnutrition: A highly predictive risk factor of short-term mortality in elderly presenting to the emergency department**. *J. Nutr. Health Aging* (2013) **17** 290-294. DOI: 10.1007/s12603-012-0398-0
18. Shidfar F., Babaii Darabkhani P., Yazdanpanah L., Karkheiran S., Noorollahi-Moghaddam H., Haghani H.. **Assessment of nutritional status in patients with Parkinson’s disease and its relationship with severity of the disease**. *Med. J. Islam. Repub. Iran* (2016) **30** 454. PMID: 28491829
19. Bauer J.M., Kaiser M.J., Anthony P., Guigoz Y., Sieber C.C.. **The Mini Nutritional Assessment—Its history; today’s practice; and future perspectives**. *Nutr. Clin. Pract.* (2008) **23** 388-396. DOI: 10.1177/0884533608321132
20. Moonen H.P.F.X., Van Zanten A.R.H.. **Bioelectric impedance analysis for body composition measurement and other potential clinical applications in critical illness**. *Curr. Opin. Crit. Care* (2021) **27** 344-353. DOI: 10.1097/MCC.0000000000000840
21. Mulasi U., Kuchnia A.J., Cole A.J., Earthman C.P.. **Bioimpedance at the bedside: Current applications; limitations; and opportunities**. *Nutr. Clin. Pract.* (2015) **30** 180-193. DOI: 10.1177/0884533614568155
22. Peacock I.W.F.. **Use of Bioimpedance Vector Analysis in Critically Ill and Cardiorenal Patients**. *Contrib. Nephrol.* (2010) **165** 226-235. DOI: 10.1159/000313762
23. Niederer L.E., Miller H., Haines K.L., Molinger J., Whittle J., MacLeod D.B., McClave S.A., Wischmeyer P.E.. **Prolonged progressive hypermetabolism during COVID-19 hospitalization undetected by common predictive energy equations**. *Clin. Nutr. ESPEN* (2021) **45** 341-350. DOI: 10.1016/j.clnesp.2021.07.021
24. Alizadehsani R., Sani Z.A., Behjati M., Roshanzamir Z., Hussain S., Abedini N., Hasanzadeh F., Khosravi A., Shoeibi A., Roshanzamir M.. **Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients**. *J. Med. Virol.* (2021) **93** 2307-2320. DOI: 10.1002/jmv.26699
25. Ahmadi S., Firoozi D., Dehghani M., Zare M., Mehrabi Z., Ghaseminasab-Parizi M., Masoumi S.J.. **Evaluation of Nutritional Status of Intensive Care Unit COVID-19 Patients Based on the Nutritional Risk Screening 2002 Score**. *Int. J. Clin. Pract.* (2022) **2022** 2448161. DOI: 10.1155/2022/2448161
26. Ting T.H.Y., Lo T.H.M., Lo W.W.T., Ding Q., Yuk D.K.L., Hui E., Tang M.W.S.. **Inadequate energy and protein intake; underweight and malnutrition are associated with in-hospital mortality among COVID-19 rehabilitation patients during the omicron outbreak in Hong Kong**. *Aging Med. (Milton)* (2022) **5** 204-210. DOI: 10.1002/agm2.12220
27. Vaezi M., Ravanshad S., Rad M.A., Zarrinfar H., Kabiri M.. **The effect of synbiotic adjunct therapy on clinical and paraclinical outcomes in hospitalized COVID-19 patients: A randomized placebo-controlled trial**. *J. Med. Virol.* (2023) **95** 28463. DOI: 10.1002/jmv.28463
28. Khani N., Soleimani R.A., Noorkhajavi G., Soleimani A.A., Abbasi A., Rad A.H.. **Postbiotics as potential promising tools for SARS-CoV-2 disease adjuvant therapy**. *J. Appl. Microbiol.* (2022) **132** 4097-4111. DOI: 10.1111/jam.15457
29. Scarpellini E., Rinninella E., Basilico M., Colomier E., Rasetti C., Larussa T., Santori P., Abenavoli L.. **From Pre- and Probiotics to Post-Biotics: A Narrative Review**. *Int. J. Environ. Res. Public Health* (2021) **19**. DOI: 10.3390/ijerph19010037
30. Pimentel R.F.W., Silva A.P., Santana A.I.C., Silva D.S.E., Ramos M.S., Souza M.C., Suen V.M.M., Maduro I.P.N.N., Ribas Filho D., D’Oliveira Júnior A.. **Effect of immuno-nutrition on serum levels of C-reactive protein and lymphocytes in patients with COVID-19: A ran-domized; controlled; double-blind clinical trial**. *Nutr. Hosp.* (2022) **39** 20-26. PMID: 34839672
31. Ruiz-León A.M., Lapuente M., Estruch R., Casas R.. **Clinical Advances in Immuno-nutrition and Atherosclerosis: A Review**. *Front. Immunol.* (2019) **10** 837. DOI: 10.3389/fimmu.2019.00837
32. Di Renzo L., Franza L., Monsignore D., Esposito E., Rio P., Gasbarrini A., Gambassi G., Cianci R., De Lorenzo A.. **Vaccines; Microbiota and Immuno-nutrition: Food for Thought**. *Vaccines* (2022) **10**. DOI: 10.3390/vaccines10020294
33. Naghibi T., Shafigh N., Mazloomzadeh S.. **Role of omega-3 fatty acids in the prevention of delirium in mechanically ventilated patients**. *J. Res. Med. Sci.* (2020) **25** 10. DOI: 10.4103/jrms.JRMS_567_18
34. Reizine F., Lesouhaitier M., Gregoire M., Pinceaux K., Gacouin A., Maamar A., Painvin B., Camus C., Le Tulzo Y., Tattevin P.. **SARS-CoV-2-Induced ARDS Associates with MDSC Expansion; Lymphocyte Dysfunction; and Arginine Shortage**. *J. Clin. Immunol.* (2021) **41** 515-525. DOI: 10.1007/s10875-020-00920-5
35. Fell J.M.. **Control of systemic and local inflammation with transforming growth factor beta containing formulas**. *J. Parenter. Enteral. Nutr.* (2005) **29** S126-S128. DOI: 10.1177/01486071050290S4S126
|
---
title: Low FODMAP Diet Relieves Visceral Hypersensitivity and Is Associated with Changes
in Colonic Microcirculation in Water Avoidance Mice Model
authors:
- Chenmin Hu
- Chenxi Yan
- Yuhao Wu
- Enfu Tao
- Rui Guo
- Zhenya Zhu
- Xiaolong Chen
- Marong Fang
- Mizu Jiang
journal: Nutrients
year: 2023
pmcid: PMC10004816
doi: 10.3390/nu15051155
license: CC BY 4.0
---
# Low FODMAP Diet Relieves Visceral Hypersensitivity and Is Associated with Changes in Colonic Microcirculation in Water Avoidance Mice Model
## Abstract
[1] Background: Irritable bowel syndrome (IBS) is a global public health problem, the pathogenesis of which has not been fully explored. Limiting fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) can relieve symptoms in some patients with IBS. Studies have shown that normal microcirculation perfusion is necessary to maintain the primary function of the gastrointestinal system. Here, we hypothesized that IBS pathogenesis might be related to abnormalities in colonic microcirculation. A low-FODMAP diet could alleviate visceral hypersensitivity (VH) by improving colonic microcirculation; [2] Methods: C57BL/6 mice were raised to establish an IBS-like rodent model using water avoidance (WA) stress or SHAM-WA as a control, one hour per day for ten days. The mice in the WA group were administered different levels of the FODMAP diet: $2.1\%$ regular FODMAP (WA-RF), $10\%$ high FODMAP diet (WA-HF), $5\%$ medium FODMAP diet (WA-MF), and $0\%$ low FODMAP diet (WA-LF) for the following 14 days. The body weight and food consumption of the mice were recorded. Visceral sensitivity was measured as colorectal distention (CRD) using the abdominal withdrawal reflex (AWR) score. Colonic microcirculation was assessed using laser speckle contrast imaging (LCSI). Vascular endothelial-derived growth factor (VEGF) was detected using immunofluorescence staining; [3] Results: The threshold values of CRD pressure in the WA-RF, WA-HF, and WA-MF groups were significantly lower than those in the SHAM-WA group. Moreover, we observed that colonic microcirculation perfusion decreased, and the expression of VEGF protein increased in these three groups of mice. Interestingly, a low-FODMAP dietary intervention could reverse this situation. Specifically, a low-FODMAP diet increased colonic microcirculation perfusion, reduced VEGF protein expression in mice, and increased the threshold of VH. There was a significant positive correlation between colonic microcirculation and threshold for VH; [4] Conclusions: These results demonstrate that a low-FODMAP diet can alter VH by affecting colonic microcirculation. Changes in intestinal microcirculation may be related to VEGF expression.
## 1. Introduction
Irritable bowel syndrome (IBS) is a functional gastrointestinal disease with a global impact [1,2], affecting 3.8–$4.8\%$ of the general population [3,4]. Chronic, recurrent abdominal pain associated with an altered stool form or frequency is the clinical characteristic of IBS [5]. Currently, the understanding of the complex pathogenesis of IBS is limited. It is believed to be related to microbiota-brain-gut axis communication disorder [6,7], visceral hypersensitivity (VH) [8], gastrointestinal infection [9], digestive tract inflammation [10], and brain function changes [11].
Research has shown that dietary manipulation is critical in managing IBS, especially fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) [12]. FODMAP is an acronym for “Fermentable Oligosaccharides, Disaccharides, Monosaccharides, and Polyols”. It exists in a variety of different and common foods. Different foods contain different kinds of FODMAP, for instance, fruits, vegetables, milk, legumes, honey, and sweeteners. Some foods contain only one kind, while others contain several kinds.
FODMAP is poorly absorbed or nonabsorbed in healthy individuals [13], so excessive small molecular substances increase the osmotic activity in the intestinal cavity, forcing water to enter the gastrointestinal tract and expand the intestinal cavity. When the unabsorbed FODMAP enters the colon, it becomes the substrate for the fermentation of gas-producing bacteria, producing many gases, such as hydrogen, methane, and short-chain fatty acids [13,14]. Excessive gas further worsens the expansion of the intestinal cavity and triggers IBS clinical symptoms in a dose-dependent manner [15]. Recently, a study showed that lactose and fructo-oligosaccharides could raise colonic glycosylation end-product production and increase visceral sensitivity in mice [16]. In addition, it is also believed that the fermentation metabolites of anaerobic bacteria, especially alcohol, ketone, and aldehyde, can affect the calcium signal and lead to the imbalance of intestinal flora, which may also be the cause of IBS [17]. In clinical practice, it is common for IBS to combine with small intestinal bacterial overgrowth (SIBO). Several symptoms are shared by both SIBO and IBS, including abdominal pain, distention, diarrhea, and bloating.
A high FODMAP diet can aggravate intestinal inflammation, damage the intestinal epithelial barrier, induce mast cell activation, and finally trigger characteristic IBS clinical symptoms, such as abdominal pain, bloating, and fecal urgency [18,19,20]. Randomized controlled clinical studies and preclinical studies have revealed that a low FODMAP diet can alleviate symptoms in some cases but not in all patients with IBS, which is superior to other interventions [21,22], although there are still many controversies [23]. The FODMAP can activate mast cells and release more inflammatory mediators, for example, histamine, which can affect the immune system [16,24]. In addition, the FODMAP diet can be sensitive to nociceptive neurons and the intestinal nervous system [25]. Furthermore, it was proved that dysfunction of the gut-brain axis is responsible for the generation ofsymptoms caused by FODMAP [26].
Microcirculation refers to blood circulation through microvessels that are less than 100 µm in diameter, including arterioles, capillaries, and venules. It plays a critical role in the human body by delivering oxygen to adjacent tissues, providing energy, and maintaining organ homeostasis. Additionally, microcirculation mediates immune system activity and hemostasis [27]. The primary function of the gastrointestinal system is to digest and absorb nutrients and prevent the invasion of bacteria and microorganisms, which intrinsically requires normal microcirculation perfusion level [28,29]. Numerous mechanisms can regulate vascular tension in gastrointestinal circulation, primarily by the nervous system, hormones, and metabolic mechanisms [29].
Over the last two decades, intestinal microcirculation has attracted increasing attention. Advanced techniques for evaluating microcirculation are developing rapidly, including intravital microscopy, laser speckle contrast imaging (LSCI), laser doppler flowmetry, and optical coherence tomography. Alexander et al. found that the incidence of ischemic colitis in patients is 3.4 times higher than that in those without IBS [30]. However, the relationship between IBS and colon ischemia remains to be established. In addition, changes in colonic microcirculation after FODMAP diet intervention still need to be investigated.
In the present study, we investigated the changes in colonic microcirculation in an animal model of IBS and after the intervention with different FODMAP diets using a direct and intuitive technique. Moreover, we analyzed critical protein signaling pathways in intestinal vasculogenesis and angiogenesis to understand the changes in potential colonic microcirculation mechanisms.
## 2.1.1. Animals
Adult male C57/BL6 mice (20–22 g) were purchased from SLAC Laboratory Animal Co., Ltd. (Shanghai, China). The animals were raised in polypropylene cages with a light-dark cycle (light was turned off at 9:00 am, turned on at 9:00 pm, and then turned off automatically). Each cage housed 4–6 animals at a humidity of 50 ± $5\%$ and a room temperature of 25–26 °C. Food and water were provided ad libitum. The animal research ethics committee approved the animal research protocol of Zhejiang University.
## 2.1.2. Dietary Formula
The mice were fed a custom-made diet (Wuxi Fanbo Biotechnology Co., Ltd., Wuxi, China) of regular FODMAP (RF) during the period of chronic water avoidance (WA) stress (Table 1). They were then randomly divided into four groups and continued to receive RF, high FODMAP diet (HF), medium FODMAP diet (MF), and low FODMAP diet (LF) (Table 1) [18]. The diet intervention lasted for 14 days [31]. Mice in the sham water-avoidance group received an RF diet.
The diets were designed to mimic human dietary consumption of FODMAP (Table 1). Comparing the composition of the raw materials between the groups, the quality differences of fructose, fructo-oligosaccharide, and galacto-oligosaccharide are the most critical. The difference was designed to be regulated by the quality of corn starch, another type of carbohydrate, such that the final weight ratio and energy ratio of protein, fat, and carbohydrate among the groups remained the same.
## 2.1.3. WA Stress Protocol and Grouping
Similar to the previous experiment [32,33], the customized test device included a transparent plastic tank (45 cm length × 32 cm width × 26 cm height) and a cuboid acrylic (3 cm length × 3 cm width × 9 cm height) fixed at the bottom center. The tank was filled with fresh room temperature water (25 °C) up to 1 cm below the acrylic bar. This was done according to the chronic WA stress protocol for one hour per day for ten consecutive days. The experimental animals were divided into SHAM-WA, WA-RF, WA-HF, WA-MF, and WA-LF groups, with 8–9 animals in each group (Figure 1).
## 2.2. Visceral Hypersensitivity Measurement
In mice, the abdominal withdrawal reflex (AWR) score was assessed using colorectal distension (CRD). We invented a novel and exquisite distension balloon (length 1 cm, diameter 0.5 cm), which was published previously [34]. After 30 min, the mice adapted to the environment. The balloon was lubricated with Vaseline, inserted into the rectum for 1 cm, and appropriately fixed under $2\%$ isoflurane (RWD Life Science, Shenzhen, China) anesthesia. In the first stage, gas was continuously injected into the balloon slowly, the reaction of the mice was observed, and the corresponding pressure value was recorded. In the second stage, the balloon was promptly inflated to constant pressure (on the order of 10, 20, 30, 40, 50, 60, 70, 80, and 90 mmHg pressure), and the AWR value of the mouse colorectum under different pressures was measured. AWR scoring standard was set as 0: no response; 1: slight head movement and no body movement; 2: abdominal muscle contraction; 3: abdominal uplift; 4: body arch-back elevation. The AWR score was measured by two independent observers using a double-blind method. Each experiment was repeated thrice to obtain an average value, and the mice were allowed to rest for 15–20 min between the 2 stages.
## 2.3. Immunofluorescence Staining
Frozen sections of 8 μm thickness were dried at 37 °C in an oven, then washed twice with phosphate-buffered saline (PBS) for 10 min each time, and twice with $0.3\%$Triton (Triton X-100, FuDebio Science, Hangzhou, China) for 10 min each time. After blocking with $2\%$ bovine serum albumin (A1933, Sigma Aldrich, Saint Louis, MO, USA) for 1 h at room temperature, the sections were incubated with primary antibodies (anti-VEGF CAT#A0280, ABclonal, Wuhan, China) at 4 °C overnight. Next, the sections were washed with PBST 4 times for 10 min each and incubated with secondary antibodies (1:1000, Dylight594, Goat anti-rabbit, FuDebio Science) for 1.5 h at room temperature. After that, the sections were washed with PBST 4 times, 10 min each time, and PBS 2 times, 5 min each time in sequence. Antifade mounting medium with 4′,6-diamidino-2-phenylindole (DAPI, Coolaber, Beijing, China) was added to the sections, and the slides were covered. Fluorescence signals were observed using a fluorescence microscope (Olympus VS200, Tokyo, Japan) under a 20× objective. The results were quantified using ImageJ software.
## 2.4. Laser Speckle Contrast Imaging
Colonic microcirculation blood flow was assessed directly using LSCI [35,36,37] (Laser Speckle Imaging system; RFLSI III; RWD Life Science, Shenzhen, China). Before initiating the study, parameters were corrected according to the manufacturer’s recommendations. An approximate infrared laser source with a wavelength of 785 nm was used to irradiate the surface of the tissue of interest, with a depth of 1 mm. The movement of particles in tissues (such as red blood cells) leads to speckle patterns randomly [38]. The sliding spatial mode with a high signal-to-noise ratio was adopted, with a 5 ms exposure time. The average microcirculatory blood flow (mFlux) results of the region of interest (ROI) were recorded using a CMO camera (image resolution 2048 × 2048-pixel, 10 frames/s) for 10 s. Finally, the mean mFlux within the ROI was calculated using the laser speckle blood flow imaging system v5.0.
The specific procedures were as follows: after isoflurane gas anesthesia was administered, the mice were placed in a supine position on a black background, the limbs were fixed with double-sided adhesive tape, the abdominal hair was removed with an electric razor, and a longitudinal incision was cut in the midline. All the intestinal segments were completely exposed. On the premise of not changing the original position of the mouse intestinal tube, each intestinal segment of the mouse was skillfully identified. To maintain moisture, exposed intestinal segments must be coated with normal saline. The laser probe was fixed 15 cm above the area to be measured. mFlux of the proximal colon was measured, and then the intestine was unfolded into a fan shape, and the mFlux of the proximal colon and the mesentery of the colon was investigated again.
## 2.5. Statistic Analyses
Statistical analyses were performed using SPSS23.0 (IBM, Armonk, NY, USA) and GraphPad Prism9.0 (GraphPad Software Inc., San Diego, CA, USA). All data are expressed as the mean ± SEM. Verifying that the original data had a normal distribution, body weight, food intake, and CRD measurements were analyzed using two-way ANOVA followed by Sidak’s multiple comparison tests. The remaining results were analyzed using a one-way ANOVA followed by Bonferroni’s post-hoc test. Correlation coefficients (r) were calculated using Pearson’s correlation coefficient. $p \leq 0.05$ was considered statistically significance.
## 3.1. Body Weight Change and Dietary Consumption
We observed fluctuations in the weight of the mice during WA. The weight of DAY0 is the baseline, and DAYn-DAY0 expresses the weight change. The results showed no statistical difference between the control and model groups during the WA stress (Figure 2A). During the diet intervention, body weight and feed consumption were measured every three days. Average food consumption = (original amount − residual amount)/number of days/number of mice in each cage. The results showed no significant differences in body weight and average feed consumption among the groups (Figure 2B,C).
## 3.2. Visceral Hypersensitivity
At an abdominal withdrawal test score of 1 point, the threshold values of colorectal distension pressure in the SHAM-WA, WA-RF, WA-HF, WA-MF, and WA-LF groups were 25.80 ± 1.88, 12.80 ± 2.22, 9.20 ± 1.59, 12.26 ± 2.03 and 20.20 ± 3.02 mmHg, respectively (Figure 3A); at a score of 2 points, the threshold values in each group were 45.20 ± 3.93, 22.80 ± 3.18, 18.00 ± 2.63, 22.60 ± 2.73 and 35.00 ± 2.88 mmHg, respectively (Figure 3B); at a score of 3 points, the threshold values in each group were 58.60 ± 3.14, 36.80 ± 3.34, 30.80 ± 5.03, 36.60 ± 3.08 and 52.20 ± 4.72, respectively (Figure 3C); at a score of 4 points, the thresholds of each group were 83.60 ± 4.84, 52.80 ± 5.12, 48.00 ± 6.14, 53.80 ± 1.83 and 70.40 ± 5.31 mmHg, respectively (Figure 3D). At the AWR1-4 score level, the thresholds of the WA-RF, WA-HF, and WA-MF groups were significantly lower than those of the SHAM-WA group, whereas the WA-LF group had a statistically significant difference.
Under the pressure of 10–90 mmHg order, the AWR scores in the SHAM-WA, WA-RF, WA-HF, WA-MF, and WA-LF groups were evaluated. The data were analyzed using two-way ANOVA (diet × pressure) followed by Sidak’s multiple comparisons test, interaction: F[32, 180] = 240.8, $p \leq 0.0001.$ It had a significant main effect on dietary intervention: F[8, 180] = 240.8, $p \leq 0.0001$; moreover, it also had a significant main effect on pressure: F[4, 180] = 68.20, $p \leq 0.0001.$ There was no significant difference at 80 and 90 mmHg between SHAM-WA and WA-HF and at 60, 70, 80, and 90 mmHg between WA-HF and WA-LF (Figure 4).
## 3.3. Colonic Microcirculation Blood Flow Decreased in the WA-RF, WA-HF, and WA-MF Groups, and Was Reversible in the WA-LF Group
Due to physiological hypoxia in healthy individuals, intestinal blood flow is vulnerable to microcirculation disorders and intestinal ischemia [39]. Laser speckle blood flow is a powerful noninvasive tool [40], which can be used as a visual method to dynamically measure changes in intestinal and mesenteric microcirculation blood flow in vivo [41]. This study presents the first data on the use of live real-time imaging tools in mice to focus on the changes in colon blood flow in IBS model mice and FODMAP diet intervention.
In the original position, the results of the colonic mFlux in SHAM-WA, WA-RF, WA-HF, WA-MF, and WA-LF groups were 618.97 ± 24.03, 352.04 ± 40.07, 348.32 ± 46.73, 359.80 ± 36.69, and 654.66 ± 50.70 Perfusion Unit (PU) (Figure 5A,B). In the fan-shaped position, the results of mesenteric vessels mFlux were 594.74 ± 15.14, 575.23 ± 81.09, 561.79 ± 30.39, 512.20 ± 39.95, and 602.25 ± 42.25; the results of the colonic mFlux in SHAM-WA, WA-RF, WA-HF, WA-MF, and WA-LF groups were 460.22 ± 17.87, 293.84 ± 29.88, 324.47 ± 29.06, 377.45 ± 19.87, and 495.01 ± 35.94 (Figure 5C–E). The results showed that the colonic mFlux in the WA-RF, WA-HF, and WA-MF groups was lower than that in the SHAM-WA group in the original and fan-shaped positions. The low-FODMAP diet intervention could reverse this situation. There was no significant difference in the mFlux of the mesenteric vessels supplying the colon among the groups. Therefore, the decrease in colonic microcirculation blood flow perfusion after WA stress and FODMAP diet intervention may have been caused by local colon lesions.
## 3.4. VEGF Fluorescence Intensity Increased in the WA-HF Group and Decreased in the WA-LF Group
We observed the colonic submucosa layer, which accounts for $70\%$ of the intestinal blood flow, and the immunofluorescence intensity of VEGF protein in the SHAM-WA, WA-RF, WA-HF, WA-MF, and WA-LF groups were 92.75 ± 5.03, 130.83 ± 9.39, 145.01 ± 7.37, 128.06 ± 6.11, 108,48 ± 6.65. The result showed that the expression of VEGF in the WA-RF, WA-HF, and WA-MF groups was significantly higher than that in the SHAM-WA group. However, the level in the WA-LF group was significantly lower than that in the WA-HF group (Figure 6A,B).
## 3.5. Colonic Microcirculation Blood Flow Was Positively Correlated with the Threshold of VH
When the AWR scores were 1, 2, 3, or 4, there was a positive correlation between colonic microcirculation blood flow and the threshold of VH, with $95\%$ confidence bands displayed by dashed lines ($r = 0.632$, 0.637, 0.467, and 0.657; $$p \leq 0.0007$$, 0.0003, 0.0002, and 0.0004, respectively) (Figure 7A–D).
## 4. Discussion
In this study, we provided evidence that regular FODMAP, high FODMAP, and middle FODMAP diet interventions can aggravate VH, while low FODMAP can alleviate WA stress in mice. In addition, colonic microcirculation blood flow decreased in the WA-RF, WA-HF, and WA-MF groups, leading to colonic microcirculation dysfunction, while the expression of VEGF protein increased. Moreover, we found that a low-FODMAP diet can increase colonic blood perfusion and reduce VEGF protein expression.
There is evidence that diet is closely related to the balance of intestinal microecology. Dietary restriction may be essential in altering microbiota composition in people with IBS. However, the results are inconsistent. Some studies demonstrated that compared with low FODMAP and high FODMAP intervention, there was no difference in α-diversity and β-diversity [42]. In contrast, other studies showed that the total abundance of low FODMAP decreased [43]. Short-term restriction of the FODMAP diet can increase the abundance of Bacteroides, Firmicutes, Clostridium, and Actinobacteria while the abundance of Bifidobacteria decreases [44,45]. Among them, *Bacteroides is* a kind of bacteria related to the degradation of sugars [46].
VH is one of the most critical characteristics of IBS pathogenesis [47]. VH for mechanical stimulation of the colon is common among patients with IBS. Utilizing the rectal balloon distention method, evidence showed lower thresholds for visceral discomfort and pain in the majority of patients with IBS [48,49,50]. It has been shown that WA is an effective model of inducing VH in animals [33,51]. Similar to the previous experiment, there was no statistical difference between the groups in terms of weight and feed consumption in the present study, eliminating other factors affecting VH. However, the mechanism underlying VH in IBS is complex. It is now believed that intestinal barrier destruction, abnormal activation of silent gastrointestinal nociceptors, peripheral intestinal nerves, and central nervous system sensitization are also involved [52,53]. Here, we found that VH significantly correlated with colonic microcirculation.
To the best of our knowledge, the present study is the first to reveal the changes in colonicmicrocirculation in an IBS mouse model and directly after FODMAP diet intervention. Two potential central mechanisms may support these results. First, regional intestinal blood-flow-regulating factors affect intestinal blood flow. Second, changes in blood flow result from altered autonomic nervous system function and neurotransmitters. In this experiment, we confirmed that there was no statistical difference in the blood flow of the mesenteric artery supplying the proximal colon among the groups, which suggests that regional intestinal segment lesions caused changes in the blood flow of the colonic microcirculation. Nevertheless, the causal relationship between colonic microcirculation reduction and IBS pathogenesis in the WA-HF group remains to be determined. Interestingly, this phenomenon can be ameliorated by a low-FODMAP diet by dilating microcirculation and improving blood infusion.
VEGF is a powerful factor in vasculogenesis and angiogenesis processes in both development and pathological conditions, mainly targeting endothelial cells; it is also known as VEGF-A [54]. Moreover, VEGF is considered an inflammatory factor that can affect vascular permeability and regulate the recruitment of inflammatory cells in colitis diseases [55]. Low-grade mucosal inflammation is considered one of the causes of IBS [56]. Here, we verified that the expression of VEGF protein was significantly higher in the HF group and significantly reduced in the LF group, indicating that the number of microvessels might increase in the HF group. A small-sample study showed that the serum VEGF level in IBS patients was higher than in ordinary people [57]. Another double-blinded randomized trial discovered that the severity of IBS symptoms in patients decreased, and circulating levels of proinflammatory cytokines, including IL-6, IL-17, and interferon-γ were significantly reduced, as well as VEGF, after turanicum wheat dietary intervention [58]. Christina et al. focused on the treatment of stress-related and psychological IBS. They found that after the one-year experience with choirs, VEGF levels were higher in both choir and nonchoir groups [59]. Experiments in other tissues confirmed that in the brain and retina, VEGF signaling is closely related to leukocyte adhesion and capillary blockage [60,61]. Nevertheless, there remains a knowledge gap associated with the mechanism of the VEGF pathway signaling and tissue microcirculation, and more research needs to be conducted.
IBS is believed to be linked to disruptions in autonomic nervous system regulation, which can damage vascular homeostasis and cause IBS symptoms and exaggerated responses to stress [62,63]. Tanaka et al. measured finger blood perfusion using laser doppler blood flow, an indicator of sympathetic nerve function. It was found that the blood flow response of IBS patients decreased, indicating excessive sympathetic nerve activity [64]. A separate study showed that rectal blood flow was decreased in IBS due to excitation of the extrinsic colonic vagus nerve and a deficit in cholinergic activity [65]. Based on this evidence, it is reasonable to speculate that sympathovagal imbalance or dysfunction may result in altered colonic microcirculatory flow in IBS patients.
Currently, diet management is considered one of the most critical decisions in IBS management strategies, especially FODMAP food restriction [1]. The research has shown that FODMAP aggravates VH by increasing the water volume in the small intestine and gas production in the colon [14]. Zhou et al. revealed that a FODMAP diet modulates barrier dysfunction, thus activating visceral pain [18]. In the meantime, too much gas and/or water fermented by intestinal bacteria can lead to an intraluminal pressure increase. Boley et al. showed that colonic blood flow decreased stepwise with increasing cavity pressure [66], although the mesenteric blood pressure remained unchanged. Different diameters of the local intestinal cavities lead to a stretch of the intestinal cavity at the expansion site [67], which may potentially induce abdominal pain. In the present study, the threshold for VH in the WA-HF group was lower than that in the WA-RF group. However, we did not observe that colonic microcirculation in the WA-HF group was less than that in the RF group.
This study had several limitations. First, the results of this study are limited to a mouse model of WA stress, although it is a classic model simulating the preclinical experiment of IBS. The mechanism underlying low FODMAP requires further investigation in clinical practice. Second, the side effects of FODMAP in the body require further research and confirmation. Third, the study’s investigation of intestinal microcirculation focused on the colon. Further research is required to determine the effects of different doses of FODMAP on microcirculation in other regions of the intestine.
## 5. Conclusions
Our study showed that a low-FODMAP diet could alleviate VH by improving colonic microcirculation. Colonic microcirculation blood flow was positively correlated with the threshold of VH. This may provide a theoretical basis for limiting the FODMAP diet for IBS management.
## References
1. Ford A.C., Sperber A.D., Corsetti M., Camilleri M.. **Irritable bowel syndrome**. *Lancet* (2020) **396** 1675-1688. DOI: 10.1016/S0140-6736(20)31548-8
2. Lacy B.E., Pimentel M., Brenner D.M., Chey W.D., Keefer L.A., Long M.D., Moshiree B.. **ACG Clinical guideline: Management of irritable bowel syndrome**. *Am. J. Gastroenterol.* (2021) **116** 17-44. DOI: 10.14309/ajg.0000000000001036
3. Palsson O.S., Whitehead W., Törnblom H., Sperber A.D., Simren M.. **Prevalence of Rome IV functional bowel disorders among adults in the United States, Canada, and the United Kingdom**. *Gastroenterology* (2020) **158** 1262-1273.e3. DOI: 10.1053/j.gastro.2019.12.021
4. Oka P., Parr H., Barberio B., Black C.J., Savarino E.V., Ford A.C.. **Global prevalence of irritable bowel syndrome according to Rome III or IV criteria: A systematic review and meta-analysis**. *Lancet Gastroenterol. Hepatol.* (2020) **5** 908-917. DOI: 10.1016/S2468-1253(20)30217-X
5. Camilleri M.. **Diagnosis and treatment of irritable bowel syndrome: A review**. *JAMA* (2021) **325** 865-877. DOI: 10.1001/jama.2020.22532
6. Margolis K.G., Cryan J.F., Mayer E.A.. **The microbiota-gut-brain axis: From motility to mood**. *Gastroenterology* (2021) **160** 1486-1501. DOI: 10.1053/j.gastro.2020.10.066
7. Jagielski C.H., Riehl M.E.. **Behavioral strategies for irritable bowel syndrome: Brain-gut or gut-brain?**. *Gastroenterol. Clin.* (2021) **50** 581-593. DOI: 10.1016/j.gtc.2021.03.006
8. Grabauskas G., Wu X., Gao J., Li J.-Y., Turgeon D.K., Owyang C.. **Prostaglandin E**. *Gastroenterology* (2020) **158** 2195-2207.e6. DOI: 10.1053/j.gastro.2020.02.022
9. Ghoshal U.C.. **Postinfection irritable bowel syndrome**. *Gut Liver* (2022) **16** 331-340. DOI: 10.5009/gnl210208
10. Ivashkin V., Poluektov Y., Kogan E., Shifrin O., Sheptulin A., Kovaleva A., Kurbatova A., Krasnov G., Poluektova E.. **Disruption of the pro-inflammatory, anti-inflammatory cytokines and tight junction proteins expression, associated with changes of the composition of the gut microbiota in patients with irritable bowel syndrome**. *PLoS ONE* (2021) **16**. DOI: 10.1371/journal.pone.0252930
11. Witt S.T., Bednarska O., Keita Å.V., Icenhour A., Jones M.P., Elsenbruch S., Söderholm J.D., Engström M., Mayer E.A., Walter S.. **Interactions between gut permeability and brain structure and function in health and irritable bowel syndrome**. *Neuroimage Clin.* (2019) **21** 101602. DOI: 10.1016/j.nicl.2018.11.012
12. Camilleri M., Boeckxstaens G.. **Dietary and pharmacological treatment of abdominal pain in IBS**. *Gut* (2017) **66** 966-974. DOI: 10.1136/gutjnl-2016-313425
13. Bellini M., Tonarelli S., Nagy A.G., Pancetti A., Costa F., Ricchiuti A., de Bortoli N., Mosca M., Marchi S., Rossi A.. **Low FODMAP Diet: Evidence, doubts, and hopes**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12010148
14. Staudacher H.M., Whelan K.. **The low FODMAP diet: Recent advances in understanding its mechanisms and efficacy in IBS**. *Gut* (2017) **66** 1517-1527. DOI: 10.1136/gutjnl-2017-313750
15. Shepherd S.J., Parker F.C., Muir J.G., Gibson P.R.. **Dietary triggers of abdominal symptoms in patients with irritable bowel syndrome: Randomized placebo-controlled evidence**. *Clin. Gastroenterol. Hepatol.* (2008) **6** 765-771. DOI: 10.1016/j.cgh.2008.02.058
16. Kamphuis J.B.J., Guiard B., Leveque M., Olier M., Jouanin I., Yvon S., Tondereau V., Rivière P., Guéraud F., Chevolleau S.. **Lactose and fructo-oligosaccharides increase visceral sensitivity in mice via glycation processes, increasing mast cell density in colonic mucosa**. *Gastroenterology* (2020) **158** 652-663.e6. DOI: 10.1053/j.gastro.2019.10.037
17. Campbell A.K., Matthews S.B., Vassel N., Cox C.D., Naseem R., Chaichi J., Holland I.B., Green J., Wann K.T.. **Bacterial metabolic ‘toxins’: A new mechanism for lactose and food intolerance, and irritable bowel syndrome**. *Toxicology* (2010) **278** 268-276. DOI: 10.1016/j.tox.2010.09.001
18. Zhou S.-Y., Gillilland M., Wu X., Leelasinjaroen P., Zhang G., Zhou H., Ye B., Lu Y., Owyang C.. **FODMAP diet modulates visceral nociception by lipopolysaccharide-mediated intestinal inflammation and barrier dysfunction**. *J. Clin. Investig.* (2018) **128** 267-280. DOI: 10.1172/JCI92390
19. Singh P., Grabauskas G., Zhou S.-Y., Gao J., Zhang Y., Owyang C.. **High FODMAP diet causes barrier loss via lipopolysaccharide-mediated mast cell activation**. *JCI Insight* (2021) **6** e146529. DOI: 10.1172/jci.insight.146529
20. Barrett J.S., Gearry R.B., Muir J.G., Irving P.M., Rose R., Rosella O., Haines M.L., Shepherd S.J., Gibson P.R.. **Dietary poorly absorbed, short-chain carbohydrates increase delivery of water and fermentable substrates to the proximal colon**. *Aliment. Pharmacol. Ther.* (2010) **31** 874-882. DOI: 10.1111/j.1365-2036.2010.04237.x
21. Black C.J., Staudacher H.M., Ford A.C.. **Efficacy of a low FODMAP diet in irritable bowel syndrome: Systematic review and network meta-analysis**. *Gut* (2022) **71** 1117-1126. DOI: 10.1136/gutjnl-2021-325214
22. Tuck C.J., Caminero A., Jiménez Vargas N.N., Soltys C.L., Jaramillo Polanco J.O., Lopez Lopez C.D., Constante M., Lourenssen S.R., Verdu E.F., Muir J.G.. **The impact of dietary fermentable carbohydrates on a postinflammatory model of irritable bowel syndrome**. *Neurogastroenterol. Motil.* (2019) **31** e13675. DOI: 10.1111/nmo.13675
23. Slomski A.. **The Low-FODMAP diet helps IBS symptoms, but questions remain**. *JAMA* (2020) **323** 1029-1031. DOI: 10.1001/jama.2020.0691
24. Kamphuis J.B.J., Reber L., Eutamène H., Theodorou V.. **Increased fermentable carbohydrate intake alters colonic mucus barrier function through glycation processes and increased mast cell counts**. *FASEB J.* (2022) **36** e22297. DOI: 10.1096/fj.202100494RRR
25. Barbara G., Wang B., Stanghellini V., de Giorgio R., Cremon C., Di Nardo G., Trevisani M., Campi B., Geppetti P., Tonini M.. **Mast cell-dependent excitation of visceral-nociceptive sensory neurons in irritable bowel syndrome**. *Gastroenterology* (2007) **132** 26-37. DOI: 10.1053/j.gastro.2006.11.039
26. Wu J., Masuy I., Biesiekierski J.R., Fitzke H.E., Parikh C., Schofield L., Shaikh H., Bhagwanani A., Aziz Q., Taylor S.A.. **Gut-brain axis dysfunction underlies FODMAP-induced symptom generation in irritable bowel syndrome**. *Aliment. Pharmacol. Ther.* (2022) **55** 670-682. DOI: 10.1111/apt.16812
27. Guven G., Hilty M.P., Ince C.. **Microcirculation: Physiology, pathophysiology, and clinical application**. *Blood Purif.* (2020) **49** 143-150. DOI: 10.1159/000503775
28. Thorburn T., Aali M., Lehmann C.. **Immune response to systemic inflammation in the intestinal microcirculation**. *Front. Biosci. Landmark Ed.* (2018) **23** 782-795. PMID: 28930572
29. Matheson P.J., Wilson M.A., Garrison R.N.. **Regulation of intestinal blood flow**. *J. Surg. Res.* (2000) **93** 182-196. DOI: 10.1006/jsre.2000.5862
30. Cole J.A., Cook S.F., Sands B.E., Ajene A.N., Miller D.P., Walker A.M.. **Occurrence of colon ischemia in relation to irritable bowel syndrome**. *Am. J. Gastroenterol.* (2004) **99** 486-491. DOI: 10.1111/j.1572-0241.2004.04097.x
31. Chen B.-R., Du L.-J., He H.-Q., Kim J.J., Zhao Y., Zhang Y.-W., Luo L., Dai N.. **Fructo-oligosaccharide intensifies visceral hypersensitivity and intestinal inflammation in a stress-induced irritable bowel syndrome mouse model**. *World J. Gastroenterol.* (2017) **23** 8321-8333. DOI: 10.3748/wjg.v23.i47.8321
32. Bradesi S., Schwetz I., Ennes H.S., Lamy C.M.R., Ohning G., Fanselow M., Pothoulakis C., McRoberts J.A., Mayer E.A.. **Repeated exposure to water avoidance stress in rats: A new model for sustained visceral hyperalgesia**. *Am. J. Physiol. Gastrointest. Liver Physiol.* (2005) **289** G42-G53. DOI: 10.1152/ajpgi.00500.2004
33. Accarie A., Vanuytsel T.. **Animal models for functional gastrointestinal disorders**. *Front. Psychiatry* (2020) **11** 509681. DOI: 10.3389/fpsyt.2020.509681
34. Tao E., Long G., Yang T., Chen B., Guo R., Ye D., Fang M., Jiang M.. **Maternal separation induced visceral hypersensitivity evaluated via novel and small size distention balloon in post-weaning mice**. *Front. Neurosci.* (2021) **15** 803957. DOI: 10.3389/fnins.2021.803957
35. Li D.-Y., Xia Q., Yu T.-T., Zhu J.-T., Zhu D.. **Transmissive-detected laser speckle contrast imaging for blood flow monitoring in thick tissue: From Monte Carlo simulation to experimental demonstration**. *Light Sci. Appl.* (2021) **10** 241. DOI: 10.1038/s41377-021-00682-8
36. Behem C.R., Graessler M.F., Pinnschmidt H.O., Duprée A., Sandhu H.K., Debus E.S., Wipper S.H., Trepte C.J.C.. **Volume based resuscitation and intestinal microcirculation after ischaemia/reperfusion injury: Results of an exploratory aortic clamping study in pigs**. *Eur. J. Vasc. Endovasc. Surg.* (2019) **57** 284-291. DOI: 10.1016/j.ejvs.2018.08.055
37. Heeman W., Steenbergen W., van Dam G., Boerma E.C.. **Clinical applications of laser speckle contrast imaging: A review**. *J. Biomed. Opt.* (2019) **24** 080901. DOI: 10.1117/1.JBO.24.8.080901
38. Stern M.D.. **In vivo evaluation of microcirculation by coherent light scattering**. *Nature* (1975) **254** 56-58. DOI: 10.1038/254056a0
39. Zheng L., Kelly C.J., Colgan S.P.. **Physiologic hypoxia and oxygen homeostasis in the healthy intestine. A review in the Theme: Cellular responses to hypoxia**. *Am. J. Physiol. Cell Physiol.* (2015) **309** C350-C360. DOI: 10.1152/ajpcell.00191.2015
40. Heeman W., Dijkstra K., Hoff C., Koopal S., Pierie J.-P., Bouma H., Boerma E.C.. **Application of laser speckle contrast imaging in laparoscopic surgery**. *Biomed. Opt. Express* (2019) **10** 2010-2019. DOI: 10.1364/BOE.10.002010
41. Li M., Miao P., Yu J., Qiu Y., Zhu Y., Tong S.. **Influences of Hypothermia on the cortical blood supply by laser speckle imaging**. *IEEE Trans. Neural Syst. Rehabil. Eng.* (2009) **17** 128-134. DOI: 10.1109/TNSRE.2009.2012499
42. Cox S.R., Lindsay J.O., Fromentin S., Stagg A.J., McCarthy N.E., Galleron N., Ibraim S.B., Roume H., Levenez F., Pons N.. **Effects of low FODMAP diet on symptoms, fecal microbiome, and markers of inflammation in patients with quiescent inflammatory bowel disease in a randomized trial**. *Gastroenterology* (2020) **158** 176-188.e7. DOI: 10.1053/j.gastro.2019.09.024
43. Halmos E.P., Christophersen C.T., Bird A.R., Shepherd S.J., Gibson P.R., Muir J.G.. **Diets that differ in their FODMAP content alter the colonic luminal microenvironment**. *Gut* (2015) **64** 93-100. DOI: 10.1136/gutjnl-2014-307264
44. McIntosh K., Reed D.E., Schneider T., Dang F., Keshteli A.H., De Palma G., Madsen K., Bercik P., Vanner S.. **FODMAPs alter symptoms and the metabolome of patients with IBS: A randomised controlled trial**. *Gut* (2017) **66** 1241-1251. DOI: 10.1136/gutjnl-2015-311339
45. Staudacher H.M., Scholz M., Lomer M.C., Ralph F.S., Irving P.M., Lindsay J.O., Fava F., Tuohy K., Whelan K.. **Gut microbiota associations with diet in irritable bowel syndrome and the effect of low FODMAP diet and probiotics**. *Clin. Nutr.* (2021) **40** 1861-1870. DOI: 10.1016/j.clnu.2020.10.013
46. Hayase E., Hayase T., Jamal M.A., Miyama T., Chang C.-C., Ortega M.R., Ahmed S.S., Karmouch J.L., Sanchez C.A., Brown A.N.. **Mucus-degrading bacteroides link carbapenems to aggravated graft-versus-host disease**. *Cell* (2022) **185** 3705-3719.e14. DOI: 10.1016/j.cell.2022.09.007
47. Zhou Q., Verne G.N.. **New insights into visceral hypersensitivity--clinical implications in IBS**. *Nat. Rev. Gastroenterol. Hepatol.* (2011) **8** 349-355. DOI: 10.1038/nrgastro.2011.83
48. Mertz H., Naliboff B., Munakata J., Niazi N., Mayer E.A.. **Altered rectal perception is a biological marker of patients with irritable bowel syndrome**. *Gastroenterology* (1995) **109** 40-52. DOI: 10.1016/0016-5085(95)90267-8
49. Distrutti E., Salvioli B., Azpiroz F., Malagelada J.R.. **Rectal function and bowel habit in irritable bowel syndrome**. *Am. J. Gastroenterol.* (2004) **99** 131-137. DOI: 10.1046/j.1572-0241.2003.04012.x
50. Bouin M., Plourde V., Boivin M., Riberdy M., Lupien F., Laganière M., Verrier P., Poitras P.. **Rectal distention testing in patients with irritable bowel syndrome: Sensitivity, specificity, and predictive values of pain sensory thresholds**. *Gastroenterology* (2002) **122** 1771-1777. DOI: 10.1053/gast.2002.33601
51. Vannucchi M.G., Evangelista S.. **Experimental models of irritable bowel syndrome and the role of the enteric neurotransmission**. *J. Clin. Med.* (2018) **7**. DOI: 10.3390/jcm7010004
52. Paine P.. **Review article: Current and future treatment approaches for pain in IBS**. *Aliment. Pharmacol. Ther.* (2021) **54** S75-S88. DOI: 10.1111/apt.16550
53. Perna E., Aguilera-Lizarraga J., Florens M.V., Jain P., Theofanous S.A., Hanning N., De Man J.G., Berg M., De Winter B., Alpizar Y.A.. **Effect of resolvins on sensitisation of TRPV1 and visceral hypersensitivity in IBS**. *Gut* (2021) **70** 1275-1286. DOI: 10.1136/gutjnl-2020-321530
54. Apte R.S., Chen D.S., Ferrara N.. **VEGF in signaling and disease: Beyond discovery and development**. *Cell* (2019) **176** 1248-1264. DOI: 10.1016/j.cell.2019.01.021
55. Tolstanova G., Khomenko T., Deng X., Chen L., Tarnawski A., Ahluwalia A., Szabo S., Sandor Z.. **Neutralizing anti-vascular endothelial growth factor (VEGF) antibody reduces severity of experimental ulcerative colitis in rats: Direct evidence for the pathogenic role of VEGF**. *J. Pharmacol. Exp. Ther.* (2009) **328** 749-757. DOI: 10.1124/jpet.108.145128
56. Holtmann G.J., Ford A.C., Talley N.J.. **Pathophysiology of irritable bowel syndrome**. *Lancet Gastroenterol. Hepatol.* (2016) **1** 133-146. DOI: 10.1016/S2468-1253(16)30023-1
57. Aksoy E.K., Çetinkaya H., Savaş B., Ensari A., Torgutalp M., Efe C.. **Vascular endothelial growth factor, endostatin levels and clinical features among patients with ulcerative colitis and irritable bowel syndrome and among healthy controls: A cross-sectional analytical study**. *Sao Paulo Med. J.* (2018) **136** 543-550. DOI: 10.1590/1516-3180.2018.0274161118
58. Sofi F., Whittaker A., Gori A.M., Cesari F., Surrenti E., Abbate R., Gensini G.F., Benedettelli S., Casini A.. **Effect of**. *Br. J. Nutr.* (2014) **111** 1992-1999. DOI: 10.1017/S000711451400018X
59. Christina Grape R.N., Theorell T., Wikström B.-M., Ekman R.. **Choir singing and fibrinogen. VEGF, cholecystokinin and motilin in IBS patients**. *Med. Hypotheses* (2009) **72** 223-225. DOI: 10.1016/j.mehy.2008.09.019
60. Joussen A.M., Poulaki V., Qin W., Kirchhof B., Mitsiades N., Wiegand S.J., Rudge J., Yancopoulos G.D., Adamis A.P.. **Retinal vascular endothelial growth factor induces intercellular adhesion molecule-1 and endothelial nitric oxide synthase expression and initiates early diabetic retinal leukocyte adhesion in vivo**. *Am. J. Pathol.* (2002) **160** 501-509. DOI: 10.1016/S0002-9440(10)64869-9
61. Ali M., Falkenhain K., Njiru B.N., Murtaza-Ali M., Ruiz-Uribe N.E., Haft-Javaherian M., Catchers S., Nishimura N., Schaffer C.B., Bracko O.. **VEGF signalling causes stalls in brain capillaries and reduces cerebral blood flow in Alzheimer’s mice**. *Brain* (2022) **145** 1449-1463. DOI: 10.1093/brain/awab387
62. Martínez-Martínez L.-A., Mora T., Vargas A., Fuentes-Iniestra M., Martínez-Lavín M.. **Sympathetic nervous system dysfunction in fibromyalgia, chronic fatigue syndrome, irritable bowel syndrome, and interstitial cystitis: A review of case-control studies**. *J. Clin. Rheumatol.* (2014) **20** 146-150. DOI: 10.1097/RHU.0000000000000089
63. Katherine Jurek M., Seavey H., Guidry M., Slomka E., Hunter S.D.. **The effects of slow deep breathing on microvascular and autonomic function and symptoms in adults with irritable bowel syndrome: A pilot study**. *Neurogastroenterol. Motil.* (2022) **34** e14275. DOI: 10.1111/nmo.14275
64. Tanaka T., Manabe N., Hata J., Kusunoki H., Ishii M., Sato M., Kamada T., Shiotani A., Haruma K.. **Characterization of autonomic dysfunction in patients with irritable bowel syndrome using fingertip blood flow**. *Neurogastroenterol. Motil.* (2008) **20** 498-504. DOI: 10.1111/j.1365-2982.2007.01039.x
65. Emmanuel A.V., Kamm M.A.. **Laser doppler flowmetry as a measure of extrinsic colonic innervation in functional bowel disease**. *Gut* (2000) **46** 212-217. DOI: 10.1136/gut.46.2.212
66. Boley S.J., Agrawal G.P., Warren A.R., Veith F.J., Levowitz B.S., Treiber W., Dougherty J., Schwartz S.S., Gliedman M.L.. **Pathophysiologic effects of bowel distention on intestinal blood flow**. *Am. J. Surg.* (1969) **117** 228-234. DOI: 10.1016/0002-9610(69)90308-0
67. Uno Y.. **Hypothesis: Mechanism of irritable bowel syndrome in inflammatory bowel disease**. *Med. Hypotheses* (2019) **132** 109324. DOI: 10.1016/j.mehy.2019.109324
|
---
title: 'Urban–Rural Disparities in Knowledge, Use and Perceived Benefits of Nutrition
Labels in China: Evidence from 10 Provinces'
authors:
- Linlin Fan
- Zhigang Wang
- Yiwen Zhao
- Ye Ma
journal: Nutrients
year: 2023
pmcid: PMC10004821
doi: 10.3390/nu15051171
license: CC BY 4.0
---
# Urban–Rural Disparities in Knowledge, Use and Perceived Benefits of Nutrition Labels in China: Evidence from 10 Provinces
## Abstract
There exist significant gaps in nutritional status between urban and rural populations in China. The previous literature has shown that more knowledge and usage of nutrition labels are instrumental in improving diet quality and health. The aim of the study is to analyze: [1] Are there urban–rural disparities in consumer knowledge, use and perceived benefits of nutrition labels in China; [2] If so, what are the magnitudes of the disparities; [3] What can explain the disparities, and how can the disparities be reduced? The Oaxaca–Blinder (O-B) decomposition is utilized to analyze the predictors of urban–rural disparities in nutrition labels based on a self-conducted study of Chinese individuals. The information from a total of 1635 individuals (aged 11–81 years) across China in 2016 was collected in the survey. We find that rural respondents have less knowledge, lower usage and perceived benefits of nutrition labels than their urban counterparts. Demographics, focus on food safety, frequent shopping locations and income jointly explain $98.9\%$ of the disparity in the knowledge of nutrition labels. Nutrition label knowledge is the predictor which contributes most to urban–rural disparity in label use—accounting for $29.6\%$ of the disparity. Nutrition label knowledge and use are the two biggest predictors of disparities in perceived benefits—accounting for $29.7\%$ and $22.8\%$ of the disparity in perceived benefits, respectively. Our study suggests that policies aiming to improve income and education, as well as raising awareness of food safety in rural areas, are promising in closing the urban–rural disparities in nutrition labels knowledge, use, diet quality and health in China.
## 1. Introduction
As China’s economy has taken off in the past decades, diet-related diseases have also gradually increased. An increasing number of people suffer from various chronic non-communicable diseases because of unhealthy diets [1]. Poor diets are a major contributor to non-communicable diseases that account for over $80\%$ of deaths in China every year [2]. The China State Council Information Office reports that chronic diseases account for $88.5\%$ of all deaths in 2019 [3]. Diet-related health problems, such as obesity, hypertension, type II diabetes and cardiovascular diseases, reduce people’s quality of life and place heavy medical expenditures burdens on both individual families and society [4,5]. The excessive consumption of saturated fat, added sugar, sodium and calories in part lead to sharp increases in those diet-related diseases and existing research shows that nutrition labels about the nutrient contents of foods help consumers make better dietary choices [6,7,8]. For example, nutrition label uses are found to reduce individuals’ daily calorie intake from saturated fat, cholesterol and sodium by $2.1\%$, 67.6 milligrams and 29.58 milligrams, respectively, while increasing average daily fiber intake by 7.51 g [6]. Consumers who frequently use nutrition labels have better diets and lower risks of diet-related comorbidities [9,10,11,12,13].
China has established mandatory nutrition labeling since 1 January 2013 [14]. The China “Guidance of Nutrition Labelling of Pre-packaged Food” (GB28050-2011) required mandatory and standardized declaration of essential nutrient content, including energy, protein, fat, carbohydrates, sodium and their respective percentages of nutrient reference values for all pre-packaged food. Despite the fact that mandatory nutrition labeling has been in effect for quite a few years, the knowledge and use of nutrition labels are still very low among Chinese consumers, particularly among rural residents [15,16]. Notably, in spite of rapid economic development in China, significant urban–rural disparities persist [17,18]. There are some China-specific institutional barriers that keep rural and urban populations apart [19]. The Hukou system, or household registration system, has contributed to the existence of a dual economy between urban and rural sectors [20]. The Hukou system restricts the mobility of labor into urban areas and operates like an “immigration visa” system but for rural migrants to urban areas. It is very difficult for rural residents to permanently change their Hukou and live in the cities so that they can enjoy equal opportunities of education, employment and healthcare as urban residents. There is a recent loosening of restrictions in smaller cities but for megacities, the Hukou system is still in effect. As a result of the dual economy, inequities persist in the level of spending on and access to education, health and social welfare programs between urban and rural areas. To this date, urban–rural disparities are not only shown in education, income and living conditions but also manifest in residents’ access to medical insurance, quality of health services and the availability of safe and quality foods that comply with nutrition labeling regulations. Rural residents also exhibit lower nutritional status and suffer more from diet-related health problems compared with urban residents [21]. Thus, it is imperative to study the factors that may improve their healthy eating behaviors, such as nutrition labels use in this vulnerable population, which could help reduce urban–rural disparities in health.
The knowledge, use and perceived benefits of nutrition labels are low among both urban and rural residents [15,16,22,23]. Several factors may hamper the use and perceived benefits of nutrition labels among Chinese consumers. For example, the information being too technical or complex is commonly cited as the most important reason why nutrition labels are not frequently used [24,25,26]. A recent systematic review revealed that traffic light schemes are more effective in inducing healthy food consumption compared with the Guideline Daily Amount and front-of-package nutrition and health claims [13]. Other factors, such as gender, age, household size, health status, shopping frequency, nutrition knowledge, diet-health concern and income, are found to be significant predictors of nutrition labels usage and knowledge [6,22,23,24,27,28].
Although extensive research has investigated the determinants of nutrition labels’ knowledge, usage and benefits, our study is the first to examine the inequities in the knowledge, use and perceived benefits of nutrition labels between urban and rural consumers in China. Existing studies mostly focus on assessing the use and understanding of nutrition labels among urban residents in China. Moreover, data samples usually come from one city or two and thus have limited information on other places in China. In contrast, we conducted a large-scale survey to collect data from both urban and rural areas in 10 provinces across China. This paper aims to answer three questions: [1] Are there urban–rural disparities in consumer knowledge, use and perceived benefits of nutrition labels; [2] If so, what are the magnitudes of the disparities; [3] What can explain the urban–rural disparities in nutrition label knowledge, usage and perceived benefits, and how can the disparities be reduced?
## 2.1. Study Sample
The data used in this paper came from a survey of urban and rural consumers in Hebei, Jiangsu, Guangdong, Sichuan, Guizhou, Guangxi, Inner Mongolia, Shanxi, Hunan and Heilongjiang conducted by the authors in October and November of 2016. The Renmin University of China Institutional Research Board (IRB) committee approved the study, and parental consent was obtained to survey teenage respondents. Stratified sampling was used, and several provinces were selected from each geographic region of China. Then, one prefecture was selected from each province, and both urban and rural areas in that prefecture were sampled. More than 200 questionnaires were distributed in each prefecture. Individuals aged 11 to 81 years were selected. Because the national urbanization rate is around $60\%$, we deliberately conducted about $60\%$ of the surveys in urban areas and the remaining $40\%$ in rural areas. Due to missing values in key explanatory and dependent variables, 481 respondents were dropped from the analysis. Another 54 observations were removed because of outliers. As a result, 1635, or $75.3\%$ of all questionnaires, were retained and used in this study. Among the remaining respondents, 696 ($42.57\%$) of them lived in rural areas, and 939 ($57.43\%$) lived in urban areas, which is consistent with the national urbanization rate of $57.35\%$ in 2016.
## 2.2. Outcome Variables
There were three outcome variables in this study—consumers’ knowledge, use and perceived benefits of nutrition labels (Table 1). Three questions were asked, and the corresponding response options were on a 5-point Likert scale. Specifically, the questions were [1] knowledge: “How well do you know about nutrition labels?” with the response options as “1 = do not understand, 2 = understand slightly, 3 = understand moderately, 4 = generally understand, 5 = totally understand”; [2] use: “How often do you use nutrition labels when shopping for food?” with the options as “1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Always”; and [3] perceived benefits: “How much do you think you have benefited from nutrition labels?” with the options as “1 = no benefits, 2 = benefit slightly, 3 = benefit moderately, 4 = benefit a lot, 5 = benefit very much”.
## 2.3. Explanatory Variables
Motivated by the literature on nutrition labels, the explanatory variables included demographics (the age, BMI and health status of the respondent, whether the respondent graduated from high school and whether the respondent has children or seniors at home), the level of attention paid to food safety issues (never = 1, rarely = 2, sometimes = 3, often = 4 and always = 5) and frequent shopping locations, income and province indicators. Rural residents were defined as those who lived in rural areas for more than six months in a year or otherwise were urban residents. Importantly, gender, marital status and household size were not included in the analysis because these variables were insignificant predictors of the nutrition label knowledge, use and perceived benefits in the regressions. The detailed definition of each variable is also shown in Table 1.
## 3. Methods
Ordinary Least Squares (OLS) regressions were separately performed on the knowledge, use and perceived benefits of nutrition labels among urban and rural respondents to investigate how the significant predictors might differ by region. Next, the Oaxaca–Blinder (O-B) decomposition was used to analyze what explains the disparities in nutrition labels knowledge, use and perceived benefits between urban and rural residents. A p-value < 0.1 was deemed statistically significant.
The Oaxaca–Blinder (O-B) decomposition has become a widespread method for decomposing the difference in an outcome between two groups in various research fields [29,30,31]. The popular tool divides the gap between two groups into two parts, which are named as “explained” and “unexplained”. The “explained” part attributes the difference in the gap to the mean of observable predictors between two groups, whereas the different effects of unobservable predictors account for the difference in the gap in the “unexplained” part [32,33].
The objective of this paper is to decompose the gaps in the knowledge, use and perceived benefit level of nutrition labels between urban and rural respondents. The common assumptions about the urban–rural disparities in nutrition labels indicate that the disparities will substantially diminish if society can effectively reduce the disparities in socioeconomic status (SES), such as income and education, and food retail environment between urban and rural residents. The O-B method is particularly helpful when one wants to test how much the urban–rural disparity could still remain in the hypothetical situation where policies successfully improved the above-mentioned predictors for the rural residents such that the mean levels of those indicators became at par with the mean levels for urban residents [30].
Specifically, the disparity in the knowledge of nutrition labels was used as an example to illustrate how the disparity is decomposed based on the O-B decomposition methodology. Initially, assuming that the knowledge level model is linear and includes a set of observable and unobservable predictors, two linear models showing the mean knowledge level for the urban and rural groups are formulated as follows, [1]Knowledgeu=Xu′βu+ϵu [2]Knowledger=Xr′βr+ϵr where Knowledge represents the mean knowledge level of nutrition labels, X is a vector that consists of predictors and a constant term, β is the corresponding coefficient vector, and ϵ is the random error term. The subscript u represents the urban group, while subscript r represents the rural group. The expected value of the random error term, ϵ, is assumed to be zero following the assumption in the linear model.
Then, the means of knowledge level for urban and rural groups can be denoted as below:[3]E(Knowledgeu)=E(Xu)′βu [4]E(Knowledger)=E(Xr)′βr According to Equations [3] and [4], the difference in mean knowledge level between urban and rural groups can be calculated as follows:[5]R=E(Xu)′βu−E(Xr)′βr Next, overall knowledge difference can be separated into two components based on the O-B decomposition, [6]R=[E(Xu)′−E(Xr)′]β*+[E(Xu)′(βu−β*)+E(Xr)′(β*−βr)] where the first term in Equation [6] is the “explained” part, indicating the difference in the knowledge level between the two groups due to the predictors. The second term represents the “unexplained” part, which contributes to the disparity of the outcome to the difference in the coefficient estimation. This part is also crucial since it contains all potential variables that affect the disparity but are not included in the predictors.
In this paper, the coefficients from pooled regressions combining two groups were used to represent the value of β*, in order to avoid base group bias and follow common practice in the literature [34,35,36]. Three O-B models are performed to decompose the disparities in knowledge, use and perceived benefits of nutrition labels between rural and urban groups. Specifically, in the first O-B model, the difference in the knowledge between rural and urban groups is decomposed into “explained” and “unexplained” portions. In the second model, the disparity in use between rural and urban groups is analyzed using knowledge as an independent variable. Lastly, both knowledge and use are treated as independent variables in the O-B model, where the differences in perceived benefits between rural and urban consumers are studied.
## 4.1. Descriptive Statistics
Table 2 summarizes the knowledge, use and perceived benefits of nutrition labels among rural and urban respondents. Results show that rural respondents have less knowledge of nutrition labels—$47.55\%$ of rural respondents vs. $38.12\%$ of urban counterparts do not understand nutrition labels. Rural respondents also have lower usage of nutrition labels (rural: $37.35\%$ vs. urban: $22.46\%$). Lastly, rural respondents also perceive lower benefits from nutrition labels ($35.92\%$ of rural vs. $27.05\%$ of urban respondents perceive no benefits or only benefit slightly from nutrition labels). However, a small percentage of both rural and urban respondents ($3.62\%$ vs. $3.59\%$) report benefiting very much from nutrition labels. Similarly, only $1.29\%$ of urban respondents and $2.24\%$ of rural respondents totally understand nutrition labels. Small percentages of rural and urban respondents ($7.76\%$ vs. $7.77\%$) always use nutrition labels when buying food (Table 2).
As can be seen from Table 3, the data in the second and third columns are sample means by urban and rural groups, and rural respondents are more socially and economically disadvantaged. Specifically, the average age of rural respondents is higher than that of urban counterparts (40.58 vs. 31.82, p-value < 0.001); rural respondents have higher BMI (23.11 vs. 22.31, p-value = 0.001); and fewer rural respondents have at least a high school education than their urban counterparts ($51.7\%$ vs. $88.8\%$, p-value < 0.001). Urban residents have better health status, such that $53.7\%$ and $16.6\%$ of them rate their health as good or excellent compared with $42.1\%$ and $17.8\%$ for rural residents (p-value < 0.001 and p-value = 0.524, respectively). More rural respondents have children and seniors at home (children: $56.5\%$ vs. $51.7\%$, p-value = 0.054; seniors: $47.7\%$ vs. $34.7\%$, p-value < 0.001). Fewer rural respondents often or always pay attention to food safety issues than rural groups ($52.8\%$ vs. $59.5\%$), and rural respondents are less likely to buy food online ($21.1\%$ vs. $40.5\%$, p-value < 0.001) or from large supermarkets ($60.3\%$ vs. $86.9\%$, p-value < 0.001) than urban residents. Rural respondents have lower annual income than their urban counterparts (4.272 vs. 7.029, p-value < 0.001).
## 4.2. OLS Regression Results
Columns [5] and [6] in Table 3 show the OLS regression results on knowledge of nutrition labels against various explanatory variables. Rural respondents who have seniors at home have lower knowledge of nutrition labels. The more often rural respondents pay attention to food safety, the more knowledge of nutrition labels they have. The rural respondents who frequently shop in large supermarkets or online also have more knowledge of nutrition labels. In comparison, rural residents who mostly shop at corner stores report having lower knowledge of nutrition labels (coeff. = −0.138, p-value < 0.1). Income is not a significant predictor of nutrition label knowledge among rural respondents. For urban respondents, similarly, the more attention paid to food safety, the more knowledgeable of nutrition labels urban residents are. A higher income also predicts better knowledge of nutrition labels among urban respondents (coeff. = 0.008, p-value < 0.1).
Columns [7] and [8] present the OLS regression results on the use of nutrition labels among rural and urban respondents, respectively. More knowledge of nutrition labels is associated with more frequent use of nutrition labels among both rural and urban respondents. The level of attention paid to food safety is also positively associated with the use of nutrition labels for both rural and urban respondents. Rural respondents who frequently shop in large supermarkets use nutrition labels more often (coeff. = 0.264, p-value < 0.01), while urban respondents who frequently shop in farmers’ markets use nutrition labels more often (coeff. = 0.100, p-value < 0.1). In contrast, urban respondents who mostly shop in corner stores use nutrition labels less often (coeff. = −0.196, p-value < 0.01). Similar to the knowledge of nutrition labels, income is a significant and positive predictor of nutrition label use among urban respondents but not in rural respondents.
Lastly, the predictors of perceived benefits from nutrition labels are analyzed among rural and urban respondents (columns [9] and [10] in Table 3). Knowledge and frequent use of nutrition labels are positively associated with perceived benefits from nutrition labels in both rural and urban residents. Age and having a high school education or above are negatively associated with perceived benefits from nutrition labels among rural residents but not among urban respondents. In contrast to the positive association between the level of attention to food safety and knowledge and use of nutrition labels, only urban residents who always pay attention to food safety perceive significantly more benefits from nutrition labels compared with those who never pay attention to food safety (coeff. = 0.444, p-value < 0.1). Similarly, although various frequent shopping locations are significant predictors of knowledge and use of nutrition labels among rural respondents, they are not significantly associated with perceived benefits among rural residents after controlling for nutrition label knowledge and usage in the regression. Interestingly, income is negatively associated with perceived benefits from nutrition labels among urban residents (coeff. = −0.009, p-value < 0.05).
## 4.3. O-B Decomposition Results
Table 4 shows the O-B decomposition results for the urban–rural disparities in knowledge, use and perceived benefits of nutrition labels. Columns [2] and [3] present the decomposition results for the difference in knowledge between urban and rural respondents. The disparity in the knowledge of nutrition labels between urban and rural groups is 0.183, indicating that urban residents have higher nutrition label knowledge compared with rural groups (also shown in Table 3). A total of $92.84\%$ (p-value < 0.01) of the overall disparity in knowledge is explained, which implies that almost all possible predictors are included in the O-B model to explain the disparity. The set of indicator variables denoting focus on food safety together have the largest contribution, accounting for $34.45\%$ of the disparity (p-value < 0.01). Specifically, the differences in whether the respondent never ($17.27\%$, p-value < 0.01) and often ($7.55\%$, p-value < 0.01) focuses on food safety between urban and rural groups contribute most to the disparities among all variables of focus on food safety. As shown in Table 3, urban residents are more likely to pay attention to food safety compared with rural residents. Therefore, if rural residents increased the frequency of focusing on food safety issues, the disparity in nutrition label knowledge would have shrunk. The second largest contributor to the disparity is demographics, accounting for 0.056 out of 0.183 ($30.77\%$, p-value < 0.01) in overall disparity between urban and rural respondents. In other words, the average knowledge of nutrition labels in rural residents will raise by 0.056 if they have identical demographic characteristics as the urban residents. The frequent shopping location indicator variables are the third-largest contributor to the disparity, explaining $23.04\%$ of the overall disparity in knowledge between rural and urban residents (p-value < 0.05). Lastly, income explains $10.63\%$ of the disparity (p-value < 0.05). So, if rural respondents had the same annual income as urban counterparts, $10.6\%$ of the disparity in knowledge of nutrition labels between these two groups would have disappeared.
The second O-B model in Table 4 (Columns [4] and [5]) is to decompose the differences in the use of nutrition labels between rural and urban residents. Table 4 shows that the overall use disparity between these two groups is 0.315, and $66.0\%$ (p-value < 0.01) of the overall disparity is explained by predictors contained in the model. This result also suggests that urban residents are more likely to use nutrition labels compared with their rural counterparts (also shown in Table 3). Particularly, we use the knowledge of nutrition labels as an explanatory variable in this O-B model. The knowledge variable has the largest contribution to the overall disparity in the use of nutrition labels among all explanatory variables. Around $30\%$ (p-value < 0.01) of the overall disparity in the use of nutrition labels is explained by differences in knowledge. The difference in demographic characteristics does not predict the urban–rural disparity in use level. This finding is contrary to the result in the knowledge disparity O-B model, where the demographic variables have a big contribution to the overall knowledge disparity. If rural respondents had the same level of focus on food safety as urban residents, the disparity in use of nutrition labels would have reduced by $14.56\%$ (p-value < 0.01). If rural respondents were to have the same frequent shopping locations as urban residents, then $23.04\%$ of the disparity in use level would disappear (p-value < 0.01). Income is not a significant predictor of the use disparity between the two groups.
The last two columns in Table 4 show the decomposition results for the gaps in perceived benefits of nutrition labels between urban and rural respondents. In the final O-B model, we add both knowledge and use as two independent variables. As observed in Table 4, the overall benefit disparity between urban and rural residents is 0.166. A total of $71.61\%$ of the overall disparity in perceived benefits can be explained by the differences in the explanatory variables included in the model. Knowledge and use are the two biggest contributors, which, together, account for more than $50\%$ of the overall urban–rural disparity in perceived benefits (p-value < 0.01). It suggests that the average benefit level would increase by 0.087 if all rural respondents had the same knowledge and use of nutrition labels as urban respondents (p-value < 0.01). Further, age explains $21.39\%$ of the disparity in perceived benefits (p-value < 0.05). Focus on food safety variables accounts for $13.36\%$ of the overall benefit disparity between two groups (p-value < 0.05). The coefficient of the income variable is −0.019 and is significant at the $10\%$ level. The negative coefficient means that closing the income disparity between urban and rural residents would actually increase the disparity in perceived benefits between urban and rural groups, a point that will be discussed in the next section.
## 5. Discussion
This study shows that both urban and rural residents have low levels of knowledge, use and perceived benefits from nutrition labels in China. Specifically, only $15.45\%$ of urban and $14.51\%$ of rural residents generally or totally understand nutrition labels. There are only $36.41\%$ of urban and $27.73\%$ of rural respondents who often or always use nutrition labels. Even smaller percentages of urban and rural residents report benefiting a lot or very much from nutrition labels (urban, $23.11\%$; rural, $20.69\%$). The reported knowledge of nutrition labels is lower compared with previous studies that found $19.2\%$ of respondents from Beijing and Baoding (two big cities in Northern China) [22] and $35.3\%$ of parents of primary and secondary school students in Shanghai [23] moderately or totally understand nutrition labels. The lower knowledge of nutrition labels found in our study is likely due to a more geographically diverse sampling that includes both urban and rural areas across China. Liu et al. [ 22] also found that $28.5\%$ of respondents regularly or always use nutrition labels, compared with $36.41\%$ of urban and $27.73\%$ of rural residents in our study. Of note, Liu et al. [ 22] collected data in 2012, while ours was gathered in 2016. The more frequent use of nutrition labels found in our study may reflect more use of nutrition labels over the years among Chinese consumers.
Despite the progress in the use of nutrition labels over the years, significant disparities persist in the knowledge, use and perceived benefits of nutrition labels between urban and rural consumers. We find that, compared with their urban counterparts, $9.43\%$ more of rural respondents do not understand nutrition labels, $14.89\%$ more of rural respondents do not use nutrition labels, and $8.87\%$ more of rural residents perceive no or slight benefits from nutrition labels. This disparity in nutrition labels knowledge, use and perceived benefits reinforces concerns about urban–rural gaps in diet-related chronic diseases and nutrition [21,37,38,39]. For example, Wang et al. [ 21] find that after adjusting for age and gender, rural residents have higher prevalence of hypertension, heart disease, cerebrovascular disease, peptic ulcer and chronic cholecystitis among other conditions. Their findings combined with the current study suggest that disparities in the knowledge and use of nutrition labels may contribute to disparities in diet quality and related health problems between urban and rural consumers. Thus, appropriate interventions are needed to address this persistent inequality in knowledge, use and perceived benefits of nutrition labels between urban and rural consumers, and close the gaps in diet quality and related health problems in China.
So, how could the urban–rural disparities in nutrition labels be reduced? The O-B analysis provides important insights into this question. Focus on food safety, demographics, frequent shopping locations and income each contributes to $34.45\%$, $30.77\%$, $23.04\%$ and $10.63\%$ of the overall disparity in nutrition label knowledge between urban and rural respondents. The results suggest that improving focus on food safety, enhancing education, increasing availability of large supermarkets and online shopping venues and raising income in rural areas are promising strategies to reduce the disparity in nutrition label knowledge. In fact, the present study shows that, after improving those explanatory variables among rural consumers, $92.84\%$ of the disparity in nutrition label knowledge would disappear. Our results are consistent with previous work that finds education and income are positive predictors of nutrition label knowledge [23,25]. We also offer some new insights to the literature—we discovered that focus on food safety issues and shopping locations are also significant predictors of nutrition label knowledge. Thus, the improvement of those predictors is a fruitful area for future interventions to increase nutrition label knowledge and mitigate the gap between urban and rural consumers.
The present study finds that urban–rural divergence in nutrition label knowledge, frequent shopping locations and focus on food safety significantly contribute to the disparity in nutrition label use. These results support previous studies that find nutrition label knowledge is a significant predictor of nutrition label use [22,27], but also offer new insights that focus on food safety and frequent shopping locations are important factors in helping close the urban–rural gap. If rural respondents had the same shopping locations and focus on food safety as urban counterparts, $32.1\%$ of the disparity in nutrition label use would be gone. Another $30\%$ of the disparity in nutrition label usage would disappear if rural respondents’ nutrition label knowledge were at par with urban consumers. These results collectively suggest that interventions that target at improving nutrition label knowledge, shopping locations and focus on food safety among rural consumers would reduce the urban–rural disparity in nutrition label use.
In terms of the disparity in perceived benefits of nutrition labels, knowledge and use are the two biggest contributors, each accounting for $29.69\%$ and $22.76\%$ of the disparity, respectively. Further, demographics, focus on food safety, and frequent shopping locations jointly explain $36.57\%$ of the disparity. The current study contributes to the literature by finding that if rural consumers’ knowledge and use of nutrition labels, demographics, focus on food safety, and frequent shopping locations were the same as their urban counterparts, $71.61\%$ of the urban–rural disparity in perceived benefits would have disappeared. Thus, policy interventions that address the disparities in knowledge and use of nutrition labels, such as improving awareness of food safety, could also be helpful to mitigate the gap in perceived benefits. Interestingly, the current study shows that increasing rural consumers’ income would actually increase the disparity in perceived benefits from nutrition labels. It is possible that high-income people are willing to spend more money to purchase food with higher perceived quality, such as organic food. In this regard, they are confident with the quality of food and do not use or recognize benefits from nutrition labels [27].
This study has several limitations. The analysis is based on self-reported data, and there could be social desirability bias that respondents overreport the knowledge and use of nutrition labels, among other variables. Moreover, our data were collected in 2016, and the income gaps between urban and rural residents have shrunk over the years since 2016. Therefore the gaps in nutrition labels may also have decreased over time. However, we argue that the important predictors of the gaps found in this study remain important to explain the disparities in the knowledge, use and perceived benefits of nutrition labels. Furthermore, while the model predictors explain a significant part of the urban–rural disparity, there are still some unexplained portions of the gaps. Omitted variables and measurement errors could contribute to the “unexplained part” of the disparity, but O-B model does not offer any further insights into which of these conjectures might be the most plausible. Another limitation of the current approach is that the outcome variables are ordered but are treated as continuous variables because the O-B approach based on ordered logit regression model is unavailable. The current analysis can only infer associations rather than causality between knowledge, use and perceived benefits of nutrition labels and predictors. Additionally, our sample includes individuals who are under 18 years old and future study is warranted to study the heterogeneous behaviors between children and adults. The average respondent in the sample had higher income and education than the national average person. Therefore, future research is needed to study the knowledge, use and perceived benefits of nutrition labels based on a more nationally representative dataset. Finally, several questions in our survey use a Likert scale of five points, which might influence our results because of the higher percentage of responses at the middle point of the scale. Despite the limitations, this study is the first to use an innovative method to analyze the urban–rural disparities in nutrition labels, and utilizes a large-scale survey from 10 provinces across China.
## 6. Conclusions
The current study finds that there are significant disparities in the knowledge, use and perceived benefits of nutrition labels between urban and rural consumers in China. Rural consumers have much lower knowledge, use and perceived benefits of nutrition labels. Reducing the disparities in education and income and raising awareness of food safety issues in rural areas are promising ways to close the urban–rural disparities in nutrition label knowledge and use. Urban–rural divergence in perceived benefits can be further reduced by closing gaps in knowledge and use of nutrition labels. Future research is warranted to assess the optimal set of policies that reduce the urban–rural gaps in the predictors discovered in this study, which contributes to equity in diet quality and health.
## References
1. Zhang J., Zhai L., Osewe M., Liu A.. **Analysis of factors influencing food nutritional labels use in Nanjing, China**. *Foods* (2020) **9**. DOI: 10.3390/foods9121796
2. Langenbrunner J.C., Marquez P.V., Wang S.. *Toward a Healthy and Harmonious Life in China: Stemming the Rising Tide of Non-Communicable Diseases* (2011)
3. **Report on Nutrition and Chronic Diseases of Chinese Residents (2020)**. (2020)
4. Afshin A., Sur P.J., Fay K.A., Cornaby L., Ferrara G., Salama J.S., Mullany E.C., Abate K.H., Abbafati C., Abebe Z.. **Health effects of dietary risks in 195 countries, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2019) **393** 1958-1972. DOI: 10.1016/S0140-6736(19)30041-8
5. He M., Su D., Zou Y., Huang L., Zhao D., Wang W., Fang Y., Huang E., Gu W., Han D.. **Comparative study on burden of diet-related chronic diseases in China between 1990 and 2016**. *Wei Sheng Yan Jiu J. Hyg.* (2019) **48** 817-821
6. Kim S.Y., Nayga R.M., Capps O.. **The effect of food label use on nutrient intakes: An endogenous switching regression analysis**. *J. Agric. Resour. Econ.* (2000) **25** 215-231
7. Pérez-Escamilla R., Haldeman L.. **Food label use modifies association of income with dietary quality**. *J. Nutr.* (2002) **132** 768-772. DOI: 10.1093/jn/132.4.768
8. Cooke R., Papadaki A.. **Nutrition label use mediates the positive relationship between nutrition knowledge and attitudes towards healthy eating with dietary quality among university students in the UK**. *Appetite* (2014) **83** 297-303. DOI: 10.1016/j.appet.2014.08.039
9. Neuhouser M.L., Kristal A.R., Patterson R.E.. **Use of food nutrition labels is associated with lower fat intake**. *Am. Diet. Assoc.* (1999) **99** 45-53. DOI: 10.1016/S0002-8223(99)00013-9
10. Graham D.J., Laska M.N.. **Nutrition label use partially mediates the relationship between attitude toward healthy eating and overall dietary quality among college students**. *J. Acad. Nutr. Diet.* (2012) **112** 414-418. DOI: 10.1016/j.jada.2011.08.047
11. Kang H.T., Shim J.Y., Lee Y.J., Linton J.A., Park B.J., Lee H.R.. **Reading nutrition labels is associated with a lower risk of metabolic syndrome in Korean adults: The 2007–2008 Korean NHANES**. *Nutr. Metab. Cardiovasc. Dis.* (2013) **23** 876-882. DOI: 10.1016/j.numecd.2012.06.007
12. Wahlich C., Gardner B., McGowan L.. **How, when and why do young women use nutrition information on food labels? A qualitative analysis**. *Psychol. Health* (2013) **28** 202-216. DOI: 10.1080/08870446.2012.716439
13. Cecchini M., Warin L.. **Impact of food labelling systems on food choices and eating behaviours: A systematic review and meta-analysis of randomized studies**. *Obes. Rev.* (2016) **17** 201-210. DOI: 10.1111/obr.12364
14. 14.GB28050-2011Implementation Guidance and Example Analysis of National Standards for Food Safety and Nutrition Standard for Pre-Packaged FoodChina Quality Inspection PressBeijing, ChinaChina Standard PressBeijing, China2016. *Implementation Guidance and Example Analysis of National Standards for Food Safety and Nutrition Standard for Pre-Packaged Food* (2016)
15. Chen X.L., Peng Y., Bai P.Q.. **Effect analysis of nutritional intervention on urbanized farmers in Pudong New Area**. *Mod. Prev. Med.* (2013) **40** 2412-2431
16. Fan Y.H., Qin M., Xu X.U., Wang Z.G.. **Principal component analysis on difference of urban and rural residents’ concern about food nutrition labels**. *Food Nutr. China* (2015) **21** 50-54
17. Kanbur R., Zhang X.. **Which regional inequality? The evolution of rural–urban and inland–coastal inequality in China from 1983 to 1995**. *J. Comp. Econ.* (1999) **27** 686-701
18. 18.
Center for Health Statistics and Information
An Analysis Report of National Health Services Survey in ChinaUnion Medical College PressBeijing, China2009Volume 2008. *An Analysis Report of National Health Services Survey in China* (2009) **Volume 2008**
19. Huang J., Rozelle S.. **Agricultural development, nutrition, and the policies behind China’s success**. *Asian J. Agric. Dev.* (2010) **7** 93-126. DOI: 10.37801/ajad2010.7.1.5
20. Zhao Y.. **Leaving the countryside: Rural-to-urban migration decisions in China**. *Am. Econ. Rev.* (1999) **89** 281-286. DOI: 10.1257/aer.89.2.281
21. Wang S., Kou C., Liu Y., Li B., Tao Y., D’Arcy C., Shi J., Wu Y., Liu J., Zhu Y.. **Rural–urban differences in the prevalence of chronic disease in northeast China**. *Pac. J. Public Health* (2015) **27** 394-406. DOI: 10.1177/1010539514551200
22. Liu R., Hoefkens C., Verbeke W.. **Chinese consumers’ understanding and use of a food nutrition label and their determinants**. *Food Qual. Prefer.* (2015) **41** 103-111. DOI: 10.1016/j.foodqual.2014.11.007
23. Ma J., Zhu Z., Chen X., Guo Y., Zhang H., Zhang Y., Zang J.. **A cross-sectional survey of nutrition labelling use and its associated factors on parents of school students in Shanghai, China**. *Public Health Nutr.* (2018) **21** 1418-1425. DOI: 10.1017/S1368980018000332
24. Lin C.T., Lee J.Y., Yen S.T.. **Do dietary intakes affect search for nutrient information on food labels?**. *Soc. Sci. Med.* (2004) **59** 1955-1967. PMID: 15312929
25. Gracia A., Loureiro M., Nayga R.M.. **Do consumers perceive benefits from the implementation of a EU mandatory nutritional labelling program?**. *Food Policy* (2007) **32** 160-174. DOI: 10.1016/j.foodpol.2006.04.002
26. Vemula S.R., Gavaravarapu S.M., Mendu V.V., Mathur P., Avula L.. **Use of food label information by urban consumers in India—A study among supermarket shoppers**. *Public Health Nutr.* (2014) **17** 2104-2114. DOI: 10.1017/S1368980013002231
27. Drichoutis A.C., Nayga R.M., Lazaridis P.. **Can nutritional label use influence body weight outcomes?**. *Kyklos* (2010) **62** 500-525. DOI: 10.1111/j.1467-6435.2009.00448.x
28. Satia J.A., Galanko J.A., Neuhouser M.L.. **Food nutrition label use is associated with demographic, behavioral, and psychosocial factors and dietary intake among African Americans in North Carolina**. *J. Am. Diet. Assoc.* (2005) **105** 392-402. DOI: 10.1016/j.jada.2004.12.006
29. Fortin N., Lemieux T., Firpo S.. **Decomposition methods in economics**. *Handbook of Labor Economics* (2011) **Volume 4** 1-102
30. Sen B.. **Using the Oaxaca–Blinder decomposition as an empirical tool to analyze racial disparities in obesity**. *Obesity* (2014) **22** 1750-1755. DOI: 10.1002/oby.20755
31. Jiang W.J., Luh Y.H.. **Gender digital divide in a patriarchal society: What can we learn from Blinder–Oaxaca decomposition?**. *Qual. Quant.* (2017) **51** 2555-2576. DOI: 10.1007/s11135-016-0409-z
32. Blinder A.S.. **Wage discrimination: Reduced form and structural estimates**. *J. Hum. Resour.* (1973) **8** 436-455. DOI: 10.2307/144855
33. Oaxaca R.. **Male-female wage differentials in urban labor markets**. *Int. Econ. Rev.* (1973) **14** 693-709. DOI: 10.2307/2525981
34. Neumark D.. **Employers’ discriminatory behavior and the estimation of wage discrimination**. *J. Hum. Resour.* (1988) **23** 279-295. DOI: 10.2307/145830
35. Lewis H.G.. **Union relative wage effects**. *Handbook of Labor Economics* (1986) **Volume 2** 1139-1181
36. Fryer R.G., Levitt S.D.. **Understanding the black-white test score gap in the first two years of school**. *Rev. Econ. Stat.* (2004) **86** 447-464. DOI: 10.1162/003465304323031049
37. Hou X.. **Urban—Rural disparity of overweight, hypertension, undiagnosed hypertension, and untreated hypertension in China**. *Asia Pac. J. Public Health* (2008) **20** 159-169. PMID: 19124310
38. Liu H., Fang H., Zhao Z.. **Urban–rural disparities of child health and nutritional status in China from 1989 to 2006**. *Econ. Hum. Biol.* (2013) **11** 294-309. DOI: 10.1016/j.ehb.2012.04.010
39. Zhang Y.X., Wang Z.X., Zhao J.S., Chu Z.H.. **Prevalence of overweight and obesity among children and adolescents in Shandong, China: Urban–rural disparity**. *J. Trop. Pediatr.* (2016) **62** 293-300. DOI: 10.1093/tropej/fmw011
|
---
title: 'Use of Caffeine-Containing Energy Drinks by Japanese Middle School Students:
A Cross-Sectional Study of Related Factors'
authors:
- Satoko Yamasaki
- Hiromi Kawasaki
- Zhengai Cui
journal: Nutrients
year: 2023
pmcid: PMC10004827
doi: 10.3390/nu15051275
license: CC BY 4.0
---
# Use of Caffeine-Containing Energy Drinks by Japanese Middle School Students: A Cross-Sectional Study of Related Factors
## Abstract
Excessive consumption of caffeine negatively affects individuals’ health. Therefore, we studied the use of energy drinks and the conditions associated with it among Japanese secondary school students. Participants were 236 students in grades 7–9 who completed anonymous questionnaires at home in July 2018. We measured the basic attributes and dietary, sleeping, and exercise habits. We used Chi-squared tests to compare differences between users and non-users of energy drinks. Logistic regression analyses were used to elucidate the complex association between the variables. The results showed that boys were more willing to consume energy drinks than girls. The reasons were ‘feeling fatigued’, ‘needing to stay awake’, ‘for curiosity’, and ‘to quench one’s thirst’. Among boys, the following were associated with the use of EDs. Buying their own snacks, not understanding nutritional labels on foods, high caffeinated beverage intake, late bed-times on weekdays, always waking up at about the same time, and weight. Health guidance is needed to prevent overconsumption and dependence on energy drinks. The cooperation of parents and teachers is needed to achieve these goals.
## 1. Introduction
In recent years, several new beverages containing caffeine, called “energy drinks” (EDs), have come on the market [1]. *In* general, the constituent components of EDs are mainly caffeine, while other ingredients are isolation and combinations that include taurine, B complex vitamins, guarana, L-carnitine, and ginseng [2]. Caffeine can produce subjective reinforcement and discriminant stimuli similar to those produced by cocaine and amphetamines via dopamine [3]. After ingestion, caffeine is rapidly absorbed by the body and reaches a peak in plasma concentrations within 30 min of ingestion [4]. Excessive consumption of caffeine negatively affects individuals’ health, including caffeine intoxication, arrythmia and hypertension, and sleep disorders [5,6,7,8,9,10]. Caffeine levels of 400 mg or more for adults and 100–400 mg for children can cause anxiety, nausea, irritability, and increased nervousness [11,12]. As children are lighter in body weight and metabolize caffeine quicker than do adults, they are more susceptible to caffeine intoxication [13].
Previous studies have reported that people who drink caffeine have worse sleep quality, longer hours, and higher levels of stress than people who do not drink caffeine [14]. In an investigation of U.S. high school students, the use of EDs was related to attention deficit/hyperactivity disorder [15]. Other research revealed a correlation between juvenile caffeine consumption and aggressive behavior and behavioral disorders [16]. In an investigation of Spanish high school students, a worse school performance were risk factor for using EDs. [ 17]. In a study of New Zealand high school students, EDs consumption was significantly related to increased depressive symptoms [18,19]. There have also been reports that use of EDs by minors is related to alcohol, tobacco, and illegal drug use [20,21,22,23]. Thus, some fear that the use of EDs may be a gateway to using illegal drugs later on [24].
In Japan, there has been an increase in the number of emergency patients that had consumed massive amounts of caffeine-containing supplements and EDs [25]. In Japan, EDs have been sold as “soft drinks” since 2005, and the market is expanding annually. Convenience stores and vending machines allow both adults and children to purchase EDs without restrictions. Although studies outside of Japan have elucidated the adverse effects of overconsuming EDs, in Japan, there have been almost no studies on the harm of EDs, despite their consumption by Japanese children [26]. To avoid the negative effects of overusing EDs, it is important to understand the spread of EDs use, usage patterns, and the backgrounds of people who use them. The purpose of this study was to elucidate the use of EDs by Japanese children and the conditions related to their use. Although elementary school children tend to have their parents buy food and drinks for them, middle school students tend to buy their own food and drinks; therefore, we examined middle school students.
## 2.1. Study Population
This study was approved by the ethics committee of Hiroshima University (E-1118) and was conducted in accordance with the principles of the Declaration of Helsinki. Participants were middle school students (grades 7–9) from Prefecture A. Since they were minors, we obtained written consent from the students, their parents, and the school principal. Participants completed self-reported anonymous questionnaires at home. The study was conducted in July 2018.
We distributed 368 copies of the questionnaire forms and recovered 275 of them. The number of effective responses was 236. The final analysis thus included 236 students (126 girls): 80 grade-seven students (45 girls), 75 grade-eight students (41 girls), and 81 grade-nine students (40 girls) (Table 1).
## 2.2.1. Objective Variable
The objective variable was the use of EDs. Those who answered “have not used” EDs in the questionnaire form were regarded as the “non-user group”. Those who said they “have used” EDs comprised the “user group”.
## 2.2.2. Explanatory Variables
The explanatory variables were basic attributes, lifestyle habits, and physical complaints. Items related to dietary habits included eating, sleeping, and exercise habits. For dietary habits, we used the Scale for the Evaluation of Healthy Eating Habits of Japanese People [27], consisting of four items about “dietary balance” and four items about “eating healthy”. Students evaluated themselves on a five-point scale (1–5). Total scores ranged from 12 to 60, with higher scores indicating healthier dietary behavior (Table 2).
As an additional indicator, we used participants’ “autonomic judgment” from the “Media Literacy Scale on Diet” [28]. The autonomic judgment subscales included the “use of food labeling” and “judgment of nutritional balance”. “ Use of food labeling” consisted of the following four items: “choosing better food items by looking at nutritional content labels like calories etc.”, “ choosing better food items by looking at the display of raw material expiration date”, “trying to look at food labels when buying food and drinks”, and “able to understand food labels”. “ Judgment of nutritional balance” consisted of the following three items: “being careful about eating a balanced diet to remain healthy”, “trying to eat meals that consist of a staple food, a main dish, and side dishes”, and “able to judge whether your own dietary balance is good”. A five-point scale was used to evaluate these, and higher scores indicated higher media literacy.
To measure sleep, we used the Athens Insomnia Scale [29]. The questionnaire consists of eight items addressing “difficulty falling asleep”, “waking up in the middle of sleep”, “waking up early”, “total sleep time”, “quality of sleep”, “mood during the day”, “activities during the day”, and “drowsiness during the day”. Participants used a four-point scale to self-evaluate things that they experienced more than three times in the past month and assign a score from 0 to 3 for each item to evaluate sleep [30]. Higher total scores (range = 0–24) indicated worse sleep habits. Furthermore, past studies showed that behaviors that promote sleep included the following [31,32,33]: “always waking up at the same time [34]”, “getting sunlight after waking up in the morning [35]”, “not consuming caffeine before sleep [36,37,38]”, “not taking naps in the afternoon [39]”, “having an early bath [32]”, “not having a snack after dinner [40]”, “not looking at screens like smartphones, television, or videogames before sleep [32]”, and “not thinking about problems or unhappy events before sleeping [31,33]”. Therefore, we calculated a total score for behavior to promote sleep by considering what “they were doing every day” as three points, what “they were not doing much every day” as two points, and what “they were unable to do at all” as one point. Since sleeping time and waking time is based on a continuous scale, we switched to a decimal system.
## 2.3. Statistical Analysis
We examined the sex differences in the above variables. Chi-squared (χ2) tests were used for comparisons. Questions were answered on a Likert scale of 2 or 4. For example, applicable every day = 1 and other = 2. A five-point scale (from 1 “not at all applicable” to 5 “very applicable”) was used. These were also divided into “yes” and “no” answers. A logistic regression analysis was conducted to elucidate the complex association between variables. The objective variable was the use of EDs: “use” was one; “no use” was zero. As dependent variables, we selected variables that had a p-value < 0.20 on the χ2-test and Mann–Whitney U-test. For the dependent variable in the logistic regression analysis, the forced input method was used; then, the variable increase method using the likelihood ratio test was used. Two-tailed significance was set at $p \leq 0.05.$ Analyses were performed using SPSS version 25.0 (IBM).
## 3.1. Student Overview
A summary of the characteristics of the students in this study is presented in Table 1.
**Table 1**
| Item | Total | Boys | Girls |
| --- | --- | --- | --- |
| Item | 236 (%) | 110 (%) | 126 (%) |
| Grade level | | | |
| Grade 7 | 80 (33.9) | 35 (31.8) | 45 (35.8) |
| Grade 8 | 75 (31.8) | 34 (30.9) | 41 (32.5) |
| Grade 9 | 81 (34.3) | 41 (37.3) | 40 (31.7) |
| Frequency of eating breakfast | | | |
| Not every day | 26 (11.0) | 12 (10.9) | 14 (11.1) |
| Every day | 210 (89.0) | 98 (89.0) | 112 (88.9) |
| Number of snacks eaten daily | | | |
| More than once | 202 (85.6) | 93 (84.5) | 109 (86.5) |
| No snacking | 34 (14.4) | 17 (15.5) | 17 (13.5) |
| Frequency of buying snacks and juice on one’s own | | | |
| Do not buy | 79 (33.5) | 35 (31.8) | 44 (34.9) |
| More than once a week | 157 (66.5) | 75 (68.2) | 82 (65.1) |
| Know the effects of caffeine | | | |
| Do not know/have heard of it | 88 (37.3) | 42 (38.2) | 46 (36.5) |
| Know a little/know/can explain | 148 (62.7) | 68 (61.8) | 80 (63.5) |
| Use smartphone in bed | | | |
| No | 181 (76.7) | 82 (74.5) | 99 (78.6) |
| Yes | 55 (23.3) | 28 (25.5) | 27 (21.4) |
| Haziness | | | |
| Often/sometimes | 85 (36.0) | 39 (35.5) | 46 (36.5) |
| Never/almost never | 151 (64.0) | 71 (64.5) | 80 (63.5) |
| Diarrhea | | | |
| Often/sometimes | 54 (22.9) | 30 (27.3) | 24 (19.0) |
| Never/almost never | 182 (77.1) | 80 (72.7) | 102 (81.0) |
| Dizziness | | | |
| Often/sometimes | 94 (39.8) | 41 (37.3) | 53 (42.1) |
| Never/almost never | 142 (60.2) | 69 (62.7) | 73 (57.9) |
| Jumpy eyelids or muscles | | | |
| Often/sometimes | 73 (30.9) | 28 (25.5) | 45 (35.7) |
| Never/almost never | 163 (69.1) | 82 (74.5) | 81 (64.3) |
| Fast heartbeat for no known reason | | | |
| Often/sometimes | 27 (11.4) | 14 (12.7) | 13 (10.3) |
| Never/almost never | 209 (88.6) | 96 (87.3) | 113 (89.7) |
| Sudden shortness of breath | | | |
| Often/sometimes | 19 (8.1) | 6 (5.5) | 13 (10.3) |
| Never/almost never | 217 (91.9) | 104 (94.5) | 113 (89.7) |
| Tremors and numbness of hands/feet | | | |
| Often/sometimes | 35 (14.8) | 16 (14.5) | 19 (15.1) |
| Never/almost never | 201 (85.2) | 94 (85.5) | 107 (84.9) |
| Understand food labels * | | | |
| Completely disagree/disagree/neither | 63 (26.7) | 35 (31.8) | 28 (22.2) |
| Strongly agree/slightly agree | 173 (73.3) | 75 (68.2) | 98 (77.8) |
| Try to look at food labels when buying food and beverages | | | |
| Completely disagree/disagree/neither | 107 (45.3) | 64 (58.2) | 43 (34.1) |
| Strongly agree/slightly agree | 129 (54.7) | 46 (41.8) | 83 (65.9) |
| Can select better food items by looking at the label ingredient name and expiration dates. | | | |
| Completely disagree/disagree/neither | 75 (31.8) | 48 (43.6) | 27 (21.4) |
| Strongly agree/slightly agree | 161 (68.2) | 62 (56.4) | 99 (78.6) |
| Can select better food items by looking at nutritional labels like calories, etc. | | | |
| Completely disagree/disagree/neither | 117 (49.6) | 64 (58.2) | 53 (42.1) |
| Strongly agree/slightly agree | 119 (50.4) | 46 (41.8) | 73 (57.9) |
| Try to eat meals containing staple foods, a main dish, and side dishes | | | |
| Completely disagree/disagree/neither | 84 (35.6) | 43 (39.1) | 41 (32.5) |
| Strongly agree/slightly agree | 152 (64.4) | 67 (60.9) | 85 (67.5) |
| Can judge if my diet is balanced | | | |
| Completely disagree/disagree/neither | 79 (33.5) | 39 (35.5) | 40 (31.7) |
| Strongly agree/slightly agree | 157 (66.5) | 71 (64.5) | 86 (68.3) |
| Do feel you want to get more exercise? | | | |
| Not applicable/slightly applicable | 55 (23.3) | 26 (23.6) | 29 (23.0) |
| Somewhat applicable/applicable | 181 (76.7) | 84 (76.4) | 97 (77.0) |
| Can you make time to exercise? | | | |
| Not applicable/slightly applicable | 96 (40.7) | 32 (29.1) | 64 (50.8) |
| Somewhat applicable/applicable | 140 (59.3) | 78 (70.9) | 62 (49.2) |
| Always able to wake up at the same time | | | |
| Not applicable/not very applicable | 52 (22.0) | 24 (21.8) | 28 (22.2) |
| Applicable every day | 184 (78.0) | 86 (78.2) | 98 (77.8) |
| Able to get sunlight after waking up | | | |
| Not applicable/not very applicable | 157 (66.5) | 64 (58.2) | 93 (73.8) |
| Applicable every day | 79 (33.5) | 46 (41.8) | 33 (26.2) |
| Do not consume caffeine four hours before sleep | | | |
| Not applicable/not very applicable | 109 (46.2) | 50 (45.5) | 59 (46.8) |
| Applicable every day | 127 (53.8) | 60 (54.5) | 67 (53.2) |
| Do not nap in the afternoons | | | |
| Not applicable/not very applicable | 73 (30.9) | 27 (24.5) | 46 (36.5) |
| Applicable every day | 163 (69.1) | 83 (75.5) | 80 (63.5) |
| Take early baths | | | |
| Not applicable/not very applicable | 170 (72.0) | 73 (66.4) | 97 (77.0) |
| Applicable every day | 66 (28.0) | 37 (33.6) | 29 (23.0) |
| Not snacking after dinner | | | |
| Not applicable/not very applicable | 91 (38.6) | 44 (40.0) | 47 (37.3) |
| Applicable every day | 145 (61.4) | 66 (60.0) | 79 (62.7) |
| Stop smartphone/TV/game/computer use one hour before sleep | | | |
| Not applicable/not very applicable | 176 (74.6) | 87 (79.1) | 89 (70.6) |
| Applicable every day | 60 (25.4) | 23 (20.9) | 37 (29.4) |
| Not thinking worries and unhappy events before sleep | | | |
| Not applicable/not very applicable | 129 (54.7) | 59 (53.6) | 70 (55.6) |
| Applicable every day | 107 (45.3) | 51 (46.4) | 56 (44.4) |
| Are there any current lifestyle habits that you could improve upon? | | | |
| No | 144 (61.0) | 58 (52.7) | 86 (68.3) |
| Yes | 92 (39.0) | 52 (47.3) | 40 (31.7) |
## 3.2. Concerning Using EDs
EDs were used by 53 boys ($48.2\%$) and 34 girls ($27.0\%$). χ2-tests revealed that male students were using EDs at a significantly greater rate than female students ($$p \leq 0.001$$). Thus, we analyzed the data by sex. Furthermore, the use of EDs by sex was non-significantly different between grade levels (overall, $$p \leq 0.217$$; boys, $$p \leq 0.232$$; girls, $$p \leq 0.849$$).
Concerning frequency among those who were using EDs, $5.6\%$ of boys and $0\%$ of girls used them every day, $5.6\%$ of boys and $5.9\%$ of girls used them once or more per week, and $88.9\%$ of boys and $94.1\%$ of girls used them one to three times per month. The response rate to the frequency question was not good. The percentage was calculated only for the students who responded. Concerning when they consumed EDs, $18.9\%$ of boys and $17.6\%$ of girls stated, “when I am tired”, $15.1\%$ of boys and $11.8\%$ of girls stated, “when I am sleepy/when I have to stay awake”, $11.3\%$ of boys and $5.9\%$ of girls stated “when I am thirsty”, $9.4\%$ of boys and $8.8\%$ of girls stated “out of curiosity”, $3.8\%$ of boys and $2.9\%$ of girls stated “when I am feeling sick”, $3.8\%$ of boys and $2.9\%$ of girls stated “when I cannot feel motivated”, $3.8\%$ of boys and $11.8\%$ of girls stated “after exercise”, $3.8\%$ of boys and $11.8\%$ of girls stated “before tests/to study”, $1.9\%$ of boys and $5.9\%$ of girls stated “when I am given one”, $3.8\%$ of boys and $2.9\%$ of girls stated “when I want to drink one”, and $7.5\%$ of boys and $0\%$ of girls had “other” reasons ($17.0\%$ of boys and $17.6\%$ of girls did not answer). The one student that used EDs every day said that he consumed six EDs each time he used them
## 3.3. Relationship between EDs Use and Lifestyle in Nominal Scale
Table 2 shows the relationship between EDs use and lifestyle habits (nominal scale). Boys with use had significantly higher percentages than those without use for buying their own snacks and juice ($$p \leq 0.001$$), not understanding food labels ($$p \leq 0.035$$), and always getting up at about the same time ($$p \leq 0.035$$). No significantly related items due to use were found for the female students. Among boys, the user group had a significantly higher proportion of students that bought snacks and juice on their own, did not understand nutritional content, and woke up at almost the same time every day than did the non-user group. There were no group differences among the female students.
**Table 2**
| Item | Total n | Boys | Boys.1 | Boys.2 | Girls | Girls.1 | Girls.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Item | Total n | Non-Users n (%) | Usersn (%) | p | Non-Users n (%) | Users n (%) | p |
| Grade level | | | | | | | |
| Grade 7 | 80 | 21 (36.8) | 14 (26.4) | 0.232 | 33 (35.9) | 12 (35.3) | 0.849 |
| Grade 8 | 75 | 19 (33.3) | 15 (28.3) | 0.232 | 31 (33.7) | 10 (29.4) | 0.849 |
| Grade 9 | 81 | 17 (29.8) | 24 (45.3) | 0.232 | 28 (30.4) | 12 (35.3) | 0.849 |
| Frequency of eating breakfast | | | | | | | |
| Not every day | 26 | 6 (10.5) | 6 (11.3) | 0.894 | 9 (9.8) | 5 (14.7) | 0.435 |
| Every day | 210 | 51 (89.5) | 47 (88.7) | 0.894 | 83 (90.2) | 29 (85.3) | 0.435 |
| Number of snacks eaten daily | | | | | | | |
| More than once | 202 | 46 (80.7) | 47 (88.7) | 0.247 | 78 (84.8) | 31 (91.2) | 0.351 |
| No snacking | 34 | 11 (19.3) | 6 (11.3) | 0.247 | 14 (15.2) | 3 (8.8) | 0.351 |
| Frequency of buying snacks and juice on one’s own | | | | | | | |
| Do not buy | 79 | 26 (45.6) | 9 (17.0) | 0.001 | 35 (38.0) | 9 (26.5) | 0.226 |
| More than once a week | 157 | 31 (54.4) | 44 (83.0) | 0.001 | 57 (62.0) | 25 (73.5) | 0.226 |
| Know the effects of caffeine | | | | | | | |
| Do not know/have heard of it | 88 | 18 (31.6) | 24 (45.3) | 0.139 | 34 (37.0) | 12 (35.3) | 0.863 |
| Know a little/know/can explain | 148 | 39 (68.4) | 29 (54.7) | 0.139 | 58 (63.0) | 22 (64.7) | 0.863 |
| Use smartphone in bed | | | | | | | |
| No | 181 | 46 (80.7) | 36 (67.9) | 0.124 | 70 (76.1) | 29 (85.3) | 0.264 |
| Yes | 55 | 11 (19.3) | 17 (32.1) | 0.124 | 22 (23.9) | 5 (14.7) | 0.264 |
| Haziness | | | | | | | |
| Often/sometimes | 85 | 20 (35.1) | 19 (35.8) | 0.934 | 32 (34.8) | 14 (41.2) | 0.508 |
| Never/almost never | 151 | 37 (64.9) | 34 (64.2) | 0.934 | 60 (65.2) | 20 (58.8) | 0.508 |
| Diarrhea | | | | | | | |
| Often/sometimes | 54 | 18 (31.6) | 12 (22.6) | 0.293 | 17 (18.5) | 7 (20.6) | 0.789 |
| Never/almost never | 182 | 39 (68.4) | 41 (77.4) | 0.293 | 75 (81.5) | 27 (79.4) | 0.789 |
| Dizziness | | | | | | | |
| Often/sometimes | 94 | 22 (38.6) | 19 (35.8) | 0.766 | 40 (43.5) | 13 (38.2) | 0.597 |
| Never/almost never | 142 | 35 (61.4) | 34 (64.2) | 0.766 | 52 (56.5) | 21 (61.8) | 0.597 |
| Jumpy eyelids or muscles | | | | | | | |
| Often/sometimes | 73 | 13 (22.8) | 15 (28.3) | 0.509 | 34 (37.0) | 11 (32.4) | 0.632 |
| Never/almost never | 163 | 44 (77.2) | 38 (71.7) | 0.509 | 58 (63.0) | 23 (67.6) | 0.632 |
| Fast heartbeat for no known reason | | | | | | | |
| Often/sometimes | 27 | 9 (15.8) | 5 (9.4) | 0.318 | 8 (8.7) | 5 (14.7) | 0.325 |
| Never/almost never | 209 | 48 (84.2) | 48 (90.6) | 0.318 | 84 (91.3) | 29 (85.3) | 0.325 |
| Sudden shortness of breath | | | | | | | |
| Often/sometimes | 19 | 3 (5.3) | 3 (5.7) | 0.927 | 8 (8.7) | 5 (14.7) | 0.325 |
| Never/almost never | 217 | 54 (94.7) | 50 (94.3) | 0.927 | 84 (91.3) | 29 (85.3) | 0.325 |
| Tremors and numbness of hands/feet | | | | | | | |
| Often/sometimes | 35 | 8 (14.0) | 8 (15.1) | 0.875 | 16 (17.4) | 3 (8.8) | 0.233 |
| Never/almost never | 201 | 49 (86.0) | 45 (84.9) | 0.875 | 76 (82.6) | 31 (91.2) | 0.233 |
| Understand food labels * | | | | | | | |
| Completely disagree/disagree/neither | 63 | 13 (22.8) | 22 (41.5) | 0.035 | 20 (21.7) | 8 (23.5) | 0.830 |
| Strongly agree/slightly agree | 173 | 44 (77.2) | 31 (58.5) | 0.035 | 72 (78.3) | 26 (76.5) | 0.830 |
| Try to look at food labels when buying food and beverages | | | | | | | |
| Completely disagree/disagree/neither | 107 | 32 (56.1) | 32 (60.4) | 0.653 | 32 (34.8) | 11 (32.4) | 0.798 |
| Strongly agree/slightly agree | 129 | 25 (43.9) | 21 (39.6) | 0.653 | 60 (65.2) | 23 (67.6) | 0.798 |
| Can select better food items by looking at the label ingredient name and expiration dates | | | | | | | |
| Completely disagree/disagree/neither | 75 | 22 (38.6) | 26 (49.1) | 0.269 | 20 (21.7) | 7 (20.6) | 0.889 |
| Strongly agree/slightly agree | 161 | 35 (61.4) | 27 (50.9) | 0.269 | 72 (78.3) | 27 (79.4) | 0.889 |
| Can select better food items by looking at nutritional labels like calories, etc. | | | | | | | |
| Completely disagree/disagree/neither | 117 | 31 (54.4) | 33 (62.3) | 0.403 | 37 (40.2) | 16 (47.1) | 0.490 |
| Strongly agree/slightly agree | 119 | 26 (45.6) | 20 (37.7) | 0.403 | 55 (59.8) | 18 (52.9) | 0.490 |
| Try to eat meals containing staple foods, a main dish, and side dishes | | | | | | | |
| Completely disagree/disagree/neither | 84 | 23 (40.4) | 20 (37.7) | 0.779 | 31 (33.7) | 10 (29.4) | 0.649 |
| Strongly agree/slightly agree | 152 | 34 (59.6) | 33 (62.3) | 0.779 | 61 (66.3) | 24 (70.6) | 0.649 |
| Can judge if my diet is balanced | | | | | | | |
| Completely disagree/disagree/neither | 79 | 20 (35.1) | 19 (35.8) | 0.934 | 32 (34.8) | 8 (23.5) | 0.228 |
| Strongly agree/slightly agree | 157 | 37 (64.9) | 34 (64.2) | 0.934 | 60 (65.2) | 26 (76.5) | 0.228 |
| Do feel you want to get more exercise? | | | | | | | |
| Not applicable/slightly applicable | 55 | 14 (24.6) | 12 (22.6) | 0.813 | 20 (21.7) | 9 (26.5) | 0.575 |
| Somewhat applicable/applicable | 181 | 43 (75.4) | 41 (77.4) | 0.813 | 72 (78.3) | 25 (73.5) | 0.575 |
| Can you make time to exercise? | | | | | | | |
| Not applicable/slightly applicable | 96 | 16 (28.1) | 16 (30.2) | 0.807 | 47 (51.1) | 17 (50.0) | 0.914 |
| Somewhat applicable/applicable | 140 | 41 (71.9) | 37 (69.8) | 0.807 | 45 (48.9) | 17 (50.0) | 0.914 |
| Always able to wake up at the same time | | | | | | | |
| Not applicable/not very applicable | 52 | 17 (29.8) | 7 (13.2) | 0.035 | 17 (18.5) | 11 (32.4) | 0.096 |
| Applicable every day | 184 | 40 (70.2) | 46 (86.8) | 0.035 | 75 (81.5) | 23 (67.6) | 0.096 |
| Able to get sunlight after waking up | | | | | | | |
| Not applicable/not very applicable | 157 | 34 (59.6) | 30 (56.6) | 0.746 | 65 (70.7) | 28 (82.4) | 0.185 |
| Applicable every day | 79 | 23 (40.4) | 23 (43.4) | 0.746 | 27 (29.3) | 6 (17.6) | 0.185 |
| Do not consume caffeine four hours before sleep | | | | | | | |
| Not applicable/not very applicable | 109 | 22 (38.6) | 28 (52.8) | 0.134 | 41 (44.6) | 18 (52.9) | 0.403 |
| Applicable every day | 127 | 35 (61.4) | 25 (47.2) | 0.134 | 51 (55.4) | 16 (47.1) | 0.403 |
| Do not nap in the afternoons | | | | | | | |
| Not applicable/not very applicable | 73 | 12 (21.1) | 15 (28.3) | 0.377 | 33 (35.9) | 13 (38.2) | 0.807 |
| Applicable every day | 163 | 45 (78.9) | 38 (71.7) | 0.377 | 59 (64.1) | 21 (61.8) | 0.807 |
| Take early baths | | | | | | | |
| Not applicable/not very applicable | 170 | 38 (66.7) | 35 (66.0) | 0.944 | 69 (75.0) | 28 (82.4) | 0.384 |
| Applicable every day | 66 | 19 (33.3) | 18 (34.0) | 0.944 | 23 (25.0) | 6 (17.6) | 0.384 |
| Not snacking after dinner | | | | | | | |
| Not applicable/not very applicable | 91 | 27 (47.4) | 17 (32.1) | 0.102 | 32 (34.8) | 15 (44.1) | 0.336 |
| Applicable every day | 145 | 30 (52.6) | 36 (67.9) | 0.102 | 60 (65.2) | 19 (55.9) | 0.336 |
| Stop smartphone/TV/game/computer use one hour before sleep | | | | | | | |
| Not applicable/not very applicable | 176 | 42 (73.7) | 45 (84.9) | 0.148 | 65 (70.7) | 24 (70.6) | 0.994 |
| Applicable every day | 60 | 15 (26.3) | 8 (15.1) | 0.148 | 27 (29.3) | 10 (29.4) | 0.994 |
| Not thinking worries and unhappy events before sleep | | | | | | | |
| Not applicable/not very applicable | 129 | 31 (54.4) | 28 (52.8) | 0.870 | 48 (52.2) | 22 (64.7) | 0.209 |
| Applicable every day | 107 | 26 (45.6) | 25 (47.2) | 0.870 | 44 (47.8) | 12 (35.3) | 0.209 |
| Are there any current lifestyle habits that you could improve upon? | | | | | | | |
| No | 144 | 33 (57.9) | 25 (47.2) | 0.260 | 61 (66.3) | 25 (73.5) | 0.439 |
| Yes | 92 | 24 (42.1) | 28 (52.8) | 0.260 | 31 (33.7) | 9 (26.5) | 0.439 |
## 3.4. Relationship between ED Use and Lifestyle on a Continuous Scale
Table 3 shows the relationship between use of EDs and lifestyle habits (continuous scale). The with-use group was significantly heavier ($$p \leq 0.031$$), using more caffeinated beverages such as coffee ($$p \leq 0.010$$), and waking up later on holidays ($$p \leq 0.031$$) than the without-use group. Among the female students, “dietary balance”, a sub-item of the Japanese Healthy Eating Behavior Rating Scale, was significantly higher in the group with use than in the group without use.
Among boys, the user group had significantly heavier body weight, consumed more caffeine-containing drinks, and woke up later on holidays than the non-user group. Among girls, dietary balance—a sub-item of the Scale for the Evaluation of Healthy Eating Habits of Japanese People—was significantly higher in the user group than in the non-user group.
## 3.5. The Results of the Logistic Regression Analysis concerning the Factors Related to Energy Drink Use among Boys and Girls
Table 4 show the results of the logistic regression analysis concerning the factors related to energy drink use among boys and girls. Based on the χ2 test and Mann–Whitney results,” Wake up almost at the same time every day”, “Buy juice and snacks on your own”, “Able to read food labels”, and “Bedtime on weekdays (continuous data) were selected for boys. For girls, “Able to get sunlight after waking up”, “Scale for the Evaluation of Healthy Eating Habits of Japanese People (dietary balance)”, “Media Literacy Scale on Diet (judgment of nutritional balance)”, “Number of cups of caffeine”, and “Amount of sleep on holidays (continuous data)” were selected. Among the male students, the odds ratio for EDs use was 6.34 ($95\%$ CI: 1.86–21.67) ($$p \leq 0.003$$) for students who reported waking up at the same time every day. The odds ratio for EDs use was 4.26 ($95\%$ CI: 1.63–11.15) ($$p \leq 0.003$$) for students who reported buying their own juice or snacks at least once a week. The odds ratio for having EDs use was 0.29 ($95\%$ CI: 0.11–0.80) ($$p \leq 0.017$$) for students who reported being able to understand food labels. The odds ratio for using EDs at bedtime on weekdays was 1.69 ($95\%$ CI: 0.99–2.87) ($$p \leq 0.054$$). No statistically significant odds ratios were found for female students.
In contrast, the odds ratio for EDs use among female students who reported waking up at the same time each day was 0.36 ($95\%$ CI: 0.13–0.95) ($$p \leq 0.039$$).
Compared to their counterparts, the odds ratios of boys that used EDs were greater for those that woke up at the same time every day, bought snacks and juice by themselves more than once a week, understood food labels, and got more sleep on school nights. No significant differences were found concerning female students.
## 4. Discussion
A study of EDs use and lifestyle habits among middle school students found that EDs use was significantly higher among boys. Among boys, the following were associated with the use of EDs. Buying their own snacks, not understanding nutritional labels on foods, high caffeinated beverage intake, late bedtimes on weekdays, always waking up at about the same time, and weight. Among girls, students with a well-balanced diet were significantly more likely to be EDs users.
There was significantly greater EDs use among male students than among female students. Similar results have been reported in studies in the U.S. and Canada in adolescent students [20,21,22,41,42]. It has been reported that boys are more likely to feel the effects of caffeine than girls and that the effects of caffeine are more likely to appear in boys [43], which led to the assumption that boys were the target for purchasing the product, which was one of the reasons why boys were more likely to use EDs [44].
EDs are also widely marketed to promote energy as well as mental and physical endurance and performance. EDs can be classified as dietary supplements, and about $\frac{1}{3}$rd of 12–17-year-olds (teens) used EDs [45,46]. In addition, according to the analysis of this study, female junior high school students are more than twice as likely as boys to be aware of their body shape and obesity [47], which suggests that they pay attention to the nutritional balance of their meals and are more regular in their diet. On the other hand, boys often purchase snacks, soft drinks, and sweet breads in addition to meals [48]. This was also assumed to be the cause.
Middle school students are in the period when the foundations of desirable lifestyle habits such as exercise, diet, and sleep are being formed. The acquisition of good lifestyle habits by junior high school students is one of the major challenges in school health. However, in recent years, there have been indications of deterioration in the sleeping conditions of children, including delayed sleep onset and decreased sleep duration [49]. Prior research has shown that poor sleep status is known to be a risk factor for the development of underlying and lifestyle-related diseases, and may lead to lower learning performance and self-esteem in junior high school students [50,51]. In addition, caffeine intake status may play a role in the deterioration of sleep conditions. Studies of children overseas have shown that those with high daily caffeine intake report higher rates of difficulty falling asleep, difficulty waking up, daytime sleepiness, and other poor sleep conditions [6,52]. The results also suggested that EDs use is related to waking up late on holidays (i.e., non-school days). This lateness is presumed to be a way to compensate for the lack of sleep on a daily basis by sleeping longer on holidays. Furthermore, the results suggest that students are using EDs to suppress drowsiness or to improve academic performance. Given that academic performance is negatively correlated with higher EDs consumption [53], students should be encouraged to go to sleep earlier and wake up earlier to reduce drowsiness rather than using EDs to force themselves to stay awake.
However, going to bed early and getting up early is one of the most difficult tasks for junior high school students. Students need to be motivated to live autonomously and balance their studies and hobbies. Attachment to family increases autonomy, while life management by preference decreases autonomy [54]. One of the concepts of autonomy includes self-control. Since the higher the self-control, the higher the sense of well-being [55], control of one’s life by non-self-motivated materials should be avoided. The high prevalence of EDs use among middle school students who buy their own snacks also requires support from families to ensure that middle school boys in particular do not leave their parents’ guidance and supervision too quickly. Even though the EDs users in this study went to bed late, they woke up at the same time in the morning, inferring that they received adequate family support.
In recent years, there has been concern about the health problems associated with excessive caffeine intake in children. Caffeine intake guidelines have been established in Western countries [56,57], tolerable doses have been indicated, and advertising and marketing rules have been tightened for caffeine-rich beverages and supplements. On the other hand, in Japan, although there are no guidelines regarding the amount of caffeine consumed, the Ministry of Health, Labor and Welfare and the Ministry of Agriculture, Forestry and Fisheries have issued warnings regarding excessive caffeine intake, and have recommended that beverages with caffeine concentrations above a certain level be labeled to prevent excessive intake [58]. In recent years, in Japan, 60–$70\%$ of the individuals arrested for using tablet-form synthetic drugs such as cannabis, methylenedioxymethamphetamine (MDMA), etc. have been minors or young people in their 20s [59]. Cannabis, MDMA, and other drugs are abused mainly by young people, and some fear that the use of EDs may be a gateway to future illegal drug use [24]. Consequently, students should be educated about the impacts that overconsumption of EDs and caffeine can have so they can exercise caution if they choose to consume EDs. Labeling adopted in other countries should be recognized as necessary in Japan.
Prior research has shown that SNS was a factor influencing the purchasing behavior of young people [60]. It has also been reported that those who have experienced EDs intake have a higher rate of SNS use than those who have not experienced EDs intake [61]. Therefore, we also believe that a certain level of social regulation regarding EDs-related advertising and sales is necessary.
EDs use was related to high consumption of other caffeine-containing drinks, indicating a risk of caffeine overconsumption. Health guidance should be provided to these individuals to prevent overconsumption and dependence. Since students’ ability to buy food by themselves was related to EDs consumption, students should receive guidance concerning their purchases at a young age (i.e., when in elementary school) to promote healthier decision-making. In addition, significantly more boys do not understand food levels, the use of EDs was related to not understanding the nutritional labels. Students need to be taught about caffeine levels and the effects that caffeine has on their bodies. Parents and physical education teachers should collaborate to ensure children understand the ingredients and nutritional value of what they are consuming. In contrast, among girls, EDs use was associated with better dietary balance. The girls indicate that those interested in the diet understand and use the ingredients. Self-medication plays an important role in controlling life for girls, including countermeasures for menstrual cramps and premenstrual tension [48,62]. We suggest the possibility of learning objectively about interest in caffeine as well.
This study had some limitations. First, this study is a cross-sectional study, and thus, causal relationships between variables cannot be clarified. Second, we employed a small sample size, and only one school was analyzed; thus, selection bias is a possibility. Furthermore, the EDs user group included those who consumed EDs regularly or occasionally. Going forward, it is necessary to increase the sample size, to examine middle school students and high school students, and elucidate the characteristics of regular users to advance research that helps prevent health damages due to the overconsumption of EDs.
## 5. Conclusions
In this exploratory study, we examined factors associated with EDs use among Japanese middle school students. The use of EDs was significantly greater among boys than girls. Among boys, the use of EDs was related to waking up at the same time every day, buying snacks and drinks by themselves, not understanding food labels, and sleeping late on school nights. It was suggested that students who forcibly suppress their drowsiness are exposed to the risk of drinking EDs.
## References
1. Beckford K., Grimes C.A., Riddell L.J.. **Australian Children’s Consumption of Caffeinated, Formulated Beverages: A Cross-Sectional Analysis**. *BMC Public Health* (2015.0) **15**. DOI: 10.1186/s12889-015-1443-9
2. Aranda M., Morlock G.. **Simultaneous Determination of Riboflavin, Pyridoxine, Nicotinamide, Caffeine and Taurine in Energy Drinks by Planar Chromatography-Multiple Detection with Confirmation by Electrospray Ionization Mass Spectrometry**. *J. Chromatogr. A* (2006.0) **1131** 253-260. DOI: 10.1016/j.chroma.2006.07.018
3. Garrett B.E., Griffiths R.R.. **The Role of Dopamine in the Behavioral Effects of Caffeine in Animals and Humans**. *Pharmacol. Biochem. Behav.* (1997.0) **57** 533-541. DOI: 10.1016/S0091-3057(96)00435-2
4. Escott-Stump S.. *Nutrition and Diagnosis-Related Care* (2015.0)
5. Seifert S.M., Seifert S.A., Schaechter J.L., Bronstein A.C., Benson B.E., Hershorin E.R., Arheart K.L., Franco V.I., Lipshultz S.E.. **An Analysis of Energy-Drink Toxicity in the National Poison Data System**. *Clin. Toxicol.* (2013.0) **51** 566-574. DOI: 10.3109/15563650.2013.820310
6. Orbeta R.L., Overpeck M.D., Ramcharran D., Kogan M.D., Ledsky R.. **High Caffeine Intake in Adolescents: Associations with Difficulty Sleeping and Feeling Tired in the Morning**. *J. Adolesc. Health* (2006.0) **38** 451-453. DOI: 10.1016/j.jadohealth.2005.05.014
7. Wikoff D., Welsh B.T., Henderson R., Brorby G.P., Britt J., Myers E., Goldberger J., Lieberman H.R., O’Brien C., Peck J.. **Systematic Review of the Potential Adverse Effects of Caffeine Consumption in Healthy Adults, Pregnant Women, Adolescents, and Children**. *Food Chem. Toxicol.* (2017.0) **109** 585-648. DOI: 10.1016/j.fct.2017.04.002
8. Sanchis-Gomar F., Leischik R., Lippi G.. **Energy Drinks: Increasing Evidence of Negative Cardiovascular Effects**. *Int. J. Cardiol.* (2016.0) **206** 153. DOI: 10.1016/j.ijcard.2016.01.107
9. Al-Shaar L., Vercammen K., Lu C., Richardson S., Tamez M., Mattei J.. **Health Effects and Public Health Concerns of Energy Drink Consumption in the United States: A Mini-Review**. *Front. Public Health* (2017.0) **5** 225. DOI: 10.3389/fpubh.2017.00225
10. Reissig C.J., Strain E.C., Griffiths R.R.. **Caffeinated Energy Drinks—A Growing Problem**. *Drug Alcohol Depend.* (2009.0) **99** 1-10. DOI: 10.1016/j.drugalcdep.2008.08.001
11. **Caffeine in Foods**
12. Bernstein G.A., Carroll M.E., Dean N.W., Crosby R.D., Perwien A.R., Benowitz N.L.. **Caffeine Withdrawal in Normal School-Age Children**. *J. Am. Acad. Child Adolesc. Psychiatry* (1998.0) **37** 858-865. DOI: 10.1097/00004583-199808000-00016
13. Miller K.E., Dermen K.H., Lucke J.F.. **Caffeinated Energy Drink Use by U.S. Adolescents Aged 13–17: A National Profile**. *Psychol. Addict. Behav.* (2018.0) **32** 647-659. DOI: 10.1037/adb0000389
14. Higbee M.R., Chilton J.M., El-Saidi M., Duke G., Haas B.K.. **Nurses Consuming Energy Drinks Report Poorer Sleep and Higher Stress**. *West. J. Nurs. Res.* (2020.0) **42** 24-31. DOI: 10.1177/0193945919840991
15. Schwartz D.L., Gilstad-Hayden K., Carroll-Scott A., Grilo S.A., McCaslin C., Schwartz M., Ickovics J.R.. **Energy Drinks and Youth Self-Reported Hyperactivity/Inattention Symptoms**. *Acad. Pediatr.* (2015.0) **15** 297-304. DOI: 10.1016/j.acap.2014.11.006
16. Kristjansson A.L., Sigfusdottir I.D., Frost S.S., James J.E.. **Adolescent Caffeine Consumption and Self-Reported Violence and Conduct Disorder**. *J. Youth Adolesc.* (2013.0) **42** 1053-1062. DOI: 10.1007/s10964-013-9917-5
17. Oliver Anglès A., Camprubí Condom L., Valero Coppin O., Oliván Abejar J.. **Prevalencia y factores asociados al consumo de bebidas energéticas en jóvenes de la provincia de Barcelona**. *Gac. Sanit.* (2021.0) **35** 153-160. DOI: 10.1016/j.gaceta.2019.08.013
18. Utter J., Denny S., Teevale T., Sheridan J.. **Energy Drink Consumption among New Zealand Adolescents: Associations with Mental Health, Health Risk Behaviours and Body Size: Energy Drinks and Adolescent Health**. *J. Paediatr. Child Health* (2018.0) **54** 279-283. DOI: 10.1111/jpc.13708
19. Tóth Á., Soós R., Szovák E., Najbauer N.M., Tényi D., Csábí G., Wilhelm M.. **Energy Drink Consumption, Depression, and Salutogenic Sense of Coherence Among Adolescents and Young Adults**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17041290
20. Terry-McElrath Y.M., O’Malley P.M., Johnston L.D.. **Energy Drinks, Soft Drinks, and Substance Use Among United States Secondary School Students**. *J. Addict. Med.* (2014.0) **8** 6-13. DOI: 10.1097/01.ADM.0000435322.07020.53
21. Azagba S., Langille D., Asbridge M.. **An Emerging Adolescent Health Risk: Caffeinated Energy Drink Consumption Patterns among High School Students**. *Prev. Med.* (2014.0) **62** 54-59. DOI: 10.1016/j.ypmed.2014.01.019
22. Mann M.J., Smith M.L., Kristjansson A.L.. **Energy Drink Consumption and Substance Use Risk in Middle School Students**. *Prev. Med. Rep.* (2016.0) **3** 279-282. DOI: 10.1016/j.pmedr.2016.03.014
23. Gallimberti L., Buja A., Chindamo S., Vinelli A., Lazzarin G., Terraneo A., Scafato E., Baldo V.. **Energy Drink Consumption in Children and Early Adolescents**. *Eur. J. Pediatr.* (2013.0) **172** 1335-1340. DOI: 10.1007/s00431-013-2036-1
24. Galimov A., Hanewinkel R., Hansen J., Unger J.B., Sussman S., Morgenstern M.. **Association of Energy Drink Consumption with Substance-Use Initiation among Adolescents: A 12-Month Longitudinal Study**. *J. Psychopharmacol.* (2020.0) **34** 221-228. DOI: 10.1177/0269881119895545
25. Kamijo Y., Takai M., Fujita Y., Usui K.. **A Retrospective Study on the Epidemiological and Clinical Features of Emergency Patients with Large or Massive Consumption of Caffeinated Supplements or Energy Drinks in Japan**. *Intern. Med.* (2018.0) **57** 2141-2146. DOI: 10.2169/internalmedicine.0333-17
26. Noi S., Chikama K., Shikano A., Tanaka R., Tanabe K., Yamada N., Watanabe A.. **The Intake of Energy Drink and the Characteristics of Consumers’Physical Symptoms/Behaviors among School-Aged Children at 10–18 Years**. *Jpn. J. School Heakth* (2020.0) **62** 166-177
27. Shimazaki T., Iio M., Lee Y.-H., Suzuki A., Konuma K., Teshima Y., Takenaka K.. **Construction of a Short Form of the Healthy Eating Behaviour Inventory for the Japanese Population**. *Obes. Res. Clin. Pract.* (2016.0) **10** S96-S102. DOI: 10.1016/j.orcp.2015.08.011
28. Akemi N., Kumi E., Yukari T.. **Development of the dietary-related media literacy scale among junior high school students**. *J. Jpn. Soc. Health Educ.* (2012.0) **20** 207-220. DOI: 10.11260/kenkokyoiku.20.207
29. Soldatos C.R., Dikeos D.G., Paparrigopoulos T.J.. **Athens Insomnia Scale: Validation of an Instrument Based on ICD-10 Criteria**. *J. Psychosom. Res.* (2000.0) **48** 555-560. DOI: 10.1016/S0022-3999(00)00095-7
30. Michiko M., Noriko H., Yoshie I.. **Actual Sleeping Habits and Related Factors Observed in Workers in the Electrical Machinery Manufacturing Sector**. *Jpn. Acad. Commun. Health Nurs.* (2017.0) **20** 6-15
31. **Sleep Guideline for Health Promotion 2014**
32. **Sleeping Early, Getting up Early and Breakfast Will Make Your Future Shine. Let’s Keep/Maintain Our Sleep Rhythm!**. (2014.0)
33. Norihisa T., Hideki T., Lnoue Y.. **Effects of Sleep Management for School Nurses at Elementary and Junior High Schools: Comparison of Self-Help Treatment and Slrrp Education**. *Jpn. Assoc. Behav. Cog. Ther.* (2014.0) **402** 83-93. DOI: 10.24468/jjbt.40.2_83
34. Crowley S.J., Carskadon M.A.. **Modifications to weekend recovery sleep delay circadian phase in older adolescents**. *Chronobiol. Int.* (2010.0) **27** 1469-1492. DOI: 10.3109/07420528.2010.503293
35. Liu X., Uchiyama M., Shibui K., Kim K., Kudo Y., Tagaya H., Suzuki H., Okawa M.. **Diurnal Preference, Sleep Habits, Circadian Sleep Propensity and Melatonin Rhythm in Healthy Human Subjects**. *Neurosci. Lett.* (2000.0) **280** 199-202. DOI: 10.1016/S0304-3940(00)00793-X
36. Mitler M.M., O’Malley M.B.. **Wake-Promoting Medications: Efficacy and Adverse Effects**. *Principles and Practice of Sleep Medicine* (2005.0) 484-498
37. Drapeau C., Hamel-Hebert I., Robillard R., Selmaoui B., Filipini D., Carrier J.. **Challenging Sleep in Aging: The Effects of 200 Mg of Caffeine during the Evening in Young and Middle-Aged Moderate Caffeine Consumers**. *J. Sleep Res.* (2006.0) **15** 133-141. DOI: 10.1111/j.1365-2869.2006.00518.x
38. Maughan R.J., Griffin J.. **Caffeine Ingestion and Fluid Balance: A Review**. *J. Hum. Nutr. Diet.* (2003.0) **16** 411-420. DOI: 10.1046/j.1365-277X.2003.00477.x
39. Barion A., Zee P.C.. **A Clinical Approach to Circadian Rhythm Sleep Disorders**. *Sleep Med.* (2007.0) **8** 566-577. DOI: 10.1016/j.sleep.2006.11.017
40. Yoshizaki T., Tada Y., Hida A., Sunami A., Yokoyama Y., Yasuda J., Nakai A., Togo F., Kawano Y.. **Effects of Feeding Schedule Changes on the Circadian Phase of the Cardiac Autonomic Nervous System and Serum Lipid Levels**. *Eur. J. Appl. Physiol.* (2013.0) **113** 2603-2611. DOI: 10.1007/s00421-013-2702-z
41. Visram S., Cheetham M., Riby D.M., Crossley S.J., Lake A.A.. **Consumption of Energy Drinks by Children and Young People: A Rapid Review Examining Evidence of Physical Effects and Consumer Attitudes**. *BMJ Open* (2016.0) **6** e010380. DOI: 10.1136/bmjopen-2015-010380
42. James J.E., Kristjansson A.L., Sigfusdottir I.D.. **A Gender-Specific Analysis of Adolescent Dietary Caffeine, Alcohol Consumption, Anger, and Violent Behavior**. *Subst. Use Misuse* (2015.0) **50** 257-267. DOI: 10.3109/10826084.2014.977394
43. Temple J.L., Ziegler A.M.. **Gender Differences in Subjective and Physiological Responses to Caffeine and the Role of Steroid Hormones**. *J. Caffeine Res.* (2011.0) **1** 41-48. DOI: 10.1089/jcr.2011.0005
44. Lebacq T., Desnouck V., Dujeu M., Holmberg E., Pedroni C., Castetbon K.. **Determinants of Energy Drink Consumption in Adolescents: Identification of Sex-Specific Patterns**. *Public Health* (2020.0) **185** 182-188. DOI: 10.1016/j.puhe.2020.05.040
45. Seifert S.M., Schaechter J.L., Hershorin E.R., Lipshultz S.E.. **Health Effects of Energy Drinks on Children, Adolescents, and Young Adults**. *Pediatrics* (2011.0) **127** 511-528. DOI: 10.1542/peds.2009-3592
46. Schneider M.B., Benjamin H.J.. **Sports Drinks and Energy Drinks for Children and Adolescents: Are They Appropriate?**. *Pediatrics* (2011.0) **127** 1182-1189. DOI: 10.1542/peds.2011-0965
47. Yakura N., Hiroe K., Kasagi T.. **A Study of Peri-adolescents’ Desire to Lose Weight-1-Association between Body Image, BMI, and Weight Loss**. *J. Child Health* (1993.0) **52** 521-524
48. Ahmed S.M., Sundby J., Aragaw Y.A., Abebe F.. **Self-Medication and Safety Profile of Medicines Used among Pregnant Women in a Tertiary Teaching Hospital in Jimma, Ethiopia: A Cross-Sectional Study**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17113993
49. Kamei Y., Iwadare Y.. **Children’s sleep**. *J. Natl. Inst. Public Health* (2012.0) **61** 11-17
50. Doi Y.. **Prevalence and health impacts of sleep disorders in Japan**. *J. Natl. Inst. Public Health* (2012.0) **61** 3-10
51. Eliasson A., Eliasson A., King J., Gould B., Eliasson A.. **Association of Sleep and Academic Performance**. *Sleep Breath* (2002.0) **6** 45-48. DOI: 10.1055/s-2002-23157
52. Pollak C.P., Bright D.. **Caffeine Consumption and Weekly Sleep Patterns in US Seventh-, Eighth-, and Ninth Graders**. *Pediatrics* (2003.0) **111** 42-46. DOI: 10.1542/peds.111.1.42
53. Champlin S.E., Pasch K.E., Perry C.L.. **Is the Consumption of Energy Drinks Associated with Academic Achievement Among College Students?**. *J. Prim. Prev.* (2016.0) **37** 345-359. DOI: 10.1007/s10935-016-0437-4
54. Murphy D.A., Greenwell L., Resell J., Brecht M.-L., Schuster M.A.. **Early and Middle Adolescents’ Autonomy Development: Impact of Maternal HIV/AIDS**. *Clin Child Psychol. Psychiatry* (2008.0) **13** 253-276. DOI: 10.1177/1359104507088346
55. Eriksson M., Boman E., Svedberg P.. **Autonomy and Health-Related Quality of Life in Adolescents**. *BMC Pediatr.* (2022.0) **22**. DOI: 10.1186/s12887-022-03607-5
56. **Scientific Opinion on the Safety of Caffeine**. *EFSA J.* (2015.0) **13** 4102
57. Rotstein J., Barber J., Strowbridge C., Hayward S., Huang R., Benrejeb Godefroy S.. **Energy Drinks: An Assessment of the Potential Health Risks in the Canadian Context**. *Int. Food Risk Anal. J.* (2013.0) **3** 1. DOI: 10.5772/56723
58. **Caffeine in Foods**
59. **Ministry of Education, Culture, Sports, Science and Technology: Health Educational Material “Invaluable Self, Irreplaceable Health”**
60. Akasaka T., Iizuka Y.. **The Impact of Instagram on Consumer Buying Behavior of Outfit Goods**. *Bull. Inst. Inf. Sci.* (2018.0) **92** 7-11
61. Umeda Y.. **Social Psychological Effects of Energy Drink Consumption and Its Impact on Taste Culture**
62. Armour M., Smith C.A., Steel K.A., Macmillan F.. **The Effectiveness of Self-Care and Lifestyle Interventions in Primary Dysmenorrhea: A Systematic Review and Meta-Analysis**. *BMC Complement Altern. Med.* (2019.0) **19**. DOI: 10.1186/s12906-019-2433-8
|
---
title: New Insights into the Phytochemical Profile and Biological Properties of Lycium
intricatum Bois. (Solanaceae)
authors:
- Houaria Bendjedou
- Houari Benamar
- Malika Bennaceur
- Maria João Rodrigues
- Catarina Guerreiro Pereira
- Riccardo Trentin
- Luísa Custódio
journal: Plants
year: 2023
pmcid: PMC10004830
doi: 10.3390/plants12050996
license: CC BY 4.0
---
# New Insights into the Phytochemical Profile and Biological Properties of Lycium intricatum Bois. (Solanaceae)
## Abstract
This work aimed to boost the valorisation of *Lycium intricatum* Boiss. L. as a source of high added value bioproducts. For that purpose, leaves and root ethanol extracts and fractions (chloroform, ethyl acetate, n-butanol, and water) were prepared and evaluated for radical scavenging activity (RSA) on 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radicals, ferric reducing antioxidant power (FRAP), and metal chelating potential against copper and iron ions. Extracts were also appraised for in vitro inhibition of enzymes implicated on the onset of neurological diseases (acetylcholinesterase: AChE and butyrylcholinesterase: BuChE), type-2 diabetes mellitus (T2DM, α-glucosidase), obesity/acne (lipase), and skin hyperpigmentation/food oxidation (tyrosinase). The total content of phenolics (TPC), flavonoids (TFC), and hydrolysable tannins (THTC) was evaluated by colorimetric methods, while the phenolic profile was determined by high-performance liquid chromatography, coupled to a diode-array ultraviolet detector (HPLC-UV-DAD). Extracts had significant RSA and FRAP, and moderate copper chelation, but no iron chelating capacity. Samples had a higher activity towards α-glucosidase and tyrosinase, especially those from roots, a low capacity to inhibit AChE, and no activity towards BuChE and lipase. The ethyl acetate fraction of roots had the highest TPC and THTC, whereas the ethyl acetate fraction of leaves had the highest flavonoid levels. Gallic, gentisic, ferulic, and trans-cinnamic acids were identified in both organs. The results suggest that L. intricatum is a promising source of bioactive compounds with food, pharmaceutical, and biomedical applications.
## 1. Introduction
Medicinal herbs contain different phytochemicals, with a broad spectrum of pharmacological effects, that have already proved to be effective therapeutic tools in the treatment of several diseases. For example, different flavonoids and other phenolic compounds display strong antioxidant activities and inhibitory properties against enzymes involved in human ailments, such as acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE), which are involved in the onset of Alzheimer’s disease (AD) and other neurodegenerative disorders, and α-glucosidase, linked with type-2 diabetes mellitus (T2DM) [1,2].
The genus Lycium (Solanaceae) comprises about 80 species distributed worldwide [3]. Algeria has four species, namely L. arabicum Boiss., L. europaeum L., L. halmifolium Mill., and L. intricatum Boiss., which are mainly distributed in the north [4]. Species belonging to the genus Lycium, especially L. barbarum L. and L. chinense Mill., have been an important source of traditional remedies against a high number of human diseases, including AD, diabetes, obesity, and cancer, and of nutritional supplements in Southeast Asia, mostly in China [5,6,7,8]. The interest in Lycium fruits, known as goji, has increased tremendously in Western countries, due to its nutritional properties (e.g., proteins, amino acids, and vitamins) and the presence of bioactive compounds (e.g., phenolics, flavonoids, and anthocyanins), with functional properties (e.g., antioxidant, anti-inflammatory) which confers goji a plethora of health promoting functions, such as, for example, anti-aging and anti-diabetic [9]. In fact, goji berries are considered a functional food, and the global distribution and diverse uses make *Lycium a* genus of global importance. Goji and other Lycium parts, such as leaves, seeds, and flowers, display substantial biological activities, like immunomodulation, retinal protection, anti-tumour, hypotensive, neuroprotective, anti-diabetic, skin care, enzyme inhibition, and antioxidant, linked with their chemical composition that include polyphenols, alkaloids, and sesquiterpenes [3,6,10,11]. For example, goji leaves have a chemical composition like berries, with reduced levels of sugars and a higher abundance of fibres [12], and are rich in bioactive metabolites (e.g., phenolic compounds and alkaloids) and present important biological activities, including antioxidant, anti-inflammatory, and anti-diabetic [12].
Research has mainly focused on L. barbarum and L. chinense [12], but other Lycium species may hold potential as sources of high added value products. Lycium intricatum Boiss., also called “Awsadj”, is a spiky shrub that can reach 3 m high, with fleshy fruits with a red colour, when mature. In Algeria, it inhabits maritime rocks and arid lands on the littoral [4,6]. In traditional medicine, a decoction of the leaves is made twice, left to cool for one day, and then applied in drops for cataracts and eye inflammations [13]. The seeds are used for helminthiasis and as a digestive, while fruits are used for the treatment of eye diseases [14]. Several bioactive molecules were previously identified in different organs of L. intricatum. For example, fatty acids, such as myristic, palmitic, palmitoleic, oleic, linoleic, and erucic acids, and sterols like ergosterol, stigmasterol, and β-sitosterol, and triterpenes like squalene, erythrodiol, and uvaol, were identified in the seeds [15]. One phenolic acid, eight phenolic acid derivatives, and six flavonoids were identified in leaves and fruits [16], and one new ionone derivative and three known compounds, namely isoscopoletin, 3,4,5-trimethoxybenzyl alcohol, and (+)-isolariciresinol, were isolated and identified in leaves [17]. To our best knowledge, only one paper has described biological activities of L. intricatum, focusing on the antioxidant activity of the methanol extract of leaves and fruits by complementary methods, namely radical scavenging properties towards 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azinobis (3-ethylbenzothiazoline-6-sulphonic acid diammonium salt) (ABTS), hydroxyl free radicals, and ferric-reducing antioxidant power (FRAP) [16]. In that work, leaves exhibited the upmost antioxidant potential, coupled to the highest levels of phenolics and flavonoids, leading the authors to conclude that L. intricatum should be further explored as a potential source of high added value bioactive products [16]. Presumably, there are no reports of the biological properties of the roots of this species.
Lycium intricatum is, therefore, considered an underexploited species, despite its high potential to serve as a source with economic and nutritional value [15]. Providing better information regarding the chemical composition and pharmacological properties of this species would pave the way to its valorisation as a source of bioactive compounds, and consequently, to agriculture and economic progress [15]. In this context, in the present work, qualitative and quantitative analyses of the phenolic composition of an ethanol crude extract and obtained fractions of roots and leaves of this species were performed by colorimetric methods and high-performance liquid chromatography, coupled to a diode-array ultraviolet detector (HPLC-UV-DAD). The extracts were also evaluated for in vitro antioxidant capacity, by complementary assays, and for enzymatic inhibitory properties toward enzymes related with the onset of AD (AChE and BuChE), T2DM (α-glucosidase), obesity/acne (lipase), and skin hyperpigmentation/food oxidation (tyrosinase).
## 2.1. Chemicals and Reagents
All the chemicals used in this work were of analytical grade. Sigma-Aldrich (Lisbon, Portugal) supplied Folin-Ciocalteau (F-C) phenol reagent, sodium acetate, sodium nitrite, DPPH, ABTS, ascorbic acid, butylated hydroxytoluene (BHT), AChE (from electric eel, Type-VIS, EC 3.1.1.7), BuChE (from horse serum, EC 3.1.1.8), acetylthiocholine iodide, butyrylthiocholine chloride, galantamine hydrobromide (from Lycoris sp.), α-glucosidase (from yeast, Saccharomyces cerevisiae, EC 3.2.1.20), 4-nitrophenyl-α-D-glucopyranoside, acarbose, lipase from porcine pancreas (Type II, EC 3.1.1.3), orlistat, tyrosinase (from mushroom, EC 1.14.18.1), L-2,3-dihydroxyphenylalanine, arbutin, and phosphate buffer. Ethylenediaminetetraacetic acid (EDTA) was obtained from VWR (Carnaxide, Portugal). Additional reagents and solvents were obtained from Merck (Lisbon, Portugal).
## 2.2. Plant Material
Roots and leaves from L. intricatum plants were harvested in 2018, in Ain El Turk, Oran, Algeria (35°44′16.7″ N, 0°43′30.5″ W, 66 m a.s.l.) during the flowering season (May). The plant was identified by Prof. Abderrazak Marouf, Institute of Science and Technology, University Centre of Naama, Naama, Algeria. A voucher specimen (OUE.2018.C1) was deposited in the Department of Biology, University of Oran1, Oran, Algeria. The roots and leaves were dried in a well-ventilated room at 30 °C for 72 h, fully grinded, and stored in the dark at room temperature (RT) until use.
## 2.3. Extraction and Partition
Dried samples (200 g) were extracted by cold maceration, three times with ethanol, (1.2 L) for 72 h at RT. The extracts were filtered through Whatman N°1 filter paper, combined, and the solvent was removed under reduced pressure at 40 °C. The crude extract (12 g) was dissolved in distilled water (240 mL) and sequentially extracted with chloroform (240 mL × 3), ethyl acetate (240 mL × 3), and n-butanol saturated with water (240 mL × 3). Obtained fractions were dried in a rotary evaporator, as previously described, for the crude extract. The crude extract and obtained fractions were resuspended in methanol, at a concentration of 10 mg/mL, and stored at −20 °C until use.
## 2.4. Total Contents of Phenolics (TPC), Flavonoids (TFC), and Hydrolysable Tannins (THTC)
TPC was evaluated by the F-C assay with absorbance measured at 760 nm. Gallic acid was used as standard, and results were expressed as milligrams of gallic acid equivalents per gram of dried extract (mg GAE/g DE). TFC was determined by the aluminium chloride colorimetric assay, the absorbance was measured at 510 nm using catechin as standard, and results were expressed as milligrams of catechin equivalents per gram of dried extract (mg CE/g DE). All methods are detailed in [18,19]. THTC were determined using potassium iodate assay, the absorbance was measured at 550 nm using tannic acid, as standard, and results were expressed as milligrams of tannic acid equivalents per gram of dried extract (mg TAE/g DE) [20].
## 2.5. HPLC-UV-DAD Analysis and Identification of Phenolic Compounds
The extracts at the concentration of 10 mg/mL were analysed by HPLC-UV-DAD (Agilent 1200 Series LC system, Waldbronn, Germany), as described elsewhere [21]. For identification of phenolic compounds, the retention parameters of each assay were compared with the standard controls and the peak purity with the UV-vis spectral reference data. Commercial standards of gallic, gentisic, trans-cinnamic, ferulic, and p-coumaric acids, gallocatechin gallate, catechin, rutin, and quercetin were prepared in methanol and analysed separately.
## 2.6.1. Radical Scavenging Activity (RSA) on DPPH Radical
Samples were tested for RSA against the DPPH radical at concentrations ranging from 10 to 1000 µg/mL, as described previously [22]. Ascorbic acid was used as a positive control at concentrations ranging from 10 to 500 µg/mL. Results were expressed as percentage of inhibition, relative to a control containing DMSO in place of the sample, and as half effective concentration (EC50 values, µg/mL).
## 2.6.2. RSA on ABTS Radical Cation
The RSA against ABTS•+ was evaluated according to Re et al. [ 23]. A stock solution of ABTS•+ (7.4 mM) was prepared in potassium persulfate (2.6 mM) and left in the dark for 12–16 h at RT. The ABTS•+ solution was then diluted with ethanol to get an absorbance of 0.7 at 734 nm (Biotek Synergy 4, Biotek, Winooski, VT, USA). Samples (10 µL), at concentrations ranging from 1 to 1000 µg/mL, were mixed with 190 µL of ABTS•+ solution in 96-well microplates, and after 6 min of incubation, the absorbance was measured at 734 nm. Results were presented as antioxidant activity (%), relative to a control containing DMSO, and as EC50 values (µg/mL). Ascorbic acid was used as a positive control at concentrations ranging from 10 to 500 µg/mL.
## 2.6.3. Ferric Reducing Antioxidant Power (FRAP)
The ability of the extracts to reduce Fe3+ was assayed by the method described by Rodrigues et al. [ 22]. Absorbance was measured at 700 nm, and increased absorbance of the reaction mixture indicated increased reducing power. Results were expressed as a percentage, relative to the positive control (BHT, 1 mg/mL), and as EC50 values (µg/mL).
## 2.6.4. Metal Chelating Activity on Iron (ICA) and Copper (CCA)
ICA and CCA were tested on samples at different concentrations (10–4000 µg/mL), as described previously [22]. The change in colour was measured on a microplate reader. EDTA was used as the positive control at concentrations ranging from 10 to 500 µg/mL. Results were expressed as percentage of inhibition, relative to a control containing DMSO in place of the sample, and as EC50 values (µg/mL).
## 2.7.1. AChE and BChE Inhibition Assay
The extracts, at concentrations ranging from 10 to 4000 µg/mL, were evaluated for their inhibitory activity against AChE and BuChE, according to Orhan et al. [ 24]. Absorbances were read at a wavelength of 412 nm using a 96-well microplate reader, and results were expressed as percent inhibition, relative to a control containing DMSO instead of extract, and as half maximal inhibitory concentration (IC50 values) (µg/mL). Galantamine (1 to 1000 µg/mL) was used as a reference.
## 2.7.2. α-Glucosidase Inhibition Assay
The α-glucosidase inhibitory activity was determined according to the method described by Kwon et al. [ 25]. The absorbances were recorded at 405 nm in a microplate reader and results were expressed as inhibition (%), related to a control containing DMSO, and as IC50 values (µg/mL). Acarbose was used as a positive control at concentrations varying from 10 to 4000 µg/mL.
## 2.7.3. Lipase Inhibition Assay
The inhibitory activity on lipase was evaluated according to the method described by McDougall et al. [ 26], adapted to 96-well microplates. Samples (20 μL), at concentrations ranging from 10 to 4000 µg/mL, were mixed with 200 μL of Tris-HCl buffer (100 mM, pH 8.2), 20 μL of the enzyme solution (1 mg/mL), and 20 μL of the substrate (4-nitrophenyl dodecanoate, 5.1 mM in ethanol). After an incubation period of 10 min at 37 °C, the absorbance was read at 410 nm. Orlistat was used as the positive control at concentrations ranging from 10 to 1000 µg/mL. Results, calculated as a percentage of inhibitory activity in relation to a control containing the corresponding solvent, in place of the sample, were expressed as IC50 values (µg/mL).
## 2.7.4. Tyrosinase Inhibition Assay
The extracts’ ability to inhibit tyrosinase was assessed following Custódio et al. [ 27], using arbutin as a positive control at concentrations ranging from 10 to 1000 µg/mL. The extracts were tested at the concentrations ranging from 10 to 4000 µg/mL. The results were calculated and expressed, as in Section 2.7.3.
## 2.8. Statistical Analysis
All the tests were carried out in triplicate. Results were expressed as mean ± standard error mean (SEM). Statistical analysis was performed by one-way analysis of variance (ANOVA), followed by Tukey and Student–Newman–Keuls post hoc test for multiple comparisons. Statistical analysis was performed by using IBM SPSS statistics V24 software from IBM. A value of $p \leq 0.05$ was considered to indicate statistical significance.
## 3.1. Phenolic Composition of the Extracts
Results on the extraction yields and total contents of phenolics, flavonoids, and tannins are summarized in Table 1. The extraction yield of the crude ethanol extracts was higher for leaves ($11.07\%$) than for roots ($1.805\%$). As a result, the extraction yields of the fractions made from the ethanol extract from leaves (range: 0.118–$3.873\%$) were higher than their counterparts obtained from roots (range: 0.021–0.463). Phenolics have recognized benefits on human health, including antioxidants and enzyme inhibitors [28]. Having this in mind, the extracts were evaluated for their total content in different phenolic groups, and results are depicted in Table 1.
Root extracts had a higher content of phenolics than leaves, with TPC in the following order: ethyl acetate fraction ≥ n-butanol fraction > ethanol extract ≥ chloroform fraction > water fraction. In roots, flavonoids peaked in the ethyl acetate fraction, followed by the n-butanol one. Finally, high levels of tannins were detected in the root’s ethyl acetate and n-butanol fractions, as well as in the ethyl acetate fraction from leaves. In fact, we observed that the ethyl acetate and the n-butanol fractions have a higher concentration of total phenolics, flavonoids, and tannins when compared to the ethanol crude extract, probably due to the enrichment in such compounds, due to the higher extractable capacity of such solvents. Similar results were obtained in a related species, L. europaeum, by Bendjedou et al. [ 11]. The obtained results clearly show the influence of the solvent on the extractability of phenolics, flavonoids, and tannins. Phenolic compounds were effectively extracted from the crude ethanol extract, with ethyl acetate and n-butanol, whereas chloroform and water allowed for lower amounts of those compounds. In a previous study on the chemical composition of roots and leaves of L. europaeum from Algeria, high levels of phenolics, flavonoids, and tannins were also detected in similar extracts [11]. However, lower contents of phenolics and flavonoids were detected in methanol extracts made from leaves and fruits of L. intricatum collected from Tunisia [16]. These differences may be related to the solvent used for the extraction and to environmental factors. In effect, the extraction of phenolics is influenced by several conditions, such as the method of extraction, climate, and geographical region of collection, which directly affect the amounts of these molecules in the plant tissues [29]. Phenolic compounds, like those found in high amounts in L. intricatum, display important bioactive properties highly relevant for human health improvement, such as anti-inflammatory, anti-anthelmintic, and anti-cataract [30,31,32], which can support the traditional medicinal uses of the plant.
The phenolic composition of the extracts of L. intricatum was further investigated through the identification of some individual phenolic compounds by HPLC-UV-DAD, and results are depicted in Figure 1 and Figure 2. Information related to the identified compounds can be found in Table 2. From the twenty-four standards tested, nine compounds were identified in those samples. Among these, five and eight compounds were detected in extracts from roots and leaves, respectively. p-coumaric acid [4] was specific to roots, while catechin [3], rutin [5], gallocatechin gallate [6], and quercetin [7] were preferentially detected in leaves. Gallic [1], gentisic [2], ferulic [8], and trans-cinnamic [9] acids were identified in both organs. To the best of our knowledge, the presence of compounds 1–4 and 6–9 in L. intricatum is described here for the first time. The detected phenolic compounds are promising nutraceutical and food additives due to their bioactivities, which include inhibition of enzymes involved in generating inflammatory and immune responses (e.g., serine protein kinases, phospholipases, lipoxygenase, cyclooxygenase, and nitric oxide synthase), modulation of glucose and lipid metabolism, and antioxidant, anticancer, and antimicrobial properties [33].
Previous reports indicated the presence of several phenolic compounds, especially phenolic acids and their derivatives, and flavonoids in fruits and leaves of L. intricatum collected from Tunisia, such as chlorogenic, feruloylquinic, mono-caffeoylquinic, dicaffeoylquinic and para-coumaroylquinic acids, caffeoyl and di-caffeoyl putrescine, quercitrin, isoquercitrin, quercetin, rutin, rutinoside, di-rhamnoside, and kaempferol [16]. Similar results were obtained in leaf ethanol extracts of related species, namely L. barbarum and L. chinensis [42,43]. Overall, the phenolic compounds identified in L. intricatum, either in the present work or in previous reports, highlight the potential use of this species as a source of natural products with health improvement potential and different biotechnological applications, as, for example, in the food and cosmetic industries.
## 3.2. Antioxidant Activity
The highest RSA was obtained with the ethyl acetate and n-butanol fractions (Table 3). The crude ethanol extracts also showed a high RSA, which was significantly higher than that obtained with the used antioxidant standard (ascorbic acid), with EC50 values ranging from 13.59 to 77.16 µg/mL and the highest values being obtained with the ethanol extracts of roots. Conversely, the water fractions of leaves had the lowest capacity to scavenge the DPPH and ABTS+ radicals.
On the other hand, the ethyl acetate and n-butanol fractions of roots and leaves had a higher capacity to reduce iron (FRAP), but the ethyl acetate fraction of leaves was more efficient than other samples in terms of copper chelating potential (CCA). Samples were not active in the iron chelation assay (ICA) (Table 3). These results suggest that some extracts contain compounds with copper chelating activity, and that these compounds may have a phenolic nature. To the best of our knowledge, there were no previous reports regarding the copper chelating potential of L. intricatum.
Samples had a high RSA, which was higher in the crude ethanol extract from roots, when compared to its leaf’s counterpart, and had a significant capacity to reduce iron, like previous findings in a related species, L. europaeum [11]. The RSA and iron reducing capacity were higher than those reported for a methanol extract from leaves and fruits of the same species collected in Tunisia [16], which may be related with different factors known to affect the synthesis of secondary metabolites and, consequently, the biological properties of obtained extracts, including different sites of collection and methods of extraction. The values of RSA obtained in the present study were like those obtained with ethanol extracts from the leaves of L. barbarum and L. chinense [43], while the capacity to reduce iron of the ethyl acetate extract was similar to that reported by Yan et al. [ 44] for leaves of L. barbarum. In leaves, the RSA, iron reducing, and copper chelating properties were higher in the ethyl acetate and n-butanol fractions, which could be linked to the enrichment in phenolic content of those samples, since it is known that phenolics are able to quench free radicals by forming resonance-stabilized phenoxyl radicals [45]. The ethyl acetate fractions generally showed higher RSA, which might be due to the presence of semi-polar molecules, including flavonoids (Table 1). These results agree with others reporting that ethyl acetate was more effective for extracting antioxidants from other plant species, including *Sasa quelpaertensis* and *Pistacia atlantica* subsp. atlantica [46,47]. The root and leaf extracts also had a considerable iron reducing capacity, indicating that they have effective electron donors capable of reducing oxidized intermediates of lipid peroxidation [48]. Interestingly, in the present study, no capability to chelate iron was detected. It has been suggested that the iron chelating activity depends on the presence of catechol groups, which seem to be mostly responsible for metal chelating [45]. Therefore, our results might indicate that the phenolics present in the extracts have few catechol groups in their structures.
Phenolic compounds have a recognized strong antioxidant capacity [49]. In this sense, we can suggest that the antioxidant activity of L. intricatum most likely reflects its high phenolic content. Nonetheless, the detected phenolic compounds may contribute to the L. intricatum antioxidant capacity through addictive and/or synergistic effects [50]. Furthermore, differences between the phenolic composition and content of root and leaf extracts can be responsible for their different behaviours against the various oxidative agents, since detected compounds can have distinct activities towards the same oxidant. For instance, phenolic acids present in the roots and leaves of L. intricatum extracts, namely gallic, gentisic, ferulic, and trans-cinnamic acids, are excellent RSA, and they may be associated with the increased activity of these extracts. Gallate and dihydroxy groups can prevent metal-induced free radicals’ formation through copper chelation, which leads to inactive complexes formation [50]. In the same way, samples were not able to chelate iron, possibly due to a differential selectivity of the antioxidants towards the several oxidising agents [50,51]. From the present results, it is clear that extracts of L. intricatum, especially those from roots, contain molecules not only able to scavenge free radicals, namely DPPH and ABTS+, but also to reduce Fe3+ and to chelate copper; thus, they may be useful in the prevention of oxidative-stress diseases, including, for example, neurodegeneration, diabetes, and skin disorders [52].
## 3.3. Enzymatic Inhibitory Properties
The extracts were further evaluated for their capacity to inhibit enzymes implicated in the onset of human diseases, including neurodegeneration, T2DM, obesity/acne, and hyperpigmentation/food oxidation, and results are summarized in Table 4. Only the chloroform and the ethyl acetate root fractions significantly inhibited AChE, while none of the extracts were able to considerably inhibit BuChE (Table 4). To the best of our knowledge, there is no published data regarding the cholinesterase inhibitory activity of L. intricatum or other neuroprotective properties. A higher inhibitory capacity towards AChE (IC50 = 92.63 µg/mL) was previously reported for the n-butanol fraction obtained from an ethanol root extract of L. europaeum [11]. Such results were in accordance with previous studies of Mocan et al. [ 53], who observed lower values in terms of cholinesterase inhibition for methanol/water (70:30, v/v) leaf extracts of L. barbarum. Interestingly, the n-butanol fraction and crude ethanol extract from roots, and the ethyl acetate fraction from leaves, were able to inhibit α-glucosidase, which were significantly higher than that obtained with the positive control, acarbose. No information was found in the literature regarding the α-glucosidase inhibitory activity of L. intricatum. The results obtained in this work are in accordance with those reported in a previous one targeting L. europaeum, where the root extracts displayed a high inhibitory capacity towards that enzyme [11]. In another study, methanol leaf extracts of L. chinense were also found to be effective against α-glucosidase activity [54]. The higher activity observed in the polar extracts, i.e., n-butanol and ethanol, could be due to their higher phenolic content. Similar results were obtained by Custódio et al. [ 55], who reported that extracts made from Quercus suber L., with the highest phenolic content, also displayed the maximum α-glucosidase inhibition. It is well established that phenolic compounds play an important role in modulating glucosidase activities and, therefore, can contribute to the management of T2DM [55,56]. The present results suggest that roots of L. intricatum contain molecules capable of inhibiting the dietary carbohydrate digestive enzyme and AChE, which may be useful for the control of glucose levels in T2DM patients and for the treatment of AD through modulation of the neurotransmitter acetylcholine in the brain. In addition, the results also suggest that the highest AChE and α-glucosidase inhibitory activities displayed by some extracts may be related with the identified compounds. In fact, previous studies have demonstrated or reviewed these inhibitory activities for gallic acid [1], catechin [3], rutin [5], and quercetin [7] [57,58,59]. However, we cannot discard both a synergistic effect and the activity of other compounds not identified in the samples. None of the extracts were active against lipase. However, they were able to inhibit tyrosinase and the inhibitory activity of n-butanol, and water fractions from roots were higher than that of the positive control, arbutin (Table 4). Although no reports were found regarding the tyrosinase inhibition of L. intricatum extracts, this capacity was already reported for root extracts of a related species, L. chinense [60]. The stronger tyrosinase inhibition capacity exhibited by the root extracts may be related to some identified compounds, namely gallic [1] and gentisic [2] acids (Figure 1), which are tyrosinase inhibitors [61,62]. The present results encourage further work aiming to deepen knowledge on the potential use of L. intricatum as a source of skin whitening products and food additives, which could be of interest for the food, cosmetic, and pharmaceutical industries. In fact, besides its involvement in melanin production, tyrosinase is also related with enzymatic browning, which is a major problem of fresh-cut fruits, and results from oxidation reactions with several enzymes and leads to modifications in the appearance of the nutritional value of food stuffs. Sulfiting agents are the most frequently used anti-browning products but have adverse health effects. Thus, safer anti-browning additives are much needed, and several natural products were already identified, including polyphenol-rich extracts [63]. Of note is the fact that, although the ethanol extract was not active in some assays, namely AChE, BuChE, lipase, and tyrosinase, the obtained fractions displayed some inhibition, allowing for the calculation of IC50 values (Table 4). This can be explained by an accumulation of molecules with enzymatic inhibition properties because of the fractionating process. In the same way, Bendjedou et al. [ 11] investigated the root and leaf extracts of L. europaeum for in vitro enzyme inhibitory activities. Obtained fractions displayed relevant inhibitory activity towards AChE, BuChE, and urease, while the crude ethanol extract was not active. These findings correlate with the results of the present study. A more detailed analysis of the phytochemical profile of the active fractions is needed to identify molecules with the antienzyme actions observed in this study.
## 4. Conclusions
This study reports, for the first time, that extracts from L. intricatum roots have radical scavenging, ferric reducing, and metal chelating activities, coupled with enzyme inhibitory activity towards AChE, α-glucosidase, and tyrosinase. These bioactivities may be related to the high abundance of total phenolics in the extracts and to some identified molecules, such as gallic acid [1], catechin [3], rutin [5], and quercetin [7]. Our results are generally similar to those obtained with well-studied Lycium species, such as L. barbarum and L. chinense, and suggest that roots and leaves of L. intricatum could be considered a source of innovative herbal products, with applications in the food and pharmaceutical industries, with particular interest in the prevention of oxidative stress, neurological diseases, diabetes, and skin disorders. Additional experiments are needed to identify and characterize the bioactive compounds present in the extracts, namely through a bioguided fractionation and isolation of pure compounds. Our results could be used to the valorisation of this promising species.
## References
1. Piero N.M., Kimuni N.S., Ngeranwa J.J.N., Orinda G.O., Njagi J.M., Maina D., Agyirifo S.D., Gathumbi K., King’e W.S., Njagi E.N.M.. **Antidiabetic and Safety of**. *J. Dev. Drugs* (2015) **4** 1000129. DOI: 10.4172/2329-6631.1000129
2. Khan S., Khan H., Ali F., Ali N., Khan F.U., Khan S.U.. **Antioxidant, Cholinesterase Inhibition Activities and Essential Oil Analysis of**. *Nat. Prod. Res.* (2016) **30** 1335-1338. DOI: 10.1080/14786419.2015.1055743
3. Zheng X.-H., Huang Y.-P., Liang Q.-P., Xu W., Lan T., Zhou G.-X.. **A New Lignanamide from the Root of**. *Molecules* (2018) **23**. DOI: 10.3390/molecules23040770
4. Quezel P., Santa S.. *Nouvelle Flore de l’Algérie et des Régions Désertiques Méridionales* (1963) 821-822
5. Qian D., Zhao Y., Yang G., Huang L.. **Systematic review of chemical constituents in the genus**. *Molecules* (2017) **22**. DOI: 10.3390/molecules22060911
6. Yao R., Heinrich M., Weckerle C.S.. **The genus**. *J. Ethnopharmacol.* (2017) **212** 50-66. DOI: 10.1016/j.jep.2017.10.010
7. Hulley I.M., Van Wyk B.E.. **Quantitative medicinal ethnobotany of Kannaland (western Little Karoo, South Africa): Non-homogeneity amongst villages**. *S. Afr. J. Bot.* (2019) **122** 225-265. DOI: 10.1016/j.sajb.2018.03.014
8. Welcome A.K., Van Wyk B.E.. **An inventory and analysis of the food plants of southern Africa**. *S. Afr. J. Bot.* (2019) **122** 136-179. DOI: 10.1016/j.sajb.2018.11.003
9. Zhang C., Wu W., Zhou L., Cheng H., Ye X., He Y.. **Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (**. *Food Chem.* (2020) **319** 126536. DOI: 10.1016/j.foodchem.2020.126536
10. Bendjedou H., Barboni L., Maggi F., Bennaceur M., Benamar H.. **Alkaloids and sesquiterpenes from roots and leaves of**. *Nat. Prod. Res.* (2021) **35** 2784-2788. DOI: 10.1080/14786419.2019.1666386
11. Bendjedou H., Bennaceur M., Benamar H., Rodrigues M.J., Pereira C., Bensouici C., Custódio L.. **In vitro Antioxidant and Enzyme Inhibitory Properties and Phenolic Contents of Crude Extracts and Fractions from Different Organs of the Halophyte**. *Curr. Bioact. Compd.* (2022) **18** 53-61. DOI: 10.2174/1573407217666210713101441
12. Lei Z., Chen X., Cao F., Guo Q., Wang J.. **Phytochemicals and bioactivities of Goji (**. *Int. J. Food Sci. Technol.* (2022) **57** 1451-1461. DOI: 10.1111/ijfs.15507
13. Volpato G., Kourková P., Zelený V.. **Healing war wounds and perfuming exile: The use of vegetal, animal, and mineral products for perfumes, cosmetics, and skin healing among Sahrawi refugees of Western Sahara**. *J. Ethnobiol. Ethnomed.* (2012) **8** 49. DOI: 10.1186/1746-4269-8-49
14. Gong H., Rehman F., Ma Y., Biao A., Zeng S., Yang T., Huang J., Li Z., Wu D., Wang Y.. **Germplasm Resources and Strategy for Genetic Breeding of**. *Front. Plant Sci.* (2022) **13** 802936. DOI: 10.3389/fpls.2022.802936
15. Boulila A., Bejaoui A.. *Lipids Health Dis.* (2015) **14** 59. DOI: 10.1186/s12944-015-0055-9
16. Abdennacer B., Karim M., Yassine M., Nesrine R., Mouna D., Mohamed B.. **Determination of phytochemicals and antioxidant activity of methanol extracts obtained from the fruit and leaves of Tunisian**. *Food Chem.* (2015) **174** 577-584. DOI: 10.1016/j.foodchem.2014.11.114
17. Bendjedou H., Maggi F., Bennaceur M., Mancinelli M., Benamar H., Barboni L.. **A new ionone derivative from**. *Nat. Prod. Res.* (2022) **36** 687-694. DOI: 10.1080/14786419.2020.1797729
18. Singleton V.L., Orthofer R., Lamuela-Raventos R.M.. **Analysis of total phenols and other oxidation substrates and antioxidants by means of Folin-Ciocalteu reagent**. *Methods Enzymol.* (1999) **299** 152-178. DOI: 10.1016/S0076-6879(99)99017-1
19. Kim D., Chun O.K., Kim Y.J., Moon H., Lee C.Y.. **Quantification of polyphenolics and their antioxidant capacity in fresh plums**. *J. Agric. Food Chem.* (2003) **51** 6509-6515. DOI: 10.1021/jf0343074
20. Cam M., Hisil Y.. **Pressurised water extraction of polyphenols from pomegranate peels**. *Food Chem.* (2010) **123** 878-885. DOI: 10.1016/j.foodchem.2010.05.011
21. Custódio L., Patarra J., Alberício F., Neng N., Nogueira J.M.F., Romano A.. **Extracts from**. *Ind. Crops Prod.* (2013) **45** 114-120. DOI: 10.1016/j.indcrop.2012.12.011
22. Rodrigues M.J., Soszynski A., Martins A., Rauter A.P., Neng N.R., Nogueira J.M.F., Varela J., Barreira L., Custódio L.. **Unravelling the antioxidant potential and the phenolic composition of different anatomical organs of the marine halophyte**. *Ind. Crops Prod.* (2015) **77** 315-322. DOI: 10.1016/j.indcrop.2015.08.061
23. Re R., Pellegrini N., Proteggente A., Pannala A., Yang M., Rice-Evans C.. **Antioxidant activity applying an improved ABTS radical cation decolorization assay**. *Free Radic. Biol. Med.* (1999) **26** 1231-1237. DOI: 10.1016/S0891-5849(98)00315-3
24. Orhan I., Kartal M., Naz Q., Ejaz A., Yilmaz G., Kan Y., Konuklugil B., Sener B., Choudhary M.I.. **Antioxidant and anticholinesterase evaluation of selected Turkish**. *Food Chem.* (2007) **103** 1247-1254. DOI: 10.1016/j.foodchem.2006.10.030
25. Kwon Y.I., Apostolidis E., Shetty K.. **In vitro studies of eggplant (**. *Bioresour. Technol.* (2008) **99** 2981-2988. DOI: 10.1016/j.biortech.2007.06.035
26. McDougall G.J., Kulkarni N.N., Stewart D.. **Berry polyphenols inhibit pancreatic lipase activity in vitro**. *Food Chem.* (2009) **115** 193-199. DOI: 10.1016/j.foodchem.2008.11.093
27. Custódio L., Silvestre L., Rocha M.I., Rodrigues M.J., Vizetto-Duarte C., Pereira H., Barreira L., Varela J.. **Methanol extracts from**. *Pharm. Biol.* (2016) **54** 1687-1696. DOI: 10.3109/13880209.2015.1123278
28. He Q., Ma P., Torshizi R.. **The Impact of Some Natural Phenolic Compounds on α-Glucosidase and Sorbitol Dehydrogenase Enzymes, and Anti-leukemia Cancer Potential, Spin Density Distributions, and in silico Studies**. *J. Oleo Sci.* (2022) **71** 863-873. DOI: 10.5650/jos.ess22029
29. Halla N., Boucherit K., Boucherit-otmani Z., Touati F.Z., Rahmani N., Aid I.. *J. King Saud Univ. Sci.* (2018) **31** 541-548. DOI: 10.1016/j.jksus.2018.03.018
30. Sunkireddy P., Jha S.N., Kanwar J.R., Yadav S.C.. **Natural antioxidant biomolecules promises future nanomedicine based therapy for cataract**. *Colloids Surf. B Biointerfaces* (2013) **112** 554-562. DOI: 10.1016/j.colsurfb.2013.07.068
31. Mukherjee N., Mukherjee S., Saini P., Roy P., Babu S.P.S.. **Phenolics and Terpenoids; the promising new search for anthelmintics: A critical review**. *Mini Rev. Med. Chem.* (2016) **16** 1415-1441. DOI: 10.2174/1389557516666151120121036
32. Bodoira R., Maestri D.. **Phenolic compounds from nuts: Extraction, chemical profiles, and bioactivity**. *J. Agric. Food Chem.* (2020) **68** 927-942. DOI: 10.1021/acs.jafc.9b07160
33. Bhuyan D.J., Basu A., Vuong Q.V.. **Phenolic compounds: Potential health benefits and toxicity**. *Utilisation of Bioactive Compounds from Agricultural and Food Production Waste* (2017) 27-59
34. Liu J., Yong H., Liu Y., Bai R.. **Recent advances in the preparation, structural characteristics, biological properties and applications of gallic acid grafted polysaccharides**. *Int. J. Biol. Macromol.* (2020) **156** 1539-1555. DOI: 10.1016/j.ijbiomac.2019.11.202
35. Abedi F., Razavi B.M., Hosseinzadeh H.. **A review on gentisic acid as a plant derived phenolic acid and metabolite of aspirin: Comprehensive pharmacology, toxicology, and some pharmaceutical aspects**. *Phytother. Res.* (2020) **34** 729-741. DOI: 10.1002/ptr.6573
36. Bae J., Kim N., Shin Y., Kim S.Y., Kim Y.J.. **Activity of catechins and their applications**. *Biomed. Dermatol.* (2020) **4** 8. DOI: 10.1186/s41702-020-0057-8
37. Aldaba-Muruato L.R., Ventura Juárez J., Perez Hernandez A.M., Hernández Morales A., Muñoz Ortega M.H., Martínez Hernández S.L., Alvarado-Sánchez B., Macías Pérez J.R.. **Therapeutic perspectives of coumaric acid: Anti necrotic, anti cholestatic and anti amoebic activities**. *World Acad. Sci. J.* (2021) **3** 47. DOI: 10.3892/wasj.2021.118
38. Ganeshpurkar A., Saluja A.K.. **The Pharmacological Potential of Rutin**. *Saudi Pharm. J.* (2017) **25** 149-164. DOI: 10.1016/j.jsps.2016.04.025
39. Kim J.K., Park S.U.. **Quercetin and its role in biological functions: An updated review**. *EXCLI J.* (2018) **17** 856-863. DOI: 10.17179/excli2018-1538
40. Zduńska K., Dana A., Kolodziejczak A., Rotsztejn H.. **Antioxidant Properties of Ferulic Acid and Its Possible Application**. *Skin Pharmacol. Physiol.* (2018) **31** 332-336. DOI: 10.1159/000491755
41. Ruwizhi N., Aderibigbe B.A.. **Cinnamic Acid Derivatives and Their Biological Efficacy**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21165712
42. Mocan A., Vlase L., Vodnar D.C., Bischin C., Hanganu D., Gheldiu A.M., Oprean R., Silaghi-Dumitrescu R., Crișan G.. **Polyphenolic content, antioxidant and antimicrobial activities of**. *Molecules* (2014) **19** 10056-10073. DOI: 10.3390/molecules190710056
43. Mocan A., Vlase L., Raita O., Hanganu D., Păltinean R., Dezsi Ş., Gheldiu A.-M., Oprean R., Crişan G.. **Comparative studies on antioxidant activity and polyphenolic content of**. *Pak. J. Pharm. Sci.* (2015) **28** 1511-1515. PMID: 26431651
44. Yan Y., Ran L., Cao Y., Qin K., Zhang X., Luo Q., Jabbar S., Abid M., Zeng X.. **Nutritional, phytochemical characterization and antioxidant capacity of Ningxia Wolfberry (**. *J. Chem. Soc. Pak.* (2014) **36** 1079
45. Trentin R., Custódio L., Rodrigues M.J., Moschin E., Sciuto K., Da Silva J.P., Moro I.. **Exploring**. *Algal Res.* (2020) **50** 101980. DOI: 10.1016/j.algal.2020.101980
46. Nakamura M., Ra J.-H., Jee Y., Kim J.-S.. **Impact of different partitioned solvents on chemical composition and bioavailability of**. *J. Food Drug Anal.* (2017) **25** 316-326. DOI: 10.1016/j.jfda.2016.08.006
47. Benamar H., Marouf A., Bennaceur M.. **Phytochemical composition, antioxidant and acetylcholinesterase inhibitory activities of aqueous extract and fractions of**. *J. Herbs Spices Med. Plants* (2018) **24** 229-244. DOI: 10.1080/10496475.2018.1446204
48. Custódio L., Escapa A.L., Fernandes E., Fajardo A., Aligué R., Alberício F., Neng N., Nogueira J.M.F., Romano A.. **Phytochemical profile, antioxidant and cytotoxic activities of the carob tree (**. *Plant Foods Hum. Nutr.* (2011) **66** 78-84. DOI: 10.1007/s11130-011-0214-8
49. Rodrigues M.J., Neves V., Martins A., Rauter A.P., Neng N.R., Nogueira J.M., Varela J., Barreira L., Custódio L.. **In vitro antioxidant and anti-inflammatory properties of**. *Food Chem.* (2016) **200** 322-329. DOI: 10.1016/j.foodchem.2016.01.048
50. Dai J., Mumper R.J.. **Plant phenolics: Extraction, analysis and their antioxidant and anticancer properties**. *Molecules* (2010) **15** 7313-7352. DOI: 10.3390/molecules15107313
51. Niki E., Noguchi N.. **Evaluation of antioxidant capacity. What capacity is being measured by which method?**. *IUBMB Life* (2000) **50** 323-329. DOI: 10.1080/15216540051081119
52. Conti V., Izzo V., Corbi G., Russomanno G., Manzo V., De Lise F., Di Donato A., Filippelli A.. **Antioxidant supplementation in the treatment of aging-associated diseases**. *Front. Pharmacol.* (2016) **7** 24. DOI: 10.3389/fphar.2016.00024
53. Mocan A., Zengin G., Simirgiotis M., Schafberg M., Mollica A., Vodnar D.C., Crişan G., Rohn S.. **Functional constituents of wild and cultivated Goji (**. *J. Enzyme Inhib. Med. Chem.* (2017) **32** 153-168. DOI: 10.1080/14756366.2016.1243535
54. Kruczek A., Krupa-Małkiewicz M., Lachowicz S., Oszmiański J., Ochmian I.. **Health-promoting capacities of in vitro and cultivated goji (**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25225314
55. Custódio L., Patarra J., Alberício F., da Rosa Neng N., Nogueira J.M.F., Romano A.. **Phenolic composition, antioxidant potential and in vitro inhibitory activity of leaves and acorns of**. *Ind. Crops Prod.* (2015) **64** 45-51. DOI: 10.1016/j.indcrop.2014.11.001
56. Abbou F., Azzi R., Ouffai K., El Haci I.A., Belyagoubi-Benhammou N., Bensouici C., Benamar H.. **Phenolic profile, antioxidant and enzyme inhibitory properties of phenolic-rich fractions from the aerial parts of**. *S. Afr. J. Bot.* (2022) **146** 196-204. DOI: 10.1016/j.sajb.2021.10.024
57. Kumar S., Narwal S., Kumar V., Prakash O.. **A-glucosidase inhibitors from plants: A natural approach to treat diabetes**. *Pharmacogn. Rev.* (2011) **5** 19. DOI: 10.4103/0973-7847.79096
58. Choudhary D.K., Chaturvedi N., Singh A., Mishra A.. **Characterization, inhibitory activity and mechanism of polyphenols from faba bean (gallic-acid and catechin) on α-glucosidase: Insights from molecular docking and simulation study**. *Prep. Biochem. Biotechnol.* (2020) **50** 123-132. DOI: 10.1080/10826068.2019.1679171
59. Khan H., Marya S., Amin M.A., Kamal S.. **Flavonoids as acetylcholinesterase inhibitors: Current therapeutic standing and future prospects**. *Biomed. Pharmacother.* (2018) **101** 860-870. DOI: 10.1016/j.biopha.2018.03.007
60. Huang H.C., Huang W.Y., Tsai T.C., Hsieh W.Y., Ko W.P., Chang K.J., Chang T.M.. **Supercritical fluid extract of**. *BMC Complement. Altern. Med.* (2014) **14**. DOI: 10.1186/1472-6882-14-208
61. Curto E.V., Kwong C., Hermersdörfer H., Glatt H., Santis C., Virador V., Hearing V.J., Dooley T.P.. **Inhibitors of mammalian melanocyte tyrosinase: In vitro comparisons of alkyl esters of gentisic acid with other putative inhibitors**. *Biochem. Pharmacol.* (1999) **57** 663-672. DOI: 10.1016/S0006-2952(98)00340-2
62. Güven Z.B., Dogan Z., Saracoglu I., Picot L., Nagatsu A., Basaran A.A.. **Food plant with antioxidant, tyrosinase inhibitory and antimelanoma activity:**. *Food Biosci.* (2022) **48** 101804. DOI: 10.1016/j.fbio.2022.101804
63. Rodrigues M.J., Castañeda-Loaiza V., Monteiro I., Pinela J., Barros L., Abreu R.M., Oliveira M.C., Reis C., Soares F., Pousão-Ferreira P.. **Metabolomic profile and biological properties of sea lavender (**. *Foods* (2021) **10**. DOI: 10.3390/foods10123104
|
---
title: Multidisciplinary Assessment and Individualized Nutritional Management of Dysphagia
in Older Outpatients
authors:
- Nikolina Jukic Peladic
- Paolo Orlandoni
- Mirko Di Rosa
- Giulia Giulioni
- Laura Bartoloni
- Claudia Venturini
journal: Nutrients
year: 2023
pmcid: PMC10004837
doi: 10.3390/nu15051103
license: CC BY 4.0
---
# Multidisciplinary Assessment and Individualized Nutritional Management of Dysphagia in Older Outpatients
## Abstract
Introduction: The evidence on the efficacy of nutrition therapy to prevent complications of dysphagia is based on observational studies that used different tools for nutritional and dysphagia assessment, and different scales for the definition of diet textures, rendering their results incomparable and the knowledge on dysphagia management inconclusive. Methods: This retrospective observational study was performed in 267 older outpatients who were assessed for dysphagia and nutritional status by a multidisciplinary team at the Clinical Nutrition Unit of IRCCS INRCA geriatric research hospital (Ancona, Italy) from 2018 to 2021. GUSS test and ASHA-NOMS measurement systems were used for dysphagia assessment, GLIM criteria for the assessment of nutritional status, and the IDDSI framework to describe the texture-modified diets. Descriptive statistics were used to summarize the characteristics of the subjects evaluated. Sociodemographic, functional and clinical parameters were compared between patients with and without BMI improvement overtime by an unpaired Student’s t test, Mann–Whitney U test or Chi square test, as appropriate. Results: Dysphagia was diagnosed in more than $96.0\%$ of subjects; $22.1\%$ ($$n = 59$$) of dysphagic subjects were also malnourished. Dysphagia was treated exclusively by nutrition therapy, prevalently by individualized texture-modified diets ($77.4\%$). For the classification of diet texture, the IDDSI framework was used. The follow-up visit was attended by $63.7\%$ ($$n = 102$$) of subjects. Aspiration pneumonia was registered only in one patient (less than $1\%$), and BMI improved in 13 of 19 malnourished subjects ($68.4\%$). The improvement of nutritional status was primarily reached in subjects whose energy intake was increased and texture of solids modified, in younger subjects, and in those taking less drugs and not reporting any weight loss before the first assessment. Conclusions: The nutritional management of dysphagia must guarantee both an adequate consistency and energy–protein intake. Evaluations and outcomes should be described with universal scales, in order to allow for comparison between studies and contribute to the collection of a critical mass of evidence on the efficacy of texture-modified diets in the management of dysphagia and its complications.
## 1. Introduction
Ageing is responsible for changes in the swallowing process that, in some cases, may compromise the efficacy and safety of swallowing and cause the medical condition known as dysphagia [1,2]. Swallowing disorders are categorized according to the swallowing phase that is affected. Oropharyngeal dysphagia (OD) is predominant in older subjects [3,4,5] and particularly frequent in those with neurologic disorders and in nursing home residents (NHRs). Among NHRs, the prevalence of dysphagia ranges between $8.1\%$ and $93.0\%$ [6,7,8], depending on the diagnostic tools and instruments used in the studies.
To prevent the onset of complications of dysphagia–aspiration pneumonia, malnutrition and dehydration, nutritional intervention is mostly adopted, with the texture modification of foods and liquids as the most frequent practice. The initiation of artificial nutrition (AN) is only sporadic [9,10,11]. Yet, for different reasons, the evidence on the adequacy and efficacy of texture modification for the management of malnutrition is scarce [12]. Some studies report the results of clinical practices where texture modification is not necessarily preceded by an accurate assessment of the protein–calorie needs of subjects with dysphagia, who are simply supposed to be malnourished. In some other studies, the nutritional assessment was carried out with unreliable tools, also due to the lack of a gold standard for its evaluation. In addition, due to the lack of an internationally accepted terminology, very different scales and levels were used for the classification of textures prescribed, making the results of different studies incomparable [13].
Scientific societies have recently proposed some tools that can help to overcome these gaps. In 2019, the major global clinical nutrition societies proposed the Global Leadership Initiative on Malnutrition (GLIM) criteria for the diagnosis of malnutrition; they have a high diagnostic accuracy in identifying patients with malnutrition and the potential to be used as a gold standard [14,15]. In 2013, the International Dysphagia Standardization Initiative (IDDSI) was founded to develop a standardized terminology for naming and classifying texture-modified food and liquids in all settings and cultures [16].
The goal of this study was to describe the characteristics and the nutrition therapies of older outpatients whose swallowing difficulties and nutritional status were assessed in a geriatric research hospital by recently proposed tools and scales. The detailed description of the use of standardized scales and tools could contribute significantly to gathering the critical mass of knowledge that is necessary to evaluate the effectiveness of texture-modified diets in preventing aspiration pneumonia and treating malnutrition, and to overcoming the knowledge gaps that prevent an efficient and evidence-based management of dysphagia.
## 2. Materials and Methods
INRCA’s ethics committee approved the study protocol in compliance with Italian national rules and standards for ethical research conduct (approval n.22034 from $\frac{19}{01}$/23). All older outpatients without a previous diagnosis of dysphagia who signed their informed consent and underwent the assessment of dysphagia and nutritional status at the Clinical Nutrition Unit of the National Institute of Health and Science on Aging (IRCCS) INRCA of Ancona, in the period from 2018 to 2021, were included. Outpatient visits were performed according to a regular protocol of the Clinical Nutrition Unit by a multi-professional team, formed of a physician nutrition specialist, dietitians and speech therapists. Standard demographic data on age, gender and living conditions were collected during interviews with patients and/or their caregivers at the first visit. Detailed data on comorbidities and medications were collected from the patients’ documentation. Values of serum albumin, serum prealbumin and C reactive protein (CRP) were collected. Two days prior to the visit, the patients were invited to fill in food diaries containing information on food and drinks consumed, as well as their texture, and sensations and difficulties experienced when swallowing.
## 2.1. Dysphagia Assessment
The speech therapist performed differential diagnoses of dysphagia. Information on the main dysphagia-related sensations and symptoms were gathered during the interviews with patients and from food diaries. The speech therapist assessed for each patient the ability to control the trunk, to cooperate during the visit, communicative deficits, awareness and oral health. For dysphagia screening, the Gugging Swallowing Screen (GUSS) was used, which allows for the assessment of dysphagia severity and the risk of aspiration for both fluid and non-fluid foods [17,18]. The GUSS test begins with the assessment of vigilance, voluntary cough and/or throat clearing and saliva swallowing (swallowing, drooling, voice change); it then proceeds to test the swallowing of semisolid, fluid and solid textures. Four levels of dysphagia severity, accompanied by different diet recommendations, are possible: severe dysphagia and high aspiration risk (0–9), moderate dysphagia and moderate risk of aspiration (10–14), mild dysphagia with mild aspiration (16–19) and normal swallowing [20]. During the assessment of dysphagia, the American Speech–Language–Hearing Association (ASHA) Functional Communication Measure swallowing subscale was also used to rate dysphagia severity based on the patient’s ability to meet nutritional needs and independence with compensatory strategies. This 7-point scale ranges from Level 1, indicating that the individual is not able to swallow anything safely by mouth and AN is necessary, to Level 7, indicating the absence of any limitation to safe swallowing [19].
## 2.2. Nutritional Assessment and Nutrition Therapy
Dietitians and nutritionists used the five-step Malnutrition Universal Screening Tool (MUST) for malnutrition screening [20]. Patients’ weight and height were measured, and body mass index (BMI) was calculated. Body weight was measured to the nearest 0.1 kg and height was determined to the nearest 0.1 cm, in subjects wearing underwear and without shoes. Height was measured with a height rod. Bedridden patients were weighed by bed scale and a special weighing set, complete with a digital scale and support spreader bar (Help 2000, Tassinari balance Srl., Bologna, Italy). Bedridden patients’ heights were estimated from ulna length, according to tables provided in the appendix of the MUST screening tool. Information on unintentional weight loss was collected during the interviews with patients and caregivers. Nutritionists used the Global Leadership Initiative on *Malnutrition criteria* (GLIM) for the assessment of the nutritional status [18]. Two phenotypic criteria—weight loss and low BMI—were used. For etiologic criteria, the inflammation was defined by clinicians, based on the clinical judgement and the assessment of diseases and conditions which may be considered as indicators of inflammation, i.e., chronic organ diseases, and on some laboratory indicators (albumin, prealbumin and CRP). Malnutrition was diagnosed when at least one phenotypic and one etiologic criterion were met, as suggested by GLIM. The malnutrition severity was graded and the distinction between stage 1 (moderate malnutrition) and stage 2 (severe malnutrition) performed, based on phenotypic criteria, using the threshold values proposed by GLIM. Dietitians assessed the texture of diets and protein calorie intake before the visit from food diaries. Wind-food software was used for a detailed nutritional analysis of patient’s intake and needs. Only after a detailed assessment of both the swallowing process and the nutritional status were the personalized nutrition therapies prescribed. Based on the patient’s conditions and needs, the nutritional interventions could imply: (a) the modification of diet form and liquid viscosity, (b) the suggestion for an increase in calorie and/or protein intake, (c) the prescription of oral nutritional supplementation, (d) different combinations of the three or (e) suggestion for AN. The recommended texture of diet was classified according to the International Dysphagia Diet Standardization Initiative (IDDSI) framework, which was adopted by the Clinical Nutrition Unit of INRCA hospital in 2018 [19]. IDDSI consists of a continuum of 8 levels (0–7), where drinks are measured from Level 0 (thin) to 4 (extremely thick), and foods are measured from Levels 3 (liquidized) to 7 (regular). Both oral and written instructions about dysphagia and its management were provided to caregivers who assisted patients at home (family members or informal caregivers who were not sanitary staff). The appropriate calorie–protein intake and the distribution of dietary protein intake across the meals during the day were recommended by the dietitians.
## 2.3. Follow-Up Visits
When it was deemed necessary, the patients were invited to attend a follow-up visit. During the follow-up visit, the patients were weighed, their BMI was calculated, and, when necessary, swallowing was also re-evaluated. The same assessment tools and methods as during the first visit were used. The effectiveness of nutritional therapy in preventing aspiration pneumonia was assessed in all the subjects that attended the follow-up visit. Its effectiveness in the treatment of malnutrition was analyzed in the subjects diagnosed as malnourished at baseline who also attended the follow-up visit, by comparing BMI values registered on the two different occasions.
## 2.4. Data Analyses
The normality of the continuous variables was tested using the Shapiro–Wilk test, and variables were reported as either mean and standard deviation (SD), or median and interquartile range (IQR), on the basis of their distribution. The comparison of the variables between groups was performed according to their distribution by an unpaired Student's t test or Mann–Whitney U test. Categorical variables were expressed as absolute frequency and percentage and analyzed by a chi square test. A 2-tailed p value of <0.05 was considered significant. Data were analyzed using STATA version 15.1 (StataCorp, College Station, TX, USA).
## 3. Results
In the period from 2018 to 2021, 267 outpatients were assessed for swallowing difficulties and nutritional status at the Clinical Nutrition Unit of INRCA. The most frequent patient-reported symptom of dysphagia was coughing during meals ($45.8\%$), followed by the sensation of a foreign body in the throat ($8.3\%$), oral residue ($7.4\%$), throat clearing ($7.2\%$) and an increased duration of meals ($7.2\%$). Almost $50.0\%$ of subjects reported having two or more symptoms of dysphagia. Most patients ($53.9\%$) reported swallowing difficulties for all consistencies: liquid, solid and mixed. All subjects presented numerous risk factors for dysphagia. As shown in Table 1, the patients were old and very old (mean age 80.5 ± 12.3 years); neurological disease was the most common chronic condition ($30.5\%$). Oral health was poor in more than $50.0\%$ of subjects (poor or no dentition). Almost $90.0\%$ of patients had difficulties communicating, and more than $30.0\%$ were incapable of collaborating with the speech therapist, nutritionist and dietitian during the visit. Subjects mostly had two to seven comorbidities ($81.3\%$) and were using 5 (polypharmacy) to 18 (severe polypharmacy) different drugs ($75.0\%$).
More than $60.0\%$ of drugs used by the assessed subjects were potentially associated with dysphagia [21,22]. Of all the drugs associated with dysphagia, $30.3\%$ were those associated with esophageal damage; $22.7\%$ were drugs that may affect consciousness and alertness, i.e., antipsychotics and neuroleptics; $18.0\%$ were drugs that may cause sleeping and confusion; and $11.0\%$ were drugs that cause xerostomia.
Dysphagia assessment was not possible for eight subjects because of an insufficient level of consciousness and collaboration. Dysphagia was diagnosed in more than $96.0\%$ of subjects who were assessed, according to both GUSS and ASHA. As shown in Table 2, its severity grade was prevalently mild and moderate.
Before the visit, more than $40.0\%$ of subjects were consuming regular or quite regular foods ($32.0\%$ Level 7; $11.2\%$ Level 6); more than $65.3\%$ were consuming thin liquids. Only $6.0\%$ of subjects were treated with AN; $1.5\%$ were both artificially and orally fed.
The nutritional screening revealed that $38.2\%$ of subjects were at risk of malnutrition; $22.1\%$ ($$n = 59$$) were diagnosed as malnourished (see Table 3. All malnourished subjects also had dysphagia. Malnutrition was severe in half of the cases ($49.1\%$; $$n = 29$$) and moderate in the other half ($50.9\%$; $$n = 30$$). Malnutrition prevalence was higher in subjects older than 70 years ($45.1\%$) compared to subjects younger than 70 years ($23.0\%$). Almost $10.0\%$ of subjects with dysphagia were obese.
After both dysphagia and nutritional status were assessed, nutritional therapy was prescribed. Texture-modified diets were prescribed or confirmed for $77.4\%$ of subjects who were assessed. Food texture was changed in $33.0\%$ of cases and the texture of liquids in $28.4\%$. The prevalence of different textures after the visit is presented in Table 4.
In $50.0\%$ of cases, subjects received the suggestion to increase their protein intake; the increase in both protein and calories was suggested in $12.1\%$.
To increase the calorie and protein intake, oral nutritional supplements (ONS) were mostly recommended. In $6.4\%$ of subjects, AN was prescribed, while in $15.5\%$, only recommendations about the protein intake in each meal and about the supervision during the meals were given. In $3.5\%$ of cases, only the increase in water intake was necessary.
Follow-up visits were scheduled for $59.9\%$ ($$n = 160$$) of subjects; these were actually performed for $63.7\%$ ($$n = 102$$) of them. The median number of days between the first and the follow-up visit was 120 (min 30; max 300).
All subjects attending the follow-up visit were following texture-modified diets. Only 1 out of 104 patients (less than $1.0\%$) reported having experienced aspiration pneumonia since the first visit at the Clinical Nutrition Unit of INRCA. Texture-modified diets successfully treated malnutrition in 13 of the 19 subjects who were diagnosed as malnourished during the first visit and attended the follow-up visit ($68.4\%$). None of them reported episodes of aspiration pneumonia.
As shown in Table 5, $76.9\%$ of the subjects with an improvement of their nutritional status had increased their calorie–protein intake after the first visit; $15.4\%$ had only increased their protein intake. Higher rates of BMI improvement were registered in subjects whose texture of solid foods was modified ($30.9\%$ vs. $16.7\%$ in subjects whose texture of solids was not modified), and in younger subjects (81.1 ± 12.1 years vs. 85.7 ± 6.9 years), but the associations were not statistically significant. The number of drugs taken and the proportion of subjects with unintentional weight loss before the first visit were also lower in subjects showing a BMI improvement after the prescribed nutritional therapy (5.92 ± 3.1 vs. 7.3 ± 3.6 and $38.5\%$ and $66.7\%$, respectively). However, the differences between the two groups were not significant, likely due to the low number of subjects in the follow-up period.
## 4. Discussion
In this study, we reported data of 267 older outpatients whose swallowing difficulties and nutritional status were assessed at the Clinical Nutrition Unit of the geriatric research hospital IRCCS INRCA, Ancona (Italy). Ninety-six percent of subjects were identified as dysphagic; $22.1\%$ were both malnourished and dysphagic. Dysphagia was treated with individualized nutritional therapy: $6.4\%$ of subjects were treated with AN; $77.4\%$ with a texture-modified diet (IDSSI from 6 to 1 for foods and from 4 to 1 for liquids). Among subjects who attended the follow-up visit, less than $1.0\%$ reported having experienced aspiration pneumonia, while the texture-modified diets successfully treated malnutrition in 13 of the 19 subjects who were diagnosed as malnourished during the first visit and attended the follow-up visit ($68.4\%$).
A high prevalence of dysphagia among the patients assessed in this study is not surprising, considering the reported risk factors: old age, the presence of neurological diseases and multiple comorbidities, poor oral health and polypharmacy [23]. In particular, we found that $60.0\%$ of subjects were taking drugs potentially associated with dysphagia, mostly by causing esophageal damage ($30.3\%$); affecting consciousness and alertness ($22.7\%$); causing sleepiness and confusion ($18.0\%$); and causing xerostomia ($11.0\%$). Previous studies have already described the high prevalence of drugs that can cause dysphagia among drug regimens in the elderly. The aforementioned studies argued that drug-induced dysphagia is prevalently caused by xerostomia, while in our study the prevalence of drugs causing dry mouth was almost negligible. Very recently, Wolf and colleagues found that antipsychotics, anti-Parkinson drugs, benzodiazepines and antidepressant medications were associated with a 1.4- to 4.4-fold higher prevalence of OD [21]. Drug therapies represent a modifiable risk factor for dysphagia; therefore, their risk–benefit balance should always be evaluated.
The nutritional assessment that was performed by GLIM criteria revealed that $22.1\%$ of subjects with dysphagia enrolled in our study were malnourished ($49.1\%$ severe malnutrition, $50.9\%$ moderate), but almost $68.0\%$ of subjects were not malnourished, and almost $10.0\%$ were even obese. No parallelism between dysphagia and malnutrition was found. Just like Lichenstine and colleagues, we also found that malnutrition was more frequent in dysphagic subjects older than 70 years [24]. In previous studies, different authors have found a prevalence rate of $13\%$ to $55\%$ for malnutrition in subjects with dysphagia [25,26,27,28,29,30,31,32,33]. The differences in the prevalence rates of malnutrition in dysphagia are related to the different settings where the subjects were assessed, but even more to the variety of tools and instruments used to assess nutritional status. Ueshima and colleagues performed the review of the studies assessing the prevalence of malnutrition in adult patients with dysphagia and identified seven nutritional diagnostic criteria used: body mass index (BMI), nutritional screening tool, anthropometric measurements, body composition, dietary assessment, blood biomarkers, and other [25]. They recommended that the GLIM criteria should be used; however, until now, ours is the only study—together with the study of Shimitsku and colleagues—performed using GLIM criteria for malnutrition assessment in subjects with dysphagia [31].
With reference to the nutritional therapies normally adopted to prevent the onset of dysphagia complications, our study confirms that texture modification is a common option ($80.6\%$), while AN is not widely adopted ($6.4\%$). As it was already mentioned, although the texture modification of diets is the most recommended practice, solid evidence on its effects on the prevention of aspiration pneumonia and treatment, as well as the prevention of malnutrition, is lacking. The available evidence derives from observational studies that used very diverse methodologies and numerous classifications of diet textures, which made it impossible to compare their results [34,35,36,37].
In our study, less than $1.0\%$ of subjects that were prescribed an individualized texture-modified diet reported episodes of aspiration pneumonia. Relative to the efficacy of texture-modified diets in treating malnutrition, we found that BMI improved in $68.4\%$ of malnourished subjects following such diets.
Previous studies reported a 1.7-times higher malnutrition risk in LTC residents consuming TMDs, compared with those on standard diets [26,38,39]. Other authors found that only protein and, in particular, calorie-enrichment of texture-modified diets may guarantee weight and BMI improvement [12,26,40,41,42]. Our results also show that BMI improvement probably depends on the adequacy of the energy intake. This result emphasizes the importance of a multidisciplinary approach in the assessment and treatment of subjects with dysphagia. In fact, texture modification without the assessment of patients’ calorie–protein needs and without their adequate provision was not found to be efficient in treating malnutrition in previous studies [12,34,36].
The main strength of our study is that both the assessment of dysphagia and nutritional status and the classification of diet textures were performed using the most recent scales and tools, internationally adopted and approved by scientific societies. Our study also provides important information on drug therapies that represent a potentially modifiable risk factor for dysphagia.
We must also stress some limitations of our study. First, the study had an observational design, and all the statistical analyses performed were descriptive. The limited sample size did not provide sufficient power to test in a multivariable model the effectiveness of an individualized texture-modified diet in preventing aspiration pneumonia and improving BMI. Additionally, the compliance to and the correct application of dietetic prescription was not supervised.
Nevertheless, our results might be helpful in orienting further research, with reference to the main issues requiring investigation, and to the selection of proper tools and scales for nutritional and dysphagia assessment and the classification of diets.
Dysphagia is a topic that requires considerable further research. Clinical trials that offer high-quality evidence on dysphagia and its management are needed. Nevertheless, the use of international standardized instruments and scales within observational studies may also represent a decisive element in overcoming the knowledge gaps that preclude an efficient and evidence-based management of dysphagia. Future studies should also further investigate to what extent the drugs potentially associated with dysphagia are actually responsible for causing it.
## 5. Conclusions
The management of dysphagia and its consequences should never disregard the evaluation of nutritional status; a proper diet for subjects with swallowing difficulties must be characterized by both an adequate consistency and an adequate protein-calorie intake. *To* generate more quality evidence on the efficacy of texture-modified diets in the management of dysphagia and its complications, it is mandatory that future studies use recently proposed GLIM criteria as the gold standard for assessing nutritional status, and the IDDSI framework, to describe the texture of modified diets.
## References
1. Thiyagalingam S., Kulinski A.E., Thorsteinsdottir B., Shindelar K.L., Takahashi P.Y.. **Dysphagia in Older Adults**. *Mayo Clin. Proc.* (2021.0) **96** 488-497. DOI: 10.1016/j.mayocp.2020.08.001
2. Ney D.M., Weiss J.M., Kind A.J., Robbins J.. **Senescent swallowing: Impact, strategies, and interventions**. *Nutr. Clin. Pract.* (2009.0) **24** 395-413. DOI: 10.1177/0884533609332005
3. Chilukuri P., Odufalu F., Hachem C.. **Dysphagia**. *Mo Med.* (2018.0) **115** 206-210. PMID: 30228723
4. Rivelsrud M.C., Hartelius L., Bergström L., Løvstad M., Speyer R.. **Prevalence of Oropharyngeal Dysphagia in Adults in Different Healthcare Settings: A Systematic Review and Meta-analyses**. *Dysphagia* (2022.0). DOI: 10.1007/s00455-022-10465-x
5. Baijens L.W., Clavé P., Cras P., Ekberg O., Forster A., Kolb G.F., Leners J.C., Masiero S., Mateos-Nozal J., Ortega O.. **European Society for Swallowing Disorders-European Union Geriatric Medicine Society white paper: Oropharyngeal dysphagia as a geriatric syndrome**. *Clin. Interv Aging* (2016.0) **11** 1403-1428. DOI: 10.2147/CIA.S107750
6. Yu L., Li Y., Zhang D., Huang W., Li R., Zhu J., Li R., Zhao J., Wang J.. **A risk prediction model for dysphagia in older patients: A single-center prospective study**. *Geriatr. Nurs.* (2022.0) **44** 24-29. DOI: 10.1016/j.gerinurse.2021.12.013
7. Takizawa C., Gemmell E., Kenworthy J., Speyer R.. **A systematic review of the prevalence of oropharyngeal dysphagia in stroke, Parkinson’s disease, Alzheimer’s disease, head injury, and pneumonia**. *Dysphagia* (2016.0) **31** 434-441. DOI: 10.1007/s00455-016-9695-9
8. Feinberg M.J., Ekberg O., Segall L., Tully J.. **Deglutition in elderly patients with dementia: Findings of videofluorographic evaluation and impact on staging and management**. *Radiology* (1992.0) **183** 811-814. DOI: 10.1148/radiology.183.3.1584939
9. Ortega O., Martín A., Clavé P.. **Diagnosis and Management of Oropharyngeal Dysphagia Among Older Persons, State of the Art**. *J. Am. Med. Dir. Assoc.* (2017.0) **18** 576-582. DOI: 10.1016/j.jamda.2017.02.015
10. Sura L., Madhavan A., Carnaby G., Crary M.A.. **Dysphagia in the elderly: Management and nutritional considerations**. *Clin. Interv. Aging* (2012.0) **7** 287-298. DOI: 10.2147/CIA.S23404
11. Jukic Peladic N., Orlandoni P., Dell’Aquila G., Carrieri B., Eusebi P., Landi F., Volpato S., Zuliani G., Lattanzio F., Cherubini A.. **Dysphagia in Nursing Home Residents: Management and Outcomes**. *J. Am. Med. Dir. Assoc.* (2019.0) **20** 147-151. DOI: 10.1016/j.jamda.2018.07.023
12. Ballesteros-Pomar M.D., Cherubini A., Keller H., Lam P., Rolland Y., Simmons S.F.. **Texture-Modified Diet for Improving the Management of Oropharyngeal Dysphagia in Nursing Home Residents: An Expert Review**. *J. Nutr. Health Aging* (2020.0) **24** 576-581. DOI: 10.1007/s12603-020-1377-5
13. Ueshima J., Shimizu A., Maeda K., Uno C., Shirai Y., Sonoi M., Motokawa K., Egashira F., Kayashita J., Kudo M.. **Nutritional Management in Adult Patients With Dysphagia: Position Paper From Japanese Working Group on Integrated Nutrition for Dysphagic People**. *J. Am. Med. Dir. Assoc.* (2022.0) **23** 1676-1682. DOI: 10.1016/j.jamda.2022.07.009
14. Cederholm T., Jensen G.L., Correia M.I.T.D., Gonzalez M.C., Fukushima R., Higashiguchi T., Baptista G., Barazzoni R., Blaauw R., Coats A.. **GLIM criteria for the diagnosis of malnutrition-A consensus report from the global clinical nutrition community**. *Clin. Nutr.* (2019.0) **38** 1-9. DOI: 10.1016/j.clnu.2018.08.002
15. Huo Z., Chong F., Yin L., Lu Z., Liu J., Xu H.. **Accuracy of the GLIM criteria for diagnosing malnutrition: A systematic review and meta-analysis**. *Clin. Nutr.* (2022.0) **41** 1208-1217. DOI: 10.1016/j.clnu.2022.04.005
16. Cichero J.A., Lam P., Steele C.M., Hanson B., Chen J., Dantas R.O., Duivestein J., Kayashita J., Lecko C., Murray J.. **Development of International Terminology and Definitions for Texture-Modified Foods and Thickened Fluids Used in Dysphagia Management: The IDDSI Framework**. *Dysphagia* (2017.0) **32** 293-314. DOI: 10.1007/s00455-016-9758-y
17. Umay E., Eyigor S., Karahan A.Y., Gezer I.A., Kurkcu A., Keskin D., Karaca G., Unlu Z., Tıkız C., Vural M.. **The GUSS test as a good indicator to evaluate dysphagia in healthy older people: A multicenter reliability and validity study**. *Eur. Geriatr. Med.* (2019.0) **10** 879-887. DOI: 10.1007/s41999-019-00249-2
18. Frank U., Radtke J., Nienstedt J.C., Pötter-Nerger M., Schönwald B., Buhmann C., Gerloff C., Niessen A., Flügel T., Koseki J.C.. **Dysphagia Screening in Parkinson’s Disease. A diagnostic accuracy cross-sectional study investigating the applicability of the Gugging Swallowing Screen (GUSS)**. *Neurogastroenterol. Motil.* (2021.0) **33** e14034. DOI: 10.1111/nmo.14034
19. 19.
American Speech-Language-Hearing Association
National Outcomes Measurement System (NOMS): Adults Speech-Language Pathology User’s GuideASHARockville, MD, USA2013. *National Outcomes Measurement System (NOMS): Adults Speech-Language Pathology User’s Guide* (2013.0)
20. **Malnutrition Universal Screening Tool, BAPEN. MAG the Malnutrition Advisory Group, a standing Committee of BAPEN. 2011**
21. Wolf U., Eckert S., Walter G., Wienke A., Bartel S., Plontke S.K., Naumann C.. **Prevalence of oropharyngeal dysphagia in geriatric patients and real-life associations with diseases and drugs**. *Sci. Rep.* (2021.0) **11** 21955. DOI: 10.1038/s41598-021-99858-w
22. Sestili M., Logrippo S., Cespi M., Bonacucina G., Ferrara L., Busco S., Grappasonni I., Palmieri G.F., Ganzetti R., Blasi P.. **Potentially Inappropriate Prescribing of Oral Solid Medications in Elderly Dysphagic Patients**. *Pharmaceutics* (2018.0) **10**. DOI: 10.3390/pharmaceutics10040280
23. Madhavan A., LaGorio L.A., Crary M.A., Dahl W.J., Carnaby G.D.. **Prevalence of and Risk Factors for Dysphagia in the Community Dwelling Elderly: A Systematic Review**. *J. Nutr. Health Aging* (2016.0) **20** 806-815. DOI: 10.1007/s12603-016-0712-3
24. Lichtenstein A.H., Rasmussen H., Yu W.W., Epstein S.R., Russell R.M.. **Modified MyPyramid for Older Adults**. *J. Nutr.* (2008.0) **138** 5-11. DOI: 10.1093/jn/138.1.5
25. Ueshima J., Momosaki R., Shimizu A., Motokawa K., Sonoi M., Shirai Y., Uno C., Kokura Y., Shimizu M., Nishiyama A.. **Nutritional Assessment in Adult Patients with Dysphagia: A Scoping Review**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13030778
26. Reyes-Torres C.A., Castillo-Martínez L., Reyes-Guerrero R., Ramos-Vázquez A.G., Zavala-Solares M., Cassis-Nosthas L., Serralde-Zúñiga A.E.. **Design and implementation of modified-texture diet in older adults with oropharyngeal dysphagia: A randomized controlled trial**. *Eur. J. Clin. Nutr.* (2019.0) **73** 989-996. DOI: 10.1038/s41430-019-0389-x
27. Miles A., Dennison K., Oad M.A., Shasha L., Royal M.. **Consumer Satisfaction of Texture Modified Meals Served in Residential Aged-Care Facilities**. *Int. J. Food Sci. Nutr. Res.* (2019.0) **1** 1. DOI: 10.31546/IJFSNR.1005
28. Tran T.P., Nguyen L.T., Kayashita J., Shimura F., Yamamoto S.. **Nutritional Status and Feeding Practice among Dysphagic Older Adult Inpatients in Vietnam**. *J. Nutr. Sci. Vitaminol.* (2020.0) **66** 224-228. DOI: 10.3177/jnsv.66.224
29. Nishida T., Yamabe K., Honda S.. **The Influence of Dysphagia on Nutritional and Frailty Status among Community-Dwelling Older Adults**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13020512
30. Sella-Weiss O.. **Association between swallowing function, malnutrition and frailty in community dwelling older people**. *Clin. Nutr. Espen.* (2021.0) **45** 476-485. DOI: 10.1016/j.clnesp.2021.06.028
31. Shimizu A., Fujishima I., Maeda K., Murotani K., Ohno T., Nomoto A., Nagami S., Nagano A., Sato K., Ueshima J.. **Association between food texture levels consumed and the prevalence of malnutrition and sarcopenia in older patients after stroke**. *Eur. J. Clin. Nutr.* (2022.0). DOI: 10.1038/s41430-022-01126-1
32. Dehom S., Lao W.P., Thompson J., Lee N., Cragoe A., Luceno C., Crawley B.. **Comorbid dysphagia and malnutrition in elderly hospitalized patients**. *Laryngoscope* (2021.0) **131** 2441-2447. PMID: 33493366
33. Namasivayam A.M., Steele C.M.. **Malnutrition and Dysphagia in Long-Term Care: A Systematic Review**. *J. Nutr. Gerontol. Geriatr.* (2015.0) **34** 1-21. DOI: 10.1080/21551197.2014.1002656
34. Wu X.S., Miles A., Braakhuis A.J.. **Texture-Modified Diets, Nutritional Status and Mealtime Satisfaction: A Systematic Review**. *Healthcare* (2021.0) **9**. DOI: 10.3390/healthcare9060624
35. O’Keeffe S.T.. **Use of modified diets to prevent aspiration in oropharyngeal dysphagia: Is current practice justified?**. *BMC Geriatr.* (2018.0) **18**. DOI: 10.1186/s12877-018-0839-7
36. Hansen T., Beck A.M., Kjaesrgaard A., Poulsen I.. **Second update of a systematic review and evidence-based recommendations on texture modified foods and thickened liquids for adults (above 17 years) with oropharyngeal dysphagia**. *Clin. Nutr. Espen.* (2022.0) **49** 551-555. DOI: 10.1016/j.clnesp.2022.03.039
37. Pauly L., Stehle P., Volkert D.. **Nutritional situation of elderly nursing home residents**. *Z. Gerontol. Geriatr.* (2007.0) **40** 3-12. DOI: 10.1007/s00391-007-0430-x
38. Smoliner C., Norman K., Scheufele R., Hartig W., Pirlich M., Lochs H.. **Effects of food fortification on nutritional and functional status in frail elderly nursing home residents at risk of malnutrition**. *Nutrition* (2008.0) **24** 1139-1144. DOI: 10.1016/j.nut.2008.06.024
39. Engh M.C.N., Speyer R.. **Management of Dysphagia in Nursing Homes: A National Survey**. *Dysphagia* (2022.0) **37** 266-276. DOI: 10.1007/s00455-021-10275-7
40. Martín A., Ortega O., Roca M., Arús M., Clavé P.. **Effect of A Minimal-Massive Intervention in Hospitalized Older Patients with Oropharyngeal Dysphagia: A Proof of Concept Study**. *J. Nutr. Health Aging* (2018.0) **22** 739-747. DOI: 10.1007/s12603-018-1043-3
41. Zanini M., Bagnasco A., Catania G., Aleo G., Sartini M., Cristina M.L., Ripamonti S., Monacelli F., Odetti P., Sasso L.. **A Dedicated Nutritional Care Program (NUTRICARE) to reduce malnutrition in institutionalised dysphagic older people: A quasi-experimental study**. *J. Clin. Nurs.* (2017.0) **26** 4446-4455. DOI: 10.1111/jocn.13774
42. Welch P.K., Dowson M., Endres J.M.. **The effect of nutrient supplements on high risk long term care residents receiving pureed diets**. *J Nutr. Elder.* (1991.0) **10** 49-62. DOI: 10.1300/J052v10n03_04
|
---
title: 'Putting the Personalized Metabolic Avatar into Production: A Comparison between
Deep-Learning and Statistical Models for Weight Prediction'
authors:
- Alessio Abeltino
- Giada Bianchetti
- Cassandra Serantoni
- Alessia Riente
- Marco De Spirito
- Giuseppe Maulucci
journal: Nutrients
year: 2023
pmcid: PMC10004838
doi: 10.3390/nu15051199
license: CC BY 4.0
---
# Putting the Personalized Metabolic Avatar into Production: A Comparison between Deep-Learning and Statistical Models for Weight Prediction
## Abstract
Nutrition is a cross-cutting sector in medicine, with a huge impact on health, from cardiovascular disease to cancer. Employment of digital medicine in nutrition relies on digital twins: digital replicas of human physiology representing an emergent solution for prevention and treatment of many diseases. In this context, we have already developed a data-driven model of metabolism, called a “Personalized Metabolic Avatar” (PMA), using gated recurrent unit (GRU) neural networks for weight forecasting. However, putting a digital twin into production to make it available for users is a difficult task that as important as model building. Among the principal issues, changes to data sources, models and hyperparameters introduce room for error and overfitting and can lead to abrupt variations in computational time. In this study, we selected the best strategy for deployment in terms of predictive performance and computational time. Several models, such as the Transformer model, recursive neural networks (GRUs and long short-term memory networks) and the statistical SARIMAX model were tested on ten users. PMAs based on GRUs and LSTM showed optimal and stable predictive performances, with the lowest root mean squared errors (0.38 ± 0.16–0.39 ± 0.18) and acceptable computational times of the retraining phase (12.7 ± 1.42 s–13.5 ± 3.60 s) for a production environment. While the Transformer model did not bring a substantial improvement over RNNs in term of predictive performance, it increased the computational time for both forecasting and retraining by $40\%$. The SARIMAX model showed the worst performance in term of predictive performance, though it had the best computational time. For all the models considered, the extent of the data source was a negligible factor, and a threshold was established for the number of time points needed for a successful prediction.
## 1. Introduction
Over the past few decades, precise diagnosis and personalized treatment have become increasingly important in healthcare [1]. Nutrition, as an important factor of personalized treatments, has a huge impact on health, from cardiovascular disease to cancer [2,3]. Nutritional habits have been linked to stronger immunity, a lower risk of noncommunicable diseases (such as diabetes and cardiovascular disease) and increased life expectancy [4,5].
Increased knowledge of the effects of nutrition on pathophysiologies of diseases, achieved with new diagnostic and monitoring technologies spanning from -omics [6,7] to wearable devices [8], has offered innovative solutions for personalized treatments. Among the most striking innovations, digital twins (DTs), which are digital replicas of human physiology, represent an emergent solution for prevention and treatment of many diseases [9,10]. DT technology holds the promise of starkly reducing the cost, time and manpower required to test effects of dietary and physical-activity plans, to run clinical trials and to create personalized diets for citizens and patients. DT models are built on data flows sourced from connected biomedical devices on the Internet of Things (IoT) and collected through digital web-based applications integrating dietary, anthropometric and physical activity data, such as the one developed by our research group [11]. Artificial intelligence algorithms have shown good performance in analysis of biometric signals [12,13]. The data streams provided by these data acquisition platforms can be analyzed with data-driven models of human metabolism, such as the personalized metabolic avatar (PMA) [14] developed by our group, to estimate personalized reactions to diets. The PMA consists of a gated recurrent unit (GRU) deep-learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. This model can perform simulations and evaluations of diet plans, allowing definition of tailored goals for achieving ideal weight. However, putting PMAs into production and transforming them in a reliable, fast and continuously updating model for predictive analytics is a difficult task. Among the principal issues, challenges can arise from changes to data sources, models and model parameters, which introduce room for error and overfitting and can lead to abrupt variations in computational time. To overcome these issues, here, we selected the best strategy for deployment in terms of predictive performance and computational time. Among statistical models, we selected, as a representative, the SARIMAX (Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors) model: the most complete for multivariate forecasting. Among deep-learning models, we selected recurrent neural networks (RNNs), such as gated recurrent units (GRUs) [14] and long short-term memory (LSTM) networks, and a new model recently introduced, the Transformer model [15], which has shown great results both in natural language processing and in time-series forecasting [16]. Moreover, we have tested the influence of the data number retrieved, which, in real settings, can vary in range from user to user, on the models. These efforts are necessary to put these models into production to augment citizens’ self-awareness, with the aim of achieving long-lasting results in pursuing a healthy lifestyle.
## 2.1. Study Population
In this single-arm, uncontrolled-pilot prospective study, a group of 10 voluntary adults ($60\%$ females and $40\%$ males, 3 overweight and 7 normal), recruited among our lab staff, self-monitored daily their weight, diet and activities completed for at least 100 days, as explained in a previous work [11]. The participants shared their personal data after signing informed consent.
## 2.2. Wearables and Devices
To track anthropometric data, the following devices were used: The MiBand 6, a smartband of Xiaomi Inc.® (Beijing, China), for estimating calories burned during exercise (walking, running, etc.).The Mi Body Composition Scale, an impedance balance of Xiaomi Inc.® (Beijing, China), for tracking weight and RMR.
These devices were already used in 4 studies on PubMed, and 11 clinical trials have been performed using the MiBand1. Validation results in estimating RMR can be retrieved in recent publications [4]. For tracking the food diary for each participant, a website app (ArMOnIA, https://www.apparmonia.com, accessed on 7 February 2023) developed by our group was used for the storing of food data. These data had already been validated in two other studies [11,14].
## 2.3. Datasets
As already shown in [14], for the development of the deep-learning models implementing PMAs, the following data were used: var1: Weight: w(t) [kg] var2: Energy Balance (EB): Eb(t) [kcal] var3: Carbohydrates: mc(t) [g]var4: Proteins: mp(t) [g]var5: Lipids: ml(t) [g] Where varj stands for variable j, with $j = 1$, …, 5.
In Figure 1a,b, the representative time series of the five selected quantities are reported.
We reframed the time-series forecasting problem as a supervised learning problem, using lagged observations (including the seven days before the prediction, e.g., t − 1, t − 2, t − 7) as input variables to forecast the current time step (t), as already explained in [12]. The inputs of our model were var1(t−7), …, var5(t−k), …, varj(t−i), …, var5(t−1), with $i = 1$, …, 7 indicating the lagged observation and $j = 1$, …, 5 indicating the input variable. Therefore, the total number of inputs for the PMA was 7∙5 = 35. In this notation, the output of the PMA is var1(t), i.e., the weight at time t.
The dataset fed to the SARIMAX model is described in the next section.
## 2.4. Description of Models
As explained in the introduction, DDMs are divided into two types: statistical and deep-learning models. To select the best option for the development of the PMA, we chose to compare 4 different models: SARIMAX: The SARIMAX model (Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors) is a linear regression model: an updated version of the ARIMA model. It is a seasonal equivalent model, like the SARIMA (Seasonal Auto-Regressive Integrated Moving Average) model, but it can also deal with exogenous factors, which are accounted for with an additional term, helping to reduce error values and improve overall model accuracy. This model is usually applied in time-series forecasting [17].
*The* general form of a SARIMA(p,d,q)(P,D,Q,s) model is [1]Θ(L)pθ(Ls)pΔdΔsDwt=Φ(L)qφ(Ls)QΔsDϵt where each term is defined as follows: Θ(L)p is the nonseasonal autoregressive lag polynomial;θ(Ls)p is the seasonal autoregressive lag polynomial;ΔdΔsDwt is the time series, differenced d times and seasonally differenced D times;Φ(L)q is the nonseasonal moving average lag polynomial;φ(Ls)Q is the seasonal moving average lag polynomial.
When dealing with n exogenous values, each defined at each time step, t, denoted as xti for i≤n, the general form of the model becomes [2]Θ(L)pθ(Ls)pΔdΔsDwt=Φ(L)qφ(Ls)QΔsDϵt+∑$i = 1$nβixti, where βi is an additional parameter accounting for the relative weight of each exogenous variable.
In Supplementary Materials (Section S1), additional details about the model are reported.
We implemented this model on Python using the StatsModels library (https://www.statsmodels.org/stable/index.html, accessed on 7 February 2023), with the SARIMAX (https://www.statsmodels.org/0.9.0/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html, accessed on 7 February 2023) class.
For the SARIMAX model, var2, var3, var4 and var5 (i.e., EB and macronutrients) are considered as exogenous variables, with the weight as output. Considering our dataset structure, the exogenous variables at time t correspond to the inputs for the forecasting of the weight at time t + 1. However, in the SARIMAX equation, the exogenous term is considered at the same time, t, with respect to the output. To overcome this issue, we shifted the exogenous values of ΔT = 1 day with respect to weight. In this way, the exogenous term changed as follows: ∑$i = 1$nβixt−1i.
LSTM: Long short-term memory (LSTM) networks [18], a variant of the simplest recurrent neural networks (RNNs), can learn long-term dependencies and are the most widely used for working with sequential data such as time-series data [19,20,21].
The LSTM cell (Figure 2) uses an input gate, a forget gate and an output gate (a simple multilayer perceptron). Depending on data’s priority, these gates allow or deny data flow/passage. Moreover, they enhance the ability of the neural network to understand what needs to be saved, forgotten, remembered, paid attention to and output. The cell state and hidden state are used to gather data to be processed in the next state.
The gates have the following equations:Input Gate: [3]i=σ(Wiht−1+Wiht),Forget Gate:[4]f=σ(Wfht−1+Wfht),Output Gate:[5]o=σ(Woht−1+Woht),Intermediate Cell State:[6]g=tanh(Wght−1+Wght),Cell State (Next Memory Input):[7]ct=(g∗i)+(f∗ct−1),New State:[8]ht=o∗tanh(ct), with Xt as the input vector, ht as the output vector, W and U as parameter matrices and f as the parameter vector.
We implemented the LSTM network using the TensorFlow Keras library (https://www.tensorflow.org/api_docs/python/tf/keras, accessed on 7 February 2023), which implements an LSTM cell as an available class on Python (https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM, accessed on 7 February 2023), which we added into a model as a monolayer neural network.
GRU: The gated recurrent unit, just like the LSTM network, is a variant of the simplest RNN but with a less complicated structure. It has an update gate, z, and a reset gate, r. These two variables are vectors that determine what information passes or does not pass to output. With the reset gate, new input is combined with the previous memory while the update gate determines how much of the last memory to keep.
The GRU has the following equations:Update Gate: [9]z=(Wzht−1+Uzxt),Reset Gate:[10]r=(Wrht−1+Urxt),Cell State:[11]c=tanh(Wc(ht−1∗r)+Ucxt),New State:[12]ht=(z∗c)((1−z)∗ht−1), A GRU cell is shown in Figure 3.
A more accurate description can be found in the Supplementary Materials (Section S3) of a previous work [14].
As for the LSTM network, we implemented the GRU in TensorFlow using a GRU cell (https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU, accessed on 7 February 2023) implemented into a monolayer neural network.
Transformer: LSTM and GRUs have been strongly established as state-of-the-art approaches in sequence modeling and transduction problems such as language modeling and machine translation [22,23,24,25,26] because of their ability to memorize long-term dependency. Since they are inherently sequential, there is no parallelization within training examples, which makes batching across training examples more difficult as sequence lengths increase. Therefore, to allow modeling of dependencies for any distance in the input or output sequences, attention mechanisms have been integrated in compelling sequence modeling and transduction models in various tasks [24,27]. Commonly [28], such attention mechanisms are used in conjunction with a recurrent network. In 2017, a team at Google Brain® developed a new model [15], called “Transformer”, with an architecture that avoids recurrence and instead relies entirely on an attention mechanism to draw global dependencies between inputs and outputs. This architecture uses stacked self-attention and pointwise, fully connected layers for both the encoder and the decoder, shown in the left and right halves of Figure 4, respectively. In Supplementary Material (Section S2), a more accurate description of the model is reported.
The implementation of the model in Python followed the Transformer starting code shared by the Google Brain team (https://keras.io/examples/timeseries/timeseries_transformer_classification/, accessed on 7 February 2023).
## 2.5.1. Implementation and Selection of Models
For each selected model, parameter scanning was performed, and the best model was selected. Below, procedures are indicated according to models.
SARIMAX: Augmented Dickey–Fuller (ADF) tests, applied to a weight time series, yielded p-values larger than α = 0.05 for $90\%$ of the overall participants. Therefore, we transformed the weight time series into a stationary one that performed first-order differentiation. The ADF test, repeated on preprocessed series, confirmed stationarities for all of the transformed time series. Following this adjustment, the terms d and D were each set to 1.
We started with fitting a SARIMAX model for all the datasets available, considering the ranges in Table 1.
In the literature, the most common way to find the best parameters for SARIMAX models is based on a simple grid search following the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), respectively. These criteria help to select the model that explains the greatest amount of variation using the fewest possible independent variables, using maximum likelihood estimation (MLE) [29], and they both penalize a model for having increasing numbers of variables, to prevent overfitting.
Therefore, we ranked the models according to the lowest AIC values. The first 5 models were then trained on the datasets, and the root mean squared error (RMSE) scores were calculated. The model with the lowest RMSE was then selected.
The LSTM and GRU Models: Hyperparameter tuning with the aim of minimizing loss function was carried out to select the best deep-learning model [14]. Typically, in time-series forecasting, tuning is carried out to reduce the RMSE of test-training forecasting.
Considering that the LSTM and GRU models had the same configuration and the same hyperparameters, we proceeded with both to parameter scanning in the range shown in Table 2.
We selected the best model via considering the lowest RMSE obtained from a prediction on the same training-test sets.
Transformer: The implementation of this model into Keras was like that of the other two neural networks, with some exceptions for the hyperparameters. We considered a grid search that would take into account the range of the hyperparameters shown in Table 3.
Differently from LSTM and GRU, there are two more parameters: head size, which is the dimensionality of the query, key and value tensors after the linear transformation, and num heads, which is the number of attention heads.
In this case, we also chose the best model via minimizing the RMSEs on the training-test sets.
## 2.5.2. Performance of Models with Datasets of Varying Length
Following model selection and parameter optimization, we compared the models, considering, as a quality index, the RMSE, which indicates errors in weight prediction with a test-set length of 7 days, considering a training set of more than 100 days (mean ± SD = 161.3 ± 22.4) for each participant.
In addition, since scarcity of data is a common problem in deployment of PMAs in production, we tested the models in more realistic settings. We thus divided the dataset of each participant into 9 independent groups of 15 days. Then, we evaluated the RMSE on a test set with a length of 1 day for each group (with a training set of 14 elements). The final RMSE was the average of these 9 RMSEs. An ANOVA followed by a Tukey test was applied for pairwise comparison of RMSEs.
## 2.5.3. Computational Time
In addition to prediction performance, the computational times were calculated for the retraining and prediction phases for the four models.
A Kruskal–Wallis test followed by a Dunn test was applied for pairwise comparison of computational times.
## 2.6. Computational Requirements and Python Libraries
Computational requirements were minimal in order to allow deployment on virtual machines available on the web. The code for the development of the models was run in Google Colab with the default settings (free plan). The code requires the following libraries: tensorflow = 2.9.2 (https://pypi.org/project/tensorflow/, accessed 7 February 2023), pandas = 1.3.5 (https://pandas.pydata.org/, accessed 7 February 2023), numpy = 1.21.6 (https://numpy.org/, accessed 7 February 2023), matplotlib = 3.2.2 (https://matplotlib.org/, 7 February 2023), seaborn = 0.11.2 (https://seaborn.pydata.org/, accessed 7 February 2023), statsmodels = 0.12.2 (https://www.statsmodels.org/stable/index.html, accessed on 7 February 2023), scipy = 1.7.3 (https://pypi.org/project/scipy/, accessed on 7 February 2023), bioinfokit = 2.1.0 (https://pypi.org/project/bioinfokit/0.3/, accessed on 7 February 2023), scikit-learn = 1.0.2 (https://scikit-learn.org/stable/, accessed 7 February 2023) and scikit-posthocs = 0.7.0 (https://scikit-posthocs.readthedocs.io/en/latest/, accessed on 7 February 2023).
## 3.1. Selection of the Optimal Model
We started with optimizing parameters for each selected model and each participant, as explained in par. 2.6.1.
For the GRU, LSTM and Transformer models, we considered an Adam optimizer and, as a loss function, the mean absolute error (MAE), defined with the formula [13]MAE=∑$i = 1$n|yi−xi|n, where yi is the actual value and xi is the prediction.
In Table S1, the selected parameters for each user are reported for each type of model. As shown in [14], we trained a model for each user to adapt it to the personalized characteristics of metabolism.
## 3.2. Comparison between Models
As explained in par. 2.6.2, to compare model performance, we used the RMSE of the prediction of the test set for each participant. Datasets were structured to make the training and test set homogeneous, ensuring that the models learned from the same data and tested their knowledge under identical conditions. In Figure 5, we report the forecasting with each model for a single participant.
From a visual inspection, the GRU and LSTM models follow the variations of weight more accurately while the SARIMAX model shows the worst result. In Figure 6, we show the RMSEs, grouped based on models, for each participant. Indeed, there is an evident difference between the SARIMAX model and the others, confirming that neural networks outperform statistical models in time-series forecasting. On the other hand, the deep-learning models show comparable RMSEs to each other.
Hence, the Transformer model did not demonstrate improvement with respect to the GRU or LSTM models, having, on the contrary, slightly worse results.
To quantify these observations, we carried out an ANOVA among the RMSEs of the models, showing a p-value lower than α=0.05 (4.31·10−4) and confirming that there was at least one model different from the others. We then performed a Tukey test for pairwise comparison, and the results, reported in Table 4, confirmed that the SARIMAX model is different from the others (adjusted p-value lower than α), while there is no statistical difference among the other three models, yielding a p-value bigger than α.
## 3.3. Analysis of the Performance with a Limited Dataset Length
As explained in Section 2.6, PMAs often operate on datasets with limited length. For example, diet diaries are often compiled for a limited amount of time. Therefore, we carried out a test to show the performances of these models, considering a limited dataset of 15 days. The model was trained to predict the weight for the day afterward.
To acquire a reliable index of the performance of each model, we tested the models on nine subsets of data in the original dataset for each participant. In this way, we could refer to a mean RMSE for each model and for each participant.
In Figure 7, the RMSE distribution of each model is reported (each point represents a user). From a visual inspection, we can conclude that, again, the SARIMAX model displays the worst results, while the others have similar performances. To confirm this observation, we carried out an ANOVA (p-value = 0.019) followed by a Tukey test. The pairwise comparison showed that only the SARIMAX model presented accuracy that was statistically different from that of the deep-learning models.
## 3.4. Performance versus Data Length
The results reported in the previous section show how the model provided accurate solutions for few data. In this section, we analyze changes in performance with decreasing data length. We considered the following subsets: $100\%$ of the dataset and 100, 80, 60, 40 and 30 days. In Figure 8, we report the RMSE versus the data length for each participant and for each model, with error bars representing the standard deviations (SDs).
While the SARIMAX model showed an important decrease in performance as data length decreased, the others were characterized with stable performances, also with data collected only for thirty days.
## 3.5. Computational Time
In the evaluation of the performance of a model in a production environment, we must consider another important parameter: the computational cost, expressed in time. This computational time is the sum of the (re)training time and the forecasting time, since in a production environment, the model must be retrained every time and data are gathered in real time. In Table 5, we report, for each model, the computational time, the (re)training time and the forecasting time. The times are averaged based on the number of participants.
It is possible to observe how the GRU and LSTM models each require about $\frac{1}{5}$ of the time requested to retrain and forecast with the Transformer model but 10× more time than that of the SARIMAX model. Therefore, a major burden of the Transformer model resides in the retraining time, since it requires more complex operations than the others.
To quantify these observations, a Kruskal–Wallis test was carried out [30] among the models, since a Shapiro–Wilk test [30] had confirmed that distributions would not be normal. The test yielded a p-value < 0.05, showing the presence of a statistical difference between the models. Therefore, a posthoc test (Dunn test [31]) was carried out to investigate the pairwise comparison. The test showed no statistical difference between the GRU and LSTM models, confirming that they have similar performances (Figure 9), which are better than that of the Transformer model.
## 4. Discussion
Obesity and cardiovascular disease, as the most serious public health challenges of the 21st century, are strongly conditioned through dietary habits. Digital health can help people to monitor themselves and prevent these diseases. The advent of wearable devices and the evolution of smartphone technology have allowed the development of an infrastructure able to retrieve data that could be used for the development of what is defined as a “Digital Twin”: a digital representation of human physiology. With this technology, it is possible to import digital health into the lifestyles of citizens, promoting a healthy lifestyle, since people would be in conditions to better know their own physiologies and responses to nutrition and physical activity. Here, we relied on the ArMOnIApp application, which is able to fetch, preprocess and analyze spontaneous and voluntary physical activities (PAs), dietary measures and anthropometric measures from a set of commercial wearables and other smart devices provided to the end user [11]. These data led to the development of a model, the PMA, that is able to give personalized responses for each end user, such as personalized reactions to the introduction of a particular food in their diet [14]. Here, we compared predictive and computational performances of several models, with the aim of providing useful parameters to put the PMA into production. Moreover, we tested the influence of the data number retrieved, which, in real settings, can vary in range from user to user, on the four models. In a production environment, the practice of automating deployment, integration and monitoring of machine-learning (ML) models is called MLOps [32], and this automation is crucial to increase the speed at which organizations can release models into production. MLOps also involves ensuring continuous quality and dynamic adaptability of projects throughout the entire model lifecycle [32].
To make an efficient and accurate PMA, data must be retrieved in real time. Therefore, web applications must be structured to continuously fetch new data as they are made available with devices, and to control data quality using algorithms. To include these functionalities, we relied on our web application, ArMOnIApp [11]. Moreover, ML models require automation of model retraining, and in this framework, the time cost for this procedure has an important role to optimize end performance. To this aim, we evaluated the time necessary for retraining of and prediction for the most used and reliable forecasting models. The results (summarized in Table 6) show how the GRU and LSTM models require about $\frac{1}{5}$ of the computational time of the Transformer model, despite this time being more than 10 times that of the SARIMAX model.
On top of these optimizations, there is a need to monitor quality of predictions. To this aim, we outlined a workflow to evaluate the performances of different models with varying data lengths. We found out that the SARIMAX model, though being the fastest, had the worst RMSE, with a great variability among users. This RMSE, being four times higher than that of the GRU or LSTM model, penalizes the SARIMAX model in the deployment of the PMA. In terms of the RMSE, the Transformer model had a better performance than the SARIMAX model as well, but was comparable with RNNs. However, the time cost was the highest (four times higher than for the GRU/LSTM model), and this criticality has a strong impact on production development.
According to the performances and computational times, we can conclude that the PMAs built on the GRU or LSTM model show optimal predictive performances with acceptable computational time, making them the best candidates for a production environment.
Another issue is the need to compare the effectiveness of training several ML models specialized in different groups versus training one unique model for all the data. To address this issue, planning to create a unique model accounting for the metabolisms of a cohort of participants will require an increased number of participants.
Before these models are put into production, several ethical concerns must be moreover addressed. In regard to privacy concerns, collection and storage of personal health data by wearable devices can potentially compromise users’ privacy if this information is shared with third parties without their consent. In this study, we retrieved health data from the Zepp API, where users have explicitly consented to data sharing. The privacy policy can be retrieved on the Zepp website (https://www.zepp.com/privacy-policy, accessed on 24 February 2023). There are, in any case, security risks. Wearable devices are often connected to the internet, making them vulnerable to hacking and data breaches. This can result in sensitive personal data being stolen or compromised, potentially leading to identity theft or other forms of fraud. In addition, discrimination is an issue to be addressed, since use of wearable devices and data collected can potentially lead to discrimination against individuals with pre-existing health conditions or disabilities. This can result in denial of insurance coverage or job opportunities. Finally, there are social implications: use of wearable devices to track personal data can promote unhealthy obsessions with self-monitoring. These issues have been constantly monitored in pilot and clinical studies, but protocols must be developed and optimized before the use of these systems on a large scale is allowed. Some of these protocols already exist or are under research [33,34].
## 5. Conclusions
Putting the PMA into production can produce diets and activity regimens that are specifically tailored to users’ needs. Thanks to the PMA, pertinent hints can be found to provide citizens and nutritionists with scientific knowledge and reliable tools, enhancing their self-awareness and assisting them in their quests for healthy lifestyles. An important development might be inclusion of newly developed lipid metabolism indicators (such as membrane lipids and fluidity of red-blood-cell membranes) as input in the PMA to research the impacts and influences of dietary components on their results [35,36,37,38,39]. Additionally, cutting-edge and promising anthropometric markers, such as VO2max and heart rate frequency, monitored using wearable technology can enhance the accuracy of weight predictions [40]. These integrations could group and cluster various PMA responses, providing insights into these variables that could affect an individual’s metabolism. Another important advancement may come from the advent of quantum computing and the achievement of quantum supremacy [41], which will revolutionize ML models, including the PMA, via increasing their performances and reducing their computational times.
## References
1. Mold J.. **Goal-Directed Health Care: Redefining Health and Health Care in the Era of Value-Based Care**. *Cureus* (2017.0) **9** e1043. DOI: 10.7759/cureus.1043
2. Bianchi V.E.. **Impact of Nutrition on Cardiovascular Function**. *Curr. Probl. Cardiol.* (2020.0) **45** 100391. DOI: 10.1016/j.cpcardiol.2018.08.003
3. Wiseman M.J.. **Nutrition and Cancer: Prevention and Survival**. *Br. J. Nutr.* (2019.0) **122** 481-487. DOI: 10.1017/S0007114518002222
4. Tourlouki E., Matalas A.-L., Panagiotakos D.B.. **Dietary Habits and Cardiovascular Disease Risk in Middle-Aged and Elderly Populations: A Review of Evidence**. *Clin. Interv. Aging* (2009.0) **4** 319-330. DOI: 10.2147/CIA.S5697
5. Pignatti C., D’adamo S., Stefanelli C., Flamigni F., Cetrullo S.. **Nutrients and Pathways That Regulate Health Span and Life Span**. *Geriatrics* (2020.0) **5**. DOI: 10.3390/geriatrics5040095
6. Kussmann M., Raymond F., Affolter M.. **OMICS-Driven Biomarker Discovery in Nutrition and Health**. *J. Biotechnol.* (2006.0) **124** 758-787. DOI: 10.1016/j.jbiotec.2006.02.014
7. Chaudhary N., Kumar V., Sangwan P., Pant N.C., Saxena A., Joshi S., Yadav A.N.. **Personalized Nutrition and -Omics**. *Comprehensive Foodomics* (2020.0) 495-507
8. Iqbal S.M.A., Mahgoub I., Du E., Leavitt M.A., Asghar W.. **Advances in Healthcare Wearable Devices**. *NPJ Flex. Electron.* (2021.0) **5** 9. DOI: 10.1038/s41528-021-00107-x
9. Ahmadi-Assalemi G., Al-Khateeb H., Maple C., Epiphaniou G., Alhaboby Z.A., Alkaabi S., Alhaboby D.. **Digital Twins for Precision Healthcare**. *Advanced Sciences and Technologies for Security Applications* (2020.0) 133-158
10. Mulder S.T., Omidvari A.H., Rueten-Budde A.J., Huang P.H., Kim K.H., Bais B., Rousian M., Hai R., Akgun C., van Lennep J.R.. **Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease During the Life Course**. *J. Med. Internet Res.* (2022.0) **24** e35675. DOI: 10.2196/35675
11. Bianchetti G., Abeltino A., Serantoni C., Ardito F., Malta D., de Spirito M., Maulucci G.. **Personalized Self-Monitoring of Energy Balance through Integration in a Web-Application of Dietary, Anthropometric, and Physical Activity Data**. *J. Pers. Med.* (2022.0) **12**. DOI: 10.3390/jpm12040568
12. Nguyen T.V.T., Huynh N.T., Vu N.C., Kieu V.N.D., Huang S.C.. **Optimizing Compliant Gripper Mechanism Design by Employing an Effective Bi-Algorithm: Fuzzy Logic and ANFIS**. *Microsyst. Technol.* (2021.0) **27** 3389-3412. DOI: 10.1007/s00542-020-05132-w
13. Wang C.N., Yang F.C., Nguyen V.T.T., Vo N.T.M.. **CFD Analysis and Optimum Design for a Centrifugal Pump Using an Effectively Artificial Intelligent Algorithm**. *Micromachines* (2022.0) **13**. DOI: 10.3390/mi13081208
14. Abeltino A., Bianchetti G., Serantoni C., Ardito C.F., Malta D., de Spirito M., Maulucci G.. **Personalized Metabolic Avatar: A Data Driven Model of Metabolism for Weight Variation Forecasting and Diet Plan Evaluation**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14173520
15. Vaswani A., Brain G., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł., Polosukhin I.. **Attention Is All You Need**. *Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017)*
16. Wu N., Green B., Ben X., O’Banion S.. **Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case**. *arXiv* (2020.0)
17. Alharbi F.R., Csala D.. **A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach**. *Inventions* (2022.0) **7**. DOI: 10.3390/inventions7040094
18. Hochreiter S., Urgen Schmidhuber J.. **Long Short-Term Memory**. *Neural Comput.* (1997.0) **9** 1735-1780. DOI: 10.1162/neco.1997.9.8.1735
19. Lipton Z.C., Berkowitz J., Elkan C.. **A Critical Review of Recurrent Neural Networks for Sequence Learning**. *arXiv* (2015.0)
20. Liu Y., Hou D., Bao J., Qi Y.. **Multi-Step Ahead Time Series Forecasting for Different Data Patterns Based on LSTM Recurrent Neural Network**. *Proceedings of the 2017 14th Web Information Systems and Applications Conference, WISA 2017* 305-310
21. Schmidhuber J., Wierstra D., Gomez F.J.. **Evolino: Hybrid neuroevolution/optimal linear search for sequence prediction**. *Proceedings of the 19th International Joint Conferenceon Artificial Intelligence (IJCAI)*
22. Elman J.L.. **Finding Structure in Time**. *Cogn. Sci.* (1990.0) **14** 179-211. DOI: 10.1207/s15516709cog1402_1
23. Mikolov T., Karafiát M., Burget L., Cernocky J.H., Khudanpur S.. **Recurrent Neural Network Based Language Model**. *Proceedings of the 11th Annual Conference of the International Speech Communication Association*
24. Bahdanau D., Cho K., Bengio Y.. **Neural Machine Translation by Jointly Learning to Align and Translate**. *arXiv* (2014.0)
25. Sutskever I., Vinyals O., Le Q.V.. **Sequence to Sequence Learning with Neural Networks**. *arXiv* (2014.0)
26. Cho K., van Merrienboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y.. **Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation**. *arXiv* (2014.0)
27. Kim Y., Denton C., Hoang L., Rush A.M.. **Structured Attention Networks**. *arXiv* (2017.0)
28. Parikh A.P., Täckström O., Das D., Uszkoreit J.. **A Decomposable Attention Model for Natural Language Inference**. *arXiv* (2016.0)
29. Myung I.J.. **Tutorial on Maximum Likelihood Estimation**. *J. Math. Psychol.* (2003.0) **47** 90-100. DOI: 10.1016/S0022-2496(02)00028-7
30. Ostertagová E., Ostertag O., Kováč J.. **Methodology and Application of the Kruskal-Wallis Test**. *Appl. Mech. Mater.* (2014.0) **611** 115-120. DOI: 10.4028/www.scientific.net/AMM.611.115
31. Dinno A.. **Nonparametric Pairwise Multiple Comparisons in Independent Groups Using Dunn’s Test**. *Stata J.* (2015.0) **15** 292-300. DOI: 10.1177/1536867X1501500117
32. Kreuzberger D., Kühl N., Hirschl S.. **Machine Learning Operations (MLOps): Overview, Definition, and Architecture**. *arXiv* (2022.0)
33. Ahmad W., Rasool A., Javed A.R., Baker T., Jalil Z.. **Cyber Security in IoT-Based Cloud Computing: A Comprehensive Survey**. *Electronics* (2022.0) **11**. DOI: 10.3390/electronics11010016
34. Langone M., Setola R., Lopez J.. **Cybersecurity of Wearable Devices: An Experimental Analysis and a Vulnerability Assessment Method**. *Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)* **Volume 2** 304-309
35. Bianchetti G., Viti L., Scupola A., di Leo M., Tartaglione L., Flex A., de Spirito M., Pitocco D., Maulucci G.. **Erythrocyte Membrane Fluidity as a Marker of Diabetic Retinopathy in Type 1 Diabetes Mellitus**. *Eur. J. Clin. Investig.* (2021.0) **51** e13455. DOI: 10.1111/eci.13455
36. Maulucci G., Cohen O., Daniel B., Ferreri C., Sasson S.. **The Combination of Whole Cell Lipidomics Analysis and Single Cell Confocal Imaging of Fluidity and Micropolarity Provides Insight into Stress-Induced Lipid Turnover in Subcellular Organelles of Pancreatic Beta Cells**. *Molecules* (2019.0) **24**. DOI: 10.3390/molecules24203742
37. Maulucci G., Cohen O., Daniel B., Sansone A., Petropoulou P.I., Filou S., Spyridonidis A., Pani G., de Spirito M., Chatgilialoglu C.. **Fatty Acid-Related Modulations of Membrane Fluidity in Cells: Detection and Implications**. *Free Radic. Res.* (2016.0) **50** S40-S50. DOI: 10.1080/10715762.2016.1231403
38. Cordelli E., Maulucci G., de Spirito M., Rizzi A., Pitocco D., Soda P.. **A Decision Support System for Type 1 Diabetes Mellitus Diagnostics Based on Dual Channel Analysis of Red Blood Cell Membrane Fluidity**. *Comput. Methods Programs Biomed.* (2018.0) **162** 263-271. DOI: 10.1016/j.cmpb.2018.05.025
39. Bianchetti G., Azoulay-Ginsburg S., Keshet-Levy N.Y., Malka A., Zilber S., Korshin E.E., Sasson S., de Spirito M., Gruzman A., Maulucci G.. **Investigation of the Membrane Fluidity Regulation of Fatty Acid Intracellular Distribution by Fluorescence Lifetime Imaging of Novel Polarity Sensitive Fluorescent Derivatives**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22063106
40. Serantoni C., Zimatore G., Bianchetti G., Abeltino A., de Spirito M., Maulucci G.. **Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness**. *Sensors* (2022.0) **22**. DOI: 10.3390/s22113974
41. Terhal B.M.. **Quantum Supremacy, Here We Come**. *Nat. Phys.* (2018.0) **14** 530-531. DOI: 10.1038/s41567-018-0131-y
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---
title: Impact of a Low-Insulin-Stimulating Bread on Weight Development—A Real Life
Randomised Controlled Trial
authors:
- Kerstin Kempf
- Martin Röhling
- Hubert Kolb
- Stephan Martin
journal: Nutrients
year: 2023
pmcid: PMC10004839
doi: 10.3390/nu15051301
license: CC BY 4.0
---
# Impact of a Low-Insulin-Stimulating Bread on Weight Development—A Real Life Randomised Controlled Trial
## Abstract
The impact on body weight development is usually analysed by comparing different diet types. Our approach was to change only one component, namely bread, common to most diets. In a single-centre triple-blind randomised controlled trial the effects of two different breads on body weight were analyzed without further lifestyle modification. Overweight adult volunteers ($$n = 80$$) were randomised 1:1 to exchange previously consumed breads for either a rye bread from milled whole grain (control) or a medium-carbohydrate, low-insulin-stimulating bread (intervention). Pre-tests demonstrated that the two bread types strongly differed in the glucose and insulin response elicited, but had similar energy content, texture and taste. The primary endpoint was the estimated treatment difference (ETD) in change of body weight after 3 months of treatment. Whereas body weight remained unchanged in the control group (−0.1 ± 2.0 kg), significant weight reduction was observed in the intervention group (−1.8 ± 2.9 kg), with an ETD of −1.7 ± 0.2 kg ($$p \leq 0.007$$), that was more pronounced in participants ≥ 55 years (−2.6 ± 3.3 kg), paralleled by significant reductions in body mass index and hip circumference. Moreover, in the intervention group, the percentage of participants with significant weight loss (≥1 kg) was twice as high as in the control group ($p \leq 0.001$). No other statistically significant changes in clinical or lifestyle parameters were noted. Simply exchanging a common insulinogenic bread for a low-insulin-stimulating bread demonstrates potential to induce weight loss in overweight persons, especially those at older age.
## 1. Introduction
Bread is the most widely consumed grain-based food in the world and is also one of the largest sources of carbohydrate in the Western diet. In Europe, it provides up to $30\%$ of the daily carbohydrate consumption in women and up to $37\%$ in men [1]. In Germany, around ~58 kg bread is consumed per person annually, whereas bread consumption is significantly lower in countries with a typically Mediterranean diet [2].
However, review articles looking at undifferentiated bread consumption could not show a significant association between bread consumption and weight loss [3,3,4,5]. This might be due to the fact, that the variety of breads is huge, and bread is definitely not just bread. In addition to providing carbohydrates, bread is also an important source of fibre, proteins, minerals, vitamins and other bioactive compounds [6]. Bread baked from milled whole grain or refined wheat flour is characterised not only by high glycaemic but also high insulin indices [2], which are known to inhibit lipolysis [7]. High-carbohydrate diets and hyperinsulinaemia [8,9,10] are associated with being overweight and obese, type 2 diabetes and cardiovascular diseases [11,12,13,14]. Since a low-insulin-releasing lifestyle has been shown to lead to clinically relevant weight reduction (≥$5\%$) in overweight or obese individuals [15] and to improve glucometabolic parameters in individuals with and without diabetes [16,17,18,19], reduction of digestible carbohydrates, which contributes to lower insulin levels [20,21], is a successful strategy for weight reduction [22]. In this context, prospective cohort studies have demonstrated that the long-term risk for being overweight or for obesity is associated with the consumption of less-complex and strongly processed bread types [23,24,25,26], whereas more complex and less processed bread is beneficial for reducing the risk of developing gastrointestinal and cardiovascular diseases, type 2 diabetes mellitus and certain types of cancer [23,24,27,28,29]. Therefore, conscious bread consumption might play an essential role in weight control.
So far, it is still unclear how exactly and to what extent bread consumption is related to regulation of body weight. In previous work [30] we could demonstrate that the carbohydrates in various types of bread cause heterogeneous levels of insulin secretion. However, intervention studies investigating the impact of low-insulin-stimulating bread on weight development and accompanying health parameters are lacking. Therefore, the aim of the present trial was to determine the insulin-stimulating potential of different bread types and concomitantly to prove the hypothesis that consumption of a low-insulin-stimulating bread compared to a conventional higher insulinogenic bread would lead to a significant difference in weight change in overweight adults.
## 2.1. Study Design and Participants
The triple-blind randomised controlled trial was conducted at the West German Centre of Diabetes and Health, Düsseldorf, Germany. Volunteers were recruited by newspaper report. Eligible participants were 18–69 years old, with body mass index (BMI) ≥ 27 kg/m2 and consumed bread on a daily basis; exclusion criteria were acute diseases, severe illness with in-patient treatment during the last 3 months, medication for weight reduction, weight change > 2 kg/week during the last month, smoking cessation during the last 3 months, or intolerance to components of the investigated breads. Between 1 August 2020, and 21 October 2021, 90 persons were screened, 6 were included in the pre-tests and 80 in the randomised controlled trial.
A three-stage procedure was followed to identify breads differing in the glucose and insulin response elicited: in the first pre-test volunteers meeting the entry criteria for the subsequent randomised controlled trial were equipped with a continuous glucose monitoring (CGM) system (FreeStyle Libre, Abbott Diabetes Care, Alameda, CA, USA). After an overnight fast participants consumed 50 g of different breads ($$n = 10$$) at the same time in the morning, in random order, on separate days. Foods and beverages throughout the rest of the day were not specified and self-chosen. Bread types were provided by a local bakery (Bäckerei Hinkel, Düsseldorf, Germany) or from STEINERfood GmbH, Sulz im Weinviertel, Austria.
## 2.2. Pre-Tests
Two breads with comparable texture but differing glucose-stimulating potential (i.e., the medium-carbohydrate, low-insulin-stimulating bread (intervention bread) and the rye bread from milled whole grain (control bread) were chosen for the second pre-test. As described before, 50 g study bread was consumed after an overnight fast on consecutive days. Glucose and insulin levels were determined in venous blood samples collected every 30 min over a period of 120 min after inserting an intravenous cannula into the forearm vein. Analyses were performed at the local laboratory [30].
## 2.3. Randomisation and Masking
For the randomised controlled trial, an unblinded statistician created the computer-generated randomisation list. Participants were equally allocated to the two groups. A closed and numbered envelope was handed out to the participants containing a verification card with coded information on the bread type for the local bakery. The study breads were baked with a comparable look and according to the verification card, the bakery employees handed out the control or intervention bread. The verification card was also used to note when and how many breads were picked up. Participants, investigators and the data analyst were blinded for group assignment. Participants were not aware of ingredients of the bread type received.
## 2.4. Procedures
Participants in the randomised controlled trial visited the study centre in fasting state, on the first day and after 3 months of intervention, for collection of anthropometric and clinical data (age, sex, body weight, height, BMI, waist circumference, blood pressure, as well as lean and fat mass). Body weight was measured in light clothing to the closest 0.1 kg, height to the closest 0.5 cm, and waist circumference at the minimum abdominal girth (about midway between the rib cage and the iliac crest). Body composition was measured using a state-of the-art body composition scale (Seca mBCA515, Seca, Hamburg, Germany). Blood pressure was determined on both arms in sitting position after a 5 min rest. Laboratory parameters were determined from venous blood samples at the local laboratory. Blood glucose was measured by photometry with an intra-assay coefficient of variability (CV) of $1.9\%$, and plasma insulin by electrochemoluminescence immunoassay (ECLIA) with an CV of $3.6\%$. Questionnaires were handed out at baseline and at follow-up to record physical activity and dietary habits during the study. Duration (0–1 h per week; 2–3 h per week; 4–5 h per week; >5 h per week) of physical activity (e.g., gardening or longer walks) was investigated via questionnaires. Dietary habits were split into three groups: vegetarian diet (i.e., fruits, vegetables, dried beans and peas, grains, seeds, and nuts, but also milk and eggs), mixed diet (i.e., potatoes, pasta, bread, meat, sausage, vegetables, salad, eggs, butter, cream), or Mediterranean diet (i.e., vegetables, fruit, salad, fish, less meat, pasta, bread, vegetable oils).
Participants picked up the breads from the local bakery, at weekly intervals, without learning about the nature of the bread type received. Participants were encouraged to eat as much bread as they normally would. No other breads, rolls or baked goods were allowed to be consumed during the 3-month intervention phase. The rye bread was made from type 997 flour, the low-insulin-stimulating bread consisted of oat flakes, sunflower seeds, flax seeds, chia seeds, psyllium husks, chopped almonds, baker’s honey, and Rhinish field beans.
## 2.5. Outcomes
For selecting the two bread types to be compared in the first pre-test, the incremental area under the curve (iAUC) of the postprandial blood glucose was calculated geometrically as the sum of the areas of the triangles and trapezoids over 120 min, excluding the area below the initial fasting concentration [30].
The primary outcome of the randomised controlled trial was the ETD in change in body weight between the two groups after 3 months. Secondary outcomes were the ETDs in change in BMI, hip circumference, waist circumference, blood pressure, triglycerides, total cholesterol, LDL cholesterol, HDL cholesterol, HbA1c, fasting blood glucose, fasting blood insulin, fat mass, and fat-free mass.
## 2.6. Statistical Analysis
The sample size calculation was based on the ‘double-sided two-sample analysis with continuity correction’ (SISA, Simple Interactive Statistical Analysis) method. Assumptions made for this calculation were based on previous nutrition studies [31], estimating a 1.0 ± 1.5 kg larger weight loss after 3 months in the intervention group who consumed the low-insulin-stimulating bread compared to the control group with the commonly consumed rye bread [22]. In order to identify such a weight reduction with a 1:1 randomisation, an accompanying power of $80\%$, a level of significance of $5\%$, and an estimated dropout rate of $10\%$ [31], 40 persons per group had to be recruited.
Intention-to-treat (ITT) analyses were performed. Missing values (due to discontinued allocated intervention) were imputed by the ‘last observation carried forward’ (LOCF) principle. Non-normally distributed data were analysed by Mann–Whitney test for between-group comparisons and by Wilcoxon signed rank test for within-group comparisons. Differences in changes after 3 months between both groups were analysed using ANCOVA with adjustment for baseline values. Normality was visually and analytically confirmed by using histogram graphs and applying the Shapiro–Wilk test. Chi-square test was used to analyse dichotomous variables. All statistical tests were two sided, and the level of significance was set at α = 0.05. All analyses were performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism 6.04 (GraphPad Software, San Diego, CA, USA).
## 3. Results
The pre-test identified four breads with medium ($14\%$) or low digestible carbohydrate (3–$4\%$) content (Table 1), which differed significantly in post-load continuous monitoring glucose kinetics from six other breads with the usual digestible carbohydrate content of 38–$54\%$.
Whereas consumption of pretzel sticks, white, rye, spelt, buckwheat, and whole-meal bread provoked an increase in glucose levels of about 20–35 mg/dL, peaking after 45 min, ingestion of medium- and low-carbohydrate breads induced almost no significant glucose increase over 120 min (Figure 1a). In detail, glucose iAUC between 0 and 120 min was the highest after consumption of 50 g of pretzel stick, white, and rye bread (>2000 mg*15 min/dL each) and between 1300 and 1600 mg*15 min/dL for spelt, buckwheat, and whole-meal bread, respectively. In contrast, after consumption of medium- or low-carbohydrate breads, the iAUC just reached values of about 100–300 mg*15 min/dL (Figure 1b).
In order to examine the influence of bread consumption on blood glucose and insulin levels in more detail, for the second part of the pre-test (and the randomised controlled trial) the medium-carbohydrate, low-insulin-stimulating bread was chosen as intervention bread and the rye bread from milled whole grain as control. Measurement of glucose in venous blood confirmed the results observed during CGM. Venous glucose and insulin levels showed the expected rise after ingestion of the rye bread but there was no significant impact on glucose or insulin levels after uptake of the medium-carbohydrate bread with low-insulin-stimulating potential (Figure 1c,d).
Prior to the study, participants of both groups consumed a mean of 3.5 slices of bread per day, mostly wholemeal wheat and mixed rye–wheat (including buns). Baseline characteristics of participants participating in the randomised controlled trial were similar for both groups (Table 2) and 69 ($86\%$) completed the allocated intervention (Figure 2).
Whereas no change of body weight was observed in the control group (−0.1 ± 2.0 kg), significant weight reduction was observed in the intervention group (−1.8 ± 2.9 kg; $$p \leq 0.0003$$), with an ETD of −1.7 ± 0.2 kg ($$p \leq 0.007$$), who consumed the low-insulin-stimulating bread for 3 months (Figure 3). Stratification into two groups by the mean age of 55 years showed no significant weight change in the control group, neither in the participants below nor above 55 years. In the intervention group, a significant weight reduction (−2.6 ± 3.3 kg; $$p \leq 0.0007$$) was observed regarding the older participants, which significantly differed from the age-matched controls ($$p \leq 0.005$$). In the control group, a similar number of persons lost or gained weight, whereas, in the intervention group, about two thirds exhibited loss of body weight of at least 1 kg and about one third had lost ≥ 3 kg ($p \leq 0.001$ for the difference between groups). This was paralleled by a significantly stronger reduction in BMI and hip circumference in the intervention group, with ETDs of −0.5 ± 0.4 kg/m2 ($$p \leq 0.002$$) and −1.7 ± 1.4 cm ($$p \leq 0.039$$). No other statistically significant changes in clinical or biochemical parameters (Supplementary Material Table S1) or in lifestyle characteristics were observed.
Lifestyle characteristics with relevance for body weight development were evaluated at baseline and at the end of treatment in those who completed the trial. During the trial, the mean number of breads consumed per week was 1.4 [1.2; 1.6] for the control vs. 1.2 [1.0; 1.5] for the intervention group ($$p \leq 0.183$$). The shape of the study bread was the same, which relates to consumption of a similar number of 50 g slices consumed per day during the study (3.1 [2.6; 3.5] in the control vs. 3.4 [2.8; 4.1] in the intervention group; $$p \leq 0.262$$). Adverse events associated with eating either bread type were not reported. Physical activity in hours per week did not significantly change during the trial, nor was there a difference between the two groups. The type of diet consumed at baseline was generally maintained during the trial. The distribution of diet types consumed did not differ between control and intervention groups.
## 4. Discussion
Since data about the effects of bread on weight development are inconsistent, we analyzed the effect of different bread types on glucose and insulin levels and compared the effects of consumption of a medium-carbohydrate, low-insulin-stimulating bread vs. a conventional rye bread from milled grain, as control, on the weight change in overweight persons in a 3-stage randomised controlled trial. CGM, as used as a scientific approach for nutritional analyses, demonstrated that the postprandial glucose courses after consumption of medium- or low-carbohydrate breads were significantly lower compared to conventional bread types. Analyses of insulin levels showed that postprandial deflections were also diminished after consumption of the intervention bread vs. control bread. Without further lifestyle changes, consumption of low-insulin-stimulating bread for 3 months led to a significant weight reduction in the intervention group—more pronounced in participants ≥ 55 years—compared to stable weight in the control group, resulting in an ETD of −1.7 ± 0.2 kg ($$p \leq 0.007$$). Thus, consumption of low-insulin-stimulating bread might be an effective and low-threshold entry into a lifestyle intervention for overweight people, especially those at older age.
Bread is a typical component of the average diet in Germany [1,2] which is reflected by a mean of 3.5 slices of bread consumed daily by the participants prior to the trial. This translates to about 100–200 g of bread per day, which perfectly fits to the estimated consumption of about 58 kg bread per person a year in Germany [32]. In countries with a typically Mediterranean diet, bread consumption is significantly lower, around 46 kg in Spain and 44 kg in Italy. In the United States mean bread consumption is also around 43 kg [32] and it provides less than $15\%$ of daily carbohydrate intake [33,34], whereas in Germany it accounts for 13–$30\%$ of daily carbohydrate intake in women and 14–$37\%$ among men [1]. As a result of conscious bread consumption, daily carbohydrate intake could be reduced in Germany; whereas, in other countries with less bread consumption, the effects might be lower.
The low-insulin-stimulating bread contained less starch than the milled whole grain rye bread. Its lower energy content was made up for by a higher fat content. Since fibre, which is present in whole grain but not in refined wheat flour, has beneficial health effects [35] the fibre content of the bread types analysed was kept similar and, moreover, there were no major differences regarding protein content, texture and taste between the two study breads. We therefore assume that the different metabolic response to the low-insulin-stimulating bread was of relevance. We had selected this bread type for comparison with a usual bread based on the absence of a detectable rise in blood glucose and insulin levels after consuming 50 g bread. Lowering the insulin response to meals has been reported previously to lower body weight in randomised controlled trials [15,16,36]. Pharmacological lowering of circulating insulin levels by diazoxide or octreotide also led to body weight reduction in most trials [9]. In mice, genetic lowering of the number of insulin genes expressed and of circulating insulin levels prevented or partially reversed diet-induced obesity [37]. The relevance of insulin levels for body weight regulation has led to the carbohydrate-insulin-concept [22,38] and mirrors results of meta-analyses that found low-fat diets inferior to low-glycaemic diets for weight reduction [39,40]. As shown in our study, when eating the three low-carb bread types, an immensely reduced glucose rise can be achieved by replacing flour from grain with protein, non-cereal flour and fibre. Studies using a wide variety of functional additives confirm the success of this approach [32,41,42] while, at the same time, a down-regulation of the appetite was observed [32].
Although the association between elevated insulin levels and obesity initiated the concept of the current study protocol, the trial did not aim to test a hypothetic metabolic mechanism or to determine the glycaemic index (GI) or the glycaemic last (GL) of the study breads. Rather, it tested whether simply exchanging one common dietary component with insulin stimulatory properties for a poorly insulin-inducing alternative would have an impact on body weight development in the absence of any recommendation to alter the daily diet composition or other aspects of participants’ lifestyles. Therefore, we can only speculate that the differences in weight development found emphasize the importance of low-insulin nutrition, especially in the elderly. Studies have shown that, with increasing age, the fasting insulin levels (measured by C-peptide) and the glucose-induced insulin secretion increase [43] and usually weight does too.
There are some strengths and limitations that need to be mentioned. The concept of the trial was to substitute the usual bread type, as an insulinogenic component of the diet, with a similar, but low-insulin-stimulating, alternative in a setting that mimicked real-world conditions. The strength of this setting is that the participants adhered to their usual lifestyle, i.e., daily eating habits and physical activity level. To keep awareness of the trial situation as low as possible, they had no contact with the study centre during the intervention period, and there was no request for repeated documentation of diet or lifestyle characteristics during the trial. Analysis of responses to questionnaires before and after the intervention phase indicated the absence of a recognisable study effect regarding the amount of bread consumed, the overall diet, and physical activity.
On the other hand, the real-world approach prevents the control of certain influences. As with every lifestyle study, there is a certain uncertainty in adherence measurement. In order to estimate the adherence, we counted the amount of bread that the participants picked up in the bakery and also asked the participants, using self-disclosure questionnaires, how many slices of bread they ate per day from the study bread; these values matched well. If an absolute degree of adherence measurement is needed, real-world studies would not be possible, and the effects could only be examined under laboratory conditions. However, the question is whether the results could be transferred in real life. Owing to the study design, the insulin-stimulating potential of the pre-study consumed breads was not measured. However, to get this information a study with an immense number of participants would have been needed. Nevertheless, concrete statements about individual breads would only have been possible to a limited extent since, although the study participants mostly consumed the same type of bread, they sometimes also ate a roll or other baked goods. Since the study was carried out during the Corona pandemic, which generally was associated with reduced physical activity and weight gain, this is not reflected in our data. In the intervention group, the proportion of people with a high level of physical activity tended to decrease and the proportion of inactivity tended to increase, but in the control group this was rather the opposite. Because of the randomisation into parallel groups, seasonal effects should have no impact on the differences between groups. However, these differences were neither statistically significant between the groups, nor in the course of the trial, and would have rather led to an underestimation of the effect.
We decided to compare the effect of 50 g of different types of bread in our randomised controlled trial. Since the breads consist of different ingredients, it could be said that the carbohydrate content was different, which could also be reflected in the glucose and insulin profiles. However, there are only two ways of doing such an analysis, i.e., either equating the carbohydrate content and then serving different portion sizes or, conversely, standardizing the portion sizes in the knowledge of different carbohydrate content. We chose the latter because simultaneous questioning demonstrated that eating bread is a kind of “ritualized” process and participants ate an equal amount of bread each day. It is also, therefore, more realistic to advise persons to eat, e.g., two slices of whole-grain bread instead of eating whole-grain bread such that you consume 50 g of carbohydrates. This is also the problem with the common measurement methods for carbohydrate quality, since for calculating the GI, an amount of a food must be consumed that contains 50 g of digestible carbohydrates [44]. In addition, GI is influenced by numerous factors, such as food composition, processing, and preparation, so that its application is inherent in practice.
By measuring glucose in venous blood, and via CGM in interstitial fluid, reliable statements about the postprandial glucose increase after bread meals can be made. However, there were methodological differences between the pre-tests: using CGM glucose levels were measured every 15 min, whereas venous blood was only taken every 30 min. In this way, it seems likely that we missed the maximum peak (after 45 min) in glucose and insulin after consumption of rye–wheat bread. In the case of the low-insulin-stimulating bread, this is obviously not the case since there was no appreciable increase in glucose and insulin levels. Overall, this led to an underestimation of the observed differences.
## 5. Conclusions
Simply exchanging a common insulinogenic bread for a low-insulin-stimulating bread demonstrates potential to induce weight loss in overweight persons. To the best of our knowledge, this is the first triple-blind randomised controlled trial comparing two different bread types, with contrasting insulin-releasing effects, for an influence on weight development over 3 months, in a study population used to daily consumption of bread. Whether the effects of exchanging the bread type for body weight reduction additionally impact insulin sensitivity or can be extended to other common dietary components with insulin-stimulating properties requires further study. In summary, consumption of low-insulin-stimulating bread can be an effective and low-threshold entry into a lifestyle intervention for overweight people, especially those of older age.
## References
1. Cust A.E., Skilton M.R., van Bakel M.M., Halkjaer J., Olsen A., Agnoli C., Psaltopoulou T., Buurma E., Sonestedt E., Chirlaque M.D.. **Total dietary carbohydrate, sugar, starch and fibre intakes in the European Prospective Investigation into Cancer and Nutrition**. *Eur. J. Clin. Nutr.* (2009) **63** S37-S60. DOI: 10.1038/ejcn.2009.74
2. Goletzke J., Atkinson F.S., Ek K.L., Bell K., Brand-Miller J.C., Buyken A.E.. **Glycaemic and insulin index of four common German breads**. *Eur. J. Clin. Nutr.* (2016) **70** 808-811. DOI: 10.1038/ejcn.2016.9
3. Serra-Majem L., Bautista-Castano I.. **Relationship between bread and obesity**. *Br. J. Nutr.* (2015) **113** S29-S35. DOI: 10.1017/S0007114514003249
4. Fardet A.. **A shift toward a new holistic paradigm will help to preserve and better process grain products’ food structure for improving their health effects**. *Food Funct.* (2015) **6** 363-382. DOI: 10.1039/C4FO00477A
5. Bautista-Castano I., Serra-Majem L.. **Relationship between bread consumption, body weight, and abdominal fat distribution: Evidence from epidemiological studies**. *Nutr. Rev.* (2012) **70** 218-233. DOI: 10.1111/j.1753-4887.2012.00454.x
6. Bautista-Castano I., Sanchez-Villegas A., Estruch R., Martinez-Gonzalez M.A., Corella D., Salas-Salvado J., Covas M.I., Schroder H., Alvarez-Perez J., Quilez J.. **Changes in bread consumption and 4-year changes in adiposity in Spanish subjects at high cardiovascular risk**. *Br. J. Nutr.* (2013) **110** 337-346. DOI: 10.1017/S000711451200476X
7. Jacob S., Hauer B., Becker R., Artzner S., Grauer P., Loblein K., Nielsen M., Renn W., Rett K., Wahl H.G.. **Lipolysis in skeletal muscle is rapidly regulated by low physiological doses of insulin**. *Diabetologia* (1999) **42** 1171-1174. DOI: 10.1007/s001250051288
8. Kolb H., Martin S.. **Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes**. *BMC Med.* (2017) **15**. DOI: 10.1186/s12916-017-0901-x
9. Kolb H., Stumvoll M., Kramer W., Kempf K., Martin S.. **Insulin translates unfavourable lifestyle into obesity**. *BMC Med.* (2018) **16**. DOI: 10.1186/s12916-018-1225-1
10. Boden G., Homko C., Barrero C.A., Stein T.P., Chen X., Cheung P., Fecchio C., Koller S., Merali S.. **Excessive caloric intake acutely causes oxidative stress, GLUT4 carbonylation, and insulin resistance in healthy men**. *Sci. Transl. Med.* (2015) **7** 304re7. DOI: 10.1126/scitranslmed.aac4765
11. Kolb H., Kempf K., Röhling M., Martin S.. **Insulin: Too much of a good thing is bad**. *BMC Med.* (2020) **18**. DOI: 10.1186/s12916-020-01688-6
12. Röhling M., Kempf K., Kolb H., Martin T., Schneider M., Martin S.. **The Epidemiological Boehringer Ingelheim Employee Study (Part 3): Association of Elevated Fasting Insulin Levels but Not HOMA-IR With Increased Intima Media Thickness and Arteriosclerosis in Middle-Aged Persons**. *Front. Cardiovasc. Med.* (2021) **8** 752789. DOI: 10.3389/fcvm.2021.752789
13. Czech M.P.. **Insulin action and resistance in obesity and type 2 diabetes**. *Nat. Med.* (2017) **23** 804-814. DOI: 10.1038/nm.4350
14. Zhang X., Li J., Zheng S., Luo Q., Zhou C., Wang C.. **Fasting insulin, insulin resistance, and risk of cardiovascular or all-cause mortality in non-diabetic adults: A meta-analysis**. *Biosci. Rep.* (2017) **37** BSR20170947. DOI: 10.1042/BSR20170947
15. Röhling M., Martin K., Ellinger S., Schreiber M., Martin S., Kempf K.. **Weight Reduction by the Low-Insulin-Method-A Randomized Controlled Trial**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12103004
16. Halle M., Röhling M., Banzer W., Braumann K.M., Kempf K., McCarthy D., Schaller N., Predel H.G., Scholze J., Fuhrer-Sakel D.. **Meal replacement by formula diet reduces weight more than a lifestyle intervention alone in patients with overweight or obesity and accompanied cardiovascular risk factors-the ACOORH trial**. *Eur. J. Clin. Nutr.* (2021) **75** 661-669. DOI: 10.1038/s41430-020-00783-4
17. Yang Q., Lang X., Li W., Liang Y.. **The effects of low-fat, high-carbohydrate diets vs. low-carbohydrate, high-fat diets on weight, blood pressure, serum liquids and blood glucose: A systematic review and meta-analysis**. *Eur. J. Clin. Nutr.* (2022) **76** 16-27. DOI: 10.1038/s41430-021-00927-0
18. Goldenberg J.Z., Day A., Brinkworth G.D., Sato J., Yamada S., Jonsson T., Beardsley J., Johnson J.A., Thabane L., Johnston B.C.. **Efficacy and safety of low and very low carbohydrate diets for type 2 diabetes remission: Systematic review and meta-analysis of published and unpublished randomized trial data**. *BMJ* (2021) **372** m4743. DOI: 10.1136/bmj.m4743
19. Hyde P.N., Sapper T.N., Crabtree C.D., LaFountain R.A., Bowling M.L., Buga A., Fell B., McSwiney F.T., Dickerson R.M., Miller V.J.. **Dietary carbohydrate restriction improves metabolic syndrome independent of weight loss**. *JCI Insight* (2019) **4** e128308. DOI: 10.1172/jci.insight.128308
20. Kempf K., Röhling M., Banzer W., Braumann K.M., Halle M., McCarthy D., Predel H.G., Schenkenberger I., Tan S., Toplak H.. **High-Protein, Low-Glycaemic Meal Replacement Decreases Fasting Insulin and Inflammation Markers-A 12-Month Subanalysis of the ACOORH Trial**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13051433
21. Kempf K., Röhling M., Banzer W., Braumann K.M., Halle M., Schaller N., McCarthy D., Predel H.G., Schenkenberger I., Tan S.. **Early and Strong Leptin Reduction Is Predictive for Long-Term Weight Loss during High-Protein, Low-Glycaemic Meal Replacement-A Subanalysis of the Randomised-Controlled ACOORH Trial**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14122537
22. Ludwig D.S., Aronne L.J., Astrup A., de Cabo R., Cantley L.C., Friedman M.I., Heymsfield S.B., Johnson J.D., King J.C., Krauss R.M.. **The carbohydrate-insulin model: A physiological perspective on the obesity pandemic**. *Am. J. Clin. Nutr.* (2021) **114** 1873-1885. DOI: 10.1093/ajcn/nqab270
23. Jacobs D.R., Meyer K.A., Kushi L.H., Folsom A.R.. **Whole-grain intake may reduce the risk of ischemic heart disease death in postmenopausal women: The Iowa Women’s Health Study**. *Am. J. Clin. Nutr.* (1998) **68** 248-257. DOI: 10.1093/ajcn/68.2.248
24. Liu S., Willett W.C., Manson J.E., Hu F.B., Rosner B., Colditz G.. **Relation between changes in intakes of dietary fiber and grain products and changes in weight and development of obesity among middle-aged women**. *Am. J. Clin. Nutr.* (2003) **78** 920-927. DOI: 10.1093/ajcn/78.5.920
25. Fuente-Arrillaga C., Martinez-Gonzalez M.A., Zazpe I., Vazquez-Ruiz Z., Benito-Corchon S., Bes-Rastrollo M.. **Glycemic load, glycemic index, bread and incidence of overweight/obesity in a Mediterranean cohort: The SUN project**. *BMC Public Health* (2014) **14**. DOI: 10.1186/1471-2458-14-1091
26. Schutte S., Esser D., Hoevenaars F.P.M., Hooiveld G.J.E.J., Priebe M.G., Vonk R.J., Wopereis S., Afman L.A.. **A 12-wk whole-grain wheat intervention protects against hepatic fat: The Graandioos study, a randomized trial in overweight subjects**. *Am. J. Clin. Nutr.* (2018) **108** 1264-1274. DOI: 10.1093/ajcn/nqy204
27. Cho S., Dietrich M., Brown C.J., Clark C.A., Block G.. **The effect of breakfast type on total daily energy intake and body mass index: Results from the Third National Health and Nutrition Examination Survey (NHANES III)**. *J. Am. Coll. Nutr.* (2003) **22** 296-302. DOI: 10.1080/07315724.2003.10719307
28. Slavin J.. **Whole grains and human health**. *Nutr. Res. Rev.* (2004) **17** 99-110. DOI: 10.1079/NRR200374
29. Gil A., Ortega R.M., Maldonado J.. **Wholegrain cereals and bread: A duet of the Mediterranean diet for the prevention of chronic diseases**. *Public Health Nutr.* (2011) **14** 2316-2322. DOI: 10.1017/S1368980011002576
30. Röhling M., Martin T., Wonnemann M., Kragl M., Klein H.H., Heinemann L., Martin S., Kempf K.. **Determination of Postprandial Glycemic Responses by Continuous Glucose Monitoring in a Real-World Setting**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11102305
31. Kempf K., Kolb H., Gartner B., Bytof G., Stiebitz H., Lantz I., Lang R., Hofmann T., Martin S.. **Cardiometabolic effects of two coffee blends differing in content for major constituents in overweight adults: A randomized controlled trial**. *Eur. J. Nutr.* (2015) **54** 845-854. DOI: 10.1007/s00394-014-0763-3
32. Gonzalez-Anton C., Artacho R., Ruiz-Lopez M.D., Gil A., Mesa M.D.. **Modification of appetite by bread consumption: A systematic review of randomized controlled trials**. *Crit. Rev. Food Sci. Nutr.* (2017) **57** 3035-3050. DOI: 10.1080/10408398.2015.1084490
33. O’Neil C.E., Keast D.R., Fulgoni V.L., Nicklas T.A.. **Food sources of energy and nutrients among adults in the US: NHANES 2003–2006**. *Nutrients* (2012) **4** 2097-2120. DOI: 10.3390/nu4122097
34. Papanikolaou Y., Fulgoni V.L.. **Grain Foods Are Contributors of Nutrient Density for American Adults and Help Close Nutrient Recommendation Gaps: Data from the National Health and Nutrition Examination Survey, 2009–2012**. *Nutrients* (2017) **9**. DOI: 10.3390/nu9080873
35. Guo H., Wu H., Sajid A., Li Z.. **Whole grain cereals: The potential roles of functional components in human health**. *Crit. Rev. Food Sci. Nutr.* (2022) **62** 8388-8402. DOI: 10.1080/10408398.2021.1928596
36. Kempf K., Altpeter B., Berger J., Reuss O., Fuchs M., Schneider M., Gartner B., Niedermeier K., Martin S.. **Efficacy of the Telemedical Lifestyle intervention Program TeLiPro in Advanced Stages of Type 2 Diabetes: A Randomized Controlled Trial**. *Diabetes Care* (2017) **40** 863-871. DOI: 10.2337/dc17-0303
37. Page M.M., Skovso S., Cen H., Chiu A.P., Dionne D.A., Hutchinson D.F., Lim G.E., Szabat M., Flibotte S., Sinha S.. **Reducing insulin via conditional partial gene ablation in adults reverses diet-induced weight gain**. *FASEB J.* (2018) **32** 1196-1206. DOI: 10.1096/fj.201700518R
38. Ludwig D.S., Ebbeling C.B.. **The Carbohydrate-Insulin Model of Obesity: Beyond “Calories In, Calories Out”**. *East Afr. Med.* (2018) **178** 1098-1103. DOI: 10.1001/jamainternmed.2018.2933
39. Mansoor N., Vinknes K.J., Veierod M.B., Retterstol K.. **Effects of low-carbohydrate diets v. low-fat diets on body weight and cardiovascular risk factors: A meta-analysis of randomised controlled trials**. *Br. J. Nutr.* (2016) **115** 466-479. DOI: 10.1017/S0007114515004699
40. Tobias D.K., Chen M., Manson J.E., Ludwig D.S., Willett W., Hu F.B.. **Effect of low-fat diet interventions versus other diet interventions on long-term weight change in adults: A systematic review and meta-analysis**. *Lancet Diabetes Endocrinol.* (2015) **3** 968-979. DOI: 10.1016/S2213-8587(15)00367-8
41. Scazzina F., Siebenhandl-Ehn S., Pellegrini N.. **The effect of dietary fibre on reducing the glycaemic index of bread**. *Br. J. Nutr.* (2013) **109** 1163-1174. DOI: 10.1017/S0007114513000032
42. Akhtar S., Layla A., Sestili P., Ismail T., Afzal K., Rizvanov A.A., Asad M.H.H.B.. **Glycemic and Insulinemic Responses of Vegetables and Beans Powders Supplemented Chapattis in Healthy Humans: A Randomized, Crossover Trial**. *BioMed Res. Int.* (2019) **2019** 7425367. DOI: 10.1155/2019/7425367
43. Ratzmann K.P., Strese J., Kohnert K.D., Jahr D., Michaelis D.. **Age-dependent relationship of fasting C-peptide concentration and insulin secretion in non-obese subjects with normal glucose tolerance**. *Exp. Clin. Endocrinol. Diabetes* (1986) **88** 57-63. DOI: 10.1055/s-0029-1210575
44. Jenkins D.J., Wolever T.M., Taylor R.H., Barker H., Fielden H., Baldwin J.M., Bowling A.C., Newman H.C., Jenkins A.L., Goff D.V.. **Glycemic index of foods: A physiological basis for carbohydrate exchange**. *Am. J. Clin. Nutr.* (1981) **34** 362-366. DOI: 10.1093/ajcn/34.3.362
|
---
title: Evidence Supporting the Involvement of the Minority Compounds of Extra Virgin
Olive Oil, through Gut Microbiota Modulation, in Some of the Dietary Benefits Related
to Metabolic Syndrome in Comparison to Butter
authors:
- María Collado Olid
- Marina Hidalgo
- Isabel Prieto
- Antonio Cobo
- Ana M. Martínez-Rodríguez
- Ana Belén Segarra
- Manuel Ramírez-Sánchez
- Antonio Gálvez
- Magdalena Martínez-Cañamero
journal: Molecules
year: 2023
pmcid: PMC10004845
doi: 10.3390/molecules28052265
license: CC BY 4.0
---
# Evidence Supporting the Involvement of the Minority Compounds of Extra Virgin Olive Oil, through Gut Microbiota Modulation, in Some of the Dietary Benefits Related to Metabolic Syndrome in Comparison to Butter
## Abstract
Extra virgin olive oil (EVOO) has proven to yield a better health outcome than other saturated fats widely used in the Western diet, including a distinct dysbiosis-preventive modulation of gut microbiota. Besides its high content in unsaturated fatty acids, EVOO also has an unsaponifiable polyphenol-enriched fraction that is lost when undergoing a depurative process that gives place to refined olive oil (ROO). Comparing the effects of both oils on the intestinal microbiota of mice can help us determine which benefits of EVOO are due to the unsaturated fatty acids, which remain the same in both, and which benefits are a consequence of its minority compounds, mainly polyphenols. In this work, we study these variations after only six weeks of diet, when physiological changes are not appreciated yet but intestinal microbial alterations can already be detected. Some of these bacterial deviations correlate in multiple regression models with ulterior physiological values, at twelve weeks of diet, including systolic blood pressure. Comparison between the EVOO and ROO diets reveals that some of these correlations can be explained by the type of fat that is present in the diet, while in other cases, such as the genus Desulfovibrio, can be better understood if the antimicrobial role of the virgin olive oil polyphenols is considered.
## 1. Introduction
For several years, studies have dealt with the influence of the intestinal microbiota and the health of the gut environment on the development of obesity, metabolic, and even neurodegenerative diseases through the gut–brain axis [1,2,3]. Diet is one of the main drivers in the microbiota composition and it is known that high-fat diets have a negative effect on health by exerting an action on the microbial taxa in the intestine which are essential for host homeostasis maintenance [4]. Changes in the gut microbiota, microbial compound production, and mucosal immune system function can contribute to cardiovascular, metabolic, and cognitive health [5].
Olive oil is not the most widely consumed oil in the world (palm and soybean oils lead to the global consumption of vegetable oils), but it has always been the principal fat added to the Mediterranean diet. Extra virgin olive oil (EVOO) is a functional food with a wide variety of healthy components, such as monounsaturated fatty acids and phenolic compounds, with beneficial effects on cardiovascular diseases due to their anti-inflammatory and antioxidant activities [6], and it has been shown that this effect is at least in part exerted along with modulation of the gut microbiota [7,8,9].
Several studies propose that the anti-inflammatory, antitumor, antioxidant, and modulatory effects of the intestinal microbiota (associated with this diet and the EVOO) on chronic inflammatory diseases could be attributed to polyphenols [revised in [10,11]]. In order to address this issue, it is interesting to compare oils with different levels of polyphenols. This can be easily undertaken because only virgin olive oils have intact polyphenol content, while other commercial olive oils, the refined ones, have lost them. EVOO is mechanically extracted directly from olives without chemical intervention, and it contains relatively high amounts of phenolic compounds and tocopherols. To improve its palatability, low-quality virgin oil undergoes chemical treatment, transforming it into refined olive oil (ROO), which has the same composition of fatty acids as EVOO but loses the minor compounds [12,13,14,15].
There are few studies about the influence of ROO on health and none on the intestinal microbiota. Because of this, our research team set out to study this fact, finding different behavior in ROO compared to EVOO as well as a different microbial profile when analyzed by denaturing gradient gel electrophoresis (DGGE) [5]. Subsequently, a metagenomic study of 16S ribosomal DNA showed clear differences in both fecal microbiota and physiological variables related to metabolic syndrome when we compared different high-fat diets (HFDs) enriched with butter, ROO, and EVOO versus the standard diet. For this, mice were fed standard or enriched chow with the different HFDs for twelve weeks, and physiological variables and fecal microbial content were measured at the end of the experiment [16]. We clearly showed a significant statistical relationship by using multiple regression models between a specific diet, physiological variables, and different taxa, and the comparison between the effects of the two olive oils allowed us to propose which EVOO-involved correlations were driven by the polyphenol content and which ones were not.
However, correlation is not causation, and in order to clarify if variations in the microbial taxa had an effect on their correlating physiological variables or if they both just changed concomitantly under the impact of the diet, we studied the results obtained in the middle of the experiment, after only six weeks of diets, comparing them with each other and also with the ones obtained at twelve weeks. Results comparing EVOO and butter at six weeks showed that several changes in the microbial composition at this time point have regression coefficients that are statistically significant with metabolic and physiological variables at twelve weeks, even though some of the taxa did not maintain statistical significance by the end of the experiment [17]. This is important because these changes can be used as markers of future risks.
In the current research, the results obtained at six weeks under the EVOO and the ROO diets were compared in order to find out which of these EVOO-driven differences have their origin in the polyphenol content lost in ROO, and the data could give information on the possible role of the intestinal microbiota in the final outcome of these diets.
## 2.1. Physiological Parameters
Food intake, water intake, diuresis, body weight, and systolic blood pressure were measured after six weeks of the experimental period. The only significant difference was found in the systolic blood pressure, where the EVOO diet showed significantly lower values than the other diets, including the ROO diet, after applying a robust ANOVA test using R (one-way analysis of means and not assuming equal variances), with a p-value of 0.004 (Figure 1a). Weight did not show significant differences at $5\%$, but it did at $10\%$ ($$p \leq 0.057$$), with lower values in EVOO and also in ROO (ANOVA test) (Figure 1b), with an average dietary intake of 3.8 g/day and weight gain of 7.9 g.
## 2.2. Sequencing, Taxa Adscription, Percentage Comparison and Multiple Regression Models
After sequencing the 35 samples until the number of reads was stable, and after trimming and filtering, 353,260 sequences were obtained with a mean length between 548 and 575 nt and a total amount of 196.91 MB. In order to associate an organism with each sequence obtained, a blast search was performed, and the reads were grouped based on family, genera, and species levels. In total, sequences were classified into 9 phyla, 89 families, 227 genera, and 538 species.
When a Kruskal–Wallis test was used to check if the distributions of the diverse phyla (Figure 2) were the same among the four diets, the phyla Tenericutes and Proteobacteria showed significant differences ($$p \leq 0.002$$ and $$p \leq 0.0005$$, respectively, Figure 3). Multiple pairwise comparisons were performed showing that there are statistically significant differences, being EVOO and ROO mean values lower than those of SD in the first case, while in the case of Proteobacteria, EVOO values were lower than both BT and ROO, ROO also being higher than SD. Cyanobacteria also showed differences ($$p \leq 0.02$$) with greater values in EVOO against SD (Figure 3). However, the high number of data points with a value of 0 in this phylum makes its significance more unreliable. A multiple linear regression analysis was performed for each physiological variable considering the phyla as independent variables, and we only obtained a model for systolic blood pressure (R2 = 0.65; $$p \leq 0.0076$$) with the phyla Tenericutes and Proteobacteria (regression coefficient estimates of −227.30 and −247.63; s.e. of ±72.24 and ±72.82; and p-values of 0.0059 and 0.0034, respectively). Since we also had available the physiological measurements at the end of the experiment, already reported in Prieto et al. [ 9] and Martínez et al. [ 16], we repeated the regression analysis with these variables as dependent ones, but no statistically significant results were obtained.
The same procedure was repeated at the family level. According to the Kruskal–Wallis test, out of the 89 families that were detected, twelve of them showed statistically significant differences among the four diets; in eight of them, the ROO diet was significantly different in a multiple pairwise analysis, but only in one case (fam. Acetobacteriaceae) was it significantly different from the EVOO diet. Figure 4 displays the box plots of these twelve families with multiple pairwise comparisons. Different multiple linear regression models were fitted to explain each physiological variable (both at 6 and 12 weeks) using as independent variables all the families with significant differences. Ten of them were involved in models with data on physiological variables from the two time points (Table 1).
We also compared the prevalence of the 227 genera obtained when studying the four diets. In this case, the Kruskal–Wallis test results indicated that fourteen of them had statistically significant differences, which are shown in Figure 5. When applying multiple pairwise comparisons, EVOO and ROO had the same significant differences only in three genera. With respect to the rest of them, in two genera both oils were significantly different from each other; in another two, only EVOO was involved in a significantly different pair; and, finally, in three more genera, ROO was the only one of the two to be statistically significant. Table 2 shows the results found after applying a multiple linear regression analysis using the genera with significant differences.
Finally, species were also studied. In this case, since the number of species was too high, we centered our attention on those showing differences with statistical significance between BT and EVOO in order to check how ROO clustered in those cases. Four species fulfilled this premise, and the comparisons are shown in Figure 6. ROO and EVOO did not behave in the same way in any of them and in one case (Marispirillum indicum), EVOO was statistically significantly different from BT and ROO. Multiple regression models were drawn and only three were found that fully complied with all the hypotheses required, one on SBP at six weeks that involved *Bacteroides finegoldii* (R2 = 0.16; $$p \leq 0.0439$$; regression coefficient estimates of −2.83; s.e. of ±1.33; and p-value of 0.0439) and two at 12 weeks for FI, involving *Rikenella microfusus* (R2 = 0.13; $$p \leq 0.0360$$; regression coefficient estimates of −0.45; s.e. of ±0.20; and p-value of 0.0361) and for WI involving *Marispirillum indicum* (R2 = 0.15; $$p \leq 0.0317$$; regression coefficient estimates of −2.06; s.e. of ±0.91; and p-value of 0.0317).
## 3. Discussion
Previously, we have shown that different high-fat diets have diverse effects on the intestinal microbiota of mice [7]. We have also shown that this effect correlates with physiological variables related to the metabolic syndrome, with systolic blood pressure being the most noticeable, correlating with the percentage of Desulfovibrio sequences in feces, increasing under a butter-enriched diet but not doing so under an EVOO-enriched one [9]. Consequently, we compared these outcomes with the physiological and microbiological profiles of mice under a refined olive oil-enriched diet, finding that both olive oils had dissimilar effects on half the families with statistically significant differences [16], including fam. Desulfovibrionaceae, being again the genus Desulfovibrio preeminent since EVOO and ROO had an opposite influence on its percentage in a multiple pairwise comparison analysis. These data allowed us to discuss whether EVOO polyphenols and unsaponifiable matter, in general, could be responsible for all the EVOO effects on the bacterial taxa that were not duplicated in the ROO experimental group.
All these results were measured after 12 weeks of diet when physiological changes were more visible. However, we had the opportunity to study the microbial intestinal environment and the physiological health of the same mice but six weeks earlier, in the midterm balance of the experiment. This analysis was performed, and comparisons and multiple regression models were drawn between the SD, BT, and EVOO groups not only at six weeks but also with the physiological and metabolic variables measured at the end of the experiment at 12 weeks [17]. Certainly, at six weeks, physiological changes are not appreciated yet, although we can already clearly detect intestinal microbial changes since these are much less subject to the global homeostasis of the organism. Remarkably, these changes in the intestinal microbiota not only had a statistically significant relationship with the concomitant physiological variables, but they also did with variables measured at the end of the experiment. Once more, Desulfovibrio had an important role in the discussion because it was again less prominent in the EVOO group at six weeks and showed a significant regression coefficient with body weight, food intake, water intake, and systolic blood pressure measured at 12 weeks of diet.
In the work we present now, we have made the analysis, adding the data obtained in the group of mice fed a refined olive oil-enriched diet at six weeks of experiment in order to derive the role of the polyphenols at this time point in the feeding process. The aim is to uncover which changes in the intestinal microbiota prompted by an EVOO diet at this time are due to its fatty acid composition and which ones are influenced by its minority compounds, and moreover, if these relations have any manifestation after a further six weeks.
With respect to the physiological variables, the only significant difference is found in systolic blood pressure, and in a relative manner. We had already reported significantly low SBP values in the EVOO group with respect to the BT and SD groups, neither of which maintained excessive SBP nor a difference between them [17]. If this effect were to be dependent on the degree of saturation of the fatty acids, it would have been logical to expect that ROO data would be similar to EVOO. However, in our current results, the ROO group clusters with the SBP values of BT and SD, which is significantly different from the EVOO data. Following this variable till the end of the experiment at 12 weeks, the blood pressure values will have evolved to be significantly higher in the BT group with respect to the three other diets (SD, EVOO, and ROO). Probably, then, in the long run, the saturation of the fatty acids may have a greater weight than the polyphenol or minor component effects.
When studying the intestinal microbiota at the phylum level, ROO behaves like EVOO in the Tenericutes profile and unlike it in Proteobacteria, where ROO clusters with BT. Proteobacteria is quite dominant in the butter group at twelve weeks [9]. At the family level, out of the twelve families with significant differences, only in one of them were EVOO and ROO significantly different from each other (Acetobacteriaceae), while in the other four, either EVOO (Christensenellaceae and Enterobacteraceae) or ROO (Spiroplasmataceae and Staphylococcaceae) was significantly different from SD while the other unsaturated fat was not, even though the two fat profiles were similar. In the rest of the families, seven in total, EVOO and ROO had an identical statistical outcome. Moreover, in four of these seven families (Rikenellaceae, Sphingobactereaceae, Gracillibacteraceae, and Mycoplasmataceae), BT behaved statistically different from both EVOO and ROO, while in another (Lactobacillaceae), the three high-fat diet groups had percentages significantly lower than the SD group, indicating a probable common effect of all types of fat. Six weeks later, at the end of the experiment, the scenario would be totally different, with further differences between both EVOO and ROO in the predominance of the bacterial taxa [16]. This fact could be indicative of the relatively limited role of the polyphenols in the effects of virgin olive oil on the intestinal microbiota in a short-term diet in contrast to a long-term diet in these cases.
With respect to the regression models found using the significant families as an independent factor, most of the significant regressions already reported in Andújar et al. [ 17] are maintained, but some are not. Thus, in the 12-week body weight model, only Sphingobacteriaceae and Spiroplasmataceae are maintained; the model for diuresis keeps only Rhodospirillaceae at six weeks, and the model for blood pressure at twelve weeks maintains only the family Sphingobacteriaceae, although in the case of fam. Spiroplasmataceae, its genus, Spiroplasma, also maintains the statistical significance. Among the regression coefficients that maintain their statistical significance, it is worth noting the relation of the families Lactobacillaceae (direct) and Gracillibacteraceae (inverse) with food intake both at six and at twelve weeks of diet. The fact of losing the significance of some regression coefficients does not diminish their importance in the other three diets because, by including a new diet, new uncontrolled factors can be introduced as well. However, if they are maintained, the result is more robust. In this way, the relationship of lactic acid bacteria with food intake all through the experiment, with and without ROO, makes this a very interesting result, especially because it is not included in the regression model for body weight at any point. However, lactobacilli have previously been related to obesity in humans [18], and this could reflect some mechanism that is no longer adjusted in overfed modern societies. On the contrary, Gracillibacteraceae has a direct relationship with body weight at six weeks but a negative one with food intake both at six and 12 weeks, and this result could be related to some kind of regulation when the animal has reached the appropriate weight. In any case, in both families, EVOO and ROO have the same effect, so these outcomes cannot be attributed to the minority compounds. Moreover, in Lactobacillaceae, the three high-fat diets have the same statistical behavior, so it could confirm the effect of HFD in general as discussed previously with only two types of fat [17].
This is not the case for fam. Acetobacteraceae and its genus Oleomonas, whose percentage is significantly lower in the EVOO diet than in the ROO group, with the BT percentage being closer to ROO and the SD percentage being closer to EVOO. The predominance of this family is low, but it is worth mentioning because it is the only one where the two olive oils have a statistically significant opposite profile. This family also happens to correlate with body weight at six weeks and inversely with food intake and water intake at twelve weeks, both without ROO [17] and with ROO (this work). Although very distantly phylogenetically related, fam. Acetobacteraceae shares with Sphingobacteriaceae the presence in its membrane of a lipid type that is rare in bacteria, sphingolipids [19]. They can be obtained directly from the diet but can also be synthesized from palmitic acid [20]. In this sense, they can be easily found in butter but also as components of the minor polar lipid fraction in olive oil [21], where palmitic acid can also be detected as part of the fatty acid profile [22]. This could explain the higher presence of this family in BT and ROO and their low profile in the standard diet. According to its lipid composition, EVOO would be expected to share ROO results, and the fact of not doing so can be explained by the impact of the virgin olive oil polyphenols and their antimicrobial effect.
When widening our analysis to the genus level, most of the genera with significant differences resembled the characteristics of their families, as discussed above. There are, though, two cases worth reviewing for two different reasons: Rikenella and Desulfovibrio. Rikenella is taxonomically framed in the homonym family, Rikenellaceae, but in this study, both taxa do not share the same profile, and it is another same family genus, Alistipes, which is much more prominent in percentage and the one that maintains the family outcome (Figure 5). While fam. Rikenellaceae and gen. Alistipes present high values and variability in EVOO, gen. Rikenella has very low values in this diet, presenting significant differences between the EVOO and the BT group. Unlike the family, this genus is included in the regression model for blood pressure with (Table 2) and without ROO [17].
Finally, the genus *Desulfovibrio is* also noteworthy, not so much for its correlations but rather for the lack of them when ROO data are considered. This genus presents a statistically significant difference in percentage between the EVOO and BT diets and has a distinct profile in ROO too, although without achieving significance with respect to any of the other diets (Figure 5). The genus percentages in the different diets already have the profile that will be attained at the end of the experiment, after 12 weeks of diet [16], when EVOO and ROO are significantly different, with its coefficient being statistically significant in the multiple regression models for FI, WI, diuresis, and total cholesterol. At six weeks, some of these correlations (12-week FI and WI, together with 12-week SBP) are drawn when compared to butter and standard diets [17], but when ROO data are compared, they no longer apply. It could be possible that this circumstance is due to the presence of the genus Rikenella, with a very similar percentage in the four diets but about ten times more prominent, diminishing the significance of Desulfovibrio in the regression model for SBP, but other explanations can also be found and added as well. Desulfovibrio is a sulfate-reducing bacterium that uses sulfur-reduced species as the last electron acceptor instead of oxygen. Compounds as such can be found in a butter-enriched diet [17,23,24] but they are not expected in ROO. However, Desulfovibrio desulfuricans has also been reported to reduce nitrates and nitrites as alternative electron acceptors to sulfates to support growth [25], and nitro-oleic acid has recently been detected in olive oil [26]. This and other possible oxidized species would explain the relatively high percentage of Desulfovibrio in ROO at six weeks and also the worse relation to blood pressure when data from ROO is considered, both at six (this work) and 12 weeks [16], since these alternative electron acceptors will not produce H2S as a reduced outcome and will not participate in some of the mechanisms hypothesized for this bacterium to contribute to a blood pressure increment in the long run [17], although other negative effects, such as total cholesterol, FI, WI, and diuresis, still persist [16]. In any case, the different behavior of this genus in the EVOO and ROO diets confirms the role of the virgin olive oil polyphenols in inhibiting the growth of Desulfovibrio in the EVOO diet; this was even more evident over the full term of the experiment when the percentages of this genus in the BT and ROO diets had doubled and EVOO and ROO had significantly different values [16].
## 4.1. Animals
Experimental procedures were followed as already described [9,16,17]. Thirty-five (six-week-old) male Swiss Webster ICR (CD-1) mice (Harlan Laboratories) weighing 30.1 ± 0.55 g at the beginning of the study were fed ad libitum for 12 weeks a freely available standard diet (SD; standard laboratory mice diet A04, $3\%$ fat, Panlab, Barcelona, Spain) ($$n = 8$$) or three high-fat diets ($35\%$ total energy) containing SD supplemented with $20\%$ either butter ($$n = 9$$) (BT), extra virgin olive oil ($$n = 9$$) (EVOO) or refined olive oil (ROO), respectively (Table 3) [9,16].
EVOO was obtained from a fully organic crop (Soler Romero, Alcaudete, Spain). Butter and ROO were obtained from a large commercial store (Hacendado, Mercadona, Jaén, Spain). Fatty acid percentage and characterization were performed (EVOO $78.6\%$ monounsaturated fatty acids -MUFA-, $4.2\%$ polyunsaturated fatty acids -PUFA-, $17.1\%$ saturated fatty acids -SFA-; butter $35.6\%$ MUFA, $1.5\%$ PUFA, $62.5\%$ SFA; ROO $76.6\%$ MUFA, $7.1\%$ PUFA, $16.3\%$ SFA; SD $0.5\%$ MUFA, $1.75\%$ PUFA, $0.75\%$ SFA); and EVOO polyphenol content was obtained from the producer (total polyphenol content was 527 mg/kg). All experimental procedures were performed in accordance with the European Communities Council Directive $\frac{86}{609}$/EEC and reviewed and approved by the Bioethics Committee of the University of Jaén, initially on 29 December 2010 for project AGR 6340 and extended for project PP$\frac{2015}{08}$/09. The procedure was followed as described previously [9,16]. Mice were housed at a constant temperature (23 °C), constant humidity ($50\%$), and a constant day length (12 h). Animals were individually housed in metabolic cages twenty-four hours at six weeks of the experiment [17] and food intake, water intake, diuresis, body weight (BW), and systolic blood pressure (SBP) were measured individually. In addition, at that moment, feces were also collected individually right after deposition, and total DNA was extracted immediately as indicated in Section 4.2 or kept at −80 °C until use. SBP was monitored by the pleithysmographic method in unanesthetized animals as previously described [9,27]. Briefly, mice were placed in plastic holders and warmed to 37 °C for each recording session. At least seven determinations were made in every session, and the mean of the stable values within a range of 5 mmHg was recorded as the SBP level. Measurements at the beginning and end were discarded. Animals were kept in cages for six more weeks; the procedure was repeated at the end of the experimental period, and blood samples were obtained as described and already reported [9,16], obtaining the values of insulin, fasting glucose, triglycerides, total cholesterol, HDL, leptin, and ghrelin [28,29,30]. All analyses were performed according to the manufacturer’s protocols.
## 4.2. Bacterial Biodiversity
In order to study the bacterial composition in feces, DNA was extracted using the QIAamp© DNA Stool Kit (QIAGEN, Hilden, Germany) as described and reported previously [9,16,17]. Thirty-five DNA samples, corresponding to eight fecal samples from eight mice under SD and twenty-seven fecal samples from nine mice fed high BT, nine fed a high EVOO diet, and nine fed a high ROO diet, were pyrosequenced at Lifesequencing (Valencia, Spain) using Roche GS-FLX-Titanium + 454 pyrosequencing technology, targeting the 16S ribosomal DNA V3–V4 region (V3fwd: 5′-TCCGTCAATTYMTTTRAGT-3′, V4rev: 5′-CCTACGGGAGGCAGCAG-3′). Thermal cycling consisted of initial denaturation at 94 °C for 2 min followed by 30 cycles of denaturation at 94 °C for 20 s, annealing at 50 °C for 30 s, and extension at 72 °C for 5 min. Thirty-five libraries were constructed, the different amplifications were individually measured, and the quantity of amplified DNA was estimated using Quant-iTTM PicoGreen (Invitrogen, Waltham, MA, USA). After the quality filter was applied and sequences were trimmed and checked for quimeras using the UCHIME v. 4.2.40 program, the resulting sequences were assigned to different taxonomic levels using the Ribosomal Database Project Classifier. Rarefaction curves were obtained for each sample, and taxonomical levels were analyzed in order to confirm that no more taxonomical groups were expected to be found if sequencing was increased.
## 4.3. Statistical Studies
For the statistical analysis, we followed the procedures described previously [9,16,17]. Whenever the ANOVA assumptions are not met to test the equality of the means according to the different types of diet, we used the Kruskal–Wallis test. Where significant differences were detected, the Dunn test for pairwise multiple comparisons with Bonferroni correction was used. The significance level considered in all tests was $5\%$. All computations were done using R 4.1.2 (Auckland, New Zealand). In addition, multiple regression models have been fitted, considering the physiological variables as dependent variables and those that have shown statistically significant differences according to the diets as explanatory variables (all regression models were fitted using the open-source statistical package Gretl 2019c, San Diego, CA, USA).
## 5. Conclusions
In summary, there are several bacterial taxa that are significant in regression models for physiological variables related to metabolic syndrome, both in a concomitant way and also after several weeks of high-fat diet feeding. Among these variables, blood pressure is remarkable because its values at twelve weeks can be explained by a multiple regression model with some of these bacteria, but no such model was found for SBP values at six weeks of diet. The different prevalence of some of these bacteria under the three high-fat diets can be easily explained just by the type of fat that is present in the diet, as is the case with the family Sphingobacteraceae and related taxa, and, therefore, their involvement in the development of the high values in the systolic blood pressure is doubtful. On the other hand, differences in the genus Desulfovibrio under the three HFDs can be better explained if a contribution of this bacterium to the increment of blood pressure values is assumed and the antimicrobial role of the virgin olive oil polyphenols is presumed in the maintenance of low levels of this genus under an EVOO high-fat diet.
## References
1. Zhang C., Zhang M., Wang S., Han R., Cao Y., Hua W.. **Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice**. *ISME J.* (2010) **4** 232-241. DOI: 10.1038/ismej.2009.112
2. Beam A., Clinger E., Hao L.. **Effect of Diet and Dietary Components on the Composition of the Gut Microbiota**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13082795
3. Miri S., Yeo J., Abubaker S., Hammami R.. **Neuromicrobiology, an emerging neurometabolic facet of the gut microbiome?**. *Front. Microbiol.* (2023) **14** 1098412. DOI: 10.3389/fmicb.2023.1098412
4. Albenberg L.G., Wu G.D.. **Diet and the intestinal microbiome: Associations, functions, and implications for health and disease**. *Gastroenterology* (2014) **146** 1564-1572. DOI: 10.1053/j.gastro.2014.01.058
5. Millman J.F., Okamoto S., Teruya T., Uema T., Ikematsu S., Shimabukuro M., Masuzaki H.. **Extra-virgin olive oil and the gut-brain axis: Influence on gut microbiota, mucosal immunity, and cardiometabolic and cognitive health**. *Nutr. Rev.* (2021) **79** 1362-1374. DOI: 10.1093/nutrit/nuaa148
6. Martínez-González M.A., Martín-Calvo N.. **The major European dietary patterns and metabolic syndrome**. *Rev. Endocr. Metab. Disord.* (2013) **14** 265-271. DOI: 10.1007/s11154-013-9264-6
7. Hidalgo M., Prieto I., Abriouel H., Cobo A., Benomar N., Gálvez A., Martínez-Cañamero M.. **Effect of virgin and refined olive oil consumption on gut microbiota. Comparison to butter**. *Food Res. Int.* (2014) **64** 553-559. DOI: 10.1016/j.foodres.2014.07.030
8. Hidalgo M., Prieto I., Abrioue H., Villarejo A.B., Ramírez M., Cobo A., Benomar N., Gálvez A., Martínez-Cañamero M.. **Changes in Gut Microbiota Linked to a Reduction in Systolic Blood Pressure in Spontaneously Hypertensive Rats Fed an Extra Virgin Olive Oil-Enriched Diet**. *Plant Foods Hum. Nutr.* (2018) **73** 1-6. DOI: 10.1007/s11130-017-0650-1
9. Prieto I., Hidalgo M., Segarra A.B., Martínez-Rodríguez A.M., Cobo A., Ramírez M., Abriouel H., Gálvez A., Martínez-Cañamero M.. **Influence of a diet enriched in virgin olive oil or butter on mouse gut microbiota and its correlation to physiological and biochemical parameters related to metabolic syndrome**. *PLoS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0190368
10. Farràs M., Martinez-Gili L., Portune K., Arranz S., Frost G., Tondo M., Blanco-Vaca F.. **Modulation of the Gut Microbiota by Olive Oil Phenolic Compounds: Implications for Lipid Metabolism, Immune System, and Obesity**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12082200
11. Vrdoljak J., Kumric M., Vilovic M., Martinovic D., Tomic I.J., Krnic M., Ticinovic Kurir T., Bozic J.. **Effects of Olive Oil and Its Components on Intestinal Inflammation and Inflammatory Bowel Disease**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14040757
12. Ramirez-Tortosa M.C., Urbano G., López-Jurado M., Nestares T., Gomez M.C., Mir A., Ros E., Mataix J., Gil A.. **Extra-virgin olive oil increases the resistance of LDL to oxidation more than refined olive oil in free-living men with peripheral vascular disease**. *J. Nutr.* (1999) **129** 2177-2183. DOI: 10.1093/jn/129.12.2177
13. Pacheco Y.M., Bemúdez B., López S., Abia R., Villar J., Muriana F.J.. **Minor compounds of olive oil have postprandial anti-inflammatory effects**. *Br. J. Nutr.* (2007) **98** 260-263. DOI: 10.1017/S0007114507701666
14. Santangelo C., Filesi C., Varì R., Scazzocchio B., Filardi T., Fogliano V., D’Archivio M., Giovannini C., Lenzi A., Morano S.. **Consumption of extra-virgin olive oil rich in phenolic compounds improves metabolic control in patients with type 2 diabetes mellitus: A possible involvement of reduced levels of circulating visfatin**. *J. Endocrinol. Investig.* (2016) **39** 1295-1301. DOI: 10.1007/s40618-016-0506-9
15. Gorzynik-Debicka M., Przychodzen P., Cappello F., Kuban-Jankowska A., Marino Gammazza A., Knap N., Wozniak M., Gorska-Ponikowska M.. **Potential health benefits of olive oil and plant polyphenols**. *Int. J. Mol. Sci.* (2018) **19**. DOI: 10.3390/ijms19030686
16. Martínez N., Prieto I., Hidalgo M., Segarra A.B., Martínez-Rodríguez A.M., Cobo A., Ramírez M., Gálvez A., Martínez-Cañamero M.. **Refined versus Extra Virgin Olive Oil High-Fat Diet Impact on Intestinal Microbiota of Mice and Its Relation to Different Physiological Variables**. *Microorganisms* (2019) **7**. DOI: 10.3390/microorganisms7020061
17. Andújar-Tenorio N., Prieto I., Cobo-Molinos A., Martínez-Rodríguez A.M., Hidalgo M., Segarra A.B., Ramírez M., Gálvez A., Martínez-Cañamero M.. **High fat diets induce early changes in gut microbiota that may serve as markers of ulterior altered physiological and biochemical parameters related to metabolic syndrome. Effect of virgin olive oil in comparison to butter**. *PLoS ONE* (2022) **17**. DOI: 10.1371/journal.pone.0271634
18. Million M., Angelakis E., Paul M., Armougom F., Leibovici L., Raoult D.. **Comparative meta-analysis of the effect of Lactobacillus species on weight gain in humans and animals**. *Microb. Pathog.* (2012) **53** 100-108. DOI: 10.1016/j.micpath.2012.05.007
19. Heaver S.L., Johnson E.L., Ley R.E.. **Sphingolipids in host–microbial interactions**. *Curr. Opin. Microbiol.* (2018) **43** 92-99. DOI: 10.1016/j.mib.2017.12.011
20. Harrison P.J., Dunn T.M., Campopiano D.J.. **Sphingolipid biosynthesis in man and microbes**. *Nat. Prod. Rep.* (2018) **35** 921-954. DOI: 10.1039/C8NP00019K
21. Alves E., Domingues M.R.M., Domingues P.. **Polar Lipids from Olives and Olive Oil: A Review on Their Identification, Significance and Potential Biotechnological Applications**. *Foods* (2018) **7**. DOI: 10.3390/foods7070109
22. Di Lecce G., Piochi M., Pacetti D., Frega N.G., Bartolucci E., Scortichini S., Fiorini D.. **Eleven Monovarietal Extra Virgin Olive Oils from Olives Grown and Processed under the Same Conditions: Effect of the Cultivar on the Chemical Composition and Sensory Traits**. *Foods* (2020) **9**. DOI: 10.3390/foods9070904
23. Rey F.E., Gonzalez M.D., Cheng J., Wu M., Ahern P.P., Gordon J.I.. **Metabolic niche of a prominent sulfatereducing human gut bacterium**. *Proc. Natl. Acad. Sci. USA* (2013) **110** 13582-13587. DOI: 10.1073/pnas.1312524110
24. Devkota S., Chang E.B.. **Interactions between Diet, Bile Acid Metabolism, Gut Microbiota, and Inflammatory Bowel Diseases**. *Dig. Dis.* (2015) **33** 351-356. DOI: 10.1159/000371687
25. Cadby I.T., Faulkner M., Cheneby J., Long J., van Helden J., Dolla A., Cole J.A.. **Coordinated response of the Desulfovibrio desulfuricans 27774, transcriptome to nitrate, nitrite and nitric oxide**. *Sci. Rep.* (2017) **7** 16228. DOI: 10.1038/s41598-017-16403-4
26. Aranda-Caño L., Sánchez-Calvo B., Begara-Morales J.C., Chaki M., Mata-Pérez C., Padilla M.N., Valderrama R., Barroso J.B.. **Post-Translational Modification of Proteins Mediated by Nitro-Fatty Acids in Plants: Nitroalkylation**. *Plants* (2019) **8**. DOI: 10.3390/plants8040082
27. Vázquez-Araújo L., Adhikari K., Chambers E., Chambers D.H., Carbonell-Barrachina A.A.. **Cross-cultural perception of six commercial olive oils: A study with Spanish and US consumers**. *Food Sci. Technol. Int.* (2015) **21** 454-466. DOI: 10.1177/1082013214543806
28. Uemura K., Mori N.. **Influence of age and sex on high-fat diet-induced increase in blood pressure**. *Nagoya J. Med. Sci.* (2006) **68** 109-114. PMID: 16967776
29. Tan Y., Li M., Wu G., Lou J., Feng M., Xu J., Zhou J., Zhang P., Yang H., Dong L.. **Short-term but not long-term high fat diet feeding protects against pressure erload-induced heart failure through activation of mitophagy**. *Life Sci.* (2021) **272** 119242. DOI: 10.1016/j.lfs.2021.119242
30. Oltra L., Reverte V., Tapia A., Moreno J.M., Salazar F.J., Llina’s M.T.. **Cardiac, renal and uterine hemodynamics changes throughout pregnancy in rats with a prolonged high fat diet from an early age**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0234861
|
---
title: Fabrication of Glutaraldehyde Vapor Treated PVA/SA/GO/ZnO Electrospun Nanofibers
with High Liquid Absorbability for Antimicrobial of Staphylococcus aureus
authors:
- Yi-Hsin Chien
- Meng-Tzu Ho
- Chin-Hsign Feng
- Jung-Hsign Yen
- Yi-Chan Chang
- Chih-Sheng Lai
- Rong-Fuh Louh
journal: Nanomaterials
year: 2023
pmcid: PMC10004850
doi: 10.3390/nano13050932
license: CC BY 4.0
---
# Fabrication of Glutaraldehyde Vapor Treated PVA/SA/GO/ZnO Electrospun Nanofibers with High Liquid Absorbability for Antimicrobial of Staphylococcus aureus
## Abstract
In this study, we aim to develop organic–inorganic hybrid nanofibers containing high moisture retention and good mechanical performance as an antimicrobial dressing platform. The main theme of this work focuses on several technical tasks including (a) the electrospinning process (ESP) to produce organic polyvinyl alcohol/sodium alginate (PVA/SA) nanofibers with an excellent diameter uniformity and fibrous orientation, (b) the fabrication of inorganic nanoparticles (NPs) as graphene oxide (GO) and ZnO NPs to be added to PVA/SA nanofibers for enhancement of the mechanical properties and an antibacterial function to *Staphylococcus aureus* (S. aureus), and then (c) the crosslinking process for PVA/SA/GO/ZnO hybrid nanofibers in glutaraldehyde (GA) vapor atmosphere to improve the hydrophilicity and moisture absorption of specimens. Our results clearly indicate that the uniformity nanofiber with 7 wt% PVA and 2 wt% SA condition demonstrates 199 ± 22 nm in diameter using an electrospinning precursor solution of 355 cP in viscosity by the ESP process. Moreover, the mechanical strength of nanofibers was enhanced by $17\%$ after the handling of a 0.5 wt% GO nanoparticles addition. Significantly, the morphology and size of ZnO NPs can be affected by NaOH concentration, where 1 M NaOH was used in the synthesis of 23 nm ZnO NPs corresponding to effective inhibition of S. aureus strains. The PVA/SA/GO/ZnO mixture successfully performed an antibacterial ability with an 8 mm inhibition zone in S. aureus strains. Furthermore, the GA vapor as a crosslinking agent acting on PVA/SA/GO/ZnO nanofiber provided both swelling behavior and structural stability performance. The swelling ratio increased up to $1.406\%$, and the mechanical strength was 1.87 MPa after 48 h of GA vapor treatment. Finally, we successfully synthesized the hybrid nanofibers of GA-treated PVA/SA/GO/ZnO accompanied with high moisturizing, biocompatibility, and great mechanical properties, which will be a novel multi-functional candidate for wound dressing composites for patients receiving surgical operations and first aid treatments.
## 1. Introduction
In recent years, the versatility and applicability of nanofiber-based products have been realized in a wide range of regions such as (a) biomedical for tissue engineering of bones or scaffolds in the orthopedics-related study, wound composite therapy, and media for drug delivery applications [1,2,3]; (b) electronic materials for electronic packaging, sensors, and fuel cells [4,5,6], and (c) industrial for filter materials, thermal insulation materials, high-performance cleaning cloths, reinforced composites, and functionally protective materials [7,8]. Generally, there are a number of common methods of nanofiber preparation, including drawing, template synthesis, phase separation, self-assembly, and electrospinning. The merits of the electrospinning process consist of simple equipment and process, high yield, cost-effectiveness, being applicable for the wide choice of polymer types, controllability of fiber size uniformity and nanofiber orientation, as well as the porosity control for the texture of membrane via experimental parameters [9,10].
Generally, the involvement of unstable jet morphologies associated with the electrospinning process depends on several operating parameters such as pump flow rate, electric field strength, needle aperture size, the surface charge density of the jet, collection distance combined with environmental conditions such as operating temperature, humidity, airflow, etc. [ 11]. Moreover, the size of electrospun fibers increases by raising the electrospinning solution’s viscosity or pump speed, the polymer agent’s molecular weight, the applied electrical field, and the humidity of the operating environment. An increase in collection distance and surface charge density or electrical conductivity of the electrospinning solution can be effectively tuned to reduce the size of electrospun fibers. Interestingly the surface of electrospun fibers is found to be associated with a great number of pores with bigger pore size under high humidity of the operating environment due to water condensation [12].
The qualified wound dressing needs to require features such as air permeability, moisture absorption, water retention, non-stickiness, acceptable mechanical strength, antibacterial ability, and biocompatibility [3]. Therefore, new types of hybrid nanofibers recently have attracted considerable attention in the area of wound dressings production for medical treatment purposes [13,14,15]. Accordingly, polyvinyl alcohol (PVA), sodium alginate (SA), poly(ε-caprolactone) (PCL), poly(glycolic acid) (PGA), and cellulose are the widely accepted polymers to fabricate electrospun nanofibers as medical surgical dressing substrates due to their excellent hygroscopicity, nontoxicity, and degradation property [16,17]. Thus, the blend and co-polymer fabrication become attractive by aptly adjusting materials properties such as hygroscopicity, toughness, mechanical strength, cellular affinity, and biodegradability. Sardou et al. reported that skin tissue regeneration shows high cell attachment and proliferation affinity by polymer blending of PCL and gelatin electrospun nanofibers [18]. Ebrahimi et al. synthesized electrospun PCL homopolymer and PCL-PEG-PCL triblock copolymer-based nanofibers for tissue engineering applications accompanied by hydrophilicity, biocompatibility, non-toxicity, non-antigenic, and non-immunogenic characteristics [19]. In this context, a polymer combined with an inorganic nanoparticle such as hydroxyapatite (HA), magnesium oxide (MgO), zinc oxide (ZnO), graphene, and silver nanoparticles (Ag NPs) to form the hybrid nanofibers corresponds to a diverse range of scaled scaffolds, mechanical, and structural integrity [20]. Rijal et al. [ 21] prepared PCL/MgO and PCL/chitosan (CS)/MgO-based composite nanofibrous membranes through an electrospinning process. The tensile strength and Young’s modulus were enhanced during the increase in MgO NPs concentration. Moreover, the presence of salt ions in the electrospinning solution results in a reduction in the fiber diameter due to raising the surface charge density of the polymer jet leading to the stretching of fibers. In addition, the Ag and ZnO NPs are commonly employed as antimicrobial agents, and the S. aureus study of PCL-Ag composite nanofibers showed an increase in the inhibition zone with higher Ag NPs concentration [22].
This study mainly aimed to investigate the binary composite fibers containing organic portions of PVA and SA and inorganic portions of GO and ZnO NPs by using the electrospinning process. Due to the overall polymerization degree of PVA reflecting high viscosity range, it is facile and easy to dissolve quickly when encountering wet conditions. Thus, a natural biopolymer SA extracted from brown algae was selected as a constituent for our dressing design in order to maintain the function of water retention and dressing structure [23]. Interestingly, glutaraldehyde (GA) was frequently utilized as a crosslinking agent in PVA-related processing since the in situ crosslinking step generates chemical bonds between different molecular chains, resulting in a stable and insoluble three-dimensional network structure with improved mechanical strength in aqueous conditions [24,25]. Therefore, the use of GA-treated PVA/SA/GO/ZnO electrospun membranes of nanofibers as wound dressings presents promising antimicrobial performance to S. aureus (as seen in Scheme 1).
## 2.1. Materials
Polyvinyl alcohol (PVA, 98.0–$98.8\%$) was purchased from Acros Organics (Geel, Belgium). Sodium alginate (SA) and glutaraldehyde were procured from Sigma-Aldrich (St. Louis, MO, USA). Natural graphite powder (>$99.7\%$) was acquired from the Great Carbon Company (Taichung City, Taiwan). Sodium nitrate (NaNO3, $99.0\%$), potassium manganate (KMnO4, $99.5\%$), and hydrogen peroxide (H2O2, $30\%$) were acquired from Showa Chemical Company (Tokyo, Japan). Hydrochloric acid (HCl and sulfuric acid (H2SO4, $98\%$) were made by Shimakyu Chemical Company (Osaka, Japan). Zinc nitrate, 6-hydrate, and sodium hydroxide (NaOH) were sourced from J.T. Baker (St. Paul, MN, USA). Ethanol was acquired from the Echo Chemical Company (Kaohsiung City, Taiwan). All reagents were used without further purification.
## 2.2. Synthesis of PVA/SA Nanofiber
The synthesis of PVA/SA nanofiber was performed following the previous literature with slight modifications [26]. In preparation for the PVS/SA electrospinning (ESP) precursor agent, the PVA powder was dissolved into DI water (5, 6, 7, 8 wt%) and stirred at 200 rpm for 2 h at 90 °C. The nanofiber precursor could be prepared by mixing the PVA (5, 6, 7, 8 wt%) and the 2 wt% SA solution at room temperature. The precursor was filled in the syringe for ESP equipment and formed the PVA/SA composite nanofibers under settings of 15 kV voltage, 15 cm working distance, and 0.5 mL/h flow rate. That adjustment of processing parameters could be attentively controlled by the viscosity of the PVA/SA precursor to achieve the best conditions for the ESP nanofibers.
## 2.3. Synthesis of Graphene Oxide (GO)
The GO is produced using a slightly modified Hummers’ method [27]. The pure graphite of 1.2 g and 0.38 g sodium nitrate were added to 31.2 mL of sulfuric acid in a serum bottle and stirred in an ice bath (0 °C) for 30 min. KMnO4 of 7.2 g was slowly added into the previous solution and kept at 0 °C to prevent an intensive exothermic reaction. While stirring in a 35 °C water bath, the solution color then gradually turned from dark green to dark brown. While the graphite was progressively oxidized, the spacings between the graphite layers subsequently expanded. The previous solution was gently added into 55.2 mL of deionized water and heated from 35 °C to 98 °C under stirring for 8 min and later cooled for another 15 min at room temperature. The above dark brown liquid was added to 120 mL of DI water and 4 mL of hydrogen peroxide (H2O2, $30\%$) as a strong oxidizing agent, and the solution finally turned light brown color.
The solution obtained by the Hummers’ method was washed by centrifugation at high speed (10,000 rpm) for 20 min, while a $5\%$ HCl aqueous solution was added in the next step for centrifugation and repeated four times in a row to reduce sulfate sedimentation in the product and by-products. The precipitation was washed later with DI water at high centrifugation speed (10,000 rpm) for 20 min and repeated for four cycles, then it was agitated with an ultrasonic homogenizer for 15 min. After collecting the dark brown solid remains in the lower layer of the centrifuge tube and drying in a vacuum oven (at 60 °C), the GO nanopowders with high oxygen content were obtained.
## 2.4. Synthesis of PVA/SA/GO Nanofiber
The sample of PVA/SA/GO nanofibers was made by the mixture of the precursor of 7 wt% PVA, 2 wt% SA, and diverse addition of aqueous GO solution (0.3, 0.5, 0.7, and 1 wt%) under 15 kV voltage, 15 cm working distance, and 0.5 mL/h flow rate. The conductivity of GO might influence the stability of the spinning jet behavior and cause a non-uniform fiber diameter or formation of a bead-like structure. To discuss the influence of the different concentrations of GO, the ESP, the TEM, SEM, and tensile testing machines were used to analyze the morphology and the mechanical strength of the PVA/SA/GO nanofibers.
## 2.5. Synthesis of ZnO Powder by Precipitation Method
The 0.5 M Zn(NO3)2 solution (100 mL) was stirred for 1 h at 70 °C until it was completely dissolved. The size of ZnO NPs could be adjusted by different NaOH concentrations. The Zn(NO3)2 solution was poured into a burette and then dropwise added to the NaOH solution through vigorous stirring [28]. The mixture gradually turned milky white. The mixture was consecutively stirred for 2 h and settled for another 24 h. After that, the mixture was filtered, and the precipitation was washed with DI water and ethanol twice to remove the by-products. Thereafter, the precipitates were dried in an oven at 70 °C and calcined at 400 °C for 3 h for ZnO NPs generation. Correspondingly, the ZnO NPs were used as one gradient of ESP precursor to produce the PVA/SA/GO/ZnO nanofiber. Note that the excessive ZnO NPs addition would destroy the fiber morphology and affect the fiber diameter evident through TEM and SEM analysis.
## 2.6. PVA/SA/GO/ZnO Antibacterial Solution Test
According to the disk diffusion method, the PVA/SA/GO/ZnO solution was dispensed in a paper disk (1 cm dia.) by micropipette to carry out the antibacterial experiment in S. aureus. The S. aureus strain was sampled by a cotton swab and spread evenly through three different directions on the surface of the medium by a rotating 60° sequence each time. Subsequently, the paper disks containing PVA/SA/GO/ZnO solution were put on the agar medium with S. aureus and then incubated for 24 h to observe the growth of the bacterium. All the bacteriostatic experiments of our specimens were collaboratively conducted in the qualified laboratory of the Laboratory Division of Taichung Veterans General Hospital for examining the dimensions of the bacteriostatic rings developed in the petri dish.
## 2.7. PVA/SA/GO/ZnO Vapor Crosslinking with Glutaraldehyde (GA)
These polymers would crosslink by vapor GA at room temperature, preventing them from easily dissolving in water. First, 5 mL of GA and PVA/SA/GO/ZnO nanofiber were taken in a closed container [29]. Normally, the spontaneously generated GA vapor at the ambient condition interactions between the –OH group (from PVA molecule) and the –CHO group (from GA molecule), the crosslinking reaction Equation [1] was shown with graphical expression as follows. The Fourier transform infrared spectroscopy (FTIR), tensile testing, water contact angle testing, swelling testing, and water retention were used for evaluation.
## 2.8. Swelling Percentage and Water Retention
The crosslinked ESP fiber was cut into 5 cm × 5 cm pieces and its dry weight was measured. The piece was swelled with phosphate-buffered saline (PBS) for 2 h. After swelling, we wiped the surface gently with wipe paper to remove excess water and weighed the sample. [ 2]Swelling percentage (%)=WS−WoW0×100 where *Ws is* the weight after swelling and *Wo is* the weight of the sample before swelling.
The water retention was the ratio between the weight of the sample after swelling in the PBS at 37 °C for 24 h (Ws) and the weight of the sample dried in the oven at 40 °C and detected by time (Wi). [ 3]Water retention (%)=WiWS×100 where *Ws is* the weight after swelling, and *Wi is* the weight of the sample dried in the oven at different times.
## 2.9. Characterization
The microstructure of the composite nanofibers structure was examined by a field emission scanning electron microscope (FE-SEM, Hitachi, S-4800, Tokyo, Japan). The lattice structure and grain size on the composite fiber and nanopowders were analyzed by multipurpose X-ray thin-film micro-area diffractometer (Bruker, D8 Discover, Billerica, MA, USA). The electrospun fibers containing GO and ZnO NPs were inspected by transmission electron microscopy (JEOL, JEM 1400, Tokyo, Japan). FTIR spectra were detected by Fourier transform infrared spectroscopy (Perkin Elmer, Spectrum One, Waltham, MA, USA). The *Raman spectra* were examined in the range from 500–2000 cm−1 at 300 K with the excitation source at 532 nm wavelength. The viscosity of the PVA solution was recorded by a viscometer (Brookfield, DV-ΙΙ+ Pro, USA). The mechanical strength was measured followed by ASTM D882. The wettability of the sample would present as the contact angle detected by a surface tension measurement apparatus (First Ten Angstroms, Inc., model FTA 125, Portsmouth, VA, USA).
## 3.1. PVA/SA Electrospun Nanofibers
The different types of PVA/SA nanofibers were fabricated via ESP technology with mass percentage concentrations (wt%) of PVA/SA precursor, applied voltage, and working distance adjustment. To our observation, the predominant factor in the ESP process was the viscosity of the precursor; for example, the bead structure or ribbon-like structure occurred during the inappropriate viscosity. To optimize the suitable viscosity, the PVA/SA precursor was prepared by different weight percent (wt%) of PVA (5, 6, 7, and 8 wt%), fixed 2 wt% SA, 15 kV voltage, and 15 cm working distance. The viscosity of the PVA/SA precursor could be determined through the cone-and-plate viscometer, and a variety of experimental parameters were shown in Table S1. The relationship between SEM images of nanofibers and viscosity is shown in Figure 1 and Table S1. Thus, the ESP precursor (5 wt% PVA) with 93 centipoises (cP) demonstrated the microbeads structure or knot-like structures of the nanofiber shown in Figure 1a. Since the viscosity value of the ESP precursor solution was too low, the electrospinning process was subjected to electric field electrostatic force traction. The jet at the front end of the needle tip was associated with disordered behavior due to the low intermolecular cohesion in the ESP solution that made the electrospun fibers less smoothly drawn. Thereafter, the viscosity of ESP precursor fluid was adjusted to 198 cP (6 wt% PVA) and 355 cP (7 wt% PVA) (Figure 1b,c), and the apparent morphology of the electrospun fibers was relatively complete without any defect structure because the electric-field induced traction force reached an equilibrium state to the cohesion of intermolecular chains in the precursor solution. Moreover, the cohesive force of molecular chains was higher than the electrostatic force as the viscosity of the precursor fluid reached 734 cP (8 wt% PVA) (Figure 1d). It might not be easy to obtain the uniformity of nanofibers since the precursor was coagulated and accumulated at the ESP needle tip. Therefore, the average diameter of nanofibers was 92 ± 19 nm, 177 ± 38 nm, 199 ± 22 nm, and 151 ± 22 nm, corresponding to the viscosity of 93 cP, 198 cP, 355 cP, and 734 cP, respectively.
Principally, the SA molecule is chosen as an ingredient of the precursor with high electrical conductivity, and sodium ions affects the fiber’s diameter and morphology. Thus, the volume of the SA solution addition was 0.5, 1, and 1.5 mL to test the appropriate condition for the nanofibers. The viscosity changed accordingly to a variety of SA solution addition. The viscosity of 432 cP, 355 cP, and 295 cP had been determined by 0.5, 1, and 1.5 mL of SA addition, respectively (Table S2). The corresponding SEM images are shown in Figure S1, and the morphology of nanofibers associated with 0.5 mL and 1.5 mL of the SA addition was presented with lots of embedded bead-like structures around the nanofibers due to the instability of the solution jet. The uniformity morphology of the PVA/SA nanofibers occurred in a 1.0 mL SA solution (2 wt%, Figure S2b). As a result, the optimized process condition of PVA/SA nanofibers was associated with the ESP solution viscosity of 355 cP (7 wt% PVA, 1 mL of 2 wt% SA) and with an average fiber diameter of 199 ± 22 nm.
## 3.2. PVA/SA/GO Electrospun Nanofibers
The PVA/SA nanofiber as dressing materials for wound treatment may unfortunately lack thenecessary toughness and mechanical strength. In this study, we tended to employ inorganic graphene oxide (GO) as the ESP precursor to further enhance the mechanical strength of PVA/SA nanofibers. Generally, GO was commonly synthesized by the Hummers’ method with higher oxygen content. The Hummers’ process was followed by the oxidation reaction of high-purity graphite (Gr) and high-strength oxidant. The characterization of GO samples was shown in Figure 2a, the XRD diffraction patterns and carbon interlayer spacing of GO samples were calculated according to Bragg’s Law, as listed in the following equation: Nλ = 2dsinθ[4] where N is an integer, λ the wavelength of the incident wave of the Cu target (0.154 nm), d the interlayer spacing in the atomic lattice, and θ the angle between the incident wave and the scattering plane. Figure 2a depicts the XRD analysis pattern of Gr with a characteristic peak at 26.5°, indicating the [002] orientation of carbon corresponding to the JCPDS card No. 08-0415, which confirmed that Gr belongs to the hexagonal crystal phase. Then, the carbon layer spacing (d) of Gr was calculated to be 3.36 Å. The characteristic peak of the XRD analysis pattern of GO shifted from 26.5° to 10.9°, indicating the [001] orientation of carbon exists. Referring to the JCPDS card No. 82-2261, the GO sample was validated to be of the cubic phase, and its layer spacing of GO was calculated as $d = 8.07$ Å. In the case of graphite oxidation, many oxygen-containing groups were sandwiched between the carbon layers, resulting in an obvious expansion in the interlayer spacing of graphite, which suggests that the GO produced by our Hummers’ process did experience a highly oxidized process.
The curve of Raman spectroscopic analysis for the GO sample is demonstrated in Figure 2b. The main characteristic peaks of GO specimens were associated with the D-band, G-band, and 2D-band fingerprints corresponding to their wavelengths located at 1330 cm−1, 1580 cm−1, and 2670 cm−1, respectively. The existence of the characteristic peak of the D-band represents that there are several possible dislocations, lattice voids, defects, or lowering of the area of the sp2 structure in the carbon material. The ratio of Raman spectral intensity of D-band and G-band (ID/IG) was used as a simple and facile indicator for judging the number of defects in the graphite structure. The ID/IG ratio of our production of the GO sample was 0.91, which signifies there are some crystal defects and sp2 carbon atoms in the GO powder specimen. Moreover, the FTIR was used to measure and analyze the functional groups of GO and Gr (Figure 2c). Prior to the FTIR measurement, we placed both GO and Gr samples in an oven for 24 h to ensure the existing moisture would not affect the subsequent FTIR results of such samples. Our FTIR analysis results show that there were only some absorption peaks, reflecting the existence of Gr. Those FTIR peaks were directly connected to a relationship with the carbon raw material bearing several functional groups. When the Gr sample was strongly oxidized and transformed into GO powder, the original O–H (wavelength at 3300–3600 cm−1) characteristic absorption peak of the sample became more obvious, such as C = O (wavelength at 1670∼1780 cm−1), C = C (wave number 1650–1675 cm−1), C-O (wave number at 1050∼1150 cm−1), coupled with some other characteristic absorption peaks also naturally appeared. Therefore, the results of the FTIR analysis confirmed that we successfully transformed the graphite material into a high-purity GO sample.
After validating the GO properties, the PVA/SA/GO precursor solution was prepared by electrospinning with varying GO concentrations of 0.3 wt%, 0.5 wt%, 0.7 wt%, and 1.0 wt%, respectively. During the 0.3 wt% GO solution addition, the non-uniformity and bead-like structure of PVA/SA/GO nanofibers generation because of less charge density of precursor led to the non-stable electrostatic force (Figure 2e). The GO concentration increased to 0.5 wt%, and the high-quality and uniform PVA/SA/GO nanofibers were obtained because the electrostatic force reached a stable state (Figure 2f). While raising GO concentration up to 0.7–1.0 wt%, a higher concentration of GO aqueous solution would accompany an enormous density of charge of the precursor liquid, allowing the ESP jet to be dragged by an applied electric field. As the electrostatic force instantaneously increased, an unstable jet of the precursor liquid around the electrospinning needle was exerted. Thus, PVA/SA/GO nanofibers became relatively non-uniform with pronounced surface-roughness, as illustrated in Figure 2g,h. Furthermore, the optimized PVA/SA/GO nanofibers were determined by a mechanical strength analysis. Following the tensile strength and elongation test results in Table S3 and Figure 2d, the PVA and PVA/SA nanofibers without GO addition showed individual tensile strength of 1.66 MPa and 1.52 MPa. In contrast, the tensile strength gradually increased to 1.61, 1.77, 1.75, and 2.03 MPa featuring GO concentrations of 0.3, 0.5, 0.7, and 1 wt% addition, respectively. Based on the tensile strength results, the maximum enhanced $34\%$ of tensile strength; however, the elongation at breaking suddenly dropped down to $11\%$ during 1 wt% GO addition. We speculated that the 1 wt% of GO addition was distributed as a two-dimensional planar texture, some of the GO might be perpendicular to the longitudinal direction of the nanofiber. Under the external stresses, the additional interaction between PVA, SA, and GO would promote the bonding of molecular chains, leading to the limited activity of GO and stress distribution. Among them, the elongation at breaking gradually diminished after the GO dopant was introduced in the nanofiber system. As a consequence, the optimized condition of PVA/SA/GO nanofibers was associated with the tensile strength of 1.77 MPa (7 wt% PVA, 1 mL of 2 wt% SA, and 0.5 wt% GO) and with the elongation of 25.54 ± $1.1\%$.
## 3.3. ZnO Nanomaterials
PVA/SA/GO nanofibers possessed both high quality and toughness dressing properties. Consequently, the ZnO nanomaterials were used to implement the objective of an antibacterial agent. The 0.5 M of Zn(NO3)2 was mixed with various concentrations of NaOH to prepare ZnO nanomaterials via the precipitation method. Hereby the chemical reactions involved were depicted as following equations [30]:[5]ZnNO32+2NaOH→ZnOH2+2NaNO3 [6]ZnOH2→∆ZnO+H2O Interestingly, the SEM images represented a diverse morphology and size of ZnO NPs according to varying NaOH concentrations from 0.5 M to 2 M (Figure 3). Under the lower alkaline concentration, the morphology of the ZnO demonstrated a non-uniform distribution of particle size (Figure 3a). Raising the alkaline concentration, the morphology of ZnO was transferred to the hexagonal (Figure 3b), the plate-like shape (Figure 3c), and the rod shape (Figure 3d), respectively. Based on the variety of the ZnO nanomaterials, we further analyzed these data in detail with an XRD pattern, as shown in Figure 3e. The XRD characteristic peaks of those ZnO samples with the standard data of JCPDS Card No. 36−145−1, where crystal planes of [100], [002], [101] were present together with diffraction peaks indicating [102], [110], [103], [200], [112], [201], [004] and [202] planes. Therefore, the prepared ZnO nanomaterials were determined to be the hexagonal wurtzite structure and they were not involved with impurity diffraction peaks. The average grain size of ZnO was further calculated using the following Scherrer equation: [7]D=Kλβcosθ where D is the average grain size, K was a constant of about 0.9 (varies according to different crystal shapes, the K value range is in the range of 0.89–1), λ the wavelength of X-ray, θ the Bragg diffraction angle, the β was the peak -to-peak width at half maximum of 2θ diffraction in the XRD pattern. When the full width at half maximum (FWHM) of the diffraction peak turned narrower and the peak intensity was enhanced, the average grain size seemed coarser. Conversely, the wider the FWHM of such a diffraction peak and the lower the diffraction peak intensity, the finer size of the grains can be obtained.
The grain size of the prepared ZnO nanomaterials was calculated by the Scherrer equation to 50, 23, 30, and 64 nm with 0.5, 1, 1.5, and 2M NaOH, respectively. The width of the characteristic peak of ZnO samples became narrower, which meant that the grain size increased with an increase in NaOH concentration. However, it was somewhat inconsistent with the trend of our observed experimental results. According to the chemical equilibrium equation [31] represented by the following Equation [8], as the molar ratio of Zn2+ and OH– was 1:2, then its chemical reaction could reach equilibrium. However, when the molar ratio of Zn2+ and OH– was 1:1, the OH– of this situation could not simply satisfy the complete reaction of Zn2+ ions. Thus, the concentration of NaOH was 1.0 M, in turn, this process parameter could lead to the smallest size of ZnO powder. [ 8]Zn2+ aq+2OH− aq→ZnOs+H2Ol Firstly, we performed the disk diffusion method by using the prepared ZnO nanomaterials for the antibacterial test of S. aureus. The experimental results demonstrated the obvious zone of inhibition (ZOI) to indicate excellent antibacterial performance within those four types of ZnO nanomaterials (Figure S3). Generally, the bacteriostatic rings were referred to as the boundary circle corresponding to the inhibiting bacteria efficiency. The ranges of ZOI of 18∼20 mm were contributed by prepared ZnO nanomaterials with 0.5 M, 1 M, 1.5 M, and 2.0 M NaOH treatment (with an average particle size ranging from 30 to 65 nm). In addition, the ZOI of S. aureus in 1 M NaOH-treated ZnO NPs (particle size in 23 nm) was slightly larger than other specimens and appeared to efficaciously create a much-pronounced effect on inhibiting the growth of microorganisms. [ 32,33]
## 3.4. PVA/SA/GO/ZnO Electrospun Nanofibers and the Antibacterial Test
Correspondingly, the 1 M NaOH-treated ZnO NPs (avg. particle size in 23 nm) was employed to fabricate PVA/SA/GO/ZnO nanofibers. The SEM images of PVA/SA/GO/ZnO nanofibers with 0.1∼1 wt% of ZnO NPs addition are shown in Figure 4a–e). After 0.1 and 0.3 wt% ZnO NPs addition, there was no obvious change in the appearance of the fibers (Figure 4a,b). Thereby, the difference in PVA/SA/GO/ZnO nanofibers indicated that a small proportion of ZnO NPs were embedded onto the surface of the electrospun fibers, which caused potential damage to the nanofibers during the amount of ZnO additives up to 0.5–0.7 wt% (Figure 4c,d). Eventually, the ZnO NPs were added to 1.0 wt%, and the PVA/SA/GO/ZnO nanofibers revealed the appearance of ZnO NPs surrounding fibers and generated aggregation illustrated in Figure 4e,f. In brief, the PVA/SA/GO/ZnO nanofibers with the excessive amount of ZnO NPs addition affected the viscosity of the electrospinning precursor and then gave rise to the spinning jet in the ESPs. Therefore, the PVA/SA/GO/ZnO antibacterial solution was demonstrated as an antibacterial solution for S. aureus by the disk diffusion method. The antibacterial conditions of the various concentrations of S. aureus to the PVA/SA/GO/ZnO antibacterial solution within a series of 1 M NaOH-treated ZnO NPs (0.1–1 wt%) are shown in Figure 4i. As shown in Figure 4g,h, the antibacterial solution without ZnO NPs as test number (No.) ①–③ showed no ZOI for both concentrations of S. aureus treatment. For Nos. ④–⑧ of PVA/SA/GO/ZnO antibacterial solution revealed that the more ZnO NPs added, the larger range of antibacterial circles (ZOI) exhibited. According to the antibacterial effect, the antibacterial results directly proved that the PVA/SA/GO/ZnO nanocomposites certainly reveal a significant antibacterial performance. It can be achieved with evidence of an antibacterial ring test for the design of dressing application, the ongoing experiment will be carried out with the PVA/SA/GO/ZnO nanofibers experiments to build up an in vivo medical testing model which will be included in our future report.
## 3.5. Glutaraldehyde Vapor Treated PVA/SA/GO/ZnO Nanofibers
To deal with a practical challenge in which the PVA as an adhesive agent in PVA/SA/GO/ZnO nanofiber regarding self-dissolving in water, the issue encounters nanofibers fused and loss of the original nanofiber structure, therefore, we came up with a strategy to tightly crosslink the PVA/SA/GO/ZnO nanofiber by using the vapor glutaraldehyde (GA) as a crosslinker due to the fact the GA at the liquid phase is naturally volatilized into the gas phase under ambient conditions. Then, the hydroxyl (–OH) group of PVA and the aldehyde group (–CHO) of GA were conducted by the crosslinking reaction, thereby improving the stability and maintaining the morphology of the PVA polymer in water without any specific sticking, dissolving, or fusing. Based on our experimental results, the appropriate parameters used to combine with vapor–GA crosslinker featuring characterizations with FTIR analysis, tensile testing, swelling properties, and water retention analysis (Figure 5). For the FTIR analysis illustrated in Figure 5a, the absorption peak of PVA at the wavenumber of 3500–3200 cm−1 represented to O–H stretching vibration of hydroxyl, 3000–2840 cm−1 represented the peak of C–H stretching vibration, 1420–1400 cm−1 represented the C–H bending vibration of CH2, and the stretching vibration of C–O–C near 1096 cm−1. For the related characteristic absorption peaks of the cross-linking molecule between GA and PVA content, we could observe in the range of 3500–3200 cm−1, where the intensity in 72 h of cross-linking was slightly higher than 48 h [34]. For the tensile strength shown in Figure 5b and Table S4, the tensile strength of PVA/SA/GO/ZnO nanofibers was 1.87 MPa, which was involved with a $5.6\%$ improvement in tensile strength of PVA/SA/GO nanofibers. Furthermore, the tensile strength of GA-treated PVA/SA/GO/ZnO nanofibers was increased to 2.00 MPa, 2.30 MPa, 2.43 MPa, and 2.30 MPa corresponding to the cross-linking time at 12 h, 24 h, 48 h, and 72 h, respectively. The experimental results supported that the time for cross-linking had an apparent tendency to enhance the tensile strength of the samples.
A significantly increased swelling ratio and water retention performance after GA-treated PVA/SA/GO/ZnO nanofibers are shown in Figure 5c,d. The swelling ratio for the sample without crosslinking treatment was $584\%$, and it increased to $1.101\%$, $1.238\%$, and $1.406\%$ after GA vapor cross-linking reaction within 12 h, 24 h, and 48 h. The 48-h GA-treated PVA/SA/GO/ZnO nanofibers exhibited the highest swelling ratio performance and were 1.4 times enhanced than uncrosslinked PVA/SA/GO/ZnO nanofibers. The reasons for the enhanced swelling rate of GA-treated PVA/SA/GO/ ZnO nanofibers are in conjunction with the tightly structured nanofibers that might provide stable porous behavior, and that the SA component played a role in obtaining high hygroscopicity. Furthermore, the water retention capacity with and without GA-treated PVA/SA/GO/ZnO nanofibers prepared by a series of drying and weighing processes every 30 min until 270 min at 40 °C was carried out. The result shows that the GA-treated PVA/SA/GO/ZnO nanofibers displayed a stronger water retention capacity than those samples without cross-linking processes (Figure 5d). The moisture content of PVA/SA/GO/ZnO nanofibers dropped to $10\%$ after the 270 min drying process. In comparison, GA-treated PVA/SA/GO/ZnO nanofibers samples could retain the moisture content of around $80\%$ after the drying step for 120 min and slowly drops down to $50\%$ after drying for 270 min. That is, the GA-treated PVA/SA/GO/ZnO nanofibers possessed a satisfactory hygroscopic ability at room temperature and kept good moisturizing abilities for more than 4 h at human skin temperature (ca. 37 °C) in order to confirm the influence of the crosslinking step on the microstructure of ZnO nanomaterials in PVA/SA/GO/ZnO electrospun nanofibers. The EDS spectrum and X-ray mapping shown in Figure 5e,i exhibits the existence of GO and ZnO nanomaterials into GA-treated PVA/SA/GO/ZnO electrospun nanofibers through crosslinking for 12 h. In addition, the EDS spectrum and X-ray mapping presented additives of both GO and ZnO nanomaterials in PVA/SA/GO/ZnO electrospun nanofibers are intact and well distributed after crosslinking for 24 h, 48 h, and 72 h, which are shown in Figure S4, respectively.
Finally, we investigated the contact angle image of PVA/SA/GO/ZnO nanofibers and GA-treated PVA/SA/GO/ZnO nanofibers to determine the surface hydrophilic/hydrophobic behavior. All the contact angle images and calculation results were shown in Figure 6. The contact angle from pure PVA nanofibers to PVA/SA and PVA/SA/GO nanofibers changed from 61.39° → 41.72° → 27.86°, indicating the SA and GO contribution to the hydrophilicity of the sample surface. Afterward, the contact angle was slightly increased to 38.81° owing to the rough surface with ZnO NPs addition. Thereafter, the contact angle of GA-treated PVA/SA/GO/ZnO nanofibers was evaluated to 64.79°, 71.70°, 72.84°, and 65.32° individually within 12 h, 24 h, 48 h, and 72 h of cross-linking reaction times (Figure 6e,h). Surprisingly, the GA-treated PVA/SA/GO/ZnO nanofibers exhibited an increasing contact angle regarding the cross-linking reaction times raising from 12 h to 48 h, but not to 72 h. Accordingly, the results did illustrate that the GA-treated PVA/SA/GO/ZnO nanofibers could perfectly maintain the hydrophilic surface and present a certain degree of moisture retention.
## 4. Conclusions
In this study, we progressively synthesized the GA-treated PVA/SA/GO/ZnO nanofibers by the electrospinning process (ESP) and successfully investigated their related properties such as nanofiber morphology, mechanical properties, swelling rate, and water retention capacity. Accordingly, the optimized process condition was the combination of 7 wt% PVA, 2 wt% SA, 0.5 wt% GO NPs, and 1M NaOH-treated ZnO NPs (23 nm) with 199 ± 22 nm in diameter, 355 cP in viscosity, and tensile strength of 1.77 MPa. The GA vapor had been used to act as a crosslinker to provide a tight network-like structure and porous-rich nanofiber to support high absorbency and moisturizing for wound healing applications. Interestingly, the GA-treated PVA/SA/GO/ZnO nanofibers displayed a swelling ratio of 1,$406\%$ and mechanical strength of 1.87 MPa. Consequently, The PVA/SA/GO/ZnO mixture effectively behaved with an antibacterial ability with an 8 mm inhibition zone in S. aureus strains. We strongly believed that such novel electrospun membranes with enhanced water and oxygen permeability would lead to better hemostasis and exudate absorption performance while keeping adequate moisture content in the wound region.
## Figures and Scheme
**Scheme 1:** *The synthesis flowchart of GA-treated PVA/SA/GO/ZnO electrospun membranes against Staphylococcus aureus (S. aureus).* **Figure 1:** *The SEM images of PVA/SA nanofiber were prepared by different precursors containing PVA (5, 6, 7, and 8 wt%) and 2 wt% SA with different viscosities: (a) 93 cP; (b) 198 cP; (c) 355 cP; (d) 734 cP. (e) the diameter of PVA/SA nanofiber followed by different viscosity. (The scale bar in the SEM image was 5 μm).* **Figure 2:** *The testing results of (a) XRD, (b) Raman, and (c) FTIR patterns of graphene oxide (GO) via the Hummers’ method. (d) The tensile strength and elongation during the breaking of PVA/SA/GO nanofibers. The SEM image of PVA/SA/GO nanofibers with varying GO concentrations of (e) 0.3 wt%, (f) 0.5 wt%, (g) 0.7 wt%, and (h) 1.0 wt%, respectively. (The scale bar in the SEM image is 5 μm).* **Figure 3:** *The SEM images of prepared ZnO nanomaterial were obtained by adjusting the NaOH concentration: (a) 0.5 M, (b) 1.0 M, (c) 1.5 M, and (d) 2.0 M, respectively. (e) the XRD diffraction patterns of ZnO are prepared by preparing different NaOH concentrations.* **Figure 4:** *The SEM images of PVA/SA/GO/ZnO nanofibers were prepared by adjusting the amount of ZnO addition: (a) 0.1 wt%, (b) 0.3 wt%, (c) 0.5 wt%, (d) 0.7 wt%, with (e) 1.0 wt%. (f) the magnification SEM image of PVA/SA/GO/ZnO nanofibers with 1 wt% ZnO NPs addition. The antibacterial results of PVA/SA/GO/ZnO antibacterial solution, in which the differences of (g) high concentration S. aureus and (h) low concentration S. aureus were compared. (i) The zones of inhibition (ZOI) of PVA/SA/GO/ZnO antibacterial solution against S. aureus. (The scale bar in the SEM image is 5 μm).* **Figure 5:** *(a) The FTIR spectrum of PVA/SA/GO/ZnO nanofibers under different crosslinking time parameters. (b) Tensile strength and elongation at break of crosslinked GA-treated PVA/SA/GO/ZnO nanofibers. (c) The swelling ratio of GA-treated PVA/SA/GO/ZnO nanofibers within the time course. (d) GA-treated PVA/SA/GO/ZnO nanofibers’ water retention capacity rate with different cross-linking times. The EDS mapping of PVA/SA/GO/ZnO nanofibers via 12 h of GA vapor cross-linking reaction: (e) carbon, (f) oxygen, (g) zinc, (h) combination of the element mapping image (carbon, oxygen, and zinc) and SEM image and (i) the EDS spectrum.* **Figure 6:** *Photographs and calculation of contact angle of (a) PVA, (b) PVA/SA, (c) PVA/SA/GO, (d) PVA/SA/GO/ZnO nanofibers, GA-treated PVA/SA/GO/ZnO nanofibers with different crosslinking reaction times: (e) 12 h, (f) 24 h, (g) 48 h, and (h) 72 h.*
## References
1. Yu B., He C., Wang W., Ren Y., Yang J., Guo S., Zheng Y., Shi X.. **Asymmetric wettable composite wound dressing prepared by electrospinning with bioinspired micropatterning enhances diabetic wound healing**. *ACS Appl. Bio Mater.* (2020) **3** 5383-5394. DOI: 10.1021/acsabm.0c00695
2. Liang Y., He J., Guo B.. **Functional hydrogels as wound dressing to enhance wound healing**. *ACS Nano* (2021) **15** 12687-12722. DOI: 10.1021/acsnano.1c04206
3. Luraghi A., Peri F., Moroni L.. **Electrospinning for drug delivery applications: A review**. *J. Control. Release* (2021) **334** 463-484. DOI: 10.1016/j.jconrel.2021.03.033
4. Cacciotti I., Pallotto F., Scognamiglio V., Moscone D., Arduini F.. **Reusable optical multi-plate sensing system for pesticide detection by using electrospun membranes as smart support for acetylcholinesterase immobilisation**. *Mater. Sci. Eng. C* (2020) **111**. DOI: 10.1016/j.msec.2020.110744
5. Si Y., Shi S., Hu J.. **Evaluation of high fluoride contaminated fractured rock aquifer in South Africa–Geochemical and chemometric approaches**. *Nano Today* (2023) **48**
6. Kabir S., Van Cleve T., Khandavalli S., Medina S., Pylypenko S., Mauger S., Ulsh M., Neyerlin K.C.. **Toward Optimizing Electrospun Nanofiber Fuel Cell Catalyst Layers: Microstructure and Pt Accessibility**. *ACS Appl. Energy Mater.* (2021) **4** 3341-3351. DOI: 10.1021/acsaem.0c03073
7. Lee C.H., Tiwari B., Zhang D., Yap Y.K.. **Water purification: Oil–water separation by nanotechnology and environmental concerns**. *Environ. Sci. Nano* (2017) **4** 514-525. DOI: 10.1039/C6EN00505E
8. Yang X., Pu Y., Li S., Liu X., Wang Z., Yuan D., Ning X.. **Electrospun polymer composite membrane with superior thermal stability and excellent chemical resistance for high-efficiency PM2. 5 capture**. *ACS Appl. Mater. Interfaces* (2019) **11** 43188-43199. DOI: 10.1021/acsami.9b15219
9. Reneker D.H., Chun I.. **Nanometre diameter fibres of polymer, produced by electrospinning**. *Nanotechnology* (1996) **7** 216. DOI: 10.1088/0957-4484/7/3/009
10. Zong X., Kim K., Fang D., Ran S., Hsiao B.S., Chu B.. **Structure and process relationship of electrospun bioabsorbable nanofiber membranes**. *Polymer* (2002) **43** 4403-4412. DOI: 10.1016/S0032-3861(02)00275-6
11. Kade J.C., Dalton P.. **Polymers for Melt Electrowriting**. *Adv. Healthc. Mater.* (2020) **10**. DOI: 10.1002/adhm.202001232
12. Robinson A.J., Pérez-Nava A., Ali S.C., González-Campos J.B., Holloway J.L., Cosgriff-Hernandez E.M.. **Comparative analysis of fiber alignmentmethods in electrospinning**. *Matter* (2021) **4** 821-844. DOI: 10.1016/j.matt.2020.12.022
13. Yen S.-C., Liu Z.-W., Juang R.-S., Sahoo S., Huang C.-H., Chen P., Hsiao Y.-S., Fang J.-T.. **Carbon nanotube/conducting polymer hybrid nanofibers as novel organic bioelectronic interfaces for efficient removal of protein-bound uremic toxins**. *ACS Appl. Mater. Interfaces* (2019) **11** 43843-43856. DOI: 10.1021/acsami.9b14351
14. Pan F., Amarjargal A., Altenried S., Liu M., Zuber F., Zeng Z., Rossi R.M., Maniura-Weber K., Ren Q.. **Bioresponsive hybrid nanofibers enable controlled drug delivery through glass transition switching at physiological temperature**. *ACS Appl. Bio Mater.* (2021) **4** 4271-4279. DOI: 10.1021/acsabm.1c00099
15. Ramírez-Agudelo R., Scheuermann K., Gala-García A., Monteiro A.P.F., Pinzón-García A.D., Cortés M.E., Sinisterra R.D.. **Hybrid nanofibers based on poly-caprolactone/gelatin/hydroxyapatite nanoparticles-loaded Doxycycline: Effective anti-tumoral and antibacterial activity**. *Mater. Sci. Eng. C* (2018) **83** 25-34. DOI: 10.1016/j.msec.2017.08.012
16. Wang X., Fang D., Yoon K., Hsiao B.S., Chu B.. **High performance ultrafiltration composite membranes based on poly (vinyl alcohol) hydrogel coating on crosslinked nanofibrous poly (vinyl alcohol) scaffold**. *J. Membr. Sci.* (2006) **278** 261-268. DOI: 10.1016/j.memsci.2005.11.009
17. Liu H., Gough C., Deng Q., Gu Z., Wang F., Hu X.. **Recent Advances in Electrospun SustainableComposites for Biomedical, Environmental, Energy,and Packaging Applications**. *Int. J. Mol. Sci.* (2020) **21**. PMID: 32512793
18. Adeli-Sardou M., Yaghoobi M.M., Torkzadeh-Mahani M., Dodel M.. **Controlled release of lawsone from polycaprolactone/gelatin electrospun nano fibers for skin tissue regeneration**. *Int. J. Biol. Macromol.* (2019) **124** 478-491. DOI: 10.1016/j.ijbiomac.2018.11.237
19. Asadi N., Alizadeh E., Salehi R., Khalandi B., Davaran S., Akbarzadeh A.. **Nanocomposite hydrogels for cartilage tissue engineering: A review**. *Artif. Cells Nanomed. Biotechnol.* (2018) **46** 465-471. DOI: 10.1080/21691401.2017.1345924
20. Zare P., Aleemardani M., Seifalian A., Bagher Z., Seifalian A.M.. **Graphene oxide: Opportunities and challenges in biomedicine**. *Nanomaterials* (2021) **11**. DOI: 10.3390/nano11051083
21. Rijal N.P., Adhikari U., Khanal S., Pai D., Sankar J., Bhattarai N.. **Magnesium oxide-poly (ε-caprolactone)-chitosan-based composite nanofiber for tissue engineering applications**. *Mater. Sci. Eng. B* (2018) **228** 18-27. DOI: 10.1016/j.mseb.2017.11.006
22. Sumitha M., Shalumon K., Sreeja V., Jayakumar R., Nair S.V., Menon D.. **Biocompatible and antibacterial nanofibrous poly (ϵ-caprolactone)-nanosilver composite scaffolds for tissue engineering applications**. *J. Macromol. Sci. Part A* (2012) **49** 131-138. DOI: 10.1080/10601325.2012.642208
23. Lee Y.J., Shin D.S., Kwon O.W., Park W.H., Choi H.G., Lee Y.R., Han S.S., Noh S.K., Lyoo W.S.. **Preparation of atactic poly (vinyl alcohol)/sodium alginate blend nanowebs by electrospinning**. *J. Appl. Polym. Sci.* (2007) **106** 1337-1342. DOI: 10.1002/app.26568
24. Zhu B., Li W., Chi N., Lewis R.V., Osamor J., Wang R.. **Optimization of glutaraldehyde vapor treatment for electrospun collagen/silk tissue engineering scaffolds**. *ACS Omega* (2017) **2** 2439-2450. DOI: 10.1021/acsomega.7b00290
25. Hulupi M., Haryadi H.. **Synthesis and characterization of electrospinning PVA nanofiber-crosslinked by glutaraldehyde**. *Mater. Today Proc.* (2019) **13** 199-204. DOI: 10.1016/j.matpr.2019.03.214
26. You X.L., He J.X., Nan N., Sun X.Q., Qi K., Zhou Y.M., Shao W.L., Liu F., Cui S.Z.. **Stretchable capacitive fabric electronic skin woven by electrospun nanofiber coated yarns for detecting tactile and multimodal mechanical stimuli**. *J. Mater. Chem. C* (2018) **6** 12981-12991. DOI: 10.1039/C8TC03631D
27. Zaaba N.I., Foo K.L., Hashim U., Tan S.J., Liu W.W., Voon C.H.. **Synthesis of Graphene Oxide using Modified Hummers Method: Solvent Influence**. *Procedia Eng.* (2017) **184** 469-477. DOI: 10.1016/j.proeng.2017.04.118
28. Gatou M.A., Lagopati N., Vagena I.A., Gazouli M., Pavlatou E.A.. **ZnO Nanoparticles from Different Precursors and Their Photocatalytic Potential for Biomedical Use**. *Nanomaterials* (2023) **13**. DOI: 10.3390/nano13010122
29. Aladejana J.T., Wu Z., Li D., Guelifack K., Wei W., Wang X., Xie Y.. **Facile Approach for Glutaraldehyde Cross-Linking of PVA/Aluminophosphate Adhesives for Wood-Based Panels**. *ACS Sustain. Chem. Eng.* (2019) **7** 18524-18533. DOI: 10.1021/acssuschemeng.9b04555
30. Premanathan M., Karthikeyan K., Jeyasubramanian K., Manivannan G.. **Selective toxicity of ZnO nanoparticles toward Gram-positive bacteria and cancer cells by apoptosis through lipid peroxidation**. *Nanomed. Nanotechnol. Biol. Med.* (2011) **7** 184-192. DOI: 10.1016/j.nano.2010.10.001
31. Moezzi A., Cortie M., McDonagh A.. **Aqueous pathways for the formation of zinc oxide nanoparticles**. *Dalton Trans.* (2011) **40** 4871-4878. DOI: 10.1039/c0dt01748e
32. Palanikumar L., Ramasamy S.N., Balachandran C.. **Size-dependent antimicrobial response of zinc oxide nanoparticles**. *IET Nanobiotechnol.* (2014) **8** 111-117. DOI: 10.1049/iet-nbt.2012.0008
33. Mirhosseini F., Amiri M., Daneshkazemi A., Zandi H., Javadi Z.S.. **Antimicrobial effect of different sizes of nano zinc oxide on oral microorganisms**. *Front. Dent.* (2019) **16** 105. DOI: 10.18502/fid.v16i2.1361
34. Bursali E.A., Coskun S., Kizil M., Yurdakoc M.. **Synthesis, characterization and in vitro antimicrobial activities of boron/starch/polyvinyl alcohol hydrogels**. *Carbohydr. Polym.* (2011) **83** 1377-1383. DOI: 10.1016/j.carbpol.2010.09.056
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---
title: Changes in Lean Tissue Mass, Fat Mass, Biological Parameters and Resting Energy
Expenditure over 24 Months Following Sleeve Gastrectomy
authors:
- Laurent Maïmoun
- Safa Aouinti
- Marion Puech
- Patrick Lefebvre
- Melanie Deloze
- Pascal de Santa Barbara
- Eric Renard
- Jean-Paul Christol
- Justine Myzia
- Marie-Christine Picot
- Denis Mariano-Goulart
- David Nocca
journal: Nutrients
year: 2023
pmcid: PMC10004853
doi: 10.3390/nu15051201
license: CC BY 4.0
---
# Changes in Lean Tissue Mass, Fat Mass, Biological Parameters and Resting Energy Expenditure over 24 Months Following Sleeve Gastrectomy
## Abstract
Sleeve gastrectomy (SG) induces weight loss but its effects on body composition (BC) are less well known. The aims of this longitudinal study were to analyse the BC changes from the acute phase up to weight stabilization following SG. Variations in the biological parameters related to glucose, lipids, inflammation, and resting energy expenditure (REE) were concomitantly analysed. Fat mass (FM), lean tissue mass (LTM), and visceral adipose tissue (VAT) were determined by dual-energy X-ray absorptiometry in 83 obese patients ($75.9\%$ women) before SG and 1, 12 and 24 months later. After 1 month, LTM and FM losses were comparable, whereas at 12 months the loss of FM exceeded that of LTM. Over this period, VAT also decreased significantly, biological parameters became normalized, and REE was reduced. For most of the BC, biological and metabolic parameters, no substantial variation was demonstrated beyond 12 months. In summary, SG induced a modification in BC changes during the first 12 months following SG. Although the significant LTM loss was not associated with an increase in sarcopenia prevalence, the preservation of LTM might have limited the reduction in REE, which is a longer-term weight-regain criterion.
## 1. Introduction
Bariatric surgery (BS) is an effective method for both acute and long-term weight loss in obese patients when other treatments have failed and when the body mass index (BMI) is greater than 40 kg/m2 (severe obesity) or greater than 35 kg/m2 with obesity-related comorbidities [1]. The loss of excess body weight is generally a criterion for successful surgery. However, while the change in absolute weight loss provides an indication of progress [2] and is easy to use in clinical practice, it cannot adequately reflect changes in fat mass (FM) and lean tissue mass (LTM), the two compartments that constitute body composition. These two indicators are more closely related to all-causes and cardiovascular mortality than to weight [3].
After BS, the greatest weight loss occurs in the first year and weight stabilization is achieved in the second [4,5]. Weight regain may start after the second or third year [4,5]. Conversely, the long-time course of body composition change is unclear, probably because the techniques of investigation—including bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiomety (DXA)—may introduce measurement bias [6]. Moreover, although BIA is less expensive than DXA, it may underestimate FM and overestimate LTM in patients with obesity [7]. The body hydration disturbance present in this population may lead to measurement errors [8]. When the reference technique is used (i.e., DXA) [9], a biphasic variation is observed. This has been characterized by an acute and concomitant reduction in FM and LTM in the first few months after BS, followed by a sustained reduction in FM during the subsequent prolonged weight-loss period [10,11,12]. Generally, the time of follow-up in these studies has been limited to the first 12 months [11,12,13], and very few longitudinal studies have been performed between 24 and 36 months [14,15]. Yet monitoring body composition change over a longer postsurgery period may provide clinically relevant information to better identify and treat patients according to lifestyle and medical care [13,16]. Moreover, although it is not clear whether body composition changes vary with the surgical procedure, studies have preferentially focused on the Roux-en-Y gastric bypass (RYGB) technique rather than sleeve gastrectomy (SG) [13], despite SG recently becoming the most common bariatric approach [17].
It was also reported that patients with weight regain presented higher %FM and lower %LTM than patients maintaining stable weight [18]. Moreover, a lower weight-adjusted resting energy expenditure (REE) was observed in the weight regain group [18], suggesting that this clinical parameter should also be routinely evaluated. These studies, thus, seem to indicate that an excessive reduction in LTM during weight loss programs may have some deleterious effects on metabolism, thermoregulation, functional capacity and weight regain [19].
While body composition analysis appears to be an improvement over simple body weight assessment, the measurement of visceral adipose tissue (VAT) may be even more relevant in patients with obesity. For example, VAT excess induces and maintains lipotoxicity and insulin resistance [20,21], playing a central role in cardiac dysfunction [22]. VAT is also an important endocrine organ that secretes pro-inflammatory factors, resulting in chronic low-grade systemic inflammation that may be involved in the pathogenesis of metabolic abnormalities [23]. However, to the best of our knowledge, few studies have investigated the VAT change after BS [24,25].
The aims of this study were to analyse the FM, LTM and VAT changes from the acute phase of body weight loss (1 month) until a recognized phase of body weight stabilization (12 and 24 months) following SG. We also analysed the variation in the biological parameters related to glucose, lipids and inflammation, as well as the resting energy expenditure.
## 2.1. Subjects and Method
The study was approved by the local research ethics committee (ID RCB: 2015-A01047-42). All patients signed a consent form before entering the study. The clinical trial number is NCT02712086.
## 2.1.1. Subjects
From November 2016 to July 2020, 83 Caucasian patients (women $$n = 63$$; $75.9\%$) from 18.4 to 60.0 years old were recruited from candidates for BS in the obesity management centre, University Hospital of Montpellier (Montpellier, France). The inclusion criteria were: inaction of other weight loss treatments, BMI > 40 or BMI ≥ 35 kg/m2 with the presence of comorbidities [type 2 diabetes (T2D), sleep apnoea syndrome or arterial hypertension (HTA)] and more than 4 years of obesity [1]. The exclusion criteria were: previous BS, pregnancy, medical treatment or physical handicap that might affect body composition evaluation, and body weight >190 kg or height >192.5 cm (limitations of the densitometry device). Physical activity levels were not specifically determined, but none of the participants was participating in a training program in the period before inclusion. Medical history was obtained by questionnaires. All the BS procedures were sleeve gastrectomy (SG), which consists of resecting most of the greater curvature to reduce gastric size and leaving a narrow stomach tube. The SGs were performed in a single institution and in only one surgical department.
## 2.1.2. Methods
This study followed a longitudinal design; its methodology was previously described in detail [10,11]. However, the data of the patients included in this study have never been published. Briefly, all the patients were evaluated the day before the operation (baseline) and 1, 12 and 24 months after SG. After SG, patients were encouraged to increase their physical activity, improve protein intake, and reduce fat intake in order to lose weight, while avoiding side effects, such as muscle mass loss or steatorrhea. For each visit, standing height and weight were measured with a stadiometer and a weight scale with a precision of 0.1 kg. BMI was determined as weight (kg)/height2 (m). The ideal body weight (IBW in kg) was obtained from the Lorentz equations to calculate ideal body weight (IBW): (height [cm]—100)—((height [cm]—150)/4) for men and (height [cm]—100)—((height [cm]—150)/2.5) for women.
## 2.1.4. Regional and Whole Body FM and LTM
FM (kg, %) and LTM (kg) were measured using DXA (Horizon A, Hologic, Inc., Waltham, MA, USA) at whole body and regional sites (upper limbs, trunk, and lower limbs). The same operator performed all scanning and analyses to ensure consistency after following standard quality control procedures. For LTM and FM, the coefficients of variation (CVs) given by the manufacturer were <$1\%$. In the abdominal region, total adipose tissue (TAT), VAT and superficial adipose tissue (SAT) were measured according to a previously validated method [28].
To define sarcopenia in terms of low LTM, we chose the most current cut-offs used for the Caucasian population. The sum of the LTM of the arms and legs defined the appendicular lean mass (ALM; kg). ALM/height2 index [ALMI(h2); kg/m2] or ALMI/body mass index [ALMI(BMI)] defined the ALM index. ALM < 20 kg and ALMI(h2) < 7 kg/m2 in men and ALM < 15 kg and ALMI(h2) < 5.5 kg/m2 in women [29] defined sarcopenia according to The European Working Group on Sarcopenia in Older People (EWGSOP2). ALM < 19.75 kg and ALMI(BMI) < 0.789 in men and ALM < 15.02 kg and ALMI(BMI) <0.512 in women defined the cut-points for low LTM for sarcopenia according to the Foundation for the National Institutes of Health (FNIH) [30]. Finally, ALMI(h2) ≤ 7.23 kg/m2 in men and ≤5.67 kg/m2 in women [31] defined sarcopenia according to the International Working Group on Sarcopenia (IWGS, Albuquerque, NM, USA).
## 2.1.5. Assays
Blood samples (35 mL) were collected in fasting conditions in the morning (8:30–9:00 am). The samples were centrifuged at 2500× g for 10 min at 4 °C. Serum samples were stored at −80 °C and most analyses were performed at the end of the study to reduce interassay variation. In premenopausal women, the blood samples were obtained at an unsynchronized menstrual stage.
Albumin, HbA1c, cholesterol, HDL, triglyceride, glucose, insulin and CRP were routinely analysed by Cobas 101, 501, 602 or 701 (Roche Diagnostic, Mannheim, Germany). The interassay and intraassay coefficients of CVs for the majority of these parameters were lower than $5\%$. The IGF-1 (Reference IS-3900) and IGFBP-3 (Reference IS-4400) assays are based on chemiluminescence technology and were analysed with IDS-iSYS (Immunodiagnostic Systems, Tyne & Ware, Boldon, UK). The intraassay and interassay CVs were 1.9 and $3.9\%$ for IGF-1 and 1.8 and $6.3\%$ for IGFBP-3. For all the biological parameters, the CVs for the intraassay and interassay variations were given by the manufacturer.
Insulin resistance was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR) according to the following formula: fasting serum insulin (mIU/mL)/fasting plasma glucose (mmol/L)/22.5 [26].
## 2.1.6. Resting Energy Expenditure (REE)
Measured REE (REEm) was performed in all patients between 8:00 and 8:30 h after overnight fasting and without the practice of physical exercise for 48 h. REEm was assessed by indirect calorimetry (Quark RMR, Cosmed, Rome, Italy), which analysed oxygen, carbon dioxide and ventilation in a mixing chamber over a period of at least 30 min. Predicted REE values (%; REEp) were calculated from the equation of Harris and Benedict modified by Roza and Shizgal [32] as follows: REEp = 667.051 + 9.74 × (weight) + 1.729 × (height) − 4.737 × (age).
## 2.1.7. Statistical Analysis
Quantitative variables are described with means and standard deviations (SD) after normality testing of the continuous variables with Shapiro–Wilk’s test. For categorical variables, the numbers and associated percentages are presented.
Paired Wilcoxon or paired Student’s tests were used, depending on the normality of the distribution, to compare the relative variations (100 × (measure 2 − measure 1)/measure 1) between baseline and 1, 12 and 24 months for the patients’ clinical characteristics; whole-body composition; android, gynoid, and abdominal adipose tissue; and biological parameters.
Relationships between preoperative characteristics (age, BMI, whole-body LTM, FM, TAT, VAT, SAT and biological parameters) and baseline or relative variations in body composition parameters at 1, 12 or 24 months were assessed using Spearman or Pearson correlation coefficients, depending on the normality of the distributions.
All analyses were two-tailed, with a p-value of < 0.05 considered statistically significant. SAS® Enterprise Guide software (version 8.2, SAS Institute, Cary, NC, USA) was used to perform the analyses and graphs were generated using R statistical software (www.r-project.org, version 4.1.3) with ggplot2 package (version 3.3.3).
## 3.1. Anthropometric Parameters
A total of 83 patients with obesity underwent preoperative and at least one postoperative assessment of body composition ($$n = 83$$ with 1-month data; $$n = 76$$ with 12-month data. and $$n = 60$$ with 24-month data). The major reasons why 23 of the 83 subjects did not complete the 24-month follow-up were the following: they chose not to repeat the measurements ($$n = 15$$), were pregnant ($$n = 3$$), were relocated for work ($$n = 3$$), could not be located for follow-up ($$n = 2$$), or died ($$n = 1$$) (Table 1).
All the baseline and variations in anthropometric characteristics and comorbidity prevalence are presented in Table 1 and Figure 1. At inclusion, the mean age was 40.9 ± 12.3 years, and the mean BMI was 40.7 ± 4.2 kg/m2. The relative mean weight loss was −9.1 ± $2.1\%$ after 1 month, −29.3 ± $8.4\%$ after 12 months, and −27.5 ± $9.6\%$ after 24 months. As expected, all the anthropometric parameters were lower at all times compared to baseline, but no additive loss was observed between 12 and 24 months. All the comorbidities decreased with time. Specifically, T2D was only present in six patients (<$10\%$) at 24 months. Among the 17 patients who presented T2D at baseline, only two were lost to follow-up (one had died).
## 3.2. Body Composition
Initial values and changes in FM and LTM are presented in Table 2 and Figure 1. At 1 month, the reduction in % relative variation at the different sites ranged between −$5.3\%$ (±$7.7\%$) at the upper limbs and −$9.0\%$ (±$4.9\%$) at the trunk for FM (kg), and between −$8.9\%$ (±$5.1\%$) at the lower limbs and −$10.4\%$ (±4.8 %) at the trunk for LTM (kg). At 12 months, all the parameters related to FM and LTM had significantly decreased, but the % relative variation in LTM was lower than in FM, which was also supported by an increase in the LTM/FM ratio. At 12 months, the reduction in % relative variation at the different sites ranged between −$39.0\%$ (±$12.1\%$) at the lower limbs and −47.9 % (±$14.1\%$) at the trunk for FM (kg), while the loss for LTM was about $20\%$ and homogeneous between sites. No subsequent loss in FM or LTM was observed at 24 months. The prevalence of T2D, HTA and obstructive sleep apnoea decreased following surgery.
## 3.3. Sarcopenia Prevalence
ALM and ALM(h2) were significantly reduced, with a comparable percentage relative variation between presurgery and 1 month, and between 1 month and 12 months, around $10\%$. Conversely, ALMI(BMI) increased at 12 and 24 months in comparison with baseline values. Sarcopenia was diagnosed in 10 patients (4 men and 6 women) at baseline and in eight patients (4 men and 4 women) at 1 month only when the FNIH (ALMI(BMI)) criteria were used. Four patients (all women) and two patients (1 man and 1 woman) presented sarcopenia at 12 and 24 months, defined mainly by FNIH and EWGSOP (Table 2 and Figure 1).
## 3.4. Android, Gynoid and Abdominal Body Composition
All parameters (total mass, LTM and FM) measured at android or gynoid regions decreased significantly at 1 and 12 months and this was accentuated with the time from surgery, following the same pattern as observed for the whole body (Table 3 and Figure 1). This was characterized by an accentuated LTM at 1 month, while, for FM, the decrease was maintained over the first 12 months. At abdominal regions, a progressive FM loss ($p \leq 0.001$) for TAT (−$7.69\%$ at 1 month, −$45.75\%$ at 12 months), VAT (−$7.16\%$ at 1 month, −$34.8\%$ at 12 months) and SAT (−$7.98\%$ at 1 month, −$46.89\%$ at 12 months) was also observed. At the android, gynoid and abdominal regions, no subsequent loss was observed between 12 and 24 months.
## 3.5. Biological Parameters
The variation in biological parameters is presented in Table 4 and Figure 2. For glucose homeostasis, a significant decrease was observed in the levels of fasting glucose, HbA1c, insulin and HOMA-IR, suggesting improved insulin sensitivity at all postsurgical time points. No subsequent loss was observed for these biological parameters after 12 months. IGF-1 and IGF-BP3 decreased simultaneously after 1 month, whereas IGF-1 had higher values and IGFBP-3 had lower values at 12 and 24 months in comparison with presurgical values. The concentration of albumin did not change significantly with time, whereas a significant decrease in CRP at 1 month was observed and accentuated at 12 and 24 months.
Compared to baseline, absolute REEm significantly decreased by −$15.7\%$ at 1 month and continued to decrease at 12 months, which totalled a reduction of −$23.2\%$. No subsequent decrease was observed between 12 and 24 months.
## 3.6. Correlations between Basal Parameters and Body Composition Change
The correlation analysis between the basal parameters and % relative body composition changes at the various time points is presented in Table 5. Few significant correlations were observed and only age seemed clearly associated with LTM and FM losses at 12 and 24 months.
## 4. Discussion
This study aimed to characterize the changes in body composition and biological and metabolic parameters in patients with obesity over a 24-month period following SG. The study demonstrated that a significant modification in body composition was associated with the body weight loss and was characterized by an acute reduction in LTM and a continuous loss of FM and VAT over the first 12 months. Concomitantly, an improvement in the lipid, glycaemic, inflammatory and somatotropic profiles was also demonstrated.
This longitudinal study, based on a cohort composed mostly of women, showed that SG induced a loss of approximately $29\%$ of the presurgical body weight and $20\%$ of the anthropometric parameters, including waist and hip circumferences, after 12 and 24 months. The positive effect of weight loss on glycaemic, lipid, inflammatory and somatotropic profiles was clearly confirmed even though some of the patients continued to be overweight or obese at 24-months postsurgery. Moreover, the LTM and FM losses were significant from the first month and maintained over the 2 years. However, the kinetics of loss seemed to be specific to the components. Thus, although the magnitude of variation was relatively comparable between FM and LTM in the first month—i.e., around 8–$10\%$—almost half of the total loss in LTM observed in the first 12 months occurred during this acute phase. The loss in FM appeared more progressive and sustained during the subsequent prolonged weight-loss period, and at 12 months it largely exceeded that of LTM (−38 vs. −$20\%$). It is interesting to note that the % relative variation in LTM and FM appeared relatively comparable between sites, including limbs, trunk and whole body throughout the study, suggesting a uniform body loss. These results are completely superimposable in terms of kinetics and intensity with those of two previously published studies by our group performed in a limited group of patients ($$n = 30$$) after the same follow-up periods [10,11]. The findings from our previous and current studies suggest that, for the same surgical procedure (i.e., SG) and patient characteristics (sex, age and BMI), the expected body composition changes are very reproducible. Comparison with other works must be made with caution because the type of surgical procedure (SG vs. RYGB vs. one-anastomosis gastric bypass) and the device used to measure body composition (BIA vs. DXA) may interfere with the results [6,13,14,33]. In a group of mainly female patients who had undergone predominantly SG, Sivakumar et al. [ 12] also reported that LTM depletion evaluated with DXA occurred predominantly in the initial month, whereas FM declined more progressively over the 12-month follow-up. A recent meta-analysis of 122 studies [13] highlighted that all types of BS cause LTM decline and, although the rate of LTM decrement decreases over time, it follows a significant downward trend over the first 12 months. It should be noted that no subgroup analysis according to the surgical procedure was performed in this meta-analysis [13]. Last, using BIA, two longitudinal studies [14,34] demonstrated that the greatest LTM loss (~7–$10\%$) occurred between 1 and 3 months and that the loss slowed down at 12 months, with values of approximately 80−$85\%$ of the presurgical values.
The highest rate of LTM loss in the first months following BS may be explained by the reduction in physical activity, inadequate protein intake, and restricted global food intake during this period of about 700 kcal/day, which can promote proteolysis to meet the metabolic demands [35,36]. The initial decrease in IGF-1, an anabolic hormone that is sensitive to nutritional intake and even more so to protein intake, might confirm this hypothesis. In humans, weight decrease is associated with IGF-1 reduction only when energy intake is below $50\%$ of the daily ration. [ 37,38]. In our study, we reported, for the first time, that, after 12 months, the IGF-1 values increased and exceeded the presurgical values. Functional hyposomatotropism is a situation frequently observed in obesity [39], and normalization of nutritional intake alone cannot explain this overcompensation. Obesity is also a disease characterized by the presence of low-grade chronic inflammation [40], and the recovery of normal IGF-1 synthesis over time after BS was reported to be independently related with CRP [39]. Consequently, the decrease in CRP found in our study might have led to an increase in IGF-1, which, in turn, might have reduced the rate of LTM loss after 1 month [41]. The favourable effect of IGF-1 in our study might even have been more effective because its free form was increased due to the reduction in its main binding protein (IGFBP-3) throughout the study. This finding has never before been reported. Ohira et al. [ 42] reported an important effect of IGF-1 on LTM because they found that preoperative values were related to maintaining skeletal muscle mass and decreasing body FM in a sex-dependent manner. Unfortunately, in this study [42], no information concerning the variation in IGF-1 after BS was reported. In line with our results, Juiz-Valina et al. [ 39] investigated 116 patients who had undergone RYGB or SG, reporting an initial decrease in IGF-1 after 1 month followed by a progressive increase in growth hormone and IGF-1 up to 12 months. Conversely, De Marinis et al. [ 43] studied 15 obese female patients 16–24 months after biliopancreatic diversion (BPD) and reported that, although the GH response to GH-releasing hormone markedly increased, the initially low IGF-1 and IGFBP-3 concentrations remained unchanged.
Very few studies have analysed body composition changes after weight stabilization over the 12 months following BS, and specifically SG [14,15,25,33,34]. Moreover, the data are generally drawn from cross-sectional analyses, which limits their scope [25,33]. Our results offer new findings by demonstrating that no substantial loss was observed for LTM and FM between 12 and 24 months, which suggests that a new steady state was established 12 months following BS. Two longitudinal studies of 24 and 36 months, using BIA, also observed minor body composition changes after 12 months following BS [14,34].
The reduction in body weight and FM after BS is a desired result, while the LTM loss may have deleterious functional and metabolic consequences. However, we observed, for the first time, that 24 months post-SG, the prevalence of low muscle mass—a criterion involved in the definition of sarcopenia evaluated by DXA—did not increase. This observation was made regardless of the threshold [ALM, ALMI(h2) or ALMI(BMI)]. The presurgical high LTM values in patients with obesity [44] and the maintenance of relative subnormal mean BMI (~28–29 kg/m2) after BS could be responsible. In line with our results, previous studies using DXA have reported that none of the patients presented pathologically low LTM from one to several years (2–18) after RYGB, thus ruling out the diagnosis of sarcopenia [25,45]. We note that the prevalence of sarcopenia may vary with the technique. For example, Vassilev et al. [ 46] used magnetic resonance imaging (RMI) and reported that $57\%$ of the patients were sarcopenic after 24 weeks following RYGB, whereas Voican et al. [ 47] used computed tomography and observed a sarcopenia prevalence of $32\%$ in patients 1 year after SG.
Although the LTM loss seems to have no or only a limited effect on the sarcopenia prevalence up to 24 months, it may alter the metabolic status because—as observed in previous studies as well as this one—muscle mass is the primary determinant of REE [19,34,48] rather than other parameters such as FM [34]. We observed that the main reductions in REE and LTM were concomitant at 1 month, with values continuing to decrease at 12 months and stabilized at 24 months. Two studies performed in patients with extreme obesity (BMI > 50 kg/m2) who underwent different surgical procedures [34,49] also reported this initial REE loss at 1 month that continued up to 24 months. However, it should be noted that body composition was not concomitantly evaluated [49] and that REE was indirectly evaluated by BIA [34], limiting the scope of these works. Our results clearly underlined that the first months, and particularly the first month, are crucial for LTM loss. Consequently, the implementation of programs in this period based on a nutritional approach, such as protein supplementation [50], or exercise interventions, particularly resistance training [51], should help preserve LTM and thus REE [52,53]. The maintenance of REE after BS is crucial and this problem needs to be resolved because low values are associated with weight regain [49,54]. The crucial role of LTM in weight homeostasis and metabolism point to the importance of evaluating body composition in each patient to improve weight loss [34]. However, as underlined by Martinez et al. [ 34], LTM is rarely evaluated in daily clinical practice.
In parallel to the conventional parameters of body composition obtained with DXA, we also evaluated the variation in abdominal adiposity. This approach may be more pertinent in this population because most of the obesity-related comorbidities are more related to VAT than whole body FM. For example, obesity, especially when linked to increases in visceral fat, has more deleterious effects on the cardiovascular system than subcutaneous compartments [22]. We demonstrated herein that abdominal FM parameters, including TAT, VAT and SAT, followed a profile similar to that of whole-body FM in terms of kinetics and intensity. Despite their pertinence, these parameters are largely under-analysed [10,24,55,56,57,58,59] and, to our knowledge, only two studies have evaluated them with comparable techniques (i.e., DXA) and surgical procedures [10,57], but with a limited follow-up duration. For example, Zang et al. [ 57] reported a single measurement point after only 3 months, with VAT decreased by approximately $22.5\%$. In our study, the relative variation in VAT was −7.8 at 1, −34.8 at 12, and −38.4 % at 24 months, and these results are totally in line with those previously published by our group at 1 and 12 months [10]. It is well established that diabetic patients have a greater amount of VAT before surgery compared to normoglycaemic subjects [24], and this observation persisted even later after surgery [25].
The identification of the basal parameters potentially associated with postsurgical body composition change is an attractive approach to implementing corrective measures. In this study, only age was positively but weakly correlated with LTM and FM loss at 12 and 24 months. Conversely, a recent study reported that the loss of LTM measured with BIA at 24 months was only associated with presurgical values of LTM and HOMA-IR independent of age, gender and BS techniques [34]. Other studies have reported that higher BMI and LTM before surgery, male gender and older age were associated with greater LTM loss [60]. Finally, higher preoperative BMI, female gender and patients undergoing SG were also found to be determinants of greater LTM [14,61]. Many factors, including gender and the age distribution of the studied group, may explain the discrepancies in the results.
## 5. Limitations and Strength
The strength of this longitudinal study is the long follow-up period after surgery (24 months) with four repeated measures from the acute loss of body weight up to body weight stabilization. Moreover, as recommended by the International Society for Clinical Densitometry (ISCD) guidelines [9], the body composition change after BS was evaluated by the gold standard technique (DXA). In addition, the concomitant evaluation of biological parameters reflecting glucose homeostasis, inflammation and metabolism may improve our knowledge on the potential link between body composition change and these parameters. We are aware that our study presents some limitations, including a limited number of patients at inclusion and after 24 months, due to the difficulties in maintaining patient compliance and the COVID-19 pandemic. However, the study group reflects the population managed in our surgical department, and the number of included patients is in line with previous studies [13], reflecting the difficulties of including and following these patients after BS. In addition, a longer study until weight regain occurs might provide more clues with factors that are closely associated with weight regain after SG. Moreover, our cohort was composed of men and women, and it cannot be excluded that a sex-specific body composition change before surgery occurred, particularly for VAT [59]. Our population was mainly women, and, thus, no subgroup analysis was performed. Last, specific data on food intake and physical activity levels were not collected. However, all patients had received the same hygienic and dietetic advice from a specialized team before and during the follow-up.
## 6. Conclusions
Our longitudinal study provides crucial findings on the kinetics and specificity of the two components of body composition change over a 2-year period after SG. FM tended to progressively decrease up to 12 months, whereas LTM loss was predominant in the first month. Taking into account the crucial role of LTM on REE regulation and potential weight regain, multidisciplinary (surgeons, dietitians, physiotherapists, …) reflection and dialogue should be encouraged to develop strategies for limiting LTM loss, particularly in the early postoperative period.
## References
1. Jensen M.D., Ryan D.H., Apovian C.M., Ard J.D., Comuzzie A.G., Donato K.A., Hu F.B., Hubbard V.S., Jakicic J.M., Kushner R.F.. **2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Obesity Society**. *J. Am. Coll. Cardiol.* (2013) **63** 2985-3023. DOI: 10.1016/j.jacc.2013.11.004
2. Albanopoulos K., Tsamis D., Natoudi M., Alevizos L., Zografos G., Leandros E.. **The impact of laparoscopic sleeve gastrectomy on weight loss and obesity-associated comorbidities: The results of 3 years of follow-up**. *Surg. Endosc.* (2016) **30** 699-705. DOI: 10.1007/s00464-015-4262-2
3. Beamish A.J., Olbers T., Kelly A.S., Inge T.H.. **Cardiovascular effects of bariatric surgery**. *Nat. Rev. Cardiol.* (2016) **13** 730-743. DOI: 10.1038/nrcardio.2016.162
4. Kennedy-Dalby A., Adam S., Ammori B.J., Syed A.A.. **Weight loss and metabolic outcomes of bariatric surgery in men versus women—A matched comparative observational cohort study**. *Eur. J. Intern. Med.* (2014) **25** 922-925. DOI: 10.1016/j.ejim.2014.10.020
5. Voorwinde V., Steenhuis I.H.M., Janssen I.M.C., Monpellier V.M., van Stralen M.M.. **Definitions of Long-Term Weight Regain and Their Associations with Clinical Outcomes**. *Obes. Surg.* (2020) **30** 527-536. DOI: 10.1007/s11695-019-04210-x
6. Frisard M.I., Greenway F.L., Delany J.P.. **Comparison of methods to assess body composition changes during a period of weight loss**. *Obes. Res.* (2005) **13** 845-854. DOI: 10.1038/oby.2005.97
7. Schiavo L., Pilone V., Tramontano S., Rossetti G., Iannelli A.. **May Bioelectrical Impedance Analysis Method Be Used in Alternative to the Dual-Energy X-Ray Absorptiometry in the Assessment of Fat Mass and Fat-Free Mass in Patients with Obesity? Pros, Cons, and Perspectives**. *Obes. Surg.* (2020) **30** 3212-3215. DOI: 10.1007/s11695-020-04614-0
8. Johnson Stoklossa C.A., Forhan M., Padwal R.S., Gonzalez M.C., Prado C.M.. **Practical Considerations for Body Composition Assessment of Adults with Class II/III Obesity Using Bioelectrical Impedance Analysis or Dual-Energy X-Ray Absorptiometry**. *Curr. Obes. Rep.* (2016) **5** 389-396. DOI: 10.1007/s13679-016-0228-5
9. Shepherd J.A., Baim S., Bilezikian J.P., Schousboe J.T.. **Executive summary of the 2013 International Society for Clinical Densitometry Position Development Conference on Body Composition**. *J. Clin. Densitom.* (2013) **16** 489-495. DOI: 10.1016/j.jocd.2013.08.005
10. Maimoun L., Lefebvre P., Aouinti S., Picot M.C., Mariano-Goulart D., Nocca D., Montpellier Study Group of Bariatric S.. **Acute and longer-term body composition changes after bariatric surgery**. *Surg. Obes. Relat. Dis.* (2019) **15** 1965-1973. DOI: 10.1016/j.soard.2019.07.006
11. Maimoun L., Lefebvre P., Jaussent A., Fouillade C., Mariano-Goulart D., Nocca D.. **Body composition changes in the first month after sleeve gastrectomy based on gender and anatomic site**. *Surg. Obes. Relat. Dis.* (2017) **13** 780-787. DOI: 10.1016/j.soard.2017.01.017
12. Sivakumar J., Chen Q., Sutherland T.R., Read M., Ward S., Chong L., Hii M.W.. **Body Composition Differences Between Excess Weight Loss ≥ 50% and < 50% at 12 Months Following Bariatric Surgery**. *Obes. Surg.* (2022) **32** 2556-2566. DOI: 10.1007/s11695-022-06128-3
13. Haghighat N., Ashtary-Larky D., Bagheri R., Aghakhani L., Asbaghi O., Amini M., Moeinvaziri N., Hosseini B., Wong A., Shamekhi Z.. **Preservation of fat-free mass in the first year after bariatric surgery: A systematic review and meta-analysis of 122 studies and 10,758 participants**. *Surg. Obes. Relat. Dis.* (2022) **18** 964-982. DOI: 10.1016/j.soard.2022.02.022
14. Barzin M., Heidari Almasi M., Mahdavi M., Khalaj A., Valizadeh M., Hosseinpanah F.. **Body Composition Changes Following Sleeve Gastrectomy Vs. One-Anastomosis Gastric Bypass: Tehran Obesity Treatment Study (TOTS)**. *Obes. Surg.* (2021) **31** 5286-5294. DOI: 10.1007/s11695-021-05722-1
15. Sherf-Dagan S., Zelber-Sagi S., Buch A., Bar N., Webb M., Sakran N., Raziel A., Goitein D., Keidar A., Shibolet O.. **Prospective Longitudinal Trends in Body Composition and Clinical Outcomes 3 Years Following Sleeve Gastrectomy**. *Obes. Surg.* (2019) **29** 3833-3841. DOI: 10.1007/s11695-019-04057-2
16. Gomez-Ambrosi J., Andrada P., Valenti V., Rotellar F., Silva C., Catalan V., Rodriguez A., Ramirez B., Moncada R., Escalada J.. **Dissociation of body mass index, excess weight loss and body fat percentage trajectories after 3 years of gastric bypass: Relationship with metabolic outcomes**. *Int. J. Obes.* (2017) **41** 1379-1387. DOI: 10.1038/ijo.2017.134
17. Angrisani L., Santonicola A., Iovino P., Formisano G., Buchwald H., Scopinaro N.. **Bariatric Surgery Worldwide 2013**. *Obes. Surg.* (2015) **25** 1822-1832. DOI: 10.1007/s11695-015-1657-z
18. Shantavasinkul P.C., Omotosho P., Muehlbauer M.J., Natoli M., Corsino L., Tong J., Portenier D., Torquati A.. **Metabolic profiles, energy expenditures, and body compositions of the weight regain versus sustained weight loss patients who underwent Roux-en-Y gastric bypass**. *Surg. Obes. Relat. Dis.* (2021) **17** 2015-2025. DOI: 10.1016/j.soard.2021.09.007
19. Marks B.L., Rippe J.M.. **The importance of fat free mass maintenance in weight loss programmes**. *Sport. Med.* (1996) **22** 273-281. DOI: 10.2165/00007256-199622050-00001
20. Montague C.T., O’Rahilly S.. **The perils of portliness: Causes and consequences of visceral adiposity**. *Diabetes* (2000) **49** 883-888. DOI: 10.2337/diabetes.49.6.883
21. Despres J.P., Lemieux I.. **Abdominal obesity and metabolic syndrome**. *Nature* (2006) **444** 881-887. DOI: 10.1038/nature05488
22. Rao V.N., Fudim M., Mentz R.J., Michos E.D., Felker G.M.. **Regional adiposity and heart failure with preserved ejection fraction**. *Eur. J. Heart Fail.* (2020) **22** 1540-1550. DOI: 10.1002/ejhf.1956
23. Fontana L., Eagon J.C., Trujillo M.E., Scherer P.E., Klein S.. **Visceral fat adipokine secretion is associated with systemic inflammation in obese humans**. *Diabetes* (2007) **56** 1010-1013. DOI: 10.2337/db06-1656
24. Favre L., Marino L., Roth A., Acierno J., Hans D., Demartines N., Pitteloud N., Suter M., Collet T.H.. **The Reduction of Visceral Adipose Tissue after Roux-en-Y Gastric Bypass Is more Pronounced in Patients with Impaired Glucose Metabolism**. *Obes. Surg.* (2018) **28** 4006-4013. DOI: 10.1007/s11695-018-3455-x
25. Santini S., Vionnet N., Pasquier J., Suter M., Hans D., Gonzalez-Rodriguez E., Pitteloud N., Favre L.. **Long-term body composition improvement in post-menopausal women following bariatric surgery: A cross-sectional and case-control study**. *Eur. J. Endocrinol.* (2022) **186** 255-263. DOI: 10.1530/EJE-21-0895
26. Ray K.K., Colhoun H.M., Szarek M., Baccara-Dinet M., Bhatt D.L., Bittner V.A., Budaj A.J., Diaz R., Goodman S.G., Hanotin C.. **Effects of alirocumab on cardiovascular and metabolic outcomes after acute coronary syndrome in patients with or without diabetes: A prespecified analysis of the ODYSSEY OUTCOMES randomised controlled trial**. *Lancet Diabetes Endocrinol.* (2019) **7** 618-628. DOI: 10.1016/S2213-8587(19)30158-5
27. Geldsetzer P., Manne-Goehler J., Marcus M.E., Ebert C., Zhumadilov Z., Wesseh C.S., Tsabedze L., Supiyev A., Sturua L., Bahendeka S.K.. **The state of hypertension care in 44 low-income and middle-income countries: A cross-sectional study of nationally representative individual-level data from 1.1 million adults**. *Lancet* (2019) **394** 652-662. DOI: 10.1016/S0140-6736(19)30955-9
28. Katzmarzyk P.T., Greenway F.L., Heymsfield S.B., Bouchard C.. **Clinical utility and reproducibility of visceral adipose tissue measurements derived from dual-energy X-ray absorptiometry in White and African American adults**. *Obesity* (2013) **21** 2221-2224. DOI: 10.1002/oby.20519
29. Cruz-Jentoft A.J., Bahat G., Bauer J., Boirie Y., Bruyere O., Cederholm T., Cooper C., Landi F., Rolland Y., Sayer A.A.. **Sarcopenia: Revised European consensus on definition and diagnosis**. *Age Ageing* (2019) **48** 601. DOI: 10.1093/ageing/afz046
30. McLean R.R., Shardell M.D., Alley D.E., Cawthon P.M., Fragala M.S., Harris T.B., Kenny A.M., Peters K.W., Ferrucci L., Guralnik J.M.. **Criteria for clinically relevant weakness and low lean mass and their longitudinal association with incident mobility impairment and mortality: The foundation for the National Institutes of Health (FNIH) sarcopenia project**. *J. Gerontol. A Biol. Sci. Med. Sci.* (2014) **69** 576-583. DOI: 10.1093/gerona/glu012
31. Fielding R.A., Vellas B., Evans W.J., Bhasin S., Morley J.E., Newman A.B., Abellan van Kan G., Andrieu S., Bauer J., Breuille D.. **Sarcopenia: An undiagnosed condition in older adults. Current consensus definition: Prevalence, etiology, and consequences. International working group on sarcopenia**. *J. Am. Med. Dir. Assoc.* (2011) **12** 249-256. DOI: 10.1016/j.jamda.2011.01.003
32. Roza A.M., Shizgal H.M.. **The Harris Benedict equation reevaluated: Resting energy requirements and the body cell mass**. *Am. J. Clin. Nutr.* (1984) **40** 168-182. DOI: 10.1093/ajcn/40.1.168
33. Buhler J., Rast S., Beglinger C., Peterli R., Peters T., Gebhart M., Meyer-Gerspach A.C., Wolnerhanssen B.K.. **Long-Term Effects of Laparoscopic Sleeve Gastrectomy and Roux-en-Y Gastric Bypass on Body Composition and Bone Mass Density**. *Obes. Facts* (2021) **14** 131-140. DOI: 10.1159/000512450
34. Martinez M.C., Meli E.F., Candia F.P., Filippi F., Vilallonga R., Cordero E., Hernandez I., Eguinoa A.Z., Burgos R., Vila A.. **The Impact of Bariatric Surgery on the Muscle Mass in Patients with Obesity: 2-Year Follow-up**. *Obes. Surg.* (2022) **32** 625-633. DOI: 10.1007/s11695-021-05815-x
35. Golzarand M., Toolabi K., Djafarian K.. **Changes in Body Composition, Dietary Intake, and Substrate Oxidation in Patients Underwent Laparoscopic Roux-en-Y Gastric Bypass and Laparoscopic Sleeve Gastrectomy: A Comparative Prospective Study**. *Obes. Surg.* (2019) **29** 406-413. DOI: 10.1007/s11695-018-3528-x
36. Coluzzi I., Raparelli L., Guarnacci L., Paone E., Del Genio G., le Roux C.W., Silecchia G.. **Food Intake and Changes in Eating Behavior After Laparoscopic Sleeve Gastrectomy**. *Obes. Surg.* (2016) **26** 2059-2067. DOI: 10.1007/s11695-015-2043-6
37. Harvie M.N., Pegington M., Mattson M.P., Frystyk J., Dillon B., Evans G., Cuzick J., Jebb S.A., Martin B., Cutler R.G.. **The effects of intermittent or continuous energy restriction on weight loss and metabolic disease risk markers: A randomized trial in young overweight women**. *Int. J. Obes.* (2011) **35** 714-727. DOI: 10.1038/ijo.2010.171
38. Tam C.S., Frost E.A., Xie W., Rood J., Ravussin E., Redman L.M., Pennington C.T.. **No effect of caloric restriction on salivary cortisol levels in overweight men and women**. *Metabolism* (2014) **63** 194-198. DOI: 10.1016/j.metabol.2013.10.007
39. Juiz-Valina P., Pena-Bello L., Cordido M., Outeirino-Blanco E., Pertega S., Varela-Rodriguez B., Garcia-Brao M.J., Mena E., Sangiao-Alvarellos S., Cordido F.. **Altered GH-IGF-1 Axis in Severe Obese Subjects is Reversed after Bariatric Surgery-Induced Weight Loss and Related with Low-Grade Chronic Inflammation**. *J. Clin. Med.* (2020) **9**. DOI: 10.3390/jcm9082614
40. Kojta I., Chacinska M., Blachnio-Zabielska A.. **Obesity, Bioactive Lipids, and Adipose Tissue Inflammation in Insulin Resistance**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12051305
41. Pellitero S., Granada M.L., Martinez E., Balibrea J.M., Guanyabens E., Serra A., Moreno P., Navarro M., Romero R., Alastrue A.. **IGF1 modifications after bariatric surgery in morbidly obese patients: Potential implications of nutritional status according to specific surgical technique**. *Eur. J. Endocrinol.* (2013) **169** 695-703. DOI: 10.1530/EJE-13-0209
42. Ohira M., Watanabe Y., Yamaguchi T., Onda H., Yamaoka S., Abe K., Nakamura S., Tanaka S., Kawagoe N., Nabekura T.. **The Relationship between Serum Insulin-Like Growth Factor-1 Levels and Body Composition Changes after Sleeve Gastrectomy**. *Obes. Facts* (2021) **14** 641-649. DOI: 10.1159/000519610
43. De Marinis L., Bianchi A., Mancini A., Gentilella R., Perrelli M., Giampietro A., Porcelli T., Tilaro L., Fusco A., Valle D.. **Growth hormone secretion and leptin in morbid obesity before and after biliopancreatic diversion: Relationships with insulin and body composition**. *J. Clin. Endocrinol. Metab.* (2004) **89** 174-180. DOI: 10.1210/jc.2002-021308
44. Maimoun L., Serrand C., Mura T., Renard E., Nocca D., Lefebvre P., Boudousq V., Avignon A., Mariano-Goulart D., Sultan A.. **Definition of an adapted cut-off for determining low lean tissue mass in older women with obesity: A comparison to current cut-offs**. *Sci. Rep.* (2022) **12** 16905. DOI: 10.1038/s41598-022-21258-5
45. Ruthes E.M.P., Lenardt B.C.C., Lass A.D., Petroski C.A., de Mello M.F., de Andrade Junior A.B., Souza C.J.F., de Matos O., Castelo-Branco C.. **Lean mass and strength profile of women submitted to bariatric surgery: Comparison of the EWGSOP2 and FNIH classification for sarcopenia—ASBS program phase II**. *Gynecol. Endocrinol.* (2022) **38** 868-873. DOI: 10.1080/09513590.2022.2119956
46. Vassilev G., Galata C., Finze A., Weiss C., Otto M., Reissfelder C., Blank S.. **Sarcopenia after Roux-en-Y Gastric Bypass: Detection by Skeletal Muscle Mass Index vs. Bioelectrical Impedance Analysis**. *J. Clin. Med.* (2022) **11**. DOI: 10.3390/jcm11061468
47. Voican C.S., Lebrun A., Maitre S., Lainas P., Lamouri K., Njike-Nakseu M., Gaillard M., Tranchart H., Balian A., Dagher I.. **Predictive score of sarcopenia occurrence one year after bariatric surgery in severely obese patients**. *PLoS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0197248
48. Johnstone A.M., Murison S.D., Duncan J.S., Rance K.A., Speakman J.R.. **Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine**. *Am. J. Clin. Nutr.* (2005) **82** 941-948. DOI: 10.1093/ajcn/82.5.941
49. Fidilio E., Comas M., Giribes M., Cardenas G., Vilallonga R., Palma F., Pelaez R.B., Simo R., Ciudin A.. **Evaluation of Resting Energy Expenditure in Subjects with Severe Obesity and Its Evolution after Bariatric Surgery**. *Obes. Surg.* (2021) **31** 4347-4355. DOI: 10.1007/s11695-021-05578-5
50. Moize V., Andreu A., Rodriguez L., Flores L., Ibarzabal A., Lacy A., Jimenez A., Vidal J.. **Protein intake and lean tissue mass retention following bariatric surgery**. *Clin Nutr* (2013) **32** 550-555. DOI: 10.1016/j.clnu.2012.11.007
51. Hassannejad A., Khalaj A., Mansournia M.A., Rajabian Tabesh M., Alizadeh Z.. **The Effect of Aerobic or Aerobic-Strength Exercise on Body Composition and Functional Capacity in Patients with BMI ≥35 after Bariatric Surgery: A Randomized Control Trial**. *Obes. Surg.* (2017) **27** 2792-2801. DOI: 10.1007/s11695-017-2717-3
52. Siervo M., Faber P., Lara J., Gibney E.R., Milne E., Ritz P., Lobley G.E., Elia M., Stubbs R.J., Johnstone A.M.. **Imposed rate and extent of weight loss in obese men and adaptive changes in resting and total energy expenditure**. *Metabolism* (2015) **64** 896-904. DOI: 10.1016/j.metabol.2015.03.011
53. Faria S.L., Kelly E., Faria O.P.. **Energy expenditure and weight regain in patients submitted to Roux-en-Y gastric bypass**. *Obes. Surg.* (2009) **19** 856-859. DOI: 10.1007/s11695-009-9842-6
54. Faria S.L., Faria O.P., Buffington C., de Almeida Cardeal M., Rodrigues de Gouvea H.. **Energy expenditure before and after Roux-en-Y gastric bypass**. *Obes. Surg.* (2012) **22** 1450-1455. DOI: 10.1007/s11695-012-0672-6
55. Bazzocchi A., Ponti F., Cariani S., Diano D., Leuratti L., Albisinni U., Marchesini G., Battista G.. **Visceral Fat and Body Composition Changes in a Female Population After RYGBP: A Two-Year Follow-Up by DXA**. *Obes. Surg.* (2015) **25** 443-451. DOI: 10.1007/s11695-014-1422-8
56. Heath M.L., Kow L., Slavotinek J.P., Valentine R., Toouli J., Thomson C.H.. **Abdominal adiposity and liver fat content 3 and 12 months after gastric banding surgery**. *Metabolism* (2009) **58** 753-758. DOI: 10.1016/j.metabol.2008.05.021
57. Zang Y., Zhu C., Wen X., Wang X., Li L., Rampersad S., Lu L., Zhou D., Qian C., Cui R.. **Laparoscopic sleeve gastrectomy improves body composition and alleviates insulin resistance in obesity related acanthosis nigricans**. *Lipids Health Dis.* (2017) **16** 209. DOI: 10.1186/s12944-017-0598-z
58. Gletsu-Miller N., Hansen J.M., Jones D.P., Go Y.M., Torres W.E., Ziegler T.R., Lin E.. **Loss of total and visceral adipose tissue mass predicts decreases in oxidative stress after weight-loss surgery**. *Obesity* (2009) **17** 439-446. DOI: 10.1038/oby.2008.542
59. Sun J., Lv H., Li M., Zhao L., Liu Y., Zeng N., Wei X., Chen Q., Ren P., Liu Y.. **How much abdominal fat do obese patients lose short term after laparoscopic sleeve gastrectomy? A quantitative study evaluated with MRI**. *Quant. Imaging Med. Surg.* (2021) **11** 4569-4582. DOI: 10.21037/qims-20-1380
60. Guida B., Cartaldi M., Busetto M., Aiello M.L., Musella M., Capone D., Parolisi S., Policastro V., Ragozini G., Belfiore A.. **Predictors of fat-free mass loss 1 years after laparoscopic sleeve gastrectomy**. *J. Endocrinol. Investig.* (2018) **41** 1307-1315. DOI: 10.1007/s40618-018-0868-2
61. Nuijten M.A.H., Monpellier V.M., Eijsvogels T.M.H., Janssen I.M.C., Hazebroek E.J., Hopman M.T.E.. **Rate and Determinants of Excessive Fat-Free Mass Loss after Bariatric Surgery**. *Obes. Surg.* (2020) **30** 3119-3126. DOI: 10.1007/s11695-020-04654-6
|
---
title: 'Estimation of Glomerular Filtration Rate in Obese Patients: Utility of a New
Equation'
authors:
- Pehuén Fernández
- María Laura Nores
- Walter Douthat
- Javier de Arteaga
- Pablo Luján
- Mario Campazzo
- Jorge de La Fuente
- Carlos Chiurchiu
journal: Nutrients
year: 2023
pmcid: PMC10004854
doi: 10.3390/nu15051233
license: CC BY 4.0
---
# Estimation of Glomerular Filtration Rate in Obese Patients: Utility of a New Equation
## Abstract
There is no consensus on the best equation to estimate glomerular filtration rate (eGFR) in obese patients (OP). Objective: to evaluate the performance of the current equations and the new Argentinian Equation (“AE”) to estimate GFR in OP. Two validation samples were used: internal (IVS, using 10-fold cross-validation) and temporary (TVS). OP whose GFR was measured (mGFR) with clearance of iothalamate between $\frac{2007}{2017}$ (IVS, $$n = 189$$) and $\frac{2018}{2019}$ (TVS, $$n = 26$$) were included. To evaluate the performance of the equations we used: bias (difference between eGFR and mGFR), P30 (percentage of estimates within ±$30\%$ of mGFR), Pearson’s correlation (r) and percentage of correct classification (%CC) according to the stages of CKD. The median age was 50 years. Sixty percent had grade I obesity (G1-Ob), $25.1\%$ G2-Ob and $14.9\%$ G3-Ob, with a wide range in mGFR (5.6–173.1 mL/min/1.73 m2). In the IVS, AE obtained a higher P30 ($85.2\%$), r (0.86) and %CC ($74.4\%$), with lower bias (−0.4 mL/min/1.73 m2). In the TVS, AE obtained a higher P30 ($88.5\%$), r (0.89) and %CC ($84.6\%$). The performance of all equations was reduced in G3-Ob, but AE was the only one that obtained a P30 > $80\%$ in all degrees. AE obtained better overall performance to estimate GFR in OP and could be useful in this population. Conclusions from this study may not be generalizable to all populations of obese patients since they were derived from a study in a single center with a very specific ethnic mixed population.
## 1. Introduction
Obesity is currently an important public health problem, its prevalence is increasing year after year throughout the world, and Latin America does not seem to be the exception [1,2]. The relationship between obesity and type 2 diabetes, hypertension, dyslipidemia, coronary heart disease, cerebrovascular accident and some types of cancer is well known [3,4]. Some authors suggest that obesity also increases the risk of initiation and progression of chronic kidney disease (CKD) [5,6,7], not only due to its relationship with its already known traditional risk factors, but also due to a direct effect on renal structure [5,8,9,10].
It is important to know as precisely as possible the glomerular filtration rate (GFR) in patients with obesity, since they belong to a risk group. The most precise method to measure GFR is by urinary inulin clearance [11], although measurement of urinary clearance with iothalamate yields similar results and is also considered the gold standard [12]. These methods are complex, impractical, expensive, and poorly available. In clinical practice, endogenous markers such as serum creatinine (SCr) are commonly used to estimate GFR using different formulas. Estimating GFR from SCr in the obese is challenging, since there may be determinants that affect SCr generation, such as diet, nutritional status, extreme body size, and hidden relative sarcopenia [13,14]. For this reason, SCr levels can be erratic in estimating GFR. The formulas commonly used in clinical practice are not calibrated for use in this population. In addition, these have been developed in populations with ethnic groups other than the typical Latin American race (mixture between natives, Spanish and Italians). For this reason, we developed a new equation using novel statistical strategies, called AE (“Argentinian Equation”), in which only subjects of Latin American origin were included in their entirety, and other predictor variables were added that could improve the prediction. There is currently no consensus on which is the best equation to estimate GFR in subjects with obesity. The aim of this study was to evaluate the performance of the currently available formulas and the new AE for estimating GFR in obese patients.
## 2. Materials and Methods
A cross-sectional study was carried out. Two samples were used to validate the equations: the internal validation sample (IVS) and the temporary validation sample (TVS).
For IVS, all individuals of Latin American origin were consecutively included, whose GFR was measured using iothalamate urinary clearance, at the Hospital Privado Universitario de Córdoba, between January 2007 and December 2017. Indications for assessment were: suspected or established renal dysfunction, renal risk or before kidney donation. The exclusion criteria were: minors, ethnic groups other than Latin American, history of cirrhosis, decompensated heart failure, more than one GFR measurement (only the first measurement was used), incorrect urine collection, hospitalized, those who presented determinations of serum creatinine, urea and/or albumin, separated more than 7 days before or after the GFR measurement. Absence of data in the single-renal variable, and all subjects with a body mass index (BMI) < 30 kg/m2. For the TVS, the same inclusion and exclusion criteria were used, only that it was created after the IVS and subjects were included between January 2018 and July 2019.
Gender, age (years), BMI (Kg/m2), history of hypertension, diabetes mellitus, and single kidney were recorded. The values of SCr (mg/dL), urea (mg/dL), albumin (gr/L) and GFR measured with urinary clearance of iothalamate (mL/min/1.73 m2) were determined.
Patients were stratified according to BMI (weight in Kg/height in m2) following the World Health Organization (WHO) obesity classification in grade I (BMI between 30 and 34.9 Kg/m2). grade II (BMI between 35 and 39.9 kg/m2) and grade III (BMI of 40 kg/m2 or more) [15].
GFR was measured by renal clearance of non-radiolabeled iothalamate, determined by high performance liquid chromatography (HPLC). The instrument used was a Gilson ® HPLC with a Model 189 UV/Visible detector with a Phenomenex® C18 column. In all cases, the procedure was carried out following a protocolized operations manual. Plasma samples were collected with heparin as an anticoagulant agent, and urine samples in sterile containers. The results are expressed adjusted to 1.73 m2 of body surface.
The determination of SCr was performed using the Jaffe kinetic method (Roche Diagnostics, Sussex, UK), traceable to the IDMS reference method, on a Modular P autoanalyzer. The calibration of the determination was performed with a commercial lyophilized calibrator for automated systems. All assays used participated in internal and external quality control programs (RIQAS, London, UK) and exceeded recommended assay quality specifications (acceptable total error). SCr values are expressed in mg/dL.
Estimated GFR was calculated from the following formulas: Cockcroft and Gault [16] adjusted for lean body mass (LBM_CG) [17], Salazar-Corcoran (SC) [18], Modification of Diet in Renal Disease adjusted for Isotope Dilution Mass Spectrometry (IDMS) standardization with 4 variables (MDRD4) and 6 variables (MDRD6) [19], Chronic Kidney Disease and Epidemiology version 2009 (CKD-EPI 2009) [20] and version 2021 (CKD-EPI 2021) [21], combined formula (CKD-MCQ) [22] between CKD- EPI 2009 and Mayo Clinic Quadratic equation (MCQ) [21], and finally the new AE (manuscript not yet published, presented and awarded at the XXII Argentine Congress of Nephrology [23]). This was developed using a quasi-likelihood model with an identity variance function (V(μ) = μ) and logarithmic link, with the intention of not having to transform the response variable Y, but rather predict it in its natural scale and without the need to assume a specific distribution for it [24]. For this model, six predictive variables were included: gender (male/female), age (years), single kidney (yes/no), square root of SCr (mg/dL), logarithm of urea (mg/dL), and serum albumin (g/L). The final equation was:[1]AE=exp (6.3106−1.7656×SCr−0.0055×age−0.0656×ln (urea)+0.060×albumin+0.224 if male−0.2052 if single kidney The internal validation of this equation was carried out through 10-fold cross-validation [25], for this study only obese patients were used.
The performance of the GFR estimates of each formula (eGFR) was evaluated in relation to the GFR measured through the urinary clearance of iothalamate (mGFR, reference value). Bias was used (defined as the difference between eGFR and mGFR). A positive bias indicates an overestimation of mGFR and vice versa. As measures of position, the median (M), first quartile (Q1) and third quartile (Q3) of the bias were used. Accuracy was evaluated with the P30, defined as the percentage of observations whose eGFR differs from the mGFR by no more than $30\%$ of the mGFR. For the agreement, the Pearson correlation coefficient (r) and the percentage of correct classification (%CC) were used, taking into account the percentage of patients well classified according to the eGFR in the 5 stages of chronic kidney disease [26], taking as reference the mGFR. Since the LBM_CG and SC equations predict creatinine clearance not adjusted for body surface area, mGFR not indexed to body surface area was used as the response variable to assess their performance, unlike the other equations. The scatter plot between mGFR and eGFR was made using the equation with the best performance.
To describe the characteristics of the patients, absolute (n) and relative (%) frequencies were used for the categorical variables, and for the continuous variables, medians (M) and Q1–Q3. To compare continuous variables, the Mann Whitney test was used and for categorical variables the Chi squared test or Fisher’s exact test, depending on the expected frequencies. All tests were two-tailed and a p value less than 0.05 was considered statistically significant.
The statistical analysis was carried out with the software: R Core Team [2021]. A: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria and GraphPad Prism version 8.0.0 for Windows, GraphPad Software, San Diego, CA, USA.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Hospital Privado Universitario de Córdoba (HP-4-264). Informed consent was obtained from all subjects involved in the study. The consent form included information on the procedure itself and on the possibility of later use of the data for research purposes.
## 3. Results
For the IVS, of the initial 755 subjects whose GFR was measured by urinary iothalamate clearance between 2007 and 2017, 566 were excluded for various reasons, and 189 subjects were included. For the TVS, of the 118 initial subjects whose GFR was measured by the same method between 2018 and 2019, 92 were excluded, and 26 were included (Figure 1).
The characteristics of the population are shown in Table 1. The median age was 50 (Q1–Q3 = 40.2–59.8) years, $52.1\%$ were women and $47.9\%$ men. The median BMI was 33.3 (31.7–37.5) kg/m2, but the maximum value was 72.3 kg/m2. The majority belonged to category I of obesity ($60\%$), to a lesser extent to category II ($25.1\%$) and category III ($14.9\%$). $18.1\%$ were diabetic, $47.1\%$ hypertensive, and $9.3\%$ single-kidney. Median SCr was 0.85 (0.71–1.1) mg/dL, urea was 31.1 (25.2–41.7) mg/dL, albumin was 4.2 (3.94–4.44) gr/L, and mGFR was 91.2 (70.2–116.2) mL/min/1.73 m2. There was a wide range in the mGFR (min-max = 5.6–173.1 mL/min/1.73 m2). $51.6\%$ had an mGFR ≥90 mL/min/1.73 m2, $27.4\%$ between 60 and 89.9 mL/min/1.73 m2, $14.4\%$ between 30 and 59.9 mL/min/1.73 m2, $4.2\%$ between 15 and 29.9 mL/min/1.73 m2 and $2.3\%$ <15 mL/min/1.73 m2. There were no statistically significant differences between the characteristics of the subjects included in the IVS and in the TVS, with the exception of SCr levels, which were slightly lower in the IVS (0.84 vs. 0.97 mg/dL; $$p \leq 0.019$$).
Table 2 shows the performance of the equations to estimate the mGFR in the IVS. The equation with the best performance was AE, with the highest P30 ($85.2\%$), correlation (0.86), %CC ($74.4\%$) and a median bias closer to 0 (−0.4 mL/min/1.73 m2). The equations with the highest P30 after AE were CKD-EPI 2009 ($84\%$), MDRD6 ($83.5\%$) and CKD-EPI 2021 ($82.4\%$). Those with the highest %CC after AE were CKD-EPI 2021 and CKD-MCQ ($72.6\%$), and CKD-EPI 2009 ($70.4\%$). CKD-EPI in its two versions and combined with MCQ obtained the same correlation as AE (0.86). CKD-EPI 2021 was the second that obtained a median bias closest to 0 (0.5 mL/min/1.73 m2). The equation that obtained the lowest performance was LBM_CG, followed by SC.
Table 3 shows the performance of the equations to estimate the mGFR in IVS, differentiating between the 3 degrees of obesity. In grade I obesity, AE was the equation with the best performance, with the highest P30 ($87.4\%$), correlation (0.89), and a median bias closer to 0 (−0.4 mL/min/1.73 m2). Its %CC ($74.8\%$) was second only to CKD-MCQ ($75.7\%$). CKD-MCQ and CKD-EPI 2021 obtained the same correlation (0.89). CKD-EPI 2009 and MDRD6 were the ones that obtained the highest P30 after AE ($86.4\%$ and $85.4\%$). CKD-EPI 2021 obtained the second bias closest to 0 (0.6 mL/min/1.73 m2). The equation with the worst performance was LBM_CG. In grade II obesity, CKD-EPI 2021 obtained a slightly higher performance than AE, with higher P30 ($84.4\%$ vs. $82.2\%$), median bias closer to 0 (−1.9 vs. −3 mL/min/1.73 m2), equal correlation (0.88) and equal %CC ($75.6\%$). In grade III obesity, AE was the equation with the highest P30 ($82.1\%$) and %CC ($67.9\%$). The equations with the highest correlation were CKD-EPI 2009 and 2021. The one that obtained a median bias closest to 0 was MDRD4 (−2.5 mL/min/1.73 m2). The performance of all the equations is reduced in grade III obesity. The only equation that obtained a P30 greater than $80\%$ in the 3 degrees of obesity was AE. *In* general terms, the two equations with the worst performance were LBM_CG and SC.
Figure 2 shows the scatter plot between the eGFR by AE and the mGFR by urinary clearance of iothalamate in the 3 degrees of obesity in IVS.
Table 4 shows the performance of the equations to estimate the mGFR in the TVS. In it, AE was also the one that obtained the highest P30 ($88.5\%$, sharing with MDRD6), correlation (0.89, sharing with 3 other equations) and %CC ($84.6\%$, sharing with CKD-EPI 2021). The median bias closest to 0 was obtained by CKD-EPI 2021 (1.1 mL/min/1.73 m2), although with greater dispersion than AE (Q1/Q3 = −$\frac{6.8}{12.1}$ vs. −$\frac{4.3}{8.6}$ mL/min/1.73 m2). In both the IVS and the TVS, a superior overall performance of AE can be observed over the other equations.
## 4. Discussion
This study evaluated the performance of currently available equations and the new AE for estimating GFR in obese patients. There is currently no consensus on the best equation to use in this population.
The SC equation was developed many years ago with obese patients with the intention of predicting creatinine clearance specifically in this population, since the equations available up to that time, such as CG, had poor performance in them [18]. Subsequently, it was observed that the use of the LBM instead of the current weight in the CG formula improves the prediction of creatinine clearance in obese subjects [27,28]. The studies in which these two equations were used to predict GFR using the mGFR with exogenous substances as the reference standard in obese patients, showed that the performance was inadequate, as in our study (either considering the prediction of the indexed mGFR or not indexed to body surface area) and many authors currently discourage their use [29,30]. The combination of CKD-EPI 2009 with MCQ had shown an improvement in the performance of each one separately, in subjects with obesity [22]. In our study, this combination improved the performance of MCQ alone, but not that of CKD-EPI 2009. When comparing the performance of CKD-EPI 2009 and MDRD4 in obese subjects in previous studies, the results are mixed [29,31,32,33,34,35,36,37]. In our study, considering the P30, there seems to be a small advantage of CKD-EPI in its two versions, over MDRD4 (except in grade III obesity). MDRD6 and CKD-EPI 2021 have not been evaluated in currently available studies. In our sample, MDRD with 6 variables improves the overall performance of MDRD with 4 variables (except in grade III obesity), and CKD-EPI 2021 has a median bias closer to 0 (except in grade III obesity) and higher % CC than CKD-EPI 2009.
The new AE obtained uniformly better performance than the other currently available equations (except in grade II obesity). This formula was developed using a different predictive model than the one used in the previous equations, which allows mGFR to be predicted directly on its natural scale, without the need to transform the response variable or assume a specific distribution beforehand [38]. We believe this is the most suitable for this type of estimation tools and the superior performance in the validation comparison demonstrated it. In this equation, unlike those currently in force, the presence of a single functioning kidney was incorporated as a predictor variable. These patients not only present less renal mass, but also suffer intraglomerular hemodynamic and structural adaptive changes [39,40], and changes in tubular creatinine secretion [41,42], which could modify the concentration of SCr and, therefore, the prediction of mGFR from it. This was the reason why we believe it is important to incorporate an adjustment coefficient in these subjects. As in MDRD6, AE incorporated urea and albumin as predictive variables. In subjects with changes in muscle mass, in which *Scr is* not a good predictor [13], these variables that do not directly depend on muscle mass could generate an extra fit to the model.
AE had 2 validations, in the internal validation the 10 fold cross-validation method was used, unlike the current equations, which is a modern statistical method of resampling, recommended and considered the best method for internal validation at present [43]. Secondly, a temporary validation was carried out, with totally independent subjects and from a later period. This validation is considered as an intermediary between internal and external validation, although some authors consider it as a subtype of external validation [43]. The comparisons of the performance of the previous equations and AE were always made separately in each of the validation samples, that is, without mixing the IVS subjects with those of the TVS. This double validation allows reaffirming the performance of the equation, although in the TVS it was not possible to perform a sub-analysis with the degrees of obesity due to the small number of subjects.
Some authors suggest that the P30 is the best metric to compare the performance of different equations, since it combines bias with precision [44,45]. A P30 value of 80 to $90\%$ is considered acceptable for the evaluation of the GFR in many circumstances of clinical practice [21]. When it is required to know the GFR with greater precision, the estimates do not replace the measurement. AE obtained a P30 greater than $80\%$ in both validation samples, and even in the 3 degrees of obesity. Although, as in previous studies, it was possible to observe that in grade III obesity all the equations reduce their performance [22,31], the only equation that obtained a P30 > $80\%$ in patients with this degree of obesity was AE.
Being a cross-sectional study, we did not evaluate the changes over time in the GFR and the variables associated with it, such as weight fluctuations and glycemic parameters [46,47]. We only set out to assess the ability to estimate the measured GFR of the different equations at one point in time. We also did not evaluate the performance of the equations that use cystatin C, since none of our patients had this measurement. In our environment there is little availability of cystatin C and it is expensive, so it is unlikely that the clinical use of equations that require this marker will be used in the short and medium term [48].
The weaknesses of this study are: it was carried out in a single center, with a sample that does not represent all obese subjects in the population, and without the use of classical external validation.
The strengths of the study are: the inclusion of patients with a wide range of GFR, the use of mGFR as a reference standard, the availability of SCr standardized to the IDMS method, and the double validation of a new equation developed in Latin America with novel statistical strategies.
## 5. Conclusions
The new AE obtained a better overall performance than the current equations to estimate the mGFR in subjects with obesity in our environment. It could be useful in this type of patients in some circumstances, although more studies are needed to confirm it. Conclusions from this study may not be generalizable to all populations of obese patients since they were derived from a study in a single center with a very specific ethnic mixed population.
## References
1. Swinburn B.A., Sacks G., Hall K.D., McPherson K., Finegood D.T., Moodie M.L., Gortmaker S.L.. **The Global Obesity Pandemic: Shaped by Global Drivers and Local Environments**. *Lancet* (2011.0) **378** 804-814. DOI: 10.1016/S0140-6736(11)60813-1
2. Ng M., Fleming T., Robinson M., Thomson B., Graetz N., Margono C., Mullany E.C., Biryukov S., Abbafati C., Abera S.F.. **Global, Regional, and National Prevalence of Overweight and Obesity in Children and Adults during 1980-2013: A Systematic Analysis for the Global Burden of Disease Study 2013**. *Lancet* (2014.0) **384** 766-781. DOI: 10.1016/S0140-6736(14)60460-8
3. Willett W.C., Dietz W.H., Colditz G.A.. **Guidelines for Healthy Weight**. *N. Engl. J. Med.* (1999.0) **341** 427-434. DOI: 10.1056/NEJM199908053410607
4. Field A.E., Coakley E.H., Must A., Spadano J.L., Laird N., Dietz W.H., Rimm E., Colditz G.A.. **Impact of Overweight on the Risk of Developing Common Chronic Diseases during a 10-Year Period**. *Arch. Intern. Med.* (2001.0) **161** 1581-1586. DOI: 10.1001/archinte.161.13.1581
5. Hsu C., McCulloch C.E., Iribarren C., Darbinian J., Go A.S.. **Body Mass Index and Risk for End-Stage Renal Disease**. *Ann. Intern. Med.* (2006.0) **144** 21-28. DOI: 10.7326/0003-4819-144-1-200601030-00006
6. Iseki K., Ikemiya Y., Kinjo K., Inoue T., Iseki C., Takishita S.. **Body Mass Index and the Risk of Development of End-Stage Renal Disease in a Screened Cohort**. *Kidney Int.* (2004.0) **65** 1870-1876. DOI: 10.1111/j.1523-1755.2004.00582.x
7. Wang Y., Chen X., Song Y., Caballero B., Cheskin L.J.. **Association between Obesity and Kidney Disease: A Systematic Review and Meta-Analysis**. *Kidney Int.* (2008.0) **73** 19-33. DOI: 10.1038/sj.ki.5002586
8. Fox C.S., Larson M.G., Leip E.P., Culleton B., Wilson P.W.F., Levy D.. **Predictors of New-Onset Kidney Disease in a Community-Based Population**. *JAMA* (2004.0) **291** 844-850. DOI: 10.1001/jama.291.7.844
9. Vivante A., Golan E., Tzur D., Leiba A., Tirosh A., Skorecki K., Calderon-Margalit R.. **Body Mass Index in 1.2 Million Adolescents and Risk for End-Stage Renal Disease**. *Arch. Intern. Med.* (2012.0) **172** 1644-1650. DOI: 10.1001/2013.jamainternmed.85
10. D’Agati V.D., Chagnac A., de Vries A.P.J., Levi M., Porrini E., Herman-Edelstein M., Praga M.. **Obesity-Related Glomerulopathy: Clinical and Pathologic Characteristics and Pathogenesis**. *Nat. Rev. Nephrol.* (2016.0) **12** 453-471. DOI: 10.1038/nrneph.2016.75
11. Smith H.W.. *The Kidney: Structure and Function in Health and Disease* (1951.0)
12. Elwood C.M., Sigman E.M., Treger C.. **The Measurement of Glomerular Filtration Rate with 125I-Sodium Iothalamate (Conray)**. *Br. J. Radiol.* (1967.0) **40** 581-583. DOI: 10.1259/0007-1285-40-476-581
13. Levey A.S., Coresh J., Tighiouart H., Greene T., Inker L.A.. **Measured and Estimated Glomerular Filtration Rate: Current Status and Future Directions**. *Nat. Rev. Nephrol.* (2020.0) **16** 51-64. DOI: 10.1038/s41581-019-0191-y
14. Prado C.M., Cushen S.J., Orsso C.E., Ryan A.M.. **Sarcopenia and Cachexia in the Era of Obesity: Clinical and Nutritional Impact**. *Proc. Nutr. Soc.* (2016.0) **75** 188-198. DOI: 10.1017/S0029665115004279
15. **Obesity: Preventing and Managing the Global Epidemic-World Health Organization-Google Libros**
16. Cockcroft D.W., Gault M.H.. **Prediction of Creatinine Clearance from Serum Creatinine**. *Nephron* (1976.0) **16** 31-41. DOI: 10.1159/000180580
17. Janmahasatian S., Duffull S.B., Ash S., Ward L.C., Byrne N.M., Green B.. **Quantification of Lean Bodyweight**. *Clin. Pharmacokinet.* (2005.0) **44** 1051-1065. DOI: 10.2165/00003088-200544100-00004
18. Salazar D.E., Corcoran G.B.. **Predicting Creatinine Clearance and Renal Drug Clearance in Obese Patients from Estimated Fat-Free Body Mass**. *Am. J. Med.* (1988.0) **84** 1053-1060. DOI: 10.1016/0002-9343(88)90310-5
19. Levey A.S., Coresh J., Greene T., Stevens L.A., Zhang Y.L., Hendriksen S., Kusek J.W., Van Lente F.. **Chronic Kidney Disease Epidemiology Collaboration Using Standardized Serum Creatinine Values in the Modification of Diet in Renal Disease Study Equation for Estimating Glomerular Filtration Rate**. *Ann. Intern. Med.* (2006.0) **145** 247-254. DOI: 10.7326/0003-4819-145-4-200608150-00004
20. Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.L., Castro A.F., Feldman H.I., Kusek J.W., Eggers P., Van Lente F., Greene T.. **A New Equation to Estimate Glomerular Filtration Rate**. *Ann. Intern. Med.* (2009.0) **150** 604-612. DOI: 10.7326/0003-4819-150-9-200905050-00006
21. Inker L.A., Eneanya N.D., Coresh J., Tighiouart H., Wang D., Sang Y., Crews D.C., Doria A., Estrella M.M., Froissart M.. **New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race**. *N. Engl. J. Med.* (2021.0) **385** 1737-1749. DOI: 10.1056/NEJMoa2102953
22. Fernández P., Chiurchiu C., de Arteaga J., Douthat W., Campazzo M., Lujan P., de la Fuente J.. **Filtrado glomerular estimado por fórmulas en pacientes obesos**. *Nefrol. Latinoam.* (2017.0) **14** 56-62. DOI: 10.1016/j.nefrol.2017.03.002
23. Lombi F., Papaginovic M.M., Fayad A., Diez G.R., Lombi F., Papaginovic M.M., Fayad A., Diez G.R.. **XXII Congreso Argentino de Nefrología CAN 2021**. *Rev. Nefrol. Dialisis Traspl.* (2022.0) **42** 1-3
24. Dunn P.K., Smyth G.K.. **Chapter 8: Generalized Linear Models: Diagnostics**. *Generalized Linear Models with Examples in R* (2018.0) 297-331
25. James G., Witten D., Hastie T., Tibshirani R.. **Resampling Methods**. *An Introduction to Statistical Learning: With Applications in R* (2013.0) 175-201
26. Levin A., Stevens P.E., Bilous R.W., Coresh J., De Francisco A.L., De Jong P.E., Griffith K.E., Hemmelgarn B.R., Iseki K., Lamb E.J.. **Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease**. *Kidney Int. Suppl.* (2013.0) **3** 5-14
27. Abouchacra S., Chaaban A., Gebran N., Hussein Q., Ahmed M., Bernieh B., Torab F., Kayyal Y., Al Omary H., Nagelkerke N.. **GFR Estimation in the Morbidly Obese Pre- and Postbariatric Surgery: One Size Does Not Fit All**. *Int. Urol. Nephrol.* (2013.0) **45** 157-162. DOI: 10.1007/s11255-012-0131-2
28. Demirovic J.A., Pai A.B., Pai M.P.. **Estimation of Creatinine Clearance in Morbidly Obese Patients**. *Am. J. Health-Syst. Pharm.* (2009.0) **66** 642-648. DOI: 10.2146/ajhp080200
29. Donker E.M., Bet P., Nurmohamed A., Serné E., Burchell G.L., Friedman A.N., Bouquegneau A., Lemoine S., Ebert N., Cirillo M.. **Estimation of Glomerular Filtration Rate for Drug Dosing in Patients with Very High or Low Body Mass Index**. *Clin. Transl. Sci.* (2022.0) **15** 2206-2217. DOI: 10.1111/cts.13354
30. López-Martínez M., Luis-Lima S., Morales E., Navarro-Díaz M., Negrín-Mena N., Folgueras T., Escamilla B., Estupiñán S., Delgado-Mallén P., Marrero-Miranda D.. **The Estimation of GFR and the Adjustment for BSA in Overweight and Obesity: A Dreadful Combination of Two Errors**. *Int. J. Obes.* (2020.0) **44** 1129-1140. DOI: 10.1038/s41366-019-0476-z
31. Bouquegneau A., Vidal-Petiot E., Vrtovsnik F., Cavalier E., Rorive M., Krzesinski J.-M., Delanaye P., Flamant M.. **Modification of Diet in Renal Disease versus Chronic Kidney Disease Epidemiology Collaboration Equation to Estimate Glomerular Filtration Rate in Obese Patients**. *Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc.-Eur. Ren. Assoc.* (2013.0) **28** iv122-iv130. DOI: 10.1093/ndt/gft329
32. Bouquegneau A., Vidal-Petiot E., Moranne O., Mariat C., Boffa J.-J., Vrtovsnik F., Scheen A.-J., Krzesinski J.-M., Flamant M., Delanaye P.. **Creatinine-Based Equations for the Adjustment of Drug Dosage in an Obese Population**. *Br. J. Clin. Pharmacol.* (2016.0) **81** 349-361. DOI: 10.1111/bcp.12817
33. Guebre-Egziabher F., Brunelle C., Thomas J., Pelletier C.C., Normand G., Juillard L., Dubourg L., Lemoine S.. **Estimated Glomerular Filtration Rate Bias in Participants with Severe Obesity Regardless of Deindexation**. *Obesity* (2019.0) **27** 2011-2017. DOI: 10.1002/oby.22574
34. Lemoine S., Guebre-Egziabher F., Sens F., Nguyen-Tu M.-S., Juillard L., Dubourg L., Hadj-Aissa A.. **Accuracy of GFR Estimation in Obese Patients**. *Clin. J. Am. Soc. Nephrol.* (2014.0) **9** 720-727. DOI: 10.2215/CJN.03610413
35. Delanaye P., Björk J., Courbebaisse M., Couzi L., Ebert N., Eriksen B.O., Dalton R.N., Dubourg L., Gaillard F., Garrouste C.. **Performance of Creatinine-Based Equations to Estimate Glomerular Filtration Rate with a Methodology Adapted to the Context of Drug Dosage Adjustment**. *Br. J. Clin. Pharmacol.* (2022.0) **88** 2118-2127. DOI: 10.1111/bcp.15132
36. Nacife C., Livino P., Fuhr L., Bezerra R., Teles F.. **Assessment of Glomerular Filtration Rate by Formulas in Obese Patients: What Is the Best Choice?**. *Clin. Nephrol.* (2022.0) **98** 92-100. DOI: 10.5414/CN110864
37. Busse D., Borghardt J.M., Petroff D., Pevzner A., Dorn C., El-Najjar N., Huisinga W., Wrigge H., Simon P., Kloft C.. **Evaluating Prediction Methods for Glomerular Filtration to Optimise Drug Doses in Obese and Nonobese Patients**. *Br. J. Clin. Pharmacol.* (2022.0) **88** 2973-2981. DOI: 10.1111/bcp.15115
38. Wedderburn R.W.M.. **Quasi-Likelihood Functions, Generalized Linear Models, and the Gauss-Newton Method**. *Biometrika* (1974.0) **61** 439-447. DOI: 10.2307/2334725
39. Kaufman J.M., Siegel N.J., Hayslett J.P.. **Functional and Hemodynamic Adaptation to Progressive Renal Ablation**. *Circ. Res.* (1975.0) **36** 286-293. DOI: 10.1161/01.RES.36.2.286
40. Kaufman J.M., DiMeola H.J., Siegel N.J., Lytton B., Kashgarian M., Hayslett J.P.. **Compensatory Adaptation of Structure and Function Following Progressive Renal Ablation**. *Kidney Int.* (1974.0) **6** 10-17. DOI: 10.1038/ki.1974.72
41. Herrera J., Rodríguez-Iturbe B.. **Stimulation of Tubular Secretion of Creatinine in Health and in Conditions Associated with Reduced Nephron Mass. Evidence for a Tubular Functional Reserve**. *Nephrol. Dial. Transplant.* (1998.0) **13** 623-629. DOI: 10.1093/ndt/13.3.623
42. Rodríguez-Iturbe B., Herrera J., Marín C., Mañalich R.. **Tubular Stress Test Detects Subclinical Reduction in Renal Functioning Mass**. *Kidney Int.* (2001.0) **59** 1094-1102. DOI: 10.1046/j.1523-1755.2001.0590031094.x
43. Moons K.G.M., Altman D.G., Reitsma J.B., Ioannidis J.P.A., Macaskill P., Steyerberg E.W., Vickers A.J., Ransohoff D.F., Collins G.S.. **Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration**. *Ann. Intern. Med.* (2015.0) **162** W1-W73. DOI: 10.7326/M14-0698
44. Earley A., Miskulin D., Lamb E.J., Levey A.S., Uhlig K.. **Estimating Equations for Glomerular Filtration Rate in the Era of Creatinine Standardization: A Systematic Review**. *Ann. Intern. Med.* (2012.0) **156** 785-795. DOI: 10.7326/0003-4819-156-11-201203200-00391
45. Shen C., Landsittel D., Irazabal M.V., Yu A.S.L., Chapman A.B., Mrug M., Grantham J.J., Bae K.T., Bennett W.M., Flessner M.F.. **Performance of the CKD-EPI Equation to Estimate GFR in a Longitudinal Study of Autosomal Dominant Polycystic Kidney Disease**. *Am. J. Kidney Dis.* (2017.0) **69** 482-484. DOI: 10.1053/j.ajkd.2016.10.021
46. Zhou Y., Huang H., Yan X., Hapca S., Bell S., Qu F., Liu L., Chen X., Zhang S., Shi Q.. **Glycated Haemoglobin A1c Variability Score Elicits Kidney Function Decline in Chinese People Living with Type 2 Diabetes**. *J. Clin. Med.* (2022.0) **11**. DOI: 10.3390/jcm11226692
47. Ceriello A., Lucisano G., Prattichizzo F., Eliasson B., Franzén S., Svensson A.-M., Nicolucci A.. **Variability in Body Weight and the Risk of Cardiovascular Complications in Type 2 Diabetes: Results from the Swedish National Diabetes Register**. *Cardiovasc. Diabetol.* (2021.0) **20** 173. DOI: 10.1186/s12933-021-01360-0
48. Inserra F., Greloni G.. **Nuevas ecuaciones basadas en creatinina y cistatina C para la TFGe, sin incluir raza**. *Rev. Nefrol. Diálisis Traspl.* (2022.0) **42** 99-102
|
---
title: Comparison of the Effects of Prebiotics and Synbiotics Supplementation on the
Immune Function of Male University Football Players
authors:
- Lufang Zhang
- Hui Xiao
- Li Zhao
- Zeting Liu
- Lanmu Chen
- Chenzhe Liu
journal: Nutrients
year: 2023
pmcid: PMC10004888
doi: 10.3390/nu15051158
license: CC BY 4.0
---
# Comparison of the Effects of Prebiotics and Synbiotics Supplementation on the Immune Function of Male University Football Players
## Abstract
This study was conducted to compare the effects of long-term prebiotic and synbiotic supplementations on the immunosuppression of male football players after daily high-intensity training and a one-time strenuous exercise. A total of 30 male university student-athletes were recruited and randomly assigned to the prebiotic (PG, $$n = 15$$) or synbiotic group (SG, $$n = 15$$), receiving a prebiotic or synbiotic once per day for six weeks. Physiological assessments were conducted by a maximal oxygen uptake (VO2max) test and an exhaustive constant load exercise ($75\%$ VO2max test). Inflammatory cytokine and secretory immunoglobulin A (SIgA) were measured. VO2max, maximal heart rate (HRmax), and lactic acid elimination rate (ER) were used to evaluate aerobic capacity. Upper respiratory tract infection (URTI) complaints were evaluated using a questionnaire. URTI incidence and duration were significantly lower in the SG group than that in the PG group ($p \leq 0.05$). At baseline, SIgA and interleukin-1β (IL-1β) levels in the SG group ($p \leq 0.01$) as well as IL-1β and IL-6 in the PG group ($p \leq 0.05$) were significantly increased, and IL-4 concentration was markedly reduced in the PG group ($p \leq 0.01$). The concentrations of IL-4, IL-10 and transforming growth factor-β1 (TGF-β1) were significantly reduced in the PG and SG group immediately after the constant load exercise. Significantly decreased HRmax and enhanced ER (increased by $193.78\%$) were detected in the SG group, not in the PG group, during the constant load experiment ($p \leq 0.05$) and the recovery period ($p \leq 0.01$), respectively. However, VO2max value was not changed. These data suggest that synbiotic supplementation for six weeks has a more positive effect than prebiotics on the immune function and athletic performance of male university football players.
## 1. Introduction
Exercise-induced immunosuppression is a common phenomenon among elite athletes after intensive exercise, which can easily increase the risk of opportunistic infection and virus reactivation [1] resulting from the changes in mucosal humoral immunity [2]. Immunosuppression in response to prolonged heavy training and match play in football is well established [3]. During this period, the risk of picking up minor infections will increase, especially an upper respiratory tract infection (URTI). Infectious pathogens enter through the mucosa of the upper respiratory tract, ultimately reducing training effects and athletic performance [4]. It has been reported that the prevalence of URTI increased in elite football players after a game [5]. Meanwhile, the level of secretory immunoglobulin A (SIgA) is decreased by $74.5\%$ [6], which is inversely correlated with the incidence rate of URTI and thus becomes a predictor of URTI [7]. In addition, heavy training is associated with elevated numbers of T helper 2 cells (Th2) and regulatory T cells (Tregs), which produce the anti-inflammatory cytokines interleukin-4 (IL-4), IL-10, and transforming growth factor-β1 (TGF-β1). This appears to increase the risk of URTI [8]. Therefore, improving athletes’ resistance to URTI is important to guarantee high-level performance during training and competitions [9].
Probiotics are living microorganisms that can benefit the health of the host when consumed in sufficient amounts [10]. Bifidobacterium and Lactobacillus strains are the most used probiotic bacteria. Prebiotics, comprised of one or more indigestible carbohydrates, such as inulin, fructooligosaccharides (FOS), galactooligosaccharides (GOS), polydextrose (PDX), and β-glucan [11], are selectively utilized by host microorganisms and confer health benefits [12]. Synbiotics are primarily composed of probiotics and prebiotics, which can bring health benefits to the host, such as modulating gut microbiota [13], alleviating gastrointestinal symptoms [14], enhancing immunity [15], reducing inflammation and oxidative stress [16], and improving blood lipids [17]. Prebiotics and synbiotics have been shown in research to support immune defense, increase SIgA levels [18], and reduce URTI incidence and severity in healthy adults [19], high-intensity fitness professionals [20], and long-distance triathletes [21]. However, the effects of prebiotics and synbiotics on the immune function in football players during intense training and one-time strenuous exercise are poorly understood. In addition, it has been reported that prebiotics [22] and synbiotics [21] can prolong the time to exhaustion as well as promote athletic performance and aerobic capacity. In this context, the aim of the present study was to compare the effects of long-term prebiotic and synbiotic supplementation on the incidence and severity of URTI symptoms, inflammatory markers, and aerobic fitness characteristics in male university football players. The results of this study can provide an experimental basis for the application of prebiotics and synbiotics in the field of sports nutrition.
## 2.1. Participants
A total of 30 male university student-athletes from Beijing Sport University were recruited. The subjects had practiced football for 9.87 ± 0.58 years, and $86.7\%$ of them were first-class players. The subjects were randomly assigned to the prebiotic group (PG) or synbiotic group (SG). During the experiment, the subjects maintained their normal diet. They ate at a fixed location. All subjects trained daily, with no change in schedule. The average training duration was more than 11 h/week (i.e., a high-intensity training load) [23].
## 2.2. Prebiotic and Synbiotic Administration
The prebiotic and synbiotic supplements in the present experiment were obtained from Jinhua Galaxy Biotechnology Co., Ltd. (Zhejiang, China) (the batch number for the prebiotics was 2021012101, and the batch number for the synbiotics was 2021032304). The prebiotics and synbiotics (2 g per packet) were both in powder form. The PG group was administered prebiotic supplements, namely GOS, FOS, inulin, PDX, strawberry powder, and maltitol. In addition to the same ingredients as the PG group, the SG group also contains three probiotic strains, ≥8 × 109 CFU of Lactobacillus casei Zhang, *Bifidobacterium lactis* V9, and ≥6 × 109 CFU of *Lactobacillus plantarum* P-8. Thus, the PG group and the SG group were regarded as the control group and experimental group, respectively. The outer packaging of the prebiotics and synbiotics was the same, and the supplements had a similar colour and smell. The subjects had to take supplements directly after lunch every day. During the experiment, the subjects were not allowed to consume any fermented food, and a two-hour time interval was maintained between taking any antibiotics and the supplements.
## 2.3. Experimental Design
The main objective of this experiment was to compare the effects of prebiotic and synbiotic supplements on the immune functions of football players after daily high-intensity training and a one-time strenuous exercise. The inclusion criteria for the subjects were as follows: no injuries during the experiment, no consumption of any prebiotics, probiotics, synbiotics, or fermented products (yogurt or other foods), and no consumption of any medications or supplements. Participants were asked to follow their regular diet for two weeks before the survey and during the programme. Prior to the experiment, all subjects were familiarised with the study process. Each participant signed an informed consent form and ensured that they could successfully complete all tests. The experimental protocol was approved by the Ethics Committee of Beijing Sport University (no. 2020167H). The participant characteristics revealed no observable differences between the SG group and PG group in terms of height, weight, age, maximal oxygen uptake (VO2max), or body mass index (BMI) (Table 1). The experimental period was six weeks. Participants had to come to the laboratory for testing before ingesting the supplement and after six weeks. The procedure and materials used were the same for the baseline and final tests. All 30 subjects completed the tests and the information collection (Figure 1).
## 2.3.1. Anthropometry
The height of the subject was determined and recorded using a unified height metre. The fasting weight of the subject was measured using a weight tester in the morning (GMCS. RCS type IV portable).
## 2.3.2. Upper Respiratory Tract Infection Questionnaire
During the six-week experiment, the subjects filled out the URTI questionnaire every day. The symptoms listed on the questionnaire were cold, sneezing, runny nose, cough, and sore throat [24]. The incidence and duration of URTI symptoms were considered to reflect the immune functions of the subjects during daily training.
## 2.3.3. Mucosal Immunity
Saliva samples were gathered before and after the subjects took the supplements. The subjects were requested to sit still for 10 min before sample collection in the morning. Each subject’s head was tilted slightly forward and then to the side, and the mouth was opened for unstimulated saliva flow directly into a 5 mL plastic test tube. Samples were centrifuged and stored in a −80°C refrigerator until assaying. The levels of SIgA, β-defensin (β-DF), α-amylase (AMS), and lysozyme (LZM) were determined using commercial ELISA kits provided by Shanghai Jianglai Biotechnology Co., Ltd. (Shanghai, China).
## 2.3.4. Maximal Oxygen Uptake
A VO2max test was conducted by adopting the classic Bruce athlete scheme for an incremental load exercise on a running platform [25]. Subjects uniformly wore an extra-small gas collection mask and heart rate (HR) band (polar V800) prior to testing. After preparing for the activity, the subjects began the test according to the experimental protocol. The subjects exercised continuously, and real-time speed, incline, and gas metabolism were accounted for using COSMED software. When the subjects reached exhaustion and could no longer carry on, the incremental load test was stopped. During the test, the experimental operator asked the subjects about their rating of perceived exertion (RPE) at each increment and monitored their HR in real time to ensure they could safely complete the incremental load test on the running platform.
To ensure that VO2max was achieved, at least three of the following criteria had to be met: [1] With an increase in exercise load, the oxygen uptake platform appears or oxygen uptake decreases; [2] with an increase in exercise load, HR does not increase; [3] respiratory quotient reaches or approaches 1.15; and [4] the RPE scale has reached the exhaustion level and can no longer maintain the current exercise load.
## 2.3.5. Constant Load Test
To clarify the effects of prebiotic and synbiotic supplementation on inflammatory factors after prolonged aerobic exercise, the subjects warmed up for 3 min at $50\%$ VO2max intensity and then exercised to exhaustion at $75\%$ VO2max intensity to complete the constant load test [26]. The exercise gradient was 0. The formula used was as follows:VO2 = 3.5 + (0.2 × speed) + (0.9 × speed × slope) The relative values of VO2max for the PG group and SG group were 53.80 ± 1.57 mL/kg/min and 53.87 ± 1.64 mL/kg/min, respectively. During the constant load test, the warm-up speeds of the PG group and SG group were 7.12 ± 0.28 km/h and 6.85 ± 0.31 km/h, respectively, and the test speeds were 11.17 ± 0.42 km/h and 10.78 ± 0.46 km/h, respectively (Table 2). The values for HR and exercise duration were recorded, and the subjects were asked about their RPE every six minutes. The exercise protocol for the final test measurements was consistent with the protocol for the baseline tests.
## 2.3.6. Lactic Acid Elimination Rate
Fingertip blood was collected from the subjects immediately, 1 min, 3 min, 5 min, and 10 min after the constant load test. The blood lactate level was determined using a blood lactate analyser (Biosen S_line Lab, EKF Diagnostics Holdings Ltd, Germany). Subjects remained in a seated position throughout the blood collection process and were not allowed to walk slowly, stretch, or perform other recovery activities. The lactic acid elimination rate (ER) was calculated using the following formula:ER=La (max)−La (10 min)T (10 min)−T (max) Here, ER is the elimination rate of blood lactic acid [27]; La (max) is the maximum value of blood lactic acid after exercise; La (10 min) is the blood lactic acid value 10 min after exercise; T (max) is the time corresponding to La (max); and T (10 min) is the 10th minute after exercise.
## 2.3.7. Inflammatory Factors and Blood Cell Count
At baseline and immediately after the constant load exercise, 5 mL of venous blood was collected from the anterior elbow vein of each subject using standard venipuncture technology. Plasma was loaded into the green blood vessel of a sodium heparin anticoagulant for the blood cell count test. Serum loaded in the red blood collection vessel of a procoagulant was centrifuged at 4000 r/min for 10 min to remove the supernatant. The tubes were stored in a refrigerator at −80 °C until further analysis. The levels of IL-1β, IL-4, IL-6, IL-8, IL-10, TGF-β1, and IgA were determined using an ELISA kit provided by Shanghai Jianglai Biotechnology Co., Ltd. (Shanghai, China) and Wuhan Eliot Biotechnology Co., Ltd. (Hubei, China). The blood count indicators—white blood cell (WBC), red blood cell (RBC), haemoglobin level (Hb), hematocrit (HCT), mean corpuscular haemoglobin (MCH), and mean corpuscular haemoglobin concentration (MCHC)—were measured using an automatic haematology analyser (BC-2800Vet, Mindray Medical International Ltd, China).
## 2.4. Statistical Analysis
SPSS 25 software was used to perform the analyses. The data were expressed in the form of mean ± standard error. Missing values were interpolated via expectation maximisation. Data in Table 1, Table 2, and Figure 2 were analyzed using an independent sample t-test. Other data were analyzed using repeated-measures ANOVA to investigate the main effects and the interactions between the group factor (prebiotic vs. synbiotic), and time factor (pre-intervention vs. post-intervention). The eta squared (η2) was also calculated to assess the effect size of the comparisons. The statistical significance level adopted was $p \leq 0.05.$
## 3.1. Effects on URTI Symptoms
In this study, the occurrence of URTI during the six weeks of prebiotic and synbiotic supplementation was recorded (Figure 2). The total number of URTI symptoms occurring in all subjects in the SG group (6.50 ± 2.00 times/week) was significantly lower than that in the PG group (14.17 ± 1.14 times/week) ($p \leq 0.01$, Figure 2A). Compared to the PG group (31.11 ± $3.30\%$), the incidence of symptomatic subjects with URTI in the SG group was markedly lower (17.78 ± $4.10\%$) ($p \leq 0.05$, Figure 2B). In addition, the URTI duration in the SG group (1.77 ± 0.19 days/time) was significantly shorter than that in the PG group (2.66 ± 0.28 days/time) ($p \leq 0.05$, Figure 2C).
## 3.2. Effects on Mucosal Immunity
The effects of six weeks of prebiotic and synbiotic supplementation on mucosal immune function during daily training were examined in this study (Figure 3). The results indicated that the level of SIgA in the SG group was significantly increased by $15.82\%$ compared to the basal level, from 7.63 ± 0.29 pg/mL to 8.72 ± 0.34 pg/mL ($p \leq 0.01$, Figure 3A). The SIgA level was significantly higher in the SG group than that in the PG group in the final test ($p \leq 0.05$, Figure 3A). The SIgA effects showed values of $$p \leq 0.015$$ and η2 = 0.391 for the group effect, $$p \leq 0.001$$ and η2 = 0.530 for time effect, and $$p \leq 0.001$$ and η2 = 0.436 for the interaction effect. However, the PG group and SG group showed no significant differences in the levels of β-DF, AMS, and LZM in saliva ($p \leq 0.05$).
## 3.3. Effects on Inflammatory Markers
Changes in inflammatory factors during daily training were examined over six weeks of administering prebiotic and synbiotic supplements (Figure. 4). The results showed that the concentration of IL-1β in the SG group was increased from 11.59 ± 0.79 pg/mL to 14.22 ± 0.81 pg/mL ($p \leq 0.01$, Figure 4A) and increased by $13.61\%$ ($p \leq 0.05$, Figure 4A) in the PG group ($p \leq 0.001$ and η2 = 0.455). The IL-4 level in the PG group decreased significantly from 5.70 ± 0.51 pg/mL to 4.66 ± 0.45 pg/mL ($p \leq 0.01$, Figure 4B) ($$p \leq 0.002$$ and η2 = 0.285). Of note, the level of IL-6 increased by $31.87\%$ in the PG group after six weeks of supplementation ($p \leq 0.05$, Figure 4C) ($$p \leq 0.020$$ and η2 = 0.178). No significant changes in the serum concentrations of IL-8, IL-10, TGF-β1, and IgA were observed before or after the intervention ($p \leq 0.05$).
Changes in inflammatory factors were also examined immediately after the constant load test (Figure 5). After six weeks of supplementation, the level of IL-1β decreased from 17.49 ± 1.63 pg/mL to 7.99 ± 3.01 pg/mL in the PG group ($p \leq 0.01$, Figure 5A) and reduced by $67.74\%$ in the SG group ($p \leq 0.01$, Figure 5A) ($p \leq 0.001$ and η2 = 0.734). IL-4 decreased by $30.64\%$ in the PG group ($p \leq 0.05$, Figure 5B) and reduced from 6.82 ± 0.77 pg/mL to 3.28 ± 1.27 pg/mL ($p \leq 0.01$, Figure 5B) in the SG group ($p \leq 0.001$ and η2 = 0.569). IL-10 significantly reduced by $57.93\%$ ($p \leq 0.01$, Figure 5E) in the SG group and decreased from 147.56 ± 14.23 pg/mL to 64.65 ± 24.91 pg/mL ($p \leq 0.01$, Figure 5E) in the PG group ($p \leq 0.001$ and η2 = 0.734). The concentration of TGF-β1 was significantly higher in the PG group than that in the SG group in both in the baseline test (8.37 ± 1.04 ng/mL vs. 4.34 ± 0.48 ng/mL, respectively; $p \leq 0.01$, Figure 5F) and the final test (3.15 ± 0.03 ng/mL vs. 1.02 ± 0.07 ng/mL, respectively; $p \leq 0.01$, Figure 5F). The TGF-β1 concentration reduced by $53.56\%$ in the PG group and by $74.12\%$ in the SG group compared to the basal level ($p \leq 0.01$, Figure 5F). The time effect presented values of $p \leq 0.001$ and η2 = 0.881, the group effect showed $p \leq 0.001$ and η2 = 0.812, and the interaction effect of $$p \leq 0.015$$ and η2 = 0.287, whereas no significant changes were observed in the concentrations of IL-6 and IL-8 in either group ($p \leq 0.05$).
## 3.4. Effects on Blood Cell Counts
After six weeks of prebiotic and synbiotic supplementation, changes in the blood cell counts were examined immediately after the constant load test (Figure 6). After the six-week intervention, the results showed that the RBC concentration was significantly decreased by $16.47\%$ ($p \leq 0.01$, Figure 6B), the Hb level was markedly reduced from 197.89 ± 10.23 g/L to 155.50 ± 1.38 g/L ($p \leq 0.01$, Figure 6C), the HCT level was significantly decreased by $16.68\%$ ($p \leq 0.01$, Figure 6D), the MCH concentration was markedly reduced from 32.52 ± 0.55 pg to 31.29 ± 0.49 pg ($p \leq 0.01$, Figure 6E), and the MCHC level was significantly decreased by $3.30\%$ ($p \leq 0.01$, Figure 6F) in the PG group. Similarly, the levels of MCH and MCHC in the SG group were significantly decreased by $3.32\%$ ($p \leq 0.01$, Figure 6E) and from 355.60 ± 3.43 g/L to 342.30 ± 2.01 g/L ($p \leq 0.01$, Figure 6F), respectively. The number of blood cells decreased significantly over the experiment, showing a strong effect of time (RBC: $$p \leq 0.009$$, η2 = 0.320; Hb: $$p \leq 0.001$$ and η2 = 0.460; HCT: $$p \leq 0.011$$ and η2 = 0.306; MCH: $p \leq 0.001$ and η2 = 0.602; MCHC: $p \leq 0.001$ and η2 = 0.708). In addition, a significant interaction was observed between the time and the group (RBC: $$p \leq 0.001$$, η2 = 0.446; Hb: $$p \leq 0.001$$, η2 =0.448; HCT: $$p \leq 0.001$$ and η2 = 0.453). No significant difference in WBC concentration was observed between the SG group and PG group ($p \leq 0.05$, Figure 6A).
## 3.5. Effects on Athletic Performance
The effects of six weeks of prebiotic and synbiotic supplementation on the athletic performance of the subjects were examined (Figure 7). The results show that there is no significant change in VO2max value in both the PG or SG group at the baseline and final test ($p \leq 0.05$, Figure 7A). However, the maximum heart rate (HRmax) during the constant load exercise was significantly reduced from 174.10 ± 3.35 bpm to 168.88 ± 4.70 bpm in the SG group ($p \leq 0.05$, Figure 7B) ($$p \leq 0.011$$ and η2 = 0.364). In addition, ER was significantly increased by $193.78\%$ compared to the basal level (from 0.33 ± $0.06\%$ to 0.76 ± $0.13\%$) during the recovery period after exercise in the SG group ($P \leq 0.01$, Figure 7C) ($$p \leq 0.013$$ and η2 = 0.295).
## 4. Discussion
The results of this study revealed that synbiotic supplementation reduced the incidence and duration of URTI, increased the levels of SIgA and IL-1β in male university football players during high-intensity training, and decreased the concentrations of IL-4, IL-10, and TGF-β1 immediately after the constant load test. The prebiotic supplementation reduced IL-4 and increased IL-1β and IL-6 at baseline as well as decreased IL-4, IL-10, and TGF-β1 levels after prolonged aerobic exercise. Additionally, increased ER in prolonged aerobic exercise were observed in the synbiotic group, not in the prebiotic group.
It is reported that prebiotics and synbiotics contribute to reducing the incidence and severity of URTI [28,29]. Dharsono et al. [ 19] and Auinger et al. [ 28] reported that taking prebiotic β-glucan enhances the immune functions of healthy adults and reduces the incidence, duration, and severity of URTI. In addition, McFarlin et al. [ 30] found that marathon runners have a significantly lower incidence of URTI after taking prebiotic β-glucan. It is also important to note that Bergendiova et al. [ 31] observed a significant reduction in the incidence of URTI symptoms when athletes who engaged in kayaking, mountain biking, swimming, shooting, running, and cycling were supplemented with prebiotic β-glucan. Hor et al. [ 32] proved that Lactobacillus casei Zhang, a probiotic component of the synbiotic used in this experiment, can alleviate URTI symptoms in healthy adults, reduce the duration of nasal, pharyngeal, and general flu as well as total respiratory illness symptoms, thus preventing sickness from strenuous exercise and increasing the chances of staying healthy. A previous study revealed a significant reduction in the number and duration of URTI episodes in healthy adults after supplementation with synbiotics [29]. In the present study, the incidence and duration of URTI were lower in the SG group than that in the PG group during six weeks of supplementation. It is suggested that supplementation with synbiotics rather than prebiotics has the ability to reduce the incidence and duration of URTI in football players.
Salivary SIgA, the main type of antibody found in mucosal secretions, [33] along with β-DF [34], AMS [35], and LZM [36] can inhibit pathogen colonization. Reduced secretion of SIgA is a risk factor for the development of URTI in physically active individuals [23]. Low resting salivary IgA concentration has been reported in some elite athletes [37]. Prebiotics and synbiotics have been reported to act directly on mucosal immune cells to promote the secretion of SIgA, thereby inhibiting the growth of pathogens and regulating immune function. Xu et al. [ 38] reported that administering the same probiotic used in this experiment could increase serum IgG and faecal SIgA levels in dogs. The present study revealed a significant increase in the SIgA level of football players after six weeks of synbiotic supplementation and markedly higher in the SG group than that in the PG group in the final test. These results are consistent with Coman et al. ’s [20] findings that synbiotic supplementation can significantly increase the salivary SIgA levels of high-intensity fitness professionals. In addition, Childs et al. [ 39] observed that the level of salivary IgA in healthy adults increased significantly after synbiotic supplementation. In contrast, prebiotic supplementation had no significant effect on SIgA in this study. Therefore, the results of this study suggest that synbiotics may enhance respiratory mucosal immune function more than prebiotics by increasing SIgA level.
T cells belong to the adaptive immune system and are essential for coordinating the immune response to invading and existing pathogens. T cells can be divided into three subgroups based on their polarised phenotypic characteristics: Th1, Th2, and Tregs [8]. Th1 cells drive cell-mediated immunity and play an important role in the defense against viral infections [40]. A reduction in the proinflammatory cytokines IL-1β, IL-6, and IL-8 produced by Th1 cells may increase the risk of infection and virus reactivation. It has been reported that high levels of Th2 cytokines are found in the bodies of athletes prone to disease [23]. Anti-inflammatory concentrations of IL-4, IL-10, and TGF-β produced by Th2 and Tregs can inhibit Th1 cell function and immune response [41]. Childs et al. [ 39] reported that synbiotics can promote Th1 response and reduce Th2 activity, thus improving the immune function of the body. Similarly, Childs et al. [ 39] observed that administering the prebiotic xylooligosaccharide to healthy adults resulted in a significant decrease in the level of the anti-inflammatory cytokine IL-10. The previous report [42] found the synbiotic increased concentration of IL-1β. In this experiment, the IL-1β level was significantly increased at baseline and the levels of IL-4, IL-10, and TGF-β1 were decreased upon prolonged aerobic exercise in the SG group. Decreased levels of IL-4 in the base state as well as IL-4, IL-10, and TGF-β1 after aerobic exercise were detected in the PG group. Therefore, synbiotics and prebiotics may not make a significant difference in reducing anti-inflammatory cytokines and increasing pro-inflammatory cytokines to improve immune function in athletes after daily training and a single bout of exercise.
WBC forms an important part of the immune system [43]. However, the results of the present study showed that six weeks of prebiotic and synbiotic supplementation had no significant effect on the WBC count of athletes. This is in agreement with Childs et al. ’s report [39] that no significant changes in leukocytes were observed after administering prebiotics and synbiotics to healthy adults. Therefore, the results of this study suggest that both prebiotic and synbiotic supplementation may not have a significant difference in the effect of WBC in football players after a single strenuous exercise.
VO2max reflects the body’s ability to inhale, transport, and utilise oxygen and is thus one of the most significant indicators of the human body’s aerobic capacity [44]. Blood indicators to assess oxygen transport capacity include RBC, Hb, HCT, MCH, and MCHC, which have similar patterns of variation [45]. In this study, both the PG group and SG group showed significant decreases in MCH and MCHC after the constant load experiment, but the decrease was more pronounced in the PG group. It is suggested that synbiotics can counteract the adverse effects of a one-time strenuous exercise and have positive results on blood health. This aligns with Farinha et al. ’s [46] results, which showed that significant increases in MCH and MCHC promoted positive changes in blood health. Although supplementation with prebiotics and synbiotics did not affect VO2max level in the subjects, a significant increase in ER was observed in the SG group compared to the PG group, suggesting that the ability to metabolize lactic acid is increased in the SG group. Furthermore, consistent with the previous result that the HRmax is significantly decreased in road cyclists after probiotic supplementation [47], obviously decreased HRmax was found in the SG group during the constant load experiment. Therefore, supplementation with synbiotics may help athletes maintain their exercise performance by promoting lactate metabolism and enhancing aerobic capacity. Salarkia et al. [ 48] and Lin et al. [ 49] reported that the improved aerobic capacities and athletic performance of female swimmers and middle-distance runners may be due to the increased resistance to URTI and the improved immune function resulting from supplementation [50]. As a consequence, the reduced incidence of URTI and enhanced immune function observed in the SG group may also be due to the factors that enhance athletic performance. However, the limitation of our study is that there was no placebo control group. In the future study, this should be taken into account.
## 5. Conclusions
In conclusion, supplementation with synbiotics is better than prebiotics at improving immune function in football players by reducing the incidence and duration of URTI and increasing SIgA level. In addition, synbiotics have a more beneficial effect than prebiotics on improving lactate metabolism and exercise performance.
## References
1. Gleeson M., Pyne D.B.. **Respiratory inflammation and infections in high-performance athletes**. *Immunol. Cell Biol.* (2016) **94** 124-131. DOI: 10.1038/icb.2015.100
2. Walsh N.P.. **Recommendations to maintain immune health in athletes**. *Eur. J. Sport Sci.* (2018) **18** 820-831. DOI: 10.1080/17461391.2018.1449895
3. Rico-Gonzalez M., Pino-Ortega J., Clemente F.M., Bustamante-Hernandez N.. **Relationship between Training Load Management and Immunoglobulin A to Avoid Immunosuppression after Soccer Training and Competition: A Theoretical Framework Based on COVID-19 for Athletes’ Healthcare**. *Healthcare* (2021) **9**. DOI: 10.3390/healthcare9070856
4. Colbey C., Cox A.J., Pyne D.B., Zhang P., Cripps A.W., West N.P.. **Upper Respiratory Symptoms, Gut Health and Mucosal Immunity in Athletes**. *Sport. Med.* (2018) **48** 65-77. DOI: 10.1007/s40279-017-0846-4
5. Mortatti A.L., Moreira A., Aoki M.S., Crewther B.T., Castagna C., de Arruda A.F., Filho J.M.. **Effect of competition on salivary cortisol, immunoglobulin A, and upper respiratory tract infections in elite young soccer players**. *J. Strength Cond. Res.* (2012) **26** 1396-1401. DOI: 10.1519/JSC.0b013e31822e7b63
6. Penailillo L., Maya L., Nino G., Torres H., Zbinden-Foncea H.. **Salivary hormones and IgA in relation to physical performance in football**. *J. Sport. Sci.* (2015) **33** 2080-2087. DOI: 10.1080/02640414.2015.1064151
7. Tiernan C., Lyons M., Comyns T., Nevill A.M., Warrington G.. **Salivary IgA as a Predictor of Upper Respiratory Tract Infections and Relationship to Training Load in Elite Rugby Union Players**. *J. Strength Cond. Res.* (2020) **34** 782-790. DOI: 10.1519/JSC.0000000000003019
8. Shaw D.M., Merien F., Braakhuis A., Dulson D.. **T-cells and their cytokine production: The anti-inflammatory and immunosuppressive effects of strenuous exercise**. *Cytokine* (2018) **104** 136-142. DOI: 10.1016/j.cyto.2017.10.001
9. Walsh N.P.. **Nutrition and Athlete Immune Health: New Perspectives on an Old Paradigm**. *Sport. Med.* (2019) **49** 153-168. DOI: 10.1007/s40279-019-01160-3
10. Suez J., Zmora N., Segal E., Elinav E.. **The pros, cons, and many unknowns of probiotics**. *Nat. Med.* (2019) **25** 716-729. DOI: 10.1038/s41591-019-0439-x
11. Maragkoudaki X., Naylor M., Papacleovoulou G., Stolarczyk E., Rees D., Pombo J.M., Abu-Hayyeh S., Czajka A., Howard J.K., Malik A.N.. **Supplementation with a prebiotic (polydextrose) in obese mouse pregnancy improves maternal glucose homeostasis and protects against offspring obesity**. *Int. J. Obes.* (2020) **44** 2382-2393. DOI: 10.1038/s41366-020-00682-5
12. Neri-Numa I.A., Pastore G.M.. **Novel insights into prebiotic properties on human health: A review**. *Food Res. Int.* (2020) **131** 108973. DOI: 10.1016/j.foodres.2019.108973
13. Yang Z., Ye S., Xu Z., Su H., Tian X., Han B., Shen B., Liao Q., Xie Z., Hong Y.. **Dietary synbiotic ameliorates constipation through the modulation of gut microbiota and its metabolic function**. *Food Res. Int.* (2021) **147** 110569. DOI: 10.1016/j.foodres.2021.110569
14. Swanson K.S., Gibson G.R., Hutkins R., Reimer R.A., Reid G., Verbeke K., Scott K.P., Holscher H.D., Azad M.B., Delzenne N.M.. **The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of synbiotics**. *Nat. Rev. Gastroenterol. Hepatol.* (2020) **17** 687-701. DOI: 10.1038/s41575-020-0344-2
15. Dawood M.A.O., Eweedah N.M., Moustafa E.M., Shahin M.G.. **Synbiotic Effects of Aspergillus oryzae and beta-Glucan on Growth and Oxidative and Immune Responses of Nile Tilapia, Oreochromis niloticus**. *Probiotics Antimicrob. Proteins* (2020) **12** 172-183. DOI: 10.1007/s12602-018-9513-9
16. Soleimani A., Motamedzadeh A., Zarrati Mojarrad M., Bahmani F., Amirani E., Ostadmohammadi V., Tajabadi-Ebrahimi M., Asemi Z.. **The Effects of Synbiotic Supplementation on Metabolic Status in Diabetic Patients Undergoing Hemodialysis: A Randomized, Double-Blinded, Placebo-Controlled Trial**. *Probiotics Antimicrob. Proteins* (2019) **11** 1248-1256. DOI: 10.1007/s12602-018-9499-3
17. Hadi A., Ghaedi E., Khalesi S., Pourmasoumi M., Arab A.. **Effects of synbiotic consumption on lipid profile: A systematic review and meta-analysis of randomized controlled clinical trials**. *Eur. J. Nutr.* (2020) **59** 2857-2874. DOI: 10.1007/s00394-020-02248-7
18. De Giani A., Sandionigi A., Zampolli J., Michelotti A., Tursi F., Labra M., Di Gennaro P.. **Effects of Inulin-Based Prebiotics Alone or in Combination with Probiotics on Human Gut Microbiota and Markers of Immune System: A Randomized, Double-Blind, Placebo-Controlled Study in Healthy Subjects**. *Microorganisms* (2022) **10**. DOI: 10.3390/microorganisms10061256
19. Dharsono T., Rudnicka K., Wilhelm M., Schoen C.. **Effects of Yeast (1,3)-(1,6)-Beta-Glucan on Severity of Upper Respiratory Tract Infections: A Double-Blind, Randomized, Placebo-Controlled Study in Healthy Subjects**. *J. Am. Coll. Nutr.* (2019) **38** 40-50. DOI: 10.1080/07315724.2018.1478339
20. Coman M.M., Verdenelli M.C., Silvi S., Cecchini C., Gabbianelli R., Amadio E., Orpianesi C., Cresci A.. **KNOWLEDGE AND ACCEPTANCE OF FUNCTIONAL FOODS: A PRELIMINARY STUDY ON INFLUENCE OF A SYNBIOTIC FERMENTED MILK ON ATHLETE HEALTH**. *Int. J. Probiotics Prebiotics* (2017) **12** 33-34
21. Roberts J.D., Suckling C.A., Peedle G.Y., Murphy J.A., Dawkins T.G., Roberts M.G.. **An Exploratory Investigation of Endotoxin Levels in Novice Long Distance Triathletes, and the Effects of a Multi-Strain Probiotic/Prebiotic, Antioxidant Intervention**. *Nutrients* (2016) **8**. DOI: 10.3390/nu8110733
22. Donatto F.F., Prestes J., Frollini A.B., Palanch A.C., Verlengia R., Cavaglieri C.R.. **Effect of oat bran on time to exhaustion, glycogen content and serum cytokine profile following exhaustive exercise**. *J. Int. Soc. Sport. Nutr.* (2010) **7** 32. DOI: 10.1186/1550-2783-7-32
23. Gleeson M., Bishop N., Oliveira M., McCauley T., Tauler P., Muhamad A.S.. **Respiratory infection risk in athletes: Association with antigen-stimulated IL-10 production and salivary IgA secretion**. *Scand. J. Med. Sci. Sport.* (2012) **22** 410-417. DOI: 10.1111/j.1600-0838.2010.01272.x
24. Tavares-Silva E., Caris A.V., Santos S.A., Ravacci G.R., Thomatieli-Santos R.V.. **Effect of Multi-Strain Probiotic Supplementation on URTI Symptoms and Cytokine Production by Monocytes after a Marathon Race: A Randomized, Double-Blind, Placebo Study**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13051478
25. Badawy M.M., Muaidi Q.I.. **Cardio respiratory response: Validation of new modifications of Bruce protocol for exercise testing and training in elite Saudi triathlon and soccer players**. *Saudi J. Biol. Sci.* (2019) **26** 105-111. DOI: 10.1016/j.sjbs.2017.05.009
26. Zandonai T., Tam E., Bruseghini P., Capelli C., Baraldo M., Chiamulera C.. **Exercise performance increase in smokeless tobacco-user athletes after overnight nicotine abstinence**. *Scand J. Med. Sci. Sport.* (2019) **29** 430-439. DOI: 10.1111/sms.13333
27. Zhang Y.J., Fan D.S.. **Elimination Rate of Serum Lactate is Correlated with Amyotrophic Lateral Sclerosis Progression**. *Chin. Med. J.* (2016) **129** 28-32. DOI: 10.4103/0366-6999.172561
28. Auinger A., Riede L., Bothe G., Busch R., Gruenwald J.. **Yeast (1,3)-(1,6)-beta-glucan helps to maintain the body’s defence against pathogens: A double-blind, randomized, placebo-controlled, multicentric study in healthy subjects**. *Eur. J. Nutr.* (2013) **52** 1913-1918. DOI: 10.1007/s00394-013-0492-z
29. Pregliasco F., Anselmi G., Fonte L., Giussani F., Schieppati S., Soletti L.. **A new chance of preventing winter diseases by the administration of synbiotic formulations**. *J. Clin. Gastroenterol.* (2008) **2** S224-S233. DOI: 10.1097/MCG.0b013e31817e1c91
30. McFarlin B.K., Carpenter K.C., Davidson T., McFarlin M.A.. **Baker’s yeast beta glucan supplementation increases salivary IgA and decreases cold/flu symptomatic days after intense exercise**. *J. Diet. Suppl.* (2013) **10** 171-183. DOI: 10.3109/19390211.2013.820248
31. Bergendiova K., Tibenska E., Majtan J.. **Pleuran (beta-glucan from Pleurotus ostreatus) supplementation, cellular immune response and respiratory tract infections in athletes**. *Eur. J. Appl. Physiol.* (2011) **111** 2033-2040. DOI: 10.1007/s00421-011-1837-z
32. Hor Y.-Y., Lew L.-C., Lau A.S.-Y., Ong J.-S., Chuah L.-O., Lee Y.-Y., Choi S.-B., Rashid F., Wahid N., Sun Z.. **Probiotic Lactobacillus casei Zhang (LCZ) alleviates respiratory, gastrointestinal & RBC abnormality via immuno-modulatory, anti-inflammatory & anti-oxidative actions**. *J. Funct. Foods* (2018) **44** 235-245. DOI: 10.1016/j.jff.2018.03.017
33. Pabst O., Cerovic V., Hornef M.. **Secretory IgA in the Coordination of Establishment and Maintenance of the Microbiota**. *Trends Immunol.* (2016) **37** 287-296. DOI: 10.1016/j.it.2016.03.002
34. Feller L., Altini M., Khammissa R.A., Chandran R., Bouckaert M., Lemmer J.. **Oral mucosal immunity**. *Oral Surg. Oral Med. Oral Pathol. Oral Radiol.* (2013) **116** 576-583. DOI: 10.1016/j.oooo.2013.07.013
35. Ali N., Pruessner J.C.. **The salivary alpha amylase over cortisol ratio as a marker to assess dysregulations of the stress systems**. *Physiol. Behav.* (2012) **106** 65-72. DOI: 10.1016/j.physbeh.2011.10.003
36. Ligtenberg A.J., Brand H.S., van den Keijbus P.A., Veerman E.C.. **The effect of physical exercise on salivary secretion of MUC5B, amylase and lysozyme**. *Arch. Oral Biol.* (2015) **60** 1639-1644. DOI: 10.1016/j.archoralbio.2015.07.012
37. MacKinnon L.T.. **Special feature for the Olympics: Effects of exercise on the immune system: Overtraining effects on immunity and performance in athletes**. *Immunol. Cell Biol.* (2000) **78** 502-509. DOI: 10.1111/j.1440-1711.2000.t01-7-.x
38. Xu H., Huang W., Hou Q., Kwok L.Y., Laga W., Wang Y., Ma H., Sun Z., Zhang H.. **Oral Administration of Compound Probiotics Improved Canine Feed Intake, Weight Gain, Immunity and Intestinal Microbiota**. *Front. Immunol.* (2019) **10** 666. DOI: 10.3389/fimmu.2019.00666
39. Childs C.E., Roytio H., Alhoniemi E., Fekete A.A., Forssten S.D., Hudjec N., Lim Y.N., Steger C.J., Yaqoob P., Tuohy K.M.. **Xylo-oligosaccharides alone or in synbiotic combination with Bifidobacterium animalis subsp. lactis induce bifidogenesis and modulate markers of immune function in healthy adults: A double-blind, placebo-controlled, randomised, factorial cross-over study**. *Br. J. Nutr.* (2014) **111** 1945-1956. DOI: 10.1017/S0007114513004261
40. Harper R.W., Zeki A.A.. **Immunobiology of the critical asthma syndrome**. *Clin. Rev. Allergy Immunol.* (2015) **48** 54-65. DOI: 10.1007/s12016-013-8407-6
41. Sharif K., Watad A., Bragazzi N.L., Lichtbroun M., Amital H., Shoenfeld Y.. **Physical activity and autoimmune diseases: Get moving and manage the disease**. *Autoimmun. Rev.* (2018) **17** 53-72. DOI: 10.1016/j.autrev.2017.11.010
42. Quero C.D., Manonelles P., Fernandez M., Abellan-Aynes O., Lopez-Plaza D., Andreu-Caravaca L., Hinchado M.D., Galvez I., Ortega E.. **Differential Health Effects on Inflammatory, Immunological and Stress Parameters in Professional Soccer Players and Sedentary Individuals after Consuming a Synbiotic. A Triple-Blinded, Randomized, Placebo-Controlled Pilot Study**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13041321
43. Knez W.L., Coombes J.S., Jenkins D.G.. **Ultra-endurance exercise and oxidative damage: Implications for cardiovascular health**. *Sport. Med.* (2006) **36** 429-441. DOI: 10.2165/00007256-200636050-00005
44. Cao J., Lei S., Wang X., Cheng S.. **The Effect of a Ketogenic Low-Carbohydrate, High-Fat Diet on Aerobic Capacity and Exercise Performance in Endurance Athletes: A Systematic Review and Meta-Analysis**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13082896
45. Bachero-Mena B., Pareja-Blanco F., Gonzalez-Badillo J.J.. **Enhanced Strength and Sprint Levels, and Changes in Blood Parameters during a Complete Athletics Season in 800 m High-Level Athletes**. *Front. Physiol.* (2017) **8** 637. DOI: 10.3389/fphys.2017.00637
46. Farinha C., Ferreira J.P., Serrano J., Santos H., Oliveiros B., Silva F.M., Cascante-Rusenhack M., Teixeira A.M.. **The impact of aquatic exercise programs on the systemic hematological and inflammatory markers of community dwelling elderly: A randomized controlled trial**. *Front. Physiol.* (2022) **13** 838580. DOI: 10.3389/fphys.2022.838580
47. Mazur-Kurach P., Fraczek B., Klimek A.T.. **Does Multi-Strain Probiotic Supplementation Impact the Effort Capacity of Competitive Road Cyclists?**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph191912205
48. Salarkia N., Ghadamli L., Zaeri F., Sabaghian Rad L.. **Effects of probiotic yogurt on performance, respiratory and digestive systems of young adult female endurance swimmers: A randomized controlled trial**. *Med. J. Islam. Repub. Iran* (2013) **27** 141-146. PMID: 24791124
49. Lin C.L., Hsu Y.J., Ho H.H., Chang Y.C., Kuo Y.W., Yeh Y.T., Tsai S.Y., Chen C.W., Chen J.F., Huang C.C.. **Bifidobacterium longum subsp. longum OLP-01 Supplementation during Endurance Running Training Improves Exercise Performance in Middle- and Long-Distance Runners: A Double-Blind Controlled Trial**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12071972
50. De Vrese M., Schrezenmeir J.. **Probiotics, prebiotics, and synbiotics**. *Adv. Biochem. Eng. Biotechnol.* (2008) **111** 1-66. DOI: 10.1007/10_2008_097
|
---
title: Anti-Oxidative and Anti-Aging Effects of Ethanol Extract of the Officinal Breynia
(Breynia vitis-idaea) In Vitro
authors:
- Chae Yun Shin
- Jiwon Jang
- Hwa Pyoung Lee
- Sang Hee Park
- Masphal Kry
- Omaliss Keo
- Byoung-Hee Lee
- Wooram Choi
- Sarah Lee
- Jae Youl Cho
journal: Plants
year: 2023
pmcid: PMC10005016
doi: 10.3390/plants12051088
license: CC BY 4.0
---
# Anti-Oxidative and Anti-Aging Effects of Ethanol Extract of the Officinal Breynia (Breynia vitis-idaea) In Vitro
## Abstract
The skin is the largest organ of the human body, and it is also the one most exposed to external environmental contaminants. The skin is the body’s first defense against harmful environmental stimuli, including ultraviolet B (UVB) rays and hazardous chemicals. Therefore, proper care of the skin is required to prevent skin-related diseases and age-related symptoms. In this study, we analyzed anti-aging and anti-oxidative effects of Breynia vitis-idaea ethanol extract (Bv-EE) in human keratinocytes and dermal fibroblasts. The Bv-EE had free radical scavenging activity and decreased the mRNA expression of MMPs and COX-2 in H2O2- or UVB-treated HaCaT cells. The Bv-EE also inhibited AP-1 transcriptional activity and phosphorylation of c-Jun N-terminal kinase, extracellular signal-regulated kinase, and mitogen-activated protein kinase 14 (p38), which are major AP-1 activators upon H2O2 or UVB exposure. Furthermore, the promoter activity and mRNA expression of collagen type I (Col1A1) increased in HDF cells treated with Bv-EE, and Bv-EE recovered the collagen mRNA expression decreased by H2O2 or UVB exposure. These results suggest that Bv-EE has anti-oxidative effects by inhibiting the AP-1 signaling pathway, and shows anti-aging effects by upregulating collagen synthesis.
## 1. Introduction
The genus Breynia belongs to the family Phyllanthaceae, which is composed of 35 plant species distributed in tropical regions of the Pacific Islands, Australia, and Asia [1]. Many reports about the medicinal benefits of Breynia species have been published. Breynia nivosa possesses analgesic, anti-inflammatory, and antimicrobial properties [2]. Breynia retusa shows antioxidant and anti-diabetic activities, and *Breynia distachia* has shown hypoglycemic and anti-Alzheimer’s activities [3,4]. Breynia vitis-idaea, commonly called Indian snowberry, is a large, evergreen shrub or treelet that grows up to 5 m in height [5]. These plants have ovate or elliptic leaves that are 1–3 cm long. The bark is yellowish grey, the flowers are small and greenish yellow or pink, and the fruits are dull red, purple, or white berries [6]. These plants are found in countries including Bangladesh, Cambodia, India, Malaya, Myanmar, Pakistan, Philippines, Sri Lanka, Thailand, and Vietnam, and have been used as a traditional herbal medicine for the treatment of wounds and chronic bronchitis, especially in China [7]. Recent studies have revealed that Breynia vitis-idaea has anti-hypoglycemic, anti-hypolipidemic, and anti-cancer activity [8,9]. Interestingly, Breynia vitis-idaea is known to contain 6-O-benzoyl arbutin, breynioside B, and 6-O-benzoyl-α-D-glucose, which are known to have antioxidant activity [5]. Despite the various therapeutic potentials of Breynia vitis-idaea, its ability to inhibit skin aging has not been studied.
The skin is the largest organ of the human body and acts as the first protective system from external threats such as noxious substances and pathogens [10,11]. Aging weakens the skin’s protective function, resulting in slow wound repair, frequent skin inflammation, and increased risk of skin cancer [12,13,14]. Skin aging is a cumulative process involving intrinsic and extrinsic factors. Among the extrinsic factors, photoaging constitutes as much as $80\%$ of skin aging [15]. Repetitive exposure of skin to UV radiation causes several skin problems, including loss of elasticity, deformation, sagging, and wrinkling [16]. UV radiation is classified as UVA (320–400 nm), UVB (290–320 nm), and UVC (200–290 nm). Although most UVC radiation is absorbed by the atmosphere, UVA and UVB rays can directly affect the skin, and the high-energy, short-wavelength UVB is more threatening to the skin than UVA [17]. Exposure of skin cells to UVB radiation triggers a signaling response called the UV response, which includes activation of the mitogen-activated protein kinase (MAPK) cascade [18]. MAPK activation induces the expression of cyclooxygenase-2 (COX-2), which catalyzes the conversion of arachidonic acid into prostaglandins and is closely implicated in aging processes [19,20,21]. UVB-induced activation of MAPK is followed by enhanced transcriptional activity of the activator protein-1 (AP-1) complex, which consists of heterodimers of c-Jun and c-Fos proteins [22,23]. Activation of the AP-1 complex leads to upregulation of extracellular-matrix (ECM)-degrading enzymes, such as matrix metalloproteinase-3 (MMP-3) and MMP-9, which are responsible for degrading the collagen and ECM that compose the dermal connective tissue [24]. Protecting the skin from UVB exposure is one way to delay skin aging. Therefore, our aim in this study was to investigate the anti-aging and antioxidant effects of an ethanol extract of Breynia vitis-idaea (Bv-EE) in UVB- and H2O2-damaged human skin cell lines.
## 2.1. Phytochemical Components of Bv-EE
The phytochemical composition of Bv-EE was analyzed by gas chromatography–mass spectrometry (GC–MS) (Figure 1). The corrected percentage peak area was divided by the sum of the corrected area to obtain the total content of each compound in Bv-EE. The major compound was identified as 2,3-dihydro-3,5-dihydroxy-6-methyl- 4H-pyran-4-one, also known as DDMP. This compound is produced in the intermediate stage of the Maillard reaction [25]. The contribution of DDMP to antioxidant activity has been well studied, and DDMP is known to exist in natural extracts and foods [26,27]. In particular, DDMP is known for its antioxidant effect and its ability to prevent and treat obesity or lipid-related metabolic diseases [28]. Other active components of Bv-EE are listed in Table 1. Nonetheless, confirming the identification of these components from Bv-EE will be continued with other extracts prepared with the same plant materials harvested according to different seasons, regions, and years to complete standardization of this extract for industrial application.
## 2.2. Bv-EE Exerts Reactive Oxygen Species Scavenging Activity
To determine whether Bv-EE has radical scavenging activity, 1-diphenyl-2-picryl-dydrazyl (DPPH) and 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) diammonium salt (ABTS) assays were performed [29]. The Bv-EE scavenged DPPH radicals in a dose-dependent manner and showed significant scavenging activity at a concentration of 6.25 μg/mL (Figure 2a). In the ABTS assay (Figure 2b), Bv-EE exhibited ABTS radical scavenging activity, with IC50 values of 10.8424 μg/mL and 5.36 μg/mL (Figure 2c,d). Ascorbic acid (300 μM) was used as a positive control (Figure 2a,b). The ferric reducing antioxidant power (FRAP) assay showed that Bv-EE can also reduce ferric acid dose-dependently (Figure 2e). In addition, the cupric ion-reducing antioxidant capacity (CUPRAC) assay showed that Bv-EE reduced Cu ions in a dose-dependent manner. Trolox (3 mg/mL) was used as the positive control. Moreover, we determined the total phenolic content and total flavonoid content of Bv-EE to be 89.33 μg/mg and 73 μg/mg, respectively (Figure 2g,h). These results together show that Bv-EE is a potential antioxidant.
## 2.3. Bv-EE Prevents Cell Death Caused by UVB and H2O2 in HaCaT cells
Because Bv-EE showed ROS scavenging activity, we tested whether it has the same effect in human keratinocytes. First, we determined whether Bv-EE has cytotoxic effects on HaCaT cells. As shown in Figure 3a, Bv-EE did not show cytotoxicity and did not impact cell viability. Next, we induced cell death using UVB (30 mJ/cm2) or H2O2 (200 μM) and found that the decreased cell number recovered with Bv-EE treatment (Figure 3b,c). In addition, to investigate whether Bv-EE can reduce ROS generation in keratinocytes, we stained HaCaT cells with 6-diamidino-2-phenylindole (DAPI) and 2′,7′–dichlorodihydrofluorescein diacetate (H2DCFDA) after Bv-EE treatment. The ROS level increased upon treatment with H2O2; however, Bv-EE treatment reduced the ROS level in a dose-dependent manner. We also analyzed the ROS level in HaCaT cells using flow cytometry and similarly found that ROS generation decreased upon Bv-EE treatment (Figure 3d). Thus, Bv-EE can reduce the ROS generation caused by UVB exposure or H2O2 treatment, suggesting that it has antioxidant and anti-aging effects.
## 2.4. Bv-EE Inhibits AP-1 Promoter Activity and mRNA Expression of Aging Factors
Because Bv-EE was able to inhibit intracellular ROS generation and increase cell viability, we examined whether those effects involve AP-1, which is a main transcription factor that regulates ROS generation and cellular responses [30]. After confirming that Bv-EE has no cytotoxic effect on HEK293T cells (Figure 4a), we examined AP-1 promoter activity using a luciferase assay. As shown in Figure 4b, the AP-1 promoter activation level decreased significantly in a dose-dependent manner upon Bv-EE treatment in Toll-interleukin-1 receptor-domain-containing adapter-inducing interferon-β (TRIF)-induced HEK293T cells. Because Bv-EE can suppress AP-1 transcriptional activity, and AP-1 is a major regulator of the transcription of aging- and oxidative-related factors [31], we analyzed whether the mRNA levels of those related factors were regulated by Bv-EE. Interestingly, the mRNA expression of MMP-3 and MMP-9 was decreased by Bv-EE in H2O2-exposed HaCaT cells (Figure 4c). The COX-2 expression level was also decreased in the Bv-EE-treated groups in a dose-dependent manner (Figure 4d). In addition, we investigated the expression of those factors in UVB-treated HaCaT cells. The mRNA expression of MMP-3 and MMP-9 was reduced upon Bv-EE treatment (Figure 4e), and the COX-2 expression level was inhibited dose-dependently (Figure 4f). Collectively, these data indicate that key factors related to aging and oxidation were inhibited by Bv-EE as a consequence of its inhibition of AP-1.
## 2.5. Bv-EE Inhibits the AP-1 Signaling Pathway
We hypothesized that Bv-EE inhibited AP-1 transcription by inhibiting an upstream signaling pathway. Therefore, we studied whether phosphorylation of the kinases involved in the AP-1 signaling pathway would be reduced by Bv-EE in UVB- or H2O2-treated HaCaT cells. First, we measured the phosphorylation levels of c-Jun and c-Fos, which are two main subunits of AP-1 [32]. Upon UVB irradiation, the expression of phosphorylated c-Jun and c-Fos was dramatically increased, and Bv-EE treatment decreased that expression in a dose-dependent manner (Figure 5a–c). We also examined the phosphorylation of AP-1 upstream kinases. Increased phosphorylation of the c-Jun N-terminal kinase (JNK) and p38 was decreased by Bv-EE treatment (Figure 5d–f). We then examined the same factors in cells subjected to H2O2 instead of UVB. We found downregulated phosphorylation of JNK, extracellular signal-regulated kinase (ERK), and p38, though the total form was unchanged (Figure 5g–i). These results indicate that Bv-EE can inhibit phosphorylation of the AP-1 signaling pathway to protect cells from UVB irradiation and oxidative stress.
## 2.6. Bv-EE Promotes Collagen Generation in Human Dermal Fibroblast (HDF) Cells
Aging and oxidative stress play important roles in wrinkle formation. Therefore, wrinkles are frequently considered an indicator of aging and oxidation [33,34]. Based on that concept, we tested whether Bv-EE can enhance the expression of wrinkle-related factors in human dermal fibroblast cells, which are responsible for collagen generation. After confirming that Bv-EE has no cytotoxic effect on HDF cells (Figure 6a), we examined whether Bv-EE can regulate Col1A1, a typical collagen-encoding gene [35]. As shown in Figure 6b, Bv-EE treatment dose-dependently upregulated Col1A1 promoter activity; retinol was used as a positive control. Correspondingly, mRNA expression of Col1A1 was also increased by Bv-EE treatment (Figure 6c). In addition, we tested whether Bv-EE can restore the Col1A1 expression decreased by exposure to UVB or H2O2. Interestingly, we found that the decreased mRNA expression of Col1A1 was recovered by Bv-EE treatment (Figure 6d,e). These results indicate that Bv-EE can promote collagen synthesis by upregulating its transcriptional activity.
## 3. Discussion
Organic and nature-derived materials are attractive approaches to skin therapy due to their minimal toxicity and side effects [36]. This is an important factor in pharmaceutical and cosmetic formulations. In this study, we used UVB- and H2O2-induced damage in vitro to study the efficacy of a novel plant extract, Bv-EE, and demonstrated its protective effects in human keratinocytes and dermal fibroblasts. Importantly, Bv-EE showed no cytotoxicity to HaCaT or HDF cells, which are the cell lines most widely used for human skin research.
Oxidative stress is the largest cause of skin damage and is associated with skin aging [37]. Therefore, the use of antioxidants has become a leading approach for anti-aging therapy [38]. Several chemicals have been approved for application in the pharmaceutical and cosmetic industries [39]. In this work, we found that Bv-EE has ROS scavenging activity (Figure 2), indicating that it can directly scavenge generated ROS. More importantly, Bv-EE inhibits ROS generation in human keratinocytes (Figure 3). UVB and H2O2 treatment induce serious oxidation processes in cells and eventually lead to cell death. Bv-EE showed a remarkable ability to prevent cell death caused by ROS responses, indicating the potential of Bv-EE as a drug to treat skin diseases.
When COX-2 and MMPs are produced in response to UVB irradiation, they play an important role in inflammatory responses in skin cells. COX-2 mediates inflammatory processes in the skin, including inflammatory hyperalgesia and nociception [40,41,42]. In this work, we discovered that Bv-EE treatment significantly decreased COX-2 and MMP transcriptional activity and mRNA expression in UVB- and H2O2-damaged keratinocytes (Figure 4). That finding clearly indicates that Bv-EE acts as an antioxidant, as well as an anti-aging agent, by recovering the damage induced by UVB irradiation and oxidative stress.
The AP-1 signaling pathway is prominently activated by external triggers, including UVB and oxidative stress. Therefore, its constitutive kinases are promising targets for disease therapies [43,44]. In this study, we focused on MAPK-related enzymes and confirmed that Bv-EE can inhibit the phosphorylation of JNK, ERK, and p38 in UVB- and H2O2-damaged keratinocytes (Figure 5). These findings suggest that the skin-protective characteristics of Bv-EE occur by regulating the AP-1 intracellular molecular signaling pathway.
One of the most typical symptoms and indicators of aging is wrinkles, which are caused by a decrease in ECM proteins such as collagen in fibroblast cells [45]. Therefore, the proper production of collagen is an important process for maintaining healthy skin [46]. In this work, we found that Bv-EE promotes collagen synthesis by increasing Col1A1 transcriptional activity and mRNA expression in human dermal fibroblasts (Figure 6). More interestingly, Bv-EE can restore the collagen synthesis decreased by UVB irradiation and H2O2 treatment (Figure 6). These findings strongly suggest that Bv-EE can help to prevent skin wrinkles by promoting collagen synthesis.
Among all the skin-protecting agents currently used in the pharmaceutical and cosmetic industries, Bv-EE has shown its potential as a natural compound with anti-aging and antioxidant properties. This study provides novel insights about an organic and natural ingredient as a promising drug candidate because of its genetic and molecular regulation of potential targets.
## 4.1. Materials
HaCaT, HDF, and HEK293T cells were purchased from the American Type Culture Collection (Rockville, MD, USA). Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), phosphate-buffered saline (PBS), penicillin–streptomycin, bovine serum albumin (BSA), gallic acid, quercetin, aluminum chloride, DPPH, ABTS, potassium sulfate, trypsin, and ascorbic acid were purchased from Hyclone (Grand Island, NY, USA). Retinol, dimethyl sulfoxide (DMSO), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), polyethylenimine (PEI), TRIzol, hydrogen peroxide (H2O2), 2,4,6-tri(2-pyridyl)-s-triazine (TPTZ), FeCl3·6H2O, Trolox, CuCl2·2H2O, NH4Ac, neocuproine, 3-(4,5-dimethylthiazol-2′, 7′-dichlorofluorescein diacetate (DCFDA), and DAPI were purchased from Sigma Aldrich Chemical Co. (St. Louis, MO, USA). The MTT stop solution was prepared by adding $10\%$ sodium dodecyl sulfate to hydrochloric acid (HCl). The luciferase assay system kit was obtained from Promega (Madison, WI, USA). The cDNA synthesis kit was received from Thermo Fisher Scientific (Waltham, MA, USA). The forward and reverse primers used in RT-PCR were synthesized by Macrogen (Seoul, Republic of Korea), and the PCR premix was obtained from Bio-D Inc. (Seoul, Republic of Korea). The polyvinylidene difluoride (PVDF) membranes and enhanced chemiluminescence (ECL) reagent were purchased from Bio-Rad (Hercules, CA, USA). Specific antibodies for the total- and phosphorylated forms of ERK, JNK, p38, c-Jun, c-Fos, and β-actin were purchased from either Cell Signaling Technology (Beverly, MA, USA) or Santa Cruz Biotechnology (Santa Cruz, CA, USA).
## 4.2. Preparation of Breynia vitis-idaea
Breynia vitis-idaea was procured from the National Institute for Biological Resources (Incheon, Republic of Korea). The dried stems (1.3 kg) were extracted in $70\%$ ethanol (3 × 18 L) at room temperature for 3 h using an ultrasonicator (Ultrasonic Cleaner UC-10, UC-20, 400 W) under dark conditions. After removing the macerate by filtration, the extracted solution was concentrated in vacuo at 40 °C using a rotary evaporator (IVT Co., Ltd., Daegu, Republic of Korea), and then freeze-dried for 48 h at −80 °C to make a completely dried powder (color of the extract: brown). The yield of this extraction was $13.97\%$.
## 4.3. GC–MS
The GC–MS analysis of dried Bv-EE (100 mg/mL in methanol) was carried out with an Agilent 8890 GC instrument (Santa Clara, CA, USA) equipped with an Agilent J&W DB-624 Ultra Insert GC column (60 m in length × 250 µm in diameter × 1.40 µm in thickness), and mass spectrometry was conducted with an Agilent 5977B MSD instrument (Santa Clara, CA, USA) equipped with a Series II triple-axis detector with a high energy dynode and long-life electron multiplier from the Cooperative Center for Research facilities of Sungkyunkwan University (Suwon, Republic of Korea), as previously reported [47,48]. Detailed conditions of the analysis are listed in Table 2. The spectrum of phytochemicals in the National Institute of Standards and Technology library was used to identify the unknown phytochemicals in Ca-EE, as reported previously [49,50].
## 4.4. Cell Culture
HaCaT cells (a human keratinocyte cell line) and HDF cells (a human fibroblast cell line) were cultured in DMEM supplemented with $10\%$ FBS and $1\%$ penicillin–streptomycin, and HEK293T cells (a human embryonic kidney cell line) were cultured in DMEM supplemented with $5\%$ FBS and $1\%$ penicillin–streptomycin. All cells were maintained in a $5\%$ CO2 incubator at 37 °C.
## 4.5. DPPH Radical Scavenging Activity Assay
The DPPH assay was performed to determine the radical scavenging capacity of the extract. We dissolved 300 μM DPPH in methanol and used ascorbic acid (100 μM) dissolved in PBS as the positive control. Different concentrations of Bv-EE were prepared, and the absorbance was measured at 517 nm and 37 °C for 15 min. Radical scavenging activity was calculated using the following equation, as reported previously [51,52]. DPPH radical scavenging activity % = [(A0 − A1)/A0] × 100 where A0 is the absorbance of DPPH, and A1 is the absorbance of the extract.
## 4.6. ABTS Radical Scavenging Activity Assay
The ABTS assay was performed to determine the radical scavenging capacity of Bv-EE. First, 7.4 mM ABTS dissolved in PBS and 2.4 mM of potassium persulfate dissolved in PBS were mixed at a ratio of 1:1 and incubated overnight. Different concentrations of Bv-EE and ascorbic acid (100 μM) were prepared, and the absorbance was measured at 730 nm and 37 °C for 15 min. The percentage of scavenging was calculated using the same method as stated previously [53].
## 4.7. CUPRAC Assay
The CUPRAC assay was performed to determine the cupric reducing antioxidant capacity of Bv-EE. First, 100 mM CuCl2·2H2O (copper (II) chloride solution) was dissolved in distilled or deionized water. NH4Ac (ammonium acetate) was dissolved in distilled or deionized water, and the pH was adjusted to 7.0. Neocuproine (Nc) solution (7.5 mM) was dissolved in pure ethanol. Next, the copper (II) chloride solution, ammonium acetate buffer, and Nc solution were mixed at a ratio of 1:1:1 to a final volume of 200 μL in e-tubes, and 200 μL of Bv-EE solution was added to each tube. Trolox (3 mg/mL) dissolved in pure ethanol was used as a positive control. After a 1 h incubation at room temperature, the mixed solution was transferred to a 96-well plate, and the absorbance was measured at 450 nm [54].
## 4.8. FRAP Assay
The FRAP assay was performed to determine the ferric reducing power of Bv-EE. First, 300 mM acetic acid buffer was prepared and mixed with anhydrous sodium acetate and glacial acetic acid (pH 3.6). Next, 10 mM TPTZ solution dissolved in distilled or deionized water and 20 mM FeCl3·6H2O dissolved in distilled or deionized water were mixed with the FeCl3 solution at a ratio of 10:1:1. Bv-EE solution was prepared at different concentrations and aliquoted to a 96-well plate at 100 μL per well. Then, 100 μL of FRAP working solution were added to each well and incubated for 15 min at 37 °C in the dark. Trolox was used as a positive control, and absorbance was measured at 593 nm [55].
## 4.9. Determination of Total Phenolic Content
At room temperature, 100 μL of Bv-EE (serially diluted five times in distilled or deionized water) and 100 μL of $10\%$ Folin–Ciocâlteu reagent were mixed with 300 μL of distilled or deionized water and incubated for 5 min. Next, 500 μL of $8\%$ sodium carbonate and 500 μL of distilled or deionized water were added to the tubes. After 30 min of incubation at room temperature in a dark room, the mixture was transferred to a 96-well plate, and the absorbance was measured at 765 nm. The calculation was performed using a standard curve obtained with gallic acid, and the total phenolic content is expressed as mg of gallic acid equivalent/g of extract.
## 4.10. Determination of Total Flavonoid Content
For this analysis, 100 μL of Bv-EE (serially diluted five times in distilled or deionized water) and 100 μL of aluminum chloride $2\%$ reagent were mixed in a 1:1 ratio, as reported previously [56,57]. After a 1 h incubation at room temperature in a dark room, the mixture was transferred to a 96-well plate, and the absorbance was measured at 420 nm. The calculation was performed using a standard curve obtained with quercetin, and the total flavonoid content is expressed as mg of quercetin equivalent/g of extract.
## 4.11. ROS Generation Assay
The 2′,7′–dichlorodihydrofluorescin diacetate (H2DCFDA) assay was used to detect intracellular ROS. HaCaT cells were pretreated with Bv-EE for 30 min and then treated with H2O2 (200 μM) for 24 h. The cells were washed with PBS, stained with 10 μM H2DCFDA, and incubated for 20 min in the dark. The cells were then fixed in formaldehyde solution (100 μL/mL) for 10 min, washed with PBS two times, stained with DAPI (1 μL/mL), and incubated for 20 min in the dark. Photographs were captured using a Nikon Eclipse Ti (Nikon, Japan) fluorescence microscope.
For flow cytometry, HaCaT cells were treated with Bv-EE and exposed to H2O2, as stated above. The cells were harvested and resuspended in 300 μL of PBS. Then, 10 μM H2DCFDA was added to the tube and incubated for 20 min in the dark. The fluorescence was detected at $\frac{485}{535}$ nm using a flow cytometer (Beckman Coulter, Brea, CA, USA), as described previously [58].
## 4.12. Cell Viability Assay
HaCaT and HEK293T cells were seeded at 3 × 105 cells/mL, and HDF cells were seeded at 1 × 105 cells/mL in 96-well plates and incubated for 24 h. Then, Bv-EE was added to all cells for 24 h. Next, 100 μL of the original media was removed, and 10 μL of MTT solution was added to each well and incubated for 4 h. To each well, 100 μL of MTT stopping solution was added and incubated overnight. The absorbance was measured at 570 nm using a multi-plate microreader, as previously reported [59,60].
## 4.13. H2O2 Treatment and UVB Irradiation
HaCaT cells were plated in 6-well plates at 2 × 105 cells/mL and incubated for 24 h. The cells were pretreated with Bv-EE for 30 min, washed with cold PBS, and exposed to H2O2 (200 μM) or UVB radiation (30 mJ/cm2). After that, the cells were treated with Bv-EE at different concentrations for 24 h, as previously reported [61]. A BLX-312 (Vilber Lourmat, France) UVB lamp was used for UVB irradiation. Cell viability was calculated as follows:Cell viability (% of control) = A1/A0 × 100 where A1 refers to treated cells, and A0 refers to normal untreated cells.
## 4.14. Semi-Quantitative Reverse Transcription-PCR (RT-PCR) and Quantitative Real-Time PCR (Real-Time PCR)
HaCaT and HDF cells were exposed to UVB and H2O2 as described above. Either Bv-EE or retinol (10 μg/mL) was added to the cells for 24 h. RNA was isolated using TRI reagent solution. cDNA was synthesized from total RNA (1 μg) using a cDNA synthesis kit (Thermo Fisher Scientific, Waltham, MA, USA). RT-PCR and real-time PCR were conducted as previously described [62,63]. The primer sequences used in this experiment are listed in Table 3.
## 4.15. Luciferase Reporter Gene Assay
HEK293T cells were seeded in a 24-well plate at 3 × 105 cells/mL. After 18 h of incubation, the HEK293T cells were co-transfected with luciferase-expressing genes (AP-1 and Col1A1) and the β-galactosidase gene using PEI. After 24 h of incubation, the cells were treated with Bv-EE or retinol for 24 h. Then, 300 μL of luciferase lysis buffer was added to each well, and the plate was frozen for 3 h at −70 °C. The luciferase assay was conducted using a luciferase assay system reported previously [64].
## 4.16. Preparation of Whole Cell Lysates and Western Blot Analysis
HaCaT cells were washed with cold PBS, collected using a cell scraper, and centrifuged at 12,000 rpm for 5 min at 4 °C. The cells were lysed for 15 min on ice in cell lysis buffer (20 mM Tris-HCl, pH 7.4; 2 mM EDTA; 2 mM ethyleneglycotetraacetic acid; 1 mM dithiothreitol; 50 mM β-glycerol phosphate; 0.1 mM sodium vanadate; 1.6 mM pervanadate; $1\%$ Triton X-100; $10\%$ glycerol; 10 μg/mL aprotinin; 10 μg/mL pepstatin; 1 µM benzamide; and 2 μM phenylmethylsulfonyl fluoride) and kept at −70 °C until use. The supernatant containing protein was collected and used for Western blotting. Protein concentrations were measured using the Bradford assay as described previously [65]. For that, 20 μg of protein from each sample was separated by $10\%$ sodium dodecyl sulfate-polyacrylamide gel electrophoresis and then transferred onto PVDF membranes (Millipore, Billerica, MA, USA). After blocking the membranes with $3\%$ BSA for 1 h at room temperature, we washed the membranes with tris-buffered saline (50 mM Tris-Cl, pH 7.5, 150 mM NaCl) and $0.1\%$ Tween-20 (TBST) three times at 10 min intervals. The membranes were incubated with primary antibodies (1:2500 dilution) overnight at 4 °C. Then, the membranes were washed with TBST three times for 10 min each and incubated with secondary antibody for 2 h at room temperature. After washing the membranes with TBST three times for 10 min each time, we detected their chemiluminescence using ECL reagent [66]. Relative band intensities were measured using ImageJ software (Wayne Rasband, NIH, Bethesda, MD, USA).
## 4.17. Statistical Analyses
All data are presented as the mean ± standard deviation of at least three independent experiments. A Mann–Whitney test was used to compare statistical differences between experimental and control groups. p-values < 0.05 were considered statistically significant. All statistical analyses were conducted using SPSS (SPSS, Chicago, IL, USA).
## 5. Conclusions
Herein, we demonstrated the anti-aging and antioxidant capacity of Bv-EE by evaluating its inhibitory effect on ROS in human keratinocytes and dermal fibroblasts exposed to UVB irradiation and H2O2. Bv-EE was able to downregulate aging factors (COX-2 and MMPs). Correspondingly, the AP-1 (c-Jun/c-Fos) signaling activity was reduced upon Bv-EE treatment, as summarized in Figure 7. On the other hand, Bv-EE upregulated Col1A1 expression in human dermal fibroblasts.
## References
1. Kawakita A., Kato M.. **Obligate pollination mutualism in**. *Am. J. Bot.* (2004) **91** 1319-1325. DOI: 10.3732/ajb.91.9.1319
2. Onyegbule F.A., Ilouno I.O., Eze P.M., Abba C.C., Chigozie V.U.. **Evaluation of the analgesic, anti-inflammatory and antimicrobial activities of leaf extracts of**. *Chem. Sci. Rev. Lett.* (2014) **3** 1126-1134
3. Murugan R., Prabu J., Chandran R., Sajeesh T., Iniyavan M., Parimelazhagan T.. **Nutritional composition, in vitro antioxidant and anti-diabetic potentials of**. *Food Sci. Hum. Wellness* (2016) **5** 30-38. DOI: 10.1016/j.fshw.2015.12.001
4. Saadullah M., Asif M., Uzair M., Afzal S., Rashid S.A., Rashad M., Bashir R., Mahmood S.. **Pharmacological evaluation of the hypoglycemic and anti-Alzheimer’s activities of aerial parts of**. *Trop. J. Pharm. Res.* (2021) **21** 579-587. DOI: 10.4314/tjpr.v21i3.18
5. Nguyen T.-M., Le X.-T., Nguyen M.-T.K.. **Chemical constituents of**. *AIP Conference Proceedings: 2017* (2017) 020041
6. Jeyasankar A., Ramar G.. **Larvicidal properties of**. *J. Vector Borne Dis.* (2014) **51** 239. PMID: 25253219
7. Meng D.-H., Wu J., Wang L.-Y., Zhao W.-M.. **Two new glycosides from**. *J. Asian Nat. Prod. Res.* (2010) **12** 535-541. DOI: 10.1080/10286021003745452
8. Nagar J.C., Chauhan L.S.. **Evaluation of antihyperglycemic and antihyperlipidemic activity of leaf extracts of**. *Pharmacogn. J.* (2016) **8** 259-263. DOI: 10.5530/pj.2016.3.15
9. FISHER C.. **Anti-cancer activity of aqueous and ethanol extracts of**. *World J. Pharm. Pharm Sci.* (2014) **4** 830-840
10. Wang Y., Li W., Xu S., Hu R., Zeng Q., Liu Q., Li S., Lee H., Chang M., Guan L.. **Protective skin aging effects of cherry blossom extract (**. *Cytotechnology* (2019) **71** 475-487. DOI: 10.1007/s10616-018-0215-7
11. You L., Cho J.Y.. **The regulatory role of Korean ginseng in skin cells**. *J. Ginseng Res.* (2021) **45** 363-370. DOI: 10.1016/j.jgr.2020.08.004
12. Maru G.B., Gandhi K., Ramchandani A., Kumar G.. **The role of inflammation in skin cancer**. *Adv. Exp. Med. Biol.* (2014) **816** 437-469. PMID: 24818733
13. Lorz L.R., Kim D., Kim M.Y., Cho J.Y.. *J. Ginseng Res.* (2020) **44** 453-460. DOI: 10.1016/j.jgr.2019.02.003
14. Grillo E., Corsini M., Ravelli C., Zammataro L., Bacci M., Morandi A., Monti E., Presta M., Mitola S.. **Expression of activated VEGFR2 by R1051Q mutation alters the energy metabolism of Sk-Mel-31 melanoma cells by increasing glutamine dependence**. *Cancer Lett.* (2021) **507** 80-88. DOI: 10.1016/j.canlet.2021.03.007
15. Kammeyer A., Luiten R.. **Oxidation events and skin aging**. *Ageing Res. Rev.* (2015) **21** 16-29. DOI: 10.1016/j.arr.2015.01.001
16. Oh J.H., Joo Y.H., Karadeniz F., Ko J., Kong C.-S.. **Syringaresinol inhibits UVA-induced MMP-1 expression by suppression of MAPK/AP-1 signaling in HaCaT keratinocytes and human dermal fibroblasts**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21113981
17. Chang H., Zou Z., Li J., Shen Q., Liu L., An X., Yang S., Xing D.. **Photoactivation of mitochondrial reactive oxygen species-mediated Src and protein kinase C pathway enhances MHC class II-restricted T cell immunity to tumours**. *Cancer Lett.* (2021) **523** 57-71. DOI: 10.1016/j.canlet.2021.09.032
18. Bode A.M., Dong Z.. **Mitogen-activated protein kinase activation in UV-induced signal transduction**. *Sci. STKE* (2003) **2003** re2. DOI: 10.1126/stke.2003.167.re2
19. Lee M.E., Kim S.R., Lee S., Jung Y.-J., Choi S.S., Kim W.J., Han J.A.. **Cyclooxygenase-2 inhibitors modulate skin aging in a catalytic activity-independent manner**. *Exp. Mol. Med.* (2012) **44** 536-544. DOI: 10.3858/emm.2012.44.9.061
20. Buckman S., Gresham A., Hale P., Hruza G., Anast J., Masferrer J., Pentland A.P.. **COX-2 expression is induced by UVB exposure in human skin: Implications for the development of skin cancer**. *Carcinogenesis* (1998) **19** 723-729. DOI: 10.1093/carcin/19.5.723
21. Kim H.H., Cho S., Lee S., Kim K.H., Cho K.H., Eun H.C., Chung J.H.. **Photoprotective and anti-skin-aging effects of eicosapentaenoic acid in human skin in vivo**. *J. Lipid Res.* (2006) **47** 921-930. DOI: 10.1194/jlr.M500420-JLR200
22. Devary Y., Rosette C., DiDonato J.A., Karin M.. **NF-κB activation by ultraviolet light not dependent on a nuclear signal**. *Science* (1993) **261** 1442-1445. DOI: 10.1126/science.8367725
23. Lu J., Guo J.-H., Tu X.-L., Zhang C., Zhao M., Zhang Q.-W., Gao F.-H.. **Tiron inhibits UVB-induced AP-1 binding sites transcriptional activation on MMP-1 and MMP-3 promoters by MAPK signaling pathway in human dermal fibroblasts**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0159998
24. Kim H.H., Shin C.M., Park C.-H., Kim K.H., Cho K.H., Eun H.C., Chung J.H.. **Eicosapentaenoic acid inhibits UV-induced MMP-1 expression in human dermal fibroblasts**. *J. Lipid Res.* (2005) **46** 1712-1720. DOI: 10.1194/jlr.M500105-JLR200
25. Kim M.-O., Baltes W.. **On the role of 2, 3-dihydro-3, 5-dihydroxy-6-methyl-4 (H)-pyran-4-one in the Maillard reaction**. *J. Agric. Food Chem.* (1996) **44** 282-289. DOI: 10.1021/jf950208h
26. Chen Z., Liu Q., Zhao Z., Bai B., Sun Z., Cai L., Fu Y., Ma Y., Wang Q., Xi G.. **Effect of hydroxyl on antioxidant properties of 2, 3-dihydro-3, 5-dihydroxy-6-methyl-4 H-pyran-4-one to scavenge free radicals**. *RSC Adv.* (2021) **11** 34456-34461. DOI: 10.1039/D1RA06317K
27. Yu X., Zhao M., Liu F., Zeng S., Hu J.. **Identification of 2, 3-dihydro-3, 5-dihydroxy-6-methyl-4H-pyran-4-one as a strong antioxidant in glucose–histidine Maillard reaction products**. *Food Res. Int.* (2013) **51** 397-403. DOI: 10.1016/j.foodres.2012.12.044
28. Xie Q., Gu X., Chen J., Liu M., Xiong F., Wu X., Zhang Y., Chen F., Chen H., Li M.. **Soyasaponins reduce inflammation and improve serum lipid profiles and glucose homeostasis in high fat diet-induced obese mice**. *Mol. Nutr. Food Res.* (2018) **62** 1800205. DOI: 10.1002/mnfr.201800205
29. Kedare S.B., Singh R.. **Genesis and development of DPPH method of antioxidant assay**. *J. Food Sci. Technol.* (2011) **48** 412-422. DOI: 10.1007/s13197-011-0251-1
30. Roebuck K.A.. **Oxidant stress regulation of IL-8 and ICAM-1 gene expression: Differential activation and binding of the transcription factors AP-1 and NF-kappaB**. *Int. J. Mol. Med.* (1999) **4** 223-253. DOI: 10.3892/ijmm.4.3.223
31. Benbow U., Brinckerhoff C.E.. **The AP-1 site and MMP gene regulation: What is all the fuss about?**. *Matrix Biol.* (1997) **15** 519-526. DOI: 10.1016/S0945-053X(97)90026-3
32. Ding M., Zhao J., Bowman L., Lu Y., Shi X.. **Inhibition of AP-1 and MAPK signaling and activation of Nrf2/ARE pathway by quercitrin**. *Int. J. Oncol.* (2010) **36** 59-67. DOI: 10.3892/ijo_00000475
33. Lee K.-T., Lee S.-Y., Jeong J.-H., Jo B.-K.. **New anti-wrinkle cosmetics**. *J. Soc. Cosmet. Sci. Korea* (2002) **28** 71-79
34. Wenk J., Brenneisen P., Meewes C., Wlaschek M., Peters T., Blaudschun R., Ma W., Kuhr L., Schneider L., Scharffetter-Kochanek K.. **UV-induced oxidative stress and photoaging**. *Curr. Probl. Dermatol.* (2001) **29** 83-94. PMID: 11225204
35. Harris A.K., Stopak D., Wild P.. **Fibroblast traction as a mechanism for collagen morphogenesis**. *Nature* (1981) **290** 249-251. DOI: 10.1038/290249a0
36. Redolfi Riva E., Micera S.. **Progress and challenges of implantable neural interfaces based on nature-derived materials**. *Bioelectron. Med.* (2021) **7** 1-10. DOI: 10.1186/s42234-021-00067-7
37. Birch-Machin M., Bowman A.. **Oxidative stress and ageing**. *Br. J. Dermatol.* (2016) **175** 26-29. DOI: 10.1111/bjd.14906
38. Halliwell B.. **Antioxidant characterization: Methodology and mechanism**. *Biochem. Pharmacol.* (1995) **49** 1341-1348. DOI: 10.1016/0006-2952(95)00088-H
39. Ou S., Kwok K.C.. **Ferulic acid: Pharmaceutical functions, preparation and applications in foods**. *J. Sci. Food Agric.* (2004) **84** 1261-1269. DOI: 10.1002/jsfa.1873
40. Seibert K., Masferrer J.. **Role of inducible cyclooxygenase (COX-2) in inflammation**. *Receptor* (1994) **4** 17-23. PMID: 8038702
41. Pillai S., Oresajo C., Hayward J.. **Ultraviolet radiation and skin aging: Roles of reactive oxygen species, inflammation and protease activation, and strategies for prevention of inflammation-induced matrix degradation–A review**. *Int. J. Cosmet. Sci.* (2005) **27** 17-34. DOI: 10.1111/j.1467-2494.2004.00241.x
42. Borg M., Brincat S., Camilleri G., Schembri-Wismayer P., Brincat M., Calleja-Agius J.. **The role of cytokines in skin aging**. *Climacteric* (2013) **16** 514-521. DOI: 10.3109/13697137.2013.802303
43. Hwang B.M., Noh E.M., Kim J.S., Kim J.M., You Y.O., Hwang J.K., Kwon K.B., Lee Y.R.. **Curcumin inhibits UVB-induced matrix metalloproteinase-1/3 expression by suppressing the MAPK-p38/JNK pathways in human dermal fibroblasts**. *Exp. Dermatol.* (2013) **22** 371-374. DOI: 10.1111/exd.12137
44. Wang Y., Schattenberg J.r.M., Rigoli R.M., Storz P., Czaja M.J.. **Hepatocyte resistance to oxidative stress is dependent on protein kinase C-mediated down-regulation of c-Jun/AP-1**. *J. Biol. Chem.* (2004) **279** 31089-31097. DOI: 10.1074/jbc.M404170200
45. MacKenna D., Summerour S.R., Villarreal F.J.. **Role of mechanical factors in modulating cardiac fibroblast function and extracellular matrix synthesis**. *Cardiovasc. Res.* (2000) **46** 257-263. DOI: 10.1016/S0008-6363(00)00030-4
46. Reilly D.M., Lozano J.. **Skin collagen through the lifestages: Importance for skin health and beauty**. *Plast. Aesthet. Res.* (2021) **8** 2. DOI: 10.20517/2347-9264.2020.153
47. Lee H.P., Kim D.S., Park S.H., Shin C.Y., Woo J.J., Kim J.W., An R.-B., Lee C., Cho J.Y.. **Antioxidant capacity of**. *Plants* (2022) **11**. DOI: 10.3390/plants11070873
48. Jang W.Y., Lee H.P., Kim S.A., Huang L., Yoon J.H., Shin C.Y., Mitra A., Kim H.G., Cho J.Y.. *Plants* (2022) **11**. DOI: 10.3390/plants11101306
49. Kim J.W., Kwon K.W., Kim M.Y., Cho J.Y.. *Plants* (2022) **11**. DOI: 10.3390/plants11131750
50. Oh J., Kim S.A., Kwon K.W., Choi S.R., Lee C.H., Hossain M.A., Kim E.S., Kim C., Lee B.H., Lee S.. *J. Ethnopharmacol.* (2023) **305** 116015. DOI: 10.1016/j.jep.2022.116015
51. Brand-Williams W., Cuvelier M.-E., Berset C.. **Use of a free radical method to evaluate antioxidant activity**. *Leb. Wiss. Technol.* (1995) **28** 25-30. DOI: 10.1016/S0023-6438(95)80008-5
52. Hwang S.-H., Kim J.H., Choi E., Park S.H., Cho J.Y.. **Antioxidative and Skin Protective Effects of**. *Evid. Based Complement. Alternat. Med.* (2021) **2021** 6692838. DOI: 10.1155/2021/6692838
53. Re R., Pellegrini N., Proteggente A., Pannala A., Yang M., Rice-Evans C.. **Antioxidant activity applying an improved ABTS radical cation decolorization assay**. *Free Radic. Biol. Med.* (1999) **26** 1231-1237. DOI: 10.1016/S0891-5849(98)00315-3
54. Apak R., Güçlü K., Özyürek M., Bener M.. **Cupric ion reducing antioxidant capacity assay for food antioxidants: Vitamins, polyphenolics, and flavonoids in food extracts**. *Advanced Protocols in Oxidative Stress I* (2008) 163-193
55. Benzie I.F., Strain J.J.. **The ferric reducing ability of plasma (FRAP) as a measure of “antioxidant power”: The FRAP assay**. *Anal. Biochem.* (1996) **239** 70-76. DOI: 10.1006/abio.1996.0292
56. Pavlesic T., Poljak S., Misetic Ostojic D., Lucin I., Reynolds C.A., Kalafatovic D., Saftic Martinovic L.. **Mint (**. *Food Technol. Biotechnol.* (2022) **60** 509-519. DOI: 10.17113/ftb.60.04.22.7703
57. Rahmawati L., Aziz N., Oh J., Hong Y.H., Woo B.Y., Hong Y.D., Manilack P., Souladeth P., Jung J.H., Lee W.S.. *Molecules* (2021) **26**. DOI: 10.3390/molecules26196073
58. Ha A.T., Rahmawati L., You L., Hossain M.A., Kim J.-H., Cho J.Y.. **Anti-inflammatory, antioxidant, moisturizing, and antimelanogenesis effects of quercetin 3-O-β-D-glucuronide in human keratinocytes and melanoma cells via activation of NF-κB and AP-1 pathways**. *Int. J. Mol. Sci.* (2021) **23**. DOI: 10.3390/ijms23010433
59. Shin K.K., Park S.H., Lim H.Y., Lorza L.R., Qomaladewia N.P., You L., Aziz N., Kim S.A., Lee J.S., Choung E.S.. **In Vitro Anti-Photoaging and Skin Protective Effects of**. *Plants* (2022) **11**. DOI: 10.3390/plants11101383
60. Hong Y.H., Aziz N., Park J.G., Lee D., Kim J.K., Kim S.A., Choi W., Lee C.Y., Lee H.P., Huyen Trang H.T.. **The EEF1AKMT3/MAP2K7/TP53 axis suppresses tumor invasiveness and metastasis in gastric cancer**. *Cancer Lett.* (2022) **544** 215803. DOI: 10.1016/j.canlet.2022.215803
61. Song C., Lorz L.R., Lee J., Cho J.Y.. **In vitro photoprotective, anti-inflammatory, moisturizing, and antimelanogenic effects of a methanolic extract of**. *Plants* (2021) **11**. DOI: 10.3390/plants11010094
62. Aziz N., Son Y.-J., Cho J.Y.. **Thymoquinone suppresses IRF-3-mediated expression of type I interferons via suppression of TBK1**. *Int. J. Mol. Sci.* (2018) **19**. DOI: 10.3390/ijms19051355
63. Lee J.-O., Kim E., Kim J.H., Hong Y.H., Kim H.G., Jeong D., Kim J., Kim S.H., Park C., Seo D.B.. **Antimelanogenesis and skin-protective activities of**. *J. Ginseng Res.* (2018) **42** 389-399. DOI: 10.1016/j.jgr.2018.02.007
64. Choi E., Kang Y.-G., Hwang S.-H., Kim J.K., Hong Y.D., Park W.-S., Kim D., Kim E., Cho J.Y.. **In vitro effects of dehydrotrametenolic acid on skin barrier function**. *Molecules* (2019) **24**. DOI: 10.3390/molecules24244583
65. Kim E., Jang J., Park J.G., Kim K.-H., Yoon K., Yoo B.C., Cho J.Y.. **Protein arginine methyltransferase 1 (PRMT1) selective inhibitor, TC-E 5003, has anti-inflammatory properties in TLR4 signaling**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21093058
66. You L., Kim M.-Y., Cho J.Y.. **Protective effect of Potentilla glabra in UVB-induced photoaging process**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26175408
|
---
title: 'Profile of a Food-Insecure College Student at a Major Southeastern University:
A Randomized Cross-Sectional Study'
authors:
- Cedric Harville
- Delores C. S. James
- Arné Burns
journal: Nutrients
year: 2023
pmcid: PMC10005036
doi: 10.3390/nu15051108
license: CC BY 4.0
---
# Profile of a Food-Insecure College Student at a Major Southeastern University: A Randomized Cross-Sectional Study
## Abstract
Ten percent of Americans are food-insecure. Few known studies have accessed college food insecurity via random sampling. An online cross-sectional survey ($$n = 1087$$) was distributed via email to a random sample of undergraduate college students. Food insecurity was determined by the USDA Food Security Short Form. Data were analyzed using JMP Pro. Results: Thirty-six percent of the students were food-insecure. Most food-insecure students were enrolled full-time ($93.6\%$), female ($81.2\%$), received financial aid ($77.9\%$), lived off-campus ($75.0\%$), non-white ($59.6\%$), and employed ($51.7\%$). Food-insecure students had a significantly lower GPA ($p \leq 0.001$ *), were more likely to be non-white ($p \leq 0.0001$ *), and were more likely to have received financial aid compared to food-secure students ($p \leq 0.0001$ *). Food-insecure students were significantly more likely to have lived in government housing, had free or reduced lunch, used SNAP and WIC benefits, and received food from a food bank during childhood ($p \leq 0.0001$ * for all). Food-insecure students were significantly less likely to report that they experienced a food shortage to counseling and wellness personnel, a resident assistant, and their parents ($p \leq 0.05$ * for all). Discussion: College students might be at greater risk for food insecurity if they are non-white, first-generation students, employed, on financial aid, and have a history of accessing government assistance during childhood.
## 1. Introduction
Food insecurity is a global health concern. A total of 13.5 million Americans ($10.2\%$) have experienced food insecurity at some point within the last year [1]. Food insecurity is defined by the United States Department of Agriculture (USDA) as the “access by all people at all times to have enough food for an active, healthy life” [1]. Trends in US food insecurity show that the current rate is at the lowest point since 1999 [1].
The USDA does not directly measure food insecurity among college student populations, but rather US adults, households, and children [2]. However, quantifying college student food insecurity in the published literature has been carried out via validated USDA scales such as the six- and ten-item food security forms [2]. College student food insecurity in the published literature prior to the Coronavirus 2019 (COVID-19) pandemic has shown rates as low as $14\%$, and nearly $59\%$ at its highest [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. Interestingly, most studies related to college food insecurity have used non-probability sampling methods [3,4,5,6,7,8,9,10,11,12,13,14,16,17,18,19,20,21,23]. Few studies [15,22] obtained a random sample of college students to assess the prevalence of food insecurity, which is generally viewed as the “gold standard” for sampling [24].
Qualitative studies have sought to explore the specific factors that might lead to college student food insecurity and found that food-insecure college students are having to sacrifice their food budgets due to having fixed bills [25]. Additionally, incidental expenses such as car troubles and medical bills occur, which results in them skipping/stretching meals and purchasing cheaper and less healthy foods [25]. One study that explored the psychosocial effects of food insecurity found that students were frustrated with their institution due to a lack of resources available for struggling students and having a feeling of hopelessness due to an inability to be employed due to potential excessive work hours [26].
This late adolescence and early adulthood period is characterized by those of college age and possibly living on their own for the first time [27]. Consequently, college students start to become independent (financially) as they are forced to develop and shape their own lifestyle, eating, and health behaviors [27]. Studies have associated college student-related food insecurity risk factors due to limited income, the increased cost of college and expenses, increased credit card reliance and debt, and limited access to supplemental programs such as the Supplemental Nutrition Assistance Program (SNAP) [3]. However, no known studies have linked students’ possible family history of reliance on government assistance as a possible indicator of collegiate food insecurity.
Understanding the profile of a food-insecure college student can potentially provide better insight to colleges and universities of how to pinpoint or target possible students that might be food-insecure. The goal of this study was to describe the profile of a food-insecure college student by assessing the prevalence of food insecurity, the history of government assistance, and food shortage contacts by food security status among a random sample of enrolled college students at a major southeastern university.
## 2.1. Sample
A self-administered online survey via Qualtrics was sent to a simple random sample of 5000 currently enrolled University of Florida (UF) undergraduate students. The total undergraduate population at UF was 35,043 (female: 19,355 ($55.2\%$), male: 15,688 ($44.8\%$)). A request was made to the UF Office of Institutional Planning and Research for the simple random sample of currently enrolled undergraduate students. The Excel file received only contained student emails with no other identifying information provided. No incentives were provided to students for participation. Data were collected from 25 April to 3 June 2017. In order to participate in the study, students had to: [1] be currently enrolled (full-time or part-time) at the University of Florida, [2] be undergraduate students, [3] have the ability to read and write in English, and [4] have a valid UF email address. Overall, 1087 students completed the survey, resulting in a response rate of $21.7\%$.
Potential respondents were emailed an introductory letter describing the study, potential benefits and harms of participation, Institutional Review Board (IRB) contact information and approval/number, and an individualized/personalized link to the survey. Additionally, respondents were not offered an incentive to take the survey. Respondents had two weeks to complete the survey after they started. Consistent with the Dillman Total Design Method of survey administration, after the introductory email, three automated personalized reminder emails were sent to non-respondents every three days [28]. Similarly, automated emails were sent to respondents who started but did not complete the survey. Respondents who completed the survey received an automated “thank you” from Qualtrics. Respondents that had missing data from the food security items were excluded from the analysis. This study was approved by the UF IRB.
## 2.2. Measure of Food Insecurity
The six-item food security scale is an instrument created by the USDA developed to determine the level of food security of individuals [29]. The six-item Food Security Short Form has been validated in previous food insecurity studies with college students [4,5]. Individuals were scored based on the number of “affirmative” responses provided. Affirmative responses consisted of the reported responses “sometimes”, “often”, and “yes”. Individuals with affirmative responses that led to raw scores of 2 or greater were considered “food-insecure”. For any score that was less than 2, participants were considered “food-secure”. This scoring was consistent with previous literature that categorized food security status into two different groups “food-insecure” and “food-secure” [4,5].
There are six-items from the USDA Food Security Short Form which measure food insecurity experienced within the last 12 months. These six items are:“In the last 12 months, the food that (I/we) bought just didn’t last, and (I/we) didn’t have money to get more. ”“In the last 12 months, (I/we) couldn’t afford to eat balanced meals. ”“In the last 12 months, since last (name of current month), did (you/you or other adults in your household) ever cut the size of your meals or skip meals because there wasn’t enough money for food?”“How often did this (cut the size of or skip meals) happen—almost every month, some months but not every month, or in only 1 or 2 months?”“In the last 12 months, did you ever eat less than you felt you should because there wasn’t enough money for food?”“In the last 12 months, were you ever hungry but didn’t eat because there wasn’t enough money for food?”
## 2.3. Measure of Government Assistance
Respondents were asked “if their family ever obtained government assistance during childhood (government housing, free/reduced lunch in school, SNAP, Women, Infants, and Children (WIC), and food banks)”. Respondents had the option to “choose all that apply” from the listed options.
## 2.4. Measures of Food Shortage Contacts
For the last item, respondents were asked to “choose all that apply” as to whether they felt comfortable discussing with a parent, roommate, counseling and wellness personnel, advisor/mentor, resident assistant, or professor if they were running short on food.
## 2.5. Data Analysis
The data for this study were analyzed using JMP Pro 12 (SAS Institute Inc., Cary, NC, USA, 1989–2021) software. Descriptive statistical analyses were used to provide group percentages and compare differences among the individual variables. Sociodemographic characteristics that were measured include: current college classification (lower class (freshman/sophomore), upper class (junior/senior)), gender identity (male/female), race (white/non-white), current age, parental status, first-generation college student, current grade point average (GPA), enrollment status (full-time/part-time), relationship status (married/single), work status (employed/unemployed), housing status (on-campus/off-campus), US citizenship (US citizen/non-US citizen), years in the US, distance/online student, fraternity/sorority member, on financial aid, estimated student debt owed, knowledge of campus food bank, and ever accessed the campus food bank. Sociodemographic characteristics were compared based on food security status using chi-square (X2) with odds ratios (OR) and $95\%$ confidence intervals (CI) and independent samples t-tests were used for continuous variables to compare food-insecure and food-secure students.
Categorization of participants based on the individual total scores on the USDA Food Security Short Form determined their food security status (0–1 = “food-secure”, ≥2 = “food-insecure”). Food security status differences were calculated using X2 for all. All statistical tests had significance set at p ≤ 0.05.
## 3.1. Sample Characteristics
The mean age for college students was 20.77 ± 3.43. Most ($78.1\%$, $$n = 838$$) were female. There was a nearly even split of white ($50.1\%$, $$n = 540$$) and non-white ($49.9\%$, $$n = 537$$) students in this study. Most of the students were upper class ($59.1\%$, $$n = 632$$). Majority of students were single ($97.4\%$, $$n = 1046$$), did not have children ($97.9\%$, $$n = 1050$$), and were born in the US ($86\%$, $$n = 924$$). Students that were not born in the US ($14\%$, $$n = 150$$) had lived in the US for on average 12.85 ± 5.33 years. Most students were enrolled full-time ($93.5\%$, $$n = 999$$) and not in a distance education program ($94.9\%$, $$n = 1012$$), were not members of a fraternity/sorority ($80.9\%$, $$n = 870$$), were not first-generation college students ($67.3\%$, $$n = 721$$), and lived off-campus ($60.2\%$, $$n = 647$$).
## 3.1.1. Food Insecurity
Students responded to a total of six items to measure their individual level of food insecurity. Out of 1087 total responses to all items, $36.1\%$ ($$n = 392$$) were found to be food-insecure within the past 12 months. Individual student responses to the USDA Food Security Short Form can be found in Table 1.
Food insecurity differences were found based on race. Most of the students that were food-insecure were non-white ($59.6\%$, $$n = 233$$), compared to the white students ($40.4\%$, $$n = 158$$). Compared to food-secure students, food-insecure students were found to be significantly more likely to be non-white students (χ2[1] = 23.25; OR = 0.54; $95\%$ (CI) = 0.42–0.69; $p \leq 0.0001$ *).
## 3.1.2. Food Insecurity and Food Bank Usage
Over half of all college students ($56.5\%$, $$n = 607$$) were aware a campus food bank existed. For the food-insecure students, $61.6\%$ ($$n = 241$$) were aware that a food bank existed on their college campus. Compared to food-secure college students, those that were food-insecure were found to be significantly more likely to be aware that a campus food bank existed (χ2[1] = 6.69; OR = 0.71; $95\%$ (CI) = 0.56–0.92; $p \leq 0.01$ *). However, only $9.1\%$ of all students had ever accessed the campus food bank. In comparison, $13.3\%$ ($$n = 32$$) of food-insecure students had ever accessed the campus food bank. Compared to food-secure college students, those that were food-insecure were found to be significantly more likely to access the campus food bank (χ2[1] = 8.63; OR = 0.44; $95\%$ (CI) = 0.25–0.77; $p \leq 0.01$ *).
## 3.1.3. Campus Meal Plan
Most students did not have a campus meal plan ($52.3\%$, $$n = 563$$). Only $33.8\%$ ($$n = 173$$) of food-insecure students had a campus meal plan, compared to $66.2\%$ ($$n = 339$$) of food-secure students. No significant differences in access to a campus meal plan by food security status were found ($p \leq 0.05$).
## 3.1.4. GPA
The average GPA was 3.45 ± 0.39 for all participants. The mean GPA of all food-insecure students was 3.36 ± 0.42. Significant differences in GPA were found based on food security status (t[1039] = 34.78; $p \leq 0.001$). Food-insecure college students had a significantly lower GPA compared to food-secure students (3.36 ± 0.42 vs. 3.50 ± 0.36).
## 3.1.5. Student Employment and Volunteer Activities
Most students ($53.7\%$, $$n = 575$$) were not employed. Students that reported working averaged 18.49 ± 10.07 h per week. A majority of food-insecure students were employed ($51.7\%$, $$n = 201$$) during college. However, a majority of food-secure students were not employed ($56.8\%$, $$n = 387$$). Food-insecure college students were significantly more likely to be employed while in school (χ2[1] = 7.19; OR = 0.71; $95\%$ (CI) = 0.55–0.91; $p \leq 0.01$ *) compared to food-secure students. Food-insecure college students currently worked an average of 18.77 ± 9.78 h per week compared to food-secure students, working 18.30 ± 10.29 h per week. No significant differences in college student hours of employment per week were found based on food security status ($p \leq 0.05$).
College students reported that they participated in volunteer activities for on average 8.64 ± 6.63 h per week. Food-insecure students were significantly more likely to participate in volunteer activities compared to food-secure students (9.42 ± 6.96 vs. 7.58 ± 6.03; t[719] = 13.73; $p \leq 0.001$).
## 3.1.6. Student Financial Aid and Debt
Most students were currently receiving financial aid ($65.1\%$, $$n = 698$$), with an average loan debt of USD 13,589.48 ± 13,209.02. A majority of food-insecure and food-secure students were currently receiving financial aid ($77.9\%$ vs. $57.6\%$). Food-insecure students were significantly more likely to receive financial aid compared to food-secure students (χ2[1] = 44.26; OR = 0.39; $95\%$ (CI) = 0.29–0.52; $p \leq 0.0001$ *). Food-insecure students had a higher loan debt compared to food-secure students, however no significant differences were found (USD 13,999.04 ± 13,242.38 vs. USD 13,194.40 ± 13,198.23).
## 3.1.7. Marital Status
Most students were single ($97.4\%$, $$n = 1046$$). Majority of food-insecure students ($98.5\%$, $$n = 384$$) and food-secure students were also single ($96.8\%$, $$n = 662$$). There were no significant differences based on marital status ($p \leq 0.05$).
## 3.1.8. US Citizenship
Most students were born in the US ($86.0\%$, $$n = 924$$). Majority of food-insecure students ($84.4\%$, $$n = 329$$) and food-secure students were born in the US ($87.0\%$, $$n = 595$$). Those that were not born in the US had lived in the US for an average of 12.85 ± 5.33 years. Food-insecure students had lived in the US for fewer years compared to food-secure students not born in the US (12.62 ± 5.07 vs. 13.02 ± 5.54 years). There were no significant differences in mean years among food-insecure and food-secure students living in the US ($p \leq 0.05$).
## 3.1.9. First-Generation Student
Most ($67.3\%$, $$n = 721$$) students were not first-generation students. Most of the food-insecure students ($61.3\%$, $$n = 239$$) and food-secure students ($70.7\%$, $$n = 482$$) were not first-generation college students. Food-insecure students were significantly more likely compared to food-secure students to be first-generation students (χ2[1] = 9.94; OR = 1.52; $95\%$ (CI) = 1.17–1.98; $p \leq 0.01$ *).
## 3.1.10. Online/Distance Education
Most students were not in an online/distance education program ($94.9\%$, $$n = 1012$$). Majority of food-insecure students ($96.1\%$, $$n = 371$$) and food-secure students were not online/distance education students ($94.3\%$, $$n = 641$$). There were no significant differences among food-insecure and food-secure students in online/distance education programs ($p \leq 0.05$).
## 3.1.11. Current Residence
Most students lived off-campus ($74.2\%$, $$n = 798$$). Majority of food-insecure students ($73.4\%$, $$n = 287$$) and food-secure students lived off-campus ($74.7\%$, $$n = 511$$). There were no significant differences among food-insecure and food-secure students based on current residence ($p \leq 0.05$).
## 3.1.12. Fraternity/Sorority
Most students were not members of a fraternity/sorority ($80.9\%$, $$n = 870$$). Majority of food-insecure students ($82.4\%$, $$n = 322$$) and food-secure students were not members of a fraternity/sorority ($80.1\%$, $$n = 548$$). There were no significant differences based on membership in a fraternity/sorority among food-insecure and food-secure students ($p \leq 0.05$).
## 3.2. Food Insecurity and Government Assistance
Participants were asked to “choose all that apply” based on five government assistance programs participated in during childhood. Most students never lived in government housing ($94.8\%$, $$n = 1019$$), never received food from a food bank ($96.0\%$, $$n = 1043$$), never received WIC ($90.8\%$, $$n = 987$$), never accessed food stamp/SNAP benefits ($85.0\%$, $$n = 924$$), and never received free school lunch/meals ($72.4\%$, $$n = 787$$). Food-insecure students were significantly more likely to have lived in government housing, had free or reduced lunch, used SNAP WIC benefits, and received food from a food bank during childhood ($p \leq 0.0001$ * for all). See Table 2 for distributions.
## 3.3. Food Insecurity and Food Shortage Contacts
Participants were asked to “choose all that apply” based on who they were comfortable telling that they were short on food. Most students reported they were comfortable telling a parent ($79.8\%$, $$n = 867$$) that they were experiencing a food shortage. Other reported responses included: telling a roommate ($44.1\%$, $$n = 479$$), counseling and wellness personnel ($20.2\%$, $$n = 220$$), advisor/mentor ($9.9\%$, $$n = 108$$), resident assistant ($5.6\%$, $$n = 61$$), and a professor ($3.7\%$, $$n = 40$$). Food-insecure students were significantly less likely to report that they experienced a food shortage to counseling and wellness personnel, a resident assistant, and their parents ($p \leq 0.05$ * for all). See Table 3 for all other variables.
## 4.1. Prevalence of Food Insecurity
In the current study, $36.1\%$ of college students were found to be food-insecure. The prevalence of food-insecure college students at this university was nearly four times greater compared to the entire state of Florida ($36.1\%$ vs. $9.9\%$), and three times greater than the food-insecure population in Alachua County, Florida ($36.1\%$ vs. $13.4\%$), where the study was conducted [1,30]. Shockingly, this prevalence was over three times higher than the general US population ($36.1\%$ vs. $10.2\%$) [1].
## 4.2. Profile of a Food-Insecure College Student
The current study found many differences in the demographics of food-insecure students compared to the total sample. Food-insecure college students in this study were significantly more likely to be non-white, on financial aid, employed, with lower GPAs, and first-generation college students compared to those that were food-secure. Consistent with the previous literature of food-insecure college students, those individuals that were non-white were more likely than white students to face food insecurity at a higher prevalence rate [3,5,6,7,8,16,19,22]. Additionally, GPA was found to be significantly lower in food-insecure students compared to those that were food-secure [4,7].
## 4.2.1. Employment among Food-Insecure College Students
Employment status and hours volunteered per week were found to have significant differences in food security status in the current study. Similarly, one study found that college students were more likely to be food-insecure if they were employed while in school and averaged 18 h of work per week [6]. Interestingly, one study that measured coping strategies among food-insecure college students found that nearly $85\%$ sought employment or worked extra hours in order to pay for food and reduce the burden experienced by being food-insecure [31].
## 4.2.2. Off-Campus Food-Insecure College Students’ Food Access
Off-campus students represented most of the college students within the study. Off-campus college students represented three times as many of the food-insecure students within the study compared to on-campus students ($75\%$ vs. $25\%$). Off-campus students could possibly experience food insecurity due to a number of factors. First, these students are less likely to have campus meal plans ($33.8\%$ vs. $66.2\%$), which possibly forces them to spend money that might not otherwise be allocated specifically for food, but rather money for books and other college-related expenses [25]. Additionally, students with access to the campus meal plans might not cover enough meals to feel secure towards the middle and end of the semesters. Only two options of meal plans are offered to off-campus students (block and flex only) [32]. Off-campus students have to make plans to cover the lack of meals available to them, whether they pay for food using cash or grocery shop. In comparison, the meal plan options for on-campus residents range between 5-day and 7-day unlimited swipe access [32]. A New York Times article discussed the term, “casual swipes”, where college students who might have extra meals/dollars leftover near or at the end of the semester swipe their cards to assist classmates that might be out of meal plan credits [33]. This might be an option for off-campus students experiencing a food shortage toward the end of the semester. Some on-campus students might access the meal plan option and use their friend’s card who live on-campus in the dining hall to receive a meal. Additionally, if the on-campus students have to spend their excess flex dollars that will not roll over to the next academic year, off-campus students could take advantage of obtaining a meal(s) with a friend.
## 4.2.3. Food-Insecure Students’ Food Shortage Contacts
Being a college student often comes with the notion of the “silent struggle”, representing the stereotypical college student that might be short on food or barely surviving on limited options [34]. In the current study, most students reported that they would tell their parents if they were short on food. Food-insecure students were significantly less likely to tell their parents that they were short on food. Interestingly, students were found to be less likely to tell their parents about a food shortage because of the perception by parents that it was normal [34]. Second, food-insecure students expressed that the shame in telling their parents would be attached because they would be admitting to not being able to provide for themselves [34]. Third, food-insecure students believed they were adults, and that their parents have their own lives and life situations to deal with [34]. These feelings might be shared by food-insecure students in the current study, especially those that might be first-generation students and those that might have grown up on government assistance and had to access a food bank on occasion(s). Additionally, along the same lines, in the current study, food-insecure college students were significantly more likely to have knowledge of and access the campus food bank. Lastly, food-insecure students were significantly more likely to be on financial aid and employed. These factors might have an impact on the “silent struggle” food-insecure college students deal with on a daily basis with regards to managing dealing with food insecurity. Having to take on and manage more personal debt, maintaining employment, and searching for donated food at food banks all appear to be characteristics of food-insecure students in this representative sample. In the current study, college students reported that they would tell their college roommate if experiencing a food shortage, which was second to parents. Although there was no statistically significant difference between food-insecure and food-secure students, college roommates represent a possible resource to alleviate food insecurity. Henry [34] found that sharing food between roommates was a coping strategy for food insecurity. However, conflicting results in quantitative studies show that college students living with roommates were significantly more likely to be food-insecure compared to living with parents/relatives [7]. Living off-campus allows students to live at a reduced cost. Dividing the cost of rent and utilities with a roommate could possibly help students have more affordable living, but at the same time that cost might be offset by the need to pay for transportation to and from school, etc. Additionally, students that have roommates may not necessarily pool their resources to share food and have communal meals. The main reason for living together is to divide the cost of rent and utilities, whereas students are “on their own” when it comes to food.
One Canadian study of food-insecure college students found that nearly $76\%$ received food from a relative or went to a relative’s home for a meal [31]. Studies suggest that parents and relatives might have a positive impact on helping college students be more food-secure, especially among lower class students [14]. Similarly, the current study found parents to be a good choice for contacts for lower class students. This might suggest that although college is the first step towards independence for students, as a group, freshman and sophomore students might be more reliant on parents and family members for resources that assist in making it through the first few years in college.
## 5. Conclusions
The current study assessed the prevalence of food insecurity among a random sample of undergraduate college students. Based on the results of this study, we can conclude that a food-insecure student is likely to be a non-white, first-generation college student, have a history of being on government assistance, be employed, be on financial aid, have knowledge of and have accessed food bank(s), and have a lower GPA. With over one-third of the sample of students found to be food-insecure, colleges must do a better job addressing possible hunger concerns on their campuses. In society (outside of the academic setting), food banks and pantries are generally used to help mitigate food security concerns among the general population.
College administrators might need to consider this as a possible option for their individual campuses. Most immediately, more work needs to be done to get the word out among students regarding how to access the local food pantry on campus. This can be carried out during orientations, with flyers placed strategically around heavily popular campus hangouts, and among the resident assistants. Additionally, health educators and dietitians might be a resource for college students to access outside of the college campus to access food and qualify for SNAP and other government assistance. Students might be able to qualify for programs such as SNAP and WIC, provided they meet the familial, income, and employment requirements. Food-insecure students might have had a family history of accessing these services, and therefore any stigma that might be associated with governmental assistance could be limited.
On a basic level, the idea that being a hungry college student is normal should be debunked. Additionally, parents of college students need to be more educated about food insecurity on campus and know that students being hungry is not normal. During preview and campus orientation sessions, campus health educators can use this time to reach out to parents and let them know of resources in and around campus that can help students.
Although parents were reported as the most likely to be contacted in case of a food shortage, it is interesting to note that food-insecure students were less likely to report parents as a resource. This finding might have implications for those food-insecure individuals who might not have the ability to tell or ask their parents for assistance in dealing with their food insecurity. Food-insecure students might not ask for assistance from their parents because their parents themselves might not have the means or ability to assist them. It is also possible that these food-insecure students might also have parents that are currently dealing with food insecurity themselves.
The current exploratory study potentially adds to the growing body of literature regarding food insecurity on college campuses. Female students represented $78\%$ of respondents. Despite using simple random sampling methods, future studies should be mindful of possible selection bias. Future food insecurity college studies could advance by being aware of sampling methodology that reduces the chance of bias. All but two prior research studies examining food insecurity on college campuses were cross-sectional. Having longitudinal studies to follow students over a four-year college education could tell a more impactful story as well as better understand what resources student’s access to address their food insecurity. These studies can also provide insight into how potential involvement of campus and governmental resources might mitigate and reduce the food insecurity that student’s experience.
## References
1. **Household Food Security in the United States in 2021**
2. Nikolaus C.J., Ellison B., Nickols-Richardson S.M.. **Are estimates of food insecurity among college students accurate? Comparison of assessment protocols**. *PLoS ONE* (2019.0) **14**. DOI: 10.1371/journal.pone.0215161
3. Gaines A., Robb C.A., Knol L.L., Sickler S.. **Examining the role of financial factors, resources and skills in predicting food security status among college students**. *Int. J. Consum. Stud.* (2014.0) **38** 374-384. DOI: 10.1111/ijcs.12110
4. Patton-López M.M., López-Cevallos D.F., Cancel-Tirado D.I., Vazquez L.. **Prevalence and correlates of food insecurity among students attending a midsize rural university in Oregon**. *J. Nutr. Educ. Behav.* (2014.0) **46** 209-214. DOI: 10.1016/j.jneb.2013.10.007
5. Chaparro M.P., Zaghloul S.S., Holck P., Dobbs J.. **Food insecurity prevalence among college students at the University of Hawai’i at Mānoa**. *Public Health Nutr.* (2009.0) **12** 2097-2103. DOI: 10.1017/S1368980009990735
6. **Food Insecurity at CUNY: Results from a Survey of CUNY Undergraduate Students**
7. Maroto M.E., Snelling A., Linck H.. **Food insecurity among community college students: Prevalence and association with grade point average**. *Community Coll. J. Res. Pract.* (2015.0) **39** 515-526. DOI: 10.1080/10668926.2013.850758
8. Bruening M., Brennhofer S., van Woerden I., Todd M., Laska M.. **Factors Related to the High Rates of Food Insecurity among Diverse, Urban College Freshmen**. *J. Acad. Nutr. Diet.* (2016.0) **116** 1450-1457. DOI: 10.1016/j.jand.2016.04.004
9. Morris L.M., Smith S., Davis J., Null D.B.. **The prevalence of food security and insecurity among Illinois university students**. *J. Nutr. Educ. Behav.* (2016.0) **48** 376-382. DOI: 10.1016/j.jneb.2016.03.013
10. Knol L.L., Robb C.A., McKinley E.M., Wood M.. **Food insecurity, self-rated health, and obesity among college students**. *Am. J. Health Educ.* (2017.0) **48** 248-255. DOI: 10.1080/19325037.2017.1316689
11. Phillips E., McDaniel A., Croft A.. **Food insecurity and academic disruption among college students**. *J. Stud. Aff. Res. Pract.* (2018.0) **55** 353-372. DOI: 10.1080/19496591.2018.1470003
12. Forman M., Mangini L., Dong Y., Hernandez L., Fingerman K.. **Food insecurity and hunger: Quiet public health problems on campus**. *J. Nutr. Sci.* (2018.0) **8** 2. DOI: 10.4172/2155-9600.1000668
13. Weaver R.R., Vaughn N.A., Hendricks S.P., McPherson-Myers P.E., Jia Q., Willis S.L., Rescigno K.P.. **University student food insecurity and academic performance**. *J. Am. Coll. Health* (2019.0) **68** 727-733. DOI: 10.1080/07448481.2019.1600522
14. Bruening M., van Woerden I., Todd M., Laska M.N.. **Hungry to learn: The prevalence and effects of food insecurity on health behaviors and outcomes over time among a diverse sample of university freshmen**. *Int. J. Behav. Nutr. Phys. Act.* (2018.0) **15** 9. DOI: 10.1186/s12966-018-0647-7
15. McArthur L.H., Ball L., Danek A.C., Holbert D.. **A high prevalence of food insecurity among university students in Appalachia reflects a need for educational interventions and policy advocacy**. *J. Nutr. Educ. Behav.* (2018.0) **50** 564-572. DOI: 10.1016/j.jneb.2017.10.011
16. El Zein A., Mathews A.E., House L., Shelnutt K.P.. **Why are hungry college students not seeking help? Predictors of and barriers to using an on-campus food pantry**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10091163
17. Knol L.L., Robb C.A., McKinley E.M., Wood M.. **Very low food security status is related to lower cooking self-efficacy and less frequent food preparation behaviors among college students**. *J. Nutr. Educ. Behav.* (2018.0) **51** 357-363. DOI: 10.1016/j.jneb.2018.10.009
18. Davidson A.R., Morrell J.S.. **Food insecurity prevalence among university students in New Hampshire**. *J. Hunger Environ. Nutr.* (2020.0) **15** 118-127. DOI: 10.1080/19320248.2018.1512928
19. Broton K.M., Weaver K.E., Mai M.. **Hunger in higher education: Experiences and correlates of food insecurity among Wisconsin undergraduates from low-income families**. *Soc. Sci.* (2018.0) **7**. DOI: 10.3390/socsci7100179
20. Hagedorn R.L., Olfert M.D.. **Food insecurity and behavioral characteristics for academic success in young adults attending an Appalachian university**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10030361
21. Hagedorn R.L., McArthur L.H., Hood L.B., Berner M., Anderson Steeves E.T., Connell C.L., Wall-Bassett E., Spence M., Babatunde O.T., Kelly E.B.. **Expenditure, coping, and academic behaviors among food-insecure college students at 10 higher education institutes in the Appalachian and Southeastern regions**. *Curr. Dev. Nutr.* (2019.0) **3** nzz058. DOI: 10.1093/cdn/nzz058
22. Leung C.W., Wolfson J.A., Lahne J., Barry M.R., Kasper N., Cohen A.J.. **Associations between food security status and diet-related outcomes among students at a large, public midwestern university**. *J. Acad. Nutr. Diet.* (2019.0) **119** 1623-1631. DOI: 10.1016/j.jand.2019.06.251
23. Martinez S.M., Grandner M.A., Nazmi A., Canedo E.R., Ritchie L.D.. **Pathways from food insecurity to health outcomes among California university students**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11061419
24. Acharya A.S., Prakash A., Saxena P., Nigam A.. **Sampling: Why and how of it**. *India J. Med. Spec.* (2013.0) **4** 330-333. DOI: 10.7713/ijms.2013.0032
25. Mukigi D., Thornton K., Binion A., Brown K., Church M., Cook M., Henry D., Hopkinson J., Mascucci C., Pruett J.. **Food insecurity among college students: An exploratory study**. *J. Nutr. Health* (2018.0) **5** 106
26. Meza A., Altman E., Martinez S., Leung C.W.. **“It’s a Feeling That One Is Not Worth Food”: A Qualitative study exploring the psychosocial experience and academic consequences of food insecurity among college students**. *J. Acad. Nutr. Diet.* (2019.0) **119** 1713-1721.e1. DOI: 10.1016/j.jand.2018.09.006
27. Worthy S.L., Jonkman J., Blinn-Pike L.. **Sensation-seeking, risk-taking, and problematic financial behaviors of college students**. *J. Fam. Econ. Issues* (2010.0) **31** 161-170. DOI: 10.1007/s10834-010-9183-6
28. Dillman D.A.. *Mail and Internet Surveys: The Tailored Design Method* (2000.0)
29. **U.S. Household Food Security Survey Module: Six-Item Short Form Economic Research Service**
30. **Food Insecurity Rate-Alachua County, Florida**
31. Farahbakhsh J., Ball G.D.C., Farmer A.P., Maximova K., Hanbazaza M., Willows N.D.. **How do student clients of a university-based food bank cope with food insecurity?**. *Can. J. Diet. Pract. Res.* (2015.0) **76** 200-203. DOI: 10.3148/cjdpr-2015-020
32. **Florida Fresh Dining at the University of Florida**
33. **Leftover Meal Plan Swipes: No Waste Here**
34. Henry L.. **Understanding food insecurity among college students: Experience, motivation, and local solutions**. *Ann. Anthropol. Pract.* (2017.0) **41** 6-19. DOI: 10.1111/napa.12108
|
---
title: Body Composition and Nutritional Status of the Spanish National Breaking Team
Aspiring to the Paris 2024 Olympic Games
authors:
- Cristina Montalbán-Méndez
- Nuria Giménez-Blasi
- Inés Aurora García-Rodríguez
- José Antonio Latorre
- Javier Conde-Pipo
- Alejandro López-Moro
- Miguel Mariscal-Arcas
- Nieves Palacios Gil-Antuñano
journal: Nutrients
year: 2023
pmcid: PMC10005051
doi: 10.3390/nu15051218
license: CC BY 4.0
---
# Body Composition and Nutritional Status of the Spanish National Breaking Team Aspiring to the Paris 2024 Olympic Games
## Abstract
Breaking is a sports dance modality that will debut for the first time at the Paris 2024 Olympic Games. This dance form combines street dance steps with acrobatics and athletic elements. It complies with gender equality, maintains aesthetic appeal, and is practised indoors. The objective of this study is to assess the characteristics of body composition and nutritional status of the athletes that make up the Breaking national team. This national team was recruited, and they underwent an analysis of body composition using bioimpedance measurement and a nutritional interview status with the completion of a survey on the frequency of the consumption of sports supplements and ergogenic aids. In addition, they completed a consumption questionnaire for a series of food groups with specified protein, lipid, and carbohydrate content. After that, parameters were analyzed in relation to their nutritional status during a complete medical examination at the Endocrinology and Nutrition Service of the Sports Medicine Center of CSD. A descriptive analysis of the results obtained was carried out to find the mean values of the variables analyzed. The analytical parameters described an adequate nutritional status, except for the mean capillary determination of 25-OH-vitamin D3, which was 24.2 ng/dL (SD: 10.3). Bone mineral density values were higher than those of the general population. This is the first time that a study of these characteristics has been carried out on Breakers, so it is highly relevant to increase knowledge in this area in order to conduct nutritional interventions aimed at improving the sports performance of these athletes.
## 1. Introduction
Breaking is a form of sports dance, whose origin dates back to the 1970s in the streets of the New York neighbourhood of the Bronx [1]. It progressively evolved during the 1980s and gained followers all over the world in the 1990s, becoming part of the International Sports Dance Federation (WSDF). In Spain, the Spanish Federation of Sports Dance (SFSD) was established in 2011, and two years later it was integrated as part of the Spanish Olympic Committee [2]. Given its great acceptance at the 2018 Youth Games, in 2020 Breaking was included in the list of sports that will participate for the first time in the 2024 Paris Olympic Games, being the first sports dance specialty that will participate as an Olympic sport.
Breaking is a dance modality that combines street dance steps with acrobatics and athletic elements. Like the rest of sports dance modalities, Breaking meets the following requirements [2]: (a) proposes gender equality, (b) has great appeal from an aesthetic and adaptability point of view, and (c) be practiced in closed areas.
The level of aerobic capacity measured by maximum oxygen consumption (VO2max) has been studied in Breaking practitioners and has been compared with other high-level sports dance specialties [3,4]. The types of injuries presented by these athletes have also been analyzed compared with other types of modern dance and with other sports specialties [5,6,7], showing that the injuries of these athletes occur more frequently and have a different etiology than other dance modalities. A 2015 study [8] described the biomechanics of Breaking movements, which justify the high level of injuries in the lower limbs, comparing the frequency and intensity of these injuries with sports such as gymnastics.
However, there is little evidence on the anthropometric and body composition characteristics of Breakers. A recent study [9] analyzed the body composition of dancers from four types of urban dance specialties to assess asymmetries in relation to the type of technical movements performed in the corresponding discipline and thus be able to relate it to the most frequent type of injuries that occur. In this study, they concluded that both male and female Breakers have significant asymmetries in the lean mass of both upper limbs. Zaletel [10] qualitatively analyzed the knowledge of nutrition and the use of nutritional supplements in Breaking in relation to other sports dance modalities.
To date, however, no studies have been carried out on the assessment of the nutritional status and body composition of Breaking practitioners, nor has the relationship between these two variables been compared with other sports dance modalities or with other Olympic sports such as gymnastics.
The objective of this work is to examine the characteristics of the body composition and the nutritional status of the national team that aspires to represent Spain in the Olympic Games (JJ) in Paris in 2024. As secondary objectives, we plan to complete the following tasks: (A) analyze the body composition of the Breaking athletes using electrical bioimpedanciometry, (B) know the nutritional status of these athletes, (C) analyze the ergogenic aids used by these athletes in order to improve sports performance, and (D) reflect on possible interventions at a nutritional level to optimize sports performance.
## 2. Material and Methods
This is an analytical, observational, and cross-sectional research study in which the characteristics of body composition and nutritional status of the athletes of the national Breaking team were evaluated.
For this study, 8 members of the national Breaking team (aged between 18 and 35 years old) were recruited. The dancers were concentrated in the area around the High-Performance Center (CAR) of Madrid, Spain and were awarded scholarships to train in this facility, given the inclusion of Breaking in the Olympic Games in Paris in 2024. The team is a representative sample of Breaking dancers in Spain. The study subjects were summoned to the Endocrinology and Nutrition Unit in the Sports Medicine Center of the CAR in Madrid between March and April 2022 to undergo a complete medical examination. In addition, an informed consent was offered to all the participants that informed them about the objectives of the study and asked for their voluntary participation. The informed consent was designed following the information model of the Spanish Ministry of Health [11].
As the first part of the examination, a fasting blood test was taken, and the following blood parameters were analyzed: Biochemical: creatinine (mg/dL), urea (mg/dL), glucose (mg/dL), lipid profile (total cholesterol and triglycerides in mg/dL), and micronutrients (serum iron in µg/dL, ferritin in ng/dL, calcium, phosphorus and magnesium in mg/dL, potassium in mmol/L, and vitamin B12).
Blood count: hemoglobin (g/L), hematocrit (%), and total leukocytes and platelets (n°/mm3). Second, an anthropometric study was performed, measuring height (cm) using a SECA stadiometer. Body composition analysis was performed by electrical bioimpedance measurement (BIA) with a body composition analyser (InBody 720, Microcaya, Bilbao, Spain), analyzing the following body parameters: weight (kg), body mass index (BMI) (kg/m2), skeletal muscle mass (SMM) (kg), % fat, and % body water. Bone mineral densiometry was measured using dual energy X-ray absorptiometry (DEXA), which is the gold standard in research as it is the method with which all other bone measurement methods are compared. This technique uses very low doses of radiation without side effects or prior anaesthesia and is a non-invasive test. It measures bone density and strength. To undergo this test, athletes signed an informed consent form beforehand. It is performed without metallic objects on the body (rings, watches, etc.) and without having undergone any contrast test in the previous two weeks. The athlete is placed in a supine position, stretched out and with arms parallel to the body without opening the fingers; a tape is placed around the ankles to ensure that the legs are immobilised, and the body is checked to ensure that it is within the measurement frame. The test lasts between 6 and 10 min, depending on height and weight. The DEXA sends an invisible thin beam with two energy peaks, which is examined by the machine and sent to the software and represented in a report [12]. For the Breaking athletes, the bone densitometry obtained a lumbar spine T-score and a femoral hip T-score. Subsequently, a complete medical examination was carried out in which capillary vitamin D3 levels were determined (measured in ng/mL).
Finally, a nutritional interview was carried out that included a survey on the frequency of consumption (FFQ) of sports supplements and ergogenic aids. Information was obtained on the Break Dancers types of supplements and dosage patterns. In addition, information on the foods consumed from food groups, such as rice, pasta, or bread; protein from meat, fish, eggs, or nuts; and the consumption of vegetables and fruit was gathered. The information allows us to know the food types the athletes consume on a daily, weekly, or monthly basis. The food consumption frequency questionnaire was previously validated by the research group [12,13].
The rigorous screening procedure allowed us to examine both independent (sex, age, years of sports practice, height) and dependent (weight, BMI, percentage of water, percentage of fat, SMM, analytical levels of nutritional markers) variables.
The data were collected in a database where the different variables analyzed were entered. Non-parametric statistical analysis was performed through descriptive study (maximum, minimum, mean, and SD) and the Spearman correlation test, using SPSS version 22.0 software and following the CHAMP declaration [14].
The researchers followed the ethical principles [15] of respect for the human being, benefit–risk balance, voluntary participation, free and informed consent, respect for the privacy, dignity, and convictions of the participant, and the responsibility and competence of the researchers. This consent was authorized by the Ethics Committee of the UOC University (Spain) and complies with the Helsinki regulations.
## 3. Results
A total of eight subjects were recruited, among whom seven were men and one was a woman (Table 1). The mean age of the sample was 27.5 years old (SD: 5.3) and the mean time spent practicing sports in Breaking was 13.5 years (SD: 4.9).
Regarding anthropometric and body composition characteristics (Table 1), the sample had a mean height of 170.7 cm (SD: 6.6), mean weight of 68.7 kg (SD: 5.3), BMI of 23.7 kg/m2 (SD: 1.3), skeletal muscle mass (SMM) of 33.9 kg (SD: 3.9), body fat percentage of $13.0\%$ (SD: 5.5), and body water percentage of $45.3\%$ (SD: 5.4).
In terms of bone mineral density, a mean femoral head T-score of 2.98 (SD: 1.21) and a lumbar spine T-score of 2.20 (SD: 1.42) were obtained. The mean capillary 25-OH-vitamin D3 determination was 24.2 ng/dL (SD: 10.3) (Table 2).
The biochemical parameters analysed (Table 2) showed the following mean values: glucose 88.4 mg/dL (SD: −6.4), creatinine 0.94 mg/dL (SD: 0.1), urea 38.3 mg/dL (SD: 9.7), total cholesterol 168.8 mg/dL (SD: 23.8), triglycerides 64.7 mg/dL (SD: 26.3), total protein 7.23 g/dL (SD: 0.4), potassium 4.58 mmol/L (SD: 0.26), magnesium 1.95 mmol/L (SD: 016), calcium 9.86 mg/dL (SD: 0.266), phosphorus 3.50 mg/dL (SD: 0.31), serum iron 100.82 µg/dL (SD: 42.1), and ferritin 102.5 ng/dL (SD: 63.42). Finally, hemoglobin 14.9 g/dL (SD: 1.1), hematocrit $44.0\%$ (SD: 2.9), leucocytes 5370/mm3 (SD: 1741), and platelets 263.25/mm3 (SD: 58.89) (Table 2).
The ergogenic aids and sports supplementation used by the study participants are shown in Table 3. Of the eight subjects in the sample, six athletes used some type of ergogenic aid, all of them male. A total of three subjects used creatine in a dose of 3 g before training. Caffeine was used to enhance performance during training by two of the subjects. One of the subjects used an isotonic drink during training, and a total of three team members used whey protein powder supplementation in doses of 20–30 g after training to support recovery. Other aids used to a lesser extent are reflected in Table 3 below.
Table 4 and Table 5 show the correlations between the variables under study. When correlating the body composition variables with biochemical and analytical values and weekly frequency of food intake (Table 4), a positive correlation was observed for the weight of the subjects under study with creatinine ($$p \leq 0.007$$) and weekly frequency of pasta consumption ($$p \leq 0.048$$); subjects’ height correlated with iron ($$p \leq 0.015$$), calcium ($p \leq 0.001$), hemoglobin ($$p \leq 0.01$$), hematocrit ($$p \leq 0.047$$), pasta consumption ($$p \leq 0.043$$), and meat consumption ($$p \leq 0.021$$) and negatively with vegetable consumption ($$p \leq 0.038$$). Muscle mass correlated positively with creatinine ($p \leq 0.001$) and pasta consumption ($$p \leq 0.038$$) and negatively with platelets ($$p \leq 0.015$$). Fat % correlated negatively with phosphorus ($$p \leq 0.043$$), while BMI correlated positively with fruit consumption ($$p \leq 0.028$$) and negatively with leucocytes ($$p \leq 0.04$$).
When correlating the weekly food consumption of the study population with their biochemical and analytical values (Table 5), total protein was positively correlated with the consumption of pulses and nuts ($$p \leq 0.031$$). Urea correlated positively with the consumption of rice ($$p \leq 0.037$$) and white fish ($$p \leq 0.032$$) and negatively with the consumption of vegetables ($$p \leq 0.048$$) and eggs ($$p \leq 0.040$$). Magnesium correlated positively ($$p \leq 0.043$$) with dairy consumption, phosphorus negatively with bread consumption ($$p \leq 0.014$$), ferritin positively with meat consumption ($$p \leq 0.042$$), hematocrit with legume consumption ($$p \leq 0.035$$), and finally, platelets correlated negatively with rice consumption ($$p \leq 0.01$$) and positively with vegetable consumption ($$p \leq 0.033$$).
## 4. Discussion
The results obtained in relation to body composition are within the normal range when compared to the general population with normal weight, as the BMI is between 18.5 and 25 kg/m2. Body fat percentage is low compared to the general population (around $13\%$ on mean) but in line with other athlete populations [16]. Body water percentage is similar to other studies with similar characteristics, and muscle mass is slightly higher [9]. In the study by Prus Dasa [9], in which anthropometric asymmetries were evaluated based on the study of body composition, it was observed that the group of Breaking athletes ($$n = 22$$) presented body composition values similar those of this study in terms of BMI, MME, and % water. In contrast, the body fat % of our group of Break *Dancers is* somewhat higher than the reference study. The mean age of the participants was 20 years old (SD: 2.00), although the mean height was slightly higher than those of the Spanish team, 177.00 cm (SD: 5.60). Our sample of Break *Dancers is* more heterogeneous in age than that found in comparative studies, as our group has a maximum of 35 years and a minimum of 18 years (SD: 5.34). The limited scientific literature on this sport makes it difficult to find comparative studies that are better adjusted to our age range. The use of different anthropometric formulas in the different studies is another limitation to be taken into account.
The analytical values included in this study are within the normal range, according to the reference values used by the laboratory of the CSD Sports Medicine Center [17], but they cannot be compared with other Breaking athletes because there is no evidence in this regard in the literature. These results show that the nutritional status from the analytical point of view of the analysed athletes is adequate, as they do not present micronutrient deficiencies or metabolic alterations, such as glycemic, lipid, or renal function profile.
In relation to VO2 max for Break Dancers, it was possible to compare them with theatrical dancers, finding that our type of athlete performs exercises of short duration, which allows them a greater cardiorespiratory demand, while theatrical dancers have higher Vo2 max peaks [3].
The mean of the sample under study in relation to bone mineral density was within the normal range considering this entire sample, with the femoral and lumbar spine T-score, both above two. This means that the values of bone mineral density are optimal for these Break Dancers in the competitive period in which they find themselves, far from presenting a risk of fragility and bone injuries that could be related to malnutrition or overtraining. These results support the importance that a non-deficient diet has on the impact of sports performance in this sport discipline.
Mean capillary vitamin D3 levels show mild insufficiency (values between 20 and 30 ng/dL) in our sample of Breaking athletes. These values are very frequent in the winter and spring months and are also more frequent in athletes training in indoor facilities. Normal values for the general population should be above 30 ng/dL, and for the athlete population vitamin D3 levels should be between 20 and 50 ng/dL, depending on the series [18]. Comparisons with other samples of Breaking athletes cannot be made because no series with these results have been published. Athletes with levels in the range of mild insufficiency were prescribed vitamin D3 supplementation (calcifediol 0.266 mg or 15,000 IU), one softgel every 15 days for 6–8 weeks and were scheduled for a repeat measurement and re-evaluation of treatment. In addition, a nutritional intervention was performed to increase intake of vitamin-D-rich oily fish and to ensure sun exposure at least 15 min per day without sunscreen.
The use of sports supplementation by the subjects in the sample is very heterogeneous. The role of ergogenic support is to improve athletic performance through supplementation in conjunction with a varied and balanced diet. More than half of the team uses at least one ergogenic aid with the aim of improving sports performance and/or body composition. In Zaletel’s study [10], which assesses the supplementation of several dance specialities, the results differ greatly from those obtained in this study, as the most used supplements are energy bars and isotonic drinks during training, while the use of multivitamins and protein supplements is similar to the data obtained in this study.
This study is a pioneering study of the body composition and nutritional status of high-level Breaking athletes who have been selected to debut at the next Olympics in Paris 2024. To date, no similar study has been carried out, nor are the normal values of the population studied known.
A quantitative analysis of the intake of the subjects in the sample would have been useful in order to be able to objectively assess whether the energy intake is in line with the estimated energy expenditure. In a review, Benardot analyses the body composition and nutritional status of artistic athletes [19], including dancers and gymnasts, concluding that the tendency of these athletes is to maintain a relative energy deficit, below their requirements, which is associated with poorer results in body composition, lower performance, and greater risk of injury. This energy deficit is often compensated for by an excessive use of sports supplementation, which is why nutritional interventions are necessary in these athletes to avoid erroneous behaviour and ensure an optimal energy balance.
The positive correlation values of creatinine, together with the weight of the athletes, may be related to the generally higher muscle mass in athletes, since total muscle mass is the most important determinant of muscle creatine content and the production derived from its waste, creatinine. This is also supported by the results obtained in the muscle mass values of the subjects under study and their creatinine [20]. It is interesting that the weekly frequency of pasta consumption is correlated with the weight of the subjects and their muscle mass, since it would be directly related to energy needs and consumption, where it seems that this increase in energy needs would be covered by an increase in the consumption of carbohydrate-rich foods such as pasta [21].
Phosphorus is involved in energy production and makes up the most representative energy unit in muscle (ATP and creatine phosphate), so it is directly linked to exercise metabolism. It works together with calcium, and it is ideal to maintain a good balance close to 1:1. Both the % fat of the subjects studied, and the consumption of bread have an inverse correlation with the serum phosphorus of these subjects, although in neither case is it deficient, which could justify the optimization of sports performance in subjects with a low % fat and a high use of energy from anaerobic sources [22].
BMI, in the case of this population of elite athletes is not used as an indicator of overweight/obesity but as another parameter of body composition. It shows a clear relationship with fruit consumption, which indicates that we are dealing with well-advised professionals. The increase in their energy needs, derived from the adaptations inherent in training, such as the increase in muscle mass and the consequent increase in BMI, is covered with carbohydrate source foods, such as fruit, pasta, or bread. We found a negative association with leucocytes as in the study by Ryder et al. [ 23]. The high biological protein value of plant foods comes mainly from legumes and nuts, and we found an association between serum protein and the frequency of consumption of these two types of food, determining the importance that their consumption could have for the optimal performance of elite athletes.
The urea levels found in the population are high and correlated with the declared frequency of rice and white fish consumption, so it would be advisable to study this fact in depth and make dietary adaptations that would benefit better urea values [24] by controlling the amounts of food to be avoided in uremia. Magnesium seems to benefit from the frequency of consumption of dairy products in this population [25], as does ferritin with meat consumption and hematocrit with consumption of legumes [26], all of which are very important for optimum performance and therefore foods that would favour the health and sporting improvements of these subjects.
This study establishes for the first-time scientific evidence on the body composition and nutritional status of high-level sports performance Breakers. As a new Olympic sport, this discipline must be understood in depth at both a technical and functional level in order to be able to carry out the necessary interventions from the point of view of training and sports nutrition with the aim of optimising the sporting performance of its practitioners. This type of study should be extended to other Breaking teams and other federated sportsmen and women in order to obtain data from a representative sample on the body composition of these sportsmen and women, as is available in other sports specialities. In addition, it could be of interest to extend the study of body composition with other tools, such as plicometry, dual energy X-ray absorptiometry (DEXA), and phase angle by electrical bioimpedance. Regarding knowledge on the nutritional assessment of Breaking athletes, other lines of research to increase knowledge on the subject would be to carry out indirect or direct calorimetry to determine the energy requirements of these athletes, analyse the frequency of consumption of the different food groups, and analyse a greater number of analytical parameters in relation to nutritional status, the level of hydration, and the degree of overtraining.
All this information should be compared with other dance modalities and/or sports modalities of an aesthetic/artistic nature (such as gymnastics or acrobatics) to establish similarities and differences of Breakers with other sports.
We encountered several limitations. Firstly, the sample size is too small to obtain significant conclusions for the study population. It would be necessary to carry out other studies, including a greater number of Breaking athletes to be able to know the anthropometric characteristics, body composition, and nutritional status of high-performance Breakers in order to be able to compare these data with other sports specialities or with the modalities of sports gymnastics.
Furthermore, in this study, both sexes were included in the sample analysed, although there was only one female subject, so the results may be biased by the lack of stratification of the sample. Further studies should be carried out in the future that include a greater number of male and female subjects in order to be able to know the body composition of Breaking athletes in both sexes and thus achieve the objectives of this work.
## 5. Conclusions
The body composition values of the Breaking athletes of the national team that will compete in the next Olympic Games in 2024 are within the normal range for the general active population and are similar to those of previous studies carried out on these athletes. The analytical assessment of the nutritional status is within the normal range, so the Breakers do not show signs of malnutrition or overtraining. The consumption of pulses, meats, dairy products, and nuts seems to influence better biochemical values of parameters related to the sports performance of the subjects under study, so they could be suitable foods for elite and Olympic athletes. The femoral and lumbar bone mineral density values are within the normal range in the sample analyzed and are higher than those of the general population. Capillary vitamin D3 levels show a mild insufficiency, so it is advisable to evaluate these levels in Breaking athletes in order to prescribe adequate supplementation and optimise sporting performance. The use of sports supplementation and/or ergogenic aids in *Breakers is* highly variable and heterogeneous. Further studies on the body composition and nutritional status of Breakers are needed to obtain reference values for this population and to be able to make appropriate interventions to maximise the sporting performance of these athletes.
## References
1. **Historia de Breaking. Olympic Chanel. COI**
2. **Historia del Baile Deportivo y su Evolución. FEBD**
3. Wyon M.A., Harris J., Adams F., Cloak R., Clarke F.A., Bryant J.. **Cardiorespiratory Profile and Performance Demands of Elite Hip-Hop Dancers: Breaking and New Style**. *Med. Probl. Perform. Artist.* (2018.0) **33** 198-204. DOI: 10.21091/mppa.2018.3028
4. Wyon M.A., Allen N., Cloak R., Beck S., Davies P., Clarke F.. **Assessment of Maximum Aerobic Capacity and Anaerobic Threshold of Elite Ballet Dancers**. *Med. Probl. Perform. Artist.* (2016.0) **31** 145-150. DOI: 10.21091/mppa.2016.3027
5. Tjukov O., Engeroff T., Vogt L., Banzer W., Niederer D.. **Injury Profile of Hip-Hop Dancers**. *J. Dance Med. Sci.* (2020.0) **24** 66-72. DOI: 10.12678/1089-313X.24.2.66
6. Uršej E., Zaletel P.. **Injury Occurrence in Modern and Hip-Hop Dancers: A Systematic Literature Review**. *Zdr. Varst.* (2020.0) **59** 195-201. DOI: 10.2478/sjph-2020-0025
7. Tsiouti N., Wyon M.. **Injury Occurrence in Break Dance an Online Cross-Sectional Cohort Study of Breakers**. *J. Dance Med. Sci.* (2021.0) **25** 2-8. DOI: 10.12678/1089-313X.031521a
8. Bronner S., Ojofeitimi S., Woo H.. **Extreme Kinematics in Selected Hip Hop Dance Sequences**. *Med. Probl. Perform Artist.* (2015.0) **30** 126-134. DOI: 10.21091/mppa.2015.3026
9. Prus D., Zaletel P.. **Body Asymmetries in Dancers of Different Dance Disciplines**. *Int. J. Morphol.* (2022.0) **40** 270-276. DOI: 10.4067/S0717-95022022000100270
10. Zaletel P.. **Knowledge and use of nutritional supplements in different dance disciplines**. *Facta Univ. Series: Phys. Educ. Sport* (2020.0) **17** 619-633. DOI: 10.22190/FUPES191211056Z
11. **Anexo VIIIA. Guía para la Correcta Elaboración de un Modelo de Hoja de Información al Paciente y Consentimiento Informado (HIP/CI). Ministerio de Sanidad, Spain**. (2017.0)
12. Rivas A., Romero A., Mariscal M., Monteagudo C., Hernández J., Olea-Serrano F.. **Validación de cuestionarios para el estudio de hábitos alimentarios y masa ósea [Validation of questionnaires for the study of food habits and bone mass]**. *Nutr. Hosp.* (2009.0) **24** 521-528. PMID: 19893861
13. Hamdan M., Monteagudo C., Lorenzo-Tovar M.-L., Tur J.-A., Olea-Serrano F., Mariscal-Arcas M.. **Development and validation of a nutritional questionnaire for the Palestine population**. *Public Health Nutr.* (2014.0) **17** 2512-2518. DOI: 10.1017/S1368980013002711
14. Mansournia M.A., Collins G.S., Nielsen R.O., Nazemipour M., Jewell N.P., Altman D.G., Campbell M.J.. **CHecklist for statistical Assessment of Medical Papers: The CHAMP statement**. *Br. J. Sports Med.* (2021.0) **55** 1002-1003. DOI: 10.1136/bjsports-2020-103651
15. Shrestha B., Dunn L.. **The Declaration of Helsinki on Medical Research involving Human Subjects: A Review of Seventh Revision**. *J. Nepal Health Res. Counc.* (2020.0) **17** 548-552. DOI: 10.33314/jnhrc.v17i4.1042
16. García Chamorro R.P., González Lorenzo M.. **Índice de masa corporal y composición corporal: Un estudio antropométrico de 2500 deportistas de alto nivel**. *Efdeportes* (2004.0) **76**
17. Ozarda Y., Ichihara K., Barth J.H., Klee G.. **Protocol and standard operating procedures for common use in a worldwide multicenter study on reference values**. *Clin. Chem. Lab. Med.* (2013.0) **51** 1027-1040. DOI: 10.1515/cclm-2013-0249
18. De la Puente M., Collado L., Ciudad M.J., Cuadrado M.A.. **Role of Vitamin D in Athletes and Their Performance: Current Concepts and New Trends**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12020579
19. Benardot D.. **Nutritional Concerns for the Artistic Athlete**. *Phys. Med. Rehabil. Clin. North Am.* (2021.0) **32** 51-64. DOI: 10.1016/j.pmr.2020.09.008
20. Anđelković M., Baralić I., Đorđević B., Stevuljević J.K., Radivojević N., Dikić N., Škodrić S.R., Stojković M.. **Hematological and Biochemical Parameters in Elite Soccer Players During A Competitive Half Season**. *J. Med. Biochem.* (2015.0) **34** 460-466. DOI: 10.2478/jomb-2014-0057
21. Choo V.L., Viguiliouk E., Mejia S.B., Cozma A., A Khan T., Ha V., Wolever T.M.S., A Leiter L., Vuksan V., Kendall C.W.C.. **Food sources of fructose-containing sugars and glycaemic control: Systematic review and meta-analysis of controlled intervention studies**. *BMJ* (2018.0) **363** k4644. DOI: 10.1136/bmj.k4644
22. Chance B., Leigh J.S., Kent J., McCully K., Nioka S., Clark B.J., Maris J.M., Graham T.. **Multiple controls of oxidative metabolism in living tissues as studied by phosphorus magnetic resonance**. *Proc. Natl. Acad. Sci. USA* (1986.0) **83** 9458-9462. DOI: 10.1073/pnas.83.24.9458
23. Ryder E., Diez-Ewald M., Mosquera J., Fernández E., Pedreanez A., Vargas R., Peña C., Fernández N.. **Association of obesity with leukocyte count in obese individuals without metabolic syndrome**. *Diabetes Metab. Syndr.* (2014.0) **8** 197-204. DOI: 10.1016/j.dsx.2014.09.002
24. Castro González M.I., Maafs Rodríguez A.G., Galindo Gómez C.. **La dieta del paciente renal: ¿Se puede incluir pescado? [Renal patient’s diet: Can fish be included?]**. *Nutr. Hosp.* (2012.0) **27** 1489-1495. PMID: 23478696
25. Perry A., Stephanou A., Rayman M.P.. **Dietary factors that affect the risk of pre-eclampsia**. *BMJ Nutr. Prev. Health* (2022.0) **5** 118-133. DOI: 10.1136/bmjnph-2021-000399
26. Lavriša Z., Hristov H., Hribar M., Seljak B.K., Gregorič M., Blaznik U., Zaletel K., Oblak A., Osredkar J., Kušar A.. **Dietary Iron Intake and Biomarkers of Iron Status in Slovenian Population: Results of SI.Menu/Nutrihealth Study**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14235144
|
---
title: 'Study of Diet Habits and Cognitive Function in the Chinese Middle-Aged and
Elderly Population: The Association between Folic Acid, B Vitamins, Vitamin D, Coenzyme
Q10 Supplementation and Cognitive Ability'
authors:
- Xinting Jiang
- Yihan Guo
- Liang Cui
- Lin Huang
- Qihao Guo
- Gaozhong Huang
journal: Nutrients
year: 2023
pmcid: PMC10005055
doi: 10.3390/nu15051243
license: CC BY 4.0
---
# Study of Diet Habits and Cognitive Function in the Chinese Middle-Aged and Elderly Population: The Association between Folic Acid, B Vitamins, Vitamin D, Coenzyme Q10 Supplementation and Cognitive Ability
## Abstract
A growing body of evidence suggests that vitamin supplements play a role in the prevention of cognitive decline. The objective of the present cross-sectional study was to evaluate the relationship between cognitive ability and folic acid, B vitamins, vitamin D (VD) and Coenzyme Q10 (CoQ10) supplementation. The sample consisted of 892 adults aged above 50 who were assessed for their cognitive status in the Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (China) from July 2019 to January 2022. According to the degree of cognitive impairment, the subjects were divided into a normal control (NC) group, subjective cognitive decline (SCD) group, mild cognitive impairment (MCI) group and Alzheimer’s disease (AD) group. The results indicated a lower risk of AD in the daily VD-supplemented subjects with MCI compared to those who were not supplemented; a lower risk of cognitive impairment in those with normal cognitive who consumed VD, folic acid or CoQ10 on a daily basis compared those who did not; and a lower risk of cognitive impairment in subjects with normal cognitive performance who consumed B vitamin supplements, either daily or occasionally, compared to those who did not. The correlation was independent of other factors that potentially affect cognition, such as education level, age, etc. In conclusion, our findings confirmed a lower prevalence of cognitive impairment in those who took vitamins (folic acid, B vitamins, VD, CoQ10) daily. Therefore, we would recommend daily supplementation of vitamins (folic acid, B vitamins, VD, CoQ10), especially group B vitamins, as a potential preventive measure to slow cognitive decline and neurodegeneration in the elderly. However, for the elderly who have already suffered from cognitive impairment, VD supplementation may also be beneficial for their brains.
## 1. Introduction
Alzheimer’s disease (AD) is the most common form of dementia. The loss of short-term memory is a hallmark of its initial stage, followed by progressive impairment in multiple cognitive areas. With the aging of the population, the prevalence of neurodegenerative diseases related to aging is increasing. The latest epidemiological surveys in China and the United States show that the adjusted prevalence rate of mild cognitive impairment (MCI) is more than twice that of AD. In China, the total prevalence of dementia in people aged 60 and over is $6.0\%$ ($95\%$CI 5.8–6.3). It is estimated that there are about 15.07 million people with dementia, of which AD accounts for about 9.83 million ($65.23\%$) [1,2].
Despite the in-depth study of pathological biomarkers of cognitive impairment in recent years, the clinical diagnosis of AD still depends on clinical phenotypic manifestations [3]. The diagnostic criteria may be challenged during the interval of several decades from the preclinical stage, with only pathological changes to the appearance of clinically identifiable symptoms in AD. The deficiency in the ability to encode and store new memories is characteristic of the initial stage of the disease called subjective cognitive decline (SCD), which is characterized by mild distraction. SCD can progress to MCI and be identified by neuropsychological tests. MCI is the earliest stage in which AD can be diagnosed, followed by gradual deterioration of cognitive and functional disorders, resulting in loss of independence and death [4]. Therefore, during the prodromal period of AD, we look forward to early identification of various protective and risk factors of AD and appropriate implementation of non-drug intervention strategies such as behavioral prevention strategies to provide a basis for delaying the progression of dementia [5].
In 2021, China published the Expert Consensus on Brain and Nutrition Intervention for Alzheimer’s Disease, which pointed out the three-level prevention of AD based on the theory of “intestinal flora-brain-intestine axis” and emphasized the importance of “early, coordinated, holistic and long-term” nutrition intervention [6]. As an important part of dietary nutrition, vitamins have a variety of functions in the central nervous system, which assist in maintaining brain health and optimal cognitive function. Supplementing various vitamins in the diet is considered a means to maintain cognitive function and even prevent Alzheimer’s disease [7]. The effects of multivitamins on cognitive decline and dementia have been widely studied. A meta-analysis found that supplementation of B-complex vitamins, especially folic acid, might play a positive role in delaying and preventing the risk of cognitive decline; ascorbic acid and a high dose of vitamin E also had positive effects on cognitive ability. With regard to vitamin D (VD) supplementation, the results observed in different trials varied widely, which led to a lack of certainty in assessing the potential benefits that VD might have on cognition [8]. In a randomized controlled trial (RCT), where 32 healthy adults aged 30 to 65 years old were given high-dose B vitamins, serum marker tests showed that high-dose B vitamins could reduce serum homocysteine (Hcy), indicating that high-dose B vitamin supplementation might effectively reduce oxidative stress and inflammation by increasing oxidative metabolism and promote myelination, cell metabolism and energy storage [9]. It was also found that folic acid (0.8 mg) and docosahexaenoic acid (DHA) (800 mg) supplementation, alone or in combination for 6 months, could reduce Amyloid β (Aβ)-related biomarkers and improve cognitive function in patients with MCI [10]. VD is involved in regulating the metabolism of calcium and phosphate in living organisms. A review reported the key role played by VD in the integrity of neurocognitive function, which led to a variety of cognitive symptoms when this integrity was compromised [11]. Another review demonstrated that evidence on the effects of vitamin and mineral supplements in MCI treatment was still limited [12]. Coenzyme Q10 (CoQ10) was found to be related to oxidative stress. The decline in cognitive ability might be attributed to a decline in antioxidant defense ability, reflected in the low plasma CoQ10 level in the elderly [13].
Overall, the aim of our study was to explore the relationship between vitamin supplements (folic acid, B vitamins, VD, CoQ10) and the cognitive level of the middle-aged and elderly. This was the first article in our series of studies on diet and cognitive function, followed by an exploration of cognitive function and daily dairy products, red wine, green tea, coffee, curry, common oil intake, dietary composition awareness and so on.
## 2.1. Population Study
This population-based cross-sectional study recruited those aged over 50 who took cognitive assessment in the Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (China) from July 2019 to January 2022. The inclusion criteria involved participants who: (I) were 50 years old and above, regardless of sex; (II) had completed the standardized examination and diagnosed the degree of cognitive impairment.
Participants were excluded if they met one of the following criteria: (I) missing clinical data or lost follow-up; (II) have suffered from depression, schizophrenia and other mental diseases; (III) were unable to cooperate with the inspection for various reasons; (IV) have suffered from various secondary cognitive disorders; (V) were unable to be grouped according to the diagnostic criteria of cognitive impairment. According to the inclusion and exclusion criteria, 892 questionnaires on eating habits were finally collected. Then, the subjects were divided into four groups according to their cognitive function, including 184 in the AD group, 296 in the MCI group, 227 in the SCD group and 185 in the NC group.
The project was approved by the Ethics Committee of the Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (China). The study was performed in accordance with the principles of the Declaration of Helsinki (approval number 2019-041). All participants provided written informed consent to participate in the study.
## 2.2. Data Collection
In this study, patients’ information was collected using dietary habit questionnaire, including: 1. Basic clinical information: demographic data (gender, age, years of education, height, weight, waistline, marital status, AD history of first-degree relatives), basic physical condition (tooth loss, one-year history of surgery), disease history (hypertension, diabetes, periodontitis, diarrhea, constipation, allergy history), drug history (metformin, antibiotics, personally recognized drugs) and personal history (smoking habits, drinking habits); 2. Vitamin supplements (folic acid, B vitamins, VD, CoQ10). The dietary habit questionnaire was filled out by family members or people familiar with the subjects’ eating habits so that the reliability of the questionnaire was not affected by the patients’ cognitive impairment.
The questionnaire of dietary habits referred to the dietary guidelines of China Nutrition Society [14], dietary approaches to stop hypertension diet (DASH diet) [15], the Mediterranean-DASH intervention for neurodegenerative delay (MIND) [16], the Dutch healthy diet food frequency questionnaire (DHD-FFQ) that was used by Wesselman [2019] [17] to survey patients with SCD and the modified Mediterranean–Ketogenic diet published by Nagpal [2019] [18].
Among them, the questionnaire question about vitamins was on whether participants supplement folic acid/B vitamins/VD/CoQ10. The answers were recorded as: 1. Daily (for those who take the above vitamins at least once a day); 2. Occasionally (for those who take the above vitamins but take them less frequently or irregularly); and 3. Not at all (for those who basically do not take the above vitamins). Using the same method, we investigated other clinical data of the subjects.
## 2.3. Assessments
All participants in the study received in-depth neuropsychological evaluation that covered a wide range of cognitive functions.
We conducted medical history inquiry, physical examination, neuropsychological test, laboratory examination (genetic detection such as apolipoprotein E (APOE)) and imaging examination (brain MRI, Aβ-PET). Aβ-PET was examined in only $\frac{1}{2}$ subjects, while MRI and APOE were completed in all subjects. The diagnosis of AD is based on NIA-AA clinical standard in 2011 [19]. The diagnosis of MCI is based on MCI generalized diagnostic standard [20] proposed by MCI International Working Group and Petersen standard [21]. The diagnosis of SCD is based on the standard proposed by Jessen et al. [ 22].
At the same time, we used Mini Mental State examination (MMSE), Montreal Cognitive Assessment—Basic (MoCA-B) [23] and The Addenbrook’s Cognitive Examination (ACE-III) [24] to evaluate the cognitive function of these subjects. All of our scales were evaluated by experienced clinical psychology staff who were trained prior to the project.
## 2.4. Data Analysis
Four groups of subjects were divided into three categories: the NC group and the SCD group in Category one; the MCI group and the AD group in Category two; the cognitive normal group (the NC group + the SCD group) and the cognitive impaired group (the MCI group + the AD group) in Category three. Chi-square test was used to compare the demographic data, basic physical condition, drug history, smoking habits, drinking habits and vitamin supplements (folic acid, B vitamins, VD, CoQ10) of the above three categories. Similarly, the continuous variables were compared by independent sample T test. The cognitive function of Category two and three was evaluated by covariance analysis, with age and education level as covariables. Classified variables are expressed by frequency (%), and continuous variables are expressed by mean ± standard deviation (M ± SD).
Multivariable logistic regression analysis was performed for the independent associations with cognitive level using the variables at $p \leq 0.05$ from the univariate analysis. We used different logistic regression models to examine the relationship between vitamin supplements (folic acid, B vitamins, VD, CoQ10) and different cognitive levels, taking vitamins (folic acid, B vitamins, VD, CoQ10) as a category (daily, occasionally, not at all). Model a did not control any variables. Model b controlled some variables, such as Category two controlled education, age, dieting to lose weight, diarrhea, allergy history and pro-cognitive drug use and Category three controlled education, age, sex, marriage, allergic history and pro-cognitive drug use. Model c adjusted for all variables in Model b + the supplementation of other vitamin variables according to the situation. The data were analyzed using SPSS25.0 software, and the difference was statistically significant with $p \leq 0.05.$
## 3. Results
According to the results of univariate analysis in Table 1, compared with the NC group, the SCD group had more women and more diarrhea. Compared with the MCI group, the AD group had shorter education years, older age, less diarrhea, less allergic history and less dieting to lose weight. Compared with the normal cognitive group, the cognitive impairment group had shorter education years, older age, more men and less allergic history. There was no significant difference in “Body Mass Index (BMI) and Waist” among the groups, suggesting that the overall nutritional status was similar. Whether the first-degree relatives suffered from AD or not was not related to the severity of cognitive impairment. As expected, the SCD, AD and cognitive impairment groups performed more poorly on all cognitive tests and took more pro-cognitive drugs compared with the corresponding groups in the three categories. The three categories did not differ with respect to disease history (periodontitis, hypertension, diabetes or constipation), drug history (metformin use, antibiotic use), smoking or alcohol history. In terms of vitamin supplementation, compared with the MCI group, the AD group consumed fewer VD supplements. Compared with the normal cognitive group, the cognitive impairment group consumed less folic acid, B vitamin, VD and CoQ10 supplements. Some of the participants answered, “Don’t answer”, so the sums of the different items do not match the total number of people in each group. However, the response rate for vitamin supplementation was high, as shown in Table 1.
Table 2 shows the results of binary logistic regression analysis. Compared with those having no VD supplement, individuals with MCI who used VD supplements on a daily basis had a lower risk of developing AD (OR = 0.551, $95\%$CI, 0.312–0.973, $$p \leq 0.040$$). After adjusting for education, age, dieting and weight loss, diarrhea, allergic history and the use of cognitive drugs, individuals with MCI who consumed VD either on a daily basis or occasionally had a lower risk of developing AD (OR = 0.395, $95\%$CI, 0.196–0.798, $$p \leq 0.010$$; OR = 0.572, $95\%$CI, 0.334–0.979, $$p \leq 0.042$$, respectively).
Table 3 shows the results of binary logistic regression analysis. Compared with those having no folic acid supplement, individuals with normal cognitive ability who used folic acid supplements on a daily basis had a lower risk of cognitive impairment (OR = 0.577, $95\%$CI, 0.369–0.903, $$p \leq 0.016$$). This relationship remained statistically significant after adjusting for education, age, sex, marriage, allergic history and the use of pro-cognitive drugs in Model b (OR = 0.570, $95\%$CI, 0.345–0.940, $$p \leq 0.028$$). Compared with those who having no B vitamin supplementation, individuals with normal cognitive ability who consumed B vitamins either on a daily basis or occasionally had a lower risk of cognitive impairment (OR = 0.389, $95\%$CI, 0.263–0.576, $p \leq 0.001$; OR = 0.522, $95\%$CI, 0.371–0.736, $p \leq 0.001$, respectively). This relationship remained statistically significant in Model b (OR = 0.391, $95\%$ CI, 0.250–0.611, $p \leq 0.001$; OR = 0.639, $95\%$CI, 0.432–0.946, $$p \leq 0.025$$, respectively). After further adjustment of folic acid supplement, VD supplement and CoQ10 supplement in Model c, individuals with normal cognitive ability who used B vitamins on a daily basis had a lower risk of cognitive impairment (OR = 0.439, $95\%$CI, 0.257–0.750, $$p \leq 0.003$$). Compared with those having no VD supplement, individuals with normal cognitive ability who used VD supplements on a daily basis had a lower risk of cognitive impairment (OR = 0.526, $95\%$CI, 0.368–0.753, $p \leq 0.001$). This relationship remained statistically significant in Model b (OR = 0.532, $95\%$CI, 0.351–0.804, $$p \leq 0.003$$). Compared with those having no CoQ10 supplement, individuals with normal cognitive ability who used CoQ10 supplements on a daily basis had a lower risk of cognitive impairment (OR = 0.498, $95\%$CI, 0.333–0.745, $$p \leq 0.001$$). This relationship remained statistically significant in Model b (OR = 0.594, $95\%$CI, 0.380–0.929, $$p \leq 0.022$$).
## 4. Discussion
This cross-sectional study examined the intrinsic relationship between folic acid, B vitamins, VD and CoQ10 supplementation and cognitive abilities in both cognitively healthy and cognitively impaired older adults. It was revealed that VD supplementation may prevent the occurrence of cognitive impairment or delay the progress of cognitive impairment, while B vitamins, folic acid and CoQ10 supplementation could prevent the occurrence of cognitive impairment only, which means that this benefit is restricted to cognitively healthy older adults and does not extend to older adults with MCI. Hence, this finding questions the benefits of B vitamins, folic acid and CoQ10 supplementation once cognitive impairment is expressed through standardized neurocognitive testing.
VD is a steroid hormone with biological activity in the form of 1.25 (OH)2 D. Humans have a combination of vitamins D2 (active product: ergocalciferol) and D3 (active product: cholecalciferol) available to them from ambient UV exposure (vitamin D3), habitual dietary intakes of VD-rich foods and vitamin supplements (both vitamins D2 and D3 are available) [25]. Therefore, it is impossible to differentiate whether the subjects take vitamins D2 or D3. VD plays an important role in proliferation and differentiation, calcium signal transduction within the brain, neuro-nutrition and neuroprotection. It may also alter nerve transmission and synaptic plasticity [26]. Animal experimental studies have found that high-dose VD supplementation in the early stage of the disease (before the AD symptoms appear) can improve cognitive performance [27], while supplementation of VD in the middle stage of AD disease could aggravate the neurodegeneration of AD [28]. In a cohort study of the population, Rai-Hua Lai et al. found that long-term supplementation of VD is likely to increase the risk of dementia in the elderly and increase mortality in patients with dementia [28]. However, the findings of Rai-Hua Lai et al. could not rule out the potential health benefits of VD supplementation for young people or preclinical AD patients. They [27,28] suggested that VD cannot change the cognitive function of the damaged brain, while our study revealed that VD may prevent the occurrence of cognitive impairment or delay the progress of cognitive impairment. The researchers proposed that the neuroprotective effect of VD might be related to a decrease in Aβ-related biomarkers [29]. A study by Shreeya S Navale et al. found that low levels of vitamin D were associated with an increased risk of dementia using genetic analysis and neuroimaging studies [30]. In the UK, researchers found that raising VD levels within a normal range (50 nml/L) can prevent $17\%$ of dementia [30]. In view of the dose–response relationship between serum 25-hydroxyvitamin D (25 (OH)D) and cognitive impairment susceptibility, Ahmad Jayedi et al. conducted a meta-analysis of 1953 dementia and 1607 AD patients, reporting that a higher level of serum 25 (OH)D was associated with a lower risk of dementia and AD [31]. VD is important for normal brain development and function in rodents and humans, as its deficiency can affect cognition [32]. However, some studies could not find evidence of a relationship between VD level and cognitive function and, consequently, did not support that VD can reduce the incidence of cognitive impairment [33,34,35]. The effect of VD on cognition may be related to baseline cognitive status [27,28,35]. Larger studies need to be conducted to explore the cognitive effects of AD on people with different baseline levels.
B vitamins consist of thiamine, riboflavin, niacin, pantothenic acid, vitamin B6, folates, biotin and vitamin B12 [36]. In our survey, the subjects were not asked to specify the type of B vitamins they supplemented. Therefore, further exploration was still required for the optimal dosage of intake and the individual assessment of the effects of each B vitamin. B vitamin supplementation can reduce cognitive impairment [36]. Previous studies mainly focused on the relationship between vitamin B6, vitamin B12, folic acid and cognition. A study in India found that the elderly living in rural areas are at higher risk of a lack of trace elements, folic acid, vitamin B12 and VD [37], which is detrimental to human immune function and brain cognition. Prospective analysis found that adequate intake of folic acid, vitamin B6 and vitamin B12 was significantly associated with better cognitive reserve, which may be due to reduced hypermethylation of redox-related genes (NUDT15 and TXNRD1) and reduced oxidative damage [38]. A meta-analysis of 95 studies reported that early and long-term B vitamin supplementation could slow down cognitive decline, while higher folic acid intake was associated with a lower risk of developing dementia in older people without dementia [39]. An animal experiment reported that folic acid supplementation could reduce DNA damage as well as delay age-related cognitive decline and neurodegeneration in aging-accelerated mice with special treatment [40]. Hyper-homocysteinemia is an independent risk factor for AD. A recent dose–response meta-analysis, including a number of prospective cohort studies, showed that the relative risk of AD increased by $15\%$ for every 5 μmol/L increase in blood Hcy [41]. It was speculated that its mechanism was not only related to the neurotoxicity of neurons caused by vascular endothelial damage but also related to the abnormal aggregation of tau protein in hippocampal neurons and the inhibition of methylation reaction [42]. Low levels of vitamin B12 are associated with low cognitive functioning in older adults [41]. RCTs reported that folic acid and vitamin B12 could cooperate to reduce the levels of Hcy and S-adenosylhomocysteine (SAH), increase the level of S-adenosylmethionine (SAM) and inhibit the expression of inflammatory factors, thus improving cognitive ability [43,44,45]. Hence, it is necessary to take enough folic acid every day to maintain normal cognitive function and reduce the risk of cognitive decline in the elderly. It is also suggested that combined supplementation of multiple nutrients may be more beneficial to improve cognitive function.
Recent studies have found that CoQ10 protects endothelial cells by promoting mitochondrial function, thus delaying age-related peripheral vascular senescence [46]. Previous studies [47,48] have found that high doses of CoQ10 may be beneficial to cognition, which is consistent with our research results. Amirreza Monsef et al. reported that a high dose of CoQ10 can improve the cognitive performance of aged healthy rats [47]. There are several explanations for why CoQ10 can prevent cognitive decline. In a study by Man Yang et al., CoQ10 was found to be an important auxiliary factor in the mitochondrial electron transfer chain. It can reduce the expression of APOE in the hippocampus by improving the energy deficiency and mitochondrial dysfunction induced by anesthesia in mice, thus alleviating the brain injury and cognitive impairment caused by sevoflurane [49]. Iman Fatemi et al. reported that chronic supplementation of CoQ10 has a potential protective effect on the brain after stroke and can reduce the sequelae of ischemia/cerebral perfusion injury, which may be related to the increase in brain-derived neurotrophic factor (BDNF) level and superoxide dismutase (SOD) activity in brain tissue [50]. Centenarians are in a state of chronic inflammation, increased oxidative stress reaction, increased CoQ10 binding protein Psap and decreased serum total CoQ10 level with age [48]. Consequently, supplementation of CoQ10 is likely to reduce the risk of age-related cognitive decline in the elderly.
The current study contributed to the growing body of research into the benefits of vitamin supplementation on cognitive health in later life by examining this association in both cognitively healthy and cognitively impaired older adults.
The main limitation of our study is its inability to determine the causal relationship between the relevant factors due to the observational study design. Moreover, this cross-sectional study does not control all confounding factors affecting cognition. We cannot rule out the potential impact of confounding factors on the results of observation. The estimates provided in this study should not be extended to other populations without additional research and validation. However, these findings still deserve further study to explore the relationship between vitamins and cognition.
## 5. Conclusions
In a word, using data from the Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (China), we found that daily VD supplementation may prevent the occurrence of cognitive impairment or delay the progress of cognitive impairment, while daily B vitamins, folic acid and CoQ10 supplementation could prevent the occurrence of cognitive impairment only. Occasional VD supplementation may reduce the risk of AD. Occasional B vitamin supplements may also reduce the risk of cognitive impairment. Consequently, we provide evidence-based recommendations that daily supplementation of VD, folic acid, B vitamins and CoQ10 may be a potential preventive measure to slow cognitive decline and neurodegeneration in the elderly. However, for the elderly who have already suffered from cognitive impairment, VD supplementation may also be beneficial to their brains.
## References
1. Jia L., Du Y., Chu L., Zhang Z., Li F., Lyu D., Li Y., Li Y., Zhu M., Jiao H.. **Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study**. *Lancet Public Health* (2020) **5** e661-e671. DOI: 10.1016/S2468-2667(20)30185-7
2. **2022 Alzheimer’s disease facts and figures**. *Alzheimers Dement.* (2022) **18** 700-789. DOI: 10.1002/alz.12638
3. Scheltens P., De Strooper B., Kivipelto M., Holstege H., Chetelat G., Teunissen C.E., Cummings J., van der Flier W.M.. **Alzheimer’s disease**. *Lancet* (2021) **397** 1577-1590. DOI: 10.1016/S0140-6736(20)32205-4
4. Krantic S.. **From Current Diagnostic Tools and Therapeutics for Alzheimer’s Disease Towards Earlier Diagnostic Markers and Treatment Targets**. *Curr. Alzheimer Res.* (2017) **14** 2-5. DOI: 10.2174/156720501401161201104858
5. Zhang X.X., Tian Y., Wang Z.T., Ma Y.H., Tan L., Yu J.T.. **The Epidemiology of Alzheimer’s Disease Modifiable Risk Factors and Prevention**. *J. Prev. Alzheimers Dis.* (2021) **8** 313-321. DOI: 10.14283/jpad.2021.15
6. Shi H., Xu J.. **Experts consensus on brain health nutrition intervention for Alzheimer’s Disease**. *Sci. Sin. Vitae* (2021) **51** 1762-1788
7. Tardy A.L., Poutean E., Marquez D., Yilmaz C., Scholey A.. **Vitamins and Minerals for Energy, Fatigue and Cognition: A Narrative Review of the Biochemical and Clinical Evidence**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12010228
8. Gil Martínez V., Avedillo Salas A., Santander Ballestín S.. **Vitamin Supplementation and Dementia: A Systematic Review**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14051033
9. Ford T.C., Downey L.A., Simpson T., McPhee G., Oliver C., Stough C.. **The Effect of a High-Dose Vitamin B Multivitamin Supplement on the Relationship between Brain Metabolism and Blood Biomarkers of Oxidative Stress: A Randomized Control Trial**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10121860
10. Bai D., Fan J., Li M., Li M., Dong C., Gao Y., Fu M., Huang G., Liu H.. **Effects of Folic Acid Combined with DHA Supplementation on Cognitive Function and Amyloid-beta-Related Biomarkers in Older Adults with Mild Cognitive Impairment by a Randomized, Double Blind, Placebo-Controlled Trial**. *J. Alzheimers Dis.* (2021) **81** 155-167. DOI: 10.3233/JAD-200997
11. Roy N.M., Al-Harthi L., Neela Sampat R.A.-M., Mahadevan S., Adawi S.A., Essa M.M., Subhi L.A., AlBalushi B., Qoronfleh M.W.. **Impact of vitamin D on neurocognitive function in dementia, depression, schizophrenia and ADHD**. *Front. Biosci.* (2021) **26** 566-611. DOI: 10.2741/4908
12. Mccleery J., Abraham R.P., Denyon D.A., Rutjes A.W., Chong L.Y., Al-Assaf A.S., Griffith D.J., Rafeeq S., Yaman H., Malik M.A.. **Vitamin and mineral supplementation for preventing dementia or delaying cognitive decline in people with mild cognitive impairment**. *Cochrane Database Syst. Rev.* (2018) **11** CD011905. DOI: 10.1002/14651858.CD011905.pub2
13. Fernandez-Portero C., Amian J.G., de la Bella R., Lopez-Lluch G., Alarcon D.. **Coenzyme Q10 levels associated to cognitive functioning and executive function in older adults**. *J. Gerontol. A Biol. Sci. Med. Sci.* (2022) **78** 1-8. DOI: 10.1093/gerona/glac152
14. **The Chinese Dietary Guidelines**. (2016)
15. Sacks F.M., Svetkey L.P., Vollmer W.M., Appel L.J., Bray G.A., Harsha D., Obarzanek E., Conlin P.R., Miller E.R.. **Effects on blood pressure of reduced dietary sodium and the dietary approaches to stop hypertension (DASH) diet**. *N. Engl. J. Med.* (2001) **344** 3-10. DOI: 10.1056/NEJM200101043440101
16. Fresan U., Bes-Rastrollo M., Segovia-Siapco G., Sanchez-Villegas A., Lahortiga F., de la Rosa P.A., Martinez-Gonzalez M.A.. **Does the MIND diet decrease depression risk? A comparison with Mediterranean diet in the SUN cohort**. *Eur. J. Nutr.* (2019) **58** 1271-1282. DOI: 10.1007/s00394-018-1653-x
17. Wesselman L.M.P., Doorduijn A.S., de Leeuw F.A., Verfaillie S.C.J., van Leeuwenstijn-Koopman M., Slot R.E.R., Kester M.I., Prins N.D., van de Rest O., de van der Schueren M.A.E.. **Dietary Patterns Are Related to Clinical Characteristics in Memory Clinic Patients with Subjective Cognitive Decline: The SCIENCe Project**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11051057
18. Nagpal R., Neth B.J., Wang S., Craft S., Yadav H.. **Modified Mediterranean-ketogenic diet modulates gut microbiome and short-chain fatty acids in association with Alzheimer’s disease markers in subjects with mild cognitive impairment**. *EBioMedicine* (2019) **47** 529-542. DOI: 10.1016/j.ebiom.2019.08.032
19. Mckhann G.M., Knopman D.S., Chertkow H., Hyman B.T., Jack C.R., Kawas C.H., Klunk W.E., Koroshetz W.J., Manly J.J., Mayeux R.. **The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease**. *Alzheimers Dement.* (2011) **7** 263-269. DOI: 10.1016/j.jalz.2011.03.005
20. Winblad B., Palmer K., Kivipelto M., Jelic V., Fratiglioni L., Wahlund L.-O., Nordberg A., Bäckman L., Albert M., Almkvist O.. **Mild cognitive impairment—Beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment**. *J. Intern. Med.* (2004) **256** 240-246. DOI: 10.1111/j.1365-2796.2004.01380.x
21. Petersen R.C., Smith G.E., Waring S.C., Ivnik R.J., Tangalos E.G., Kokmen E.. **Mild Cognitive Impairment Clinical Characterization and Outcome**. *Arch. Neurol.* (1999) **56** 303-308. DOI: 10.1001/archneur.56.3.303
22. Jessen F., Amariglio R.E., Van Boxtel M., van Boxtel M., Breteler M., Ceccaldi M., Chetelat G., Dubois B., Dufouil C., Ellis K.A.. **A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease**. *Alzheimers Dement.* (2014) **10** 844-852. DOI: 10.1016/j.jalz.2014.01.001
23. Chen K.L., Xu Y., Chu A.Q., Ding D., Liang X.N., Nasreddine Z.S., Dong Q., Hong Z., Zhao Q.H., Guo Q.H.. **Validation of the Chinese Version of Montreal Cognitive Assessment Basic for Screening Mild Cognitive Impairment**. *J. Am. Geriatr. Soc.* (2016) **64** e285-e290. DOI: 10.1111/jgs.14530
24. Pan F.F., Wang Y., Huang L., Huang Y., Guo Q.H.. **Validation of the Chinese Version of Addenbrooke’s Cognitive Examination III for detecting mild cognitive impairment**. *Aging Ment. Health.* (2022) **26** 384-391. DOI: 10.1080/13607863.2021.1881757
25. Kulda V.. **Vitamin D Metabolism**. *Vnitr. Lek.* (2012) **58** 400-404. PMID: 22716179
26. Bivona G., Gambino C.M., Iacolino G., Ciaccio M.. **Vitamin D and the nervous system**. *Neurol. Res.* (2019) **41** 827-835. DOI: 10.1080/01616412.2019.1622872
27. Morello M., Landel V., Lacassagne E., Baranger K., Annweiler C., Feron F., Millet P.. **Vitamin D Improves Neurogenesis and Cognition in a Mouse Model of Alzheimer’s Disease**. *Mol. Neurobiol.* (2018) **55** 6463-6479. DOI: 10.1007/s12035-017-0839-1
28. Lai R.H., Hsu C.C., Yu B.H., Lo Y.R., Hsu Y.Y., Chen M.H., Juang J.L.. **Vitamin D supplementation worsens Alzheimer’s progression: Animal model and human cohort studies**. *Aging Cell* (2022) **21** e13670. DOI: 10.1111/acel.13670
29. Jia J., Hu J., Huo X., Miao R., Zhang Y., Ma F.. **Effects of vitamin D supplementation on cognitive function and blood Abeta-related biomarkers in older adults with Alzheimer’s disease: A randomised, double-blind, placebo-controlled trial**. *J. Neurol. Neurosurg. Psychiatry* (2019) **90** 1347-1352. PMID: 31296588
30. Navale S.S., Mulugeta A., Zhou A., Llewellyn D.J., Hypponen E.. **Vitamin D and brain health: An observational and Mendelian randomization study**. *Am. J. Clin. Nutr.* (2022) **116** 531-540. DOI: 10.1093/ajcn/nqac107
31. Jayedi A., Rashidy-Pour A., Shab-Bidar S.. **Vitamin D status and risk of dementia and Alzheimer’s disease: A meta-analysis of dose-response (dagger)**. *Nutr. Neurosci.* (2019) **22** 750-759. DOI: 10.1080/1028415X.2018.1436639
32. Gall Z., Szekely O.. **Role of Vitamin D in Cognitive Dysfunction: New Molecular Concepts and Discrepancies between Animal and Human Findings**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13113672
33. Rossom R.C., EspelandS M.A., Manson J.E., Dysken M.W., Johnson K.C., Lane D.S., LeBlanc E.S., Lederle F.A., Masaki K.H., Margolis K.L.. **Calcium and vitamin D supplementation and cognitive impairment in the women’s health initiative**. *J. Am. Geriatr. Soc.* (2012) **60** 2197-2205. DOI: 10.1111/jgs.12032
34. Vinueza Veloz A.F., Carpio Arias T.V., Vargas Mejía J.S., Tapia Veloz E.C., Piedra Andrade J.S., Nicolalde Cifuentes T.M., Heredia Aguirre S.I., Vinueza Veloz M.F.. **Cognitive function and vitamin B12 and D among community-dwelling elders: A cross-sectional study**. *Clin. Nutr. ESPEN* (2022) **50** 270-276. DOI: 10.1016/j.clnesp.2022.05.004
35. Kang J.H., Vyas C.M., Okereke O.I., Ogata S., Albert M., Lee I.M., D’Agostino D., Buring J.E., Cook N.R., Grodstein F.. **Effect of vitamin D on cognitive decline: Results from two ancillary studies of the VITAL randomized trial**. *Sci. Rep.* (2021) **11** 23253. DOI: 10.1038/s41598-021-02485-8
36. Zheng Y., Chen Z.Y., Ma W.J., Wang Q.Z., Liang H., Ma A.G.. **B Vitamins Supplementation Can Improve Cognitive Functions and May Relate to the Enhancement of Transketolase Activity in A Rat Model of Cognitive Impairment Associated with High-fat Diets**. *Curr. Med. Sci.* (2021) **41** 847-856. DOI: 10.1007/s11596-021-2456-5
37. Sundarakumar J.S., Shahul Hameed S.K., Ravindranath V.. **Burden of Vitamin D, Vitamin B12 and Folic Acid Deficiencies in an Aging, Rural Indian Community**. *Front. Public Health* (2021) **9** 707036. DOI: 10.3389/fpubh.2021.707036
38. An Y., Feng L., Zhang X., Wang Y., Wang Y., Tao L., Qin Z., Xiao R.. **Dietary intakes and biomarker patterns of folate, vitamin B6, and vitamin B12 can be associated with cognitive impairment by hypermethylation of redox-related genes NUDT15 and TXNRD1**. *Clin. Epigenet.* (2019) **11** 139. DOI: 10.1186/s13148-019-0741-y
39. Wang Z., Zhu W., Xing Y., Jia J., Tang Y.. **B vitamins and prevention of cognitive decline and incident dementia: A systematic review and meta-analysis**. *Nutr. Rev.* (2022) **80** 931-949. DOI: 10.1093/nutrit/nuab057
40. Zhou D., Lv X., Wang Y., Liu H., Luo S., Li W., Huang G.. **Folic acid alleviates age-related cognitive decline and inhibits apoptosis of neurocytes in senescence-accelerated mouse prone 8: Deoxythymidine triphosphate biosynthesis as a potential mechanism**. *J. Nutr. Biochem.* (2021) **97** 108796. DOI: 10.1016/j.jnutbio.2021.108796
41. Zhou F., Chen S.. **Hyperhomocysteinemia and risk of incident cognitive outcomes: An updated dose-response meta-analysis of prospective cohort studies**. *Ageing Res. Rev.* (2019) **51** 55-66. DOI: 10.1016/j.arr.2019.02.006
42. Smith A.D., Refsum H.. **Homocysteine, B Vitamins and Cognitive Impairment**. *Annu. Rev. Nutr.* (2016) **36** 211-239. DOI: 10.1146/annurev-nutr-071715-050947
43. Chen H., Liu S., Ge B., Zhou D., Li M., Li W., Ma F., Liu Z., Ji Y., Huang G.. **Effects of Folic Acid and Vitamin B12 Supplementation on Cognitive Impairment and Inflammation in Patients with Alzheimer’s Disease: A Randomized, Single-Blinded, Placebo-Controlled Trial**. *J. Prev. Alzheimers Dis.* (2021) **8** 249-256. DOI: 10.14283/jpad.2021.22
44. Chen H., Liu S., Ji L., Wu T., Ji Y., Zhou Y., Zheng M., Zhang M., Xu W., Huang G.. **Folic Acid Supplementation Mitigates Alzheimer’s Disease by Reducing Inflammation: A Randomized Controlled Trial**. *Mediat. Inflamm.* (2016) **2016** 5912146. DOI: 10.1155/2016/5912146
45. Ma F., Zhou X., Li Q., Zhao J., Song A., An P., Du Y., Xu W., Huang G.. **Effects of Folic Acid and Vitamin B12, Alone and in Combination on Cognitive Function and Inflammatory Factors in the Elderly with Mild Cognitive Impairment: A Single-blind Experimental Design**. *Curr. Alzheimer Res.* (2019) **16** 622-632. DOI: 10.2174/1567205016666190725144629
46. Huo J., Xu Z., Hosoe K., Kubo H., Miyahara H., Dai J., Mori M., Sawashita J., Higuchi K.. **Coenzyme Q10 Prevents Senescence and Dysfunction Caused by Oxidative Stress in Vascular Endothelial Cells**. *Oxid. Med. Cell Longev.* (2018) **2018** 3181759. DOI: 10.1155/2018/3181759
47. Monsef A., Shahidi S., Komaki A.. **Influence of Chronic Coenzyme Q10 Supplementation on Cognitive Function, Learning, and Memory in Healthy and Diabetic Middle-Aged Rats**. *Neuropsychobiology* (2019) **77** 92-100. DOI: 10.1159/000495520
48. Nagase M., Yamamoto Y., Matsumoto N., Arai Y., Hirose N.. **Increased oxidative stress and coenzyme Q10 deficiency in centenarians**. *J. Clin. Biochem. Nutr.* (2018) **63** 129-136. DOI: 10.3164/jcbn.17.124
49. Yang M., Tan H., Zhang K., Lian N., Yu Y., Yu Y.. **Protective effects of Coenzyme Q10 against sevoflurane-induced cognitive impairment through regulating apolipoprotein E and phosphorylated Tau expression in young mice**. *Int. J. Dev. Neurosci.* (2020) **80** 418-428. DOI: 10.1002/jdn.10041
50. Fatemi I., Saeed Askari P., Hakimizadeh E., Kaeidi A., Esmaeil Moghaddam S., Pak-Hashemi M., Allahtavakoli M.. **Chronic treatment with coenzyme Q10 mitigates the behavioral dysfunction of global cerebral ischemia/reperfusion injury in rats**. *Iran. J. Basic Med. Sci.* (2022) **25** 39-45. PMID: 35656440
|
---
title: 'Metabolic Profile of Patients with Long COVID: A Cross-Sectional Study'
authors:
- Daniel Carvalho de Menezes
- Patrícia Danielle Lima de Lima
- Igor Costa de Lima
- Juliana Hiromi Emin Uesugi
- Pedro Fernando da Costa Vasconcelos
- Juarez Antônio Simões Quaresma
- Luiz Fábio Magno Falcão
journal: Nutrients
year: 2023
pmcid: PMC10005061
doi: 10.3390/nu15051197
license: CC BY 4.0
---
# Metabolic Profile of Patients with Long COVID: A Cross-Sectional Study
## Abstract
A significant proportion of patients experience a wide range of symptoms following acute coronavirus disease 2019 (COVID-19). Laboratory analyses of long COVID have demonstrated imbalances in metabolic parameters, suggesting that it is one of the many outcomes induced by long COVID. Therefore, this study aimed to illustrate the clinical and laboratory markers related to the course of the disease in patients with long COVID. Participants were selected using a clinical care programme for long COVID in the Amazon region. Clinical and sociodemographic data and glycaemic, lipid, and inflammatory screening markers were collected, and cross-sectionally analysed between the long COVID-19 outcome groups. Of the 215 participants, most were female and not elderly, and 78 were hospitalised during the acute COVID-19 phase. The main long COVID symptoms reported were fatigue, dyspnoea, and muscle weakness. Our main findings show that abnormal metabolic profiles (such as high body mass index measurement and high triglyceride, glycated haemoglobin A1c, and ferritin levels) are more prevalent in worse long COVID presentations (such as previous hospitalisation and more long-term symptoms). This prevalence may suggest a propensity for patients with long COVID to present abnormalities in the markers involved in cardiometabolic health.
## 1. Introduction
There have been millions of confirmed cases of coronavirus disease 2019 (COVID-19) globally. Based on recent reviews, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may be related to not only the involvement of respiratory organs but also non-respiratory systems, causing problems such as male infertility [1], liver disease [2] and gastrointestinal disease [3], indicating a multiorgan involvement by COVID-19.
A significant proportion of patients continue to experience a wide range of physical, mental, and psychological symptoms following their acute illness, characterising a post-acute involvement commonly referred to as long COVID [4,5]. Patients with this long-term condition may have persistent symptoms for more than four weeks after the acute phase of COVID-19 [6,7,8,9,10].
Metabolic dysfunction related to metabolic syndrome (MS) is suggested to be one of the many outcomes induced by long COVID [11,12]. Laboratory analyses of long COVID have demonstrated imbalances in cardiometabolic parameters, such as lipid, glycaemic, and obesity-related markers [7]. In addition, monitoring inflammatory biomarkers such as the erythrocyte sedimentation rate (ESR), serum ferritin, and C-reactive protein (CRP), as well as other markers associated with metabolism, can help to understand the evolution and maintenance of long COVID symptoms [13].
Thus, this study aimed to determine the clinical and laboratory profiles of patients with long COVID and explore the interactions between metabolic abnormalities and long-term outcomes. Our main findings show that abnormal metabolic profiles are more prevalent in worse long COVID presentations.
## 2. Materials and Methods
This is a prospective, cross-sectional, observational study that was conducted in strict accordance with the principles of the Declaration of Helsinki and reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational studies. This study was approved by the Ethics Committee for Research Involving Human Beings of the State University of Pará (Protocol No. 4.252.664). All the participants provided written informed consent.
A total of 258 adults (≥18 years old) of both sexes diagnosed with long COVID were selected between March 2020 and December 2021, following the order of voluntary registration in a clinical follow-up programme for patients with long COVID in the Brazilian Amazon region. The following criteria were considered for the diagnosis of long COVID: (I) the acute symptomatic phase of COVID-19 confirmed by a reverse transcription–quantitative polymerase chain reaction (RT-qPCR), with symptoms consistent with COVID-19 not attributable to any other cause; and (II) at least one long-term symptom related to COVID-19, such as cough, dyspnoea, chest pain, muscle weakness, loss of balance, tremor, fatigue, muscle pain, headache, visual disturbances, insomnia, and/or lower limb oedema, not attributable to another differential diagnosis, for at least four weeks after the onset of symptoms. The time interval between symptom onset and diagnostic confirmation ranged up to 3 days, while the interval between the diagnosis of COVID-19 and the long COVID patients’ clinical and laboratory evaluation ranged from 32 to 632 days.
We excluded 10 patients with thyroid disorders (hyperthyroidism or hypothyroidism) and 33 patients with diabetes since these are conditions that could interfere with the values of metabolic laboratory markers. The use of drugs that could alter these levels, such as corticosteroids, was also considered as an exclusion criterion, but none of the patients included used medications at the time of data collection. The other 215 patients were allocated into the following groups: (i) according to the outcomes associated with COVID-19: “hospitalisation in acute phase”; “long COVID period” (period from the onset of symptoms to the time of data collection); and “number of long COVID symptoms”; (ii) according to laboratory abnormalities related with the metabolic profile: “laboratory metabolic disturbances” (fasting blood glucose (FBG): ≥126 mg/dL and/or glycated haemoglobin A1c (HbA1c) ≥$6.5\%$ and/or low-density cholesterol (LDL-C) ≥160 mg/dL and/or triglycerides ≥200 mg/ dL) (Figure 1). These groups were composed of different patients, and data collection was carried out in a single step.
After fasting for at least 8 h, 3 mL of blood was collected in two Vacuette® tubes (Greiner Bio-One, Kremsmünster, Austria), one with ethylenediaminetetraacetic acid anticoagulant, for the analysis of whole blood, which consisted of the quantification of HbA1c and ESR; and another tube with a separator gel and clot activator for the analysis of serum as follows: LDL-C, high-density cholesterol (HDL-C), total cholesterol, triglycerides, FBG, serum ferritin, and CRP. As a reference parameter, the values used by the respective reagent manufacturers were adopted and classified as “desirable” and “risk” in relation to the metabolic profile (Supplementary Table S1). Of the 215 patients, 65 were not tested for HbA1c.
Soon after, with a maximum interval of 24 h, the patients were interviewed to obtain their demographic and clinical data, such as name, age, sex, monthly income, job status, smoking status, long COVID symptoms and period, comorbidities, hospital admission during the acute phase, length of stay, and the medications used. In addition, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured at rest, and the patient’s weight and height were measured, followed by the calculation of the body mass index (BMI). Of the 215 patients, 49 did not report their job status and monthly income.
The clot activator tube was centrifuged for 5 min at 3000 rpm (Daiki 80-2B centrifuge, Ionlab, Araucária, Brazil), followed by the determination of the quantitative values of serum metabolic markers using a CMD 600 semi-automatic compact auto-analyser with a reagent line for clinical chemistry (Wiener Lab Group, Rosario, Argentina). Lipid markers were quantified in addition to glucose and serum ferritin. Whole-blood samples were analysed using the same equipment, and HbA1c levels were quantified. The ESR was also determined in whole-blood samples after 1 h of rest. The presence of CRP (Latex PCR SD LabTest, Lagoa Santa, Brazil) was qualitatively analysed.
The collected data were tabulated in an Excel™ spreadsheet (Microsoft Corporation, Redmond, DC, USA) and analysed using GraphPad Prism™ software version 8.4.3 (GraphPad Software, San Diego, CA, USA). Data normality was assessed with the D’Agostino Pearson test, using the mean and standard deviation for data description. For comparisons between groups, the Mann–Whitney and chi-square tests were used to compare the variables without a normal distribution, while an analysis of variance (ANOVA) was used for comparisons with a normal distribution. Multiple logistic regression analysis was used to verify the predictors and associations between different study variables. Linear correlation between metabolic and inflammatory markers was evaluated using Pearson’s correlation coefficient in the long COVID outcome groups. A two-tailed p-value of <0.05 was considered statistically significant.
## 3. Results
Of the 215 patients included, 138 were female, and 161 were aged between 18 and 59 years, with 78 hospitalisations during the acute phase of COVID-19. The main symptoms reported were fatigue ($$n = 184$$), dyspnoea ($$n = 178$$), and muscle weakness ($$n = 168$$). The long COVID period ranged from 32 to 632 days, with an average of 247.7 days (standard deviation (SD) 151.2). The most common self-reported comorbidity was hypertension ($$n = 64$$), and 89 patients had a BMI suggestive of obesity (≥30 kg/m2). Even though most laboratory mean values remained close to the desirable reference thresholds, the ESR levels were increased. Patients with laboratory metabolic disturbances ($$n = 102$$) reported more muscle weakness ($$n = 88$$) and higher mean ferritin levels and age (Table 1).
Of the 166 participants who reported their monthly income and job status, $56.6\%$ ($$n = 94$$) performed paid activities, with $65.9\%$ ($$n = 62$$) being employees, business owners, or military personnel, and $34\%$ ($$n = 32$$) being self-employed. Among the remaining $43.3\%$ ($$n = 72$$) who did not work, $62.5\%$ ($$n = 45$$) were unemployed, $13.8\%$ ($$n = 10$$) were retired, $16.6\%$ ($$n = 12$$) were homemakers, and $6.9\%$ ($$n = 5$$) were students. Regarding those who had a monthly income (employees, business owners, military personnel, self-employed, and retirees, $$n = 104$$), $12.5\%$ ($$n = 13$$) had an income of up to USD 200, $36.5\%$ ($$n = 38$$) had an income between USD 200 and 400, $35.5\%$ ($$n = 37$$) had an income between USD 400 and 1000, $12.5\%$ ($$n = 13$$) had an income between USD 1000 and 2000, and $2.8\%$ ($$n = 3$$) had an income of more than USD 2000.
The mean triglyceride and ferritin levels were higher in hospitalised patients ($$n = 78$$) than in non-hospitalised patients ($$n = 137$$). The group of patients with up to 90 days of long COVID ($$n = 35$$) showed abnormalities in HbA1c and ferritin levels when compared to its counter-group (>90 days, $$n = 180$$). On the other hand, patients with more than 365 days of long COVID ($$n = 45$$) had higher mean BMI than those with a period of long COVID of less than a year ($$n = 170$$). Furthermore, the mean measurements of BMI and HbA1c mean values were higher in the group of patients with more than six simultaneous symptoms ($$n = 121$$) when compared to its counter-group (up to six simultaneous symptoms, $$n = 94$$). In contrast, ferritin levels were higher in patients with up to six symptoms ($$n = 94$$), compared with the group with more than six symptoms ($$n = 121$$) (Table 2). The group of patients with up to 90 days of long COVID ($$n = 35$$) presented a mean of 7 (SD 2.8) simultaneous symptoms, while patients with more than 365 days of long COVID ($$n = 45$$) showed a mean of 6.8 (SD 2.6) symptoms.
Hospitalisation and higher HbA1c and ferritin levels were associated with a long COVID period of up to 90 days. High BMI was associated with long COVID for more than 365 days. In addition, female sex, hospitalisation, and high BMI increased the risk of presenting more than six simultaneous symptoms in patients with long COVID. Fatigue was associated with a long COVID period of over 90 days (Table 3). A stronger correlation between triglycerides and FBG levels was observed, as was the case for triglycerides and ferritin in patients with more than six symptoms. Regarding the long COVID period, the correlation between triglycerides and ferritin was stronger in patients up to 90 days (Figure 2).
## 4. Discussion
Most of the 215 patients included were females aged 18–59. Seventy-eight of the patients had been hospitalised during the COVID-19 acute phase, and the main long COVID symptoms reported were fatigue, dyspnoea, and muscle weakness. Among the hospitalised patients, the mean levels of triglycerides and ferritin were higher than those in the non-hospitalised group. In patients with a shorter period of long COVID, most quantitative laboratory tests, especially HbA1c and ferritin levels, were higher. Furthermore, BMI and HbA1c levels were higher in patients with more than six symptoms. Female sex, hospitalisation, and a high BMI were associated with more symptoms. Finally, triglyceride levels were correlated with FBG and ferritin levels in the worst long COVID outcomes.
The most commonly reported long COVID symptoms in recent studies are fatigue, dyspnoea, chest pain, and muscle weakness, among other non-specific manifestations [4,11]. This study demonstrated a similar profile of involvement, with fatigue being the most frequent symptom. In addition, most of the affected population consisted of women aged approximately 50 years, in agreement with several recent investigations [10,14,15,16].
Patients who had been hospitalised during the COVID-19 acute phase showed abnormal levels of triglycerides and ferritin. Although there is evidence that hospitalisation during the onset of symptoms is a risk factor for long COVID susceptibility and severity [17,18], it is unclear whether hospitalisation is directly responsible for the abnormality in the markers mentioned above and how this interaction occurs considering the vast multiorgan pathophysiology of long COVID. However, the non-specific harmful effects of hospitalisation certainly influence the long-term abnormalities in these markers, as demonstrated in the current study.
Our results suggest that patients with a shorter period of COVID symptoms have a higher risk of complications associated with metabolic health since significant abnormalities were observed in HbA1c and ferritin levels, in addition to several minor abnormalities in other studied markers in these patients. This might be explained by the temporal proximity to the acute involvement of COVID-19 since even though the patient is still presenting with long COVID symptoms, most of the laboratory markers investigated here showed normalisation in their mean levels in more extended periods of long COVID. This supposed benign evolution can be related to evidence that the number of symptoms tends to decrease over time [18,19,20], although when comparing patients with up to three months and with more than one year of long COVID, we could not observe a significant difference in the number of simultaneous symptoms reported.
It was shown here that BMI measures suggestive of obesity and abnormalities in triglycerides, HbA1c, and ferritin were prevalent in worse clinical long COVID scenarios, such as previous acute hospitalisation and presentation of more symptoms, indicating a worse long COVID involvement, in agreement with other studies [7,21,22]. This prevalence may suggest that patients with long COVID may be more likely to present abnormalities in the markers involved in cardiometabolic health, and consequently, with complications related to MS. It has been hypothesised that acute inflammation driven by SARS-CoV-2 infection could dysregulate metabolic pathways, for instance, through the impairment of insulin signalling, which could lead to abnormalities in cardiometabolic homeostasis [13,23].
Regarding ferritin, it is important to emphasise that the results showed a prevalence of higher levels based on the proximity to the acute phase of COVID-19, as previously presented in the literature [13,24,25]. Thus, the groups of hospitalised patients and those with a shorter period of long COVID had higher ferritin levels than their counter-groups. High ferritin levels have been found to be related to a poor prognosis of acute COVID-19 and may also be elevated in the long COVID-19 phase [24,26]. However, in this study, the levels of ferritin were unexpectedly higher in the group of patients with up to six symptoms of long COVID, indicating an abnormality in this marker in a group with a less severe outcome. This could be explained by the fact that, in this study, the group with fewer symptoms consisted of more patients with a shorter duration of long COVID, leading to a higher prevalence of high ferritin levels in this group, but further investigations are advisable.
The lack of a control group is a limitation of this study. A control group would allow for a comparison between patients with and without prolonged symptoms. Additionally, evaluating screening clinical and laboratory markers may not be enough to detail all the particularities of metabolic health, considering the complexity of long COVID. On the other hand, monitoring the markers involved in metabolism that are widely available certainly enhances our understanding of the metabolic profile of patients with long COVID. To the best of our knowledge, this is the first study to demonstrate a possible metabolic imbalance in patients with up to 632 days of long COVID, highlighting MS-related markers while linking these metabolic abnormalities to the clinical context of long COVID.
Here, interesting clues regarding the relationship between long COVID and metabolic-health-related markers are provided, examining how the metabolic profile is presented in patients with long COVID. Illustrating how common markers in clinical practice, especially those involved in glycaemic and lipid metabolism, relate to the presentation of the disease, by presenting population prevalence patterns, could provide useful insight into possible risk patterns and markers and help to better manage these patients. The findings presented here, together with other evidence [7,11,22], may provide a basis for more robust and detailed investigations in the future.
## 5. Conclusions
This study has examined how the metabolic profile is affected in patients with long COVID, illustrating how common markers in clinical practice relate to the course of the disease. Our main findings indicate that abnormal triglyceride, HbA1c, BMI, and ferritin levels are prevalent in worse long COVID presentations, such as hospitalisation in the acute phase and more concomitant symptoms. This prevalence may suggest a propensity for patients with long COVID to present abnormalities in the markers involved in cardiometabolic health. Therefore, it is recommended that health systems be prepared to receive an increasing number of patients affected by conditions related to MS, given the probable influence of long COVID. It is also suggested that further investigations, especially regarding the cellular metabolic mechanisms shared by MS and long COVID, be conducted in case symptoms persist. Importantly, cohort studies that follow patients with long COVID for an extended period are advisable and could provide a better understanding of how the metabolic profile develops in these patients.
## References
1. Delli Muti N., Finocchi F., Tossetta G., Salvio G., Cutini M., Marzioni D., Balercia G.. **Could SARS-CoV-2 infection affect male fertility and sexuality?**. *APMIS* (2022) **130** 243-252. DOI: 10.1111/apm.13210
2. Liptak P., Nosakova L., Rosolanka R., Skladany L., Banovcin P.. **Acute-on-chronic liver failure in patients with severe acute respiratory syndrome coronavirus 2 infection**. *World J. Hepatol.* (2023) **15** 41-51. DOI: 10.4254/wjh.v15.i1.41
3. Khreefa Z., Barbier M.T., Koksal A.R., Love G., Del Valle L.. **Pathogenesis and Mechanisms of SARS-CoV-2 Infection in the Intestine, Liver, and Pancreas**. *Cells* (2023) **12**. DOI: 10.3390/cells12020262
4. Huang C., Huang L., Wang Y., Li X., Ren L., Gu X., Kang L., Guo L., Liu M., Zhou X.. **6-month consequences of COVID-19 in patients discharged from hospital: A cohort study**. *Lancet* (2021) **397** 220-232. DOI: 10.1016/S0140-6736(20)32656-8
5. Yaksi N., Teker A.G., Imre A.. **Long COVID in Hospitalized COVID-19 Patients: A Retrospective Cohort Study**. *Iran. J. Public Health* (2022) **51** 88-95. DOI: 10.18502/ijph.v51i1.8297
6. Petersen M.S., Kristiansen M.F., Hanusson K.D., Danielsen M.E., Steig B., Gaini S., Strøm M., Weihe P.. **Long COVID in the Faroe Islands: A Longitudinal Study Among Nonhospitalized Patients**. *Clin. Infect. Dis.* (2021) **73** e4058-e4063. DOI: 10.1093/cid/ciaa1792
7. Al-Aly Z., Xie Y., Bowe B.. **High-dimensional characterization of post-acute sequelae of COVID-19**. *Nature* (2021) **594** 259-264. DOI: 10.1038/s41586-021-03553-9
8. Greenhalgh T., Knight M., A’Court C., Buxton M., Husain L.. **Management of post-acute covid-19 in primary care**. *BMJ* (2020) **370** m3026. DOI: 10.1136/bmj.m3026
9. Raveendran A.V.. **Long COVID-19: Challenges in the diagnosis and proposed diagnostic criteria**. *Diabetes Metab. Syndr.* (2021) **15** 145-146. DOI: 10.1016/j.dsx.2020.12.025
10. Bai F., Tomasoni D., Falcinella C., Barbanotti D., Castoldi R., Mulè G., Augello M., Mondatore D., Allegrini M., Cona A.. **Female gender is associated with long COVID syndrome: A prospective cohort study**. *Clin. Microbiol. Infect.* (2022) **28** 611.e9-611.e16. DOI: 10.1016/j.cmi.2021.11.002
11. Michelen M., Manoharan L., Elkheir N., Cheng V., Dagens A., Hastie C., O’Hara M., Suett J., Dahmash D., Bugaeva P.. **Characterising long COVID: A living systematic review**. *BMJ Glob. Health* (2021) **6** e005427. DOI: 10.1136/bmjgh-2021-005427
12. Frere J.J., tenOever B.R.. **Cardiometabolic syndrome-an emergent feature of Long COVID?**. *Nat. Rev. Immunol.* (2022) **22** 399-400. DOI: 10.1038/s41577-022-00739-8
13. Pasini E., Corsetti G., Romano C., Scarabelli T.M., Chen-Scarabelli C., Saravolatz L., Dioguardi F.S.. **Serum Metabolic Profile in Patients with Long-Covid (PASC) Syndrome: Clinical Implications**. *Front. Med.* (2021) **8** 714426. DOI: 10.3389/fmed.2021.714426
14. Stavem K., Ghanima W., Olsen M.K., Gilboe H.M., Einvik G.. **Persistent symptoms 1.5–6 months after COVID-19 in non-hospitalised subjects: A population-based cohort study**. *Thorax* (2021) **76** 405-407. DOI: 10.1136/thoraxjnl-2020-216377
15. Einvik G., Dammen T., Ghanima W., Heir T., Stavem K.. **Prevalence and Risk Factors for Post-Traumatic Stress in Hospitalized and Non-Hospitalized COVID-19 Patients**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph18042079
16. Logue J.K., Franko N.M., McCulloch D.J., McDonald D., Magedson A., Wolf C.R., Chu H.Y.. **Sequelae in Adults at 6 Months After COVID-19 Infection**. *JAMA Netw. Open* (2021) **4** e210830. DOI: 10.1001/jamanetworkopen.2021.0830
17. Sudre C.H., Murray B., Varsavsky T., Graham M.S., Penfold R.S., Bowyer R.C., Pujol J.C., Klaser K., Antonelli M., Canas L.S.. **Attributes and predictors of long COVID**. *Nat. Med.* (2021) **27** 626-631. DOI: 10.1038/s41591-021-01292-y
18. Carvalho-Schneider C., Laurent E., Lemaignen A., Beaufils E., Bourbao-Tournois C., Laribi S., Flament T., Ferreira-Maldent N., Bruyère F., Stefic K.. **Follow-up of adults with noncritical COVID-19 two months after symptom onset**. *Clin. Microbiol. Infect.* (2021) **27** 258-263. DOI: 10.1016/j.cmi.2020.09.052
19. Prieto M.A., Prieto O., Castro H.M.. **Covid prolongado: Estudio de corte transversal [Long covid: Cross sectional study]**. *Rev. Fac. Cien. Med. Univ. Nac. Cordoba.* (2021) **78** 33-36. DOI: 10.31053/1853.0605.v78.n1.32048
20. Zhao Y.-M., Shang Y.-M., Song W.-B., Li Q.-Q., Xie H., Xu Q.-F., Jia J.-L., Li L.-M., Mao H.-L., Zhou X.-M.. **Follow-up study of the pulmonary function and related physiological characteristics of COVID-19 survivors three months after recovery**. *EClinicalMedicine* (2020) **25** 100463. DOI: 10.1016/j.eclinm.2020.100463
21. Aung N., Khanji M.Y., Munroe P.B., Petersen S.E.. **Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study**. *Front. Genet.* (2020) **11** 586308. DOI: 10.3389/fgene.2020.586308
22. Vimercati L., De Maria L., Quarato M., Caputi A., Gesualdo L., Migliore G., Cavone D., Sponselli S., Pipoli A., Inchingolo F.. **Association between Long COVID and Overweight/Obesity**. *J. Clin. Med.* (2021) **10**. DOI: 10.3390/jcm10184143
23. Reiterer M., Rajan M., Gómez-Banoy N., Lau J.D., Gomez-Escobar L.G., Ma L., Gilani A., Alvarez-Mulett S., Sholle E.T., Chandar V.. **Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2**. *Cell. Metab.* (2021) **33** 2174-2188.e5. DOI: 10.1016/j.cmet.2021.09.009
24. Sonnweber T., Boehm A., Sahanic S., Pizzini A., Aichner M., Sonnweber B., Kurz K., Koppelstätter S., Haschka D., Petzer V.. **Persisting alterations of iron homeostasis in COVID-19 are associated with non-resolving lung pathologies and poor patients’ performance: A prospective observational cohort study**. *Respir. Res.* (2020) **21** 276. DOI: 10.1186/s12931-020-01546-2
25. Dahan S., Segal G., Katz I., Hellou T., Tietel M., Bryk G., Amital H., Shoenfeld Y., Dagan A.. **Ferritin as a Marker of Severity in COVID-19 Patients: A Fatal Correlation**. *Isr. Med. Assoc. J.* (2020) **22** 494-500. PMID: 33236582
26. Para O., Caruso L., Pestelli G., Tangianu F., Carrara D., Maddaluni L., Tamburello A., Castelnovo L., Fedi G., Guidi S.. **Ferritin as prognostic marker in COVID-19: The FerVid study**. *Postgrad. Med.* (2022) **134** 58-63. DOI: 10.1080/00325481.2021.1990091
|
---
title: 'Rationale, Design and Participants Baseline Characteristics of a Crossover
Randomized Controlled Trial of the Effect of Replacing SSBs with NSBs versus Water
on Glucose Tolerance, Gut Microbiome and Cardiometabolic Risk in Overweight or Obese
Adult SSB Consumer: Strategies to Oppose SUGARS with Non-Nutritive Sweeteners or
Water (STOP Sugars NOW) Trial and Ectopic Fat Sub-Study'
authors:
- Sabrina Ayoub-Charette
- Néma D. McGlynn
- Danielle Lee
- Tauseef Ahmad Khan
- Sonia Blanco Mejia
- Laura Chiavaroli
- Meaghan E. Kavanagh
- Maxine Seider
- Amel Taibi
- Chuck T. Chen
- Amna Ahmed
- Rachel Asbury
- Madeline Erlich
- Yue-Tong Chen
- Vasanti S. Malik
- Richard P. Bazinet
- D. Dan Ramdath
- Caomhan Logue
- Anthony J. Hanley
- Cyril W. C. Kendall
- Lawrence A. Leiter
- Elena M. Comelli
- John L. Sievenpiper
journal: Nutrients
year: 2023
pmcid: PMC10005063
doi: 10.3390/nu15051238
license: CC BY 4.0
---
# Rationale, Design and Participants Baseline Characteristics of a Crossover Randomized Controlled Trial of the Effect of Replacing SSBs with NSBs versus Water on Glucose Tolerance, Gut Microbiome and Cardiometabolic Risk in Overweight or Obese Adult SSB Consumer: Strategies to Oppose SUGARS with Non-Nutritive Sweeteners or Water (STOP Sugars NOW) Trial and Ectopic Fat Sub-Study
## Abstract
Background: Health authorities are near universal in their recommendation to replace sugar-sweetened beverages (SSBs) with water. Non-nutritive sweetened beverages (NSBs) are not as widely recommended as a replacement strategy due to a lack of established benefits and concerns they may induce glucose intolerance through changes in the gut microbiome. The STOP Sugars NOW trial aims to assess the effect of the substitution of NSBs (the “intended substitution”) versus water (the “standard of care substitution”) for SSBs on glucose tolerance and microbiota diversity. Design and Methods: The STOP Sugars NOW trial (NCT03543644) is a pragmatic, “head-to-head”, open-label, crossover, randomized controlled trial conducted in an outpatient setting. Participants were overweight or obese adults with a high waist circumference who regularly consumed ≥1 SSBs daily. Each participant completed three 4-week treatment phases (usual SSBs, matched NSBs, or water) in random order, which were separated by ≥4-week washout. Blocked randomization was performed centrally by computer with allocation concealment. Outcome assessment was blinded; however, blinding of participants and trial personnel was not possible. The two primary outcomes are oral glucose tolerance (incremental area under the curve) and gut microbiota beta-diversity (weighted UniFrac distance). Secondary outcomes include related markers of adiposity and glucose and insulin regulation. Adherence was assessed by objective biomarkers of added sugars and non-nutritive sweeteners and self-report intake. A subset of participants was included in an Ectopic Fat sub-study in which the primary outcome is intrahepatocellular lipid (IHCL) by 1H-MRS. Analyses will be according to the intention to treat principle. Baseline results: Recruitment began on 1 June 2018, and the last participant completed the trial on 15 October 2020. We screened 1086 participants, of whom 80 were enrolled and randomized in the main trial and 32 of these were enrolled and randomized in the Ectopic Fat sub-study. The participants were predominantly middle-aged (mean age 41.8 ± SD 13.0 y) and had obesity (BMI of 33.7 ± 6.8 kg/m2) with a near equal ratio of female: male ($51\%$:$49\%$). The average baseline SSB intake was 1.9 servings/day. SSBs were replaced with matched NSB brands, sweetened with either a blend of aspartame and acesulfame-potassium ($95\%$) or sucralose ($5\%$). Conclusions: Baseline characteristics for both the main and Ectopic Fat sub-study meet our inclusion criteria and represent a group with overweight or obesity, with characteristics putting them at risk for type 2 diabetes. Findings will be published in peer-reviewed open-access medical journals and provide high-level evidence to inform clinical practice guidelines and public health policy for the use NSBs in sugars reduction strategies. Trial registration: ClinicalTrials.gov identifier, NCT03543644.
## 1. Introduction
Sugar-sweetened beverages (SSBs) are the single largest contributor of added sugars in the diet [1,2]. Health authorities are universal in discouraging the consumption of SSBs [2,3,4,5] as excess intake is associated with weight gain, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD) [6,7,8,9,10]. Non-nutritive sweetened beverages (NSBs), the greatest largest source of non-nutritive sweeteners (NNS) [11], provide a sweet alternative to SSBs without the calories. Health authorities are near universal in their recommendation that water preferentially replace SSBs. However, these health authorities are mixed in their recommendations regarding NSBs as a replacement strategy for SSBs with some recommending their use [12] and others, including Health Canada and the World Health Organization (WHO), recommending against their use over due to concerns that they have not demonstrated the intended benefits [5,13,14,15,16,17,18]. Whether NSBs are comparable to water as a replacement strategy for SSBs and reduce adiposity and adiposity-related non-communicable diseases (NCDs) is unclear. NNS mimic the taste of sugar, but are used in much smaller quantities [19], as they are much sweeter than sucrose [20]. The NNS that sweeten NSBs have been shown to be safe [20,21,22,23] and are approved for use in Canada [24] and regulatory authorities globally [25]. NSBs may be sweetened by a single NNS or NNS blends. For example, in Canada, Coke Zero, Diet Coke, Diet Pepsi, and Diet 7UP are sweetened by aspartame and acesulfame-potassium (ace-k), while Diet Orange *Crush is* sweetened by sucralose alone.
Recent comprehensive syntheses of the evidence of randomized controlled trials and prospective cohort studies have contributed to the uncertainty regarding NNS. A WHO-commissioned systematic review and meta-analysis of NNS concluded that most health outcomes showed no difference, meaning they failed to show the expected re-ductions in body mass index (BMI), body weight, plasma insulin levels, insulin resistance and β-cell function in randomized controlled trials (RCTs) or associations with lower risk of NCDs [26]. A second comprehensive systematic review and meta-analysis of RCTs showed that NNS did not result in the expected weight loss and were associated with higher risk of weight gain, type 2 diabetes, and cardiovascular disease in prospective cohort studies [27]. An important criticism of these systematic reviews and meta-analyses of RCTs has been their failure to account for the nature of the comparator and the calories displaced by NSBs, as caloric and noncaloric comparators were pooled together or non-caloric comparators were used as the sole comparator, which would lead to an underestimation of the effect of NSBs in their intended substitution for SSBs [28,29,30,31,32]. When analyses were restricted to comparisons with SSBs (allowing for caloric displacement), there were the expected decreases in blood glucose levels and blood pressure in these same syntheses [26,27]. These evidence syntheses also did not differentiate between the food sources that contain NNS, in which it can be difficult to achieve differences in energy as gram amounts of sugars are replaced with milligram amount of NNS with starches and fats as fillers making up the difference by weight [26]. Epidemiologic analyses have proven equally problematic, as baseline or prevalent exposure of NNS and NCD outcomes is well recognized to be at high risk of reverse causality and residual confounding from behavioral clustering [29,31,32,33]. More recent systematic reviews and meta-analyses designed to address these criticisms by looking specifically at the substitution of NSBs for SSBs as a comparison of a single food matrix and the most important source of NNS (i.e., NSBs) and free sugars (i.e., SSBs) in the diet. These systematic reviews and meta-analyses showed the expected differences in adiposity and adiposity-related markers in both the randomized controlled trials [16,34,35,36,37] and prospective cohort studies [38]. These syn-theses reinforced the importance of energy displacement and food matrix and support the shift in dietary guidelines from a focus on single nutrients towards a focus on foods, in recognition that focus on single nutrients may miss important interactions related to the food matrix in which the nutrients are contained [39].
Various biological mechanisms have been invoked to support the signals for concern regarding NNS consumption (e.g., through the brain’s reward center [40], and/or through gastrointestinal sweet-taste receptors [41]), but there is a particular concern that NNSs may induce and promote glucose intolerance through changes to the composition and diversity of the gut microbiome [30,42]. A single study showed a worsening of glucose tolerance and changes in gut microbiota beta-diversity in a post-hoc “responder” group after six days of saccharin capsules administration at the maximum acceptable daily intake (ADI) dose (5 mg/kg of body weight/day) [43]. Despite this study’s methodological weaknesses (short duration, small sample size, before-and-after design etc.), it reinforced a negative view of NNSs in the media [44,45,46,47,48,49,50,51]. More recent intervention trials designed to address the gaps described have failed to confirm these initial results [42,52,53,54], using various single NNS (aspartame, sucralose, and saccharin) in healthy normal weight adults on markers of glycemic control and gut microbiota beta-diversity. However, there is still controversy in this field, as a 2022 study showed a worsening of glucose tolerance and changes in gut microbiota beta-diversity in a different post-hoc “responder” group after 14 days of saccharin and sucralose sachet administration at 34 and $20\%$ of the ADI dose, respectively [55]. These studies still leave many pragmatic questions unanswered such as, dose- and time-dependent effects, the effect of the most commonly consumed NNS and NNS blends (which is the most common way that blends being the most common way NNS are consumed worldwide [25], especially since different NNS may have different physio-logical effects) and in the food sources which they are mostly found (i.e., NSBs) [56,57]. More importantly, none of these studies answered the question of the intended use of NNS, which is to displace calories from sugars, particularly from SSBs for disease prevention.
There is an urgent need to address the remaining uncertainties related to NSBs. Health Canada, in particular, has indicated that studies of sugar reduction strategies that use NNSs with microbiome outcomes are an important research priority [58]. We undertook the Strategies To OPpose SUGARS with Non-nutritive sweeteners Or Water (STOP Sugars NOW) trial, a Canadian Institutes for Health Research (CIHR)-funded randomized controlled trial to assess the effect of a “real world” strategy of SSBs reduction using NSBs on glucose tolerance and gut microbiota changes as well as intermediate cardiometabolic risk factors and mediators. We also undertook a sub-study on liver fat and related ectopic muscle fat and intermediate NAFLD outcomes (Ectopic Fat sub-study), given the recognition of NAFLD as an early metabolic lesion in the pathogenesis of type 2 diabetes and an increasingly common public health problem [59]. To address the issue of the nature of the comparator, we designed a study to assess NSBs in the context of three prespecified substitutions of clinical and public health concern: NSBs for SSBs (“intended substitution” with caloric displacement), water for SSBs (“standard of care substitution” with caloric displacement) and NSBs for water (“reference substitution” without caloric displacement).
## 2.1. Trial Design
The STOP Sugars NOW trial is a four-week single-center, open label, randomized controlled crossover trial with three arms (SSB, NSB, water) comparing the effect of replacing SSBs with NSBs (“intended replacement”) versus water (“standard of care”) on the gut microbiota diversity and glucose tolerance. After a two-week run-in phase, each participant acted as their own control receiving the three interventions for four-weeks each in a random order, with intervention phases separated by a minimum of a four-week washout phase. All trial visits were conducted at Unity Health Toronto, St. Michael’s Hospital, Toronto, ON. Canada. The trial protocol conforms to the ethical guidelines of the Tri-Council Policy Statement 2 [60], and the study was conducted according to the guidelines of the Declaration of Helsinki and approved by the research ethics board of St. Michael’s Hospital (protocol code: 17-292, 16 February 2018). All participants provided written informed consent to the main trial, and separately but optional, to the Ectopic Fat sub-study. The trial is registered on ClinicalTrials.gov (NCT03543644), and Supplemental File S1 includes the full trial protocol. All methods described here apply for the Ectopic Fat sub-study except where indicated.
## 2.2. Inclusion and Exclusion Criteria
Table 1 shows the detailed list of inclusion and exclusion criteria. Participants were included if they were consuming at least one 355 mL serving of SSB per day. Additional inclusion criteria include: between the ages of 18 and 75 years, BMI that is classified as overweight or obese (BMI ≥ 23 kg/m2 for Asian individuals and ≥25 kg/m2 for other individuals), and high waist circumference (using ethnic specific cut-offs [61,62,63,64]) but otherwise healthy with no antibiotic use in the last three months [65,66,67,68,69]. Main exclusion criteria were if they were pregnant or breastfeeding or planning on becoming pregnant during the trial and if they had any disease, among others. The Ectopic Fat sub-study followed the same inclusion and exclusion criteria with the addition of one factor for exclusion: any condition or circumstance which would prevent from having an 1H-MRS scan (e.g., having prostheses or metal implants, tattoos, or claustrophobia).
## 2.3. Randomization and Allocation Concealment
Table 2 shows the Latin square randomization table. Randomization was performed after successful completion of the run-in phase and first visit. Randomization, with no stratification, was conducted centrally by an offsite statistician at the Applied Health Research Centre (AHRC) at St. Michael’s Hospital using the Research Data Capture (REDCap) program. Participants were randomly allocated to six possible sequences using blocked (Latin squares) randomization with a similar number of participants allocated to each treatment sequence. Allocation concealment was achieved by the secured electronic delivery of a single sequence for each consecutive participant. RedCap was chosen as it exceeds privacy and security standards which enables anonymization, secure information storage, retrieval, and sharing of data.
## 2.4. Interventions
Table 3 shows the list of intervention beverages and types of NNS sweeteners available for the trial. There were three interventions: SSBs (355 mL, 140 kcal, 42 g sugars per serving); NSBs (355 mL, 0 kcal, 0 g sugars per serving); and water (still or carbonated) (355 mL, 0 kcal, 0 g sugars per serving). To allow for pragmatic substitutions using available market products, the calories of the intervention groups were not matched, and the dose prescription of each intervention (number of 355 mL servings) for each participant was matched to their baseline SSB intake. Participants were provided with the SSB of their choice, equivalent NSB that was sweetened by either acesulfame-potassium (ace-k) or sucralose, as these will be measured objectively as markers of adherence (see below) [71], or water. If participants were consuming NSBs in addition to SSBs, they forwent their NSBs. The beverages given to participants during the trial were not necessarily selected to be in the same lot.
## 2.5. Blinding
Blinding of the participants and trial personnel was not possible due to the nature of the intervention. However, outcome assessors and statisticians were blinded to the identity of the treatments.
## 2.6. Trial Flow
Figure 1 shows the flow of participants through the trial and the data collection schedule. Participants were recruited through postings and flyer handouts, transit advertisements, online listings (Craigslist and Kijiji) and through a digital marketing group (Trialfacts). After informed consent review and in-person screening, eligible participants who consented to participate in the trial were instructed on data collection procedures and were enrolled in a minimum two-week run-in phase.
After randomization and for the trial duration, participants were asked to maintain their usual background diet and exercise regimen. Before every visit, participants were instructed to collect a whole stool sample, a 24 h urine sample, and a weighed three-day diet record (3DDR); to consume a minimum of 150 g of carbohydrate for each of the three days prior; and to arrive fasted for 10–12 h [72,73]. Common examples of 150 g of carbohydrates were shared with participants prior to their visit (e.g., 2–3 slices of bread, 1 cup of cooked rice/pasta, 1 medium potato, etc.). At each trial visit, if participants were enrolled in the Ectopic Fat sub-study, they complete an 1H-MRS scan. Then, the standard protocol was followed for the administration of a 2 h 75 g oral glucose tolerance test (OGTT) (time points −30, 0, 30, 60, 90, 120) [74], which was followed by breakfast prepared by the study staff.
Once all measures were collected, participants were provided the intervention beverages and beverage log sheets, urine and stool containers and instructions for their next visit. After successful randomization, participants were compensated for their travel expenses and for their time. Participants who were lost to follow-up were compensated for each trial visit that was completed.
The four-week duration of each intervention phase was chosen to allow changes in our family of primary outcomes (glucose tolerance and gut microbiota diversity), as previous studies have seen changes in microbiota diversity with seven days to two weeks intervention [43,55]. To control for any carry-over effects of one beverage type over another, each of the three intervention phases was separated by a minimum four-week washout phase where participants reverted to their regular SSB intake. Participants were given beverage logs to complete over the washout. Antibiotic use during a washout or intervention phase required either prolongation of the washout period or stoppage of the intervention phase followed by a minimum 30-day washout period measured from the time of completion of the antibiotic course [69].
To promote adherence, all intervention beverages were provided either by pick-up by the participant or by home delivery; participants completed beverage log sheets and motivational phone calls and emails were made every two weeks.
## 2.7. Primary Outcomes
The family of primary outcomes of the main trial includes changes from baseline in oral glucose tolerance, as measured by the glucose incremental area under the curve (iAUC), and the gut microbial beta-diversity, as measured by the weighted UniFrac distance matrix. The weighted UniFrac distance matrix was chosen as it was found to be more accurate [75]. This will be computed using the QIIME2 [76] pipeline with DNA sequences (sequenced using the Illumina MiSeq platform) from the 16S rRNA gene, with primers targeting the V3–V4 region. The QIIME2 pipeline will be available on GitHub upon request.
The primary outcome of the Ectopic Fat sub-study is changes from baseline in intrahepatocellular lipid (IHCL), as measured by 1H-MRS.
## 2.8. Secondary Outcomes
The secondary outcomes of the main trial are changes from baseline in waist circumference, body weight, fasting plasma glucose (FPG), 2 h plasma glucose (2 h-PG) and the Matsuda whole body insulin sensitivity index (Matsuda ISIOGTT) [77].
The secondary outcomes of the Ectopic Fat sub-study are change from baseline in intramyocellular lipid (IMCL), fatty liver index (FLI) [78], alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutamyl transferase (GGT) and alkaline phosphatase (ALP).
## 2.9. Adherence Outcomes
Adherence outcomes are changes from baseline in self-reported beverage intake from beverage logs, returned beverage containers, and objective biomarkers of SSBs (increased 13C/12C ratios in serum fatty acids [79] and increased urinary fructose [80]), water (decreased 13C/12C ratios in serum fatty acids [79] and decreased urinary fructose [80]), and NSB (increased urinary acesulfame potassium and/or sucralose [71]) intake.
## 2.10. Exploratory Outcomes
Exploratory outcomes of the main trial include changes from baseline in blood pressure, fasting blood lipid profile, fasting plasma insulin, 75 g OGTT derived indices (iAUC plasma insulin, maximum concentrations (Cmax) and time to maximum concentrations (Tmax)) of plasma glucose and insulin, mean incremental plasma glucose and insulin, the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) [81,82], beta-cell function as measured by the insulin secretion-sensitivity index-2 (ISSI-2) [83,84], early insulin secretion index [85,86], satiety, hunger, and food cravings, diet quality, alpha-diversity, other beta-diversity indexes and metagenomic inference from compositional data in silico from whole stool [87].
## 2.11. Power Calculation
Table 4 shows the power (sample size) calculation for the main trial and the Ectopic Fat sub-study, using the “power” package in STATA17 (StataCorp, College Station, TX, USA). The main trial has over $89\%$ power and the Ectopic Fat sub-study has over $80\%$ power to show a difference between the NSB, water and SSB arms in 60 participants in the two primary outcomes of glucose tolerance and gut microbiota beta-diversity in the main trial and 25 participants in the primary outcome of IHCL in the Ectopic Fat sub-study. Assuming a drop-out rate of $20\%$, we planned to recruit 75 and 32 participants, respectively.
The main trial is powered to detect a difference of 44.81 mmol/L/min (standard de-viation (SD) = 113 mmol/L/min) (i.e., $20\%$) in iAUC glucose [88,89] and 0.04 (SD = 0.07) in microbiota beta-diversity [43]. Considering it is a cross-over trial with a within-person correlation of 0.7, these differences were chosen based on effects smaller than the be-ta-diversity and glycemic response effect observed in the study of Suez et al. [ 2014] [43], which was the only study available at the time. The trial is also powered to detect clinically meaningful differences in all secondary outcomes except the adiposity outcomes (body weight and waist circumference) between the three interventions [35,90,91]. To control for false discovery, the truncated Benjamini-Hochberg method with parallel gatekeeping for control of false discovery will be implemented [93,94,95,96]. By this method the unused portion of the alpha from the primary outcomes is passed onto the secondary family of outcomes if either one of the primary outcomes is statistically significant. The sample size calculations were based on the most conservative alpha estimates from this method. If none of the primary endpoints reaches significance, then the secondary outcomes will be analyzed as exploratory variables without adjustment for false discovery rate. All exploratory out-comes will be assessed without adjustment for false discovery rate.
The Ectopic Fat sub-study is powered to detect a difference of $5\%$ (SD = $10\%$) in IHCL [92], using a within-person correlation of 0.67. The truncated Benjamini-Hochberg method with parallel gatekeeping procedure will not be used for the Ectopic Fat sub-study, as it is considered exploratory in nature.
## 2.12. Outcome Assessment
Supplementary File S1 presents the methods for the assessment of the primary, secondary, adherence, and exploratory outcomes.
## 2.13. Statistical Analysis
Data will be analyzed in STATA 17 (StataCorp, College Station, TX, USA). Primary analyses will be according to intention-to-treat (ITT) principle with multiple imputations or other appropriate statistics. Additional prespecified analyses will be undertaken that include completers and the per-protocol analyses. Repeated measures mixed effect models will be used to assess changes in the family of primary outcomes (gut microbiota beta-diversity (weighed UniFrac distance) and mean glucose iAUC (75 g OGTT)) between the groups and in our secondary outcomes (waist circumference, body weight, FPG, 2 h-PG and Matsuda ISIOGTT) with adjustment for sequence effects, trial completion during the coronavirus disease 2019 (COVID-19) pandemic, withdrawal/drop-out during the COVID-19 pandemic, and antibiotic use during the trial. Other adjustments will be considered based upon an assessment of any imbalances during the trial. Pairwise comparisons will be performed using Tukey–Kramer adjustment to assess differences for the three prespecified substitutions: SSBs for NSBs (“intended substitution” with caloric displacement), SSBs for water (“standard of care substitution” with caloric displacement) and NSBs for water (“reference substitution” without caloric displacement). We will use the truncated Benjamini–Hochberg false discovery rate controlling method with a parallel gatekeeping procedure to correct for multiple outcomes for all the primary and secondary endpoint comparisons in the main analysis as described above [93,96]. To assess effect modification, a priori subgroup analysis will be conducted by age, sex, ethnicity, antibiotic use during the trial, baseline BMI, baseline waist circumference, baseline FPG, baseline 2 h-PG, baseline iAUC, medication use, NNS blend consumed from trial beverages in the NSB arm, SSB type and background NNS use. Subgroup analyses by baseline SSB dose (as number of 355 mL serving per day and percent energy from sugars), consumption patterns, energy compensation, trial completion during the COVID-19 pandemic and caffeine intake (cola vs. non-cola beverages) that have emerged as relevant prior to data analyses will also be considered.
## 3.1. CONSORT Statement
Figure 2 shows the CONSORT statement for the main trial and the Ectopic Fat sub-study. Enrollment began on 1 June 2018, with the first participant undergoing randomization on 22 November 2018. The last participant finished the Ectopic Fat sub-study on 18 February 2020 (prior to the start of the COVID-19 pandemic). The main trial was interrupted owing to research closures at St. Michael’s Hospital due to the COVID-19 pandemic between March 2020 and September 2020, which resulted in visits being halted for 10 participants. During this time, participants were placed on a washout period pending the restart of research; any intervention that needed to be stopped prior to completion was restarted from baseline. The trial resumed with the permission of the Research Ethics Board in September 2020 with completion of the last trial participant on 15 October 2020.
The planned recruitment was expanded from 75 to 81 participants for the main trial and from 25 to 32 for the Ectopic Fat sub-study to increase the power for the planned analyses. The increase in power was approved before the COVID-19 pandemic and was completed to allow participants already enrolled in the run-in phase to have a chance to participate.
A total of 1088 individuals completed the telephone screening questionnaire, of which 260 were eligible for in-person screening and 156 provided written informed consent. Main reasons for the failed screening included failure to contact ($$n = 398$$), lack of interest ($$n = 163$$) and ineligibility ($$n = 267$$) mainly due to non-consumption of SSBs ($$n = 57$$) or the presence of disease ($$n = 48$$). After the in-person screening, 141 individuals were eligible for the main trial, of which 62 individuals consented to the Ectopic Fat sub-study. Due to dropouts (main trial = 38; Ectopic Fat sub-study = 21) and loss-to-follow up (main trial = 22; Ectopic Fat sub-study = 9) during the screening and run-in phases, a total of 81 eligible participants (Ectopic Fat sub-study = 32) were randomized to a treatment sequence. One participant met the exclusion criterion for the presence of gastrointestinal disease, was randomized in error to the main trial and was withdrawn shortly after randomization. Their randomization sequence was not reused, and the participant was not counted among the randomized participants and will not be included in any analyses. A total of 66 participants out of 80 completed the main trial ($83\%$ retention), and 26 out of 32 participants completed the Ectopic Fat sub-study ($81\%$ retention). Of the 10 participants who were enrolled in the main trial during the COVID-19 pandemic, one participant dropped out during the pandemic (due to changes in lifestyle (interest in cutting back on SSB consumption)), three participants who had more than one phase to complete were withdrawn by the investigators (as it was determined there was no reasonable prospect of their completion during the projected subsequent waves of COVID-19), and the remaining six participants who had only a single phase to complete were able to return and finish the trial. The reasons for attrition unrelated to the COVID-19 pandemic ranged from lack of time to participate, moving away, change in lifestyle (cutting back on SSB consumption) or loss-to-follow-up.
## 3.2. Baseline Characteristics
Table 5 shows the baseline characteristics of the 80 randomized participants in the main trial and the 32 participants randomized in the Ectopic Fat sub-study participants. Baseline characteristics are descriptively described here: Mean (SD) age was 42.34 years (12.99 years) (Ectopic Fat sub-study: 42.16 years (12.91 years)), with approximately even sex distribution (main trial, female = $51\%$; Ectopic Fat sub-study, female = $50\%$). BMI and waist circumference were 33.71 kg/m2 (6.75 kg/m2) (Ectopic Fat sub-study, 33.70 kg/m2 (6.03 kg/m2)) and 108.69 cm (13.50 cm) (Ectopic Fat sub-study, 110.31 cm (13.67 cm)), respectively. The mean baseline (SD) systolic and diastolic blood pressure were 116.37 mmHg (12.49 mmHg) (Ectopic Fat sub-study: 76.68 mmHg (9.12 mmHg)) and 76.24 mmHg (9.03 mmHg) (Ectopic Fat sub-study, 72.34 mmHg (9.76 mmHg)), respectively. Mean baseline (SD) FPG was 5.57 mmol/L (1.19 mmol/L) (Ectopic Fat sub-study: 5.77 mmol/L (1.75 mmol/L)) and 2 h-PG were 7.26 mmol/L (3.11 mmol/L) (Ectopic Fat sub-study: 7.99 mmol/L (4.05 mmol/L)). Mean baseline (SD) IHCL for the participants in the Ectopic Fat sub-study was $9.7\%$ ($9.2\%$). Most trial participants are of European (main trial: $45\%$; Ectopic Fat sub-study, $56\%$) descent. About one-third of participants in the main trial had achieved an undergraduate education ($34\%$), whereas a similar proportion of participants in the Ectopic Fat sub-study had a high school diploma ($31\%$). About half of the participants worked full-time (main trial: $50\%$; Ectopic Fat sub-study: $44\%$), and one-quarter of participants in the main trial reported drinking no alcohol ($26\%$), with only $4\%$ drinking alcohol daily. Among the Ectopic Fat sub-study, a similar proportion reported drinking alcohol once every 2–3 months ($28\%$) with only $6\%$ drinking alcohol daily.
Overall, $29\%$ and $38\%$ of participants in the main trial and Ectopic Fat sub-study, respectively, reported being on regular medications. Some participants reported taking supplements (main trial: $29\%$; Ectopic Fat sub-study: $25\%$). Two participants ($9\%$) included in the main trial (one ($13\%$) in the Ectopic Fat sub-study) used marijuana regularly without a medical prescription.
Most participants did not regularly consume NNS in the past six months before baseline (main trial: $55\%$; Ectopic Fat sub-study: $63\%$), while some participants reported background NNS intake from beverages (main trial: $26\%$; Ectopic Fat sub-study: $31\%$), table-top added sweeteners (main trial: $6\%$; Ectopic Fat sub-study: $3\%$), foods (main trial: $5\%$; Ectopic Fat sub-study: $0\%$), or a mix of these sources (main trial: $8\%$; Ectopic Fat sub-study: $3\%$). Baseline SSB preference revealed Coca-Cola (main trial: $44\%$; Ectopic Fat sub-study: $53\%$) as the most popular SSB consumed, with an overall average of 1.84 servings (355 mL) per day (total intake: 653.2 mL/d) in the main trial and 2.02 servings (355 mL) (total intake: 717.1 mL/d) per day in the Ectopic Fat sub-study. From this information, projected NSB equivalent intake indicated that an overwhelming majority of participants consumed a blend of aspartame and ace-k (main trial: $95\%$; Ectopic Fat sub-study: $94\%$) during the NSB phase. Overall, baseline characteristics in the Ectopic Fat sub-study were descriptively similar to those in the main trial.
## 4. Discussion
The STOP Sugars NOW *Trial is* a pragmatic, single-center, open label, randomized controlled multiple crossover trial with three four-week treatment phases (SSB, NSB, water) comparing the effect of the substitution of NSBs (“intended substitution”) versus water (“standard of care substitution”) for SSBs on the gut microbiota and glucose tolerance as well as other intermediate cardiometabolic outcomes in adults who consume ≥1 SSBs daily and are overweight or obese with a high waist circumference. The Ectopic Fat sub-study uses the same design to assess the effect on ectopic fat in liver (IHCL) by 1H-MRS as well as other ectopic fat in muscle (IMCL) and related intermediate markers of NAFLD. This trial will be the first to investigate the effect of the intended use of NSBs to replace SSBs, compared with the “standard of care” water, in a pragmatic and controlled manner in those who are regular consumers of SSBs and at high risk of the sequelae of overconsumption of SSBs.
Baseline characteristics for both the main and Ectopic Fat sub-study were not different. Overall, the study participants in both the main trial and the Ectopic Fat sub-study represent an overweight or obese group with characteristics putting them at risk for type 2 diabetes and other NCDs.
The NNS tested in the present trial are representative of those available on the market in Canada and globally [97]. Consumption of NSBs has been increasing [11,98] with approximately $10\%$ of Canadians consuming NSBs [99]. Canadian market share data show that Diet Pepsi, Diet Coke and Coke Zero are the leading NSB brands in Canada [100]. As NSBs are the most important food source of NNS, it can be inferred that the blend of aspartame and ace-k, followed by sucralose alone, are the most common NNS in foods in Canada, and this data reflects the global market [97].
## 4.1. Strengths and Limitations
The present trial overcomes several limitations of previous trials of the use of NSBs as a replacement strategy for SSBs. First, it uses a “real-world” fully pragmatic design with NSBs products available on the Canadian and global markets [56,100], compared with previous work that mostly administered single NNS in capsule form and often in greater amounts than products available on the market, which limits the generalizability of conclusions [42,43,52,53,54]. Second, comparing the effect of NSBs to SSBs (“intended sub-stitution”) and the standard of care water (“reference substitution”) allows for the dis-entanglement of the effect of energy from that of the NNS, as it has been hypothesized that NNS may have consequences for health independent of energy content that result from the chemical compounds themselves [41,56,101]. The substitution of NSBs for SSBs allows for the displacement of energy, which is often overlooked in controlled trial syntheses, including a recent WHO-commissioned review [26]. The substitution of water for SSBs (“standard of care substitution”) and comparison of water with NSBs (“reference substitution”), will clarify whether NSBs are like water in their effect on gut microbiota and attributing metabolic disease risk. Third, unlike many studies that have investigated changes in the gut microbiota beta-diversity as an outcome [102], the STOP Sugars NOW trial is powered to detect a change in both primary outcomes of glucose tolerance and change in the beta-diversity of the gut microbiota, using Food and Drug Administration recommended statistical analyses [96]. Fourth, our use of objective urinary and serum biomarker analysis to assess adherence to all interventions overcomes many reporting or recall biases. Fifth, our sample population that includes participants who are overweight or obese with a high waist circumference represents an at-risk population for type 2 diabetes. This makes the results of the trial relevant to guidelines and policies for type 2 diabetes prevention. Sixth, the results from this trial will contribute to the development of clinical trial methodologies, especially related to the integration of microbiota data with clinical data, which is an emerging area of research [103]. Finally, our results will be directly translatable to most areas of the world which share similar NNS and NSB availability.
Some limitations remain. First, the multiple crossover design with three treatment phases, two washout periods, and multiple measurements may have contributed to a reduction in retention. Our retention of $82.5\%$ for the main trial and $81.3\%$ for the Ectopic Fat sub-study, however, would meet criteria for good retention [104]. We also anticipated this level of retention in our sample size calculation and have been able to maintain adequate statistical power for our primary and secondary outcomes with good balance in the treatment sequences. Second, as with any intervention trial, participants may un-consciously compensate for the reduced energy and caffeine intake from NSBs or water interventions by consuming additional calories in their background diet [34]. Despite instructions to maintain usual background diets and activity levels throughout the trial, the inventions may also have engendered other health behaviors leading participants to change their diets and activity levels consciously or unconsciously [105]. As part of the ITT principle, any changes resulting from the interventions would be considered an effect of the interventions and will be captured in our analysis of the participant’s 3DDRs, activity logs, and case report forms, and assessed by subgroup analyses. The design which included both negative (SSBs) and positive (water) controls will also help us to isolate the effect of the change in NSBs from other changes which may attenuate or enhance the effects of the interventions. Further, we will be conducting subgroup analysis to explore the effects of caffeine on our outcomes. Third, as most of the participants in the NSB phase consumed a blend of aspartame and ace-k, we will not be able to isolate the effect of a single NNS on glucose tolerance and the gut microbiota. Related to this limitation, our results will not inform the effect of other less-common NNS, such as neotame or saccharin, as these NNS are not used to sweeten commercially available NSBs in Canada. These results, however, will be representative of the product availability in the Canadian, North American, and global market [56,97,100]. Fourth, although our trial sample shows traits akin to the general population of Canada where the prevalence of overweight and obesity is now $63\%$ [106,107] and akin to average SSB consumers in North America [10,11,108], we do acknowledge that there is some evidence of volunteer bias in our sample, as most of our participants represent higher socioeconomic background (higher education and full-time work status). Therefore, we are potentially neglecting inclusion of people who are at higher risk for type 2 diabetes and other NCDs, and who have less access to resources to manage the disease [109,110]. Fifth, the COVID-19 pan-demic posed some challenges. Ten participants were enrolled in the trial during the COVID-19 pandemic, of which six completed the last phase of the trial after a prolonged washout period, three were withdrawn by the investigators for no reasonable prospect of completion, and one dropped out during the pandemic due to changes in lifestyle. It is reasonable to expect that the background diet and lifestyle of these participants and their ability to adhere to the interventions and trial protocol were directly affected by the pandemic. It was decided to adjust for study participation during the pandemic in our primary models and conduct sensitivity analyses in which these participants were excluded in modified ITT, completers, and per protocol analyses. Sixth, the washout period may have been of insufficient duration and may result in carry-over effects from previous the intervention, especially for changes in the gut microbiota. However, the microbiome is very resilient; acute changes in diet or lifestyle revert to baseline within 48 hours [111]. Short-term dietary changes, especially of only one component of the diet, is often not sufficient to majorly perturb the gut microbiome in a permanent way [112]. The STOP Sugars NOW *Trial is* changing only one aspect in our participant’s diets for a short-term intervention: sugar-sweetened beverage intake for four weeks. During the washout phases, participants were instructed to revert to their usual sugar-sweetened beverage intake, and we believe that the resiliency of the gut microbiome will overcome the short-term dietary changes. Finally, as we are using 16S rRNA gene sequencing to assess gut microbiota outcomes, we will not have species level resolution, and we will not be able to directly infer gut microbiota functions. However, previous research done on the effect of non-nutritive sweeteners on the gut microbiota and the risk for diabetes [42,43,52,53,54], have also sequenced the 16S rRNA gene. Therefore, our results will be directly comparable to the current literature on this topic.
## 4.2. Implications
The role of NSBs in modifying the risk for type 2 diabetes from SSBs and the extent to which the human gut microbiota might mediate this process is of great importance in understanding chronic disease pathogenesis, prevention, and management for guideline and policy makers. Excess intake of calories from sugars, particularly from SSBs, has been linked to the rise in NCDs [6,7,8,9]. Public health agencies in Canada and around the world have responded by urging the public to reduce their consumption of SSBs [2,113,114,115]. Replacement strategies for SSBs that leverage beverages with the same sweet taste, like NSBs, may be more appealing than water and thus may facilitate a reduction in SSB intake. Although the NNSs used to sweeten NSBs are considered safe by regulatory authorities [22,23], public health recommendations are conflicting [2,18,114,116]. Some research suggests that NNS may increase the risk for type 2 diabetes through changes in the microbiota in humans, but this finding needs confirmation [42,88]. The proposed STOP Sugars NOW trial will provide important data to address this concern using representative NSBs and NNS blends in a representative sample of at-risk obese individuals.
## 5. Conclusions
The STOP Sugars NOW trial is the first to pragmatically investigate the effect of the intended use of NSBs to replace SSBs compared with the “standard of care” water in those who are regular consumers of SSBs and at high risk of the sequelae of overconsumption of SSBs. We successfully recruited and randomized 80 participants in the main trial and 32 participants in the Ectopic Fat sub-study. Baseline characteristics for both the main and Ectopic Fat sub-study were similar and meet our eligibility criteria. Overall, the study participants in both the main trial and the Ectopic Fat sub-study represent an overweight or obese group with characteristics putting them at risk for type 2 diabetes and other NCDs. Additionally, the study sample is representative of the average SSB consumer in Canada, making the study’s results applicable to disease prevention in target population for public health interventions to reduce SSB intake. The results of this unique trial will be presented at scientific meetings, published in peer-reviewed academic journals, and included in updated systematic reviews and meta-analyses. The results will inform guidelines for NSBs as a replacement strategy for SSBs, compared with “standard of care” (water), aiding in knowledge translation related to the health effects of NSBs in their intended substitution for SSBs; improving health outcomes by educating healthcare providers and patients, stimulating industry innovation; and guiding future research design.
## References
1. Brisbois T.D., Marsden S.L., Anderson G.H., Sievenpiper J.L.. **Estimated intakes and sources of total and added sugars in the Canadian diet**. *Nutrients* (2014.0) **6** 1899-1912. DOI: 10.3390/nu6051899
2. **Sugar, Heart Disease and Stroke**
3. **Sugar & Diabetes**
4. **Cut down on Added Sugars**
5. **Canada’s Dietary Guidelines for Health Professionals and Policy Makers**
6. Malik V.S., Pan A., Willett W.C., Hu F.B.. **Sugar-sweetened beverages and weight gain in children and adults: A systematic review and meta-analysis**. *Am. J. Clin. Nutr.* (2013.0) **98** 1084-1102. DOI: 10.3945/ajcn.113.058362
7. Imamura F., O’Connor L., Ye Z., Mursu J., Hayashino Y., Bhupathiraju S.N., Forouhi N.G.. **Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: Systematic review, meta-analysis, and estimation of population attributable fraction**. *BMJ* (2015.0) **315** h3576. DOI: 10.1136/bmj.h3576
8. Jayalath V.H., de Souza R.J., Ha V., Mirrahimi A., Blanco-Mejia S., Di Buono M., Jenkins A.L., Leiter L.A., Wolever T.M., Beyene J.. **Sugar-sweetened beverage consumption and incident hypertension: A systematic review and meta-analysis of prospective cohorts**. *Am. J. Clin. Nutr.* (2015.0) **102** 914-921. DOI: 10.3945/ajcn.115.107243
9. Xi B., Huang Y., Reilly K.H., Li S., Zheng R., Barrio-Lopez M.T., Martinez-Gonzalez M.A., Zhou D.. **Sugar-sweetened beverages and risk of hypertension and CVD: A dose-response meta-analysis**. *Br. J. Nutr.* (2015.0) **113** 709-717. DOI: 10.1017/S0007114514004383
10. Malik V.S., Li Y., Pan A., De Koning L., Schernhammer E., Willett W.C., Hu F.B.. **Long-term consumption of sugar-sweetened and artificially sweetened beverages and risk of mortality in US adults**. *Circulation* (2019.0) **139** 2113-2125. DOI: 10.1161/CIRCULATIONAHA.118.037401
11. Sylvetsky A.C., Rother K.I.. **Trends in the consumption of low-calorie sweeteners**. *Physiol. Behav.* (2016.0) **164** 446-450. DOI: 10.1016/j.physbeh.2016.03.030
12. **Scientific Report of the 2020 Dietary Guidelines Advisory Committee: Advisory Report to the Secretary of Agriculture and the Secretary of Health and Human Services**
13. Johnson R.K., Lichtenstein A.H., Anderson C.A.M., Carson J.A., Després J.-P., Hu F.B., Kris-Etherton P.M., Otten J.J., Towfighi A., Wylie-Rosett J.. **Low-calorie sweetened beverages and cardiometabolic health: A science advisory from the american heart association**. *Circulation* (2018.0) **138** e126-e140. DOI: 10.1161/CIR.0000000000000569
14. **Standards of Medical Care in Diabetes—2020**
15. **Evidence-Based Nutrition Guidelines for the Prevention and Management Of Diabetes**
16. Rios-Leyvraz M., Montez J.. *Health Effects of the Use of Non-Sugar Sweeteners: A Systematic Review and Meta-Analysis* (2022.0) 10-174
17. **Launch Event for the Public Consultation on the Draft WHO Guideline on Use of Non-Sugar Sweeteners**
18. Sievenpiper J.L., Chan C.B., Dworatzek P.D., Freeze C., Williams S.L.. **Diabetes Canada Clinical Practice Guidelines Expert Committee—Chapter 11: Nutrition Therapy**. *Can. J. Diabetes* (2018.0) **42** S64. DOI: 10.1016/j.jcjd.2017.10.009
19. **Sweeteners**
20. Kroger M., Meister K., Kava R.. **Low-calorie sweeteners and other sugar substitutes: A review of the safety issues**. *Compr. Rev. Food Sci. Food Saf.* (2006.0) **5** 35-47. DOI: 10.1111/j.1541-4337.2006.tb00081.x
21. Magnuson B.A., Roberts A., Nestmann E.R.. **Critical review of the current literature on the safety of sucralose**. *Food Chem. Toxicol.* (2017.0) **106** 324-355. DOI: 10.1016/j.fct.2017.05.047
22. **American Heart Association/American Diabetes Association Scientific Statement:Non-nutritive Sweeteners: A Potentially Useful Option—with Caveats**
23. Fitch C., Keim K.S.. **Position of the Academy of Nutrition and Dietetics: Use of nutritive and nonnutritive sweeteners**. *J. Acad. Nutr. Diet.* (2012.0) **112** 739-758. DOI: 10.1016/j.jand.2012.03.009
24. **9. List of Permitted Sweeteners (Lists of Permitted Food Additives)**
25. Russell C., Baker P., Grimes C., Lindberg R., Lawrence M.A.. **Global trends in added sugars and non-nutritive sweetener use in the packaged food supply: Drivers and implications for public health**. *Public Health Nutr.* (2022.0) 1-13. DOI: 10.1017/S1368980022001598
26. Toews I., Lohner S., de Gaudry D.K., Sommer H., Meerpohl J.J.. **Association between intake of non-sugar sweeteners and health outcomes: Systematic review and meta-analyses of randomised and non-randomised controlled trials and observational studies**. *BMJ* (2019.0) **364** k4718. DOI: 10.1136/bmj.k4718
27. Azad M.B., Abou-Setta A.M., Chauhan B.F., Rabbani R., Lys J., Copstein L., Mann A., Jeyaraman M.M., Reid A.E., Fiander M.. **Nonnutritive sweeteners and cardiometabolic health: A systematic review and meta-analysis of randomized controlled trials and prospective cohort studies**. *CMAJ* (2017.0) **189** E929-E939. DOI: 10.1503/cmaj.161390
28. Malik V.S.. **Non-sugar sweeteners and health**. *BMJ* (2019.0) **364** k5005. DOI: 10.1136/bmj.k5005
29. Khan T.A., Sievenpiper J.L.. **Low-energy sweeteners and cardiometabolic health: Is there method in the madness?**. *Am. J. Clin. Nutr.* (2020.0) **112** 917-919. DOI: 10.1093/ajcn/nqaa260
30. Mela D.J., McLaughlin J., Rogers P.J.. **Perspective: Standards for research and reporting on low-energy (“artificial”) sweeteners**. *Adv. Nutr.* (2020.0) **11** 484-491. DOI: 10.1093/advances/nmz137
31. Ashwell M., Gibson S., Bellisle F., Buttriss J., Drewnowski A., Fantino M., Gallagher A.M., De Graaf K., Goscinny S., Hardman C.A.. **Expert consensus on low-calorie sweeteners: Facts, research gaps and suggested actions**. *Nutr. Res. Rev.* (2020.0) **33** 145-154. DOI: 10.1017/S0954422419000283
32. Sievenpiper J.L., Khan T.A., Ha V., Viguiliouk E., Auyeung R.. **The importance of study design in the assessment of nonnutritive sweeteners and cardiometabolic health**. *Can. Med. Assoc. J.* (2017.0) **189** E1424-E1425. DOI: 10.1503/cmaj.733381
33. Khan T.A., Malik V.S., Sievenpiper J.L.. **Letter by Khan et al. regarding article, “Artificially sweetened beverages and stroke, coronary heart disease, and all-cause mortality in the women’s health initiative”**. *Stroke* (2019.0) **50** e167-e168. DOI: 10.1161/STROKEAHA.119.025571
34. Rogers P., Hogenkamp P., De Graaf C., Higgs S., Lluch A., Ness A., Penfold C., Perry R., Putz P., Yeomans M.. **Does low-energy sweetener consumption affect energy intake and body weight? A systematic review, including meta-analyses, of the evidence from human and animal studies**. *Int. J. Obes.* (2016.0) **40** 381-394. DOI: 10.1038/ijo.2015.177
35. McGlynn N.D., Khan T.A., Wang L., Zhang R., Chiavaroli L., Au-Yeung F., Lee J.J., Noronha J.C., Comelli E.M., Blanco Mejia S.. **Association of Low- and No-Calorie Sweetened Beverages as a Replacement for Sugar-Sweetened Beverages with Body Weight and Cardiometabolic Risk: A Systematic Review and Meta-analysis**. *JAMA Netw. Open* (2022.0) **5** e222092. DOI: 10.1001/jamanetworkopen.2022.2092
36. Laviada-Molina H., Molina-Segui F., Pérez-Gaxiola G., Cuello-García C., Arjona-Villicaña R., Espinosa-Marrón A., Martinez-Portilla R.J.. **Effects of nonnutritive sweeteners on body weight and BMI in diverse clinical contexts: Systematic review and meta-analysis**. *Obes. Rev.* (2020.0) **21** e13020. DOI: 10.1111/obr.13020
37. Lee J., Khan T., Malik V., Hill J., Jeppesen P., Rahelic D., Kahleova H., Salas-Salvado J., Kendall C.W., Sievenpiper J.. **Relation of Change or Substitution of Low Calorie Sweetened Beverages with Cardiometabolic Outcomes: A Systematic Review and Meta-Analysis of Prospective Cohort Studies**. *Curr. Dev. Nutr.* (2020.0) **4** 1432. DOI: 10.1093/cdn/nzaa061_060
38. Lee J.J., Khan T.A., McGlynn N., Malik V.S., Hill J.O., Leiter L.A., Jeppesen P.B., Rahelić D., Kahleová H., Salas-Salvadó J.. **Relation of Change or Substitution of Low-and No-Calorie Sweetened Beverages with Cardiometabolic Outcomes: A Systematic Review and Meta-analysis of Prospective Cohort Studies**. *Diabetes Care* (2022.0) **45** 1917-1930. DOI: 10.2337/dc21-2130
39. Sievenpiper J.L., Dworatzek P.D.. **Food and dietary pattern-based recommendations: An emerging approach to clinical practice guidelines for nutrition therapy in diabetes**. *Can. J. Diabetes.* (2013.0) **37** 51-57. DOI: 10.1016/j.jcjd.2012.11.001
40. Hunter S.R., Reister E.J., Cheon E., Mattes R.D.. **Low calorie sweeteners differ in their physiological effects in humans**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11112717
41. Burke M.V., Small D.M.. **Physiological mechanisms by which non-nutritive sweeteners may impact body weight and metabolism**. *Physiol. Behav.* (2015.0) **152** 381-388. DOI: 10.1016/j.physbeh.2015.05.036
42. Thomson P., Santibanez R., Aguirre C., Galgani J.E., Garrido D.. **Short-term impact of sucralose consumption on the metabolic response and gut microbiome of healthy adults**. *Br. J. Nutr.* (2019.0) **122** 856-862. DOI: 10.1017/S0007114519001570
43. Suez J., Korem T., Zeevi D., Zilberman-Schapira G., Thaiss C.A., Maza O., Israeli D., Zmora N., Gilad S., Weinberger A.. **Artificial sweeteners induce glucose intolerance by altering the gut microbiota**. *Nature* (2014.0) **514** 181-186. DOI: 10.1038/nature13793
44. Chang K.. **Artificial Sweeteners May Disrupt Body’s Blood Sugar Controls**
45. Kirkey S.. **Fake Sugars Linked to Obesity, Heart Disease**
46. Faherty S.. **Artificial Sweeteners May Have Despicable Impacts on Gut Microbes**
47. Sifferlin A.. **Artificial Sweeteners are Linked to Weight Gain —Not Weight Loss**
48. Vergano D.. **Artificial Sweeteners May Trigger Blood Sugar Risks**
49. Leung W.. **Sugar Substitutes Associated with Weight Gain and Health Problems, Study Says**
50. Brait E.. **More Research Needed into the Effects Sugar Substitutes Have on Health**
51. Thompson D.. **Do Artificial Sweeteners Raise Odds for Obesity?**
52. Serrano J., Smith K.R., Crouch A.L., Sharma V., Yi F., Vargova V., LaMoia T.E., Dupont L.M., Serna V., Tang F.. **High-dose saccharin supplementation does not induce gut microbiota changes or glucose intolerance in healthy humans and mice**. *Microbiome* (2021.0) **9** 1-18. DOI: 10.1186/s40168-020-00976-w
53. Ahmad S.Y., Friel J., Mackay D.. **The Effects of Non-Nutritive Artificial Sweeteners, Aspartame and Sucralose, on the Gut Microbiome in Healthy Adults: Secondary Outcomes of a Randomized Double-Blinded Crossover Clinical Trial**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12113408
54. Ahmad S.Y., Friel J.K., MacKay D.S.. **The effect of the artificial sweeteners on glucose metabolism in healthy adults: A randomized, double-blinded, crossover clinical trial**. *Appl. Physiol. Nutr. Metab.* (2020.0) **45** 606-612. DOI: 10.1139/apnm-2019-0359
55. Suez J., Cohen Y., Valdés-Mas R., Mor U., Dori-Bachash M., Federici S., Zmora N., Leshem A., Heinemann M., Linevsky R.. **Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance**. *Cell* (2022.0) **185** 3307-3328. DOI: 10.1016/j.cell.2022.07.016
56. Magnuson B.A., Carakostas M.C., Moore N.H., Poulos S.P., Renwick A.G.J.N.r.. **Biological fate of low-calorie sweeteners**. *Nutr. Rev.* (2016.0) **74** 670-689. DOI: 10.1093/nutrit/nuw032
57. Bright O.-J.M., Wang D.D., Shams-White M., Bleich S.N., Foreyt J., Franz M., Johnson G., Manning B.T., Mattes R., Pi-Sunyer X.. **Research priorities for studies linking intake of low-calorie sweeteners and potentially related health outcomes: Research methodology and study design**. *Curr. Dev. Nutr.* (2017.0) **1** e000547. DOI: 10.3945/cdn.117.000547
58. **Operating Grant: Sugar and Health ARCHIVED**
59. Taylor R., Al-Mrabeh A., Sattar N.. **Understanding the mechanisms of reversal of type 2 diabetes**. *Lancet Diabetes Endocrinol.* (2019.0) **7** 726-736. DOI: 10.1016/S2213-8587(19)30076-2
60. **Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans, December 2018**
61. Alberti K.G.M., Zimmet P., Shaw J.. **The metabolic syndrome—a new worldwide definition**. *Lancet* (2005.0) **366** 1059-1062. DOI: 10.1016/S0140-6736(05)67402-8
62. Alberti K., Eckel R., Grundy S., Zimmet P., Cleeman J., Donato K., Fruchart J., James W., Loria C., Smith Jr S.. **A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity**. *Circulation* (2009.0) **120** 1640-1645. PMID: 19805654
63. 63.
World Health Organization
Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert ConsultationWorld Health OrganizationGeneva, Switzerland2011831. *Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation* (2011.0) 8-31
64. Lear S., James P., Ko G., Kumanyika S.. **Appropriateness of waist circumference and waist-to-hip ratio cutoffs for different ethnic groups**. *Eur. J. Clin. Nutr.* (2010.0) **64** 42-61. DOI: 10.1038/ejcn.2009.70
65. Korem T., Zeevi D., Zmora N., Weissbrod O., Bar N., Lotan-Pompan M., Avnit-Sagi T., Kosower N., Malka G., Rein M.. **Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses**. *Cell Metab.* (2017.0) **25** 1243-1253. DOI: 10.1016/j.cmet.2017.05.002
66. Huaman J.-W., Mego M., Manichanh C., Cañellas N., Cañueto D., Segurola H., Jansana M., Malagelada C., Accarino A., Vulevic J.. **Effects of prebiotics vs a diet low in FODMAPs in patients with functional gut disorders**. *Gastroenterology* (2018.0) **155** 1004-1007. DOI: 10.1053/j.gastro.2018.06.045
67. Ibrügger S., Gøbel R.J., Vestergaard H., Licht T.R., Frøkiær H., Linneberg A., Hansen T., Gupta R., Pedersen O., Kristensen M.. **Two randomized cross-over trials assessing the impact of dietary gluten or wholegrain on the gut microbiome and host metabolic health**. *J. Clin. Trials.* (2014.0) **4**. DOI: 10.4172/2167-0870.1000178
68. Roager H.M., Vogt J.K., Kristensen M., Hansen L.B.S., Ibrügger S., Mærkedahl R.B., Bahl M.I., Lind M.V., Nielsen R.L., Frøkiær H.. **Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: A randomised cross-over trial**. *Gut* (2019.0) **68** 83-93. DOI: 10.1136/gutjnl-2017-314786
69. Elvers K.T., Wilson V.J., Hammond A., Duncan L., Huntley A.L., Hay A.D., Van Der Werf E.T.. **Antibiotic-induced changes in the human gut microbiota for the most commonly prescribed antibiotics in primary care in the UK: A systematic review**. *BMJ open* (2020.0) **10** e035677. DOI: 10.1136/bmjopen-2019-035677
70. Leung A., Daskaklopoulou S., Dasgupta K., McBrien K., Butalia S., Zarnke K., Nerenberg K., Harris K., Nakhla M., Cloutier L.. **Hypertension Canada’s 2017 guidelines for diagnosis, risk assessment, prevention, and treatment of hypertension in adults**. *Can. J. Cardiol.* (2017.0) **33** 557-576. DOI: 10.1016/j.cjca.2017.03.005
71. Logue C., Dowey L.R.C., Strain J.J., Verhagen H., McClean S., Gallagher A.M.. **Application of Liquid Chromatography-Tandem Mass Spectrometry To Determine Urinary Concentrations of Five Commonly Used Low-Calorie Sweeteners: A Novel Biomarker Approach for Assessing Recent Intakes?**. *J. Agric. Food Chem.* (2017.0) **65** 4516-4525. DOI: 10.1021/acs.jafc.7b00404
72. Hales C., Randle P.. **Effects of low-carbohydrate diet and diabetes mellitus on plasma concentrations of glucose, non-esterified fatty acid, and insulin during oral glucose-tolerance tests**. *Lancet* (1963.0) **1** 790-794. DOI: 10.1016/S0140-6736(63)91501-0
73. Wilkerson H.L., Butler F.K., Francis J.O.S.. **The effect of prior carbohydrate intake on the oral glucose tolerance test**. *Diabetes* (1960.0) **9** 386-391. DOI: 10.2337/diab.9.5.386
74. 74.
WHO Study Group on Diabetes Mellitus & World Health Organization
Diabetes Mellitus: Report of a WHO Study Group [meeting held in Geneva from 11 to 16 February 1985]World Health OrganizationGeneva, Switzerland1985793. *Diabetes Mellitus: Report of a WHO Study Group [meeting held in Geneva from 11 to 16 February 1985]* (1985.0) 7-93
75. Lozupone C., Knight R.. **UniFrac: A new phylogenetic method for comparing microbial communities**. *Appl. Environ. Microbiol.* (2005.0) **71** 8228-8235. DOI: 10.1128/AEM.71.12.8228-8235.2005
76. Bolyen E., Rideout J.R., Dillon M.R., Bokulich N.A., Abnet C.C., Al-Ghalith G.A., Alexander H., Alm E.J., Arumugam M., Asnicar F.. **Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2**. *Nat. Biotechnol.* (2019.0) **37** 852-857. DOI: 10.1038/s41587-019-0209-9
77. Matsuda M., DeFronzo R.A.. **Insulin sensitivity indices obtained from oral glucose tolerance testing: Comparison with the euglycemic insulin clamp**. *Diabetes Care* (1999.0) **22** 1462-1470. DOI: 10.2337/diacare.22.9.1462
78. Bedogni G., Bellentani S., Miglioli L., Masutti F., Passalacqua M., Castiglione A., Tiribelli C.. **The Fatty Liver Index: A simple and accurate predictor of hepatic steatosis in the general population**. *BMC Gastroenterol.* (2006.0) **6** 1-7. DOI: 10.1186/1471-230X-6-33
79. Lacombe R.S., Giuliano V., Chouinard-Watkins R., Bazinet R.P.J.L.. **Natural Abundance Carbon Isotopic Analysis Indicates the Equal Contribution of Local Synthesis and Plasma Uptake to Palmitate Levels in the Mouse Brain**. *Lipids* (2018.0) **53** 481-490. DOI: 10.1002/lipd.12046
80. Johner S.A., Libuda L., Shi L., Retzlaff A., Joslowski G., Remer T.. **Urinary fructose: A potential biomarker for dietary fructose intake in children**. *Eur. J. Clin. Nutr.* (2010.0) **64** 1365-1370. DOI: 10.1038/ejcn.2010.160
81. Matthews D.R., Hosker J., Rudenski A., Naylor B., Treacher D., Turner R.. **Homeostasis model assessment: Insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man**. *Diabetologia* (1985.0) **28** 412-419. DOI: 10.1007/BF00280883
82. Salgado A., Carvalho L., Oliveira A., Santos V., Vieira J., Parise E.. **Insulin resistance index (HOMA-IR) in the differentiation of patients with non-alcoholic fatty liver disease and healthy individuals**. *Arq. Gastroenterol.* (2010.0) **47** 165-169. DOI: 10.1590/S0004-28032010000200009
83. Retnakaran R., Shen S., Hanley A.J., Vuksan V., Hamilton J.K., Zinman B.. **Hyperbolic relationship between insulin secretion and sensitivity on oral glucose tolerance test**. *Obesity* (2008.0) **16** 1901-1907. DOI: 10.1038/oby.2008.307
84. Retnakaran R., Qi Y., Goran M., Hamilton J.. **Evaluation of proposed oral disposition index measures in relation to the actual disposition index**. *Diabet. Med.* (2009.0) **26** 1198-1203. DOI: 10.1111/j.1464-5491.2009.02841.x
85. Kahn S.E., Montgomery B., Howell W., Ligueros-Saylan M., Hsu C.-H., Devineni D., McLeod J.F., Horowitz A., Foley J.E.. **Importance of early phase insulin secretion to intravenous glucose tolerance in subjects with type 2 diabetes mellitus**. *J. Clin. Endocrinol. Metab.* (2001.0) **86** 5824-5829. DOI: 10.1210/jcem.86.12.8105
86. Phillips D., Clark P., Hales C., Osmond C.. **Understanding oral glucose tolerance: Comparison of glucose or insulin measurements during the oral glucose tolerance test with specific measurements of insulin resistance and insulin secretion**. *Diabet. Med.* (1994.0) **11** 286-292. DOI: 10.1111/j.1464-5491.1994.tb00273.x
87. Langille M.G., Zaneveld J., Caporaso J.G., McDonald D., Knights D., Reyes J.A., Clemente J.C., Burkepile D.E., Vega Thurber R.L., Knight R.. **Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences**. *Nat. Biotechnol.* (2013.0) **31** 814-821. DOI: 10.1038/nbt.2676
88. Wolever T.M., Chiasson J.-L., Csima A., Hunt J.A., Palmason C., Ross S.A., Ryan E.A.. **Variation of postprandial plasma glucose, palatability, and symptoms associated with a standardized mixed test meal versus 75 g oral glucose**. *Diabetes Care* (1998.0) **21** 336-340. DOI: 10.2337/diacare.21.3.336
89. **Draft Guidance Document on Food Health Claims Related to the Reduction in Post-Prandial Glycaemic Response**
90. Maersk M., Belza A., Stødkilde-Jørgensen H., Ringgaard S., Chabanova E., Thomsen H., Pedersen S.B., Astrup A., Richelsen B.. **Sucrose-sweetened beverages increase fat storage in the liver, muscle, and visceral fat depot: A 6-mo randomized intervention study**. *Am. J. Clin. Nutr.* (2012.0) **95** 283-289. DOI: 10.3945/ajcn.111.022533
91. Braunstein C.R., Noronha J.C., Glenn A.J., Viguiliouk E., Noseworthy R., Khan T.A., Au-Yeung F., Blanco Mejia S., Wolever T.M., Josse R.G.. **A double-blind, randomized controlled, acute feeding equivalence trial of small, catalytic doses of fructose and allulose on postprandial blood glucose metabolism in healthy participants: The Fructose and Allulose Catalytic Effects (FACE) Trial**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10060750
92. Campos V., Despland C., Brandejsky V., Kreis R., Schneiter P., Chiolero A., Boesch C., Tappy L.. **Sugar- and artificially sweetened beverages and intrahepatic fat: A randomized controlled trial**. *Obesity* (2015.0) **23** 2335-2339. DOI: 10.1002/oby.21310
93. Benjamini Y., Hochberg Y.. **Controlling the false discovery rate: A practical and powerful approach to multiple testing**. *J. R. Stat. Soc. Ser. B* (1995.0) **57** 289-300. DOI: 10.1111/j.2517-6161.1995.tb02031.x
94. Benjamini Y., Drai D., Elmer G., Kafkafi N., Golani I.. **Controlling the false discovery rate in behavior genetics research**. *Behav. Brain Res.* (2001.0) **125** 279-284. DOI: 10.1016/S0166-4328(01)00297-2
95. Benjamini Y., Yekutieli D.. **The control of the false discovery rate in multiple testing under dependency**. *Ann. Statist.* (2001.0) **29** 1165-1188. DOI: 10.1214/aos/1013699998
96. **Multiple Endpoints in Clinical Trials: Guidance for Industry**
97. Tran N.L., Barraj L.M., Hearty A.P., Jack M.M.. **Tiered intake assessment for low-and no-calorie sweeteners in beverages**. *Food Addit. Contam. Part A* (2021.0) **38** 208-222. DOI: 10.1080/19440049.2020.1843717
98. Dunford E.K., Taillie L.S., Miles D.R., Eyles H., Tolentino-Mayo L., Ng S.W.J.N.. **Non-Nutritive Sweeteners in the Packaged Food Supply—An Assessment across 4 Countries**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10020257
99. Garriguet D.. **Beverage consumption of Canadian adults**. *Health Rep.* (2008.0) **19** 23. PMID: 19226924
100. **Soft Drinks Market Analyzer Database**
101. Swithers S.E.. **Artificial sweeteners produce the counterintuitive effect of inducing metabolic derangements**. *Trends Endocrinol. Metab.* (2013.0) **24** 431-441. DOI: 10.1016/j.tem.2013.05.005
102. Casals-Pascual C., González A., Vázquez-Baeza Y., Song S.J., Jiang L., Knight R.. **Microbial diversity in clinical microbiome studies: Sample size and statistical power considerations**. *Gastroenterology* (2020.0) **158** 1524-1528. DOI: 10.1053/j.gastro.2019.11.305
103. Narayana J.K., Mac Aogáin M., Goh W.W.B., Xia K., Tsaneva-Atanasova K., Chotirmall S.H.. **Mathematical-based microbiome analytics for clinical translation**. *Comput. Struct. Biotechnol. J.* (2021.0) **19** 6272-6281. DOI: 10.1016/j.csbj.2021.11.029
104. Diana Sherifali R., Robyn L.. **Diabetes Canada clinical practice guidelines expert committee**. *Can. J. Diabet.* (2018.0) **42** S6-S9
105. Sedgwick P., Greenwood N.. **Understanding the Hawthorne effect**. *BMJ* (2015.0) **351** h4672. DOI: 10.1136/bmj.h4672
106. **Health Fact Sheets. Overweight and Obese Adults**. (2018.0)
107. Jones A.C., Kirkpatrick S.I., Hammond D.. **Beverage consumption and energy intake among Canadians: Analyses of 2004 and 2015 national dietary intake data**. *Nutr. J.* (2019.0) **18** 1-14. DOI: 10.1186/s12937-019-0488-5
108. Nikpartow N., Danyliw A.D., Whiting S.J., Lim H.J., Vatanparast H.J.P.h.n.. **Beverage consumption patterns of Canadian adults aged 19 to 65 years**. *Public Health Nutr.* (2012.0) **15** 2175-2184. DOI: 10.1017/S1368980012003898
109. Brancati F.L., Whelton P.K., Kuller L.H., Klag M.J.. **Diabetes mellitus, race, and socioeconomic status a population-based study**. *Ann. Epidemiol.* (1996.0) **6** 67-73. DOI: 10.1016/1047-2797(95)00095-X
110. **Diabetes Care Gaps and Disparities in Canada**
111. Singh R.K., Chang H.-W., Yan D., Lee K.M., Ucmak D., Wong K., Abrouk M., Farahnik B., Nakamura M., Zhu T.H.. **Influence of diet on the gut microbiome and implications for human health**. *J. Transl. Med.* (2017.0) **15** 1-17. DOI: 10.1186/s12967-017-1175-y
112. Fassarella M., Blaak E.E., Penders J., Nauta A., Smidt H., Zoetendal E.G.. **Gut microbiome stability and resilience: Elucidating the response to perturbations in order to modulate gut health**. *Gut* (2021.0) **70** 595-605. DOI: 10.1136/gutjnl-2020-321747
113. **Guideline: Sugars Intake for Adults and Children**
114. **Summary of Guiding Principles and Recommendations**
115. **Diabetes Canada’s Position on Sugars**
116. 116.
U.S. Department of Health and Human Services and U.S. Department of Agriculture
2015—2020 Dietary Guidelines for Americans. 8th Edition.Available online: https://health.gov/dietaryguidelines/2015/guidelines/(accessed on 20 January 2022). *2015—2020 Dietary Guidelines for Americans. 8th Edition.*
|
---
title: Phytochemical Profile of Trigonella caerulea (Blue Fenugreek) Herb and Quantification
of Aroma-Determining Constituents
authors:
- Arpine Ayvazyan
- Thomas Stegemann
- Mayra Galarza Pérez
- Manuel Pramsohler
- Serhat Sezai Çiçek
journal: Plants
year: 2023
pmcid: PMC10005085
doi: 10.3390/plants12051154
license: CC BY 4.0
---
# Phytochemical Profile of Trigonella caerulea (Blue Fenugreek) Herb and Quantification of Aroma-Determining Constituents
## Abstract
The herb of *Trigonella caerulea* (Fabaceae), commonly known as blue fenugreek, is used for the production of traditional cheese and bread varieties in the Alpine region. Despite its frequent consumption, only one study so far has focused on the constituent pattern of blue fenugreek, revealing qualitative information on some flavor-determining constituents. However, with regard to the volatile constituents present in the herb, the applied methods were insufficient and did not take relevant terpenoids into account. In the present study, we analyzed the phytochemical composition of T. caerulea herb applying a set of analytical methods, such as headspace-GC, GC-MS, LC-MS, and NMR spectroscopy. We thus determined the most dominant primary and specialized metabolites and assessed the fatty acid profile as well as the amounts of taste-relevant α-keto acids. In addition, eleven volatiles were quantified, of which tiglic aldehyde, phenylacetaldehyde, methyl benzoate, n-hexanal, and trans-menthone were identified as most significantly contributing to the aroma of blue fenugreek. Moreover, pinitol was found accumulated in the herb, whereas preparative works led to the isolation of six flavonol glycosides. Hence, our study shows a detailed analysis of the phytochemical profile of blue fenugreek and provides an explanation for its characteristic aroma and its health-beneficial effects.
## 1. Introduction
Trigonella caerulea (L.) Ser. ( Fabaceae subfam. Papilionoideae), commonly known as blue fenugreek, is a flowering annual cultivated in the Alps and mountains of eastern and southeastern Europe [1,2]. Unlike the closely related fenugreek (T. foenum-graecum), which is a major component in most curry mixtures and therefore well-known and consumed around the world, T. caerulea is of regional importance [3,4,5]. Blue fenugreek seeds are used as a spice in Georgian cuisine and the Caucasus region, whereas the young seedlings are eaten with oil and salt [4,5,6,7]. In Switzerland, T. caerulea herb is added to the traditional Schabziger cheese in amounts of 2.0 to $2.5\%$, while it is mixed with flour for the flavoring of bread in South Tyrol, the German speaking part of Northern Italy [4,8]. For bread production, 2 g of blue fenugreek herb are added to 500 g of flour [9]. In South Tyrol, blue fenugreek is stored for a period of up to six months by many traditional farmers before being made commercially available. This measure should increase the aromatic flavor of the herb and the bread made thereof, respectively. The traditional usage of blue fenugreek in the *Alps is* reflected in its German name, where it is referred to as “Schabzigerklee” or “Brotklee”, but also called by the ethically questionable term “Zigainerkraut”, which means herb of the gypsies.
The few phytochemical studies on T. caerulea mostly focused on the seeds, which were analyzed together with the seeds of T. foenum-graecum and other Trigonella species [3,10,11]. Brenac and Sauvaire [10] investigated sterols and steroidal sapogenins of seven Trigonella species and sitosterol was found to be the major sterol in blue fenugreek seeds, with more than $50\%$ of the total sterol content (2 mg/g dry weight). The major steroidal sapogenin was diosgenin, with approximately two-thirds of the total content (8 mg/g dry weight), which is of interest as diosgenin is used for the synthesis of cortisol. Dinu et al. [ 11] investigated the fatty acid profile and determined α-linolenic acid, linoleic acid, and palmitic acid as the dominant compounds in T. caerulea seeds. In the same study, T. foenum-graecum seeds were also analyzed, showing a very similar fatty acid pattern. The most recent study on the seeds was conducted by Farag et al. [ 3], who combined ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS), and gas chromatography coupled to mass spectrometry (GC-MS) for the analysis of three Trigonella species. GC-MS analysis confirmed the aforementioned observed fatty acids, showing several amino acids, keto acids, and other organic acids in blue fenugreek seeds. Liquid chromatography coupled to mass spectrometry (LC-MS) analysis revealed a plethora of flavonoids and saponins, which were, however, not clearly defined. Neither was the origin of these compounds evident, as a seed mixture of three different Trigonella species was used.
In contrast to the seeds, the blue fenugreek herb was only investigated once for its phytochemical composition, with the focus on the flavor-determining constituents [8]. Thereby, different chromatographic methods were applied for evaluating the profiles of short-chain fatty acids, aldehydes, and α-keto acids. Of the latter compound class, four constituents were defined as key components, namely pyruvic acid, α-ketoglutaric acid, α-ketoisovaleric acid, and α-ketoisocapronic acid. A recent study quantified the amount of oxalic acid in blue fenugreek herb and other edible plants, such as licorice, sweet potato, okra, cocoa, and different types of legumes [5], and the amount of total oxalate was determined as $1.25\%$ in the dried herb, among which $0.07\%$ were soluble oxalate.
With regard to the characteristic aroma of blue fenugreek, the flavor-determining role of α-keto acids is reasonable, as these compounds are also responsible for the aroma of fermented food products [12]. However, with respect to the volatile constituents, the application of thin layer chromatography, and the limited focus on only aldehydes falls short as also terpenoid constituents can have a great impact on the aroma of plants [8]. Thus, our aim was to investigate the metabolic profile of blue fenugreek herb with a wider scope and up-to-date analytical methods in order to eventually identify additional flavor-contributing constituents. In addition, other specialized (secondary) plant metabolites, e.g., flavonoids, should also be taken into consideration. This is even more important as blue fenugreek is used as a dietary supplement (in combination with kale) and the respective product is standardized on the amount on flavonoid glycosides (>12 mg/g extract) [13].
## 2.1.1. Polar Metabolites
Polar primary metabolites were analyzed using a metabolomics approach [14]. The polar fraction of a chloroform-methanol-water extract was investigated by means of GC-MS and untargeted profiling. An example chromatogram and compound list of the commercial samples T. caerulea sample 1 to T. caerulea sample 3 (TC1–TC3) are provided in the supporting information (Figure S1, Table S1). Two peaks were found dominating in the chromatogram, namely those of sucrose and the methylated sugar alcohol pinitol (Figure 1). In addition, noteworthy signals were detected for fructose and glucose, the sugar alcohols mannitol, myo-inositol, and glycerol, as well as for malic acid, malonic acid, phosphate, and succinic acid.
## 2.1.2. Fatty Acid Profile
Fatty acid analysis was accomplished by esterification with methanol and subsequent gas chromatography coupled to flame ionization detection and mass spectrometry (GC-FID/MS) analysis. Palmitic acid and α-linolenic acid were found to be the most abundant fatty acids (Figure 2). Moreover, significant amounts of linoleic acid and stearic acid were found in the herb of blue fenugreek, as well as small amounts of arachidic acid, myristic acid, and margaric acid. A sample chromatogram is depicted in Figure S2 and results are given in Table 1.
## 2.1.3. Quantification of α-Keto Acids
Using ultra-high performance chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) and multiple reaction monitoring, we determined the contents of ten α-keto acids (Figure 3) after conversion into their O-(2,3,4,5,6-pentafluorobenzyl)oxime derivatives by the method described by Noguchi et al. [ 15]. Among the thus quantified compounds were also the proposed key components pyruvic acid, α-ketoglutaric acid, α-ketoisovaleric acid, and α-ketoisocapronic acid (Figure S3). Additional relevant α-keto acids were retrieved by means of GC-MS and the method of Lee et al. [ 16]. A total of eleven α-keto acids were found in considerable amounts and commercial standards were therefore obtained. Of the eleven standards, however, oxaloacetic acid did not show useful results and had to be excluded.
The results of the three commercial samples TC1–TC3 are shown in Table 2. In all three samples, glyoxylic acid and α-ketoglutaric acid were the two dominating α-keto acids with amounts of 40 to 86 mg/kg dry plant material. Moreover, high amounts of pyruvic acid (8.1 to 14 mg/kg) were determined, whereas the remaining seven α-keto acids quantified in this study showed concentrations of 0.4 to 4.3 mg/kg. The total content of α-keto acids was found between 115 (TC3) and 185 (TC2) mg/kg, thus showing distinct variations. However, the three samples also differed with regard to their α-keto acid pattern, i.e., α-ketobutyric acid, showing 3 to 5 times higher amounts in sample TC2, or the ratio of the two major components ranging from 1.8 (TC1) to 0.8 (TC3).
## 2.2.1. Quantification of Volatile Constituents in Commercial Samples
Volatile constituents of blue fenugreek herb were quantified using headspace-GC-MS/MS and external calibration with eleven compounds, which were found in relevant concentrations (Figure 4 and Figure S4).
The results are given in Table 3 and show three dominating constituents in all three samples, namely tiglic aldehyde, trans-menthone, and camphor. The amounts on camphor (10 mg/kg) and trans-menthone (8.3 to 8.6 mg/kg) were comparable in all three samples, whereas the concentration on tiglic aldehyde was differing significantly with 8.4, 17, and 6.8 mg/kg, respectively. Other compounds found in considerable amounts were benzaldehyde, phenylacetaldeyhde (hyacinthin), safranal, and bornyl acetate, being present in amounts of 1 mg/kg or above.
Moreover, all quantified compounds (except p-cymene in samples TC1 and TC2) showed concentrations above their respective olfactory threshold values (Table 3), and thus contribute to the aroma of blue fenugreek herb. Five compounds were found to predominantly affect the odor of blue fenugreek, namely the aldehydes tiglic aldehyde, phenylacetaldehyde, and n-hexanal, as well as methyl benzoate and trans-menthone (Table 4). Other compounds with values significantly above their olfactory thresholds were camphor, menthol, and benzaldehyde, as well as p-cymene in sample TC3.
## 2.2.2. Isolation and Identification of Flavonoids
In order to detect eventual non-volatile secondary metabolites, ultra-high performance liquid chromatography coupled to photodiode array detection (UHPLC-PDA) analysis of a crude methanol extract of blue fenugreek herb was performed (Figure S5). Thereby, several flavonoids were detected, showing characteristic UV spectra with absorption maxima at 249 to 265 nm and at 347 to 354 nm, respectively. Additional UHPLC-MS/MS analysis revealed the two major flavonoids to be triglycosides, whereas the minor components showed two sugar moieties. The fragmentation pattern of the flavonoids revealed both hexoside as well as deoxyhexoside moieties, while the remaining aglycone fragments pointed at kaempferol and quercetin scaffolds. As no absolute determination of the structures could be performed without isolating the respective constituents, larger amounts of plant material were extracted and made subjects for a preparative phytochemical work-up.
Repeated chromatographic separation using liquid-liquid fractionation, vacuum liquid chromatography, size exclusion chromatography, and semi-preparative HPLC led to the isolation of the two major flavonoids (1 and 2) along with four minor constituents (3 to 6). After 1D and 2D nuclear magnetic resonance (NMR) spectroscopic experiments and comparison of the acquired data with the data obtained from the literature, the isolated compounds were identified as quercetin 3-O-(2″-O-α-L-rhamnopyranosyl)-β-D-glucopyranoside 7-O-β-D-rhamnopyranoside [1] [25], kaempferol 3-O-(2″-O-α-L-rhamnopyranosyl)-β-D-glucopyranoside 7-O-β-D-rhamnopyranoside [2] [25], quercetin 3-O-(2″-O-α-L-rhamnopyranosyl)-β-D glucopyranoside [3] [26,27], quercetin 3-O-β-D-glucopyranoside 7-O-β-D-rhamnopyranoside [4] [25,28], kaempferol 3-O-(2″-O-α-L-rhamnopyranosyl)-β-D-glucopyranoside [5] [26,29], and kaempferol 3-O-β-D glucopyranoside 7-O-β-D-rhamnopyranoside [6] [25] (Figure 5, Tables S2–S7).
## 3. Discussion
The first interesting finding of our detailed phytochemical analyses was revealed using metabolic profiling, by which we discovered pinitol as one of two major carbohydrates (Figure S1, Table S1). The relative concentration was more or less the same in all of the investigated samples and comparable to that of sucrose, the second highly abundant carbohydrate in blue fenugreek herb. A study on the occurrence and accumulation on pinitol in T. foenum-graecum found the compound to undergo seasonal variation, thereby showing an increase in content in the leaves during the generative period of plant vegetation [30]. In contrast, the content in the stems remained stable for the whole time of the investigations (56 to 126 days after sowing). Eighty-six days after sowing, which was comparable to the harvesting date of our study, T. foenum-graecum showed about half of the amount of sucrose and the same level as glucose in the leaves. Although no quantification of the polar metabolites was conducted in our study, at the same time point, the amount of pinitol in T. caerulea was significantly higher than that of glucose, and rather comparable to the level of sucrose. Apart from the role of pinitol in plant carbohydrate metabolism, the compound was also found to exhibit anti-hyperglycemic effects in vivo, which seem to derive from insulin-sensitizing or insulin-mediating properties, respectively [31,32,33].
Other compounds with health-beneficial effects present in blue fenugreek herb were linoleic acid and α-linolenic acid. In all of the investigated samples, the latter compound was found to be one of the two dominant fatty acids, which was only surpassed by palmitic acid (Figure S2, Table 1). The fatty acid composition was also the topic of a comparison study of fenugreek and blue fenugreek seeds, which reported high amounts of linoleic acid and α-linolenic acid in the two species and much lower amounts of palmitic acid [11].
In the same study, the occurrence of flavonoids was also proposed, however, with no details on eventual flavonoid types or sugar moieties. A metabolite profiling study on the seeds of three Trigonella species, also showed the occurrence of flavonoids [3], namely C- and O-glycosides of apigenin and luteolin, respectively. However, no assignment of the sugar moieties or eventual linkages were given, indicating the limitations of the metabolomics approach for many secondary metabolites. Furthermore, even more interestingly, no mentions on the occurrence of flavonol derivatives were made, and thus on the flavonoid types detected in our study. Therefore, different scaffolds seem to be present in the seeds and the herb of T. caerulea, with the herb containing predominantly di- and triglycosides of kaempferol and quercetin (Figure 5). This is of interest as a commercial dietary supplement containing blue fenugreek and kale leaves (4:1) is standardized on the content of flavonoids [13], even though no reports on the flavonoid composition of T. carulea herb have been made so far. This preparation, a hydroethanolic ($36\%$) extract, is intended to prevent skin aging by the antioxidative properties of its ingredients, which was partly demonstrated in a recent study [13]. The identification of the major flavonoids in blue fenugreek by our work might lead to the compounds responsible for the skin-protecting effect and thus lay the basis for future compound-related investigations.
Referring to the study of Ney [8], which so far was the only detailed phytochemical investigation of blue fenugreek herb, the author reported pyruvic acid, α-ketoglutaric acid, α-ketoisovaleric acid, and α-ketoisocaproic acid as being key components of T. caerulea herb. Ney [8] determined the α-keto acids after a reduction to the respective amino acids and subsequent ion exchange chromatography. In our work, we chose the conversion of the α-keto acids to their O-(2,3,4,5,6-pentafluorobenzyl)oxime derivatives and quantification via LC-MS/MS (Figure S3, Table 2). We thus quantified most of the α-keto acids described by Ney [8], including the supposed key components. Of those, pyruvic acid and even more α-ketoglutaric acid were indeed found in high amounts in all of the investigated samples. However, α-ketoisovaleric acid and α-ketoisocaproic acid were present in lower amounts and in the range of other keto acids, such as α-ketobutyric acid, α-ketovaleric acid, and α-ketoanteisocaproic acid. In addition, we found high concentrations of glyoxylic acid, being the major α-keto acid in two of three commercial samples (Table 2).
Even more differing than the results of the α-keto acids, were our findings on the aldehyde composition. Using headspace GC-MS/MS, we determined a completely different profile of aldehydes than Ney [8], who was using thin layer chromatography after conversion to the respective dinitrophenylhydrazone derivatives. With tiglic aldehyde, n-hexanal, benzaldehyde, phenylacetaldehyde, and safranal, we identified five aldehydes with concentrations above 1 mg/kg in the dried herb (Table 3), which were not mentioned by Ney [8]. Out of these, tiglic aldehyde, phenylacetaldehyde, and n-hexanal were found to have a great impact on the aroma of blue fenugreek herb (Table 4). Other compounds clearly affecting the flavor of blue fenugreek were methyl benzoate and trans-menthone. With odor activity values above fifty, these five components should also play a role in the aroma of the traditional Schabziger cheese, for which blue fenugreek herb is added in concentrations of 2.0 to $2.5\%$ [8]. For the flavoring of bread in South Tyrol, instead, only tiglic aldehyde and phenylacetaldehyde would reach the respective concentrations [9]. Apart from trans-menthone, we also determined high amounts of the monoterpene camphor, with a concentration of around 10 mg/kg in all measured samples (Table 3).
Certainly, the explanation of plant aromas by odor activity values is oversimplified as olfactory thresholds were determined in aqueous solutions and do not necessarily show the same values in other matrices. In addition, synergistic and masking effects can affect flavor perception [17]. Still, the findings of our study contribute to the knowledge on blue fenugreek herb and thereby provide an explanation for its characteristic aroma. As the aroma of the herb is said to be increasing over time, further studies will concentrate on eventual processes occurring during storage.
With regard to the initially mentioned high amounts on oxalic acid ($1.25\%$) and the recommended maximum daily intake of no more than 100 mg oxalates for people affected by hyperoxaluria [5], 320 to 400 g of Schabziger cheese or bread made out of 2 kg blue fenugreek-containing flour would therefore have to be consumed. Although these amounts are rather high, the intake of oxalate for people at risk must not have to be underestimated, especially when additional oxalate-rich foods (spinach, rhubarb), teas (licorice), or dietary supplements are consumed.
To summarize, with the results of our study, new and significant knowledge on the constituent pattern of T. caerulea, a plant of growing popularity, is presented. Our findings give explanations for the smell and taste of blue fenugreek and thus for its culinary use. Moreover, with the identification of the major flavonoids, our study reveals the compounds responsible for the supposed beneficial antioxidant effects in nutraceutical preparations.
## 4.1. Plant Material and Chemical Reagents
Dried plant material for isolation and analytical studies (Südtiroler Brotklee Zigainerkraut, Lot $\frac{17}{2019}$) was obtained from Feichter Bernhard (Toblach, Italy). Additional samples for analytical studies were purchased from Alfred Galke GmbH (Bad Grund, Germany; Schabziger Bio geschnitten, Lot 34060) and from Lebensbaum (Diepholz, Germany; Schabziger Klee geschnitten und getrocknet). Specimens of all three plant samples (TC1–TC3) are located at the Department of Pharmaceutical Biology in Kiel, Germany.
Pyruvic acid (sodium pyruvate, Lot A0393530) was obtained from ThermoFisher Scientific (Waltham, MA, United States), glyoxylic acid monohydrate (Lot WXBC8625V), 2-ketobutyric acid (Lot BCBW9441), α-ketoisocapronic acid (4-methyl-2-oxovaleric acid, Lot 0000101887), α-ketoglutaric acid (Lot BCBX6537), phenylpyruvic acid (Lot BCBV9597), 4-hydroxyphenylpyruvic acid (Lot BCCC9999), 2-oxovaleric acid (Lot BCCC2216), α-ketoanteisocapronic acid (3-methyl-2-oxopentanoic acid, Lot 19,897-8), α-ketoisovaleric acid (sodium 3-methyl-2-oxobutyrate, Lot 0000000796), oxaloacetic acid (Lot SLBQ4770V), tiglic aldehyde (Lot 06813CE), hexanal (Lot 02429AA), hyacinthin (Lot S21728-164), safranal (Lot 00526EE), cymene (Lot 01805AE), isobornyl acetate (Lot 1091784) menthone (Lot BCCF7127), and O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride (Lot BCCD2221) were purchased from Sigma-Aldrich (St. Louis, MO, United States). N-methyl-N-(trimethylsilyl)-trifluoroacetaminde (MSTFA, Lot 417263702), benzaldehyde (Lot 50696AJ) and sodium hydroxide (Lot 6771) were obtained from Carl Roth GmbH (Karlsruhe, Germany).
LC-MS grade formic acid, Diaion HP-20, and Sephadex LH-20 were purchased from Sigma Aldrich. Silica gel (40–63 µm) for column chromatography, TLC plates (silica gel 60 F254), acetonitrile and water (both of LC-MS grade), gradient grade methanol, and other (analytical grade) solvents were obtained from VWR International GmbH (Darmstadt, Germany). Water used for isolation was doubly distilled in-house. Dimethyl sulfoxide-d6 ($99.80\%$, Lot S1051, Batch 0119E) for NMR spectroscopy was purchased from Euriso-top GmbH, Saarbrücken, Germany, and conventional 5 mm NMR sample tubes were obtained from Rototec-Spintec GmbH (Griesheim, Germany).
## 4.2. General Experimental Procedures
Thin layer chromatography for the detection of flavonoids was performed using ethyl acetate–water–acetic acid–formic acid (20:5.4:2.2:2.2) as the eluent and diphenylboryloxy ethylamine–macrogol as the spraying reagent. Pressurized solvent extraction was performed with a Speed Extractor E961 and preparative MPLC was carried out with a Buchi PrepChrom C-700 chromatograph and a Buchi PrepChrom C18 column (250 × 30.0 mm, 15 m particle size) (Büchi, Flawil, Switzerland). Semi-preparative HPLC was accomplished using a Waters Alliance e2695 separations module with an Alliance 2998 photodiode array, and a waters fraction collector (WFC) III (Waters, Milford, MA, USA) using a VP Nucleodur C18 column (250 × 10 mm, 5 µm particle size, Macherey-Nagel GmbH and Co. KG, Düren, Germany).
UHPLC-MS/MS analyses were carried out on a Shimadzu Nexera 2 liquid chromatograph connected to an LC-MS triple quadrupole mass spectrometer using electrospray ionization (Shimadzu, Kyoto, Japan). A Phenomenex Luna Omega C18 column (100 × 2.1 mm, 1.6 µm particle size, Phenomenex, Aschaffenburg, Germany) was employed for the analysis of extracts, fractions, and pure compounds during isolation. Quantification of α-keto acids was accomplished with a Phenomenex Kinetex Biphenyl column (100 × 2.1 mm, 1.7 µm particle size). Headspace GC-MS/MS analyses were performed on a Trace 1310 gas chromatograph equipped with split/splitless (SSL) and programmable temperature vaporizer (PTV) inlets and a TSQ Duo mass spectrometer and the GC-MS instrument used for fatty acid and polar metabolite analysis was a Focus GC gas chromatograph equipped with an SSL inlet, a flame ionization detector and an ISQ mass spectrometer (ThermoFisher Scientific, Waltham, MA, USA). Column used for all GC analyses was a ThermoFisher TG-5SilMS (30 m × 0.25 mm × 0.25 µm). NMR spectra were recorded using a Bruker Avance III 400 NMR spectrometer (Bruker, Rheinstetten, Germany) operating at 400.33 MHz for the proton channel and at 100.66 MHz for the 13C channel by means of a 5 mm PABBO broad-band probe with a z gradient unit.
## 4.3. Extraction and Isolation of Secondary Metabolites
A total of 855 g dried and ground plant material was extracted 5 times with a mixture of acetone-water (80:20) using ultra-sonication for 15 min and subsequent maceration for 24 h each. After combining the extracts, the acetone was evaporated and the remaining water was subsequently extracted with ethyl acetate followed by 1-butanol to give 20.5 g and 25.2 g of extract, respectively, after evaporation of the solvents. The remaining water layer yielded 71.2 g. The 1-butanol phase was dissolved in 400 mL water and chromatographed over Diaion HP-20 material, using subsequent elution with water, methanol $25\%$ (400 mL), methanol $50\%$ (600 mL), methanol $75\%$ (600 mL) and methanol $100\%$ (600 mL).
The methanol $25\%$ fraction (1.06 g) was subjected to Sephadex LH-20 using methanol-water (50:50) as eluent. Of the resulting 6 fractions, fraction 2 (0.20 g) was chromatographed with preparative MPLC using water (A) and methanol (B) as solvents with the following gradient: $20\%$ B to $50\%$ B in 60 min, and to $95\%$ B in 30 min. From the obtained 6 fractions (2A–2F), fraction 2B yielded 11.3 mg of compound 1 and fraction 2D yielded 9.5 mg of compound 2, respectively.
The methanol $50\%$ fraction (2.40 g) was also chromatographed with Sephadex LH-20 giving 10 fractions, of which fractions 8 and 9 were further separated by semipreparative HPLC using water (A) and acetonitrile (B) as solvents with the following gradient $15\%$ B to $30\%$ B in 30 min, and to $50\%$ B in 40 min. Thus, 10.1 mg of compound 3 and 10.5 mg of compound 5 were obtained.
The methanol $75\%$ fraction (2.57 g) was as well-subjected to Sephadex LH-20 using methanol-water (50:50) as an eluent and obtaining 12 fractions. Fractions 9 and 10 were chromatographed with semipreparative HPLC in the same manner as before, yielding 15.6 mg of compound 4 and 12.8 mg of compound 6.
## 4.4.1. Analysis of Polar Metabolites
Analysis of polar metabolites was accomplished with GC-MS and the method adapted by Fiehn [14]. Briefly, 10 mg of plant material was suspended in 100 µL of water and vortexed for 5 min before adding 300 µL of methanol and 100 µL of chloroform. The extraction was incubated for 1 h over ice and 100 µL of the supernatant (water–methanol) was dried under a stream of nitrogen overnight. An amount of 100 µL of a solution of 20 mg/mL methoxylamine hydrochloride in pyridine was added to the dried extract and incubated at 30 °C for 60 min. Subsequently, 150 µL of MSTFA containing $1\%$ TMCS was added and the reaction was incubated for another 60 min at 45 °C. An amount of 1 µL of the derivatized sample was analyzed by GC-MS with the following parameters: 100 °C hold for 5 min, 25 °C/min to 160 °C hold 1 min, 10 °C/min to 300 °C hold for 12 min. MS parameters: full-scan 50–500 m/z. Compounds were identified using the NIST database version 2020.
## 4.4.2. Analysis of Fatty Acid Profile
A total of 50 mg of plant material was analyzed for the fatty acid composition by directly weighing into a 5 mL reaction tube. An amount of 1000 µL of $2.5\%$ H2SO4 in methanol was added and incubated at 80 °C for 1 h. After cooling at room temperature, 500 µL of n-hexane was added, followed by 1500 µL of saturated sodium chloride solution in water. The n-hexane phase was transferred into a 2 mL glass vial. An amount of 1 µL was directly injected into the GC-MS. The GC parameters were as follows: 50 °C for 5 min and heated with 5 °C/min to 160 °C hold 1 min, 5 °C/min to 300 °C and hold for 5 min. MS parameters: full-scan from 50–500 m/z, ion source temperature: 280 °C. Relative quantification was done under the same conditions using FID.
## 4.4.3. Analysis of Volatile Constituents
An amount of 100 mg of dried and ground plant material was directly weighed into a 20 mL headspace vial, 100 ng of internal standard (toluene-d5) was added, and the vial was incubated for 30 min at a temperature of 90 °C. An amount of 1000 µL of the headspace was taken with a heated syringe and injected into the GC-MS. The GC program was as follows: 35 °C hold 1 min, 5 °C/min to 120 °C and hold 1 min, 30 °C/min to 300 °C and hold 1 min. The MS parameters were as follows: 43–300 m/z scan with MS Source at 280 °C. Quantification of compounds was achieved by external calibration using original references standards. Odor activity values for each compound were calculated by dividing the obtained concentration by the respective olfactory activity threshold [34].
## 4.4.4. Analysis of α-Keto Acids
An amount of 1000 mg of dried and ground plant material was extracted using pressurized solvent extraction (70 °C, 100 bar, 1 min heat up, 5 min hold, 2 min discharge) using one cycle of n-hexane and three cycles of methanol, in principle, following the extraction procedure reported by Hidalgo et al. [ 12]. The methanol extract was evaporated to dryness and reconstituted in 1000 µL of methanol. Following the procedure of Noguchi et al. [ 15], which was used for quantitation, 5 µL of the solution (or standard solution) was mixed with 45 µL of a mixture of acetonitrile and $0.1\%$ (w/w) NaOH solution (1:1). An amount of 20 µL of this mixture was treated with a mixture of 10 mg/mL PFBHA and kept at room temperature for 30 min before adding 10 µL of acetone and 100 µL of acetonitrile-NaOH $0.1\%$ (w/w) solution (1:1). For qualitative analysis, the PFBOximes were further derivatized using MSTFA (after evaporation of the solvent) and analyzed using GC-MS/MS, following the method described by Lee et al. [ 16], thus retrieving the most relevant α-keto acids for quantitative analysis. For quantitation of α-keto acids, LC-MS/MS was applied in the multiple reaction mode using external calibration with α-keto acid standard solutions over a range of 0.9 to 600 µmol/L. Transitions used for quantification and respective collision energies are given in the supporting information (Figure S3).
## 5. Conclusions
Using a set of analytical techniques, we identified and quantified the key constituents responsible for the smell and taste of T. caerulea herb, which is used for flavoring bread and cheese in some regions of the Alps, and which has also become increasingly popular in other parts of Europe. After pyruvic acid and α-ketoglutaric acid were previously determined as important components for the taste of blue fenugreek, we proved glyoxylic acid as another relevant α-keto acid, showing the highest amount in two of the three measured samples. Apart from the importance of α-keto acids, we demonstrated the contribution of several volatiles to the aroma of T. caerulea herb, such as tiglic aldehyde, phenylacetaldehyde, methyl benzoate, n-hexanal, and trans-menthone. Thus, our study also provides an explanation for the characteristic smell of blue fenugreek herb. As a last outcome of our study, the major flavonoids present in T. caerulea herb were identified and, subsequently, the value-determining constituents of blue fenugreek nutraceutical preparations.
## References
1. 1.Trigonella caerulea (L.) SerAvailable online: http://www.worldfloraonline.org/taxon/wfo-0000214019(accessed on 9 February 2023)
2. Dangi R.S., Lagu M.D., Choudhary L.B., Ranjekar P.K., Gupta V.S.. **Assessment of Genetic Diversity in**. *BMC Plant Biol.* (2004.0) **4**. DOI: 10.1186/1471-2229-4-13
3. Farag M.A., Rasheed D.M., Kropf M., Heiss A.G.. **Metabolite Profiling in**. *Anal. Bioanal. Chem.* (2016.0) **408** 8065-8078. DOI: 10.1007/s00216-016-9910-4
4. Hanelt P.. *Mansfeld’s Encyclopedia of Agricultural and Horticultural Crops: (Except Ornamentals)* (2001.0) 864-866
5. Siener R., Seidler A., Hönow R.. **Oxalate-Rich Foods**. *Food Sci. Technol.* (2021.0) **41** 169-173. DOI: 10.1590/fst.10620
6. Rodov V., Vinokur Y., Gogia N., Chkhikvishvili I.D.. **Hydrophilic and Lipophilic Antioxidant Capacities of Georgian Spices for Meat and their Possible Health Implication**. *Georgian Med. News* (2010.0) **179** 61-65
7. Ranjbar M., Karamian R., Hajmoradi Z.. **Composition of the Essential Oil of**. *Chem. Nat. Compd.* (2009.0) **45** 116-117. DOI: 10.1007/s10600-009-9234-0
8. Ney K.H.. **Untersuchung des Aromas von Ziegerklee (**. *Gordian* (1986.0) **86** 9-10
9. Gasteiger H., Wieser G., Bachmann H.. *So Kocht Südtirol. Eine Kulinarische Reise von den Alpen in den Süden* (2003.0) 452
10. Brenac P., Sauvaire Y.. **Chemotaxonomic Value of Sterols and Steroidal Sapogenins in the Genus**. *Biochem. Syst. Ecol.* (1996.0) **24** 157-164. DOI: 10.1016/0305-1978(95)00105-0
11. Dinu M., Ancuceanu R., Ilie D.-C., Munteanu C., Aram C.. **A Comparative Morphological and Chemical Study of the Seeds from**. *Farmacia* (2013.0) **61** 13
12. Hidalgo F.J., Navarro J.L., Delgado R.M., Zamora R.. **Determination of α-Keto Acids in Pork Meat and Iberian Ham via Tandem Mass Spectrometry**. *Food Chem.* (2013.0) **140** 183-188. DOI: 10.1016/j.foodchem.2013.02.052
13. Kappler K., Grothe T., Srivastava S., Jagtap M.. **Evaluation of the Efficacy and Safety of Blue Fenugreek Kale Extract on Skin Health and Aging: In-vitro and Clinical Evidences**. *Clin. Cosmet. Investig. Dermatol.* (2022.0) **15** 2051-2064. DOI: 10.2147/CCID.S368576
14. Fiehn O.. **Metabolomics by Gas Chromatography–Mass Spectrometry: Combined Targeted and Untargeted Profiling**. *Curr. Protoc. Mol. Biol.* (2016.0) **114** 30.4.1-30.4.32. DOI: 10.1002/0471142727.mb3004s114
15. Noguchi K., Mizukoshi T., Miyano H., Yamada N.. **Development of a new LC-MS/MS method for the quantification of keto acids**. *Chromatography* (2014.0) **35** 117-123. DOI: 10.15583/jpchrom.2014.017
16. Lee S.H., Kim S.O., Chung B.C.. **Gas Chromatographic-Mass Spectrometric Determination of Urinary Oxoacids Using O-(2,3,4,5,6-Pentafluorobenzyl)oxime-Trimethylsilyl Ester Derivatization and Cation-Exchange Chromatography**. *J. Chromatogr. B Biomed. Sci. Appl.* (1998.0) **719** 1-7. DOI: 10.1016/S0378-4347(98)00388-0
17. Abraham M.H., Sánchez-Moreno R., Cometto-Muñiz J.E., Cain W.S.. **An Algorithm for 353 Odor Detection Thresholds in Humans**. *Chem. Senses* (2012.0) **37** 207-218. DOI: 10.1093/chemse/bjr094
18. Kelley L.E., Cadwallader K.R.. **Identification and Quantitation of Potent Odorants in Spearmint Oils**. *J. Agric. Food Chem.* (2018.0) **66** 2414-2421. DOI: 10.1021/acs.jafc.6b04852
19. Liu C., Wang C., Zheng T., Zhao M., Gong W., Wang Q., Yan L., Zhang W.. **Characterization of Key Odor-Active Compounds in Sun-Dried Black Tea by Sensory and Instrumental-Directed Flavor Analysis**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11121740
20. Chen C., Zhou W., Yu H., Yuan J., Tian H.. **Evaluation of the Perceptual Interactions among Aldehydes in a Cheddar Cheese Matrix According to Odor Threshold and Aroma Intensity**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25184308
21. Lin H., Liu Y., He Q., Liu P., Che Z., Wang X., Huang J.. **Characterization of Odor Components of Pixian Douban (Broad Bean Paste) by Aroma Extract Dilute Analysis and Odor Activity Values**. *Int. J. Food Prop.* (2019.0) **22** 1223-1234. DOI: 10.1080/10942912.2019.1636816
22. Sosa-Moguel O., Pino J.A., Sauri-Duch E., Cuevas-Glory L.. **Characterization of Odor-Active Compounds in Three Varieties of Ciruela (**. *Int. J. Food Prop.* (2018.0) **21** 1008-1016. DOI: 10.1080/10942912.2018.1479858
23. Niu Y., Wang P., Xiao Q., Xiao Z., Mao H., Zhang J.. **Characterization of Odor-Active Volatiles and Odor Contribution Based on Binary Interaction Effects in Mango and Vodka Cocktail**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25051083
24. Lillard D.A.. *Aqueous Odor Thresholds of Organic Pollutants in Industrial Effluents* (1975.0) 8
25. Mulinacci N., Vincieri F.F., Baldi A., Bambagiotti-Alberti M., Sendl A., Wagner H.. **Flavonol Glycosides from**. *Phytochemistry* (1995.0) **38** 531-533. DOI: 10.1016/0031-9422(94)00554-7
26. Kazuma K., Noda N., Suzuki M.. **Malonylated Flavonol Glycosides from the Petals of**. *Phytochemistry* (2003.0) **62** 229-237. DOI: 10.1016/S0031-9422(02)00486-7
27. Zhou X., Peng J., Fan G., Wu Y.. **Isolation and Purification of Flavonoid Glycosides from Trollius ledebouri Using High-Speed Counter-Current Chromatography by Stepwise Increasing the Flow-Rate of the Mobile Phase**. *J. Chromatogr. A* (2005.0) **1092** 216-221. DOI: 10.1016/j.chroma.2005.07.064
28. Kim H.J., Kim B.-G., Ahn J.H.. **Regioselective Synthesis of Flavonoid Biglycosides Using**. *Appl. Microbiol. Biotechnol.* (2013.0) **97** 5275-5282. DOI: 10.1007/s00253-013-4844-7
29. Iwashina T., Yamaguchi M.A., Nakayama M., Onozaki T., Yoshida H., Kawanobu S., Ono H., Okamura M.. **Kaempferol Glycosides in the Flowers of Carnation and their Contribution to the Creamy White Flower Color**. *Nat. Prod. Commun.* (2010.0) **5** 1903-1906. DOI: 10.1177/1934578X1000501213
30. Lahuta L.B., Szablińska J., Ciak M., Górecki R.J.. **The Occurrence and Accumulation of D-Pinitol in Fenugreek (**. *Acta Physiol. Plant* (2018.0) **40** 155. DOI: 10.1007/s11738-018-2734-4
31. Narayanan C.R., Joshi D.D., Mujumdar A.M., Dhekne V.V.. **Pinitol—A New Anti-Diabetic Compound from the Leaves of**. *Curr. Sci.* (1987.0) **56** 139-141
32. Hernández-Mijares A., Bañuls C., Peris J.E., Monzó N., Jover A., Bellod L., Victor V.M., Rocha M.. **A Single Acute Dose of Pinitol from a Naturally-Occurring Food Ingredient Decreases Hyperglycaemia and Circulating Insulin Levels in Healthy Subjects**. *Food Chem.* (2013.0) **141** 1267-1272. DOI: 10.1016/j.foodchem.2013.04.042
33. Do G.-M., Choi M.-S., Kim H.-J., Woo M.-N., Lee M.-K., Jeon S.-M.. **Soy Pinitol Acts Partly as an Insulin Sensitizer or Insulin Mediator in 3T3-L1 Preadipocytes**. *Genes Nutr.* (2008.0) **2** 359-364. DOI: 10.1007/s12263-007-0071-0
34. Grosch W.. **Detection of Potent Odorants in Foods by Aroma Extract Dilution Analysis**. *Trends Food Sci. Technol.* (1993.0) **4** 68-73. DOI: 10.1016/0924-2244(93)90187-F
|
---
title: Ct, IL-18 polymorphism, and laboratory biomarkers for predicting chemosensory
dysfunctions and mortality in COVID-19
authors:
- Shukur Wasman Smail
- Esmaeil Babaei
- Kawa Amin
journal: Future Science OA
year: 2023
pmcid: PMC10005086
doi: 10.2144/fsoa-2022-0082
license: CC BY 4.0
---
# Ct, IL-18 polymorphism, and laboratory biomarkers for predicting chemosensory dysfunctions and mortality in COVID-19
## Body
Wuhan, China was the origin of the SARS-CoV-2 which resulted in the COVID-19 [1]. The disease might be asymptomatic or produce mild, severe or critical symptoms [2]. Symptoms vary from individual to individual and country to country. Cough, myalgia, fever, and sore throat are the most prevalent symptoms [3]. The SARS-CoV-2 virus could attack the taste buds and olfactory epithelium, causing olfactory dysfunction (OD) and gustatory dysfunction (GD) [4]. GD comprises hypogeusia and dysgeusia, whereas OD comprises hyposmia and anosmia; their prevalence ranged from 5 to $88\%$ [5–7]. In Spain, only $43.3\%$ of COVID-19 have anosmia [8]. European populations have a higher incidence of OD compared with Asian populations [6,9]. Varying demographics may display different frequencies of OD because of discrepancies in the amount of ACE2 expressed in the olfactory epithelium [10,11]. The incidence of GD also varies because of the different expressions of ACE2 in the oral epithelium [12]. Variations in alcohol consumption and smoking habits between countries may result in a difference in ACE2 expression, which could lead to diverse signs and symptoms, such as chemosensory dysfunctions [13,14]. The single nucleotide polymorphism (SNP) of brain-derived neurotrophic factor (BDNF) is implicated in the degree of OD [15]. Polymorphisms in the carbonic anhydrase VI gene may be associated with the area-specific properties of early GD in COVID-19 patients and the taste-related effects of those who have recovered from COVID-19 [16].
The clinical laboratory has been a fundamental part of stratifying the severity and prognosis of the disease [17–19]. Systolic blood pressure and oxygen saturation (SpO2) were assessed alongside other biological measurements. It has been reported that the levels of D-dimer and C-reactive protein (CRP) are notably increased in individuals suffering from COVID-19 [20]. The gold standard technology of real-time polymerase chain reaction (RT-PCR) was utilized throughout the pandemic to diagnose SARS-CoV-2. Despite this, some scientists remain doubtful of this technology [21]. Moreover, cycle threshold (Ct) value in RT-PCR is also essential for interpretation. It is necessary to associate Ct value with disease severity to eliminate this doubt.
RT-PCR reaction's Ct value is the number of cycles at which the fluorescence of the PCR product is detectable above the background signal [22]. The amount of Ct cycles of RT-PCR must be calculated to inspect the virus in the sample. As a result, the *Ct is* an indirect measurement of the amount of RNA in the sample, meaning that a low *Ct is* associated with a high viral load and vice versa [23]. Unfortunately, the Ct value is not included in the lab report for the patient. The Ct value is a significant factor in the prognosis of COVID-19, however it is not included in the patient's laboratory report [23–25].
Interleukin-18 (IL-18), a pro-inflammatory cytokine, was present at a high level among COVID-19 patients. The positioning of the IL-18 gene is on chromosome 11 in the q22.2–22.3 [26]. It is assumed that two SNPs can influence the expression of the IL-18 gene and are linked to IL-18 concentration. Research has demonstrated that the SNPs located in the IL-18 promoter region, such as rs187238 (-137 G >C) and rs1946518 (-607 C >A or T >G), can affect the expression of IL-18 [27]. Alteration of the -607 IL-18 SNP influences the concentration of IL-18 in the body by modulating the cAMP response element-binding protein (CREB). There is evidence that the severity of COVID-19 is linked to high levels of IL-18 in the blood. *The* genetic variations of this cytokine may affect its expression and the amount present in the blood [28]. There are many papers explaining role of IL-6 and its polymorphism in prediction severity and mortality of COVID-19 [29–31], but there are few papers regarding role of IL-18 SNP in prediction mortality of the disease.
The purpose of the research was to identify the relationship between Ct value with GD, OD, and SpO2 in patients with SARS-CoV-2 infection in Northern Iraq. We aimed to assess the importance of determining Ct value and SpO2 measurements upon admission in relation to regular laboratory markers (CRP and D-dimer), in order to predict COVID-19 mortality. According to our knowledge, no article has examined the association between the -607 T/G SNP of the IL-18 gene and mortality in COVID-19. Therefore, this study was conducted to assess the association between the -607 T/G polymorphism of the IL-18 gene and the mortality risk in the Kurdish population.
## Abstract
### Aim
Patients with COVID-19 often experience chemosensory dysfunction. This research intends to uncover the association of RT-PCR Ct value with chemosensory dysfunctions and SpO2. This study also aims to investigate Ct, SpO2, CRP, D-dimer, and -607 IL-18 T/G polymorphism in order to find out predictors of chemosensory dysfunctions and mortality.
### Materials & methods
This study included 120 COVID-19 patients, of which 54 were mild, 40 were severe and 26 were critical. CRP, D-dimer, RT-PCR, and IL-18 polymorphism were evaluated. Results & conclusion: Low Ct was associated with SpO2 dropping and chemosensory dysfunctions. IL-18 T/G polymorphism did not show an association with COVID-19 mortality; conversely, age, BMI, D-dimer and Ct values did.
## Plain language summary
This research intends to uncover the association of RT-PCR Ct value with GD, OD, and SpO2. It also aims to investigate Ct, SpO2, CRP, D-dimer and -607 IL-18 T/G polymorphism as predictors of chemosensory dysfunctions and mortality. This study included 120 COVID-19 patients, of which 54 were mild, 40 were severe and 26 were critical. Low Ct was associated with SpO2 dropping, GD, and OD. IL-18 T/G polymorphism did not show an association with COVID-19 mortality, conversely, age, BMI, D-dimer and Ct values did.
## Graphical abstract
## Patients
From 1 May to 30 May 2021, 120 COVID-19 patients were recruited, with 60 ($50\%$) males and 60 ($50\%$) females, as displayed in Table 1. Participants of this study originated from several cities in Northern Iraq and displayed signs and symptoms corresponding to SARS-CoV-2 infection. The infection was confirmed via RT-PCR. As per Chinese guidelines, the COVID-19 participants were categorized into three groups: “mild” 54 ($45\%$), “severe” 40 ($33.33\%$), and “critical” 26 ($21.67\%$) [32,33]. The protocol that divided COVID-19 patients into mild, moderate, severe, and critical groups was slightly modified for the current study; the mild and moderate groups were merged into the mild group. According to this protocol, mild cases either don't exhibit pneumonia or exhibit mild pneumonia; severe cases manifest pneumonia characterized by shortness of breath and their SpO2 <93 and critical patients suffer from respiratory failure, septic shock, or multiple organ failure [33].
**Table 1.**
| Parameters | Mild (mean ± SEM) | Severe (mean ± SEM) | Critical (mean ± SEM) | p-value |
| --- | --- | --- | --- | --- |
| n | 54 | 40 | 26 | – |
| Gender (M/F) | 30/24 | 22/18 | 8/18 | – |
| Age (Years) | 39.44 ± 2.191 | 48.10 ± 2.570 | 58.54 ± 2.839 | <0.0001 |
| BMI (kg/m2) | 26.79 ± 0.781 | 30.54 ± 1.326 | 29.96 ± 1.573 | 0.020 |
| D-dimer (μg/ml) | 0.450 ± 0.100 | 10.12 ± 3.284 | 46.60 ± 7.883 | <0.0001 |
| SpO2 (%) | 94.15 ± 0.901 | 63.85 ± 4.311 | 49.08 ± 3.847 | <0.0001 |
| CRP (mg/l) | 5.200 ± 2.672 | 67.20 ± 13.07 | 65.82 + 8.615 | <0.0001 |
| Ct value | 24.65 ± 0.6444 | 23.25 ± 0.593 | 15.64 ± 2.139 | <0.0001 |
During the thirty-day follow-up, all patients in the critical group passed away in the hospital. All COVID-19 patients were non-vaccinated; the patients did not involve in any therapeutic intervention. For the purpose of genotyping, the COVID-19 study participants were re-categorized into two groups based on mortality: those who survived (mild and severe) ($$n = 94$$) and those who did not survive (critical) ($$n = 26$$). Patients who did not have access to clinical information or did not have a blood or swab sample were excluded.
## Sample & data collection
Blood samples were taken from all the participants prior to the administration of any drugs. An oro-nasopharyngeal swab (eSwab, Copan, USA) was taken from the back of the throat and the nasopharynx to investigate the SARS-CoV-2 virus [34]. The emergency departments of the hospital provided data concerning COVID-19, such as comorbidities, demographics, and medical history. In terms of comorbidities, COVID-19 patients were found to have hypertension (HTN), diabetes mellitus (DM), rheumatoid arthritis (RA), and cancer. 78 patients ($65\%$) didn't have any other underlying disease. While 42 patients ($35\%$) had a personal history of comorbidity. HTN was found to be the most regularly occurring comorbidity among COVID-19 cases, with $42.85\%$ ($$n = 18$$). DM, on the other hand, was seen in $19.08\%$ of cases ($$n = 8$$). Ten people with COVID-19 ($23.81\%$) reported having more than two comorbidities. Cancer was seen in two cases, with a rate of 4.76 percent, while other diseases occurred in four cases at a rate of 9.53 percent.
## Methods for measurement of parameters
D-dimer and CRP were evaluated by Cobas c311 (Roche, Germany). A Masimo pulse oximeter was utilized to measure SpO2. The SARS-CoV-2 detection was conducted via RT-PCR (Bio-Rad, USA); all the procedures from RNA extraction, cDNA synthesis, and virus detection in the swab were done by Bio-Rad reliance SARS-CoV-2 RT-PCR assay kit (Bio-Rad, USA).
## DNA extraction & genotyping of IL-18 rs1946518 SNP
As instructed by the manufacturer, a DNA extract kit (Qiagen, Germany) was used to extract DNA from whole blood. The primers used in this study were designed on this website: http://primer1.soton.ac.uk/primer1.html.
Outer forward: 5“CCTACAATGTTACAACACTTAAAAT”3 Outer reverse: 5“ATAAGCCCTAAATATATGTATCCTTA”3 Inner forward: 5“GATACCATCATTAGAATTTTGTG”3 Inner reverse: 5“GCAGAAAGTGTAAAAATTATCAA”3
The 20 μL reaction tube for the PCR procedure included 2 μL of genomic DNA, 10 μL of mastermix (Ampliqon, Denmark), 1 μL of each primer, and 4 μL of deionized distilled water. The thermocycling protocol began with an initial denaturation step at 94°C for 7 minutes, followed by 30 cycles of 94°C for 40 seconds, 54°C for 40 seconds, 72°C for 1 minute, and a final extension of 72°C for 5 minutes.
The PCR products were then visualized on a $2\%$ agarose gel electrophoresis stained with ethidium bromide and examined under ultraviolet light (Brown, 2016). Gel electrophoresis results of the TG genotype revealed three bands (440 bp, 208 bp, 278 bp), the TT genotype yielded two bands (440 bp and 208 bp) and the GG genotype displayed two bands (440 bp and 278 bp) (Supplementary Figure 1). For ensuring of the result, $20\%$ of samples were sent for sanger sequencing (Macrogen, Korea), only outer forward and reverse primers were used for sequencing. The result of the sanger sequence was shown on the NCBI via the accession code (OP896193 and OP896194) (not released yet). It was analyzed via the Geneious prime program and confirmed by NCBI nucleotide blast (Supplementary Figure 2). NC_000011.10 was used as a reference sequence (https://www.ncbi.nlm.nih.gov/nucleotide/NC_000011.10?report=genbank&log$=nuclalign&blast_rank=1&RID=SCAV7SFP01N).
## Statistical analysis
The SPSS 28 (IBM, USA), GraphPad Prism 9 (GraphPad Software, Inc., USA), and MedCalc 20 (MedCalc Software, Ltd., Belgium) were used for statistical analysis and making graphs. All of the data was found to be in accordance with the Shapiro-Wilk and D'Agostino normality tests, as well as the Levene's test which showed homogeneity of variance. A two-group comparison was conducted using an independent t-test, while a one-way ANOVA was used for comparing more than two groups. A Tukey test was implemented as a post-hoc test for multiple comparisons.
Pearson correlation analysis was used to determine linear relationships between quantitative variables. Binary univariate logistic and multiple regression were used for the prediction of variables in severity and mortality. The receiver operating characteristic (ROC) curve was utilized in order to predict mortality. The mortality prognosis of laboratory parameters was established by means of the cut-off (with the use of Youden index) from the ROC curve. The predictive accuracy of biomarkers was assessed through positive predictive value (PPV) and negative predictive value (NPV). The Chi-square (χ2) test was applied to assess the COVID-19 genotype and allele frequencies. A p-value of 0.05 or below was considered being statistically significant.
## Comparison of demographics & laboratory markers
Age and CRP were much higher in the severe and critical groups of COVID-19 than in the mild group (Table 1, Figure 1a and e). Concerning BMI, our results showed that statistical significance ($$p \leq 0.02$$) was only found between the severe and mild groups (Table 1, Figure 1b). In the mild, severe, and critical groups, the average amount of D-dimer was (0.450 ± 0.100), (10.12 ± 3.284), and (46.60 ± 7.883), respectively. The difference in D-dimer between the mild and severe groups was not statistically significant, but there were significant differences between the other categories (Table 1, Figure 1c). Compared with the mild group, severe and critical COVID-19 showed a significant ($p \leq 0.0001$) drop in SpO2 (Table 1, Figure 1d).
**Figure 1.:** *Comparison of variables among groups of COVID-19 patients.
One-way ANOVA was used for the comparison of (A) Age. (B) BMI. (C) D-dimer. (D) SpO2. (E) CRP. (F) Ct value. Tukey test was applied as a post-hoc test for multiple comparisons.*p-value < 0.05; **p-value < 0.01; ***p-value < 0.001 and ****p-value < 0.0001.CRP: C-reactive protein; Ct: Cyclic threshold; D-dimer: Degradation product of fibrin; NS: Non-significant; SpO2: Oxygen saturation.*
## Comparison of Ct value among groups of COVID-19 patients
Among COVID-19 patients, the Ct value decreased significantly in the critical groups compared with the mild and severe groups ($$p \leq 0.025$$ and $p \leq 0.0001$, respectively). In contrast, there were no statistically significant differences in the Ct value between the mild and severe groups ($$p \leq 0.541$$) (Table 1, Figure 1). Ct values for male patients (22.22 ± 1.113) and female patients (22.24 ± 0.961) were not significantly different ($$p \leq 0.992$$) (Table 2, Figure 2a). Patients who survived had a Ct value of 24.05 0.455, while those who did not had a Ct value of 15.64 ± 2.139. There was a statistically significant difference ($p \leq 0.0001$) between them (Table 2, Figure 2b). Comorbid patients had a mean Ct value of 20.54 ± 1.461, while non-comorbid patients had a mean Ct value of 23.14 ± 0.776; there was no significant difference between them ($$p \leq 0.089$$) (Table 2, Figure 2c).
The mean Ct value of COVID-19 patients with GD was 19.84 ± 1.092 and that of patients without GD was 24.96 ± 0.639; there was a significant difference between them ($$p \leq 0.0003$$) (Table 2, Figure 2d). Patients with OD had a mean Ct value of 19.80 ± 1.099, while those without OD had a mean Ct value of 25.01 ± 0.606. A substantial difference was observed between their Ct values ($$p \leq 0.0002$$) (Table 2, Figure 2e).
## Binary univariate regression model for GD & OD
Ct value was incorporated into a binary univariate logistic regression model to predict OD and GD. RT-PCR Ct values are independent predictors of OD and GD. As shown in Table 3, our model revealed a significant relationship between the Ct value and GD ($$p \leq 0.001$$, OR = 0.700) and OD ($$p \leq 0.001$$, OR = 0.688). A one-unit increase in the Ct value reduces the probability of an individual having GD 1.43 ($\frac{1}{0.7}$) times, and the probability of OD to be 1.45 ($\frac{1}{0.68}$) times.
**Table 3.**
| Dependent variables | Independent variables | B | OR | 95% CI for OR | p-value |
| --- | --- | --- | --- | --- | --- |
| GD | Ct value | -0.357 | 0.7 | 0.566–0.865 | 0.001 |
| OD | Ct value | -0.374 | 0.688 | 0.554–0.855 | 0.001 |
## Multiple linear regression model for SpO2
To find out if there is an association between lab parameters and the risk of SpO2 dropping. The models were made with multiple linear regression, and the “stepwise” method was used to choose the variables that went into the models and acted as predictors. The calibration of the model was checked by determining whether the related variables had multicollinearity (tolerance and variance inflation factor (VIF)). By doing stepwise multiple linear regression analysis on age, BMI, D-dimer, CRP, Ct value, and SpO2, we found that model 1 (Ct value alone), model 2 (Ct value and age), model 3 (Ct value, age, and BMI), and model 4 (Ct value, age, BMI, and D-dimer) were strongly associated to SpO2 dropping in COVID-19 patients. Simultaneously, the inclusion of CRP did not enhance the regression model, and as a result, it was omitted from the stepwise analysis. As shown in Table 4, age, BMI, and D-dimer were predictors for SpO2 dropping. While the Ct value could predict the rise in SpO2.
**Table 4.**
| Dependent variable | Models | Independent variables | B | p-value |
| --- | --- | --- | --- | --- |
| SpO2 | Model 1 | Ct value | 2.435 | <0.0001 |
| SpO2 | Model 2 | Ct value | 1.893 | <0.0001 |
| SpO2 | Model 2 | Age | -0.546 | 0.005 |
| SpO2 | Model 3 | Ct value | 1.674 | <0.0001 |
| SpO2 | Model 3 | Age | -0.509 | 0.007 |
| SpO2 | Model 3 | BMI | -1.039 | 0.018 |
| SpO2 | Model 4 | Ct value | 1.215 | 0.013 |
| SpO2 | Model 4 | Age | -0.422 | 0.023 |
| SpO2 | Model 4 | BMI | -1.238 | 0.005 |
| SpO2 | Model 4 | D-dimer | -0.233 | 0.037 |
## The correlation between the variables
The Pearson correlation was done between variables (age, D-dimer, CRP, BMI, Ct value, and SpO2). The significant correlation coefficients (r) were found between SpO2 and Ct value (r = -0.600, $p \leq 0.0001$), SpO2 and age (r = -0.513, $p \leq 0.0001$), CRP and SpO2 (r = -0.449, $p \leq 0.0001$), SpO2 and D-dimer (r = -0.476, $$p \leq 0.001$$) and SpO2 and BMI (r = -0.402, $$p \leq 0.001$$). The detailed correlations can be seen in Table 5, Figure 3.
## ROC curve analysis as a predictor of mortality in COVID-19
In Table 6 and Figure 4, ROC curves were constructed for the predicting mortality of 120 COVID-19 patients. Areas under the curve (AUC) have been reported for the predicting mortality of COVID-19, which is equal to 0.899, 0.956, 0.797, 0.879, and 0.989, for Ct value, D-dimer, CRP, SpO2, and their combinations, respectively. The results also showed that the AUC with the best performance in biomarkers was the combination of Ct value, D-dimer, CRP, and SpO2. They allow us to predict the mortality of the disease with a sensitivity of $92.31\%$ and a specificity of $100\%$; their NPV and PPV were 97.9 and $100\%$, respectively. A Ct value of 20.47 is the cutoff point for hospital mortality prediction. The ideal cutoff point for predicting mortality for D-dimer, CRP, and SpO2 was 6.81μg/ml, 33.56 mg/l, and $70\%$, respectively.
## Association of - 607 IL-18 SNP to susceptibility to SARS-CoV-2 infection
In this study, we analyzed the IL-18 polymorphism between survivors and non-survivors of COVID-19. $61.54\%$ of non-survivors (16 out of 26) and $67.02\%$ of survivors from COVID-19 (63 out of 94), can be identified as heterozygous (TG) for the rs1946518 IL-18 polymorphism. $26.92\%$ of non-survivors and $27.66\%$ of survivors from COVID-19 were homozygous for the TT genotype. Regarding the GG genotype, $11.54\%$ of the non-survivors were homozygous for it, and $5.32\%$ of the COVID-19 survivors showed polymorphism in homozygosity. When performing the χ2, we found that the IL-18 TG and GG genotypes were not significantly associated with increased mortality of COVID-19 compared with the IL-18 TT genotypes ($$p \leq 0.9$$, OR = 1.060, $95\%$ CI = 0.413–2.764) and ($$p \leq 0.378$$, OR = 0.449, $95\%$ CI = 0.094–2.063) respectively. There was no significant association in the comparison of the dominant model (TT + TG vs GG: OR = 0.431, $95\%$ CI: 0.102–1.736; $$p \leq 0.368$$) and the recessive model (TT vs TG/GG: OR = 0.964, $95\%$ CI: 0.379–2.478; $$p \leq 0.9$$) (Table 7).
**Table 7.**
| Polymorphism | Non-survivor (n = 26) | Non-survivor (n = 26).1 | Survivor (n = 94) | Survivor (n = 94).1 | OR | 95% CI | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | n | % | n | % | | | |
| TT | 7 | 26.92 | 26 | 27.66 | 1.0 | – | – |
| TG | 16 | 61.54 | 63 | 67.02 | 1.06 | 0.413–2.764 | 0.9 |
| GG | 3 | 11.54 | 5 | 5.32 | 0.449 | 0.094–2.063 | 0.378 |
| TG + GG | 19 | 73.08 | 68 | 72.34 | 0.964 | 0.379–2.478 | 0.9 |
| TT + TG | 23 | 88.46 | 89 | 94.68 | 0.431 | 0.102–1.736 | 0.368 |
| T | 30 | 57.69 | 115 | 61.17 | 0.866 | 0.476–1.635 | 0.749 |
| G | 22 | 42.31 | 73 | 38.83 | | | |
Furthermore, the carrier frequency of the T allele was not significantly lower in the non-survivor group ($57.69\%$) compared with the survivor COVID-19 group (61.17). While the carrier frequency of the G allele was not significantly higher in the non-survivor ($42.31\%$) compared with the survivor's COVID-19 ($38.83\%$). Finally, there was no statistical difference in the frequencies of the two alleles (T vs G: OR = 0.866, $95\%$ CI: 0.476–1.635; $$p \leq 0.749$$) (Table 7).
## Discussion
The respiratory and cardiovascular systems are the primary target of SARS-CoV-2, yet, there is a significant amount of proof to support the importance of chemosensory dysfunctions when assessing and diagnosing COVID-19 patients. The decreased sense of taste and smell in the current COVID-19 pandemic strongly shows a SARS-CoV-2 infection. Furthermore, in addition to standard laboratory values (D-dimer and CRP), parameters such as SpO2 and the Ct value of the RT-PCR should be considered [35–38].
Our results revealed the patients who exhibited severe and critical symptoms had a higher CRP than mild cases of COVID-19, CRP levels were associated negatively with SpO2; a published paper confirms that this result reported that an exaggerated elevation of CRP in patients with COPD was negatively correlated with SpO2 [39]. Despite elevation of CRP in critical COVID-19 patients, but our regression model didn't support its use as predictor for mortality, this finding is in contrast to Huyut MT, Ilkbahar F [40] and Xie Jet al. [ 41] who displayed that CRP was an important predictor for the progression of COVID-19 disease.
D-dimer levels increased in COVID-19's severe and critical groups. This result parallels a published paper by Yu H-Het al. [ 42], who documented that D-dimer was higher in patients suffering from severe COVID-19 [42]. D-dimer levels correlated negatively with SpO2; therefore, D-dimer may be a precise target to be assessed to indicate the mortality, as seen in the ROC curve analysis of this study. Yalçin, 2020 also confirmed our result; D-dimer and SpO2 were shown to be negatively correlated by him [43]. Huyut MTet al. [ 44] also documented that D-dimer was a predictor for mortality in COVID-19.
A low Ct value was found in COVID-19 patients suffering from GD and GD. This result is parallel with many papers that were done in this field [45–47]. There are many explanations for virus-induced GD and OD. First, the virus induces stomatitis and rhinitis, that leads to the breakdown of the neurosensory cells by the antibody. Second, the virus attacks the central nervous system, peripheral nervous system, cerebral cortex, and exclusively cranial nerves related to taste and smell. Lastly, the virus attacks directly ACE2 expressed on olfactory epithelium and taste buds [48]. The Ct value did not show the gender-based difference in the current study. This result coincides with another finding [49]. However, in COVID-19, The males have been infected more severely than females [50]; the increased mortality in a male might not be because of the viral load, but it may be other reasons: first, the bad habits of males such as smoking and drinking [51]; second, the testosterone in the male has immunosuppressive activity and increases the expression of TMPRSS2 which is the co-receptor for binding SRAS-CoV-2 virus. In addition, the estrogen in the female has immune-boosting properties against the virus [52]. There was a variation in the immune response dependent on gender. Suppressing toll-like receptor (TLR)-induced interferon (IFN) release decreases the male's capability to eradicate the virus [53,54].
The Ct value was lower in non-survivor patients in this study since the high load of the virus may lead to damage to the lung and induce pneumonia [55]. The multiple regression and correlation in this study showed that the Ct value associated with the dropping level of SpO2. There are many challenges in adding CT values to clinical reports: there is no industry-wide standard cut-off; it may vary depending on the type of commercial kits and the stage of the disease; and finally, the ability of the healthcare provider taking the swab and the COVID-19 patients' tolerance may also affect CT values [56]. After addressing these issues and enhancing RT-PCR’ sensitivity, adding the Ct value to the results of the test might be an excellent decision.
SpO2 assesses the respiratory function and arterial oxygenation of COVID-19 and its level decreased in severe and critical groups compared with mild groups. Correlation showed that age, BMI, CRP, and D-dimer were negatively associated, while Ct value was positively associated with a level of SpO2. The reason for the association of age with COVID-19 mortality could be due to the presence of comorbidities in older age [57]. An age related malfunction in T-lymphocyte and B-lymphocyte cells results in their inability to clear the virus [58]. Obesity is one of the risk factors for increased mortality in COVID-19 [59,60]. When COVID-19 patients have a high BMI, pro-inflammatory cytokines are also increased, causing damage to lung cells and a decline in SpO2 levels [61–63].
This study evaluated the association of polymorphisms of - 607 SNP of IL-18 with COVID-19 mortality among the Kurdish population. The - 607 SNP of IL-18 changes the immune response to many viruses, including hepatitis B virus [64], hepatitis C virus, cytomegalovirus [65], and human immune deficiency virus [66]. The cytokine genes are very polymorphic that may change the concentration of cytokine; IL-18 has - 607 SNP in the promoter that causes elevation of IL-18. Serum elevation of IL-18 can be regarded as a bad prognostic factor in COVID-19 in the Brazilian population [67]. The current study documented that - 607 SNP of IL-18 was not linked to mortality in COVID-19. Chen W-Jet al. [ 68] proved that the TG genotype of - 607 SNP of IL-18 was associated with viral shedding in SARS-CoV-1.
This study has some limitations. First, the sample size is relatively small, if the sample was taken from all governments in Iraq so that the sample represents the population of Iraq. Second, the blood was only taken at the time of admission; it was better to retake blood at regular intervals. Third, healthy controls were not enrolled in the current study. Finally, reports of the prevalence of olfactory and taste disturbances in patients with SARS-CoV-2 have been based mainly on questionnaires, which may overestimate the association of these alterations with this disease.
Future studies should measure SpO2, Ct value, GD, and OD changes in a follow-up study at early infection and monitor their levels during several stages of disease progression. Despite evidence of the existence of alteration of taste and smell as a symptom of COVID-19, it is still required to carry out experimental studies that explain the mechanism by which the infection produces this wide range of alterations, as well as their exact prevalence to find effective strategies for prevention, diagnosis, treatment, and rehabilitation of these conditions.
## Conclusion
The leading utility of our finding may assist physicians in focusing on measuring SpO2, Ct beside D-dimer at the early stage of the disease since both parameters were significantly decreased in COVID-19 patients with a poor prognosis at the time of hospital admission. Besides involving Ct in the mortality of COVID-19, a low value of Ct value is also associated with GD and OD. Age and BMI were also predictors of COVID-19 mortality. Therefore, individuals with low SpO2 and Ct values but high D-dimer should be monitored carefully to prevent death. The current study's findings suggest that Ct value and its interpretation comments on RT-PCR's report can be used as an early warning system to predict mortality in COVID-19. Once the challenges of Ct value have been overcome, Ct values can be utilized in conjunction with other laboratory biomarkers and clinical characteristics to manage the disease. -607 IL-18 T/G SNP was not associated with mortality in COVID-19. It may be of great diagnostic and therapeutic importance to understand the genetic basis of COVID-19.
## References
1. Lekhraj Rampal M, Seng LB. **Coronavirus disease (COVID-19) pandemic**. *Med. J. Malays* (2020) **75** 95
2. Marshall JC, Murthy S, Diaz J. **A minimal common outcome measure set for COVID-19 clinical research**. *Lancet Infect Dis* (2020) **20** e192-e197. PMID: 32539990
3. WHO.. **Coronavirus disease (COVID-19)**. (March 24, 2020)
4. Lechien JR, Chiesa-Estomba CM, De Siati DR. **Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study**. *Eur Arch Oto-Rhino-L* (2020) **277** 2251-2261
5. Haehner A, Draf J, Dräger S, Hummel T. **Predictive value of sudden olfactory loss in the diagnosis of COVID-19**. *ORL* (2020) **82** 175-180. PMID: 32526759
6. Menni C, Valdes AM, Freidin MB. **Real-time tracking of self-reported symptoms to predict potential COVID-19**. *Nat. Med.* (2020) **26** 1037-1040. PMID: 32393804
7. Mao L, Jin H, Wang M. **Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China**. *JAMA Neurol* (2020) **77** 683-690. PMID: 32275288
8. Pollán M, Pérez-Gómez B, Pastor-Barriuso R. **Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study**. *Lancet* (2020) **396** 535-544. PMID: 32645347
9. Chiesa-Estomba CM, Lechien JR, Saussez S. **The alteration of smell and taste in COVID-19 patients. A diagnostic resource in primary care**. *Aten Primaria* (2020) **52** 592-593. PMID: 32620292
10. Desforges M, Le Coupanec A, Dubeau P, Bourgouin A, Lajoie L, Dubé M. **Coronavirus humanos y otros virus respiratorios:¿ patógenos oportunistas subestimados del sistema nervioso central?**. *Virus* (2019) **12** 14
11. Cao Y, Li L, Feng Z. **Comparative genetic analysis of the novel coronavirus (2019-nCoV/SARS-CoV-2) receptor ACE2 in different populations**. *Cell Discov* (2020) **6** 1-4. PMID: 31934347
12. Xu H, Zhong L, Deng J. **High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa**. *Int J Oral Sci* (2020) **12** 1-5. PMID: 31900382
13. Huyut M, Soygüder S. **The multi-relationship structure between some symptoms and features seen during the new coronavirus 19 infection and the levels of anxiety and depression post-Covid**. *East J Med* (2022) **27** 1-10
14. Friske MM, Giannone F, Senger M, Seitz R, Hansson AC, Spanagel R. **Chronic alcohol intake regulates expression of SARS-CoV2 infection-relevant genes in an organ-specific manner**. *Alcohol. Clin. Exp. Res.* (2023)
15. Roh D, Lee D-H, Kim SW. **The association between olfactory dysfunction and cardiovascular disease and its risk factors in middle-aged and older adults**. *Sci Rep* (2021) **11** 1248. PMID: 33441955
16. Tsuchiya H. **Gustatory and Saliva Secretory Dysfunctions in COVID-19 Patients with Zinc Deficiency**. *Life* (2022) **12** 353. PMID: 35330104
17. Mertoglu C, Huyut MT, Arslan Y, Ceylan Y, Coban TA. **How do routine laboratory tests change in coronavirus disease 2019?**. *Scand J Clin Lab Invest* (2021) **81** 24-33. PMID: 33342313
18. Huyut MT, Üstündağ H. **Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: a retrospective observational study**. *Medical Gas Res* (2022) **12** 60
19. Huyut MT, Huyut Z. **Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19**. *Int Immunopharmacol* (2021) **100** 108127. PMID: 34536746
20. Ali AM, Rostam HM, Fatah MH, Noori CM, Ali KM, Tawfeeq HM. **Serum troponin, D-dimer, and CRP level in severe coronavirus (COVID-19) patients**. *Immun Inflamm Dis* (2022) **10** e582. PMID: 34939346
21. Dramé M, Tabue Teguo M, Proye E. **Should RT-PCR be considered a gold standard in the diagnosis of COVID-19?**. *J. Med. Virol.* (2020) **92** 2312-2313. PMID: 32383182
22. Al Bayat S, Mundodan J, Hasnain S. **Can the cycle threshold (Ct) value of RT-PCR test for SARS CoV2 predict infectivity among close contacts?**. *J Infect Public Health* (2021) **14** 1201-1205. PMID: 34416598
23. Tom MR, Mina MJ. **To interpret the SARS-CoV-2 test, consider the cycle threshold value**. *J Clin Infect Dis* (2020) **71** 2252-2254
24. Rajyalakshmi B, Samavedam S, Reddy PR, Aluru N. **Prognostic value of “Cycle Threshold” in confirmed COVID-19 patients**. *Indian J Crit Care Med* (2021) **25** 322. PMID: 33790515
25. Albahrani S, Alghamdi M, Zakary N. **Initial viral cycle threshold values in patients with COVID-19 and their clinical significance**. *Eur. J. Med. Res.* (2022) **27** 101. PMID: 35765070
26. Nolan KF, Greaves DR, Waldmann HJG. **The human interleukin 18 gene IL 18 maps to 11q22. 2-q22. 3, closely linked to the DRD2 gene locus and distinct from mapped IDDM loci**. *Genomics* (1998) **51** 161-163. PMID: 9693051
27. Giedraitis V, He B, Huang W-X, Hillert JJJON. **Cloning and mutation analysis of the human IL-18 promoter: a possible role of polymorphisms in expression regulation**. *J. Neuroimmunol.* (2001) **112** 146-152. PMID: 11108943
28. Ivanski F, Fermino BL, De Souza Cassela PLC. **ESTUDO DA ASSOCIAÇÃO DE POLIMORFISMOS GENÉTICOS DA IL-18 NA COVID-19**. *Braz J Infect Dis* (2022) **26** 102593
29. Sun H, Guo P, Zhang L, Wang F. **Serum Interleukin-6 Concentrations and the Severity of COVID-19 Pneumonia: A Retrospective Study at a Single Center in Bengbu City, Anhui Province, China, in January and February 2020**. *Med Sci Monit* (2020) **26** e926941. PMID: 33175722
30. Zhang J, Hao Y, Ou W. **Serum interleukin-6 is an indicator for severity in 901 patients with SARS-CoV-2 infection: a cohort study**. *J Transl Med* (2020) **18** 406. PMID: 33121497
31. Giannitrapani L, Augello G, Mirarchi L. **Outcome predictors in SARS-CoV-2 disease (COVID-19): the prominent role of IL-6 levels and an IL-6 gene polymorphism in a western Sicilian population**. *J. Infect.* (2022) **85** 174-211
32. Gui-Qiang W, Lei Z, Xia W, Yan-Mei J, Fu-Sheng W. **Diagnosis and treatment protocol for COVID-19 patients (tentative 8th edition): interpretation of updated key points**. *Infect Dis Immun* (2021) **1** 17-19
33. Nhc Natcm. **Diagnosis and Treatment Protocol for COVID-19 Patients (Tentative 8th Edition)**. *Infect Dis Immun* (2020) **20** 8-16
34. Ek P, Böttiger B, Dahlman D, Hansen KB, Nyman M, Nilsson AC. **A combination of naso- and oropharyngeal swabs improves the diagnostic yield of respiratory viruses in adult emergency department patients**. *Infect Dis* (2019) **51** 241-248
35. Huyut MT, Velichko A, Belyaev M. **Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers**. *Appl Sci* (2022) **12** 12180
36. Huyut MT, Velichko A. **Diagnosis and Prognosis of COVID-19 disease using routine blood values and LogNNet neural network**. *Sensors* (2022) **22** 4820. PMID: 35808317
37. Huyut M. **Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models**. *IRBM* (2023) **44** 100725. PMID: 35673548
38. Velichko A, Huyut MT, Belyaev M, Izotov Y, Korzun D. **Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application**. *Sensors* (2022) **22** 7886. PMID: 36298235
39. Aksu F, Çapan N, Aksu K. **C-reactive protein levels are raised in stable Chronic obstructive pulmonary disease patients independent of smoking behavior and biomass exposure**. *J Thorac Dis* (2013) **5** 414-421. PMID: 23991296
40. Huyut MT, Ilkbahar F. **The effectiveness of blood routine parameters and some biomarkers as a potential diagnostic tool in the diagnosis and prognosis of Covid-19 disease**. *Int Immunopharmacol* (2021) **98** 107838. PMID: 34303274
41. Xie J, Covassin N, Fan Z. **Association between hypoxemia and mortality in patients with COVID-19**. *Presented at: Mayo Clinic Proceedings.* (2020) **95** 1138-1147. PMID: 32376101
42. Yu H-H, Qin C, Chen M, Wang W, Tian D-S. **D-dimer level is associated with the severity of COVID-19**. *Thromb. Res.* (2020) **195** 219-225. PMID: 32777639
43. Yalçin KS, Kasapoğlu B, Alanli R, Küçükay MB, Koşar A. **The association of oxygen saturation, tomography findings and D-dimer levels in coronavirus disease 2019 patients**. *Blood Coagul. Fibrinolysis* (2020) **31** 558-561. PMID: 33181759
44. Huyut MT, Huyut Z, İlkbahar F, Mertoğlu C. **What is the impact and efficacy of routine immunological, biochemical and hematological biomarkers as predictors of COVID-19 mortality?**. *Int Immunopharmacol* (2022) **105** 108542. PMID: 35063753
45. Jain A, Pandey AK, Kaur J. **Is there a correlation between viral load and olfactory & taste dysfunction in COVID-19 patients?**. *Am. J. Otolaryngol.* (2021) **42** 102911-102911. PMID: 33476975
46. Cho RH, To ZW, Yeung ZW. **COVID-19 viral load in the severity of and recovery from olfactory and gustatory dysfunction**. *The Laryngoscope* (2020) **130** 2680-2685. PMID: 32794209
47. Taziki Balajelini MH, Rajabi A, Mohammadi M. **Virus load and incidence of olfactory, gustatory, respiratory, gastrointestinal disorders in COVID-19 patients: a retrospective cohort study**. *Clin. Otolaryngol.* (2021) **46** 1331-1338. PMID: 34358409
48. Finsterer J, Stollberger C. **Causes of hypogeusia/hyposmia in SARS-CoV2 infected patients**. *J. Med. Virol.* (2020) **92** 1793-1794. PMID: 32311107
49. Buchan BW, Hoff JS, Gmehlin CG. **Distribution of SARS-CoV-2 PCR cycle threshold values provide practical insight into overall and target-specific sensitivity among symptomatic patients**. *Am. J. Clin. Pathol.* (2020) **154** 479-485. PMID: 32687186
50. Mertoglu C, Huyut MT, Olmez H, Tosun M, Kantarci M, Coban TA. **COVID-19 is more dangerous for older people and its severity is increasing: a case-control study**. *Med Gas Res* (2022) **12** 51. PMID: 34677152
51. Griswold MG, Fullman N, Hawley C. **Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016**. *Lancet* (2018) **392** 1015-1035. PMID: 30146330
52. Chakravarty D, Nair SS, Hammouda N. **Sex differences in SARS-CoV-2 infection rates and the potential link to prostate cancer**. *Commun Biol* (2020) **3** 1-12. PMID: 31925316
53. Stack G, Jones E, Marsden M. **CD200 receptor restriction of myeloid cell responses antagonizes antiviral immunity and facilitates cytomegalovirus persistence within mucosal tissue**. *PLoS Pathog* (2015) **11** e1004641. PMID: 25654642
54. Bwire GM. **Coronavirus: Why Men are More Vulnerable to Covid-19 Than Women?**. *SN Compr Clin Med* (2020) **2** 874-876. PMID: 32838138
55. Kishaba T, Tamaki H, Shimaoka Y, Fukuyama H, Yamashiro S. **Staging of acute exacerbation in patients with idiopathic pulmonary fibrosis**. *Lung* (2014) **192** 141-149. PMID: 24221341
56. Jamal SA, Farooq MU, Bidari V. **Challenges associated with using cycle threshold (Ct) value of reverse-transcription polymerase chain reaction (RT-PCR) as a criteria for infectiousness of coronavirus disease 2019 (COVID-19) patients in India**. *Infect. Control Hosp. Epidemiol.* (2022) **43** 1730-1731. PMID: 34338176
57. Antos A, Kwong ML, Balmorez T, Villanueva A, Murakami S. **Unusually High Risks of COVID-19 Mortality with Age-Related Comorbidities: An Adjusted Meta-Analysis Method to Improve the Risk Assessment of Mortality Using the Comorbid Mortality Data**. *Infect Dis Rep* (2021) **13** 700-711. PMID: 34449622
58. Zhou F, Yu T, Du R. **Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study**. *Lancet* (2020) **395** 1054-1062. PMID: 32171076
59. Suleyman G, Fadel RA, Malette KM. **Clinical characteristics and morbidity associated with coronavirus disease 2019 in a series of patients in metropolitan Detroit**. *JAMA Netw Open* (2020) **3** e2012270-e2012270. PMID: 32543702
60. Petrakis D, Margină D, Tsarouhas K. **Obesity - a risk factor for increased COVID-19 prevalence, severity and lethality**. *Mol. Med. Rep* (2020) **22** 9-19. PMID: 32377709
61. Faam B, Zarkesh M, Daneshpour MS, Azizi F, Hedayati M. **The association between inflammatory markers and obesity-related factors in Tehranian adults: tehran lipid and glucose study**. *Iran J Basic Med Sci* (2014) **17** 577-582. PMID: 25422750
62. Moriconi D, Masi S, Rebelos E. **Obesity prolongs the hospital stay in patients affected by COVID-19, and may impact on SARS-COV-2 shedding**. *Obes Res Clin Pract* (2020) **14** 205-209. PMID: 32534848
63. Costela-Ruiz VJ, Illescas-Montes R, Puerta-Puerta JM, Ruiz C, Melguizo-Rodríguez L. **SARS-CoV-2 infection: the role of cytokines in COVID-19 disease**. *Cytokine Growth Factor Rev.* (2020) **54** 62-75. PMID: 32513566
64. Karra VK, Gumma PK, Chowdhury SJ. **IL-18 polymorphisms in hepatitis B virus related liver disease**. *Cytokine* (2015) **73** 277-282. PMID: 25802197
65. Pérez-Flores I, Santiago JL, Fernández-Pérez C. **Impacts of Interleukin-18 Polymorphisms on the Incidence of Delayed-Onset Cytomegalovirus Infection in a Cohort of Kidney Transplant Recipients**. *Open Forum Infect Dis* (2019) **6** ofz325. PMID: 31660404
66. Sobti R, Sharma V, Abitew A. **IL-18 Gene promoter region 607C/A polymorphism in HIV-1 infected north indian population**. *Balkan J Med Genet* (2011) **14** 41. PMID: 24052711
67. Coutinho LL, Oliveira CN, Albuquerque PL. **Elevated IL-18 predicts poor prognosis in critically ill COVID-19 patients at a Brazilian hospital in 2020-21**. *Future microbiology* (2022) **17** 1287-1294. PMID: 36111789
68. Chen W-J, Yang J-Y, Lin J-H. **Nasopharyngeal shedding of severe acute respiratory syndrome—associated coronavirus is associated with genetic polymorphisms**. *J Clin Infect Dis* (2006) **42** 1561-1569
|
---
title: Prolonged Egg Supplement Advances Growing Child’s Growth and Gut Microbiota
authors:
- Sophida Suta
- Apinya Surawit
- Pichanun Mongkolsucharitkul
- Bonggochpass Pinsawas
- Thamonwan Manosan
- Suphawan Ophakas
- Tanyaporn Pongkunakorn
- Sureeporn Pumeiam
- Kitti Sranacharoenpong
- Sawannee Sutheeworapong
- Patcha Poungsombat
- Sakda Khoomrung
- Pravit Akarasereenont
- Iyarit Thaipisuttikul
- Bhoom Suktitipat
- Korapat Mayurasakorn
journal: Nutrients
year: 2023
pmcid: PMC10005095
doi: 10.3390/nu15051143
license: CC BY 4.0
---
# Prolonged Egg Supplement Advances Growing Child’s Growth and Gut Microbiota
## Abstract
Protein-energy malnutrition still impacts children’s growth and development. We investigated the prolonged effects of egg supplementation on growth and microbiota in primary school children. For this study, 8–14-year-old students ($51.5\%$ F) in six rural schools in Thailand were randomly assigned into three groups: [1] whole egg (WE), consuming 10 additional eggs/week ($$n = 238$$) ($$n = 238$$); [2] protein substitute (PS), consuming yolk-free egg substitutes equivalent to 10 eggs/week ($$n = 200$$); and [3] control group (C, ($$n = 197$$)). The outcomes were measured at week 0, 14, and 35. At the baseline, $17\%$ of the students were underweight, $18\%$ were stunted, and $13\%$ were wasted. At week 35, compared to the C group the weight and height difference increased significantly in the WE group (3.6 ± 23.5 kg, $p \leq 0.001$; 5.1 ± 23.2 cm, $p \leq 0.001$). No significant differences in weight or height were observed between the PS and C groups. Significant decreases in atherogenic lipoproteins were observed in the WE, but not in PS group. HDL-cholesterol tended to increase in the WE group (0.02 ± 0.59 mmol/L, ns). The bacterial diversity was similar among the groups. The relative abundance of Bifidobacterium increased by 1.28-fold in the WE group compared to the baseline and differential abundance analysis which indicated that Lachnospira increased and Varibaculum decreased significantly. In conclusion, prolonged whole egg supplementation is an effective intervention to improve growth, nutritional biomarkers, and gut microbiota with unaltered adverse effects on blood lipoproteins.
## 1. Introduction
Protein-energy malnutrition (PEM) is still a major nutritional problem in the world. It has repercussions on schoolchildren’s growth and development [1]. Inadequate protein intake results in reduced growth and an immune system that is susceptible to disease and infection in early life, and also affects school performance and intelligence status [2], particularly among vulnerable groups [3,4]. Recent data showed that $24.7\%$ of children in Southeast Asian countries were malnourished [5], many of whom lived in households with insecure incomes. School closures led to the disruption of the free school lunch program, exposing millions of children to food insecurity [4]. Our preliminary survey of students in this study in 2021 after the COVID-19 pandemic showed that financial difficulties caused by the lockdown forced families to choose low quality food choices, exacerbating severe malnutrition and disparity in many societies [4].
In Thailand, the government has provided free lunch and milk every school day for primary school children since 1993 and malnutrition has improved over time [6]. However, the recent Thailand National Health Examination Survey showed that about 400,000 ($3.5\%$) Thai children were stunted, while 470,000 ($4.1\%$) were still underweight. In contrast, the prevalence of overnutrition in children has increased and is associated with the early onset of noncommunicable adult chronic diseases [7]. This double burden of malnutrition can be caused by the imbalance of macronutrients and micronutrient intake, particularly vitamin A, iron, vitamin D, and calcium [6,8]. Eggs are a common food around the world that provides approximately 150 kcal/100 g, >$50\%$ of adequate intake of critical micronutrients, and high-quality protein, and are more affordable than other animal-derived foods [9]. Eggs are a rich source of choline [10], which plays an integral role in neurotransmitters, cell membrane signaling, and lipid metabolism [11,12]. Recent evidence suggests that the early introduction of one egg per day for six months markedly improved growth in young children [13]. Eggs have been shown to improve growth, as well as reduce wasting and acute malnutrition [14].
Malnutrition has been associated with intestinal dysbiosis [15] by altering the healthy and pathogenic microbiota that efficiently processes foods or produces vitamins. These changes can impact the healthy mucosal immune system. Alterations in the composition of the gut microbiota have been observed in cardiovascular disease (CVD) and malnutrition [16].
For example, the number of species in the *Proteobacteria phylum* increases in malnourished infants, while the number of species in the phyla Bifidobacterium and Lactobacillus decreases [17]. However, recent short-term studies in people revealed that the microbiota is not modified after 4 weeks of egg consumption. Liu et al. showed in a novel but extensive 2-week intervention that it altered vascular function, namely flow-mediated dilation, brachial-ankle pulse wave velocity, and gut microbial function; yet the clear mechanism remains elucidated [18,19]. Therefore, egg consumption may not only help address malnutrition, but may also ameliorate problems with vascular and intestinal function related to alterations in the gut microbiota [19]. Although the short-term benefits of egg supplementation may have been demonstrated, there is considerable controversy regarding its long-term consequences and the underlying mechanism by which egg consumption modifies dysbiosis [11,14]. Therefore, we investigated the effects of prolonged egg supplementation on growth, blood biochemical indices, and gut microbiome in school-aged Thai children.
## 2.1. Study Design and Setting
This cluster randomized controlled trial with parallel design was conducted at six rural primary schools in Nakhon Pathom (Central), Chachoengsao, Chon Buri (Eastern), and Ratchaburi (Western) in Thailand from May 2019 to March 2020. This study was aimed at rural schools where the prevalence of malnutrition was still problematic. The school locations were considered rural areas due to the low population density and no franchise convenience stores within a 10-kilometer radius. We chose rural schools where >$10\%$ of all students were underweight based on the weight-for-age (W/A) measurements. The study protocol was approved by the Institutional Review Board of Siriraj Hospital, Mahidol University (COA No. Si $\frac{322}{2017}$). Written informed consent was obtained from the parents or legal guardians of the participating children prior to starting the study. This clinical trial was registered with Clinicaltrials.gov (Protocol NCT04896996). This study followed the Consolidated Standards of Reporting Trials (CONSORT) guideline for cluster randomized trials [20].
## 2.2. Sample Size Calculation
The sample size was calculated based on the ability to match the participants in three groups. The effect size of 0.1 for the significant comparison differences between many means was estimated by Cohen, D. A two-tailed significance level of 0.05 and $80\%$ power was used to calculate the sample size for repeated measures ANOVA between factors using G-Power version 3.1.
## 2.3. Participants and Intervention
We recruited students from six rural primary schools and the eligibility criteria included students ages 8–14 years. The participants were excluded if they had an egg allergy. The trial profile is presented in Figure 1. All participants in each school were recruited and randomly assigned to three groups based on the weight-for-age criteria to ensure that all groups were homogeneous: [1] whole egg (WE)—consumed 10 additional whole chicken eggs/week, [2] protein substitute (PS)—consumed a yolk-free egg substitute equivalent to 10 eggs/week, and [3] control group. A cluster randomization was chosen: each classroom in each school was assigned to a group in one of the three groups to reduce group confusion and maintain group compliance. All six schools were asked to prepare the same school lunch menus if possible to standardize the calories and nutritional composition of the meals according to the national school lunch program [6].
Before conducting the intervention, all participants were asked to maintain their usual consumption of eggs and dietary cholesterol for four weeks (washout period [week-4]). Participants who were randomized to an intervention (WE and PS) continued their usual dietary habits. The intervention was delivered individually to each classroom at their general lunch time. The WE group received cycle ready-to-eat commercial menu items (S.W. Foodtech., Co., Ltd., Bangkok, Thailand) such as hard-boiled whole eggs, scrambled eggs, stewed eggs, omelets, etc., while the PS group received ready-to-eat commercial menu items such as hard-boiled egg whites or chicken sausages. On average, WE participants received 800 to 850 kcal/d, 2100 to 2260 mg of dietary cholesterol, and 70 to 80 g of protein, while PS participants received 810 to 850 kcal/d, 50 to 220 mg of dietary cholesterol, and 70 to 80 g of protein during the 5 school days. The participants in the control group received standard school lunches according to the Thai school lunch program. No group received additional meals or supplementation on the weekends. The participants recruited in this study were followed up at the baseline, 14 weeks, and 35 weeks.
## 2.4. Diet Assessment
The participants were invited to participate in semi-structured face-to-face food recall and validated questionnaires with dietitians three times during the study period. The behavior and dietary intake of the children were obtained from a 3-day dietary record [21] to standardize calories and nutritional composition. The energy and nutrient intakes reported in each recall were summed to estimate the observed intakes of complementary feeding. The micronutrients and macronutrients were controlled by the Thai school lunch program (NECTEC), Pathumthani, Thailand) to maintain homogeneity among the schools. Finally, energy intake and nutrients were calculated using INMUCAL–Nutrient Software version 4.0 (INMU), Nakhon Pathom, Thailand.
## 2.5.1. Anthropometric Measurements
The body weight (BW) and height (HT) were measured (Tanita HD-395, Tanita Corporation, Tokyo, Japan and Institute of Nutrition, Mahidol University [INMU], Thailand). The BW and HT data were converted into percentiles for W/A, height for age (H/A) and weight for height (W/H) using the Thai Growth program software (version 1.05, INMU, Nakhon Pathom, Thailand) [22]. Furthermore, subpopulations have also been characterized according to nutritional status, including underweight, stunting, and wasting, which were defined as Z < −1.5 standard deviations (SDs).
## 2.5.2. Blood Test
Fasting blood samples were taken for DNA extraction (Supplement Method S1 and Supplement Method S2) and to evaluate hematology (hemoglobin (Hb), hematocrit (Ht), mean corpuscular volume (MCV), transferrin, prealbumin, albumin, fasting blood sugar (FBS), total cholesterol (TC), triglyceride (TG), HDL-cholesterol, LDL-cholesterol, vitamin D, and insulin-like growth factor 1 (IGF-1) were quantified in an accredited clinical laboratory (Siriraj Hospital, Bangkok, Thailand).
## 2.5.3. Gut Microbiota Analysis
In total, 15 g of feces were randomly collected in $25\%$ of the participants. Microbial DNA was isolated from 250 mg of feces using a QIAamp PowerFecal Pro DNA Kit (QIAGEN, Hilden, Germany). The samples were sent to the Centre d’expertise et de Services Génome Québec (Génome Québec, Montréal, Canada) for 16S rRNA sequencing. The V4 region of the 16S rRNA gene was amplified using primer 515F–806R, reverse-barcoded: GTGCCAGCMGCCGCGGTAA/ GGACTACHVGGGTWTCTAAT, according to the manufacturer’s protocols. AmpliconSeq sequencing was performed on the NovaSeq platform (Génome Québec, Montréal, Canada) (detail in Supplement Method S3).
## 2.6. Statistical Analysis
Prespecified analyses were performed in three subgroups, as defined by characteristics at randomization: age, sex, W/A, H/A, and W/H. Continuous variables were expressed as mean ± SD and discrete variables as percentages. ANOVA and chi-square tests were used to assess the demographic characteristics and anthropometric data. For repeated measurements, the generalized estimating equation (GEE) was used to determine the effects of group and time for the parameters measured at the baseline, week 14, and week 35. Parameters with only two time points were analyzed using paired t-tests to test. GEE was used to determine the differences in absolute changes in dependent variables between the groups. Significant differences were defined as a p-value less than 0.05. Statistical analyses were performed using STATA version 17.0 (Stata Corporation, College Station, TX, USA). The gut microbiome used the NovaSeq 6000 platform (Génome Québec, Montréal, Canada) and the sequence reads were processed using QIIME2 version 2021.4 (details in Supplement Method S3).
## 3.1. Participants
Table 1 represents the baseline characteristics of 635 participants aged 9.8 ± 1.4 years of age. Approximately 12–$21\%$ of the participants were underweight and 15–$22\%$ were stunted; in contrast, the proportion of overweight and obese participants was over $12\%$ and $6\%$, respectively, and $70\%$ had low prealbumin levels and low vitamin D levels (Table 1). These results indicate that about a third of this population faced malnutrition of macronutrients or micronutrients. The loss of follow-up was 46 participants ($7\%$) due to illness, relocation, blood draw problems, or personal reasons (Figure 1). No significant differences were observed in the overall mean dietary energy intake and macronutrients, including carbohydrates, protein, fat, and fiber, except cholesterol, between the groups during the study period (Supplement Table S1). Significant differences in the cholesterol levels (mg/day) were observed in the WE (368.5 ± 92.4 mg/day) as compared to the PS (230.3 ± 62.6 mg/day) and control group (236.9 ± 65.2 mg/day), ($p \leq 0.001$).
## 3.2.1. Whole Egg Consumption Improved Growth
At week 35, the child growth and malnutrition improved markedly in the WE and PS compared to the C group in almost all anthropometric measures (Table 2 and Supplement Table S2). We observed significant increases in BW and HT in the WE compared to the PS and C group beginning at week 14 and noticeably at week 35. The participants in the WE markedly gained a mean of 21.7 ± $13.5\%$ (4.4 ± 13.7 kg), while participants in the PS and C groups gained a mean of 20.9 ± $15.2\%$ (3.6 ± 13.5 kg) and 19.5 ± $12.4\%$ (3.6 ± 13.3 kg), respectively (WE vs. PS, $p \leq 0.001$; WE vs. C group, $p \leq 0.001$). The HT in WE increased by 24.6 ± $8.5\%$ (6.9 ± 13.8 cm), while HT in the PS and C group increased by 22.7 ± $9.7\%$ (3.7 ± 13.6 cm) and 21.6 ± $9.3\%$ (3.4 ± 13.5 cm), respectively (WE vs. PS, $p \leq 0.001$; WE vs. C group, $p \leq 0.001$, [Figure 2A,B]). The increase in WE was significantly higher than the reference value recommended by the WHO for children in that age group. No significant differences in BW or HT were observed between the PS and C group after the intervention. In a subpopulation analysis (Figure 2C–E), a higher proportion of participants in the WE than in the PS and C group dramatically improved underweight, stunting, and wasting by 37–$41\%$, 39–$47\%$, and 35–$44\%$ (vs. PS [26–$36\%$, 22–$36\%$, and 27–$31\%$] and C [24–$37\%$, 16–$37\%$, and 26–$38\%$]), respectively. Furthermore, children who were overweight, obese, or with a tall stature grew more in both WE and PS than in the C group. WE had a greater improvement in H/A and W/A while PS had a remarkable improvement in BW but not in HT. In brief, child growth and malnutrition markedly improved in prolonged egg supple-mentation.
## 3.2.2. Plasma Protein
At the baseline, the prealbumin levels < 2.91 μmol/L, as a sensitive indicator of low nutritional status, were found in $5\%$, $6\%$, and $6\%$ in the WE, PS and C groups, respectively. The plasma concentrations of both prealbumins increased significantly by 0.24 μmol/L ($95\%$ CI, 0.12 to 0.35) in WE compared to the PS and C groups at week 14 and 35 ($p \leq 0.001$ [Table 2 and Supplement Table S3]).
## 3.2.3. Cardiometabolic Variables
TC, TG, and HDL levels markedly increased at week 14 compared to the baseline in all groups ($p \leq 0.05$), while the HDL levels increased significantly only in the WE group but not in the PS and C groups at week 14. Subsequently, at week 35, the TC levels returned to similar levels in all groups compared to the baseline (ns), while the TG levels showed a marked decrease in the PS and WE groups but not in the C group, compared to the baseline and week 14 ($p \leq 0.05$). Surprisingly, the HDL levels increased in the WE group at week 35 (0.08 mmol/L ($95\%$ CI, 0.03 to 0.13 [$$p \leq 0.001$$]). No significant differences in LDL-C concentration were observed in all groups. However, the mean HDL-C concentration at week 35 had trend increases in the WE group (1.48 ± 0.21 mmol/L) as compared to PS (1.46 ± 0.26 mmol/L) and the C group (1.47 ± 0.26 mmol/L) (WE vs. PS, $$p \leq 0.410$$; WE vs. C, $$p \leq 0.510$$) shown in Table 2, Figure 2F–I, and Supplement Table S3. Therefore, prolonged egg supplementation modestly improved the lipid levels.
## 3.2.4. Gut Microbiota
A total of 455658 ASVs were detected, corresponding to 2 kingdoms, 29 phyla, 61 classes, 137 orders, 233 families, and 519 genera. Of the 9 genera with the highest abundance in the host group (Figure 3A), there was a significant change in the relative abundance between the baseline and week 35 in WE. The Bifidobacterium, found to have a positive effect on the child growth in undernourished children [23], increased up to 1.28 times and Prevotella increased 2.63 times and 2.68 times in the WE and C groups, respectively. After egg supplementation in WE, Prevotella increased, as reported in an earlier study [24]. Both the alpha or beta bacterial diversity in the WE, PS, and C groups did not significantly change (Figure 3B,C). *The* genera with higher abundances after supplementation represent a positive direction in the bar graph. In contrast, the genera with lower abundances after supplementation were represented in a negative direction. The abundance of Agathobacter, Candidatus Soleaferrea, and Clostridia vadinBB60 was significantly increased in control group. Enterobacteriaceae decreased significantly in the control group. Furthermore, the abundance of genera of Eubacterium Ventriosum, Anaerofilum, and Incertae Sedis increased significantly in the control and PS groups (Figure 3D). These results indicated that prolonged egg supplementation promoted healthy gut microbiota.
## 4. Discussion
This randomized controlled trial (RCT) was the first long-term intervention that provided 2 additional whole eggs per school day for 35 weeks, beginning in the first semester and continuing through the second semester in multiple regions of Thailand. We confirmed that this had a positive biological impact on adolescent growth, particularly improving stunting and underweight. This intervention was associated with improved biomarkers, including lipoproteins, microbiota, and healthy dietary patterns in children.
The World Health Organization (WHO) reported a $22.9\%$ prevalence of stunting (H/A Z < −2SD from the median of WHO child growth standards) among children under 5 years of age and a trend in child malnutrition that will be greater than 10 to $50\%$ in Africa, the eastern Mediterranean, and Southeast Asia, including Thailand [25]. We observed that more than $10\%$ of rural primary school children were underweight, stunted or wasted, had low vitamin D levels, low prealbumin levels, or were anemic. These conditions involved an inadequate intake of macronutrients and micronutrients. Our results showed that additional egg consumption may influence healthier dietary patterns. In Thailand, eggs are often eaten with rice, a filling meal that can reduce the need for snacks and desserts. In fact, a previous study in U.S. children showed that egg consumption was significantly associated with higher amounts of several nutrients, including protein, total and saturated fat, alpha-linolenic acid, DHA, lutein + zeaxanthin, choline, potassium, phosphorus, selenium, riboflavin, vitamin D, vitamin A, and vitamin E [26]. Similarly, a cross-sectional survey in the U.S. reported that eggs and foods containing eggs can be an important part of a healthy dietary pattern when balanced with other foods rich in nutrients [27]. Currently, in the post-COVID-19 pandemic, the world is facing socioeconomic inequality, which can lead to starvation and malnutrition. Many low-cost commercial foods are high in calories; on the contrary, they often have poor nutrient profiles.
This finding confirms an RCT that egg consumption significantly improved growth in young children [13]. In Ecuador, supplementation with 1 egg per day in infants for 6 months was reported to have reduced stunting by $47\%$ and increased linear growth by 0.63 length-for-age Z (LAZ) [28]. Mosites et al. showed that in western Kenya the height gain of the child was associated with the consumption of milk and eggs [29] and that an egg was considered a reference food, comparable to breast milk. An egg white is made up of albumin protein—related to muscle mass gain, cell regeneration, and the maintenance of immunity [30]. However, egg yolks also have protein in their composition, as well as vitamin A, vitamin E, vitamin D, and, the most expressive of this complex, choline. Choline is a nutrient that plays a role in human metabolism and cell membrane structure, and acts on the transmission of nerve impulses [31]. During pregnancy and lactation, it is essential for the development of the nervous system of the fetus [32,33]. In older adults, Liu et al. suggest that choline plays a role in maintaining the nerve impulse circuit, preventing age-related cognitive decline, and maintaining memory [34]. The egg is one of the few foods that has vitamin D (fat-soluble vitamins), responsible for the deposition of bone calcium and the mineralization of the skeleton [35]. It also has vitamins A and E, which have an antioxidant action. Moreover, the egg has in its composition several minerals such as calcium, phosphorus, iron, magnesium, manganese, zinc, copper, and selenium - found in the egg and which meet $50\%$ of the needs of adults and children [36].
Furthermore, we found that egg supplementation improved the blood lipid profiles, including HDL-C levels [37]. Similarly, daily egg consumption promotes HDL lipid composition and function [38]. Fernandez et al. reported that eating whole eggs increases the size of HDL lipoprotein particles and increases the activity of lecithin-cholesterol acyltransferase (LCAT) [39]. The yolk has mono- and polyunsaturated fatty acids, considered good fats for heart health, a small amount of saturated fat, and has cholesterol in its composition, which has already been proven by numerous studies not to be associated with an increased risk of cardiovascular disease and stroke [40,41]. Recently, U.S. cohort studies and meta-analysis data showed that moderate egg consumption (up to one egg per day) is not associated with a potentially lower risk of cardiovascular disease in Asian populations [41].
Regarding the structure of the gut microbiome after whole egg supplementation, we observed increased levels of Bifidobacterium in the WE group. Bifidobacterium is a human milk oligosaccharide (HMO) used by bacteria [42]. They are considered to have health-promoting benefits in humans [43]. These microbes produce a variety of useful metabolites, which benefit the host’s immune system [42]. On the contrary, a decrease in this microbiota has been associated with a high incidence of diseases, such as irritable bowel syndrome (IBS) [44]. In Thai children, the abundance of *Bifidobacterium is* negatively correlated with the consumption of fish and beef [45]. In our study, the abundance of Lachnospira was significantly higher after WE supplementation. Lachnospira are anaerobic, fermentative, and chemoorganotrophic [46]. Normally, this genus is well known as one of the SCFA producers throughout the whole grain fermenter [47]. Vanegas et al. reported that short-term supplementation of whole or refined grains increased the abundance of Lachnospira significantly [48]. Our results showed that the abundance of Varibaculum was significantly lower after whole egg supplementation. Furthermore, there is little evidence of the relationship between Varibaculum and host health at the genus level. Kang et al. reported that the abundance of Varibaculum was significantly higher in patients with invasive cervical cancer (CAN) compared to healthy controls [49].
This research has strengths which suggest that its findings may have important implications for public policy. First, this is a large-scale, one-year randomized controlled trial. We collected data from rural schoolchildren, including central, eastern, and western Thailand, homogenized by geographical and food patterns. Second, we used tools for the evaluation of food intake to achieve a high level of precision of nutrition data. Third, this study showed an important verified discovery that the fight against malnutrition, especially in low- and middle-income communities, could be achieved by using locally available high-quality proteins such as eggs, milk, and chicken. This also impacts healthier food choices and children’s behavior. However, there are some limitations of this study. First, whole egg consumption in the protein substitute and control groups on weekends and during school breaks is difficult to control. Second, the whole egg group and the protein substitute group had at least one secondary school class, suggesting that these may be confounding variables for anthropometric analysis.
## 5. Conclusions
In conclusion, long-term whole egg supplementation is a feasible, low-cost, and effective intervention to significantly increase growth and improve important biomarkers in young school-age children without adverse effects on blood cholesterol levels. It also promotes intestinal microbial diversity by maintaining an intestinal microbiota composition that benefits health. More information is needed on the mechanistic effects of egg consumption on gut microbiota and growth.
## References
1. Winichagoon P.. **Scaling up a community-based program for maternal and child nutrition in Thailand**. *Food Nutr. Bull.* (2014.0) **35** S27-S33. DOI: 10.1177/15648265140352S104
2. Poh B.K., Rojroonwasinkul N., Le Nyugen B.K., Budiman B., Ng L.O., Soonthorndhada K., Xuyen H.T., Deurenberg P., Parikh P.. **Relationship between anthropometric indicators and cognitive performance in Southeast Asian school-aged children**. *Br. J. Nutr.* (2013.0) **110** S57-S64. DOI: 10.1017/S0007114513002079
3. Padhani Z.A., Das J.K., Akhtar S., Ismail T., Bhutta Z.A.. **Tackling protein-calorie malnutrition during world crises**. *Ann. Nutr. Metab.* (2022.0) **78** 27-37. DOI: 10.1159/000522242
4. Mayurasakorn K., Pinsawas B., Mongkolsucharitkul P., Sranacharoenpong K., Damapong S.N.. **School closure, COVID-19 and lunch programme: Unprecedented undernutrition crisis in low-middle income countries**. *J. Paediatr. Child Health* (2020.0) **56** 1013-1017. DOI: 10.1111/jpc.15018
5. **Levels and Trends in Child Malnutrition 2019**
6. Petchoo J., Kaewchutima N., Tangsuphoom N.. **Nutritional quality of lunch meals and plate waste in school lunch programme in Southern Thailand**. *J. Nutr. Sci.* (2022.0) **11** E35. DOI: 10.1017/jns.2022.31
7. Shinde S., Wang D., Fawzi W.W.. **School-based interventions targeting double burden of malnutrition and educational outcomes of adolescents in low- and middle-income countries: Protocol for a systematic review**. *Syst. Rev.* (2021.0) **10** 204. DOI: 10.1186/s13643-021-01756-9
8. Rojroongwasinkul N., Kijboonchoo K., Wimonpeerapattana W., Purttiponthanee S., Yamborisut U., Boonpraderm A., Kunapan P., Thasanasuwan W., Khouw I.. **SEANUTS: The nutritional status and dietary intakes of 0.5–12-year-old Thai children**. *Br. J. Nutr.* (2013.0) **110** S36-S44. DOI: 10.1017/S0007114513002110
9. Carrera P.M., Gao X., Tucker K.L.. **A study of dietary patterns in the Mexican-American population and their association with obesity**. *J. Am. Diet. Assoc.* (2007.0) **107** 1735-1742. DOI: 10.1016/j.jada.2007.07.016
10. Zeisel S.H., Mar M.H., Howe J.C., Holden J.M.. **Concentrations of choline-containing compounds and betaine in common foods**. *J. Nutr.* (2003.0) **133** 1302-1307. DOI: 10.1093/jn/133.5.1302
11. Iannotti L.L., Lutter C.K., Bunn D.A., Stewart C.P.. **Eggs: The uncracked potential for improving maternal and young child nutrition among the world’s poor**. *Nutr. Rev.* (2014.0) **72** 355-368. DOI: 10.1111/nure.12107
12. Lutter C.K., Iannotti L.L., Stewart C.P.. **Cracking the egg potential during pregnancy and lactation**. *Sight Life* (2016.0) **30** 74-80
13. Iannotti L.L., Lutter C.K., Stewart C.P., Gallegos Riofrío C.A., Malo C., Reinhart G., Palacios A., Karp C., Chapnick M., Cox K.. **Eggs in Early Complementary Feeding and Child Growth: A Randomized Controlled Trial**. *Pediatrics* (2017.0) **140** e20163459. DOI: 10.1542/peds.2016-3459
14. McKune S.L., Stark H., Sapp A.C., Yang Y., Slanzi C.M., Moore E.V., Omer A., Wereme N’Diaye A.. **Behavior change, egg consumption, and child nutrition: A cluster randomized controlled trial**. *Pediatrics* (2020.0) **146** e2020007930. DOI: 10.1542/peds.2020-007930
15. Kane A.V., Dinh D.M., Ward H.D.. **Childhood malnutrition and the intestinal microbiome**. *Pediatr. Res.* (2015.0) **77** 256-262. DOI: 10.1038/pr.2014.179
16. Tang W.W., Kitai T., Hazen S.L.. **Gut microbiota in cardiovascular health and disease**. *Circ. Res.* (2017.0) **120** 1183-1196. DOI: 10.1161/CIRCRESAHA.117.309715
17. Iddrisu I., Monteagudo-Mera A., Poveda C., Pyle S., Shahzad M., Andrews S., Walton G.E.. **Malnutrition and Gut Microbiota in Children**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13082727
18. Thomas M.S., DiBella M., Blesso C.N., Malysheva O., Caudill M., Sholola M., Cooperstone J.L., Fernandez M.L.. **Comparison between Egg Intake versus Choline Supplementation on Gut Microbiota and Plasma Carotenoids in Subjects with Metabolic Syndrome**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14061179
19. Liu X., Shao Y., Sun J., Tu J., Wang Z., Tao J., Chen J.. **Egg consumption improves vascular and gut microbiota function without increasing inflammatory, metabolic, and oxidative stress markers**. *Food Sci. Nutr.* (2022.0) **10** 295-304. DOI: 10.1002/fsn3.2671
20. Schulz K.F., Altman D.G., Moher D.. **CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials**. *BMJ* (2010.0) **340** c332. DOI: 10.1136/bmj.c332
21. Gibson R.S.. *Principles of Nutritional Assessment* (2005.0) 41-46
22. 22.
Institute of Nutrition Research Mahidol University (INMU)
INMU Thai Growth Program for Nutritional Assessment (Using Weight for Height References from Thai National Survey, Department of Health, Ministry of Public Health)Institute of Nutrition ResearchNakhon Pathom, Thailand2002. *INMU Thai Growth Program for Nutritional Assessment (Using Weight for Height References from Thai National Survey, Department of Health, Ministry of Public Health)* (2002.0)
23. Onubi O.J., Poobalan A.S., Dineen B., Marais D., McNeill G.. **Effects of probiotics on child growth: A systematic review**. *J. Health Popul. Nutr.* (2015.0) **34** 8. DOI: 10.1186/s41043-015-0010-4
24. Perin J., Burrowes V., Almeida M., Ahmed S., Haque R., Parvin T., Biswas S., Azmi I.J., Bhuyian S.I., Talukder K.A.. **A Retrospective Case–Control Study of the Relationship between the Gut Microbiota, Enteropathy, and Child Growth**. *Am. J. Trop. Med. Hyg.* (2020.0) **103** 520-527. DOI: 10.4269/ajtmh.19-0761
25. **World Health Statistics 2018: Monitoring Health for the SDGs, Sustainable Development Goals: World Health Organization 2018**
26. Papanikolaou Y., Fulgoni III V.L.. **Egg consumption in US children is associated with greater daily nutrient intakes, including protein, lutein+ zeaxanthin, choline, α-linolenic acid, and docosahexanoic acid**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11051137
27. Papanikolaou Y., Fulgoni III V.L.. **Patterns of Egg Consumption Can Help Contribute to Nutrient Recommendations and Are Associated with Diet Quality and Shortfall Nutrient Intakes**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13114094
28. Iannotti L.L., Chapnick M., Nicholas J., Gallegos-Riofrio C.A., Moreno P., Douglas K., Habif D., Cui Y., Stewart C., Lutter C.K.. **Egg intervention effect on linear growth no longer present after two years**. *Matern. Child Nutr.* (2020.0) **16** e12925. DOI: 10.1111/mcn.12925
29. Mosites E., Thumbi S.M., Otiang E., McElwain T.F., Njenga M.K., Rabinowitz P.M., Rowhani-Rahbar A., Neuhouser M.L., May S., Palmer G.H.. **Relations between household livestock ownership, livestock disease, and young child growth**. *J. Nutr.* (2016.0) **146** 1118-1124. DOI: 10.3945/jn.115.225961
30. Børsheim E., Tipton K.D., Wolf S.E., Wolfe R.R.. **Essential amino acids and muscle protein recovery from resistance exercise**. *Am. J. Physiol. Endocrinol. Metab.* (2002.0) **283** E648-E657. DOI: 10.1152/ajpendo.00466.2001
31. Zeisel S.H., Da Costa K.A.. **Choline: An essential nutrient for public health**. *Nutr. Rev.* (2009.0) **67** 615-623. DOI: 10.1111/j.1753-4887.2009.00246.x
32. Jadavji N.M., Deng L., Malysheva O., Caudill M.A., Rozen R.. **MTHFR deficiency or reduced intake of folate or choline in pregnant mice results in impaired short-term memory and increased apoptosis in the hippocampus of wild-type offspring**. *Neurosci* (2015.0) **300** 1-9. DOI: 10.1016/j.neuroscience.2015.04.067
33. Korsmo H.W., Jiang X., Caudill M.A.. **Choline: Exploring the growing science on its benefits for moms and babies**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11081823
34. Liu L., Qiao S., Zhuang L., Xu S., Chen L., Lai Q., Wang W.. **Choline intake correlates with cognitive performance among elder adults in the United States**. *Behav. Neurol.* (2021.0) **2021** 2962245. DOI: 10.1155/2021/2962245
35. Lips P., Van Schoor N.M.. **The effect of vitamin D on bone and osteoporosis**. *Best Pract Res. Clin. Endocrinol. Metab.* (2011.0) **25** 585-591. DOI: 10.1016/j.beem.2011.05.002
36. Papanikolaou Y., Fulgoni III V.L.. **Increasing egg consumption at breakfast is associated with increased usual nutrient intakes: A modeling analysis using NHANES and the USDA child and adult care food program school breakfast guidelines**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13041379
37. Mayurasakorn K., Sitphahul P., Hongto P.O.. **Supplement of three eggs a week improves protein malnutrition in Thai children from rural areas**. *J. Med. Assoc. Thai.* (2011.0) **93** 301-309. DOI: 10.1096/fasebj.24.1_supplement.94.8
38. Andersen C.J., Blesso C.N., Lee J., Barona J., Shah D., Thomas M.J., Fernandez M.L.. **Egg consumption modulates HDL lipid composition and increases the cholesterol-accepting capacity of serum in metabolic syndrome**. *Lipids* (2013.0) **48** 557-567. DOI: 10.1007/s11745-013-3780-8
39. Fernandez M.L.. **Effects of eggs on plasma lipoproteins in healthy populations**. *Food Funct.* (2010.0) **1** 156-160. DOI: 10.1039/c0fo00088d
40. Qin C., Lv J., Guo Y., Bian Z., Si J., Yang L., Chen Y., Zhou Y., Zhang H., Liu J.. **Associations of egg consumption with cardiovascular disease in a cohort study of 0.5 million Chinese adults**. *Heart* (2018.0) **104** 1756-1763. DOI: 10.1136/heartjnl-2017-312651
41. Drouin-Chartier J.P., Chen S., Li Y., Schwab A.L., Stampfer M.J., Sacks F.M., Rosner B., Willett W.C., Hu F.B., Bhupathiraju S.N.. **Egg consumption and risk of cardiovascular disease: Three large prospective US cohort studies, systematic review, and updated meta-analysis**. *BMJ* (2020.0) **368** m513. DOI: 10.1136/bmj.m513
42. Ruiz L., Delgado S., Ruas-Madiedo P., Sánchez B., Margolles A.. **Bifidobacteria and their molecular communication with the immune system**. *Front. Microbiol.* (2017.0) **8** 2345. DOI: 10.3389/fmicb.2017.02345
43. Wong C.B., Odamaki T., Xiao J.Z.. **Insights into the reason of Human-Residential Bifidobacteria (HRB) being the natural inhabitants of the human gut and their potential health-promoting benefits**. *FEMS Microbiol. Rev.* (2020.0) **44** 369-385. DOI: 10.1093/femsre/fuaa010
44. Rodiño-Janeiro B.K., Vicario M., Alonso-Cotoner C., Pascua-García R., Santos J.. **A review of microbiota and irritable bowel syndrome: Future in therapies**. *Adv. Ther.* (2018.0) **35** 289-310. DOI: 10.1007/s12325-018-0673-5
45. La-Ongkham O., Nakphaichit M., Leelavatcharamas V., Keawsompong S., Nitisinprasert S.. **Distinct gut microbiota of healthy children from two different geographic regions of Thailand**. *Arch. Microbiol.* (2015.0) **197** 561-573. DOI: 10.1007/s00203-015-1089-0
46. Vacca M., Celano G., Calabrese F.M., Portincasa P., Gobbetti M., De Angelis M.. **The controversial role of human gut lachnospiraceae**. *Microorganisms* (2020.0) **8**. DOI: 10.3390/microorganisms8040573
47. Myhrstad M.C., Tunsjø H., Charnock C., Telle-Hansen V.H.. **Dietary fiber, gut microbiota, and metabolic regulation—Current status in human randomized trials**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12030859
48. Vanegas S.M., Meydani M., Barnett J.B., Goldin B., Kane A., Rasmussen H., Brown C., Vangay P., Knights D., Jonnalagadda S.. **Substituting whole grains for refined grains in a 6-wk randomized trial has a modest effect on gut microbiota and immune and inflammatory markers of healthy adults**. *Am. J. Clin. Nutr.* (2017.0) **105** 635-650. DOI: 10.3945/ajcn.116.146928
49. Kang G.U., Jung D.R., Lee Y.H., Jeon S.Y., Han H.S., Chong G.O., Shin J.H.. **Dynamics of Fecal microbiota with and without invasive cervical cancer and its application in early diagnosis**. *Cancers* (2020.0) **12**. DOI: 10.3390/cancers12123800
|
---
title: A Sarcopenia Index Derived from Malnutrition Parameters in Elderly Haemodialysis
Patients
authors:
- M. L. Sánchez-Tocino
- S. Mas-Fontao
- C. Gracia-Iguacel
- M. Pereira
- I. González-Ibarguren
- A. Ortiz
- M. D. Arenas
- E. González Parra
journal: Nutrients
year: 2023
pmcid: PMC10005100
doi: 10.3390/nu15051115
license: CC BY 4.0
---
# A Sarcopenia Index Derived from Malnutrition Parameters in Elderly Haemodialysis Patients
## Abstract
[1] Background: Persons with chronic kidney disease may have sarcopenia characterized by the loss of muscle mass and loss of muscle strength. However, EWGSOP2 criteria to diagnose sarcopenia are technically challenging, especially in elderly persons on hemodialysis. Sarcopenia may be associated with malnutrition. We aimed at defining a sarcopenia index derived from malnutrition parameters for use in elderly haemodialysis patients. [ 2] Methods: A retrospective study of 60 patients aged 75 to 95 years treated with chronic hemodialysis was conducted. Anthropometric and analytical variables, EWGSOP2 sarcopenia criteria and other nutrition-related variables were collected. Binomial logistic regressions were used to define the combination of anthropometric and nutritional parameters that best predict moderate or severe sarcopenia according to EWGSOP2, and performance for moderate and severe sarcopenia was assessed by the area under the curve (AUC) of receiver operating characteristic (ROC) curves. [ 3] Results: The combination of loss of strength, loss of muscle mass and low physical performance correlated with malnutrition. We developed regression-equation-related nutrition criteria that predicted moderate sarcopenia (elderly hemodialysis sarcopenia index-moderate, EHSI-M) and severe sarcopenia (EHSI-S) diagnosed according to EWGSOP2 with an AUC of 0.80 and 0.866, respectively. [ 4] Conclusions: *There is* a close relationship between nutrition and sarcopenia. The EHSI may identify EWGSOP2-diagnosed sarcopenia from easily accessible anthropometric and nutritional parameters.
## 1. Introduction
The word sarcopenia derives from Greek, meaning a scarcity (penia) of flesh (sarx). Irwin Rosenberg first used the term sarcopenia in 1988, identifying a clinical condition characterized by the loss of skeletal muscle mass in the context of ageing [1]. However, the definition of sarcopenia has evolved over the years, and several definitions have been proposed. The available definitions always include muscle mass; in addition, some include muscle strength and most include physical performance [2,3,4,5,6]. Sarcopenia is primarily associated with old age [7], and in 2016, it was listed as a disease in the International Classification of Diseases (CIE-10, MC version) with the code M62.84 [8,9].
In February 2018, the European Working Group on Sarcopenia revised and updated its definition of sarcopenia (EWGSOP2), which is the most widely used, in a new consensus document [10]. Sarcopenia is defined by EWGSOP2 as a skeletal muscle disease understood as the loss of muscle mass and loss of strength. Therefore, sarcopenia combines the concepts of myopenia (decreased muscle mass) and dynapenia (decreased muscle strength). Assessment of sarcopenia by EWGSOP2 may be technically challenging, especially for haemodialysis (HD) patients, requiring techniques such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), magnetic resonance imaging (MRI) or computed tomography (CT), which are not frequently available in routine clinical settings for use for this purpose, in addition to functional evaluations that may not be possible for some HD patients. There is thus a need for simpler parameters that guide clinicians in peripheral or low-resource HD units.
The prevalence of sarcopenia in HD patients ranges from $4\%$ to $64\%$ [11,12,13] depending on the diagnostic criteria applied. Following the EWGSOP2 definition, we previously reported a prevalence of sarcopenia in elderly HD patients of $20\%$ [14].
In chronic kidney disease (CKD) patients, multiple causes lead to an imbalance between muscle synthesis and catabolism, and the term uraemic myopenia has been coined [15,16,17,18,19,20,21,22,23,24,25]. This leads to decreased muscle quantity, an altered muscle structure, muscle atrophy and reduced muscle strength [26,27]. However, there is not always a linear relationship between skeletal muscle size and strength. These patients may have a disproportionate loss of strength despite having muscle mass within normal limits [14].
Chronic renal failure is characterized by nutritional disturbances and systemic inflammation accompanied by increased catabolism, which increase morbidity and mortality. Malnutrition and inflammation may contribute to sarcopenia. However, there are several definitions of malnutrition in CKD patients that include muscle assessment, and, thus, sarcopenia and malnutrition are interconnected concepts [28]. The International Society of Renal Metabolism and Nutrition has defined protein energy wasting as a pathological state where there is a continued depletion of both protein stores and energy reserves [29,30], including the simultaneous loss of muscle. In our population, $37\%$ of HD patients had protein energy wasting [31]. The management of sarcopenia includes optimizing the diet [32].
In the present study, we explored the relationship between biochemical and body composition criteria for the diagnosis of malnutrition and developed, from an HD population over 75 years of age, a simple index, the elderly hemodialysis sarcopenia index (EHSI), that provides information on sarcopenia diagnosed according to EWGSOP2 criteria with a high area under the curve (AUC).
## 2.1. Study Design
A retrospective study was conducted on patients in chronic hemodialysis in three outpatient units and one hospital of the Fundación Renal Íñigo Álvarez de Toledo in Spain (Hospital Fundación Jiménez Díaz de Madrid and outpatient units Centro Santa Engracia (Madrid), Centro de Bejar and Centro de Ciudad Rodrigo (Salamanca)) in February 2019, i.e., prior to the coronavirus disease 2019 (COVID-19) pandemic. Inclusion criteria were age from 75 to 95 years, the capability to perform physical fitness assessment tests or dynamometry and patients who had been on HD for more than 3 months. All patients were dialyzed for 210 min per session, 3 days a week, with a maximum blood flow of 300 mL/min with conventional dialysis.
The study was approved by the ethics committee of the Hospital Universitario Fundación Jiménez Díaz (acta no. $\frac{03}{19}$) and complied with the standards recognized by the Declaration of Helsinki of the World Medical Association, as well as the Standards of Good Clinical Practice, in addition to compliance with Spanish legislation on biomedical research (Law $\frac{14}{2007}$). All participants signed informed consent for their participation.
## 2.2. Study Variables
Both sarcopenia and nutritional status were assessed.
The EWGSOP2 three-stage diagnostic work-up was used to assess sarcopenia: (A) Probability: Loss of grip strength was determined by the hand grip (HG) using an electric CAMRY® Model EH101 dynamometer with the participant standing with their arm extended along the body and not supporting it or moving the wrist. Maximum grip strength was maintained for 3 s, with a rest of 1 min between each repetition, making two attempts in both arms. The strongest grip achieved by the dominant arm was the one used for the study [33]. The cutoff points that determine the probability of sarcopenia (dynapenia) are strength less than 27 kg in men and less than 16kg in women [34].
(B) Confirmation: An assessment was performed of the appendicular skeletal muscle mass by bioimpedance (ASM) defined as the sum of the muscle mass of the four limbs [7]. A MALTRON® bioimpedance device, model BioScan touch i8, was used in the second HD session of the week, in the second hour of treatment, since the device allows an assessment while HD is underway. The Maltron bioscan 916 device is validated for assessing body composition in situations when extracellular water (ECW) is changing. Thanks to this software, dry weight can be calculated when no more volume is extracted from the ECW despite ongoing ultrafiltration, and no change in resistance is observed [35]. The cutoff points to diagnose sarcopenia as the loss of muscle mass (myopenia) are ASM less than 20 kg in males and less than 15 kg in females [36].
(C) Severity: Physical performance was assessed. It was determined by the variable gait speed (GS) evaluated as the time required to walk 4 m and expressed in meters per second, considering whether any assistance (cane, walker, another person, etc.) was required to maintain balance while walking. Walking included one meter in front and one meter behind the four meters that were assessed so that the results would not be influenced by acceleration and deceleration [37]. The cutoff point that determines severe sarcopenia is a speed <0.8 m/s [6].
## Nutritional Status Was Assessed by the Malnutrition–Inflammation Score (MIS), Anthropometric Variables, Biochemical Variables and Body Composition
(A) The MIS is a fully quantitative score adapted from the subjective global assessment used for the early identification of malnutrition–inflammation. The MIS is associated with nutritional parameters, inflammatory status and mortality [38]. It is a validated questionnaire for the dialysis population consisting of 10 components, each scored from 0 to 3, for a range of values from 0 to 30: weight change, appetite, gastrointestinal symptoms, functional capacity related to nutritional factors, comorbidities including years on dialysis, subcutaneous fat loss, muscle mass, body mass index (BMI), serum albumin and total iron binding capacity. Above 10 points, patients have extreme malnutrition; from 7 to 10 points, very severe malnutrition; from 5 to 7 points, moderate–severe malnutrition; from 2 to 5 points, mild–moderate malnutrition; and below 2 points, normonutrition [39].
(B) Anthropometric variables. The BMI was determined as weight (kg)/height (m)2. BMI is used as a marker of obesity. In Caucasian populations, the BMI cutoff point for obesity is 30 kg/m2 [40]. A weight loss of at least $5\%$ in 12 months or less or a BMI < 20 kg/m2 is diagnostic of cachexia [41].
Brachial circumference (BC) is an indicator of decreased tissue protein reserves used in older adults, as it is easy to measure. Its use in conjunction with other measurements, such as the tricipital and bicipital folds, may provide more complete information on caloric-protein reserves [42].
The waist-to-hip ratio (WHI) was assessed from waist and hip circumferences. The WHI assesses intra-abdominal fat. The waist circumference (WC) and WHI better assess cardiovascular risk than the BMI [43].
Tricipital, abdominal and subscapular skin folds indicate total body fat. They were assessed with a caliper by calculating the average value of 3 measurements in millimeters. In the elderly population, skin folds may be less reliable [44].
(C) Analytical variables: The following parameters were measured: serum albumin, proteins, hemoglobin, hematocrit, cholesterol, lymphocytes, protein catabolism rate and 25OH vitamin D [41,45,46]. In addition, dialytic efficacy was assessed using Daurgidas’ Kt/Vurea [47].
(D) Body composition was determined by bioimpedance, assessing muscle mass, fat mass, fat-free mass, extracellular mass, total cell mass, body water, extracellular water, intracellular water, overhydration and hydration of lean mass. Body impedance (Z) is a function of 2 components or vectors—resistance (R) and reactance (Xc)—according to the equation Z2 = R2 + Xc2. R represents the resistance of the tissues to the passage of an electric current and *Xc is* the additional opposition due to the capacitance of these tissues and cell membranes [48]. Electrical conductivity is higher in lean tissue than in adipose tissue, since the former contains almost all the water and electrolytes [49].
## 2.3. Statistics
Statistical analyses were performed with the IBM SPSS Statistics v20 program (IBM, Armonk, NY, USA). Quantitative variables are presented as the mean and standard deviation or median (interquartile range). Qualitative variables are presented as absolute numbers and percentages. Student’s t-test or Wilcoxon test was used to compare two quantitative variables, and Pearson’s or Spearman coefficient was used for correlation studies. The association between qualitative variables was evaluated using the chi-square test. The level of statistical significance was established at p ≤ 0.05. Binomial logistic regressions were used to define the combination of anthropometric and nutritional parameters that best predict moderate or severe sarcopenia according to EWGSOP2, and performance for moderate and severe sarcopenia was assessed by the area under the curve (AUC) of receiver operating characteristic (ROC) curves.
## 3. Results
Table 1 shows the characteristics of the study population, also divided into men and women. Men and women differed in Kt/Vurea and anthropometry, as expected. There were also differences in lean and fluid composition but not in sarcopenia and/or frailty criteria.
Table 2 describes the analytical, clinical, body composition and nutrition data of participants in two groups with normonutrition/mild–moderate malnutrition or severe/extreme malnutrition according to the MIS (≤5 and >5 points, respectively). Malnourished patients had a higher KTVurea, as expected due to the lower muscle mass, urea distribution volume, albumin and weight, less muscle and body water and greater frailty measured with FRAIL.
Table 3 shows the correlations between nutritional parameters, including the MIS, with the sarcopenia criteria used by EWGSOP2 for probability, confirmation and severity. There were no significant correlations of nutritional parameters with those of the probability and confirmation of sarcopenia measured by muscle mass. However, severe sarcopenia correlated with parameters of malnutrition. The combination of loss of strength, muscle mass and physical performance correlated with malnutrition.
## Prediction of Sarcopenia
In subjects presenting sarcopenia criteria according to EWGSOP2, a mild correlation with nutritional parameters was observed; however, the presence of malnutrition was not significantly associated with the presence of sarcopenia. Moderate sarcopenia was observed in $30\%$ of normonourished patients and in $50\%$ of malnourished patients. Severe sarcopenia was observed in $20\%$ of the normonourished population and in $40\%$ of those who are malnourished.
We used binomial logistic regressions to define the combination of anthropometric and nutritional parameters that best predicts moderate or severe sarcopenia according to EWGSOP2.
A combination of gender, age, serum albumin, phosphate and cholesterol (3.0055 + 1.2218[gender] − 0.1358[age] + 0.5977[albumin] + 0.7246[phosphate] + 0.0202[cholesterol]) predicted moderate sarcopenia with a specificity of $87\%$ and sensitivity of $61\%$, yielding an AUC of 0.8 with a precision of 0.7119 for a cutoff value of 0.745. We called this the elderly hemodialysis sarcopenia index-moderate, EHSI-M.
Severe sarcopenia was predicted with a specificity of $94\%$ and a sensitivity of $76\%$ (AUC 0.866 and a precision of 0.815 for a cutoff of 0.71) by 12.3261 − 1.8643[gender] − 0.1830[age] − 0.0430[MIS] + 0.257[cholesterol] + 1.1139[lymphocytes] + 1.1987[subscapular fold]. We called this the elderly hemodialysis sarcopenia index-severe, EHSI-S.
## 4. Discussion
The present study confirmed the close relationship between nutrition and sarcopenia in patients over 75 years of age on hemodialysis. Additionally, we provide a tool, the elderly hemodialysis sarcopenia index (EHSI), that may be useful to estimate the risk of sarcopenia according to EWGSOP2 in elderly hemodialysis patients. This tool will be useful for centers that do not have regular access to bioimpedance that can be performed during the hemodialysis session for monitoring sarcopenia. A holistic intervention, including a nutritional intervention, is important to avoid sarcopenia and the effects of sarcopenia on frailty, quality of life, dependence and mortality in these patients [50].
Nutritional parameters did not correlate with the suspicion and confirmation of sarcopenia according to EWGSOP2 as assessed by the loss of strength and muscle mass. However, severe sarcopenia (loss of strength, mass and gait speed) correlated with different parameters of malnutrition. Only the combination of loss of strength, muscle mass and physical performance correlated with malnutrition. These data are consistent with prior data suggesting that malnutrition contributed to sarcopenia/severe sarcopenia in 315 Asian maintenance HD patients by reducing muscle mass, strength and physical performance [51]. We observed moderate sarcopenia (loss of strength and loss of muscle mass) in $30\%$ of normonourished patients and in $50\%$ of malnourished patients. Severe sarcopenia was found in $20\%$ of normonourished patients and in $40\%$ of those malnourished.
The origin of sarcopenia is multifactorial. Uremic patients may lose muscle function independent of adequate muscle mass [52]. Muscle mass and strength are associated with exercise [53] but not with adequate nutrition without exercise.
Acidosis increases the risk of sarcopenia in CKD patients [54]. Alkali supplementation to treat acidosis increased lean body mass[54], mid-arm muscle circumference and lower limb muscle strength [55]. Diets rich in vegetables, fruits and greens also improved acidosis [56,57].
A high protein intake (1 to 1.2 g/kg/day) improved muscle health and prevented sarcopenia in elderly people with normal renal function [58,59]. However, this relationship has not been demonstrated in CKD patients, who may be prescribed protein intake restriction (0.55–0.6 g/kg/day, or 0.6–0.8 g/kg/day in diabetics as per 2020 guidelines) to slow CKD progression, which may negatively impact muscle mass and function [60]. In CKD patients on dialysis, a higher intake (1.0–1.2 mg/kg) is recommended [16].
Despite this reflection, the latest revision published in 2020 of the KDOQI clinical practice guidelines for nutrition in patients with CKD [60] maintains the recommendation of a protein intake in predialysis stages of 0.55–0.6 g/kg/day. We believe that in elderly patients with stage 3–5 CKD, the risk of malnutrition and sarcopenia should be assessed as a differentiating value for recommending an intense restriction of protein intake, independent of the progression, or lack thereof, of the deterioration of renal disease.
A systematic review and network meta-analysis revealed the benefits of exercise on muscle strength. However, the combination of exercise and nutrition did not improve muscle strength and physical performance above exercise alone [61]. In elderly HD patients, EWGSOP2 sarcopenia criteria improved with muscular exercise [62].
Given that diagnosing sarcopenia using EWGSOP2 criteria may be time consuming and technically challenging, efforts have been made to identify sarcopenia using simpler analytical and clinical criteria. Recently, a sarcopenia index was described in persons with cardiovascular disease. The sarcopenia index includes five independent factors (sex, age, BMI, adiponectin and sialic acid) and had a high accuracy in ROC curve analysis (sensitivity of $94.9\%$ and specificity of $69.9\%$) [63]. However, it still contained uncommon analytes (adiponectin and sialic acid) that may render the index of little value in patients with kidney failure, as their circulating levels increase as the glomerular filtration rate decreases [1,2]. In this equation, there is a negative relationship between age and the likelihood of sarcopenia; older patients possibly have less sarcopenia, as a protective factor, so those with more sarcopenia may be less likely to reach older ages. We have now developed two regression formulas to test for sarcopenia in elderly patients on hemodialysis using easily accessible clinical data. EHSI-M and EHSI-S may be useful for easy patient monitoring for sarcopenia in low-resource centers, which represent the majority of global HD units, allowing early intervention. Additionally, even high-resource centers may not have as regular access as needed to tools such as dual-energy X-ray absorptiometry. Resonance imaging and computed tomography may be needed to diagnose sarcopenia according to EWGSOP2 criteria, while bioimpedance may be problematic in HD patients given the rapid fluid changes during dialysis and the desire of patients to leave for home as early as possible after HD sessions.
Several limitations should be acknowledged, including the need for external validation of the equations to assess sarcopenia in persons from different ancestries and continents. For this purpose, we provide easy calculators that allow centers across the world to test the EHSI-M and EHSI-S and prospectively explore their prognostic value and their response to interventions. In addition, we used BIA instead of the gold standard dual-energy X-ray absorptiometry, resonance imaging and computed tomography to assess EWGSOP2. Among the strengths, tests were performed by highly trained personnel, the BIA techniques employed allowed assessments during the HD session and we provide data in an understudied but growing population, very elderly HD patients, for whom the performance of tests needed for EWGSOP2 may be problematic.
## 5. Conclusions
In conclusion, there was a close relationship between nutrition and sarcopenia. A tool is provided, developed from nutrition and simple analytical criteria, that may identify moderate and severe sarcopenia according to EWGSOP2 criteria and that can be used to monitor patients at a low cost in terms of time and technical resources. The progression of malnutrition and/or sarcopenia should be a factor to be considered when restricting protein intake in patients with ACKD G3-5.
## References
1. Rosemberg I.H.. **Epidemiologic and methodologic problems in determining nutritional status of older persons. Proceedings of a conference. Albuquerque: New Mexico, 19–21 October 1988**. *Am. J. Clin. Nutr.* (1989) **50** 1121-1235
2. Muscaritoli M., Anker S.D., Argilés J., Aversa Z., Bauer J.M., Biolo G., Boirie Y., Bosaeus I., Cederholm T., Costelli P.. **Consensus definition of sarcopenia, cachexia and pre-cachexia: Joint document elaborated by Special Interest Groups (SIG) “cachexia-anorexia in chronic wasting diseases” and “nutrition in geriatrics”**. *Clin. Nutr.* (2010) **29** 154-159. DOI: 10.1016/j.clnu.2009.12.004
3. Cruz-Jentoft A.J., Baeyens J.P., Bauer J.M., Boirie Y., Cederholm T., Landi F., Martin F.C., Michel J.P., Rolland Y., Schneider S.M.. **Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People**. *Age Ageing* (2010) **39** 412-423. DOI: 10.1093/ageing/afq034
4. Suzan V., Suzan A.A.. **A bibliometric analysis of sarcopenia: Top 100 articles**. *Eur. Geriatr. Med.* (2021) **12** 185-191. DOI: 10.1007/s41999-020-00395-y
5. Morley J.E., Abbatecola A.M., Argiles J.M., Baracos V., Bauer J., Bhasin S., Cederholm T., Coats A.J.S., Cummings S.R., Evans W.J.. **Sarcopenia with limited mobility: An international consensus**. *J. Am. Med. Dir. Assoc.* (2011) **12** 403-409. DOI: 10.1016/j.jamda.2011.04.014
6. Studenski S.A., Peters K.W., Alley D.E., Cawthon P.M., McLean R.R., Harris T.B., Ferrucci L., Guralnik J.M., Fragala M.S., Kenny A.M.. **The FNIH Sarcopenia Project: Rationale, Study Description, Conference Recommendations, and Final Estimates**. *J. Gerontol. Ser. A Biol. Sci. Med. Sci.* (2014) **69** 547. DOI: 10.1093/gerona/glu010
7. Cruz-Jentoft A.J., Landi F., Topinková E., Michel J.-P.. **Understanding sarcopenia as a geriatric syndrome**. *Curr. Opin. Clin. Nutr. Metab. Care* (2010) **13** 1-7. DOI: 10.1097/MCO.0b013e328333c1c1
8. Anker S.D., Morley J.E., von Haehling S.. **Welcome to the ICD-10 code for sarcopenia**. *J. Cachexia Sarcopenia Muscle* (2016) **7** 512-514. DOI: 10.1002/jcsm.12147
9. Vellas B., Fielding R.A., Bens C., Bernabei R., Cawthon P.M., Cederholm T., Cruz-Jentoft A.J., Del Signore S., Donahue S., Morley J.. **Implications of ICD-10 for Sarcopenia Clinical Practice and Clinical Trials: Report by the International Conference on Frailty and Sarcopenia Research Task Force**. *J. Frailty Aging* (2018) **7** 2-9. DOI: 10.14283/jfa.2017.30
10. Cruz-Jentoft A.J., Sayer A.A.. **Sarcopenia**. *Lancet* (2019) **393** 2636-2646. DOI: 10.1016/S0140-6736(19)31138-9
11. Isoyama N., Qureshi A.R., Avesani C.M., Lindholm B., Bárány P., Heimbürger O., Cederholm T., Stenvinkel P., Carrero J.J.. **Comparative Associations of Muscle Mass and Muscle Strength with Mortality in Dialysis Patients**. *Clin. J. Am. Soc. Nephrol.* (2014) **9** 1720. DOI: 10.2215/CJN.10261013
12. Lamarca F., Carrero J.J., Rodrigues J.C.D., Bigogno F.G., Fetter R.L., Avesani C.M.. **Prevalence of sarcopenia in elderly maintenance hemodialysis patients: The impact of different diagnostic criteria**. *J. Nutr. Health Aging* (2014) **18** 710-717. DOI: 10.1007/s12603-014-0505-5
13. Kim J.K., Choi S.R., Choi M.J., Kim S.G., Lee Y.K., Noh J.W., Kim H.J., Song Y.R.. **Prevalence of and factors associated with sarcopenia in elderly patients with end-stage renal disease**. *Clin. Nutr.* (2014) **33** 64-68. DOI: 10.1016/j.clnu.2013.04.002
14. Sánchez-Tocino M.L., Miranda-Serrano B., Gracia-Iguacel C., De-Alba-Peñaranda A.M., Mas-Fontao S., López-González A., Villoria-González S., Pereira-García M., Ortíz A., González-Parra E.. **Sarcopenia assessed by 4-step EWGSOP2 in elderly hemodialysis patients: Feasibility and limitations**. *PLoS ONE* (2022) **17**. DOI: 10.1371/journal.pone.0261459
15. Noce A., Marrone G., Ottaviani E., Guerriero C., Di Daniele F., Pietroboni Zaitseva A., Di Daniele N.. **Uremic Sarcopenia and Its Possible Nutritional Approach**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13010147
16. Nishi H., Takemura K., Higashihara T., Inagi R.. **Uremic Sarcopenia: Clinical Evidence and Basic Experimental Approach**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12061814
17. Takemura K., Nishi H., Inagi R.. **Mitochondrial Dysfunction in Kidney Disease and Uremic Sarcopenia**. *Front. Physiol.* (2020) **11** 565023. DOI: 10.3389/fphys.2020.565023
18. Avin K.G., Chen N.X., Organ J.M., Zarse C., O’Neill K., Conway R.G., Konrad R.J., Bacallao R.L., Allen M.R., Moe S.M.. **Skeletal Muscle Regeneration and Oxidative Stress Are Altered in Chronic Kidney Disease**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0159411
19. Fahal I.H.. **Uraemic sarcopenia: Aetiology and implications**. *Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. Eur. Ren. Assoc.* (2014) **29** 1655-1665. DOI: 10.1093/ndt/gft070
20. Kittiskulnam P., Chertow G.M., Carrero J.J., Delgado C., Kaysen G.A., Johansen K.L.. **Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis**. *Kidney Int.* (2017) **92** 238-247. DOI: 10.1016/j.kint.2017.01.024
21. Avin K.G., Moorthi R.N.. **Bone is Not Alone: The Effects of Skeletal Muscle Dysfunction in Chronic Kidney Disease**. *Curr. Osteoporos. Rep.* (2015) **13** 173-179. DOI: 10.1007/s11914-015-0261-4
22. Bhat M., Kalam R., Qadri S.S., Madabushi S., Ismail A.. **Vitamin D deficiency-induced muscle wasting occurs through the ubiquitin proteasome pathway and is partially corrected by calcium in male rats**. *Endocrinology* (2013) **154** 4018-4029. DOI: 10.1210/en.2013-1369
23. Sanders P.M., Russell S.T., Tisdale M.J.. **Angiotensin II directly induces muscle protein catabolism through the ubiquitin-proteasome proteolytic pathway and may play a role in cancer cachexia**. *Br. J. Cancer* (2005) **93** 425-434. DOI: 10.1038/sj.bjc.6602725
24. Cigarrán S., Pousa M., Castro M.J., González B., Martínez A., Barril G., Aguilera A., Coronel F., Stenvinkel P., Carrero J.J.. **Endogenous testosterone, muscle strength, and fat-free mass in men with chronic kidney disease**. *J. Ren. Nutr. Off. J. Counc. Ren. Nutr. Natl. Kidney Found.* (2013) **23** e89-e95. DOI: 10.1053/j.jrn.2012.08.007
25. Conley K.E., Jubrias S.A., Cress M.E., Esselman P.. **Exercise efficiency is reduced by mitochondrial uncoupling in the elderly**. *Exp. Physiol.* (2013) **98** 768-777. DOI: 10.1113/expphysiol.2012.067314
26. Domański M., Ciechanowski K.. **Sarcopenia: A major challenge in elderly patients with end-stage renal disease**. *J. Aging Res.* (2012) **2012** 754739. DOI: 10.1155/2012/754739
27. Greco A., Paroni G., Seripa D., Addante F., Dagostino M.P., Aucella F.. **Frailty, disability and physical exercise in the aging process and in chronic kidney disease**. *Kidney Blood Press. Res.* (2014) **39** 164-168. DOI: 10.1159/000355792
28. Cruz-Jentoft A.J., Kiesswetter E., Drey M., Sieber C.C.. **Nutrition, frailty, and sarcopenia**. *Aging Clin. Exp. Res.* (2017) **29** 43-48. DOI: 10.1007/s40520-016-0709-0
29. Fouque D., Kalantar-Zadeh K., Kopple J., Cano N., Chauveau P., Cuppari L., Franch H., Guarnieri G., Ikizler T.A., Kaysen G.. **A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease**. *Kidney Int.* (2008) **73** 391-398. DOI: 10.1038/sj.ki.5002585
30. Gracia-Iguacel C., González-Parra E., Mahillo I., Ortiz A.. **Criteria for classification of protein-energy wasting in dialysis patients: Impact on prevalence**. *Br. J. Nutr.* (2019) **121** 1271-1278. DOI: 10.1017/S0007114519000400
31. Gracia-Iguacel C., González-Parra E., Pérez-Gómez M.V., Mahíllo I., Egido J., Ortiz A., Carrero J.J.. **Prevalencia del síndrome de desgaste proteico-energético y su asociación con mortalidad en pacientes en hemodiálisis en un centro en españa**. *Nefrologia* (2013) **33** 495-505. PMID: 23897181
32. Bloom I., Shand C., Cooper C., Robinson S., Baird J.. **Diet Quality and Sarcopenia in Older Adults: A Systematic Review**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10030308
33. Jamal S.A., Leiter R.E., Jassal V., Hamilton C.J., Bauer D.C.. **Impaired muscle strength is associated with fractures in hemodialysis patients**. *Osteoporos. Int.* (2006) **17** 1390-1397. DOI: 10.1007/s00198-006-0133-y
34. Dodds R.M., Syddall H.E., Cooper R., Benzeval M., Deary I.J., Dennison E.M., Der G., Gale C.R., Inskip H.M., Jagger C.. **Grip Strength across the Life Course: Normative Data from Twelve British Studies**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0113637
35. Zhu F., Kuhlmann M.K., Sarkar S., Kaitwatcharachai C., Khilnani R., Leonard E.F., Greenwood R., Levin N.W.. **Adjustment of dry weight in hemodialysis patients using intradialytic continuous multifrequency bioimpedance of the calf**. *Int. J. Artif. Organs* (2004) **27** 104-109. DOI: 10.1177/039139880402700205
36. Cesari M., Kritchevsky S.B., Newman A.B., Simonsick E.M., Harris T.B., Penninx B.W., Brach J.S., Tylavsky F.A., Satterfield S., Bauer D.C.. **Added Value of Physical Performance Measures in Predicting Adverse Health-Related Events: Results from the Health, Aging, and Body Composition Study**. *J. Am. Geriatr. Soc.* (2009) **57** 251. DOI: 10.1111/j.1532-5415.2008.02126.x
37. Abellan Van Kan G., Rolland Y., Andrieu S., Bauer J., Beauchet O., Bonnefoy M., Cesari M., Donini L.M., Gillette-Guyonnet S., Inzitari M.. **Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force**. *J. Nutr. Health Aging* (2009) **13** 881-889. DOI: 10.1007/s12603-009-0246-z
38. Kalantar-Zadeh K., Kopple J.D., Block G., Humphreys M.H.. **A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients**. *Am. J. Kidney Dis.* (2001) **38** 1251-1263. DOI: 10.1053/ajkd.2001.29222
39. Kalantar-Zadeh K., Kopple J.D., Humphreys M.H., Block G.. **Comparing outcome predictability of markers of malnutrition-inflammation complex syndrome in haemodialysis patients**. *Nephrol. Dial. Transpl.* (2004) **19** 1507-1519. DOI: 10.1093/ndt/gfh143
40. Formiguera X., Cantón A.. **Obesity: Epidemiology and clinical aspects**. *Best Pract. Res. Clin. Gastroenterol.* (2004) **18** 1125-1146. DOI: 10.1016/S1521-6918(04)00091-5
41. Oliveira E.M., Silva R.P.D., Lemos M.D.C.C., Burgos M.G.P.A., Costa D.M.N., Maio R.. **Frequency of sarcopenia, cachexia, and associated factors in patients with chronic kidney disease in dialysis treatment**. *Nutr. Hosp.* (2020) **37** 1157-1165. DOI: 10.20960/nh.02954
42. Méndez Estévez E., Romero Pita J., Fernández Domínguez M.J., Troitiño Álvarez P., García Dopazo S., Jardón Blanco M., Rey Charlo M., Rivero Cotilla M.I., Rodríguez Fernández C., Menéndez Rodríguez M.. **Do our elderly have an adequate nutritional status?**. *Nutr. Hosp.* (2013) **28** 903-913. PMID: 23848118
43. Zimmet P., Magliano D., Matsuzawa Y., Alberti G., Shaw J.. **The metabolic syndrome: A global public health problem and a new definition**. *J. Atheroscler. Thromb.* (2005) **12** 295-300. DOI: 10.5551/jat.12.295
44. Pérez Miguelsanz M.J., Cabrera Parra W., Varela Moreiras G., Garaulet M.. **Regional distribution of the body fat: Use of image techniques as tools for nutritional diagnosis**. *Nutr. Hosp.* (2010) **25** 207-223. PMID: 20449529
45. González Madroño A., Mancha A., Rodríguez F.J., de Ulibarri J.I., Culebras J.. **The use of biochemical and immunological parameters in nutritional screening and assessment**. *Nutr. Hosp.* (2011) **26** 594-601. PMID: 21892580
46. Boudville N., Inderjeeth C., Elder G.J., Glendenning P.. **Association between 25-hydroxyvitamin D, somatic muscle weakness and falls risk in end-stage renal failure**. *Clin. Endocrinol.* (2010) **73** 299-304. DOI: 10.1111/j.1365-2265.2010.03821.x
47. Daugirdas J.T.. **Kt/V (and especially its modifications) remains a useful measure of hemodialysis dose**. *Kidney Int.* (2015) **88** 466-473. DOI: 10.1038/ki.2015.204
48. Alvero-Cruz J.R., Correas L., Ronconi M., Fernández R., Manzañido P.. **La bioimpedancia eléctrica como método de estimación de la composición corporal: Normas prácticas de utilización**. *Rev. Andal. Med. Deport.* (2011) **4** 167-174
49. Quesada Leyva L., León Ramentol C.C., Betancourt Bethencourt J., Nicolau Pestana E.. **Elementos teóricos y prácticos sobre la bioimpedancia eléctrica en salud Theoretical and practical facts about health electric bioimpedance**. *AMC* (2016) **20** 565-578
50. Sánchez-Tocino M.L., Miranda-Serrano B., López-González A., Villoria-González S., Pereira-García M., Gracia-Iguacel C., González-Ibarguren I., Ortíz-Arduan A., Mas-Fontao S., González-Parra E.. **Sarcopenia and Mortality in Older Hemodialysis Patients**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14112354
51. Kurajoh M., Mori K., Miyabe M., Matsufuji S., Ichii M., Morioka T., Kizu A., Tsujimoto Y., Emoto M.. **Nutritional Status Association With Sarcopenia in Patients Undergoing Maintenance Hemodialysis Assessed by Nutritional Risk Index**. *Front. Nutr.* (2022) **9** 896427. DOI: 10.3389/fnut.2022.896427
52. Carrero J.J., Johansen K.L., Lindholm B., Stenvinkel P., Cuppari L., Avesani C.M.. **Screening for muscle wasting and dysfunction in patients with chronic kidney disease**. *Kidney Int.* (2016) **90** 53-66. DOI: 10.1016/j.kint.2016.02.025
53. Fiatarone M.A., O’Neill E.F., Ryan N.D., Clements K.M., Solares G.R., Nelson M.E., Roberts S.B., Kehayias J.J., Lipsitz L.A., Evans W.J.. **Exercise training and nutritional supplementation for physical frailty in very elderly people**. *N. Engl. J. Med.* (1994) **330** 1769-1775. DOI: 10.1056/NEJM199406233302501
54. Dubey A.K., Sahoo J., Vairappan B., Haridasan S., Parameswaran S., Priyamvada P.S.. **Correction of metabolic acidosis improves muscle mass and renal function in chronic kidney disease stages 3 and 4: A randomized controlled trial**. *Nephrol. Dial. Transpl.* (2020) **35** 121-129. DOI: 10.1093/ndt/gfy214
55. De Brito-Ashurst I., Varagunam M., Raftery M.J., Yaqoob M.M.. **Bicarbonate supplementation slows progression of CKD and improves nutritional status**. *J. Am. Soc. Nephrol.* (2009) **20** 2075-2084. DOI: 10.1681/ASN.2008111205
56. Goraya N., Simoni J., Jo C.H., Wesson D.E.. **A comparison of treating metabolic acidosis in CKD stage 4 hypertensive kidney disease with fruits and vegetables or sodium bicarbonate**. *Clin. J. Am. Soc. Nephrol.* (2013) **8** 371-381. DOI: 10.2215/CJN.02430312
57. Cases A., Cigarrán-Guldrís S., Mas S., Gonzalez-Parra E.. **Vegetable-based diets for chronic kidney disease? It is time to reconsider**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11061263
58. Morton R.W., Murphy K.T., McKellar S.R., Schoenfeld B.J., Henselmans M., Helms E., Aragon A.A., Devries M.C., Banfield L., Krieger J.W.. **A systematic review, meta-analysis and meta-regression of the effect of protein supplementation on resistance training-induced gains in muscle mass and strength in healthy adults**. *Br. J. Sports Med.* (2018) **52** 376-384. DOI: 10.1136/bjsports-2017-097608
59. Bauer J., Biolo G., Cederholm T., Cesari M., Cruz-Jentoft A.J., Morley J.E., Phillips S., Sieber C., Stehle P., Teta D.. **Evidence-based recommendations for optimal dietary protein intake in older people: A position paper from the PROT-AGE Study Group**. *J. Am. Med. Dir. Assoc.* (2013) **14** 542-559. DOI: 10.1016/j.jamda.2013.05.021
60. Ikizler T.A., Burrowes J.D., Byham-Gray L.D., Campbell K.L., Carrero J.J., Chan W., Fouque D., Friedman A.N., Ghaddar S., Goldstein-Fuchs D.J.. **KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update**. *Am. J. Kidney Dis.* (2020) **76** S1-S107. DOI: 10.1053/j.ajkd.2020.05.006
61. Wu P.Y., Huang K.S., Chen K.M., Chou C.P., Tu Y.K.. **Exercise, Nutrition, and Combined Exercise and Nutrition in Older Adults with Sarcopenia: A Systematic Review and Network Meta-analysis**. *Maturitas* (2021) **145** 38-48. DOI: 10.1016/j.maturitas.2020.12.009
62. Sánchez-Tocino M.L., González-Parra E., Miranda Serrano B., Gracia-Iguacel C., María de-Alba-Peñaranda A., López-González A., García Olegario M., Ortíz A., Mas-Fontao S.. **Evaluation of the impact of an intradialytic exercise programme on sarcopaenia in very elderly haemodialysis patients**. *Clin. Kidney J.* (2022) **15** 1514-1523. DOI: 10.1093/ckj/sfac046
63. Harada H., Kai H., Shibata R., Niiyama H., Nishiyama Y., Murohara T., Yoshida N., Katoh A., Ikeda H.. **New diagnostic index for sarcopenia in patients with cardiovascular diseases**. *PLoS ONE* (2017) **12**. DOI: 10.1371/journal.pone.0178123
|
---
title: 'Deep Eutectic Liquids as a Topical Vehicle for Tadalafil: Characterisation
and Potential Wound Healing and Antimicrobial Activity'
authors:
- Bayan Alkhawaja
- Faisal Al-Akayleh
- Ashraf Al-Khateeb
- Jehad Nasereddin
- Bayan Y. Ghanim
- Albert Bolhuis
- Nisrein Jaber
- Mayyas Al-Remawi
- Nidal A. Qinna
journal: Molecules
year: 2023
pmcid: PMC10005105
doi: 10.3390/molecules28052402
license: CC BY 4.0
---
# Deep Eutectic Liquids as a Topical Vehicle for Tadalafil: Characterisation and Potential Wound Healing and Antimicrobial Activity
## Abstract
Deep eutectic solvents (DESs) and ionic liquids (ILs) offer novel opportunities for several pharmaceutical applications. Their tunable properties offer control over their design and applications. Choline chloride (CC)-based DESs (referred to as Type III eutectics) offer superior advantages for various pharmaceutical and therapeutic applications. Here, CC-based DESs of tadalafil (TDF), a selective phosphodiesterase type 5 (PDE-5) enzyme inhibitor, were designed for implementation in wound healing. The adopted approach provides formulations for the topical application of TDF, hence avoiding systemic exposure. To this end, the DESs were chosen based on their suitability for topical application. Then, DES formulations of TDF were prepared, yielding a tremendous increase in the equilibrium solubility of TDF. Lidocaine (LDC) was included in the formulation with TDF to provide a local anaesthetic effect, forming F01. The addition of propylene glycol (PG) to the formulation was attempted to reduce the viscosity, forming F02. The formulations were fully characterised using NMR, FTIR and DCS techniques. According to the obtained characterisation results, the drugs were soluble in the DES with no detectable degradation. Our results demonstrated the utility of F01 in wound healing in vivo using cut wound and burn wound models. Significant retraction of the cut wound area was observed within three weeks of the application of F01 when compared with DES. Furthermore, the utilisation of F01 resulted in less scarring of the burn wounds than any other group including the positive control, thus rendering it a candidate formula for burn dressing formulations. We demonstrated that the slower healing process associated with F01 resulted in less scarring potential. Lastly, the antimicrobial activity of the DES formulations was demonstrated against a panel of fungi and bacterial strains, thus providing a unique wound healing process via simultaneous prevention of wound infection. In conclusion, this work presents the design and application of a topical vehicle for TDF with novel biomedical applications.
## 1. Introduction
Ionic liquids (ILs) are usually defined as a neoteric class of molten salts with a liquid state at temperatures below 100 °C, with some of them being liquid at room temperature. They comprise discrete inorganic or organic anions and organic cations with favourable solvation properties, low vapour pressure, chemical adaptability, and thermal stability [1,2]. Due to their combination of organic character with ionic nature, ILs have been adopted as alternatives to organic solvents with ubiquitous applications [3,4].
Deep eutectic solvents (DESs) have received huge amounts of attention as green substitutes to ILs, sharing most of the unique properties of ILs. Some reports consider DESs to be a new class of ILs [5]. However, DESs have superior desirable properties compared to ILs, such as lower preparation cost, facile preparation techniques, non-toxicity and biodegradability [6]. DESs are usually formed by direct mixing of Lewis or Brønsted acids and bases in specific ratios. The formed systems have noticeably lower melting points compared to their individual components [7,8]. DESs formed between quaternary ammonium salts and carboxylic acids are amongst the most investigated systems [9].
As one of the most versatile types of DESs, choline chloride salt-based DESs (referred to as Type III eutectics) have been widely utilised in chemical applications, such as catalysis, extraction from biomass, and synthesis reactions [6,10,11,12]. With the unique advantages of choline-based DESs, notably an interest in their tunable biological applications, they have been extensively evaluated in a myriad of pharmaceutical applications [13,14,15,16]. As an alternative to organic solvents, DESs have provided green alternatives with various medical implications, such as providing vehicles for transdermal drug delivery and wound healing applications [17,18,19].
Non-healing or chronic wounds impose an enormous burden on and expenditure within global health sectors [20,21]. Wound healing refers to a dynamic biological process comprising four main physiological stages, namely, haemostasis, inflammation, proliferation, and tissue remodeling. Wound healing phases should be sequenced and stewarded timely [22]. Various local or systemic factors could interfere with one or more stages of wound healing, which ultimately could impair the healing process or lead to the development of chronic wounds [22,23]. One of the main factors that impairs the normal wound healing process is hypoxia, which, although initially essential for healing, is detrimental for wound healing if prolonged [24].
The merits of using selective phosphodiesterase type 5 (PDE-5) enzyme inhibitors in the wound healing process have been widely investigated. PDE-5 is an enzyme involved in the degradation of cyclic guanosine monophosphate (cGMP), and its inhibitors have a vasodilation effect. In addition, they also prolong the effect of nitric oxide (NO) both cellularly and endovascularly [25,26,27]. NO is an important signaling molecule that could facilitate the wound healing process via several mechanisms, including promoting cellular proliferation, tissue remodeling and angiogenesis [28]. Amongst the selective PDE-5 inhibitors, tadalafil (TDF) has shown an intriguing PDE selectivity [27,29].
Previously, Alwattar et al. adopted spray-dried TDF in wound healing [27]. Moreover, orally delivered TDF was tested in a porcine burn model and shown to result in faster reepithelialisation and reduced scarring [30]. In addition, a positive impact of oral PDE-inhibitors in skin flap healing viability was demonstrated in a rat model [31]. However, oral administration of TDF causes a range of adverse and undesirable side effects, such as headache, myalgia, and flushing [32]. Therefore, the topical application of TDF could be a better solution for wound management.
It is well established that improving the delivery mechanisms and pharmacokinetic characteristics of existing drugs is financially preferable to the launching of a new one [33,34]. Hence, we set out to investigate choline chloride-based DES as a carrier vehicle for TDF and to repurpose TDF for wound management. Moreover, to obtain an optimal healing process, lidocaine (LDC) was adopted in the wound management formula. LDC is a local anaesthetic drug; hence, it was incorporated to manage the pain associated with wounds [35,36].
The advantages of the use of topical PDE-5 inhibitors combined with the beneficial properties of DES-based drug delivery represent the rationale behind the wound healing method investigated in this work. Herein, this work demonstrated the utility of DESs of TDF and LDC as a strategy for topical application as well as for the management of wounds, while possessing good antimicrobial activity.
## 2.1. Preparation of DESs and Drug-Loaded DES Formulations
Initially, we set out to validate the suitability of the DES formulations for topical application. *In* general, topically applied products entail adequate rheological behaviour, spreadability, and appropriate skin tolerability [37]. Rheological testing of topically applied products is essential for product evaluation. Generally, these experiments are performed primarily to ascertain suitability for product manufacturing purposes, uniformity, extrudability, stability and topical applicability [38]. Although decreasing the viscosity would enhance the applicability and penetration of the topical product, choosing the optimum viscosity usually varies depending on the purpose of the treatment and the final topical product.
To this end, different compositions of blank DES were prepared using various molar equivalents of malonic acid (MA) to CC (1:1, 1:2, and 2:1), with or without propylene glycol (PG) (Table 1).
The rheological study results are presented in Figure 1 and compared to a commercially available topical cream for wounds, ialuset Plus. In order of increasing viscosity the formulations ranked B03 ˃ B01 ˃ B02. The relatively high viscosity of B03 hinders its topical application as a drug carrier system; hence, it was not optimal for the purpose of our study. On the other hand, B01 showed lower viscosity behaviour than B02 at room temperature (25 °C) and therefore could be a candidate blank formula (Figure 1) To further enhance the fluidity of the DESs, the co-solvent PG was incorporated at different ratios. PG has been widely utilised in topical applications, notably to enhance drug permeation through the skin [39]. The rheological behaviour of DESs with PG was compared with ialuset Plus. The viscosity of B04 was closer to that of the commercial product at 25 °C at a shear rate of 50 s−1 (360 ± 7.1 MPa and 380 ± 5.1 MPa for B04 and ialuset Plus, respectively) (Figure 1).
Next, the contact angle was measured for DESs with PG, with a lower contact angle indicating a less hydrophobic character. The incorporation of PG resulted in a significant reduction in the DES contact angles on the hydrophobic glass surface, with B04 and B05 formulations being the lowest (Table 1). Owing to the suitable viscosity behaviour for topical application, B04 formulation showed close spreadability readings when compared with the commercial product (6.0 ± 0.12 and 6.3 ± 0.11 cm, respectively) (Table 1). Based on the aforementioned results with respect to the commercial product, B01 and B04 could be candidate formulations to be adopted for topical application purposes.
Having evaluated the optimum DESs for topical application, we then studied the equilibrium solubility of TDF in B01. Accordingly, the equilibrium solubility of TDF in B01 (4.3 mg/mL) was found to be 1133-fold higher when compared with the aqueous solubility of TDF at room temperature (4.3 mg/mL and 0.003 mg/mL, respectively) [40]. Indeed, TDF is practically insoluble in water [40,41] and, hence, a significant improvement in the saturated solubility in DES could be a significant advantage for enhancing the bioavailability of TDF. The latter is, however, beyond the aim of this study.
LDC belongs to the class I drugs, which have high solubility and permeability according to the biopharmaceutical classification system [42]. LDC could have an effect on the solubility of TDF in DES, which was therefore evaluated using increasing molar rations of LDC:TDF. The equilibrium solubility of TDF in B01 was evaluated separately and likewise with increasing molar ratios of LDC. A notable improvement in the solubility of TDF in DES was observed upon addition of LDC, with an almost twofold increase in solubility when mixed at 3:1 molar ratio (Figure 2). Our results were in accordance with previous findings reported by Marei and co-workers, where LDC was shown to enhance the solubility of a group of nonsteroidal anti-inflammatory drugs [43]. Hence, in this work, LDC was added to the formulation as a local anaesthetic to ease the pain associated with wounds; in addition, we demonstrated that it enhanced the solubility of TDF in the blank DES. Hence, LDC exhibited double action by acting as a solubility enhancer and being pharmacologically active as a topical anaesthetic.
The prepared drug formulations within this work and their composition are illustrated in Table 2. Given that B01 and B04 gave optimal topical behaviour comparable with the commercial drug, two main formulations were prepared: formulation 1 (F01) of TDF and LDC was prepared with B01 as a vehicle, while F02 was prepared using B04 as a vehicle. Lastly, F03 was prepared using TDF without LDC to assess the impact of LDC on the wound healing process.
## 2.2. Characterisation of DES Formulations
Detailed characterisation of the prepared formulations containing TDF and LDC was performed using a range of analytical techniques, including NMR, ATR-FTIR, and DSC.
## 2.2.1. Nuclear Magnetic Resonance (NMR)
Analyses were conducted to investigate the structural characteristics of the blank and drug-loaded formulations (Figure 3). However, as the final concentrations of both TDF and LDC were considerably lower in the tested samples, well-pronounced peaks corresponding to the DES were observed in the 1H NMR spectrum. Therefore, detailed structural assignments of the chemical shifts associated with the structure of the studied drugs were unattainable even after the solvent suppression method was employed. Nevertheless, with the expansion around the aromatic region (6–8 ppm), peaks corresponding to both TDF and LDC chemical structures were detected in the expected ratios, confirming the presence of both drugs without any degradation after their incorporation into the DES (Figure 3C). Moreover, 1H NMR analysis of the individual drugs was conducted to further ascertain these observations, and the results were in accordance with the DES formulation loaded with the drugs (Figures S3 and S4).
## 2.2.2. Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy (ATR-FTIR)
Figure 4A shows the FTIR spectra of MA, CC, the blank DES formulation, TDF, LDC, and formulation 1. The most notable peaks in the FTIR spectrum of CC were the peaks at 3220 cm−1 and 1482 cm−1, corresponding to OH stretching and bending peaks of CC respectively, as well as the peak at 1348 cm−1, likely corresponding to C–N bending [44]. In the case of MA, the peaks at 3220 cm−1 and 3287 cm−1 corresponded to the OH stretching of the two hydroxyl groups, and the peaks observed at 1720 cm−1 and 1688 cm−1 corresponded to the C=O stretching of the two carbonyl groups present. Other notable peaks in the MA spectrum were the peaks at 1390 cm−1 and 1418 cm−1, which correspond to OH bending. In the B01 spectrum, peaks of note were the broad peak centered around 2936 cm−1, with notable shoulder peaks at 3300 cm−1 and 3031 cm−1, which likely corresponds to the shifted OH stretching peaks of CC and MA. Furthermore, a notable peak was seen 1at 1718 cm−1, likely corresponding to the shifted C=O stretching of MA. The OH bending peaks of MA were observed at 1379 cm−1 and 1417 cm−1, along with the peak at 1478 cm−1, likely corresponding to the shifted OH bending peak of CC. The weak C–N stretching peak of CC was not visible in the DES spectrum. This suggests that the mechanism of formation of the DES is likely due to hydrogen bonding mediated by the C=O groups of MA with the OH group of CC. Characteristic peaks in the LDC spectrum were the NH stretching peak seen at 3454 cm−1, the C=O stretching peak at 1672 cm−1, and the NH bending peak at 1656 cm−1. Characteristic peaks in the TDF spectrum were the peaks seen at 3326 cm−1, 1676 cm−1, 1646 cm−1, and 1628 cm−1, which likely correspond to NH stretching, stretching of the two C=O groups, and NH bending, respectively. The FTIR spectrum of F01 (containing the DES in which TDF and LDC were dissolved) was largely similar to the spectrum of the placebo DES, with the previously reported peaks at 3300 cm−1, 3031 cm−1, and 1718 cm−1. No peaks which are characteristic of either drug were visible in the spectra, most likely due to the concentration of both drugs in the formulation being below the limits of detection by ATR-FTIR.
Figure 4B shows the spectra of F01, PG, and F02. The characteristic peaks of PG were seen at 3311 cm−1, 1459 cm−1, and 1412 cm−1, corresponding to OH stretching and OH bending, respectively. In the spectrum of the viscosity-adjusted formulation (F02), the aforementioned C=O stretching peak of the B01 formulation was seen to shift to 1721 cm−1. The OH stretching peak of PG was also visible, albeit shifted to 3326 cm−1, which is suggestive of hydrogen bonding between the DES formulation and PG.
## 2.2.3. Differential Scanning Calorimetry (DSC)
Figure 5A shows thermograms of the raw materials (CC, MA, TDF, and LDC) during the first heating scan. The melting point (Tm) of CC was seen at 253 °C. The Tm of MA was seen at 136 °C. The Tm of LDC was seen at 80 °C, and the melting point of TDF was found to be around 303 °C.
In the cooling cycle (Figure 5B), no recrystallisation was observed for MA, TDF, and LDC. Furthermore, glass transitions (Tg) were observed for both LDC (at −15 °C) and TDF (at 129 °C). An exothermic event indicative of recrystallisation was seen at 53 °C in the thermogram of MA (enthalpy: 1.81 J/g, corresponding to ≈$0.8\%$ recrystallisation of the 214.47 J/g observed in the first heating cycle).
In the second heating cycle (Figure 5C), an endothermic peak indicative of melting was seen in the thermogram of MA at 89 °C (enthalpy: 1.59 J/g, corresponding to ≈$0.7\%$). The amount recrystallised that was observed in the cooling cycle (Figure 5B) corresponds to the amount recovered in the second heating cycle (Figure 5C), suggesting that the observed event is indeed the recrystallisation and melting of MA. The depression of the Tm of MA from 136 °C to 89 °C is likely due to the excessive amorphisation of MA in DSC (>$99\%$) causing depression in MA melting. The aforementioned Tg of TDF was clearly visible in the second cooling cycle.
Figure 5D shows DSC thermograms of B01, F01, and F02. No endothermic events corresponding to the melting of either of the DES components MA and CC were seen, nor any endothermic events indicative of the presence of either drug in the crystalline form.
The results obtained from DSC, coupled with FTIR data, suggest that at the aforementioned ratio, the MA:CC interaction results in the formation of a homogenous, single-phase liquid with no signs of recrystallisation of either material. Furthermore, the absence of any endothermic events is indicative of complete solubilisation of both TDF and LDC in the DES formulation.
Furthermore, due to the contact-dependent nature of ATR-FTIR, any crystalline content within the DES formulations would have been significantly more prominent than what was observed in Figure 4B. This suggests that, within the limits of detection of DSC and FTIR, no crystalline content is present within the formulation, with all constituents (MA, CC, LDC, and TDF) appearing to have formed a homogenous, single-phase solution. Moreover, PG does not appear to have any negative influence on the solubilisation potential of the DES formulation, as no changes in thermal behaviour were seen in DSC upon the addition of PG.
## 2.3. Stability Evaluation of the TDF Formulations
The stability of the F01 and F02 formulations was determined after incubation at ambient temperature for three months. No precipitate was noticed, nor any change in the physical appearance of the formulation under ambient storage conditions. More importantly, the quantity of TDF was assayed with HPLC in F01 and F02, and was found to be $84\%$ and $91\%$, respectively, of the amount present at the start of the experiment. These results reflect that TDF and LDC exhibited excellent stability in both formulations under ambient storage conditions.
## 2.4. Effect of DES Formulations on Cut Wound Healing Process
TDF is a PDE-5-inhibitor primarily employed for erectile dysfunction. By inhibition of PDE and elevating the level of cGMP, the effects of NO, including relaxation of smooth muscle cells and enhancing vasodilation, will dominate. NO is a central player in the wound-healing process through modulating vascular homeostasis and inflammation, and has antimicrobial effects. Previous studies demonstrated the positive impact of oral TDF in the burn healing process [21,45]. We proposed that the topical application of TDF could improve the wound healing process without the need for systemic exposure to the drug, whereas LDC could help in relieving the pain associated with wounds without any negative impact on the healing process, as previously demonstrated [46]. This was tested using a rat wound-healing model. In the assessment of cut wound healing, F01 showed retraction of the wound area at week 3, unlike B01, which showed a slight decrease in the wound area at week 4. F01 formed scars at week 4, while B01 formed scars at week 5. Scars disappeared at week 6 in both treated groups. Pus formation was observed in both treated groups until week 2 (Figure 6 and Figure 7).
The group treated with B04 (DES with PG) showed significant retraction in wound size not earlier than the third week of dosing, which left a scar at the following week but disappeared after five weeks of treatment. In regard to animals treated with F02, mild wound retraction was observed at the third week of dosing. However, scar formation persisted during the following two weeks and disappeared after six weeks of treatment (Figure 6 and Figure 7).
Treatment with F03 (TDF alone) showed some lag time in healing until week 4, when a significant retraction in wound size was noted. Marks of wound were absent at week 5, when no scar formation was noted (Figure 6 and Figure 7).
It could be concluded that the prepared formulations with TDF were not very effective for cut wound healing when compared with the blank B04. The most consistent recovery of cut wounds was observed in group B04, unlike other groups, which showed mild effects in retracting the wound area within the first three weeks of treatment. The positive control (ialuset Plus) showed a fast recovery rate in comparison to all treatments. The positive control showed a consistent and clean (no pus) skin healing process, and also showed mild formation of scars in the third week which disappeared in the fourth week. The sham group (no treatment) healed well within the first three weeks.
## 2.5. Effect of DES Formulations on Burn Wound Healing Process
Regarding burn wound healing, the use of all treatments on burn wounds showed significant retraction and healing at the third week, except for F03, which showed no significant change on burn wounds until the fourth week. At week four, small wounds were evident, which formed scars at the fifth week. Scars persisted in all groups until the sixth week, except for F01, ialuset Plus and F03, as they were found to be negligible in those groups (Figure 8). The morphological changes within the skin layers are shown in Figure 9; wounds treated with F01 showed formation of serohematic scabs that did not persist and peeled off indicating healthy, repaired skin underneath, as confirmed by histopathological examinations at day 28 of the study.
Collectively, F01 showed less scarring of burn wounds than any other group including the positive control, thus being a candidate formula for burn dressing formulations (Figure 8 and Figure 9). A rapid wound healing process of the skin has been reported to cause formation of fibrotic scar tissue [47]. Therefore, the slow healing process of F01 was found to be the preferred treatment for burn healing.
When compared with F02, F01 exhibited slower healing results, which could be related to the presence of PG in F02. Previously, PG formulation was found to be ineffective for the skin wound healing process [48]; however, it has been utilised in formulations, notably to enhance drug permeation through the skin from topical preparations [39]. In F02, PG was incorporated in a 1:1 ratio to enhance the spreadability of the treatment, which was found to be effective for rapid retraction and healing of burn wounds at the third week. This could be related to the enhanced penetration of the TDF through the intact skin. Nevertheless, in our hands, we found that our treatments (F01 and F02) showed slower healing processes when compared with the commercial product, and F01 exhibited less scarring than any other group, which is more preferred in burn healing.
## 2.6. Antimicrobial Activity Testing (In Vitro)
Generally, most DESs have antibacterial activity against a wide range of Gram-positive and Gram-negative bacteria. According to Zakrewsky and co-workers, DES of CC and MA exhibited strong activity against bacterial biofilms of both *Pseudomonas aeruginosa* and *Salmonella enterica* serovar Typhimurium [19].
Skin wounds are usually complicated by the presence of microbes, ranging from contamination and colonisation to invasive infection [23]. Therefore, we set out to evaluate the antimicrobial activity of the developed formulations. The antimicrobial activity of DES and formulation 1 were tested against a panel of bacterial and fungal strains, and their MIC values are reported in Table 3. This showed that good antibacterial activity was observed with B01, which was not significantly altered by the presence of TDF and LDC. However, B01 was not active against the yeast C. albicans.
## 3.1. Materials
Tadalafil (TDF) and lidocaine HCl (LDC) were kindly gifted by the Jordanian Pharmaceutical Company (JPM) Amman, Jordan. Choline chloride and malonic acid were purchased from Tokyo Chemical Industry Co., Ltd. (TCI), Tokyo, Japan.
## 3.2. HPLC Analysis
HPLC (Shimadzu LC20AT HPLC system, Shimadzu LTD, Japan) was employed for the determination of TDF concentration in the DES. The analysis method adopted for TDF was developed and validated in an earlier report [49]. Briefly, the mobile phase, consisting of acetonitrile and phosphate buffer ($60\%$:$40\%$) with pH 7 adjusted using phosphoric acid, was circulated through a C-18 column (150 × 4.6 mm) packed with a particle size of 5 μm. The flow rate was kept at 0.8 mL/min and the wavelength of detection was 262 nm. The standard curves of TDF and LDC were found to be linear (Figures S1 and S2).
## 3.3. Choline Chloride–Malonic Acid (Blank Formulations) DES Preparation
Different compositions of blank DES were prepared using various molar equivalents of malonic acid (MA) to choline chloride (CC) (1:1, 1:2, and 2:1), with or without propylene glycol (PG) (Table 1). The DES mixtures were prepared by mixing CC and MA; the mixtures were left for 24 h with continuous stirring at room temperature until a clear and homogenous liquid was obtained. This was then left to equilibrate in a shaking water bath for 12 h at 190 rpm and 25 °C. Lastly, the prepared DESs were mixed with propylene glycol (PG) at different ratios, as shown in Table 1.
## 3.4.1. Rheology Study
The viscosity of prepared DESs were studied using a rheometer (Physica MCR 302, Anton Paar, Austria) with different geometries (concentric cylinder, cone-and-plate, and parallel-plate), coupled with a Cp 50 double gap concentric cylinder measurement system. Initially, the instrument was calibrated, and each DES (5–10 mL) was loaded between the concentric cylinders. The measurement conditions were shear rate (0.1–100), temperature set (−10–32 °C), cone angle 1 and zero-gap 0.1.
## 3.4.2. Contact Angle Measurements
The contact angles of a drop of DES were measured on a polyethylene plastic surface using a contact angle goniometer (OCA 15 EC, Data Physics instruments GmbH, Filderstadt, Germany) and analysed using SCA20 Software (, Dataphysics, Germany.) for optical contact angle (OCA) and portable contact angle meter (PCA) (PCA-1, Kyowa Interface Science, Niiza, Japan). For each measurement, a 500 µg Hamilton syringe was filled with the sample and anchored on the device. The dosing volume of each drop was 4 µg with a dosing rate of 1 µg/s. High-resolution images of each drop were captured using a fixed camera (Sony, Tokyo, Japan).
## 3.4.3. Spreadability
The spreadability of the DESs was investigated by placing 1 g of the DES preparation in the center of a 20 × 20 cm glass plate. It was then covered with another slide and left for one minute. The diameter of the spread area (cm) was measured. The results are presented as the mean along with the standard deviation from three independent experiments.
## 3.5. Determination of TDF Solubility in DESs (Shake-Flask Technique)
For the determination of the equilibrium solubility of the TDF in a blank formulation, an excess amount of TDF was added to the DES. Then, the system was kept shaking (190 RPM) for 24 h in a thermostatic water bath at 25 °C. The excess (undissolved) TDF was separated by centrifugation (Stuart SCF1 Mini Centrifuge Spinner, Bosco M-24A centrifuge, Hamburg, Germany) at 14,000 RPM for 5 min. After suitable dilution, the drug concentration was determined using HPLC.
## 3.6. Enhancing TDF Solubility Using LDC
A fixed amount TDF was dissolved in DES with increasing molar ratios of LDC (0.5, 1, 2 and 3). The equilibrium solubility of TDF was determined as previously described using the HPLC method.
## 3.7. Stability Study of TDF Formulations
The stability study of the TDF formulations was determined after incubation at ambient temperature for three months. Physical appearance, colour change, and precipitation were evaluated. Chemical stability was determined using HPLC.
## 3.8. Nuclear Magnetic Resonance (NMR)
NMR datasets were collected on a 500-MHz Bruker instrument (Bruker Avance III, BRUKER)using DMSO-d6. NMR spectra for CC-MA DES and formulation containing the drugs are shown in Figure 3A–C.
NMR assignments for B01: 1H NMR (500 MHz, DMSO) δ 12.69 (s, 2H), 5.58 (s, 1H), 3.87–3.78 (m, 2H), 3.46–3.39 (m, 2H), 3.26 (s, 2H), 3.16 (s, 1H). 13C NMR (126 MHz, DMSO) δ 168.81, 67.40, 67.38, 67.36, 55.49, 53.60, 53.57, 53.54, 42.47.
## 3.9. Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy (ATR-FTIR)
ATR-FTIR spectra for the blank and final formulations were acquired using a Perkin Elmer UATR Spectrum (PerkinElmer Inc., Waltham, MA, USA) within the range 4000–550 cm−1. Spectra were acquired in absorbance mode, with a resolution of 2 cm−1 and 32 scans per sample. The spectra were exported in Comma Separated Values (CSV) format and analysed using Spectrogryph version 1.2.15. No pre-processing (PerkinElmer Inc., Waltham, MA, USA) was applied to the spectra before analysis.
## 3.10. Differential Scanning Calorimetry (DSC)
Differential scanning calorimetry (DSC) thermograms were acquired using a TA Q25 Discovery instrument (TA Instruments, Newcastle, DE, USA). Scans were acquired using a heat–cool–reheat cycle at a heating/cooling rate of 10 °C/min. Choline chloride and tadalafil were scanned from 25–320 °C during the first heating cycle, equilibrated at 320 °C for one minute, and then cooled to −20 °C, followed by a second heating cycle up to 320 °C. Malonic acid was scanned at a temperature range of 25–250 °C during the first heating cycle, and was then equilibrated at 250 °C before cooling to −20 °C, followed by a second heating cycle up to 250 °C. Lidocaine was analysed at a temperature range of 25–100 °C, followed by cooling down to −20 °C, and a second heating cycle up to 100 °C. All formulations were analysed at a temperature range of 25–320 °C for the first heating cycle, 320–−20 °C for the cooling cycle, and −20 °C–320 °C for the second heating cycle. Standard aluminium pans containing samples weighing 2–4 mg were used for all scans. Nitrogen purge gas was used for all runs, with a constant flow rate of 50 mL/min.
## 3.11. Wound Healing Model (In Vivo)
Forty-nine adult male Sprague Dawley rats with an average weight of 250 ± 20 g were housed at the Laboratory Animal Research Unit of the University of Petra Pharmaceutical Center (UPPC), Amman, Jordan. Rats were kept under controlled temperatures of 22 ± 2 °C, at a humidity 60 ± $5\%$ and with a 12-h light/dark cycle. The study was conducted in accordance with the University of Petra Institutional Guidelines on Animal Use, which adopts the guidelines of the Federation of European Laboratory Animal Science Association (FELASA), ethical approval number (1A/$\frac{1}{2020}$).
Animals were grouped into seven groups ($$n = 7$$), each group receiving its corresponding treatments, in addition to a sham group that received no treatment and a positive control group treated with a commercially available topical cream, namely, ialuset Plus, a product of IBSA Farmaceutici, Lodi, Italy.
The back of each animal was shaved using an electronic clipper and skin was observed to confirm absence of any irritation or scars. The following day, animals were placed on a surgical board (Kent Scientific, 1116 Litchfield Street Torrington, CT 06790, CT, USA) and anaesthetised with $2.5\%$ isoflurane (Hikma Pharmaceuticals, Amman, Jordan) using a general anaesthesia system (SomnoSuite, Kent Scientific Corporation, city, CT, USA). Upon anaesthesia, wound and burn sections were created on the backs of the animals. An excision wound was made using a skin punch (diameter size: 0.8 mm) while a burn wound was generated by thermal damage through direct application of heat by placing a hot 80 °C metal plate on the skin for 10 s, as described by Masson-Meyers [50].
A thin layer of each formulation was applied aseptically once daily for six consecutive weeks. Exactly 1 g quantity of tested formulation or reference product was applied and distributed over the wound using sterile gloved hands to mimic clinical conditions.
Animals were observed for six consecutive weeks for wound healing, scar formation and scar healing. Samples of the skin were collected weekly from one animal of each group and were fixed in buffered formalin for H&E staining and histopathology examination.
## 3.12. Statistical Analysis
To determine significance between groups, one-way ANOVA was used followed by post-hoc Tukey’s HSD test, unless stated otherwise, using IBM SPSS Statistics 25, IBM Corporation (New York, NY, USA). Values are expressed as mean ± standard error of the mean (SEM). Results were considered significant for p-values ≤ 0.05. An independent samples t-test was conducted to analyse significance between candidate groups, in comparison to sham and positive control groups individually.
## 3.13. Antimicrobial Activity Testing (In Vitro)
The strains used were *Escherichia coli* MC4100 [51], *Pseudomonas aeruginosa* PA01 [52], *Staphylococcus aureus* NCTC6571, *Enterococcus faecalis* ATCC19422, and Candida albicans SC5314 [53]. The minimal inhibitory concentrations (MICs) were evaluated with a microbroth dilution method [45] using Mueller Hinton broth (MHB) for bacteria, or MHB supplemented with $2\%$ glucose for C. albicans. The MIC values, expressed as µL/mL, were defined as the lowest concentration where no growth was visible after 24 h of incubation at 37 °C.
## 4. Conclusions
The work yielded an effective formulation for wound healing, particularly burn wounds. CC-based DES was prepared and the selection criteria for the blank formulations suitable for topical application was compared with a commercial product for wound healing, ialuset Plus. TDF exhibited high equilibrium solubility in DES formulations, and the developed formulations were fully characterised to ensure the suitability of this vehicle for applying TDF topically. The facile preparation approach of the formulations along with the long-term stability are amongst the main pros of this work. The combination of LDC and TDF was found to be optimal for the devoted aims of this work, as the former could provide a local anaesthetic effect without impairment of the healing process. The attained in vivo results for F01 were comparable to the commercial product in the healing time of cuts as well as burn wounds, and resulted in less scarring in burn wounds than other groups, including the commercial product. Lastly, the antimicrobial activity of the formulations containing DESs could be advantageous to counteract possible minor microbial infections associated with wounds.
## References
1. Al-Akayleh F., Khalid R.M., Hawash D., Al-Kaissi E., Al-Adham I.S., Al-Muhtaseb N., Collier P.J.. **Antimicrobial potential of natural deep eutectic solvents**. *Lett. Appl. Microbiol.* (2022.0) **75** 607-615. DOI: 10.1111/lam.13699
2. Singh S.K., Savoy A.W.. **Ionic Liquids Synthesis and Applications: An Overview**. *J. Mol. Liq.* (2020.0) **297** 112038. DOI: 10.1016/j.molliq.2019.112038
3. Clarke C.J., Tu W.C., Levers O., Bröhl A., Hallett J.P.. **Green and Sustainable Solvents in Chemical Processes**. *Chem. Rev.* (2018.0) **118** 747-800. DOI: 10.1021/acs.chemrev.7b00571
4. Hallett J.P., Welton T.. **Room-Temperature Ionic Liquids: Solvents for Synthesis and Catalysis. 2**. *Chem. Rev.* (2011.0) **111** 3508-3576. DOI: 10.1021/cr1003248
5. Smith E.L., Abbott A.P., Ryder K.S.. **Deep Eutectic Solvents (DESs) and Their Applications**. *Chem. Rev.* (2014.0) **114** 11060-11082. DOI: 10.1021/cr300162p
6. Ünlü A.E., Arlkaya A., Takaç S.. **Use of Deep Eutectic Solvents as Catalyst: A Mini-Review**. *Green Process. Synth.* (2019.0) **8** 355-372. DOI: 10.1515/gps-2019-0003
7. Carriazo D., Serrano M.C., Gutiérrez M.C., Ferrer M.L., del Monte F.. **Deep-Eutectic Solvents Playing Multiple Roles in the Synthesis of Polymers and Related Materials**. *Chem. Soc. Rev.* (2012.0) **41** 4996-5014. DOI: 10.1039/c2cs15353j
8. Deetlefs M., Seddon K.R.. **Assessing the Greenness of Some Typical Laboratory Ionic Liquid Preparations**. *Green Chem.* (2010.0) **12** 17-30. DOI: 10.1039/B915049H
9. Abbott A.P., Boothby D., Capper G., Davies D.L., Rasheed R.K.. **Deep Eutectic Solvents Formed between Choline Chloride and Carboxylic Acids: Versatile Alternatives to Ionic Liquids**. *J. Am. Chem. Soc.* (2004.0) **126** 9142-9147. DOI: 10.1021/ja048266j
10. Abbott A.P., Bell T.J., Handa S., Stoddart B.. **Cationic Functionalisation of Cellulose Using a Choline Based Ionic Liquid Analogue**. *Green Chem.* (2006.0) **8** 784-786. DOI: 10.1039/b605258d
11. Alam M.A., Muhammad G., Khan M.N., Mofijur M., Lv Y., Xiong W., Xu J.. **Choline Chloride-Based Deep Eutectic Solvents as Green Extractants for the Isolation of Phenolic Compounds from Biomass**. *J. Clean. Prod.* (2021.0) **309** 127445. DOI: 10.1016/j.jclepro.2021.127445
12. Lomba L., García C.B., Ribate M.P., Giner B., Zuriaga E.. **Applications of Deep Eutectic Solvents Related to Health, Synthesis, and Extraction of Natural Based Chemicals**. *Appl. Sci.* (2021.0) **11**. DOI: 10.3390/app112110156
13. Amoroso R., Hollmann F., Maccallini C.. **Choline Chloride-Based DES as Solvents/Catalysts/Chemical Donors in Pharmaceutical Synthesis**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26206286
14. Riaz M., Akhlaq M., Naz S., Uroos M.. **An Overview of Biomedical Applications of Choline Geranate (CAGE): A Major Breakthrough in Drug Delivery**. *RSC Adv.* (2022.0) **12** 25977-25991. DOI: 10.1039/D2RA03882J
15. Vaidya A., Mitragotri S.. **Ionic Liquid-Mediated Delivery of Insulin to Buccal Mucosa**. *J. Control. Release* (2020.0) **327** 26-34. DOI: 10.1016/j.jconrel.2020.07.037
16. Saha M., Saha M., Rahman M.S., Hossain M.N., Raynie D.E., Halim M.A.. **Molecular and Spectroscopic Insights of a Choline Chloride Based Therapeutic Deep Eutectic Solvent**. *J. Phys. Chem. A* (2020.0) **124** 4690-4699. DOI: 10.1021/acs.jpca.0c00851
17. Santos M.M., Branco L.C.. **Ionic Liquids and Deep Eutectic Solvents for Application in Pharmaceutics**. *Pharmaceutics* (2020.0) **12**. DOI: 10.3390/pharmaceutics12100909
18. Wang Y., Zhang Y., Su J., Zhang X., Wang J., Tu Q.. **Preparation of a Multifunctional Wound Dressing Based on a Natural Deep Eutectic Solvent**. *ACS Sustain. Chem. Eng.* (2020.0) **8** 14243-14252. DOI: 10.1021/acssuschemeng.0c05754
19. Zakrewsky M., Lovejoy K.S., Kern T.L., Miller T.E., Le V., Nagy A., Goumas A.M., Iyer R.S., DelSesto R.E., Koppisch A.T.. **Ionic Liquids as a Class of Materials for Transdermal Delivery and Pathogen Neutralization**. *Proc. Natl. Acad. Sci. USA* (2014.0) **111** 13313-13318. DOI: 10.1073/pnas.1403995111
20. Dong R., Guo B.. **Smart Wound Dressings for Wound Healing**. *Nano Today* (2021.0) **41** 101290. DOI: 10.1016/j.nantod.2021.101290
21. Malone-Povolny M.J., Maloney S.E., Schoenfisch M.H.. **Nitric Oxide Therapy for Diabetic Wound Healing**. *Adv. Healthc. Mater.* (2019.0) **8** e1801210. DOI: 10.1002/adhm.201801210
22. Gosain A., DiPietro L.A.. **Aging and Wound Healing**. *World J. Surg.* (2004.0) **28** 321-326. DOI: 10.1007/s00268-003-7397-6
23. Guo S., DiPietro L.A.. **Factors Affecting Wound Healing**. *J. Dent. Res.* (2010.0) **89** 219. DOI: 10.1177/0022034509359125
24. Bishop A.. **Role of Oxygen in Wound Healing**. *J. Wound Care* (2008.0) **17** 399-402. DOI: 10.12968/jowc.2008.17.9.30937
25. Abu Dayyih W., Abu Rayyan W., Al-Matubsi H.Y.. **Impact of Sildenafil-Containing Ointment on Wound Healing in Healthy and Experimental Diabetic Rats**. *Acta Diabetol.* (2020.0) **57** 1351-1358. DOI: 10.1007/s00592-020-01562-0
26. Al-Akayleh F., Jaber N., Al-Remawi M., Al Odwan G., Qinna N.. **Chitosan-biotin topical film: Preparation and evaluation of burn wound healing activity**. *Pharm. Devel. Technol.* (2022.0) **27** 479-489. DOI: 10.1080/10837450.2022.2079132
27. Alwattar J.K., Chouaib R., Khalil A., Mehanna M.M.. **A Novel Multifaceted Approach for Wound Healing: Optimization and in Vivo Evaluation of Spray Dried Tadalafil Loaded pro-Nanoliposomal Powder**. *Int. J. Pharm.* (2020.0) **587** 119647. DOI: 10.1016/j.ijpharm.2020.119647
28. Mahmoud N.N., Hikmat S., Ghith D.A., Hajeer M., Hamadneh L., Qattan D., Khalil E.A.. **Gold nanoparticles loaded into polymeric hydrogel for wound healing in rats: Effect of nanoparticles’ shape and surface modification**. *Inter. J. Pharm.* (2019.0) **565** 174-186
29. Brock G.B., McMahon C.G., Chen K.K., Costigan T., Shen W., Watkins V., Anglin G., Whitaker S.. **Efficacy and Safety of Tadalafil for the Treatment of Erectile Dysfunction: Results of Integrated Analyses**. *J. Urol.* (2002.0) **168** 1332-1336. DOI: 10.1016/S0022-5347(05)64442-4
30. Goldsmith K., Goradia E., McClain S.A., Sandoval S., Singer A.J.. **The Effect of Tadalafil on Reepithelialization and Scarring of Partial Thickness Porcine Burns**. *Wound Repair Regen.* (2020.0) **28** 26-32. DOI: 10.1111/wrr.12770
31. Souza R.A.C., Martinelli-Kläy C.P., d’Acampora A.J., Bernardes G.J.S., Sgrott S.M., Souza L.A.C., Lombardi T., Sudbrack T.R.. **Effects of Sildenafil and Tadalafil on Skin Flap Viability**. *Arch. Dermatol. Res.* (2022.0) **314** 151-157. DOI: 10.1007/s00403-021-02196-0
32. Galiè N., Brundage B.H., Ghofrani H.A., Oudiz R.J., Simonneau G., Safdar Z., Shapiro S., White R.J., Chan M., Beardsworth A.. **Tadalafil Therapy for Pulmonary Arterial Hypertension**. *Circulation* (2009.0) **119** 2894-2903. DOI: 10.1161/CIRCULATIONAHA.108.839274
33. Daadoue S., Al-Remawi M., Al-Mawla L., Idkaidek N., Khalid R.M., Al-Akayleh F.. **Deep Eutectic Liquid as Transdermal Delivery Vehicle of Risperidone**. *J. Mol. Liq.* (2022.0) **345** 117347. DOI: 10.1016/j.molliq.2021.117347
34. Al-Akayleh F., Adwan S., Khanfer M., Idkaidek N., Al-Remawi M.. **A Novel Eutectic-Based Transdermal Delivery System for Risperidone**. *AAPS PharmSciTech* (2021.0) **22** 1-11. DOI: 10.1208/s12249-020-01844-4
35. Calatayud J., González Á.. **History of the Development and Evolution of Local Anesthesia Since the Coca Leaf**. *Anesthesiology* (2003.0) **98** 1503-1508. DOI: 10.1097/00000542-200306000-00031
36. Dave R.A., Morris M.E.. **Novel High/Low Solubility Classification Methods for New Molecular Entities**. *Int. J. Pharm.* (2016.0) **511** 111. DOI: 10.1016/j.ijpharm.2016.06.060
37. Al-Akayleh F., Mohammed Ali H.H., Ghareeb M.M., Al-Remawi M.. **Therapeutic Deep Eutectic System of Capric Acid and Menthol: Characterization and Pharmaceutical Application**. *J. Drug Deliv. Sci. Technol.* (2019.0) **53** 101159. DOI: 10.1016/j.jddst.2019.101159
38. Rawat A., Gupta S.S., Kalluri H., Lowenborg M., Bhatia K., Warner K.. **Rheological Characterization in the Development of Topical Drug Products**. *AAPS Adv. Pharm. Sci. Ser.* (2019.0) **36** 3-45. DOI: 10.1007/978-3-030-17355-5_1/FIGURES/31
39. Carrer V., Alonso C., Pont M., Zanuy M., Córdoba M., Espinosa S., Barba C., Oliver M.A., Martí M., Coderch L.. **Effect of Propylene Glycol on the Skin Penetration of Drugs**. *Arch. Dermatolog. Res.* (2019.0) **312** 337-352. DOI: 10.1007/s00403-019-02017-5
40. Kim S., Chen J., Cheng T., Gindulyte A., He J., He S., Li Q., Shoemaker B.A., Thiessen P.A., Yu B.. **PubChem in 2021: New Data Content and Improved Web Interfaces**. *Nucleic Acids Res.* (2021.0) **49** D1388-D1395. DOI: 10.1093/nar/gkaa971
41. **Tadalafil. C22H19N3O4. PubChem**
42. Rahman M.S., Roy R., Jadhav B., Hossain M.N., Halim M.A., Raynie D.E.. **Formulation, Structure, and Applications of Therapeutic and Amino Acid-Based Deep Eutectic Solvents: An Overview**. *J. Mol. Liq.* (2021.0) **321** 114745. DOI: 10.1016/j.molliq.2020.114745
43. Marei H.F., Arafa M.F., Essa E.A., El Maghraby G.M.. **Lidocaine as Eutectic Forming Drug for Enhanced Transdermal Delivery of Nonsteroidal Anti-Inflammatory Drugs**. *J. Drug Deliv. Sci. Technol.* (2021.0) **61** 102338. DOI: 10.1016/j.jddst.2021.102338
44. Zhang H., Lang J., Lan P., Yang H., Lu J., Wang Z.. **Study on the Dissolution Mechanism of Cellulose by ChCl-Based Deep Eutectic Solvents**. *Materials* (2020.0) **13**. DOI: 10.3390/ma13020278
45. Andrews J.M.. **Determination of Minimum Inhibitory Concentrations**. *J. Antimicrob. Chemother.* (2001.0) **48** 5-16. DOI: 10.1093/jac/48.suppl_1.5
46. Sheil M., Chambers M., Polkinghorne A., Sharpe B.. **Topical Application of Lidocaine and Bupivacaine to Disbudding Wounds in Dairy Calves: Safety, Toxicology and Wound Healing**. *Animals* (2021.0) **11**. DOI: 10.3390/ani11030869
47. Leavitt T., Hu M.S., Marshall C.D., Barnes L.A., Lorenz H.P., Longaker M.T.. **Scarless Wound Healing: Finding the Right Cells and Signals**. *Cell Tissue Res.* (2016.0) **365** 483. DOI: 10.1007/s00441-016-2424-8
48. Dart A.J., Cries L., Jeffcott L.B., Hodgson D.R., Rose R.J.. **Effects of 25% Propylene Glycol Hydrogel (Solugel) on Second Intention Wound Healing in Horses**. *Vet. Surg.* (2002.0) **31** 309-313. DOI: 10.1053/jvet.2002.33585
49. Reddy B.P., Reddy K.A., Reddy M.S.. **Validation and Stability Indicating RP-HPLC Method for the Determination of Tadalafil API in Pharmaceutical Formulations**. *Res. Pharm. Biotechnol.* (2010.0) **2** 1-006
50. Masson-Meyers D.S., Andrade T.A.M., Caetano G.F., Guimaraes F.R., Leite M.N., Leite S.N., Frade M.A.C.. **Experimental Models and Methods for Cutaneous Wound Healing Assessment**. *Int. J. Exp. Pathol.* (2020.0) **101** 21-37. DOI: 10.1111/iep.12346
51. Peters J.E., Thate T.E., Craig N.L.. **Definition of the Escherichia Coli MC4100 Genome by Use of a DNA Array**. *J. Bacteriol.* (2003.0) **185** 2017. DOI: 10.1128/JB.185.6.2017-2021.2003
52. HOLLOWAY B.W.. **Genetic Recombination in Pseudomonas Aeruginosa**. *J. Gen. Microbiol.* (1955.0) **13** 572-581. DOI: 10.1099/00221287-13-3-572
53. Gillum A.M., Tsay E.Y.H., Kirsch D.R.. **Isolation of the Candida Albicans Gene for Orotidine-5’-Phosphate Decarboxylase by Complementation of S. Cerevisiae Ura3 and E. Coli PyrF Mutations**. *Mol. Gen. Genet.* (1984.0) **198** 179-182. DOI: 10.1007/BF00328721
|
---
title: Circulating Magnesium and Risk of Major Adverse Cardiac Events among Patients
with Atrial Fibrillation in the ARIC Cohort
authors:
- Linzi Li
- Pamela L. Lutsey
- Lin Yee Chen
- Elsayed Z. Soliman
- Mary R. Rooney
- Alvaro Alonso
journal: Nutrients
year: 2023
pmcid: PMC10005106
doi: 10.3390/nu15051211
license: CC BY 4.0
---
# Circulating Magnesium and Risk of Major Adverse Cardiac Events among Patients with Atrial Fibrillation in the ARIC Cohort
## Abstract
Background: *Serum magnesium* (Mg) has been reported to be inversely associated with the risk of atrial fibrillation (AF), coronary artery disease (CAD), and major adverse cardiovascular events (MACE). The association between serum Mg and the risk of MACE, heart failure (HF), stroke, and all-cause mortality among patients with AF has not been evaluated. Objective: We aim to examine whether higher serum *Mg is* associated with a lower risk of MACE, heart failure (HF), stroke, and all-cause mortality among patients with AF. Methods: We evaluated prospectively 413 participants of the Atherosclerosis Risk in Communities (ARIC) Study with a diagnosis of AF at the time of Mg measurement participating in visit 5 (2011–2013). Serum Mg was modeled in tertiles and as a continuous variable in standard deviation units. Endpoints (HF, MI, stroke, cardiovascular (CV) death, all-cause mortality, and MACE) were identified and modeled separately using Cox proportional hazard regression adjusting for potential confounders. Results: *During a* mean follow-up of 5.8 years, there were 79 HFs, 34 MIs, 24 strokes, 80 CV deaths, 110 MACEs, and 198 total deaths. After adjustment for demographic and clinical variables, participants in the second and third tertiles of serum Mg had lower rates of most endpoints, with the strongest inverse association for the incidence of MI (HR 0.20, $95\%$ CI 0.07, 0.61) comparing top to bottom tertile. Serum Mg modeled linearly as a continuous variable did not show clear associations with endpoints except MI (HR 0.50, $95\%$ CI 0.31, 0.80). Due to the limited number of events, the precision of most estimates of association was relatively low. Conclusions: Among patients with AF, higher serum Mg was associated with a lower risk of developing incident MI and, to a lesser extent, other CV endpoints. Further studies in larger patients with AF cohorts are needed to evaluate the role of serum Mg in preventing adverse CV outcomes in these patients.
## 1. Background
Magnesium (Mg), as one of the most abundant cations in the human body, plays a critical role in several physiological, biochemical, and cellular processes that regulate cardiovascular function. Mg is related to the pathogenesis and mechanism of some cardiovascular diseases, such as heart failure (HF), hypertension, and cardiac arrhythmias [1]. Mg exerts antiarrhythmic effects on cells through modulation of myocardial excitability, influencing the risk of cardiac arrhythmias [2]. Existing literature has documented that serum Mg deficiency could identify patients with a higher risk of postoperative atrial fibrillation (AF) after coronary artery bypass surgery [3,4]. Some studies also suggest that Mg supplementation reduces AF incidence after cardiac surgery [5,6,7]. In the community-based Framingham Offspring Heart Study and the Atherosclerosis Risk in Communities (ARIC) Study, an inverse association between serum Mg and the risk of AF has been described [8,9,10].
Serum *Mg is* also related to other cardiovascular conditions [11]. Evidence shows that serum Mg was associated with coronary heart and vascular disease deaths and hospitalizations and all-cause mortality inversely in a nationally representative population-based sample [12,13]. Low serum Mg also independently predicted all-cause and cardiovascular mortality, adjusting for established cardiovascular risk factors [14]. Among patients with myocardial infarction (MI), low serum Mg was associated with major adverse cardiac events (MACE), including death, recurrent MI, stroke, and any revascularization [15].
AF shares risk factors and usually co-exists with coronary artery disease (CAD) [16]. However, there is little information regarding the association between serum Mg and MACE among patients with AF. Using data from the ARIC study, which has obtained information on cardiovascular risk factors, AF, and other cardiac events endpoints, we investigated how serum *Mg is* related to MACE, heart failure (HF), and all-cause mortality among patients with AF. We hypothesized that lower serum Mg concentrations are associated with a higher risk of MACE, HF, and all-cause mortality among patients with AF.
## 2.1. Study Population and Design
The ARIC *Study is* a community-based prospective cohort conducted in four United States communities: Forsyth County, North Carolina; Washington County, Maryland; selected Minneapolis suburbs, Minnesota; and Jackson, Mississippi. The study aims to investigate cardiovascular risk factors. The detailed study design was described elsewhere [17]. In 1987–1989, approximately 4000 individuals aged 45–64 years in each field center were enrolled. In total, 15,792 participants completed an extensive baseline examination. After the baseline examination (visit 1), participants attended follow-up visits, occurring in 1990–1992 (visit 2), 1993–1995 (visit 3), 1996–1998 (visit 4), 2011–2013 (visit 5), 2016–2017 (visit 6), and 2018–2019 (visit 7). During each study visit, information on clinical and lifestyle variables was collected. In this study, we included those with available serum Mg measurements and diagnosis of AF at the time of examination in visit 5. At other visits, no Mg concentration was measured or measured using the same assay. We excluded those who had missing serum Mg concentrations, those who did not have AF or had missing AF status at the time Mg was measured, as well as non-whites from the Minneapolis and Washington County field centers, and individuals who self-reported their race as other than white or African American in the Forsyth County field center because of small numbers in some race-center combinations. This resulted in 413 unique individuals in the analytic dataset (Figure 1). All the participants were followed until the end of 2019 (end of 2017 for Jackson participants). Institutional review boards approved the study protocol at participating institutions. All study participants provided written informed consent.
## 2.2. Assessment of Serum Mg Concentrations
At visit 5, participants had blood samples collected after eight hours of fasting following standardized protocols. Serum samples were stored at −80 °C and shipped to a central facility. In 2016, Mg concentrations were measured using these serum samples with a colorimetric (xylidyl blue) method in Roche COBAS 6000 chemistry analyzer (Roche Diagnostics, Indianapolis, IN, USA). The coefficient of variation was $1.9\%$ using measurements from 242 duplicates [18]. Circulating Mg refers to the total blood level in this study.
## 2.3. Definition of Prevalent AF and Incident Endpoints
Prevalent AF at visit 5 was defined as [1] electrocardiographic evidence of the arrhythmia at the current or prior study visits, or [2] between visits 1 and 5, any presence of AF in hospitalization discharge diagnosis not associated with open cardiac surgery [19].
The separate endpoints in this study were incident HF, MI, stroke, cardiovascular (CV) death, and all-cause mortality during follow-up. The combined endpoint was MACE (MI, stroke, cardiovascular death). Incident HF was determined as the first HF hospitalization or death due to HF. HF hospitalizations were identified if ICD-9-CM 428.xx or ICD-10-CM I50.xx codes were included as discharge diagnoses, and HF deaths were identified if ICD-9 428 or ICD-10 I50 codes were listed in the death certificate [20]. Incident MIs were defined as definite or probable MI, death due to coronary heart disease, or any electrocardiographic evidence of a silent MI detected [21]. Incident strokes were defined as definite or probable strokes by criteria used in the National Survey of Stroke [22]. CV deaths were defined as ICD-9 401-459 or ICD-10 I10-I99. Stroke and CV death cases were confirmed by a computerized algorithm and physician reviewer independently [21,23]. All death events were determined from death certificates where causes of death were coded using ICD-9 or ICD-10 [24].
## 2.4. Ascertainment of Other Covariates
The following variables measured at visit 5 were covariates in this study: age, sex, race, study center, body mass index (BMI), smoking status (current, former, never), alcohol drinking status (current, former, never), systolic blood pressure (SBP), diastolic blood pressure (DBP), low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), diabetes history, use of anti-hypertension medication other than diuretics, use of diuretics (loop diuretics and other diuretics), use of blood lipid-lowering medication, estimated glomerular filtration rate (eGFR), serum potassium, serum creatinine, use of anticoagulants, use of aspirin, use of beta-blocker, use of angiotensin-converting enzyme (ACE) inhibitor, use of angiotensin II receptor antagonists, use of aldosterone antagonist, use of antiarrhythmics, MI history, stroke history, and HF history. All demographic variables were self-reported and clinical variables were measured at the visit examination. Diabetes was defined as fasting blood glucose ≥126 mg/dL, non-fasting blood glucose ≥200 mg/dL, use of glucose-lowering medication, or a self-reported physician diagnosis of diabetes. Participants were asked to bring any medications and supplements taken during the two weeks prior to the exam. Medication use was determined by staff review at the time of the visit.
## 2.5. Statistical Analysis
Serum Mg concentration was categorized based on approximate tertiles (1.2–1.9, 2.0, 2.1–2.7 mg/dL) and, separately, using the thresholds defining normal range (1.7 and 2.2 mg/dL) [25]. According to these categories, baseline characteristics of the study population were described. The follow-up time was days from visit 5 to any incident outcome, loss to follow-up, or 31 December 2019, whichever happened earlier. For each outcome-specific analysis (HF, stroke, MI), we excluded those who had a prior history before visit 5, but not for the combined endpoint (MACE). The incidence rates of each outcome were calculated. Cox proportional hazard regression was used to estimate the hazard ratios (HRs) and $95\%$ confidence intervals (CIs) between serum Mg concentration and the incident endpoints. Serum Mg concentrations were modeled as tertiles, normal range categories, and continuously (per 1 SD or ~0.21 mg/dL). The following models were run for each outcome: [1] crude model, [2] adjusted for age, sex, race, and study center, [3] model 2 plus adjustment for BMI, smoking status, drinking status, SBP, DBP, LDL, HDL, diabetes, use of diuretics and antihypertensive medication (loop diuretics, other diuretics, other antihypertensive medication, and no antihypertensive medication), use of lipid-lowering medications, eGFR, serum potassium, serum creatinine, use of anticoagulants and use of aspirin, and [4] model 3 plus an adjustment for MI history, stroke history, and HF history. Additionally, we used restricted cubic spline functions with 3 knots to visually examine the associations between serum Mg concentration and the incident endpoints. Direct adjusted survival curves were plotted for the incident endpoints using a SAS macro [26]. We also ran an additional model for each endpoint, adjusting for covariates in model 3 and beta-blocker use, ACE inhibitor use, angiotensin II receptor antagonist use, aldosterone antagonist use, and antiarrhythmic agent use. All the analyses were completed with SAS statistical software (v. 9.4, SAS Institute Inc., Cary, NC, USA).
## 2.6. Results
Characteristics of 413 ARIC participants grouped by Mg concentration tertiles and categories are shown in Table 1 and Supplemental Table S1. The tertile ranges were 1.2–1.9, 2.0, and 2.1–2.7 mg/dL. The number of participants in tertiles was unequal because tied Mg values were assigned to the same group. Those with normal serum Mg levels (1.7–2.2 mg/dL) were more likely to be male, and those with lower serum Mg levels (<1.7 mg/dL) were more likely to be younger, white, and have higher BMI.
Over an average follow-up of 5.7 years, 79 HFs, 34 MIs, 24 strokes, 80 CV deaths, 110 MACEs, and 198 total deaths were identified in the study population (Table 2). There tended to be a linear dose-response inverse association of serum Mg tertiles with incident MI, U-shape associations with incident stroke, CV death, MACE, and all-cause mortality, and an L-shape association with HF (Supplemental Figure S1). In the models adjusted for age, sex, race, and study center, participants in the second tertile of serum Mg had a lower risk of MI, MACE, and all-cause mortality than patients in the first tertile. The HRs ($95\%$ CIs) for MI, MACE, and all-cause mortality were 0.36 (0.14, 0.95), 0.48 (0.28, 0.83), and 0.62 (0.42, 0.92), respectively. There was an inverse association comparing participants in the third tertile and the first tertile for incident MI in all models adjusted for covariates [HRs ($95\%$ CI): model 1 0.43 (0.10, 0.93), model 2 0.23 (0.08, 0.65), model 3 0.20 (0.07, 0.61)]. There was evidence of dose-response for incident MI in model 2 and model 3. However, in other models, associations for individuals in the second and third tertiles of Mg level were attenuated and did not show statistically significant differences in the risks of HF, MI, stroke, CV death, MACE, and all-cause mortality. As 1 SD (~0.21 mg/dL) increased in Mg concentration, the risk of MI decreased by $50\%$ (HR 0.50, $95\%$ CI 0.31, 0.80). Mg concentration modeled as a continuous variable was not associated with the risk of incident HF, stroke, and MACE, nor with CV death and all-cause mortality.
In a secondary analysis using normal serum Mg level (1.7–2.2 mg/dL) as the reference category, there were no statistically significant differences in the risk of HF, MI, stroke, MACE, CV death, or all-cause mortality among individuals with low (<1.7 mg/dL) or high (>2.2 mg/dL) serum Mg (Supplemental Table S2). Adjusting for additional medication use did not change the results significantly (Supplemental Table S3).
## 3. Discussion
Our study found that higher serum Mg was associated with a lower risk of most cardiovascular outcomes (MI, HF, MACE, CV death, and all-cause mortality) in patients with AF, but showed a consistently significant association with incident MI after adjusting for other cardiovascular risk factors which may mediate the association between Mg and incident cardiovascular outcomes.
To our knowledge, literature documenting the association between circulating Mg concentrations and cardiac events among patients with AF in the long term is exiguous. Previous studies in ARIC have reported that low serum Mg was associated with an increased risk of incident HF [27], sudden cardiac death [28], and CHD, including definite or probable MI or definite CHD death [29]. In other studies, an inverse association between serum Mg and CV death and all-cause mortality has been reported [13,14]. We observed an inverse association between Mg and most cardiovascular outcomes in demographic-adjusted models, but not after adjusting for additional CVD risk factors which may mediate the association between Mg and CVD. The lack of independent significant associations for most outcomes in our study could be due to several reasons. First, the association of serum Mg among patients with AF with CV endpoints could be different from that in a healthy population. Second, the study population was relatively old, with a high prevalence of multiple chronic health conditions. The effect of other cardiovascular risk factors could overwhelm potential causal pathways, reducing the absolute impact of serum Mg. Third, the limited number of events reduced the precision of effect estimates. Some of the outcomes’ associations were in the hypothesized direction, though not statistically significant.
Limited evidence suggests that in the acute management of non-postoperative acute AF, Mg therapy does not perform better than placebo in preventing major adverse outcomes, including death (RR 0.85, $95\%$ 0.44–1.61 in a metanalysis of 273 patients and 29 events) [30]. However, due to the small number of patients and events in this meta-analysis, the lack of a statistically significant association does not imply evidence of no effect of Mg on CV outcomes among patients with AF. Thus, further investigation with larger sample sizes is warranted in order to evaluate the role of serum Mg and Mg supplementation among those with existing AF as a secondary prevention strategy.
Previous epidemiological studies have demonstrated an association between serum Mg and hypertension in different populations, implying that Mg may play a role in regulating blood pressure through vascular smooth muscle cell relaxation [31,32,33]. Evidence showed that intracellular and extracellular Mg deficiency might participate in insulin resistance and metabolic syndrome development [34,35,36]. Improved low-grade inflammation was suggested as the potential mechanism by which Mg had a beneficial effect on hypertension, type 2 diabetes, and metabolic syndrome [35]. However, it remains unclear how serum Mg influences the pathophysiology of incident MI, especially among AF patients. Since hypertension, type 2 diabetes, and metabolic syndrome are common complications of MI, Mg might affect incident MI development by improving systemic inflammation and endothelial function.
There were strengths in our study, including the use of a community-based study sample, assessment of AF and cardiac events endpoint, measurements of cardiovascular risk factors allowing control for confounding, and the long follow-up. Our study also had limitations. First, the age of the study population was 78 years on average, limiting the generalizability of the results to populations in other age ranges. Second, for incident stroke and MI, the number of events was relatively small, resulting in imprecise confidence intervals. Third, due to the observational study design, the causal inference between serum Mg and cardiovascular outcomes is limited.
In conclusion, among patients with existing AF, higher serum Mg was associated with a lower risk of incident MI, but no significant association was observed with stroke, MACE, HF, and all-cause mortality. To better understand whether serum Mg and Mg therapy has a role in the prevention of MACE in patients with AF, further studies in larger patient samples are needed.
The authors’ responsibilities were as follows: LL and AA designed the study; LL conducted the literature search, performed the statistical analysis, drafted the paper, and had primary responsibility for the final content; and all authors wrote the paper and read and approved the final manuscript. None of the authors reported a conflict of interest related to the study.
## References
1. Kolte D., Vijayaraghavan K., Khera S., Sica D.A., Frishman W.H.. **Role of magnesium in cardiovascular diseases**. *Cardiol. Rev.* (2014.0) **22** 182-192. DOI: 10.1097/CRD.0000000000000003
2. Larsson S.C., Drca N., Michaëlsson K.. **Serum Magnesium and Calcium Levels and Risk of Atrial Fibrillation**. *Circ. Genom. Precis. Med.* (2019.0) **12** e002349. DOI: 10.1161/CIRCGEN.118.002349
3. Zaman A.G., Alamgir F., Richens T., Williams R., Rothman M.T., Mills P.G.. **The role of signal averaged P wave duration and serum magnesium as a combined predictor of atrial fibrillation after elective coronary artery bypass surgery**. *Heart* (1997.0) **77** 527. DOI: 10.1136/hrt.77.6.527
4. Kiziltepe U., Eyileten Z.B., Sirlak M., Tasoz R., Aral A., Eren N.T., Uysalel A., Akalin H.. **Antiarrhythmic effect of magnesium sulfate after open heart surgery: Effect of blood levels**. *Int. J. Cardiol.* (2003.0) **89** 153-158. DOI: 10.1016/S0167-5273(02)00449-7
5. Alghamdi A.A., Al-Radi O.O., Latter D.A.. **Intravenous magnesium for prevention of atrial fibrillation after coronary artery bypass surgery: A systematic review and meta—Analysis**. *J. Card. Surg.* (2005.0) **20** 293-299. DOI: 10.1111/j.1540-8191.2005.200447.x
6. Fairley J.L., Zhang L., Glassford N.J., Bellomo R.. **Magnesium status and magnesium therapy in cardiac surgery: A systematic review and meta-analysis focusing on arrhythmia prevention**. *J. Crit. Care* (2017.0) **42** 69-77. DOI: 10.1016/j.jcrc.2017.05.038
7. Hazelrigg S.R., Boley T.M., Cetindag I.B., Moulton K.P., Trammell G.L., Polancic J.E., Shawgo T.S., Quin J.A., Verhulst S.. **The efficacy of supplemental magnesium in reducing atrial fibrillation after coronary artery bypass grafting**. *Ann. Thorac. Surg.* (2004.0) **77** 824-830. DOI: 10.1016/j.athoracsur.2003.08.027
8. Misialek J.R., Lopez F.L., Lutsey P.L., Huxley R.R., Peacock J.M., Chen L.Y., Soliman E.Z., Agarwal S.K., Alonso A.. **Serum and dietary magnesium and incidence of atrial fibrillation in whites and in African Americans—Atherosclerosis Risk in Communities (ARIC) study**. *Circ. J.* (2013.0) **77** 323-329. DOI: 10.1253/circj.CJ-12-0886
9. Khan A.M., Lubitz S.A., Sullivan L.M., Sun J.X., Levy D., Vasan R.S., Magnani J.W., Ellinor P.T., Benjamin E.J., Wang T.J.. **Low serum magnesium and the development of atrial fibrillation in the community: The Framingham Heart Study**. *Circulation* (2013.0) **127** 33-38. DOI: 10.1161/CIRCULATIONAHA.111.082511
10. Markovits N., Kurnik D., Halkin H., Margalit R., Bialik M., Lomnicky Y., Loebstein R.. **Database evaluation of the association between serum magnesium levels and the risk of atrial fibrillation in the community**. *Int. J. Cardiol.* (2016.0) **205** 142-146. DOI: 10.1016/j.ijcard.2015.12.014
11. Rooney M.R., Alonso A., Folsom A.R., Michos E.D., Rebholz C.M., Misialek J.R., Chen L.Y., Dudley S., Lutsey P.L.. **Serum magnesium and the incidence of coronary artery disease over a median 27 years of follow-up in the Atherosclerosis Risk in Communities (ARIC) Study and a meta-analysis**. *Am. J. Clin. Nutr.* (2020.0) **111** 52-60. DOI: 10.1093/ajcn/nqz256
12. Gartside P., Glueck C.. **The important role of modifiable dietary and behavioral characteristics in the causation and prevention of coronary heart disease hospitalization and mortality: The prospective NHANES I follow-up study**. *J. Am. Coll. Nutr.* (1995.0) **14** 71-79. DOI: 10.1080/07315724.1995.10718476
13. Ford E.S.. **Serum magnesium and ischaemic heart disease: Findings from a national sample of US adults**. *Int. J. Epidemiol.* (1999.0) **28** 645-651. DOI: 10.1093/ije/28.4.645
14. Reffelmann T., Ittermann T., Dörr M., Völzke H., Reinthaler M., Petersmann A., Felix S.B.. **Low serum magnesium concentrations predict cardiovascular and all-cause mortality**. *Atherosclerosis* (2011.0) **219** 280-284. DOI: 10.1016/j.atherosclerosis.2011.05.038
15. An G., Du Z., Meng X., Guo T., Shang R., Li J., An F., Li W., Zhang C.. **Association between low serum magnesium level and major adverse cardiac events in patients treated with drug-eluting stents for acute myocardial infarction**. *PLoS ONE* (2014.0) **9**. DOI: 10.1371/journal.pone.0098971
16. Akao M.. **Atrial fibrillation and coronary artery disease: Resembling twins?**. *J. Cardiol.* (2014.0) **63** 169-170. DOI: 10.1016/j.jjcc.2013.09.010
17. Wright J.D., Folsom A.R., Coresh J., Sharrett A.R., Couper D., Wagenknecht L.E., Mosley T.H., Ballantyne C.M., Boerwinkle E.A., Rosamond W.D.. **The ARIC (Atherosclerosis Risk In Communities) Study: JACC Focus Seminar 3/8**. *J. Am. Coll. Cardiol.* (2021.0) **77** 2939-2959. DOI: 10.1016/j.jacc.2021.04.035
18. Alonso A., Rooney M.R., Chen L.Y., Norby F.L., Saenger A.K., Soliman E.Z., O’Neal W.T., Hootman K.C., Selvin E., Lutsey P.L.. **Circulating electrolytes and the prevalence of atrial fibrillation and supraventricular ectopy: The Atherosclerosis Risk in Communities (ARIC) study**. *Nutr. Metab. Cardiovasc. Dis.* (2020.0) **30** 1121-1129. DOI: 10.1016/j.numecd.2020.03.010
19. Alonso A., Agarwal S.K., Soliman E.Z., Ambrose M., Chamberlain A.M., Prineas R.J., Folsom A.R.. **Incidence of atrial fibrillation in whites and African-Americans: The Atherosclerosis Risk in Communities (ARIC) study**. *Am. Heart J.* (2009.0) **158** 111-117. DOI: 10.1016/j.ahj.2009.05.010
20. Kottgen A., Russell S.D., Loehr L.R., Crainiceanu C.M., Rosamond W.D., Chang P.P., Chambless L.E., Coresh J.. **Reduced Kidney Function as a Risk Factor for Incident Heart Failure: The Atherosclerosis Risk in Communities (ARIC) Study**. *J. Am. Soc. Nephrol.* (2007.0) **18** 1307-1315. DOI: 10.1681/ASN.2006101159
21. White A.D., Folsom A.R., Chambless L.E., Sharret A.R., Yang K., Conwill D., Higgins M., Williams O.D., Tyroler H., Investigators A.. **Community surveillance of coronary heart disease in the Atherosclerosis Risk in Communities (ARIC) Study: Methods and initial two years’ experience**. *J. Clin. Epidemiol.* (1996.0) **49** 223-233. DOI: 10.1016/0895-4356(95)00041-0
22. Robins M., Baum H.. **The National Survey of Stroke. Incidence**. *Stroke* (1981.0) **12** I45-I57. PMID: 7222166
23. Kwon Y., Norby F.L., Jensen P.N., Agarwal S.K., Soliman E.Z., Lip G.Y.H., Longstreth W.T., Alonso A., Heckbert S.R., Chen L.Y.. **Association of Smoking, Alcohol, and Obesity with Cardiovascular Death and Ischemic Stroke in Atrial Fibrillation: The Atherosclerosis Risk in Communities (ARIC) Study and Cardiovascular Health Study (CHS)**. *PLoS ONE* (2016.0) **11**. DOI: 10.1371/journal.pone.0147065
24. Nagarajarao H.S., Penman A.D., Taylor H.A., Mosley T.H., Butler K., Skelton T.N., Samdarshi T.E., Aru G., Fox E.R.. **The Predictive Value of Left Atrial Size for Incident Ischemic Stroke and All-Cause Mortality in African Americans**. *Stroke* (2008.0) **39** 2701-2706. DOI: 10.1161/STROKEAHA.108.515221
25. **Magnesium Blood Test**
26. Hu Z.-H., Wang H.-L., Gale R.P., Zhang M.-J.. **Correction to: A SAS macro for estimating direct adjusted survival functions for time-to-event data with or without left truncation**. *Bone Marrow Transplant.* (2022.0) **57** 693. DOI: 10.1038/s41409-021-01533-1
27. Lutsey P.L., Alonso A., Michos E.D., Loehr L.R., Astor B.C., Coresh J., Folsom A.R.. **Serum magnesium, phosphorus, and calcium are associated with risk of incident heart failure: The Atherosclerosis Risk in Communities (ARIC) Study**. *Am. J. Clin. Nutr.* (2014.0) **100** 756-764. DOI: 10.3945/ajcn.114.085167
28. Peacock J.M., Ohira T., Post W., Sotoodehnia N., Rosamond W., Folsom A.R.. **Serum magnesium and risk of sudden cardiac death in the Atherosclerosis Risk in Communities (ARIC) Study**. *Am. Heart J.* (2010.0) **160** 464-470. DOI: 10.1016/j.ahj.2010.06.012
29. Liao F., Folsom A.R., Brancati F.L.. **Is low magnesium concentration a risk factor for coronary heart disease? The Atherosclerosis Risk in Communities (ARIC) Study**. *Am. Heart J.* (1998.0) **136** 480-490. DOI: 10.1016/S0002-8703(98)70224-8
30. Onalan O., Crystal E., Daoulah A., Lau C., Crystal A., Lashevsky I.. **Meta-Analysis of Magnesium Therapy for the Acute Management of Rapid Atrial Fibrillation**. *Am. J. Cardiol.* (2007.0) **99** 1726-1732. DOI: 10.1016/j.amjcard.2007.01.057
31. Laurant P., Touyz R.M.. **Physiological and pathophysiological role of magnesium in the cardiovascular system: Implications in hypertension**. *J. Hypertens.* (2000.0) **18** 1177-1191. DOI: 10.1097/00004872-200018090-00003
32. Correa S., Guerra-Torres X.E., Waikar S.S., Mc Causland F.R.. **Serum magnesium, blood pressure, and risk of hypertension and chronic kidney disease progression in the CRIC study**. *Hypertension* (2021.0) **78** 1771-1780. DOI: 10.1161/HYPERTENSIONAHA.121.17694
33. Guerrero-Romero F., Rodríguez-Morán M., Hernández-Ronquillo G., Gómez-Díaz R., Pizano-Zarate M.L., Wacher N.H., Mondragón-González R., Simental-Mendia L.E., Martínez A.M.S., Villaseñor A.S.Á.. **Low serum magnesium levels and its association with high blood pressure in children**. *J. Pediatr.* (2016.0) **168** 93-98.e91. DOI: 10.1016/j.jpeds.2015.09.050
34. Barbagallo M., Dominguez L.J.. **Magnesium metabolism in type 2 diabetes mellitus, metabolic syndrome and insulin resistance**. *Arch. Biochem. Biophys.* (2007.0) **458** 40-47. DOI: 10.1016/j.abb.2006.05.007
35. Evangelopoulos A.A., Vallianou N.G., Panagiotakos D.B., Georgiou A., Zacharias G.A., Alevra A.N., Zalokosta G.J., Vogiatzakis E.D., Avgerinos P.C.. **An inverse relationship between cumulating components of the metabolic syndrome and serum magnesium levels**. *Nutr. Res.* (2008.0) **28** 659-663. DOI: 10.1016/j.nutres.2008.07.001
36. Barbagallo M., Dominguez L.J., Galioto A., Ferlisi A., Cani C., Malfa L., Pineo A., Paolisso G.. **Role of magnesium in insulin action, diabetes and cardio-metabolic syndrome X**. *Mol. Asp. Med.* (2003.0) **24** 39-52. DOI: 10.1016/S0098-2997(02)00090-0
|
---
title: Effect of Walnut Supplementation on Dietary Polyphenol Intake and Urinary Polyphenol
Excretion in the Walnuts and Healthy Aging Study
authors:
- Rita I. Amen
- Rawiwan Sirirat
- Keiji Oda
- Sujatha Rajaram
- Ifeanyi Nwachukwu
- Montserrat Cofan
- Emilio Ros
- Joan Sabate
- Ella H. Haddad
journal: Nutrients
year: 2023
pmcid: PMC10005107
doi: 10.3390/nu15051253
license: CC BY 4.0
---
# Effect of Walnut Supplementation on Dietary Polyphenol Intake and Urinary Polyphenol Excretion in the Walnuts and Healthy Aging Study
## Abstract
Among all tree nuts, walnuts contain the highest total polyphenols by weight. This secondary data analysis examined the effect of daily walnut supplementation on the total dietary polyphenols and subclasses and the urinary excretion of total polyphenols in a free-living elderly population. In this 2-year prospective, randomized intervention trial (ID NCT01634841), the dietary polyphenol intake of participants who added walnuts daily to their diets at $15\%$ of daily energy were compared to those in the control group that consumed a walnut-free diet. Dietary polyphenols and subclasses were estimated from 24 h dietary recalls. Phenolic estimates were derived from Phenol-Explorer database version 3.6. Participants in the walnut group compared to the control group had a higher intake of total polyphenols, flavonoids, flavanols, and phenolic acids in mg/d (IQR): 2480 [1955, 3145] vs. 1897 [1369, 2496]; 56 [42,84] vs. 29 [15, 54]; 174 [90, 298] vs. 140 [61, 277]; and 368 [246, 569] vs. 242 [89, 398], respectively. There was a significant inverse association between dietary flavonoid intake and urine polyphenol excretion; less urinary excretion may imply that some of the polyphenols were eliminated via the gut. Nuts had a significant contribution to the total polyphenols in the diet, suggesting that a single food like walnuts added to habitual diet can increase the polyphenol intake in a Western population.
## 1. Introduction
It is well-recognized that walnuts have a favorable nutrient and fatty acid profile, and their consumption is effective in reducing blood lipids [1,2] and in modifying inflammation and endothelial dysfunction [3,4,5], thus reducing the risk of cardiovascular disease [2,6]. The risk lowering effects of walnuts as demonstrated by supplementing diets with the nuts, are greater than predicted based on the amount and nature of the fat consumed [7]. Evidence suggests that the phenolic phytochemicals found in walnuts and other nuts increase antioxidant defenses and reduce inflammation [7]. A recent review and meta-analysis summarizing the findings from several randomized controlled trials showed that incorporating polyphenol-rich foods impacts blood lipids by increasing high-density lipoprotein (HDL) and lowering low-density lipoprotein (LDL) [8]. Diets rich in polyphenols such as the Mediterranean diet, emphasizing olive oil and walnuts, report decreased blood lipids and inflammatory markers [6,9].
Walnuts are composed of an outer green husk with a hard shell inside the husk containing a walnut kernel covered with a seed coat or pellicle, where most polyphenols reside [10,11]. Among the nuts, walnuts contain the highest concentrations of polyphenols, averaging 2500 gallic acid equivalent (GAE) per 100 g [12]. Current comprehensive analyses of walnut polyphenols using chromatographic, and mass spectrometric techniques have identified hundreds of compounds in the walnut kernel including hydrolysable and condensed tannins, flavonoids, flavanols, phenolic acids, and lignans [13,14]. A study investigated the postprandial effect of walnut intake on the plasma total polyphenols and showed increased concentrations of plasma polyphenols 30 min following ingestion, which reached a peak at 90 min [1]. Therefore, walnuts may contribute to dietary polyphenols and may offer protective health benefits.
Several studies have recently explored dietary compositional changes produced by adding walnuts to the diet. In a randomized parallel design intervention, participants at risk for type 2 diabetes who added walnuts to their habitual diet increased their energy, protein, total fat, and magnesium intake [15] and had a non-significant decrease in sodium, empty calories, and dairy products [16]. In similar studies, individuals randomized to the walnut group showed higher intakes of protein, polyunsaturated fatty acids, both omega-3 and omega-6, but lower intakes of carbohydrates, animal protein and saturated fatty acids [17], and, in a cross-over study, participants who consumed walnuts additionally increased their dietary fiber, calcium, phosphorus, magnesium, and zinc intake [18]. However, no studies have as yet investigated whether the inclusion of walnuts in the diet influences the dietary intake of polyphenols or urinary polyphenol excretion. In this secondary data analyses of the Walnuts and Healthy Aging study (WAHA) [19], we investigated the impact of consuming walnuts on the dietary intake of total polyphenols and their sub-classes (flavonoids, flavanols, and lignans), and on the total urinary polyphenol excretion. Therefore, the aim of the current secondary analysis of data from the WAHA study was to determine whether long-term inclusion of walnuts (*Juglans regia* L.) in the daily diet increases polyphenol intake and the urinary excretion of phenolic metabolites.
## 2.1. Study Design and Participants
The WAHA study was a 2-year parallel group, observer-blinded randomized controlled trial (RCT) examining the effect of the usual diet supplemented with walnuts at $15\%$ (30–60 g/d) of energy compared to a walnut free habitual diet on the aging outcomes in elderly participants [17,20,21,22,23,24,25,26,27]. The parent study was a dual center clinical trial and was carried out in Barcelona, Spain and at Loma Linda University (LLU) in California, USA, from 2014 to 2016. However, only data collected from participants at LLU were used in the current secondary data analyses. The WAHA study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the Loma Linda University Institutional Review Board (IRB 5120066). All participants provided their written informed consent before enrolment. The WAHA study clinical trial (NCT01634841) is registered at www.clinicaltrials.gov (accessed on 23 February 2023).
Detailed information about the WAHA study has been published elsewhere [19,26]. Briefly, candidates for this study were elderly ambulatory men and women aged 63–79 years. Exclusion criteria were inability to undergo neuropsychological testing; previously diagnosed neurodegenerative disease; prior stroke, significant head trauma, or brain surgery; relevant psychiatric illness; major depression; morbid obesity; uncontrolled diabetes; uncontrolled hypertension; prior chemotherapy; allergy to walnuts; habitual consumption of tree nuts (>2 servings/week); or customary use of fish oil, flaxseed oil, and/or soy lecithin. A total of 656 subjects were recruited and assessed for eligibility by the LLU team and 356 met the eligibility criteria and were randomized into the study. Of the total sample of randomized participants, 300 were selected for the current analysis, as shown in the flowchart in Figure 1.
## 2.2. Sociodemographic, Anthropometric, and Biochemical Outcomes
Demographic data, anthropometric measurements, and dietary and lifestyle habits were collected from the participants at the baseline according to the study protocols [19]. Anthropometric measurements were carried out by trained professionals, and sociodemographic data and lifestyle habits were inputted using self-reported study questionnaires. Blood and spot urine samples were collected at the baseline and at the end of each year of intervention, aliquoted, and stored at −80 °C until analysis. All routine biochemical analyses and the determination of urinary creatinine concentration were performed at the completion of the study in the same laboratory to control for between-assay variability, as previously reported [20].
## 2.3. Estimation of Dietary Nutrient and Polyphenol Intake
Collection of dietary recall data and nutrient analysis was performed using the Nutrition Data System for Research (software version 2018) developed by the Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN. The 24-h dietary recalls were obtained by telephone or face-to-face interviews using a multiple-pass approach to capture information about the food items, beverages, and dietary supplements consumed during the past 24 h and the nutrient estimates were acquired using the systems’ nutrient database. A total of five unannounced dietary recalls per participant were obtained at random times during the study, and these included at least one weekend day. The dietary intake data were collected by trained research dietitians and conducted at diverse intervals over the 2 year study duration to account for seasonal variations of food intake [17].
The polyphenol content of foods and beverages reported in the 24-h recalls were generated from the Phenol-Explorer database (version 3.6) [28]. The Phenol-Explorer database compiles the total polyphenol content of foods based on analyses performed using the Folin–Ciocalteu (F–C) reagent, whereas chromatography methods are used to estimate polyphenol subclasses. In the current study, the following variables were estimated based on data from Phenol-Explorer: the subclass total flavonoids consisted of flavones, flavonols plus anthocyanins, and the subclass phenolic acids were phenolics obtained either by chromatography or by chromatography after hydrolysis; the subclass flavanols were obtained by chromatography or normal phase HPLC; and the subclass lignans was obtained by chromatography after hydrolysis.
The contribution of food items to the total polyphenols, flavonoids, flavanols, phenolic acids, and lignans were entered into the dietary database in milligrams per 100 g per day. Food items found in the 24-h dietary recalls (24-HDR) and food composition data were matched and the intake of total dietary polyphenols and phenol subclasses was estimated using the following equation: 24-HDRs = Σ Pn × Gn. Here, p is the mg of phenolic compound per 100 g food, and G is the reported portion size of food in grams.
## 2.4. Urinary Total Polyphenols
Spot urine samples [29] were collected from the WAHA participants at the baseline and at the end of the first and second years of the study. Spot urine samples were collected in the morning at the time participants came in for their fasting blood draw but were not the first void. Samples were processed and stored at −80 °C until use. The total urinary polyphenol concentrations in the spot urine samples were determined using the modified rapid Folin–Ciocalteu (F–C) method, as previously described [30]. Briefly, following solid phase extraction for the removal of interfering substances using Oasis Max cartridges (Waters Corp. Milford, MA, USA), the samples were loaded on 96-well plates (Waters Corp., Milford, MA, USA) for testing using the Folin–Ciocalteu reagent. The Bio Tek Synergy HT spectrometer (Bio Tek, Winooski, VT, USA) was used to measure the resulting absorbance at 765 nm. All analyses were run in triplicate using gallic acid as the standard. Urine creatinine was determined using the Jaffe’ alkaline picrate microplate method as published [30].
## 2.5. Statistical Analyses
From the LLU cohort, a total of $$n = 356$$ subjects were randomized, but only 300 subjects were included in this secondary data analysis. A total of 34 subjects were excluded due to missing data. Dietary polyphenol variables of total polyphenols, total flavonoids, flavanols, phenolic acids, and lignans were energy-adjusted using the residual method and then averaged for each subject. Spot urine polyphenol concentrations in mg GAE/L were adjusted by creatinine concentration to account for urine dilution. Mann–Whitney tests were used for these variables for between-group comparisons. Means and standard deviations (SD) of polyphenol intake by food group were reported.
In the descriptive analysis of urinary polyphenols, the means (SD) by treatment and time were determined. To compare the morning spot urine polyphenol excretion between treatment groups, linear regression mixed models fitted for both variables (mg GAE/L, mg GAE/g Cr) included the treatment, time, treatment × time interaction, age, gender, and BMI as fixed-effects terms and the participants as a random-effects term. To examine the association between spot urine polyphenol excretion at year 2 and the dietary intake of polyphenols and subclasses, a linear regression model was fitted for each combination of urine polyphenol (dependent variable) and log dietary polyphenol (independent variable), while adjusting for age, gender, and BMI. All analyses were performed using R version 4.2.2 with a significance level at $p \leq 0.05.$
## 3. Results
The subject characteristics as observed in Table 1 show that there were more women than men enrolled in the study. In the walnut group, $63\%$ were women and $37\%$ were men, and in the control group, $68\%$ were women and $32\%$ were men.
Table 2 describes the mean dietary intake of macronutrients by the treatment group over a 2-year period. A total of 1242 sessions were held to collect 24-h dietary recalls from the -participants. A total of five 24-h dietary recalls were collected from most participants (range 1–5 recalls) at random times during the duration of the study. The walnut group had a significantly higher energy intake, total dietary fiber, and total fat intake compared to the control group.
Table 3 describes the mean dietary intake of phenolics by treatment group for a 2 year period. Compared to the control group, participants in the walnut group had a significantly higher mean intake of total polyphenols, flavonoids, flavanols, and phenolic acids in mg/d. There were no significant differences in lignan intake.
Table 4 shows the contribution of the various food groups to the daily intake of total polyphenols and phenolic subclasses by treatment group. Of the food groups consumed, the mean intake of nuts showed that walnuts significantly ($p \leq 0.001$) contributed to the total polyphenol intake in the walnut group in mg/d 632 [182] compared to the control 40 [7]. Results of the polyphenol intake by food group also showed that nuts were a significant contributor to all other major subclasses including flavonoids (flavones, flavonols, and anthocyanidins, with the exception of lignan ($$p \leq 0.513$$) compared to the other food categories.
Table 5 shows the comparison of urinary polyphenol excretion between the control and walnut groups at the baseline and at the end of years 1 and 2. Urinary polyphenols and creatinine were measured in the spot urine samples obtained from the participants at the same clinic visit when the fasting blood samples were drawn. From the baseline, the excretion of polyphenols in the walnut group in the first year approached significance at a 0.066 p value, but not in the second year or when the values were adjusted for urinary creatinine excretion. The values were similar at the baseline and years 1 and 2 in the control group. The results show that there were no significant differences between the intervention groups at any time point.
Table 6 describes the association between dietary and urinary polyphenols in year 2. Results of the linear models showed that there was a significantly negative association between the total urinary polyphenols and the log of total dietary flavonoids ($$p \leq 0.0316$$). There were no significant associations with any other dietary polyphenols.
## 4. Discussion
In this sub-study of the WAHA trial, we showed that the daily ingestion of walnuts for 2 years significantly increased the total dietary polyphenols and the subclasses of flavonoids, flavanols and phenolic acids in healthy elderly participants. To our knowledge, this is the first study to show that the inclusion of a single food (i.e., walnuts), with no other changes made to the usual diet, could significantly increase the total polyphenol intake. As expected, those who ate walnuts daily also showed higher intakes of energy, fiber, total fat, and unsaturated fatty acids.
The results of this trial also show that participants in the walnut group consumed significantly higher amounts of total polyphenols and flavonoids (flavones, flavonols, and anthocyanidins), flavanols, and phenolic acids from nuts compared to those in the control group. This finding demonstrates that a single food such as walnuts can increase the intakes of total polyphenols and the polyphenol subclasses except for lignans. The walnut group had a higher intake of total polyphenols from fruits, and the flavanols and lignans from vegetables. The median daily intake of the total polyphenols of the control and walnut groups at 1897 mg/d and 2480 mg/d, respectively, of this elderly cohort residing in California was higher compared to that reported in adults in the U.S. by the National Health and Nutrition Examination Survey (NHANES) at 884 mg per 1000 kcal per day [31]. Like the current study, beverages such as tea, coffee, red wine, and fruit juices, vegetables, and fruits were the main contributors to the total polyphenols, flavonoids, and phenolic acids by NHANES [31] and through a recent systematic review of 91 studies from multiple countries [32]. A study [33] that examined the intake of dietary polyphenols by vegetarian status showed that a coffee intake, being a single food item, was the number one contributor to phenolic intake. Given that the WAHA intervention participants consumed walnuts at ~$15\%$ of their energy intake, the total polyphenol content of 2431.52 per 100 g walnuts would have added polyphenols to their diet [4].
While the walnut group showed a higher daily intake of polyphenols, this was not reflected in the urinary excretion of polyphenols tested in the spot urine samples obtained at the baseline, and at the end of either year 1 or year 2. Increased polyphenol metabolites have been identified in urine following the consumption of plant-based foods, suggesting that selected urinary polyphenols could be useful biomarkers to assess the intakes of polyphenol-rich foods and diets [34,35]. Most bioavailable dietary polyphenols have a relatively short half-life, estimated at 1 to 24 h following intake, and studies quantifying polyphenol biomarkers in urine have used 24-h urine collections following the consumption of test foods or diets [36,37,38]. Studies that have used 24-h urine samples were able to capture a wide range of polyphenols and positive relationships between dietary intake and urinary excretion of polyphenols [39,40]. One can conclude that the morning spot void used in our study could have resulted in poor collection of most polyphenols excreted in the urine over a 24-h period. However, similar studies found an increase in the concentration of phenolics in the spot morning urine following the intake of polyphenol-rich foods such fruits and vegetables [41,42]. Therefore, it is unclear why the daily inclusion of walnuts in the diet did not result in consistent increases in the concentration of polyphenols in our fasting spot urine samples collected following the first morning void. It is important to note that the rapid Folin–Ciocalteu (F–C) assay with solid-phase extraction optimized by Medina-Remón et al. [ 43] was used in this study to determine the total polyphenols in urine was validated in the spot urine samples collected from individuals consuming fruits, vegetables, tea, and red wine [44], and it has not been validated using walnuts.
In relation to the total polyphenol concentrations in the spot urine samples, our analyses did not show associations either with the dietary total polyphenols or flavanols and phenolic acid subclasses, but disclosed an inverse association with an intake of the flavonoid (flavanones + flavones) category. Studies in which the total urine polyphenols were measured with the F–C assay have shown weak to moderate associations with dietary polyphenol intakes in the 24-h [39], 12-h overnight [33], and morning urine sample collections [43]. In adults prescribed a high vegetable and fruit diet, the fasting spot urine samples collected after the first morning void and tested using liquid chromatography-mass spectroscopy disclosed an inconsistent association between the total urinary polyphenols and total polyphenol intake, while the linear mixed model analysis showed a non-significant inverse association between the total urine polyphenols and polyphenols from fruit [41]. The inverse correlation with fruit ingestion is consistent with our findings, since fruits are rich sources of the flavanone and flavone subclasses. It has been hypothesized that inverse correlations may be due to components of the food matrix that inhibit the intestinal absorption and urinary excretion of polyphenols [45,46]. The observed lack of associations or inverse associations may also be explained by the short half-life of bioavailable polyphenols and their metabolites, which may be absorbed and excreted within a short time period following intake [38,47]. Future studies should utilize 24-h urine collection following the ingestion of walnuts as the best method of capturing the majority of polyphenols. Additionally, reduced urinary excretion may imply that some of the polyphenols were eliminated via the gut, an effect likely to have a favorable impact on the intestinal microbiome [48,49,50,51,52,53].
The quality of phenolic compounds present in walnuts is diverse, ranging from simple phenolic acids and flavonoids to highly polymerized molecules such as tannins. Walnut phenolics are usually found at the highest concentration in the seed coat (also called the pellicle) surrounding the edible kernel and may be bound to other plant components such as carbohydrates and proteins. Consequently, some polyphenolic compounds might not be released in compositional testing studies. Walnuts are distinguished by the predominance of the hydrolysable gallotannins, glansrins (ellagitanins), and ellagic acid and the condensed tannins that are polymers of flavan-3-ol (flavanol) catechin and epicatechin subunits. The Polyphenol-Explorer 3.6 database reports an average amount of total polyphenol assayed by the F–C reagent as 1575 mg/100 g of kernel. Average amounts per 100 g of the total flavonoids, flavanols, and phenolic acids in walnuts are reported as 65 mg, 60 mg, and 449 mg, respectively [28,54,55,56,57].
Aside from the wide diversity and complexity of the phenolic substances found in walnuts, a number of other factors complicate efforts to obtain the exact accounts of their polyphenol composition. The concentration of phenolic compounds from different genomic walnut species and cultivars have been found to vary widely, with mean coefficients of variation of $25\%$ or greater [58]. In addition, the climate, soil characteristics, agricultural practices, storage, and manipulation influence the phenolic content of the nuts [58,59]. Studies have shown substantial differences in the composition depending on the solvent or method (maceration, sonication) employed to extract phenols from walnuts [60]. The results are also influenced by whether the walnut kernel is raw, mildly heated or roasted, or whether it is defatted prior to extraction [4]. Current liquid chromatography techniques coupled with high resolution mass spectrometry and electrospray ionization tandem mass spectrometry have successfully been employed to identify and quantify phenolic compounds in walnuts found in soluble free, soluble esters, or conjugated and insoluble bound forms, thus providing more inclusive phenolic profiles than those reported by Phenol-Explorer [14,61].
It is important to note that the concentration of polyphenol in urine is determined by factors beyond the walnut phenol content and its structural matrix, but is related to human physiology, mainly sex and age, along with factors such as digestive and metabolic efficiency and the gut microbiota [62]. Depending on their structural complexity and solubility, it has been estimated that only 5–$10\%$ of the total dietary polyphenols reaching the small intestine are absorbed, with the maximum plasma concentrations attained at 30 min following ingestion. Urinary excretion generally peaks after about 8 h of intake [38,62,63]. Unabsorbed polyphenols reach the large intestine, where they undergo enzymatic action by the microbiota to produce a variety of metabolites. One of the major categories of phenolic compounds in walnuts are ellagitannins [64], which are hydrolyzed to produce ellagic acid and further acted upon by the gut microbiota to produce a series of metabolites known as urolithins [65]. Urolithins are better absorbed than ellagitannins and are thus transported to peripheral tissues or excreted through the urine [5,38,66]. Studies have shown urolithins to be valid biomarkers of walnut consumption [63,67,68] with higher concentrations of the metabolite found 12 h or longer following walnut ingestion [69].
Our study has many strengths. The WAHA study has a significantly long duration of intervention (2 years). The study also includes a relatively large number of participants who demonstrated excellent compliance, with a retention rate of $90\%$. Moreover, the dietary intake data were extensive, having been acquired using multiple 24-h recalls (up to five recalls) obtained throughout the 2 year period and carefully matched with Phenol-Explorer values to obtain a profile of its polyphenol content.
The main limitation was that the urine samples were obtained from fasting participants following the first morning void, and as such, may not have captured a large enough quantity and diversity of phenolic metabolites excreted in a 24-h urine or in a longer collection period. It is well-known that the half-life of most polyphenol metabolites is relatively short and typically appear in urine within 1 to 24 h following ingestion [36]. Some phenol metabolites produced by microbiota such as urolithins may not be detected or quantified by the F–C reagent assay used in this study. A potential limitation is that despite an open recruitment policy, our study participants included a higher proportion of females than males. Additionally, the diets of the participants in the walnut group showed a higher mean energy and fat intake than the habitual diet group, which was partially mitigated through energy adjustment.
## 5. Conclusions
A single food such as walnuts eaten daily can increase dietary polyphenol intake. This is important as we now know that polyphenols have significant health benefits, being powerful anti-inflammatory and antioxidant phytochemicals. To reduce the risk for age-related chronic diseases, it may be prudent to include nuts such as walnuts as part of the usual diet to not only benefit from the unsaturated fatty acids and other nutrients that have CVD and neuroprotective effects, but also increase the polyphenol intake, which can synergistically influence the disease risk in a favorable manner.
## References
1. Torabian S., Haddad E., Rajaram S., Banta J., Sabate J.. **Acute effect of nut consumption on plasma total polyphenols, antioxidant capacity and lipid peroxidation**. *J. Hum. Nutr. Diet.* (2009) **22** 64-71. DOI: 10.1111/j.1365-277X.2008.00923.x
2. Guasch-Ferré M., Li J., Hu F.B., Salas-Salvadó J., Tobias D.K.. **Effects of walnut consumption on blood lipids and other cardiovascular risk factors: An updated meta-analysis and systematic review of controlled trials**. *Am. J. Clin. Nutr.* (2018) **108** 174-187. DOI: 10.1093/ajcn/nqy091
3. Ros E., Núñez I., Pérez-Heras A., Serra M., Gilabert R., Casals E., Deulofeu R.. **A walnut diet improves endothelial function in hypercholesterolemic subjects: A randomized crossover trial**. *Circulation* (2004) **109** 1609-1614. DOI: 10.1161/01.CIR.0000124477.91474.FF
4. Vinson J.A., Cai Y.. **Nuts, especially walnuts, have both antioxidant quantity and efficacy and exhibit significant potential health benefits**. *Food Funct.* (2012) **3** 134-140. DOI: 10.1039/C2FO10152A
5. Ros E., Izquierdo-Pulido M., Sala-Vila A.. **Beneficial effects of walnut consumption on human health: Role of micronutrients**. *Curr. Opin. Clin. Nutr. Metab. Care* (2018) **21** 498-504. DOI: 10.1097/MCO.0000000000000508
6. Guo X., Tresserra-Rimbau A., Estruch R., Martinez-Gonzalez M.A., Medina-Remon A., Castaner O., Corella D., Salas-Salvado J., Lamuela-Raventos R.M.. **Effects of Polyphenol, Measured by a Biomarker of Total Polyphenols in Urine, on Cardiovascular Risk Factors After a Long-Term Follow-Up in the PREDIMED Study**. *Oxid. Med. Cell Longev.* (2016) **2016** 2572606. DOI: 10.1155/2016/2572606
7. Sanchez-Gonzalez C., Ciudad C.J., Noe V., Izquierdo-Pulido M.. **Health benefits of walnut polyphenols: An exploration beyond their lipid profile**. *Crit. Rev. Food Sci. Nutr.* (2017) **57** 3373-3383. DOI: 10.1080/10408398.2015.1126218
8. Poti F., Santi D., Spaggiari G., Zimetti F., Zanotti I.. **Polyphenol Health Effects on Cardiovascular and Neurodegenerative Disorders: A Review and Meta-Analysis**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20020351
9. Medina-Remón A., Casas R., Tressserra-Rimbau A., Ros E., Martínez-González M.A., Fitó M., Corella D., Salas-Salvadó J., Lamuela-Raventos R.M., Estruch R.. **Polyphenol intake from a Mediterranean diet decreases inflammatory biomarkers related to atherosclerosis: A sub study of the PREDIMED trial**. *Br. J. Clin. Pharmacol.* (2017) **83** 114-128. DOI: 10.1111/bcp.12986
10. Rusu M.E., Gheldiu A.-M., Mocan A., Moldovan C., Popa D.-S., Tomuta I., Vlase L.. **Process optimization for improved phenolic compounds recovery from walnut (**. *Molecules* (2018) **23**. DOI: 10.3390/molecules23112814
11. Ni Z.-J., Zhang Y.-G., Chen S.-X., Thakur K., Wang S., Zhang J.-G., Shang Y.-F., Wei Z.-J.. **Exploration of walnut components and their association with health effects**. *Crit. Rev. Food Sci. Nutr.* (2022) **62** 5113-5129. DOI: 10.1080/10408398.2021.1881439
12. Kris-Etherton P.M., Yu-Poth S., Sabate J., Ratcliffe H.E., Zhao G., Etherton T.D.. **Nuts and their bioactive constituents: Effects on serum lipids and other factors that affect disease risk**. *Am. J. Clin. Nutr.* (1999) **70** 504S-511S. DOI: 10.1093/ajcn/70.3.504s
13. Regueiro J., Sánchez-González C., Vallverdú-Queralt A., Simal-Gándara J., Lamuela-Raventós R., Izquierdo-Pulido M.. **Comprehensive identification of walnut polyphenols by liquid chromatography coupled to linear ion trap–Orbitrap mass spectrometry**. *Food Chem.* (2014) **152** 340-348. DOI: 10.1016/j.foodchem.2013.11.158
14. Wu S., Shen D., Wang R., Li Q., Mo R., Zheng Y., Zhou Y., Liu Y.. **Phenolic profiles, and antioxidant activities of free, esterified and bound phenolic compounds in walnut kernel**. *Food Chem.* (2021) **350** 129217. DOI: 10.1016/j.foodchem.2021.129217
15. Njike V.Y., Costales V.C., Petraro P., Annam R., Yarandi N., Katz D.L.. **The resulting variation in nutrient intake with the inclusion of walnuts in the diets of adults at risk for type 2 diabetes: A randomized, controlled, crossover trial**. *Am. J. Health Promot.* (2019) **33** 430-438. DOI: 10.1177/0890117118791120
16. Njike V.Y., Yarandi N., Petraro P., Ayettey R.G., Treu J.A., Katz D.L.. **Inclusion of walnut in the diets of adults at risk for type 2 diabetes and their dietary pattern changes: A randomized, controlled, cross-over trial**. *BMJ Open Diabetes Res. Care* (2016) **4** e000293. DOI: 10.1136/bmjdrc-2016-000293
17. Bitok E., Jaceldo-Siegl K., Rajaram S., Serra-Mir M., Roth I., Feitas-Simoes T., Ros E., Sabaté J.. **Favorable nutrient intake and displacement with long-term walnut supplementation among elderly: Results of a randomized trial**. *Br. J. Nutr.* (2017) **118** 201-209. DOI: 10.1017/S0007114517001957
18. Natto Z.S., Siapco G., Jaceldo-Siegl K., Haddad E.H., Sabaté J.. **Food and Nutrient Displacement by Walnut Supplementation in a Randomized Crossover Study**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14051017
19. Rajaram S., Valls-Pedret C., Cofán M., Sabaté J., Serra-Mir M., Pérez-Heras A.M., Arechiga A., Casaroli-Marano R.P., Alforja S., Sala-Vila A.. **The Walnuts and Healthy Aging Study (WAHA): Protocol for a nutritional intervention trial with walnuts on brain aging**. *Front. Aging Neurosci.* (2017) **8** 333. DOI: 10.3389/fnagi.2016.00333
20. Sala-Vila A., Valls-Pedret C., Rajaram S., Coll-Padros N., Cofan M., Serra-Mir M., Perez-Heras A.M., Roth I., Freitas-Simoes T.M., Domenech M.. **Effect of a 2-year diet intervention with walnuts on cognitive decline. The Walnuts and Healthy Aging (WAHA) study: A randomized controlled trial**. *Am. J. Clin. Nutr.* (2020) **111** 590-600. DOI: 10.1093/ajcn/nqz328
21. Rajaram S., Cofan M., Sala-Vila A., Haddad E., Serra-Mir M., Bitok E., Roth I., Freitas-Simoes T.M., Kaur A., Valls-Pedret C.. **Effects of Walnut Consumption for 2 Years on Lipoprotein Subclasses Among Healthy Elders: Findings from the WAHA Randomized Controlled Trial**. *Circulation* (2021) **144** 1083-1085. DOI: 10.1161/CIRCULATIONAHA.121.054051
22. Domenech M., Serra-Mir M., Roth I., Freitas-Simoes T., Valls-Pedret C., Cofan M., Lopez A., Sala-Vila A., Calvo C., Rajaram S.. **Effect of a Walnut Diet on Office and 24-Hour Ambulatory Blood Pressure in Elderly Individuals**. *Hypertension* (2019) **73** 1049-1057. DOI: 10.1161/HYPERTENSIONAHA.118.12766
23. Ros E., Rajaram S., Sala-Vila A., Serra-Mir M., Valls-Pedret C., Cofán M., Roth I., Doménech M., Freitas T., Calvo C.. **Effect of a 1-year walnut supplementation on blood lipids among older individuals: Findings from the walnuts and healthy aging (WAHA) study**. *FASEB J.* (2016) **30** 293.294
24. López de las Hazas M.-C., Gil-Zamorano J., Cofán M., Mantilla-Escalante D.C., Garcia-Ruiz A., del Pozo-Acebo L., Pastor O., Yañez-Mo M., Mazzeo C., Serra-Mir M.. **One-year dietary supplementation with walnuts modifies exosomal miRNA in elderly subjects**. *Eur. J. Nutr.* (2021) **60** 1999-2011. DOI: 10.1007/s00394-020-02390-2
25. Freitas-Simoes T.-M., Cofán M., Blasco M.A., Soberón N., Foronda M., Serra-Mir M., Roth I., Valls-Pedret C., Doménech M., Ponferrada-Ariza E.. **Walnut consumption for two years and leukocyte telomere attrition in Mediterranean elders: Results of a randomized controlled trial**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10121907
26. Bitok E., Rajaram S., Jaceldo-Siegl K., Oda K., Sala-Vila A., Serra-Mir M., Ros E., Sabate J.. **Effects of Long-Term Walnut Supplementation on Body Weight in Free-Living Elderly: Results of a Randomized Controlled Trial**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10091317
27. Cofán M., Rajaram S., Sala-Vila A., Valls-Pedret C., Serra-Mir M., Roth I., Freitas-Simoes T.M., Bitok E., Sabaté J., Ros E.E.. **Effects of 2-year walnut-supplemented diet on inflammatory biomarkers**. *J. Am. Coll. Cardiol.* (2020) **76** 2282-2284. DOI: 10.1016/j.jacc.2020.07.071
28. Rothwell J.A., Perez-Jimenez J., Neveu V., Medina-Remon A., M’Hiri N., García-Lobato P., Manach C., Knox C., Eisner R., Wishart D.S.. **Phenol-Explorer 3.0: A major update of the Phenol-Explorer database to incorporate data on the effects of food processing on polyphenol content**. *Database* (2013) **2013** bat070. DOI: 10.1093/database/bat070
29. Valls-Pedret C., Lamuela-Raventós R.M., Medina-Remon A., Quintana M., Corella D., Pinto X., Martínez-González M.Á., Estruch R., Ros E.. **Polyphenol-rich foods in the Mediterranean diet are associated with better cognitive function in elderly subjects at high cardiovascular risk**. *J. Alzheimer’s Dis.* (2012) **29** 773-782. DOI: 10.3233/JAD-2012-111799
30. Medina-Remón A., Barrionuevo-González A., Zamora-Ros R., Andres-Lacueva C., Estruch R., Martínez-González M.-Á., Diez-Espino J., Lamuela-Raventos R.M.. **Rapid Folin–Ciocalteu method using microtiter 96-well plate cartridges for solid phase extraction to assess urinary total phenolic compounds, as a biomarker of total polyphenols intake**. *Anal. Chim. Acta* (2009) **634** 54-60. DOI: 10.1016/j.aca.2008.12.012
31. Huang Q., Braffett B.H., Simmens S.J., Young H.A., Ogden C.L.. **Dietary polyphenol intake in US adults and 10-year trends: 2007–2016**. *J. Acad. Nutr. Diet.* (2020) **120** 1821-1833. DOI: 10.1016/j.jand.2020.06.016
32. Del Bo’ C., Bernardi S., Marino M., Porrini M., Tucci M., Guglielmetti S., Cherubini A., Carrieri B., Kirkup B., Kroon P.. **Systematic review on polyphenol intake and health outcomes: Is there sufficient evidence to define a health-promoting polyphenol-rich dietary pattern?**. *Nutrients* (2019) **11** 1355. PMID: 31208133
33. Burkholder-Cooley N., Rajaram S., Haddad E., Fraser G.E., Jaceldo-Siegl K.. **Comparison of polyphenol intakes according to distinct dietary patterns and food sources in the Adventist Health Study-2 cohort**. *Br. J. Nutr.* (2016) **115** 2162-2169. DOI: 10.1017/S0007114516001331
34. Clarke E.D., Rollo M.E., Pezdirc K., Collins C.E., Haslam R.L.. **Urinary biomarkers of dietary intake: A review**. *Nutr. Rev.* (2020) **78** 364-381. DOI: 10.1093/nutrit/nuz048
35. Ito H., Gonthier M.-P., Manach C., Morand C., Mennen L., Rémésy C., Scalbert A.. **Polyphenol levels in human urine after intake of six different polyphenol-rich beverages**. *Br. J. Nutr.* (2005) **94** 500-509. DOI: 10.1079/BJN20051522
36. Manach C., Williamson G., Morand C., Scalbert A., Remesy C.. **Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies**. *Am. J. Clin. Nutr* (2005) **81** 230S-242S. DOI: 10.1093/ajcn/81.1.230S
37. Spencer J.P., Abd El Mohsen M.M., Minihane A.M., Mathers J.C.. **Biomarkers of the intake of dietary polyphenols: Strengths, limitations, and application in nutrition research**. *Br. J. Nutr* (2008) **99** 12-22. DOI: 10.1017/S0007114507798938
38. Santhakumar A.B., Battino M., Alvarez-Suarez J.M.. **Dietary polyphenols: Structures, bioavailability, and protective effects against atherosclerosis**. *Food Chem. Toxicol.* (2018) **113** 49-65. DOI: 10.1016/j.fct.2018.01.022
39. Zamora-Ros R., Rabassa M., Cherubini A., Urpi-Sarda M., Llorach R., Bandinelli S., Ferrucci L., Andres-Lacueva C.. **Comparison of 24-h volume and creatinine-corrected total urinary polyphenol as a biomarker of total dietary polyphenols in the Invecchiare InCHIANTI study**. *Anal. Chim. Acta* (2011) **704** 110-115. DOI: 10.1016/j.aca.2011.07.035
40. Burkholder-Cooley N.M., Rajaram S.S., Haddad E.H., Oda K., Fraser G.E., Jaceldo-Siegl K.. **Validating polyphenol intake estimates from a food-frequency questionnaire by using repeated 24-h dietary recalls and a unique method-of-triads approach with 2 biomarkers**. *Am. J. Clin. Nutr.* (2017) **105** 685-694. DOI: 10.3945/ajcn.116.137174
41. Clarke E.D., Rollo M.E., Collins C.E., Wood L., Callister R., Philo M., Kroon P.A., Haslam R.L.. **The relationship between dietary polyphenol intakes and urinary polyphenol concentrations in adults prescribed a high vegetable and fruit diet**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12113431
42. Mennen L.I., Sapinho D., Ito H., Bertrais S., Galan P., Hercberg S., Scalbert A.. **Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods**. *Br. J. Nutr.* (2006) **96** 191-198. DOI: 10.1079/BJN20061808
43. Medina-Remon A., Tresserra-Rimbau A., Arranz S., Estruch R., Lamuela-Raventos R.M.. **Polyphenols excreted in urine as biomarkers of total polyphenol intake**. *Bioanalysis* (2012) **4** 2705-2713. DOI: 10.4155/bio.12.249
44. Tresserra-Rimbau A., Medina-Remón A., Pérez-Jiménez J., Martínez-González M., Covas M.I., Corella D., Salas-Salvadó J., Gómez-Gracia E., Lapetra J., Arós F.. **Dietary intake and major food sources of polyphenols in a Spanish population at high cardiovascular risk: The PREDIMED study**. *Nutr. Metab. Cardiovasc. Dis.* (2013) **23** 953-959. DOI: 10.1016/j.numecd.2012.10.008
45. Scalbert A., Williamson G.. **Dietary intake, and bioavailability of polyphenols**. *J. Nutr.* (2000) **130** 2073S-2085S. DOI: 10.1093/jn/130.8.2073S
46. D’Archivio M., Filesi C., Varì R., Scazzocchio B., Masella R.. **Bioavailability of polyphenols: Status and controversies**. *Int. J. Mol. Sci.* (2010) **11** 1321-1342. DOI: 10.3390/ijms11041321
47. Rechner A.R., Kuhnle G., Hu H., Roedig-Penman A., van den Braak M.H., Moore K.P., Rice-Evans C.A.. **The metabolism of dietary polyphenols and the relevance to circulating levels of conjugated metabolites**. *Free Radic. Res.* (2002) **36** 1229-1241. DOI: 10.1080/246-1071576021000016472
48. Garcia-Munoz C., Vaillant F.. **Metabolic fate of ellagitannins: Implications for health, and research perspectives for innovative functional foods**. *Crit. Rev. Food Sci. Nutr.* (2014) **54** 1584-1598. DOI: 10.1080/10408398.2011.644643
49. García-Mantrana I., Calatayud M., Romo-Vaquero M., Espín J.C., Selma M.V., Collado M.C.. **Urolithin metabotypes can determine the modulation of gut microbiota in healthy individuals by tracking walnuts consumption over three days**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11102483
50. Cortés-Martín A., Selma M.V., Tomás-Barberán F.A., González-Sarrías A., Espín J.C.. **Where to investigate the puzzle of polyphenols and health? Postbiotics and gut microbiota are associated with human metabotypes**. *Mol. Nutr. Food Res.* (2020) **64** 1900952. DOI: 10.1002/mnfr.201900952
51. Kasprzak-Drozd K., Oniszczuk T., Stasiak M., Oniszczuk A.. **Beneficial effects of phenolic compounds on gut microbiota and metabolic syndrome**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22073715
52. Hidalgo M., Oruna-Concha M.J., Kolida S., Walton G.E., Kallithraka S., Spencer J.P., de Pascual-Teresa S.. **Metabolism of anthocyanins by human gut microflora and their influence on gut bacterial growth**. *J. Agric. Food Chem.* (2012) **60** 3882-3890. DOI: 10.1021/jf3002153
53. Aravind S.M., Wichienchot S., Tsao R., Ramakrishnan S., Chakkaravarthi S.. **Role of dietary polyphenols on gut microbiota, their metabolites and health benefits**. *Food Res. Int.* (2021) **142** 110189. DOI: 10.1016/j.foodres.2021.110189
54. Gu L., Kelm M.A., Hammerstone J.F., Beecher G., Holden J., Haytowitz D., Gebhardt S., Prior R.L.. **Concentrations of proanthocyanidins in common foods and estimations of normal consumption**. *J. Nutr.* (2004) **134** 613-617. PMID: 14988456
55. Colaric M., Veberic R., Solar A., Hudina M., Stampar F.. **Phenolic acids, syringaldehyde, and juglone in fruits of different cultivars of**. *J. Agric. Food Chem.* (2005) **53** 6390-6396. DOI: 10.1021/jf050721n
56. Harnly J.M., Doherty R.F., Beecher G.R., Holden J.M., Haytowitz D.B., Bhagwat S., Gebhardt S.. **Flavonoid content of US fruits, vegetables, and nuts**. *J. Agric. Food Chem.* (2006) **54** 9966-9977. DOI: 10.1021/jf061478a
57. Li L., Tsao R., Yang R., Liu C., Zhu H., Young J.C.. **Polyphenolic profiles and antioxidant activities of heartnut (**. *J. Agric. Food Chem.* (2006) **54** 8033-8040. DOI: 10.1021/jf0612171
58. Trandafir I., Cosmulescu S.. **Total phenolic content, antioxidant capacity and individual phenolic compounds of defatted kernel from different cultivars of walnut**. *Erwerbs-Obstbau* (2020) **62** 309-314. DOI: 10.1007/s10341-020-00501-1
59. Slatnar A., Mikulic-Petkovsek M., Stampar F., Veberic R., Solar A.. **Identification, and quantification of phenolic compounds in kernels, oil, and bagasse pellets of common walnut (**. *Food Res. Int.* (2015) **67** 255-263. DOI: 10.1016/j.foodres.2014.11.016
60. Labuckas D.O., Maestri D.M., Perello M., Martínez M.L., Lamarque A.L.. **Phenolics from walnut (**. *Food Chem.* (2008) **107** 607-612. DOI: 10.1016/j.foodchem.2007.08.051
61. Woźniak M., Waśkiewicz A., Ratajczak I.. **The Content of Phenolic Compounds and Mineral Elements in Edible Nuts**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27144326
62. Cardona F., Andrés-Lacueva C., Tulipani S., Tinahones F.J., Queipo-Ortuño M.I.. **Benefits of polyphenols on gut microbiota and implications in human health**. *J. Nutr. Biochem.* (2013) **24** 1415-1422. DOI: 10.1016/j.jnutbio.2013.05.001
63. Del Rio D., Rodriguez-Mateos A., Spencer J.P., Tognolini M., Borges G., Crozier A.. **Dietary (poly) phenolics in human health: Structures, bioavailability, and evidence of protective effects against chronic diseases**. *Antioxid. Redox Signal.* (2013) **18** 1818-1892. DOI: 10.1089/ars.2012.4581
64. Fukuda T., Ito H., Yoshida T.. **Antioxidative polyphenols from walnuts (**. *Phytochemistry* (2003) **63** 795-801. DOI: 10.1016/S0031-9422(03)00333-9
65. Garcia-Aloy M., Hulshof P.J.M., Estruel-Amades S., Oste M.C.J., Lankinen M., Geleijnse J.M., de Goede J., Ulaszewska M., Mattivi F., Bakker S.J.L.. **Biomarkers of food intake for nuts and vegetable oils: An extensive literature search**. *Genes Nutr.* (2019) **14** 7. DOI: 10.1186/s12263-019-0628-8
66. Alkhaldy A., Edwards C.A., Combet E.. **The urinary phenolic acid profile varies between younger and older adults after a polyphenol-rich meal despite limited differences in in vitro colonic catabolism**. *Eur. J. Nutr.* (2019) **58** 1095-1111. DOI: 10.1007/s00394-018-1625-1
67. Cerdá B., Tomás-Barberán F.A., Espín J.C.. **Metabolism of antioxidant and chemopreventive ellagitannins from strawberries, raspberries, walnuts, and oak-aged wine in humans: Identification of biomarkers and individual variability**. *J. Agric. Food Chem.* (2005) **53** 227-235. DOI: 10.1021/jf049144d
68. Tomás-Barberán F.A., González-Sarrías A., García-Villalba R., Núñez-Sánchez M.A., Selma M.V., García-Conesa M.T., Espín J.C.. **Urolithins, the rescue of “old” metabolites to understand a “new” concept: Metabotypes as a nexus among phenolic metabolism, microbiota dysbiosis, and host health status**. *Mol. Nutr. Food Res.* (2017) **61** 1500901. DOI: 10.1002/mnfr.201500901
69. Haddad E.H., Gaban-Chong N., Oda K., Sabaté J.. **Effect of a walnut meal on postprandial oxidative stress and antioxidants in healthy individuals**. *Nutr. J.* (2014) **13** 4. DOI: 10.1186/1475-2891-13-4
|
---
title: Relationships between Obesity, Exercise Preferences, and Related Social Cognitive
Theory Variables among Breast Cancer Survivors
authors:
- Nashira I. Brown
- Dorothy W. Pekmezi
- Robert A. Oster
- Kerry S. Courneya
- Edward McAuley
- Diane K. Ehlers
- Siobhan M. Phillips
- Philip Anton
- Laura Q. Rogers
journal: Nutrients
year: 2023
pmcid: PMC10005113
doi: 10.3390/nu15051286
license: CC BY 4.0
---
# Relationships between Obesity, Exercise Preferences, and Related Social Cognitive Theory Variables among Breast Cancer Survivors
## Abstract
Breast cancer survivors with obesity have an increased risk of cancer recurrence, second malignancy, and comorbidities. Though physical activity (PA) interventions are needed, investigation of the relationships between obesity and factors influencing PA program aspects among cancer survivors remain understudied. Thus, we conducted a cross-sectional study examining associations amongst baseline body mass index (BMI), PA program preferences, PA, cardiorespiratory fitness, and related social cognitive theory variables (self-efficacy, exercise barriers interference, social support, positive and negative outcome expectations) from a randomized controlled PA trial with 320 post-treatment breast cancer survivors. BMI was significantly correlated with exercise barriers interference ($r = 0.131$, $$p \leq 0.019$$). Higher BMI was significantly associated with preference to exercise at a facility ($$p \leq 0.038$$), lower cardiorespiratory fitness ($p \leq 0.001$), lower walking self-efficacy ($p \leq 0.001$), and higher negative outcome expectations ($$p \leq 0.024$$), independent of covariates (comorbidity score, Western Ontario and McMaster *Universities osteoarthritis* index score, income, race, education). Those with class I/II obesity reported a higher negative outcome expectations score compared with class III. Location, walking self-efficacy, barriers, negative outcome expectations, and fitness should be considered when designing future PA programs among breast cancer survivors with obesity.
## 1. Introduction
The prevalence of breast cancer survivors continues to grow as diagnosis, treatment, and control advance. However, quality of life after diagnosis among this population is affected by modifiable lifestyle behaviors, including physical activity (PA) engagement [1]. American Cancer Society Guidelines for PA recommend at least 150 min of exercise per week and 2 days of strength training per week for cancer survivors to help reduce risk of cancer recurrence, second malignancy, and comorbidities (e.g., obesity) [2]. Notably, a minority ($13.9\%$) of breast cancer survivors meet PA guidelines and only $35\%$ maintain a healthy BMI [3]. Moreover, adult cancer survivors experience a more rapid rise in obesity [4], which has been associated with low cardiorespiratory fitness, a strong predictor of all-cause and cardiovascular mortality, following cancer treatment(s) [5], and thus are in need of effective lifestyle intervention [4].
Best practices for assisting this population in adopting a physically active lifestyle include using evidence-based theories [6] and program preferences [7] for PA program design. The social cognitive theory (SCT) is one of the most widely applied health behavior theories in PA research [8,9]. This framework posits that behavior is a dynamic interaction of a triad of factors (i.e., personal cognitive, the physical and social environment (socioenvironmental), and behavioral). SCT constructs that are commonly targeted in PA interventions are self-efficacy, exercise barriers interference, outcome expectations, and social support. Self-efficacy, or the confidence in one’s ability to take action and overcome obstacles and situations to reach a goal, is deemed as the significant primary personal factor that mediates behavior change [8], especially PA behaviors in women with breast cancer [10]. This construct has been associated with body mass index (BMI) in cancer survivors [11,12], along with other constructs, such as social support and exercise barriers interference [12].
In addition to SCT constructs, research focusing on what individuals prefer could be vital to optimizing participant engagement and acceptability when designing PA programs [12]. Although multiple studies have reported PA preferences for breast cancer survivors [13], very few have examined how preferences may vary by level of obesity [12,14]. Further, the few studies in populations without a history of cancer have shown that PA preferences (i.e., intervention delivery, supervision, and scheduling) differed among those with and without obesity [15,16]. Given the paucity of research on the relationship between obesity and potential differences in preferences among breast cancer survivors with cancer, further examination is warranted to develop effective PA programs.
Thus, to inform future PA promotion programs for breast cancer survivor populations with obesity, we performed a secondary analysis of baseline data from a randomized PA intervention trial [17]. Our study purpose was to examine the associations between BMI and factors influencing program content and delivery preferences (source, mode, structure, location, furthest distance willing to travel, furthest distance willing to travel if someone else paid for gas, price willing to pay for exercise program, program type, supervision, alone or with group), current PA (accelerometer), cardiorespiratory fitness, and related SCT constructs (i.e., self-efficacy (barriers and task), exercise barriers interference, social support, outcome expectations (negative outcome expectations and positive outcome expectations)) among the enrolled breast cancer survivors ($$n = 320$$). We hypothesized that BMI would be significantly associated with program preferences, PA, cardiorespiratory fitness, and related SCT constructs.
## 2.1. Study Design
This cross-sectional study examined relationships amongst BMI, PA program preferences, PA, cardiorespiratory fitness, and related SCT variables. Data for these secondary analyses were taken from the baseline survey for a randomized controlled PA behavior change trial with 320 post-treatment breast cancer survivors. Participants enrolled in the primary trial (Better exercise adherence after treatment for cancer (BEAT Cancer); $$n = 222$$) [18] were combined with participants enrolled to a trial supplement (accelerometer calibration sub-study entitled “Comparing doubly-labeled water to accelerometer to assess PA measurement error during and after a physical activity behavior change intervention” (COMPARE); $$n = 98$$).
## 2.2. Study Sample
Three hundred and twenty post-primary treatment breast cancer survivors were recruited through newspaper advertisements, cancer support groups, flyers posted in relevant locations (e.g., hospitals, physician offices, cancer centers/clinics), and areas frequented by women (e.g., retail stores, beauty salons). Eligible women met the following criteria: English speaking, between the ages of 18 and 70 years of age with a history of ductal carcinoma in situ (DCIS) or Stage I, II, or IIIA breast cancer and post-primary chemotherapy or radiation therapy, medically cleared for participation by their physician and underactive (participating in no more than 60 min of moderate intensity PA or no more than 30 min of vigorous intensity activity per week, on average, over the past 6 months).
## 3.1. Demographics and BMI
Self-reported participant demographics included age, race, ethnicity, years of education, annual household income, employment status, marital status, cancer stage at diagnosis, history of chemotherapy, history of radiation therapy, hormonal therapy type, functional comorbidity index score [19], and the Western Ontario and McMaster *Universities osteoarthritis* index (WOMAC) [20]. The functional comorbidity index score, or the number of comorbidities, was assessed by totaling the number of “yes” responses to 18 diagnoses with possible scores of 0–18, 0 indicating no comorbidities and 18 indicating the highest number of comorbidities [19]. The WOMAC, 24-item scale, assessed lower extremity joint pain (5 items), stiffness (2 items), and physical dysfunction (17 items) [20]. Scores from the subscales were summed with possible score ranging from 0 to 68, with higher scores indicating greater pain, stiffness, and physical dysfunction [20]. Weight and height were measured in person by trained research staff using a calibrated scale and stadiometer. Brand and model were study site specific (University of Alabama at Birmingham (Detecto Model 439); Southern Illinois University(Continental Health-O-Meter #400 DML medical scale); University of Illinois (Seca 763 Digital Column Scale)) [21]. BMI was calculated using the measured weight and height (weight (kg)/height (m2)) [21].
## 3.2. Exercise Program Preferences
Exercise program preferences were assessed using a 15-item multiple choice self-administered survey that has been used in prior studies among breast cancer survivors [12,14,22,23]. The counseling preference items included queries regarding counseling source (i.e., cancer exercise physiologist, personal trainer, medical doctor, nurse, health club exercise specialist, cancer patient/survivor), mode of delivery (i.e., face-to-face, phone, video, written material, internet, audiotape, interactive workbook), and company (i.e., individual or with a group). Exercise training preference items focused on location (i.e., at home, outdoors, at work, health club, cancer exercise center), exercise type (i.e., walking, water, bike, jogging, resistance, yoga, Pilates), and supervision (i.e., supervised, or unsupervised). Programming preference items inquired about program type (i.e., aerobic, strength, or both), structure (i.e., flexible vs. scheduled), maximum price willing to pay for an exercise program (i.e., $0, $1–10/month, $11–20/month, $21–30/month, $31–40/month, $40+/month), farthest distance willing to travel to an exercise program (0 miles, 1–15 miles, 16–30 miles, 31–45 miles, 46–60 miles, 60+ miles), and the farthest distance willing to travel to an exercise program if cost of gas was covered (0 miles, 1–15 miles, 16–30 miles, 31–45 miles, 46–60 miles, 60+ miles).
## 3.3. PA
Weekly minutes of moderate-plus-vigorous intensity PA were assessed with ActiGraph accelerometer (model: GT3X, Pensacola, FL, USA). Participants were instructed (orally and written) to wear the device for at least 10 waking hours for seven (primary trial) or 10 (COMPARE) consecutive days [24]. The parameters used to validate the minimum wear time of 4 days was comprised of wear time ≥10 waking hours. The cut points that were used to establish moderate-to-vigorous activity intensity were: moderate (1952–5724 counts/min) and vigorous (5725+ counts/min) [21]. Minutes of vigorous intensity activity were doubled prior to adding minutes of moderate intensity activity and calculating weekly minutes of moderate-to-vigorous activity.
## 3.4. Cardiorespiratory Fitness
Following the American College of Sports Medicine guidelines for testing [25], cardiorespiratory fitness (relative VO2 peak) was estimated with submaximal treadmill testing [26] in which speed and elevation were gradually increased until the participant achieved $85\%$ of age-predicted maximal heart rate. Following the modified Naughton protocol, tests were begun at a slower speed and progressed at lower increments, as in past studies with individuals who are sedentary, older, fatigued, or have balance complications [21,25]. The oxygen cost of walking at the treadmill grade and speed achieved at $85\%$ of predicted heart rate was estimated using published regression equations and is expressed in mL/kg/min [27].
## 3.5.1. Self-Efficacy
Both barriers and task self-efficacy were assessed. One’s confidence in his/her ability to act and overcome obstacles and situations to reach a goal was assessed using a reliable (α = 0.97–0.98) 9-item scale, Barriers Self-Efficacy, designed for breast cancer patients [28]. Walking task self-efficacy was assessed using a valid and reliable ($r = 0.89$ and α = 0.96) 6-item scale, Self-Efficacy for Walking, to measure confidence in walking at a moderate pace for six different intervals of time (i.e., 5, 10, 15, 20, 25, and 30 min) [29]. Both measures of self-efficacy asked participants to indicate their confidence (0–$100\%$, at $10\%$ intervals (i.e., not at all confident, 0–$20\%$; slightly confident, 20–$40\%$; moderately confident, 40–$60\%$; very confident, 60–$80\%$; extremely confident, 80–100)) [28,30]. Responses were averaged separately for barriers and task self-efficacy with a range of possible scores (0–100).
## 3.5.2. Exercise Barriers Interference
Perceived barriers (or barriers interference), or how often recognized obstacles (i.e., lack of time, fear of injury, fatigue, lack of energy, lack of company, cost of exercising, lack of enjoyment, lack of equipment, family responsibilities, inconvenient exercise schedule, lack of interest, lack of knowledgeable exercise staff, feeling nauseated, no facilities/space, not a priority, procrastination, pain/discomfort, not in routine, lack of self-discipline, lack of skills, weather) interfered with exercise, was assessed using a 21-item, 5-point Likert scale (1 = rare to 5 = very often) measure, Exercise Barriers Interference, that has demonstrated reliability (α = 0.92) among breast cancer survivors [31]. Responses were summed for a total exercise barriers interference score [31,32,33] with a range of possible scores (21–105).
## 3.5.3. Social Support
Social support, or the perception of encouragement to engage in PA, from other sources (i.e., friends and family) [31] was measured via a 4-item (friends, 2-items, family, 2-items), 5-point Likert scale (0 = none to 4 = very often), Social Support for Physical Activity, with an internal consistency of 0.80 [31]. Responses were summed for a total social support score with possible scores ranging from 0 to 16 [34,35].
## 3.5.4. Outcome Expectations
Outcome expectations, or the anticipated positive and/or negative consequences of engaging in a behavior (e.g., exercise) was evaluated using a reliable (α = 0.79 and 0.70, respectively) 17-item (14 positive expectations and 3 negative expectations), 5-point Likert scale (1 = strongly disagree to 5 = strongly agree); responses were summed for positive and negative outcomes separately (i.e., higher score indicates greater perceived benefit (positive expectations) or greater perceived risk (negative expectations)) with possible scores ranging from 14 to 70 and 3 to 15, respectively [32].
## 3.5.5. Statistical Analyses
Descriptive analyses were conducted to summarize participant characteristics. BMI was analyzed as a continuous variable and as a 3-level categorical outcome (i.e., non-obese (BMI ≤ 29.9 kg/m2), obese classes I/II (BMI = 30–39.9 kg/m2), and obese class III (BMI ≥ 40 kg/m2)). A 3-level BMI was created to facilitate identifying potentially important differences at a higher BMI that may be missed when analyzing with a continuous BMI.
The associations with continuous BMI were analyzed using Pearson correlation coefficients (continuous correlates) and independent groups t-test (continuous correlates). The associations with 3-level categorical BMI were analyzed using one-way analysis of variance (ANOVA), followed by the Tukey post-hoc test (continuous correlates), and also the chi-square test (categorical or dichotomous correlates). Continuous study variables were examined for normality of distribution using box plots, stem-and-leaf plots, normal probability plots, and the Kolmogorov–Smirnov test. Variables that were determined as being non-normally distributed were also examined using the non-parametric Kruskall–Wallis test. Since the parametric were similar to the non-parametric results, we report the parametric results for ease of interpretation, and previous studies have reported parametric results obtained from these or similar variables. Follow-up multiple linear or logistic regression were performed based on the type of dependent variable.
No imputations were performed for missing data since the amount of missing data were very small (<$1\%$); there were no missing data for most of the study variables. All analyses were performed using IBM SPSS Statistics software (SPSS) Version 28 (Armonk, NY, USA: IBM Corp), and $p \leq 0.05$ was deemed as statistically significant.
Exercise program preferences were dichotomized due to the small stratum-specific sample sizes with an emphasis on preferences more likely to alter program design (e.g., facility vs. other). Hence, each preference was reviewed based on the number of participants preferring each option within a specific preference question and then collapsed based on preference with greater potential to alter a program design (e.g., facility vs. other options not requiring a facility).
Accelerometer-measured PA was analyzed as dichotomous, categorical outcome (met PA recommendations: >150 min of moderate-to-vigorous PA vs. did not meet PA recommendations: <150 min of moderate-to-vigorous PA) based on the American Cancer Society guidelines for PA for cancer survivors and to simplify interpretation [2].
## 3.6. Covariates and Adjusted Analyses
The variables investigated as potential covariates included age, race, ethnicity, education, income, employment, marital status, cancer stage at diagnosis, months since diagnosis, history of chemotherapy, history of radiation, hormonal therapy, number of comorbidities, and WOMAC score. A variable was considered a covariate if it was statistically significantly associated with both BMI and one or more of the correlates of interest (PA preferences, PA, cardiorespiratory fitness, and SCT variables). The identified covariates were then used for adjusted multiple variable regression analyses performed as indicated, with linear regression analysis performed for continuous outcomes and logistic regression analysis performed for dichotomous outcomes. All regression coefficients were tested for statistical significance. The R2 value was examined as goodness of fit measure.
## 4.1. Participants
The participant characteristics are summarized in Table 1. Overall, participants were post-treatment breast cancer survivors with over half ($52.2\%$) having obesity (mean BMI of 31.1 ± 7.34 kg/m2). Most participants were white ($83\%$), non-Hispanic ($98.7\%$), employed ($67.5\%$) with an annual household income greater than $50,000 ($67.6\%$), and a history of chemotherapy ($61.6\%$) or radiation ($65.6\%$). At cancer diagnosis, most were stage 1 ($39.1\%$).
## 4.2. BMI and Program Preferences
A statistically significant relationship between BMI and a dichotomized program preference was found for only one preference, regardless of whether BMI was analyzed as a continuous variable ($$p \leq 0.009$$) or as a 3-level outcome ($$p \leq 0.038$$). Participants who preferred to exercise at a facility had a higher BMI (versus lower) (32.8 ± 8.6 vs. 30.3 ± 6.6 respectively). No other program preferences were associated with the continuous or 3-level BMI outcomes.
## 4.3. BMI, Current PA, and Cardiorespiratory Fitness
There were no statistically significant differences or associations with levels of current PA (moderate-to-vigorous PA or meets recommendations) when BMI was analyzed as a 3-level categorical outcome (Table 2 and Table 3). Similarly, no statistically significant association between PA and continuous BMI was noted (Table 4). However, there was a significant inverse correlation between BMI and cardiorespiratory fitness (r = −0.414, $p \leq 0.001$) in which higher BMI was related to lower cardiorespiratory fitness (Table 4). Results from a one-way ANOVA yielded statistically significant differences in cardiorespiratory fitness between levels of BMI (Table 2). A Tukey post-hoc test indicated that there was a significant difference in cardiorespiratory fitness between all levels of BMI (all $p \leq 0.001$). Specifically, cardiorespiratory fitness was significantly lower among obese class III vs. non-obese and obese class I/II.
## 4.4. BMI and SCT Variables
Pearson correlation coefficients between continuous BMI and PA-related SCT variables are provided in Table 4. Higher BMI was significantly correlated with higher exercise barriers interference ($r = 0.131$, $$p \leq 0.019$$) and lower walking self-efficacy (r = −0.364, $p \leq 0.001$). No other SCT variables were significantly correlated with BMI.
The one-way ANOVA showed statistically significant differences in walking self-efficacy and negative outcome expectations as reflected in Table 2. A Tukey post-hoc test indicated that there was a significant difference in walking self-efficacy scores between all levels of BMI (all $p \leq 0.001$). Walking self-efficacy scores were significantly lower among obese class III vs. non-obese and obese class I/II. As for the negative outcome expectations, obese class I/II reported significantly higher negative outcome expectations than non-obese ($$p \leq 0.024$$). The remaining constructs did not show any statistically significant differences.
## 4.5. Adjusted Associations
Multiple variable linear regression analyses were performed to examine the independent relationships between BMI (dependent variable) and fitness, exercise barriers interference, walking self-efficacy, and identified covariates (comorbidity score, WOMAC, income, race, and education). The model was statistically significant (F[9, 306] = 15.875, $p \leq 0.001$, R2 = 0.318) indicating that walking self-efficacy, cardiorespiratory fitness, comorbidity score, and race are independently associated with BMI ($p \leq 0.05$). Table 5 provides the analyses results.
## 4.6. Logistic Regression Models
Because preferring a facility was associated with BMI and walking self-efficacy, a post-hoc binomial logistic regression was performed to determine the relationship between location preference (dependent variable) and BMI independent of walking self-efficacy. The adjusted logistic regression model including BMI, walking self-efficacy, and location was statistically significant, χ2[2] = 10.292, $$p \leq 0.006.$$ For every 1-unit increase in BMI, there is a 1.04 greater odds of preferring to exercise at a facility independent of walking self-efficacy score with a $95\%$ confidence interval of (1.001–1.073).
## 5. Discussion
In this study, we sought to examine the associations between BMI and factors influencing program content and delivery (preferences), current PA (accelerometer-measured), cardiorespiratory fitness, and related SCT constructs (i.e., self-efficacy (barriers and task), exercise barriers interference, social support, outcome expectations (negative outcome expectations and positive outcome expectations)) among breast cancer survivors. We found that BMI was significantly correlated with exercise barriers interference. Further, we found significant associations between higher BMI and preference to exercise at a facility, lower cardiorespiratory fitness, and lower walking self-efficacy, independent of covariates (comorbidity score, Western Ontario and McMaster *Universities osteoarthritis* index score, income, race, education). These findings suggest that BMI influences PA program preferences, multiple psychosocial factors, and fitness which could be useful for informing future PA interventions for this population.
Overall, our sample of breast cancer survivors preferred to exercise at home or had no preference, which is consistent with the existing literature among diverse cancer survivors [36]. A recent systematic review reported a similar finding that adults with obesity prefer to engage in exercise “close to home”, while Hussien et al. found that adults with severe obesity prefer exercising outdoors [37]. However, when assessing preference by BMI category, we found that as obesity increased (i.e., obese class I/II to class III), there was an increased preference for exercising at a facility. This is similar to findings from another study among rural breast cancer survivors with overweight and obesity [38]. Potential hypotheses are that survivors with obesity may appreciate the support/supervision of facility staff (e.g., personal trainers, health coaches, health club exercise specialist), facility-provided equipment that limits weight bearing, and social context (i.e., seeing others exercise). Further, breast cancer survivors were recruited to participate in the current study, which required in-person visits to a facility.
The current study did not find a statistically significant association between BMI and current PA, similar to a past study among adults with obesity and multiple sclerosis [39]. Our sample consisted of breast cancer survivors who self-reported that they were not currently active (i.e., engaging in no more than 60 min of moderate intensity PA or no more than 30 min of vigorous intensity activity per week), as noted earlier in the inclusion criteria, which may have impacted our ability to test this relationship. However, a majority of the sample met PA recommendations based on free-living PA measured by accelerometer. We did find that overall, breast cancer survivors with and without obesity had low cardiorespiratory fitness levels compared to healthy women without cancer [40]. However, those with higher BMI had significantly lower cardiorespiratory fitness as in past studies among adults with obesity and no history of cancer [41,42]. Engaging cancer survivors, especially those with obesity, in cardiorespiratory fitness enhancing activities (i.e., PA, weight loss) is critical [43].
We found a statistically significant relationship between BMI and several SCT constructs. Specifically, breast cancer survivors with higher BMI had significantly lower walking self-efficacy and higher exercise barriers interference scores. Past studies have found associations between BMI and exercise barriers interference [44], exercise self-efficacy [45], and family social support [46] among adults with obesity, no cancer history [46], and presence of chronic illness (i.e., multiple sclerosis) [39]. The lower levels of self-reported walking self-efficacy among cancer survivors with higher BMI in the current study could possibly be due in part to limited mobility caused by excess body weight and reduced fitness levels, which makes it more difficult to walk at a moderately fast pace without stopping. With regard to exercise barriers interference, participants with increased weight reported more barriers to PA which has been previously described [15]. Similar to walking self-efficacy, bearing more body weight increases the likelihood of barriers (i.e., lack of energy, lack of confidence, lack of enjoyment, lack of interest, pain/discomfort).
## 5.1. Clinical and Research Implications
The findings from the current study have important implications for clinicians, healthcare professionals, and researchers who prescribe exercise and develop PA interventions for breast cancer survivors, especially with obesity. When prescribing exercise/PA for this vulnerable population, clinicians need to individually assess and consider medical (i.e., BMI), preference (i.e., location), SCT (i.e., walking self-efficacy), and fitness-related (i.e., cardiorespiratory fitness) factors as this could influence participation and the likelihood of meeting PA recommendations.
Although breast cancer survivors with obesity were more likely to prefer to engage in exercise at a facility (versus at home or no preference), potential barriers (e.g., financial, lack of accessibility) may arise for those without facility access. Therefore, distance-delivered interventions (e.g., mHealth, telephone, virtual) could serve as alternative options for delivering such programs. As for walking self-efficacy, exercise/PA programs can target and improve this component through incorporating individual behavior change techniques (e.g., goal setting, self-monitoring, and implementing solutions) [47,48]. The low levels of fitness found in the current study are more indicative of a greater need for PA and a potential intervention target. Thus, this suggests that structured exercise progression focused on improving cardiorespiratory fitness may be needed for this group. Lastly, although our study does not assess strength training, future interventions could leverage a preference for facility-based exercise to increase this type of exercise. In turn, individuals with obesity may be better able to do strength training at first, with a progression to greater aerobic exercise as muscle strength improves.
Taken as a whole, future research efforts need to be aimed at gaining a nuanced understanding regarding specific preferences (e.g., *Why is* facility more preferred by survivors with obesity?) through a qualitative approach. Moreover, future research directions should include assessing whether tailoring interventions and programs to individual health-related factors and preferences increases active living among this population.
## 5.2. Strengths and Limitations
The findings of the current study provide insight on a population that might need tailored or targeted PA programing. Specifically, our data related to location preference and health-related factors (i.e., walking self-efficacy and cardiorespiratory fitness) that can affect participation in programs can be useful for future intervention design. However, there are limitations. First, the entire sample consisted of post-treatment cancer survivors, mainly educated, affluent Caucasian women. Hence, findings may not be generalizable to the larger population of cancer survivors (e.g., those who are not White, have not undergone treatment, or are currently receiving treatment, less educated, less affluent, or male). Furthermore, although we used accelerometers to objectively measure minutes of PA, there are limitations (i.e., estimation of minutes of sedentary behaviors and PA). For example, it is unclear whether the device (waist-worn) accurately distinguishes the difference between sitting and standing idle, and upper body movement [49,50]. Moreover, accelerometers assess free-living PA rather than volitional, which results in a higher prevalence of meeting PA recommendations which are often based on leisure-time exercise alone. Lastly, our sample consisted of fewer participants with class III obesity (than with class I/II), which warrants future research with more representation from this group.
## 6. Conclusions
Cancer survivors are living longer due to advances in the diagnosis and control of cancer. Healthy lifestyle habits (physical activity, weight control) can enhance the quality of these years by reducing risk of cancer recurrence and other chronic diseases. Effective lifestyle programs are needed and should take into consideration the physical activity preferences and potential influences specific to this population, particularly among those with obesity who are most in need. Location, walking self-efficacy, and fitness are factors that should be considered for future PA programs among breast cancer survivors who have obesity. Tailoring for such individuals should involve a theoretically driven program which targets walking self-efficacy and involves activities appropriate for various levels of cardiorespiratory fitness.
## References
1. Campbell K.L., Winters-Stone K.M., Wiskemann J., May A.M., Schwartz A.L., Courneya K.S., Zucker D.S., Matthews C.E., Ligibel J.A., Gerber L.H.. **Exercise Guidelines for Cancer Survivors: Consensus Statement from International Multidisciplinary Roundtable**. *Med. Sci. Sport. Exerc.* (2019) **51** 2375-2390. DOI: 10.1249/MSS.0000000000002116
2. Rock C.L., Thomson C., Gansler T., Gapstur S.M., McCullough M.L., Patel A.V., Bandrews K.S., Bandera E.V., Spees C.K., Robien K.. **American Cancer Society guideline for diet and physical activity for cancer prevention**. *CA Cancer J. Clin.* (2020) **70** 245-271. DOI: 10.3322/caac.21591
3. Arem H., Mama S., Duan X., Rowland J., Bellizzi K., Ehlers D.. **Prevalence of Healthy Behaviors among Cancer Survivors in the United States: How Far Have We Come?**. *Cancer Epidemiol. Biomark. Prev.* (2020) **29** 1179-1187. DOI: 10.1158/1055-9965.EPI-19-1318
4. Greenlee H., Shi Z., Molmenti C.L.S., Rundle A., Tsai W.Y.. **Trends in Obesity Prevalence in Adults With a History of Cancer: Results From the US National Health Interview Survey, 1997 to 2014**. *J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol.* (2016) **34** 3133-3140. DOI: 10.1200/JCO.2016.66.4391
5. Zlotoff D.A., Nayor M.. **Can We Estimate Cardiorespiratory Fitness in Breast Cancer Survivors Without Exercise?**. *JACC Cardio Oncol.* (2021) **3** 692-694. DOI: 10.1016/j.jaccao.2021.10.001
6. Gourlan M., Bernard P., Bortolon C., Romain A.J., Lareyre O., Carayol M., Ninot G., Boiché J.. **Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials**. *Health Psychol. Rev.* (2016) **10** 50-66. DOI: 10.1080/17437199.2014.981777
7. Ma J.K., A Floegel T., Li L.C., Leese J., A De Vera M., Beauchamp M.R., Taunton J., Liu-Ambrose T., Allen K.D.. **Tailored physical activity behavior change interventions: Challenges and opportunities**. *Transl. Behav. Med.* (2021) **11** 2174-2181. DOI: 10.1093/tbm/ibab106
8. Rimer B.K., Glanz K.. *Theory at a Glance: A Guide for Health Promotion Practice* (2005)
9. Glanz K.R.B., Viswanath K.. *Health Behavior and Health Education: Theory, Research, and Practice* (2015)
10. Stacey F.G., James E.L., Chapman K., Courneya K.S., Lubans D.. **A systematic review and meta-analysis of social cognitive theory-based physical activity and/or nutrition behavior change interventions for cancer survivors**. *J. Cancer Surviv.* (2015) **9** 305-338. DOI: 10.1007/s11764-014-0413-z
11. Maxwell-Smith C., Hagger M.S., Kane R., Cohen P.A., Tan J., Platell C., Makin G.B., Saunders C., Nightingale S., Lynch C.. **Psychological correlates of physical activity and exercise preferences in metropolitan and nonmetropolitan cancer survivors**. *Psychooncology* (2021) **30** 221-230. DOI: 10.1002/pon.5553
12. Rogers L.Q., Markwell S.J., Verhulst S., McAuley E., Courneya K.S.. **Rural breast cancer survivors: Exercise preferences and their determinants**. *Psychooncology* (2009) **18** 412-421. DOI: 10.1002/pon.1497
13. Elshahat S., Treanor C., Donnelly M.. **Factors influencing physical activity participation among people living with or beyond cancer: A systematic scoping review**. *Int. J. Behav. Nutr. Phys. Act.* (2021) **18** 50. DOI: 10.1186/s12966-021-01116-9
14. Rogers L.Q., Courneya K.S., Verhulst S., Markwell S.J., McAuley E.. **Factors Associated With Exercise Counseling and Program Preferences Among Breast Cancer Survivors**. *J. Phys. Act. Health* (2008) **5** 688-705. DOI: 10.1123/jpah.5.5.688
15. Baillot A., Chenail S., Polita N.B., Simoneau M., Libourel M., Nazon E., Riesco E., Bond D.S., Romain A.J.. **Physical activity motives, barriers, and preferences in people with obesity: A systematic review**. *PLoS ONE* (2021) **16**. DOI: 10.1371/journal.pone.0253114
16. Short C.E., Vandelanotte C., Duncan M.. **Individual characteristics associated with physical activity intervention delivery mode preferences among adults**. *Int. J. Behav. Nutr. Phys. Act.* (2014) **11** 25. DOI: 10.1186/1479-5868-11-25
17. Rogers L.Q., Courneya K.S., Oster R.A., Anton P.M., Phillips S., Ehlers D.K., McAuley E.. **Physical activity intervention benefits persist months post-intervention: Randomized trial in breast cancer survivors**. *J. Cancer Surviv.* (2023) 1-13. DOI: 10.1007/s11764-022-01329-2
18. Rogers L.Q., Courneya K.S., Anton P.M., Hopkins-Price P., Verhulst S., Vicari S.K., Robbs R.S., Mocharnuk R., McAuley E.. **Effects of the BEAT Cancer physical activity behavior change intervention on physical activity, aerobic fitness, and quality of life in breast cancer survivors: A multicenter randomized controlled trial**. *Breast Cancer Res. Treat.* (2015) **149** 109-119. DOI: 10.1007/s10549-014-3216-z
19. Groll D.L., To T., Bombardier C., Wright J.G.. **The development of a comorbidity index with physical function as the outcome**. *J. Clin. Epidemiol.* (2005) **58** 595-602. DOI: 10.1016/j.jclinepi.2004.10.018
20. Rogers J.C., Irrgang J.. **Measures of adult lower extremity function: The American Academy of orthopedic surgeons lower limb questionnaire, the activities of daily living scale of the knee outcome survey (ADLS), foot function index (FFI), functional assessment system (FAS), Harris hip score (HHS), index of severity for hip osteoarthritis (ISH), index of severity for knee osteoarthritis (ISK), knee injury and osteoarthritis outcome score (KOOS), and Western Ontario and McMaster universities osteoarthritis index (WOMAC™)**. *Arthritis Care Res. Off. J. Am. Coll. Rheumatol.* (2003) **49** S67-S84
21. Rogers L.Q., McAuley E., Anton P.M., Courneya K.S., Vicari S., Hopkins-Price P., Verhulst S., Mocharnuk R., Hoelzer K.. **Better exercise adherence after treatment for cancer (BEAT Cancer) study: Rationale, design, and methods**. *Contemp. Clin. Trials* (2012) **33** 124-137. DOI: 10.1016/j.cct.2011.09.004
22. Rogers L., Courneya K., Shah P., Dunnington G., Hopkins-Price P.. **Exercise stage of change, barriers, expectations, values and preferences among breast cancer patients during treatment: A pilot study**. *Eur. J. Cancer Care* (2007) **16** 55-66. DOI: 10.1111/j.1365-2354.2006.00705.x
23. Courneya K.S., McKenzie D.C., Mackey J.R., Gelmon K., Reid R.D., Friedenreich C.M., Ladha A.B., Proulx C., Vallance J.K., Lane K.. **Moderators of the effects of exercise training in breast cancer patients receiving chemotherapy: A randomized controlled trial**. *Cancer* (2008) **112** 1845-1853. DOI: 10.1002/cncr.23379
24. Carter S.J., Hunter G.R., Blackston J.W., Liu N., Lefkowitz E.J., Van Der Pol W.J., Morrow C.D., Paulsen J.A., Rogers L.Q.. **Gut microbiota diversity is associated with cardiorespiratory fitness in post-primary treatment breast cancer survivors**. *Exp. Physiol.* (2019) **104** 529-539. DOI: 10.1113/EP087404
25. Wasserman K., Hansen J.E., Sue D.Y., Whipp B.J., Froelicher V.F.. **Principles of exercise testing and interpretation**. *J. Cardiopulm. Rehabil. Prev.* (1987) **7** 189. DOI: 10.1097/00008483-198704000-00014
26. Roitman J.. *ACSM’s Resource Manual for Guidelines for Exercise Testing and Prescription* (1998)
27. Balady G., Berra K.A., Golding L.A., Gordon N.F., Mahler D.A., Myers J.N., Sheldahl L.M.. *ACSM’s Guidelines for Exercise Testing and Prescription* (2000)
28. Rogers L.Q., Courneya K.S., Verhulst S., Markwell S., Lanzotti V., Shah P.. **Exercise barrier and task self-efficacy in breast cancer patients during treatment**. *Support. Care Cancer* (2006) **14** 84-90. DOI: 10.1007/s00520-005-0851-2
29. McAuley E., Mihalko S.. **Advances in sport and exercise psychology measurement. Measuring exercise-related self-efficacy**. *Fit. Inf. Technol.* (1998) **1998** 371-389
30. McAuley E., Mailey E.L., Mullen S.P., Szabo A.N., Wójcicki T.R., White S.M., Gothe N., Olson E.A., Kramer A.F.. **Growth trajectories of exercise self-efficacy in older adults: Influence of measures and initial status**. *Health Psychol.* (2011) **30** 75-83. DOI: 10.1037/a0021567
31. Rogers L.Q., McAuley E., Courneya K.S., Verhulst S.J.. **Correlates of physical activity self-efficacy among breast cancer survivors**. *Am. J. Health Behav.* (2008) **32** 594-603. DOI: 10.5993/AJHB.32.6.4
32. Rogers L.Q., Shah P., Dunnington G., Greive A., Shanmugham A., Dawson B., Courneya K.S.. **Social cognitive theory and physical activity during breast cancer treatment**. *Oncol. Nurs. Forum* (2005) **32** 807-815. DOI: 10.1188/05.ONF.807-815
33. Lewis C.E., Raczynski J.M., Heath G.W., Levinson R., Hilyer J.C., Cutter G.R.. **Promoting physical activity in low-income African-American communities: The PARR project**. *Ethn. Dis.* (1993) **3** 106-118. PMID: 8324487
34. Sallis J.F., Grossman R.M., Pinski R.B., Patterson T.L., Nader P.R.. **The development of scales to measure social support for diet and exercise behaviors**. *Prev. Med.* (1987) **16** 825-836. DOI: 10.1016/0091-7435(87)90022-3
35. Sallis J.F., Hovell M.F., Hofstetter C.R., Faucher P., Elder J.P., Blanchard J., Caspersen C.J., Powell K.E., Christenson G.M.. **A multivariate study of determinants of vigorous exercise in a community sample**. *Prev. Med.* (1989) **18** 20-34. DOI: 10.1016/0091-7435(89)90051-0
36. Wong J.N., McAuley E., Trinh L.. **Physical activity programming and counseling preferences among cancer survivors: A systematic review**. *Int. J. Behav. Nutr. Phys. Act.* (2018) **15** 48. DOI: 10.1186/s12966-018-0680-6
37. Hussien J., Brunet J., Romain A.J., Lemelin L., Baillot A.. **Living with severe obesity: Adults’ physical activity preferences, self-efficacy to overcome barriers and motives**. *Disabil. Rehabil.* (2022) **44** 590-599. DOI: 10.1080/09638288.2020.1773944
38. Vallance J., Lavallee C., Culos-Reed N., Trudeau M.. **Rural and small town breast cancer survivors’ preferences for physical activity**. *Int. J. Behav. Med.* (2013) **20** 522-528. DOI: 10.1007/s12529-012-9264-z
39. So W.-Y., Kalron A.. **The Association between Body Mass Index and Leisure-Time Physical Activity in Adults with Multiple Sclerosis**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17030920
40. Imboden M.T., Harber M.P., Whaley M.H., Finch W.H., Bishop D.L., Kaminsky L.A.. **Cardiorespiratory Fitness and Mortality in Healthy Men and Women**. *J. Am. Coll. Cardiol.* (2018) **72** 2283-2292. DOI: 10.1016/j.jacc.2018.08.2166
41. Hung T.-H., Liao P.-A., Chang H.-H., Wang J.-H., Wu M.-C.. **Examining the relationship between cardiorespiratory fitness and body weight status: Empirical evidence from a population-based survey of adults in Taiwan**. *Sci. World J.* (2014) **2014** 463736. DOI: 10.1155/2014/463736
42. Zeiher J., Manz K., Kuntz B., Perumal N., Keil T., Mensink G.B.M., Finger J.D.. **Individual and interpersonal correlates of cardiorespiratory fitness in adults—Findings from the German Health Interview and Examination Survey**. *Sci. Rep.* (2020) **10** 445. DOI: 10.1038/s41598-019-56698-z
43. Aadland E., Robertson L.. **Physical activity is associated with weight loss and increased cardiorespiratory fitness in severely obese men and women undergoing lifestyle treatment**. *J. Obes.* (2012) **2012** 810594. DOI: 10.1155/2012/810594
44. Leone L.A., Ward D.. **A mixed methods comparison of perceived benefits and barriers to exercise between obese and nonobese women**. *J. Phys. Act. Health* (2013) **10** 461-469. DOI: 10.1123/jpah.10.4.461
45. Brooks J.M., Deiches J., Xiang X., Batsis J.A., Chan F., DiMilia P., Chiu C., Thompson K., Bartels S.. **Differences in Self-Reported Physical Activity, Exercise Self-Efficacy and Outcome Expectancies, and Health Status by Body Mass Index Groups in People with Chronic Pain**. *J. Rehabil.* (2018) **84** 46-52. PMID: 32089565
46. Hartman S.J., Dunsiger S.I., Pekmezi D.W., Barbera B., Neighbors C.J., Marquez B., Marcus B.H.. **Impact of Baseline BMI upon the Success of Latina Participants Enrolled in a 6-Month Physical Activity Intervention**. *J. Obes.* (2011) **2011** 921916. DOI: 10.1155/2011/921916
47. Bailey R.R.. **Goal Setting and Action Planning for Health Behavior Change**. *Am. J. Lifestyle Med.* (2019) **13** 615-618. DOI: 10.1177/1559827617729634
48. Kitagawa T., Higuchi Y., Todo E., Ueda T., Ando S., Murakami T.. **Tailored feedback reduced prolonged sitting time and improved the health of housewives: A single-blind randomized controlled pilot study**. *Women Health* (2020) **60** 212-223. DOI: 10.1080/03630242.2019.1616043
49. Kalisch T., Theil C., Gosheger G., Ackmann T., Schoenhals I., Moellenbeck B.. **Measuring sedentary behavior using waist- and thigh-worn accelerometers and inclinometers—Are the results comparable?**. *Ther. Adv. Musculoskelet. Dis.* (2022) **14** 1759720x221079256. DOI: 10.1177/1759720X221079256
50. Lee I.M., Shiroma E.. **Using accelerometers to measure physical activity in large-scale epidemiological studies: Issues and challenges**. *Br. J. Sport. Med.* (2014) **48** 197-201. DOI: 10.1136/bjsports-2013-093154
|
---
title: Effect of Hot- and Cold-Water Treatment on Broccoli Bioactive Compounds, Oxidative
Stress Parameters and Biological Effects of Their Extracts
authors:
- Ivana Šola
- Dino Davosir
- Emilie Kokić
- Jana Zekirovski
journal: Plants
year: 2023
pmcid: PMC10005114
doi: 10.3390/plants12051135
license: CC BY 4.0
---
# Effect of Hot- and Cold-Water Treatment on Broccoli Bioactive Compounds, Oxidative Stress Parameters and Biological Effects of Their Extracts
## Abstract
The goal of this work was to define resistant and susceptible variables of young broccoli (*Brassica oleracea* L. convar. botrytis (L.) Alef. var. cymosa Duch.) plants treated with cold and hot water. Additionally, we wanted to single out variables that could potentially be used as biomarkers of cold/hot-water stress in broccoli. Hot water changed more variables ($72\%$) of young broccoli than cold water ($24\%$) treatment. Hot water increased the concentration of vitamin C for $33\%$, hydrogen peroxide for $10\%$, malondialdehyde for $28\%$, and proline for $147\%$. Extracts of broccoli stressed with hot water were significantly more efficient in the inhibition of α-glucosidase (65.85 ± $4.85\%$ compared to 52.00 ± $5.16\%$ of control plants), while those of cold-water-stressed broccoli were more efficient in the inhibition of α-amylase (19.85 ± $2.70\%$ compared to 13.26 ± $2.36\%$ of control plants). Total glucosinolates and soluble sugars were affected by hot and cold water in an opposite way, which is why they could be used as biomarkers of hot/cold-water stress in broccoli. The possibility of using temperature stress to grow broccoli enriched with compounds of interest to human health should be further investigated.
## 1. Introduction
Due to climate change, sudden and intense changes in weather conditions are becoming more frequent. Immobile organisms such as plants cannot “take refuge” from high or low temperatures but adapt their physiology to the new conditions to survive [1,2]. The intensity and direction of these changes depend on both the temperature and the plant species. Since adaptations at the metabolic level are fast, changes in the ambient temperature can be detected on the level of biochemical responses by which plants react to the new conditions [3,4,5]. Such changes are usually crucial yield-limiting factors for plants [6].
Studies conducted on various plant species, such as wheat, rice, corn, chili peppers, okra, tobacco and soybeans, have shown that heat stress affects plants in various, mostly negative, ways [7]. In corn, which was exposed to daytime temperature of 38 °C and nighttime temperature of 28 °C for 14 days, plasma, chloroplast and mitochondrial membranes were damaged [8]. In tobacco stored at a temperature of 43 °C for 2 h in the early stage of growth, a decrease in the net rate of photosynthesis as well as a decrease in antioxidant activity was observed [9]. In okra, at temperatures between 32 °C and 34 °C during growth, yield as well as the quality parameters of the legume, such as fiber content, decreased [10]. Additionally, high-temperature stress altered the expression of genes involved in the regulation of osmoprotectants, detoxifying enzymes, transporters and regulatory protein synthesis [11,12,13].
Plants that are sensitive to low temperatures can already suffer damage at temperatures close to 15 °C, while plants that are tolerant survive at temperatures slightly below 5 °C [14]. Cold stress induces different ultrastructural changes, primarily relating to membrane alterations [15]. Acclimatization to cold affects the composition of lipids in cells by increasing the proportion of unsaturated fatty acids that build phospholipids [16,17]. This is necessary to maintain the functionality of the plasma membrane. Plants adjust to low temperatures by synthesizing cryoprotective molecules such as soluble sugars, sugar alcohols and low-energy nitrogen compounds [18]. They decrease the activity of ATP synthase, inhibit Rubisco regeneration and photophosphorylation [19]. The tolerance of gene expression for improving adaptive capacity is also changed under cold stress [20].
Broccoli (Brassica oleracea) is rich in vitamins (C, K); β-carotene, a precursor of vitamin A; dietary fibers; polyphenols; fatty acids; minerals and glucosinolates—phytochemicals that are predominantly represented in Brassica vegetables [21]. These compounds contribute to the health benefits of broccoli, such as antioxidant, antiproliferative and antidiabetic properties and the protection of the cardiovascular system [22,23,24]. Temperature and irradiation have been recognized as the most important factors for the production of consumer-orientated quality broccoli [25]. Since broccoli is native to moderate climatic zones such as the Mediterranean region, high or low temperatures will cause perturbations in its phytochemical profile to survive. Such phytochemical perturbations might have consequences on the biological effects of a plant and its products [26].
As part of this work, we investigated the influence of hot and cold water on the metabolism of young broccoli (*Brassica oleracea* L. convar. botrytis (L.) Alef. var. cymosa Duch.) plants. Our aim was to (i) define the susceptible and resistant parameters of this plant during low- and high-temperature water stress, (ii) determine the degree of metabolism change of broccoli due to these two types of stress, and (iii) determine the degree of change in the biological effects of broccoli extracts due to the types of stress. The approach we chose was as follows: we spectrophotometrically determined the quantity of (a) total phenolics, flavonoids, flavonols, proanthocyanins, tannins, phenolic and hydroxycinnamic acids; (b) soluble sugars and total glucosinolates; (c) parameters of oxidative stress, hydrogen peroxide, proline and malondialdehyde and (d) photosynthetic pigments; (e) then, using the RP-HPLC method, we separated, tentatively identified and quantified individual phenolics and L-ascorbic acid in broccoli before and after in vitro simulated human digestion; (f) determined the antioxidant capacity; (g) measured the effect of broccoli extracts on the activity of the enzymes α-amylase, α-glucosidase and lipase and (h) statistically, using one-way analysis of variance (ANOVA), principal component analysis (PCA), hierarchical clustering and Pearson’s correlations, correlated all of the measured variables of broccoli in order to conclude which of the parameters, in what direction and at what intensity are affected by hot/cold-water treatment. The results showed that hot water changed more variables ($72\%$) of young broccoli than cold-water ($24\%$) treatment. Total glucosinolates and soluble sugars were affected by hot and cold water in opposite ways; therefore, they could be used as biomarkers of hot/cold-water stress in broccoli. Among the bioactive compounds and oxidative stress parameters, the variable that was most significantly affected was proline, which was 2.5 times higher after hot-water treatment. Among the variables presenting the biological activity of the extract, the most significantly affected was α-amylase inhibition, which was 1.5 times more inhibited by cold-water treatment than with the extract of the control group. Statistical analyses showed greater similarity between the control group and the group treated with cold water, while the group treated with hot water separated more significantly.
## 2.1. Effect of Hot- and Cold-Water Stress on Different Groups of Phenolic Compounds, Soluble Sugars and Glucosinolates in Broccoli
In order to survive, plants adapt their metabolism according to their environmental conditions. In our previous work, we showed that the phytochemical profile could even be changed intentionally to create plants enriched with the bioactive compounds of interest [27]. Since temperature is known to be one of the main modificators of the phytochemical composition of plants [28], in this study, we investigated the impact of hot- and cold-water stress on the broccoli metabolism. The amount of total phenolics, flavonoids, tannins and phenolic acids did not differ significantly between the experimental groups (Table 1). In the control group, total phenolics were recorded as 10.56 ± 0.55 mg gallic acid equivalents (GAE)/g dw, total flavonoids 16.31 ± 0.89 mg quercetin equivalents (QE)/g dw, total tannins 5.05 ± 0.12 mg catechin equivalents (CatE)/g dw and total phenolic acids 2.23 ± 0.36 caffeic acid equivalents (CAE)/g dw.
The proportion of hydroxycinnamic acids, as well as flavonols, significantly decreased in the group treated with hot water, the amount of hydroxycinnamic acids was 0.10 ± 0.01 mg caffeic acid equivalent/g dw and the amount of flavonols was 0.12 ± 0.01 mg QE/g dw. On the other hand, in the group treated with cold water it did not differ significantly compared to the control. This suggests that hydroxycinnamic acids and flavonols in broccoli are more sensitive to high-temperature water stress than low temperature. Regarding the low growing temperature, similar results as ours were recorded in spinach [29]; namely, not just that winter sweet treatment did not decrease flavonoid composition in spinach, but it even increased some of the components at certain timepoints.
Proanthocyanins, due to their astringent properties, allow the plant to defend against pathogens and predators. Additionally, due to their antioxidant potential, they also protect plants from abiotic stress [30,31,32,33]. The concentration of total proanthocyanins was the highest in the group treated with hot water, 1.21 ± 0.064 mg CatE/g dw. On the other hand, in the group treated with cold water, this value was not significantly different from the value of the control group. We hypothesize that broccoli might accumulate proanthocyanins by stimulation of their biosynthesis and/or inhibition of their catabolism as one of the acclimation mechanisms against hot-water stress. Contrary to our result, a higher proportion of proanthocyanins was recorded in the skin and seeds of the fruits of grapevines grown at low temperatures [34]. This indicates a specific response of different plant species at the level of total proanthocyanins to temperature stress.
Sugars are known as one of the main groups of osmoprotectants [35,36,37]. In our study, the amount of soluble sugars was significantly different between all three experimental groups. They were the highest in the group treated with hot water; 31.14 ± 1.47 mg sucrose equivalent (SucE)/g dw. Much like in our work, high-temperature stress caused an increase in sugar content in most genotypes of the *Vigna aconitifolia* species [38] and in lablab bean [39]. However, contrary to our expectation, in the group treated with cold water, the proportion was significantly lower than in the control group, 15.07 ± 1.61 mg SucE/g dw. The accumulation of soluble sugars is a common phenomenon during cold stress [40]. Therefore, this result is interesting and suggests the need for additional analyses. Soluble sugars help the plant adapt to temperature conditions by supporting the hardening of plant cell membranes and thereby preventing cell destruction. Such action by soluble sugars was described under conditions of cold shock [41]. Given that we did not completely freeze the plants, but only watered them with cold water, it is possible that the shock was insufficient to elicit the biosynthesis of soluble sugars.
Glucosinolates and their hydrolysis products play a key role in the growth and development of plants, their taste and their defense systems [42,43,44]. In our work on young broccoli, we recorded significant changes in the proportion of total glucosinolates after both treatments. Cold water significantly increased the proportion of these compounds, 34.96 ± 2.37 mg sinigrin equivalent (SinE)/g dw. On the contrary, hot water decreased their amount to the value of 24.07 ± 1.34 mg SinE/g dw. This indicated a high degree of susceptibility of broccoli glucosinolates to temperature stress, and the specificity of the response depending on temperature. These results also suggest the potential to use low temperatures to grow broccoli enriched with glucosinolates. Similarly to our study, the amount of all types of glucosinolates decreased with increasing temperature in red cabbage [45]. Likewise, low temperature increased the concentration of total glucosinolates in kale [46,47].
## 2.2. Effect of Hot- and Cold-Water Stress on Parameters of Oxidative Stress and Pigments in Broccoli
A direct result of stress-induced cellular changes is the overproduction of highly reactive and toxic oxygen species (ROS) that damage proteins, lipids and carbohydrates and cause oxidative stress [48]. In our work, both types of stress significantly affected the amount of hydrogen peroxide in broccoli plants; however, this occurred in an opposite way (Table 2). More precisely, hot-water treatment significantly increased the concentration of hydrogen peroxide from 2.33 ± 0.13 µM/g dw in the control group to 2.56 ± 0.30 µM/g dw. Cold water decreased it to the concentration of 1.93 ± 0.12 µM/g dw. Proline was affected in the same way, as hot water significantly increased the concentration to 3.31 ± 0.09 µM/g dw, which was almost 2.5 times higher than in the control group. Cold water decreased it to 1.15 ± 0.12 µM/g dw. This result suggests that hydrogen peroxide and proline can be used as biomarkers of hot/cold-water stress in young broccoli. Lipid peroxidation, that is, malondialdehyde concentration, was affected by hot-water treatment only and was increased from 23.92 ± 0.74 µM/g dw in the control group to a concentration of 30.62 ± 1.39 µM/g dw. When we look at all three parameters of oxidative stress together, we notice that the most significant change occurred in the group treated with hot water. This indicated the highest level of oxidative stress induction in plants treated with hot water, which occurred on the level of proline. Similar to our result, an increased level of proline under high-temperature stress was recorded in lablab bean (Dolichos lablab) [39].
If the share of photosynthetic pigments in a plant is significantly changed, the possibility of using solar energy for growth will be changed, and thus the development and functioning of the plant will also change. In our work, the values of all pigments, except for chlorophyll a, were significantly different between the group treated with hot water and the remaining two groups. The treatment with hot and cold water did not significantly affect the concentration of chlorophyll a in young broccoli plants, but hot water significantly increased the concentration of chlorophyll b, raising it to 5.35 ± 0.34 mg/g dw. We recorded the same results for lycopene and porphyrins; hot water significantly increased their concentration (0.28 ± 0.016 mg lycopene/g dw, and 28.27 ± 1.09 mg porphyrins/g dw), while cold water did not affect it. A similar result with drought treatment was also previously recorded in young Chinese cabbage plants [49]. Likewise, an increase in the proportion of chlorophyll b was also recorded in rice genotypes that were highly resistant to high-temperature stress [50]. It is assumed that a high proportion of chlorophyll b is associated with high temperature tolerance. Based on our results, we hypothesize that photosynthetic pigments in young broccoli plants, except for chlorophyll a, are more susceptible to high- than to low-temperature stress. Given that chlorophyll a is the primary pigment of photosynthesis, the stability of this structure in different environmental conditions is extremely important for plant survival. This was probably the reason why its share in broccoli did not change due to temperature stress. A different result was recorded in cucumber seedlings [51]. More precisely, cucumber seedlings were treated with low (7 °C) and high (42 °C) temperatures, and in both cases a decrease in the proportion of total chlorophyll was recorded. However, a more significant decrease in the proportion occurring with low- rather than high-temperature treatment was recorded.
It is interesting to note that in the case of the concentration of total carotenoids, the situation was exactly the opposite. Hot water significantly lowered their concentration to was 0.40 ± 0.19 mg/g dw only, while there was no significant difference between the control group and the group treated with cold water. The same result was recorded with different genotypes of rice [50] and wheat [52]. When we compare the treatments in our study, similarly to chlorophyll, lycopene and porphyrins, hot-water treatment had a more significant effect on total carotenoids than cold-water treatment. The reason why hot water decreased carotenoids might be the price paid for enhanced chlorophyll, lycopene and total porpyhrin synthesis. Chlorophyll is obviously more important in the defense of young broccoli against hot water than total carotenoids. Additionally, young broccoli is less resistant to hot than to cold water.
## 2.3. Effect of Hot- and Cold-Water Stress on the Concentration of Individual Phenolic Compounds and Vitamin C in Broccoli
Both types of temperature stress significantly increased the concentration of p-coumaric acid compared to the control group (Table 3). Treatment with hot water caused a more intense increase in the concentration of this acid than treatment with cold water. Specifically, a concentration of 14.22 ± 0.68 mg/g dw was recorded in the control group, 16.72 ± 0.51 mg/g dw in the group treated with cold water and 19.04 ± 0.29 mg/g dw in the group treated with hot water. Previous research has shown a differential accumulation of phenolic compounds in plants depending on the type of abiotic stress [53]. Given that we applied two different types of stress, we also expected different concentrations of phenolic compounds depending on whether the broccoli was treated with hot or cold water. p-coumaric acid is important for the plant organism because it is a precursor of polyphenolic compounds, such as flavonoids and other phenolic acids, as well as lignin [54], a compound in plant cell walls that contributes to their strength and protection from environmental conditions. We assume that this is one of the possible reasons why both types of stress led to an increase in the concentration of p-coumaric acid; to supply the plant with the main precursor of a series of defense compounds. The same result was also recorded in Chinese cabbage [55], different wheat cultivars exposed to heat stress [56], tomato [57] and the genotype Predator of the *Festuca trachyphylla* species [58]. However, contrary to our results, in the leaves of grapevines that were subjected to low-temperature stress for a week, a decrease in the concentration of p-coumaric acid was recorded [59]. This suggests the specificity of the defense response mediated by p-coumaric acid in different plant species. For p-coumaric acid, the ability to alleviate the symptoms of diabetes has been proven [60,61,62] through various mechanisms. The fact that cold-water treatment significantly increased the concentration of this acid suggests a great potential of the application of this type of stress for the cultivation of broccoli with increased health value.
The concentration of ferulic acid in broccoli was not significantly affected by cold-water stress, but hot-water stress significantly increased its concentration to 64.99 ± 2.62 mg/g dw. In the control group and the group treated with cold water, the value was 49.41 ± 4.80 mg/g dw and 43.18 ± 3.28 mg/g dw, respectively. Recently, it was reported that high-temperature stress did not significantly affect the concentration of this component in wheat [56]. A decrease in the concentration of ferulic acid in grapevine leaves treated with low temperature was recorded [59]. On the other hand, in tomato seedlings [57] and Chinese cabbage [55], an increase in the concentration of ferulic acid was recorded after low-temperature stress. This points to a specific response of different species to temperature stress at the level of this phenolic acid. Such differences in results can be attributed to differences in the intensity and duration of stress, the stage of plant development and the part of the plant analyzed, e.g., flower, fruit, leaf, stem and root [56].
Contrary to the previous one, sinapic acid in broccoli was extremely sensitive to stress caused by hot water and its concentration decreased almost by half, to 339.80 ± 37.67 mg/g dw. Cold-water treatment had no significant effect. For comparison, an increase in the concentration of this acid in Chinese cabbage treated with cold stress was recently recorded [55], and no significant change was observed in the concentration of this acid in wheat grown at elevated temperatures [56]. This suggests the importance of plant matrices for the stability/susceptibility of a bioactive compound under the impact of environmental stress.
When we compare all three identified phenolic acids, we can see that p-coumaric acid in young broccoli is sensitive to both types of stress, while ferulic and sinapic acid are susceptible to hot-water treatment only. Hot water affected all three phenolic acids with a very similar intensity: change in a concentration between $32\%$ and $36\%$. However, p-coumaric and ferulic acid concentration was increased at the expense of sinapic acid, whose concentration was decreased. Based on this, we conclude that under hot-water stress, young broccoli will synthesize more intensively phenolic acids containing two hydroxyl groups only (p-coumaric acid), or two hydroxyl groups and one methoxy group (ferulic acid). Phenolic acid containing two hydroxyl and two methoxy groups (sinapic acid) will be decreased, probably because two methoxy groups sterically hinder the antioxidant potential of hydroxyl groups and hence reduce the functionality of sinapic acid in plant defense against ROS. From another point of view, as with a study on durum wheat cultivars [56], hot-water treatment increased the minor identified phenolic acids, i.e., p-coumaric and ferulic. The fact that cold-water stress affected p-coumaric acid only suggests that for young broccoli, cold water is less stressful than hot water on the level of these phenolic acids. Additionally, half the increase than that of hot-water treatment of an acid containing two hydroxyl groups, without any methoxy group, was enough to protect broccoli from cold-water stress.
The flavonols we tentatively identified in broccoli using the RP-HPLC method were kaempferol and quercetin. Kaempferol was the predominant flavonol in young broccoli. Its concentration was almost three times higher than that of quercetin. The treatment with cold water did not significantly affect the concentration of kaempferol; however, treatment with hot water led to a significant decrease in its concentration. Based on this, we assume that the broccoli matrix “protects” kaempferol better from cold stress than from elevated temperatures. On the other hand, in tomato seedlings treated with low and high temperatures, the concentration of kaempferol increased [57]. We hypothesize that in young broccoli some other phytochemicals are harnessed to defend it against hot-water treatment, and not kaempferol. One of these could be quercetin.
The concentration of quercetin in broccoli increased significantly under the influence of hot water, to 35.11 ± 0.73 mg/g dw, compared to 32.93 ± 0.49 in the control group. Cold water did not show a significant effect. For comparison, in treated tomato seedlings, the concentration of quercetin increased after both low- and high-temperature treatment [45]. Given that high-temperature water stress in broccoli caused an increase of quercetin and a decrease of kaempferol, we assume that quercetin plays a more significant role in the defense of this species against high temperature, although it was present in a significantly lower concentration than kaempferol. This is a good example that the quantity is not crucial, but the chemical structure, i.e., the mode of action in the cells. Obviously, hot-water treatment supports flavonols with higher number of hydroxyl groups instead of those with less. Additionally, based on this result, we assume that quercetin and kaempferol in broccoli are in an antagonistic relationship with each other during heat stress. Similar relationship between these two flavonols we had previously recorded in Crocus varieties grown at different altitudes [63]. Namely, quercetin:kaempferol ratio was higher in varieties collected at mountains where the UVB radiation was more intense and has a stronger effect on plants. Therefore, we conclude that in a situation of a significant stress, plants will increase the biosynthesis of a flavonol containing more hydroxyl groups to better scavenge reactive oxygen species (ROS).
Vitamin C (L-ascorbic acid) is one of the most important parameters of the nutritional quality of food plants and is an essential nutrient for the human body [64,65,66]. The concentration of vitamin C increased significantly in broccoli treated with hot water to 1522.99 ± 70.30 mg/g dw. In the group treated with cold water, there was no significant change compared to the control: 1143.09 ± 33.32 mg/g dw and 1144.43 ± 66.64 mg/g dw, respectively. As in our work, in the species *Festuca arundinacea* Schreb [66], tall fescue [67] and in lablab bean [39], the concentration of vitamin C increased significantly in parallel with the increase in temperature. In the work carried out on citrus fruits, which are the richest in vitamin C, it was shown that the temperature of fruit processing significantly affected the concentration of vitamin C. Thus, the concentration of vitamin C was higher in juice squeezed at a temperature of 20 °C than at a temperature of 80 °C [68]. This indicates the thermolability of vitamin C during the processing of plant material and, more importantly, the completely different effect of high temperature on this vitamin in intact living plant and harvested plant material. It might be that the vitamin C in a living cell is to important to be destroyed under high-temperature stress, and therefore it is increased at the cost of some other phytochemicals.
## 2.4. Concentration of Individual Phenolic Compounds and Vitamin C during In Vitro Simulated Digestion of Broccoli: Comparison of Control, Hot-Water- and Cold-Water-Stressed Plants
Due to the interaction of the extract matrix with gastrointestinal enzymes and bacteria in the human digestive system, the concentration of bioactive compounds varies between the phases of digestion [69,70]. Since the composition of the plant matrix depends on the growing conditions, we analyzed whether hot- and cold-water stress affected the bioactive compound concentration in the salivary, gastric and intestinal phases of digestion using an in vitro model. Flavonol kaempferol concentration in control plants was decreased after the salivary phase of digestion, while hot- and cold-water stressed plants did not show any differences in this compound concentration during digestion (Figure 1A). p-coumaric acid in control plants was increased after the gastric and intestinal phase, while in hot- and cold-water-stressed plants there was no change in its concentration during in vitro digestion (Figure 1B). Treatment with hot water increased the concentration of this acid in each of the digestion phases except the gastric one, where it did not differ compared to the control group of plants. Based on this result, we concluded that hot-water-stressed broccoli presents a better source of p-coumaric acid in the human digestion system than broccoli treated with room-temperature water. Ferulic acid concentration was increased after the intestinal phase in control and cold-water-stressed plants, while it was not changed in hot-water-stressed ones (Figure 1C). Same as with p-coumaric acid, hot-water-stressed broccoli presented a better source of ferulic acid in the human digestion system than broccoli treated with room-temperature water. Sinapic acid in control plants was decreased after the salivary phase of digestion, while in cold-water-stressed plants it was increased in each of the digestion phases (Figure 1D). When we compare the level of sinapic acid for each digestion phase between the groups of plants, we can see that hot/cold water oppositely affected its concentration to that of p-coumaric and ferulic acid. Namely, L-ascorbic acid (vitamin C) concentration was not changed during the in vitro digestion of control and cold-water-stressed plants. However, it decreased after the intestinal phase of hot-water-stressed plants (Figure 1E). By comparing samples within the digestion phase, no differences could be detected. This is interesting because in the original extract, the concentration of vitamin C was significantly higher in hot-water-stressed broccoli (Table 3). This means that during digestion, the matrix of hot-water-stressed broccoli cannot “protect” this vitamin from being degraded.
Along with the concentration of identified compounds, we also calculated the bioavailability of these compounds (Supplementary Table S1). In control plants, the bioavailability of p-coumaric and sinapic acid was higher after the gastric and intestinal phases of in vitro digestion and did not differ between those two phases. Ferulic acid was more bioavailable after the intestinal phase, while salivary and gastric did not differ between each other. Kaempferol availability did not differ among the salivary and gastric phases and could not be detected after the intestinal phase of digestion at all. Bioavailability of vitamin C did not differ between any of the phases. In cold-water-stressed plants, p-coumaric acid was more bioavailable in the gastric than intestinal phase; salivary phase bioavailability did not differ during either of the remaining phases. Ferulic acid was the most bioavailable in the intestinal phase, while sinapic acid, kaempferol and vitamin C bioavailability did not differ between the phases. Kaempferol was under the detection limit in the intestinal phase. In hot-water-stressed plants, we recorded no difference between the digestion phases for any of the identified compounds. When we compare the bioavailability of each of the compounds in each of the digestion phases between the control and tested plants, we can see that p-coumaric acid bioavailability in the salivary phase decreased after both types of stress. Ferulic acid bioavailability was not affected by cold-water stress in any of the phases, while hot-water stress decreased its availability in the intestinal phase. On the contrary, sinapic acid availability was increased after cold-water treatment in every digestion phase. Hot-water stress increased its availability in the salivary phase only, while availability in other phases was not changed. Bioavailability of kaempferol was affected by cold-water stress only; it was increased in the salivary and gastric phase. Hot-water stress did not significantly change kaempferol bioavailability. Interestingly, vitamin C bioavailability was not significantly affected by any type stress in any of the digestion phases.
## 2.5. Effect of Hot- and Cold-Water Stress on Antioxidant Potential of Broccoli Extracts
Oxidative stress is a common response of plants exposed to extreme temperatures [71,72]. ROS were recognized as ubiquitous markers of oxidative stress and signaling events for the induction of adaptive stress responses [73]. During temperature stress, the increased production of ROS is a major risk factor for plant cells, and therefore it is crucial that the concentration of ROS-detoxifying compounds also increases [74]. Although almost all organisms possess antioxidants and several enzyme systems such as superoxide dismutase, catalase, glutathione peroxidase and glutathione reductase to protect them against oxidative damage, these systems cannot completely defend them from damage. Therefore, antioxidants or foods containing high concentrations of antioxidants are needed to help scavenge free radicals and reduce oxidative damage. The currently available synthetic antioxidants, including butylated hydroxytoluene (BHT) and butylated hydroxyanisole (BHA), have many unwanted side effects on human health [75], which limits their application. Therefore, the trend to replace them with natural antioxidants that are physiologically easier to tolerate has gained importance. Given that there are different types of antioxidants in plant cells, we used three different methods of measuring antioxidant potential, ABTS, DPPH and FRAP, to obtain the most reliable information. ABTS, unlike DPPH and FRAP, is soluble in both aqueous and organic media, which makes it more suitable for the analysis of hydrophilic and lipophilic antioxidants and pigments [76]. Therefore, we expected to record a difference compared to the control group primarily with this method, but this was not the case. None of the treatments significantly changed the antioxidant potential measured by the ABTS and FRAP methods, but both caused a decrease in the potential measured by the DPPH method (Table 4). This difference in results between different methods was due to the different types of antioxidants present in the samples that react differently with the radicals used. A similar result, related to the suitability of the methods, was recorded when measuring the antioxidant potential of the mycelium of different types of mushrooms [75]. Based on these results, we conclude that the use of low- or high-temperature water cannot significantly improve the antioxidant potential of broccoli. Even more so, according to the DPPH method, this potential was reduced. A similar result has already been recorded on *Brassica oleracea* vegetables; in the acephala (kale) group, the ABTS method showed a lowering of the antioxidant potential, while in the capitata (cabbage) group, there was no significant impact [77]. Likewise, in Chinese cabbage treated with low temperature, the antioxidant potential measured by the DPPH and ORAC method, as well as the activity of the key antioxidant enzymes catalase and peroxidase, decreased [55].
## 2.6. Effect of Hot- and Cold-Water Stress on the Potential of Extracts of Young Broccoli Plants to Inhibit α-Amylase, α-Glucosidase and Lipase Enzyme
The search for natural inhibitors of α-amylase, α-glucosidase and lipase enzyme is becoming more intense because synthesized drugs have unwanted side effects [78,79]. Many fruits have been shown to contain inhibitors of these enzymes [28,80,81,82]; however, there is much less data for the inhibitory potential of vegetables [83,84].
Enzyme α-amylase participates in the hydrolysis of starch and glycogen, and its inhibition is an approach for carbohydrate intake disorders treatment, such as diabetes and obesity, as well as dental caries and periodontal disease [85]. Plants are a great, renewable source of chemical compounds with the potential to inhibit α-amylase and can be used as therapeutic or functional foods [86,87]. So far, about 800 plant species with antidiabetic properties have been recorded [85]. Among others, extracts of lyophilized radish sprouts (*Raphanus sativus* cv. Rambo) inhibited α-amylase activity [87]. Moreover, extracts of two broccoli varieties, *Brassica oleracea* L. convar. Italica botrytis (L.) Alef. var. cymosa Duch. and *Brassica oleracea* acephala L. convar. acephala (DC.) Alef. var. sabellica L. showed the ability to inhibit α-amylase activity [88]. In our work, the group treated with cold water inhibited the activity of this enzyme significantly more, achieving a level of inhibition of 19.85 ± $2.70\%$, than the control group and the group treated with hot water (Figure 2A). The α-amylase inhibition ability of the group treated with hot water and the control group was not significantly different: 12.11 ± $1.68\%$ and 13.26 ± $2.36\%$, respectively. Neither plant group was more efficient in the inhibition of α-amylase than the standard inhibitor acarbose with a concentration of 20 mg/mL, which inhibited 46.07 ± $0.19\%$ of enzyme activity. Considering our results, if one of the goals of broccoli consumption would be an improved inhibition of α-amylase, it would certainly be reasonable to treat broccoli with cold water during cultivation. This result also suggests further research into the possibility of using cold-water stress to increase the antidiabetic properties of plant species.
Inhibition of α-glucosidase activity is another possibility for regulating blood glucose level. Different types of α-glucosidase inhibitors have been found in microorganisms and plants, including alkaloids, phenolics, curcuminoids and terpenoids [28,89,90]. Regarding the α-glucosidase activity in our study, hot-water treatment significantly increased the inhibition potential of broccoli extracts to 65.85 ± $4.85\%$, compared to 52.00 ± $5.16\%$ in control plants (Figure 2B). Cold-water treatment did not significantly affect the inhibition potential of extracts toward this enzyme, which reached 54.26 ± $3.24\%$. As in the case of α-amlyase, the standard compound, acarbose, showed the highest inhibition potential, 78.69 ± $1.47\%$.
Obesity is a strong risk factor for diseases such as hypertension, arteriosclerosis and diabetes [91]. One of the ways to prevent obesity is the inhibition of fat absorption from the intestines, that is, inhibition of pancreatic lipase which breaks down triglycerides into free fatty acids and glycerol. Only a small number of substances, such as orlistat (tetrahydrolipstatin), inhibit the activity of lipase [92]; however, such substances cause unpleasant side effects in the gastrointestinal system and kidneys and, in addition, are expensive. It is known that many plant species inhibit the activity of lipases thanks to the presence of specialized metabolites such as polyphenols, which are strong inhibitors of pancreatic lipase [93]. Recently, the potential of methanolic extracts of twelve medicinal plant species and four types of agricultural waste to inhibit lipase activity was analyzed [94]. The results showed that methanol extracts of white poplar twigs (Populus alba), green *Ononis vaginalis* and *Asparagus stipularis* have excellent inhibitory ability: $98\%$, $94\%$ and $92\%$, respectively. For comparison, the standard orlistat with a concentration of 1 μg/mL showed $90\%$ inhibition. Further analyses revealed that the flavonoids taxifolin and ampelopsin were the strongest inhibitors, followed by p-hydroxybenzoic acid. To the best of our knowledge, there are no published data about the influence of temperature stress on the ability of plant extracts to inhibit lipase. Therefore, as part of our work, we investigated whether temperature stress would change the phytochemical composition of a plant to such an extent that the ability of its extract to inhibit lipase would also significantly change. The results showed that treatment with hot water significantly reduced the ability of broccoli extract to inhibit lipase activity; the inhibition level was 43.32 ± $1.75\%$ (Figure 2C). In the group treated with cold water, the ability to inhibit lipase did not differ significantly compared to the control group; it was 49.54 ± $0.80\%$ and 48.58 ± $1.34\%$, respectively. Additionally, the inhibition values were relatively close to the inhibition percentage of the orlistat standard at a concentration of 20 mg/mL, 56.96 ± $0.72\%$. This indicates the potential of broccoli for this purpose and suggests further research in this direction. This result also suggests that high temperature has a negative effect on broccoli compounds with inhibitory activity toward lipase, and if one wants to preserve this potential, broccoli should not be exposed to high temperatures.
## 2.7. Representation and Distribution of Decreased, Unchanged and Increased Variables in Young Broccoli Plants Due to Hot/Cold-Water Treatment
Based on all the measured variables, we concluded that hot water changed more variables of young broccoli than cold-water treatment. To be precise, $72\%$ of measured variables were changed after hot-water-, and $24\%$ after cold-water treatment (Supplementary Figure S1, Supplementary Table S2). Among the changed variables upon hot-water treatment, $62\%$ of them increased and $38\%$ decreased, while upon cold-water stress, $43\%$ increased and $57\%$ decreased.
Regarding the identified individual compounds, the order of compounds in each group is the same; the most represented was L-ascorbic acid with $56\%$ in the control group, $57\%$ in the group treated with water of reduced temperature and $72\%$ in the group treated with water at an elevated temperature (Supplementary Figure S2). The second most abundant was sinapic acid; however, unlike L-ascorbic acid, its concentration in the treated groups was lower than in the control group. Specifically, it was $31\%$ in the control group, $30\%$ in the group treated with cold water and only $16\%$ in the group treated with hot water. The third most abundant compound was kaempferol, followed by ferulic acid, quercetin and, finally, p-coumaric acid.
Some of the variables were affected by both types of stress in the same direction, and some in the opposite direction. For example, total flavonols, proanthocyanins, phenolic acids, hydroxycinnamic acids, soluble sugars, glucosinolates, ferulic and sinapic acid, kaempferol, hydrogen peroxide, proline, malondialdehyde, α-amylase and lipase activity were affected oppositely by hot- and cold-water treatment (Supplementary Figures S3 and S4). Among the bioactive compounds and oxidative stress parameters, the variable that was most significantly affected was proline. It was 2.5 times higher after hot-water treatment (Supplementary Figure S4B). Among the variables presenting the biological activity of the extract, the most significantly affected was α-amylase inhibition. With cold-water-treatment it was 1.5 times more inhibited than with the control group extract (Supplementary Figure S4C). The resistable variables in young broccoli that were not changed by either of the stress types were chlorophyll a, total phenolics, flavonoids, tannins and phenolic acids, as well as antioxidant potential measured by the ABTS and FRAP methods.
## 2.8.1. Principal Component Analysis
Two principal components (PC 1 and PC 2) explained $100\%$ of the variability; PC1 explained $79.68\%$ and PC2 explained $20.32\%$ (Figure 3). The analysis clearly separated the three analyzed groups of plants (Figure 3A). With respect to PC 1, the hot-water-treated plants separated from the other two groups. With respect to PC 2, the cold-water-treated plants were further away from the control group than the hot-water-treated group. Figure 3B shows that chlorophylls, lycopene, porphyrins, vitamin C, phenolic compounds and sugars, proline, hydrogen peroxide and malondialdehyde, and the ability to inhibit the activity of α-glucosidase contributed dominantly to the separation of the hot-water-treated group. The ability to inhibit the activity of α-amylase and lipase enzymes, glucosinolates, hydroxycinnamic acids and flavonols dominantly contributed to the separation of the group treated with cold water. Finally, total phenolics, tannins and antioxidant potential (measured by any of the three methods) contributed the most to the separation of the control group from the test groups.
## 2.8.2. Hierarchical Clustering
The hierarchical clustering method showed a greater similarity between the control group and the group treated with cold water, while the group treated with hot water separated more significantly (Figure 4). This was in accordance with the results of the PCA shown on Figure 3, and shows that the hot-water treatment changed the broccoli more significantly than the cold-water treatment, based on the measured variables.
## 2.8.3. Pearson’s Correlation Coefficients
The values of Pearson’s correlation coefficients between phytochemical and oxidative stress parameters and biological activity of extracts are shown in Table 5. A very high positive correlation was noticeable between the antioxidant activity measured by the ABTS and DPPH methods with total phenolics and tannins in broccoli. Similarly, as in our work, a positive correlation between antioxidant properties and phenolic content had already been recorded for lignin samples isolated from energy crops and agro-industrial byproducts [95].
Hydrogen peroxide levels almost $100\%$ positively correlated with total phenolic acids, and also very highly positively correlated with ferulic acid. On the contrary, almost $100\%$ negatively correlated with total glucosinolates and very highly negatively with total hydroxycinnamic acids and kaempferol. Proline almost $100\%$ positively correlated with total proanthocyanins and soluble sugars, and also very highly positively with total flavonoids, chlorophylls, lycopene, porphyrines, vitamin C, ferulic acid and quercetin. On the other hand, it correlated very highly negatively with total flavonols, hydroxycinnamic acids, carotenoids, sinapic acid and kaempferol. Malondialdehyde almost $100\%$ positively correlated with total proanthocyanidins, proline and vitamin C, and also very highly positively with total flavonoids, soluble sugars, chlorophylls, lycopene, porphyrins, ferulic acid and quercetin. Almost $100\%$ negatively correlated with total flavonols, and very highly negatively with total hydroxycinnamic acids, carotenoids, sinapic acid and kaempferol.
Regarding the ability to inhibit α-amylase enzyme activity, we recorded a very high positive correlation with total glucosinolates.
Inhibition of α-glucosidase activity very highly positively correlated with the highest number of measured parameters, with total flavonoids, proanthocyanins, soluble sugars, photosynthetic pigments, vitamin C, p-coumaric and ferulic acid, quercetin, proline and malondialdehyde.
Inhibition of lipase enzyme activity correlated almost $100\%$ positively with total flavonols in broccoli. Likewise, the inhibition of this enzyme correlated very highly positively with total hydroxycinnamic acids and carotenoids, sinapic acid and kaempferol.
## 3.1. Plant Material
Broccoli seeds (*Brassica oleracea* L. convar. botrytis (L.) Alef. var. cymosa Duch.) were purchased from International Seeds Processing (ISP) GmbH (Quedlinburg, Germany) and plants were grown on a sterile substrate in pots to the stage with 2 true leaves. After that, the plants were subjected to ice water treatment (10 pieces of ice cubes, each with diameter of 2 cm, were applied daily to the substrate in the pots in which the broccoli were grown and were left until they melted) and hot-water treatment (the substrate was watered daily with the same volume of water at a temperature of 80 °C) until the stage of development with 6–8 true leaves, which was 12–14 days. The control group of plants was treated with the same volume of water at room temperature. After collecting the aerial part of a plant, it was immediately frozen in liquid nitrogen and then lyophilized. After lyophilization, the plant material was homogenized to a powder level and extracts of different concentrations were prepared in different solvents, depending on the analysis method; concentrations and solvents are listed later in each of the methods. The material included three biological replicates and three technical replicates were weighed from each biological replicate.
## 3.2. Determination of the Proportion of Total Phenols
To determine the proportion of total phenolic compounds, we prepared extracts with a concentration of 20 mg/mL in $70\%$ ethanol and applied a method with Folin–Ciocalteu reagent [96]. The absorbance was measured on an optical microplate reader (Fluostar Optima) at a wavelength of 765 nm. Gallic acid concentration in the range of 0.5–2.5 mg/mL was used to prepare the calibration line. The results are presented as milligrams of gallic acid equivalents per gram of dry weight (mg GAE/g dw) of the sample.
## 3.3. Determination of the Proportion of Total Flavonoids
The content of total flavonoids was determined according to Zhishen et al. [ 1999] [97], with slight modifications. To 100 μL of extract, a volume of 400 μL of deionized water and 30 μL of NaNO2 ($5\%$) was added. After 5 min incubation at room temperature, a volume of 30 μL of AlCl3 ($10\%$) was added and the mixture was incubated at room temperature for an additional 6 min. Then, a volume of 200 μL of NaOH (1M) was added, and made up to a volume of 1 milliliter with deionized water. The absorbance of the reaction mixture was read at 520 nm. A quercetin solution with a concentration in the range of 0.00625–0.5 mg/mL was used to prepare the calibration line. The results are presented as milligrams of quercetin equivalents per gram of dry weight (mg QE/g dw) of the sample.
## 3.4. Determination of the Proportion of Total Proanthocyanins
Extracts with a concentration of 20 mg/mL were prepared in $70\%$ ethanol and analyzed for the presence of total proanthocyanins as previously described [49]. The absorbance was measured on a microplate reader at a wavelength of 500 nm. A catechin solution with a concentration in the range of 0.025–1 mg/mL was used to prepare the calibration line. The results are presented as milligrams of catechin equivalents per gram of dry weight (mg CatE/g dw) of the sample.
## 3.5. Determination of the Proportion of Total Tannins
Total tannins were analyzed as in our previous work [49]. The absorbance was measured on a Nanodrop 2000c spectrophotometer at a wavelength of 700 nm. Catechin solutions with concentrations in the range of 0.00625–0.5 mg/mL were used to prepare the calibration line. The results are presented as milligrams of catechin equivalents per gram of dry weight (mg CatE/g dw) of the sample.
## 3.6. Determination of the Proportion of Total Phenolic Acids
Total phenolic acids were analyzed according to a previously published method [98]. Absorbance was measured on a microplate reader at wavelengths of 495 nm. Caffeic acid concentration in the range of 0.025–1 mg/mL was used to prepare the calibration line. The results are presented as milligrams of caffeic acid equivalents per gram of dry weight (mg CAE/g dw) of the sample.
## 3.7. Determination of the Proportion of Total Hydroxycinnamic Acids and Flavonols
Total hydroxycinnamic acids and flavonols were analyzed as in our previous work [96]. Absorbance was measured on a Nanodrop 2000c spectrophotometer at wavelengths of 320 nm for hydroxycinnamic acids and 360 nm for flavonols. A solution of caffeic acid with a concentration in the range of 0.05–0.7 mg/mL for hydroxycinnamic acids, and a solution of quercetin with a concentration of 0.00626–0.1 mg/mL for flavonols was used to prepare the calibration line. The results are presented as milligrams of caffeic acid equivalents per gram of dry weight (mg CAE/g dw) of the sample for hydroxycinnamic acids and as milligrams of quercetin equivalents per gram of dry weight (mg QE/g dw) of the sample for flavonols.
## 3.8. Determination of the Proportion of Soluble Sugars
Soluble sugars were determined as in our previous work [49]. Absorbance was measured on a microplate reader at wavelengths of 485 nm. Sucrose solution with a concentration in the range of 0.0390625–10 mg/mL was used to prepare the calibration line. The results are presented as milligrams of sucrose equivalents per gram of dry weight (mg SucE/g dw) of the sample.
## 3.9. Determination of the Proportion of Total Intact Glucosinolates
To determine the proportion of total intact glucosinolates, we prepared the extracts in hot $70\%$ methanol and applied the already described method [99]. The absorbance was measured on a microplate reader at a wavelength of 425 nm. An aqueous solution of sinigrin in the concentration range of 0.01–0.10 mg/mL was used as a standard. The results are presented as milligrams of sinigrin equivalents per gram of dry weight (mg SinE/g dw) of the sample.
## 3.10. Determination of Parameters of Oxidative Stress
For the estimation of the level of lipid peroxidation, the method by Linić et al. [ 2021] was used [100]. Color intensity was quantified by measuring the absorbance at 532 and 600 nm on a MultiSkan SkyHigh Microplate Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). MDA content in the samples was calculated based on the molar extinction coefficient of 155 mM−1 cm−1 adapted for the measurement using the microplate reader and expressed as ng of MDA per g of dry weight (dw).
Hydrogen peroxide (H2O2) content was determined as described in Junglee et al. [ 2014] [101]. The color intensity was quantified by measuring the absorbance at 390 nm on a MultiSkan SkyHigh Microplate Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Hydrogen peroxide content in the samples was calculated indirectly based on the calibration curve of standard H2O2 solutions of known concentrations (2475.27–61.91 μM). The results were expressed as ng of H2O2 per g of dw.
Proline content was evaluated using a method according to Ljubej et al. [ 2021] [47]. The color intensity was quantified by measuring the absorbance at 520 nm on a FLUOstar Optima microplate reader (BMG LABTECH, Ortenberg, Germany). The proline content in the samples was calculated indirectly based on the calibration curve of standard L-proline solutions of known concentrations (1.25–0.009 mg/mL). The results were expressed as mg of L-proline per g of dw.
## 3.11. Determination of the Proportion of Chlorophyll, Carotenoids and Porphyrins
The concentration of pigments was determined according to the method of Sumanta et al. [ 2014] [102]. We prepared an extract with a concentration of 15 mg/mL in $80\%$ acetone. The mixture was mixed on a vortex mixer and then centrifuged at a temperature of 4 °C at 13,000× g rpm for 5 min. We separated the supernatant in a test tube, and the precipitate was extracted two more times with $80\%$ acetone. The absorbances on a spectrophotometer (Thermo Scientific Nanodrop 2000c) at wavelengths of 470, 575, 590, 628, 647 and 663 nm were measured.
## 3.12. In Vitro Simulated Human Digestion of Extracts
The in vitro model of human digestion was performed as in our previous work (Šola et al., 2020a) [27]. The model mimics three phases of digestive process in humans; namely, mouth, stomach and small intestine. After digestion, samples were centrifuged at 11,000× g rpm for 10 min at 4 °C and supernatants were stored at −20 °C until further analyses.
## 3.13. Separation, Identification and Quantification of Individual Compounds by the HPLC Method
Separation, identification and quantification of individual compounds was carried out on an Agilent 1100 Series device with a UV/VIS detector, non-polar Poroshell 120 SB-C18 column 4.6 × 75 mm with a particle size of 2.7 μm, and pre-column Zorbax Rx-C18 4.6 × 12.5 mm with a particle size of 5 μm. Mobile phase A was $0.2\%$ acetic acid (acetic acid:H2O; 0.2:99.8; v/v), and mobile phase B was $0.2\%$ acetic acid and $80\%$ methanol (acetic acid: MeOH:H2O; 0.2:80:19.8; v/v). The solvent gradient profile was as follows: at 0 min = $\frac{100}{0}$, at 42 min = $\frac{20}{80}$, at 43 min = $\frac{0}{100}$, at 45 min = $\frac{0}{100}$, at 45.1 min = $\frac{100}{0.}$ The flow rate was 1 mL/min and the injected volume of the samples was 25 μL. Flavonoids were analyzed at a wavelength of 360 nm, phenolic acids at 310 nm and L-ascorbic acid at 254 nm. We identified the compounds by comparing the retention times of the peaks obtained from the analysis of the extracts with the retention times of the peaks obtained from the analysis of the standards. Quantification of the compounds was carried out using the calibration curves of the corresponding standards.
## 3.14. Determination of Antioxidant Capacity
The antioxidant capacity was determined using three standard methods, ABTS, FRAP and DPPH, adapted to small volumes, as described in Šola et al. ( 2020b) [96]. An aqueous solution of Trolox in the concentration range of 0.04–0.60 mg/mL was used to create the calibration line. The results are presented as equivalents of Trolox.
## 3.15. Determination of the Effect of Broccoli Extracts on α-Amylase, α-Glucosidase and Lipase Enzyme Activity
The inhibition of α-amylase enzyme activity was measured as in Šola et al. [ 2022] [96]. An aqueous solution of acarbose with a concentration of 20 mg/mL was used as a positive control. The solvent in which the extract was prepared, $70\%$ ethanol, was used as a negative control. Enzyme inhibitory activity was calculated from the equation: % inhibition = 100 − [(At − Atb)/(Ac − Acb)) × 100], where At was the absorbance of the test (with amylase), Atb was the absorbance of test blank (without amylase), Ac was the absorbance of control (with amylase) and Acb was the absorbance of control blank (without amylase).
The inhibition of α-glucosidase was determined using the pre-incubation method as described in Rusak et al. [ 2021] [103]. An aqueous solution of acarbose with a concentration of 20 mg/mL was used as a positive control. The solvent in which the extract was prepared, $70\%$ ethanol, was used as a negative control. Enzyme inhibitory activity was calculated as for the α-amylase.
Pancreatic lipase inhibition assay was carried out according to Spínola et al. [ 2019] [104]. An ethanolic solution of orlistat with a concentration of 20 mg/mL was used as a positive control. The solvent in which the extract was prepared, $70\%$ ethanol, was used as a negative control. Enzyme inhibitory activity was calculated as for the α-amylase.
All the absorbance measurements were performed with microplate reader Fluostar Optima (BMG Labtech GmbH, Offenburg, Germany).
## 3.16. Statistical Data Processing
Data were statistically processed using the computer software STATISTICA 12.0 (Stat Soft INC., Tulsa, OK, USA). The comparison was performed using one-way analysis of variance (ANOVA) and the application of a post hoc test of multiple comparisons (Duncan’s New Multiple Range Test, DNMRT). Values that differ at the p ≤ 0.05 level were considered statistically significant. Principal component analysis (PCA) was used to visualize the relationship between the samples, as well as the measured parameters. For additional visualization of the grouping of individual investigated groups based on the measured parameters, the hierarchical clustering (HC) method was used, which uses the Euclidean distance as a measure of similarity or dissimilarity between samples. To analyze the correlation between the measured parameters, the values of the Pearson’s correlation coefficients were calculated. Correlation coefficient values of 0.60–0.79 indicate a high degree of correlation, and correlation coefficient values of 0.80–1.00 indicate a very high degree of correlation [105].
## 4. Conclusions
Hot- and cold-water treatment specifically affected young broccoli. Hot water changed more variables ($72\%$) of young broccoli than cold water ($24\%$) treatment. Among the changed variables upon hot-water treatment, $62\%$ increased and $38\%$ decreased, while upon cold-water stress, $43\%$ were increased and $57\%$ decreased. The effect on total glucosinolates was exactly the opposite; hot water significantly decreased the proportion of total glucosinolates, while cold water increased it. Hot water also increased the concentration of total proanthocyanins, vitamin C, hydrogen peroxide, proline, malondialdehyde, chlorophyll b, lycopene and porphyrins in young broccoli. On the other hand, it lowered the concentration of carotenoids. Sinapic acid in broccoli was extremely sensitive to stress caused by hot water and its concentration decreased almost by half. Kaempferol and quercetin were oppositely affected by hot water; the first was decreased, while the latter increased. Total glucosinolates and soluble sugars were affected by hot and cold water in an opposite way; hot water decreased glucosinolates and increased soluble sugars, while cold water increased glucosinolates and decreased soluble sugars. Therefore, glucosinolates and soluble sugars could be used as biomarkers of hot/cold-water stress in broccoli. Hot-water-stressed broccoli presented a better source of p-coumaric and ferulic acid both before and after in vitro simulated human digestion system than broccoli treated with room-temperature water. Regarding the effect on biological activity of broccoli extracts, those of hot-water-stressed plants were significantly more efficient in the inhibition of α-glucosidase, while those of cold-water-stressed broccoli were more efficient in the inhibition of α-amylase than control plants. Among the bioactive compounds and oxidative stress parameters, the variable that was most significantly affected was proline, which was 2.5 times higher after hot-water treatment. Among the variables presenting biological activity of the extract, the most significantly affected was α-amylase inhibition; with cold-water treatment, it was 1.5 times more inhibited than with the extract of the control group. Furthermore, detailed research into the possibility of broccoli adaptation to temperature stress is highly crucial.
## References
1. Singh V.. *Environmental Plant Physiology: Botanical Strategies for a Climate Smart Planet* (2020)
2. He M., He C.-Q., Ding N.-Z.. **Abiotic stresses: General defenses of land plants and chances for engineering multistres tolerance**. *Front. Plant Sci.* (2018) **9** 1771. DOI: 10.3389/fpls.2018.01771
3. Cramer G.R., Urano K., Delrot S., Pezzotti M., Shinozaki K.. **Effects of abiotic stress on plants: A systems biology perspective**. *BMC Plant Biol.* (2011) **11**. DOI: 10.1186/1471-2229-11-163
4. Knight M.R., Knight H.. **Low-temperature perception leading to gene expression and cold tolerance in higher plants**. *New Phytol.* (2012) **195** 737-751. DOI: 10.1111/j.1469-8137.2012.04239.x
5. Prinsloo G., Nogemane N.. **The effects of season and water availability on chemical composition, secondary metabolites and biological activity in plants**. *Phytochem. Rev.* (2018) **17** 889-902. DOI: 10.1007/s11101-018-9567-z
6. Farooq M.A., Kaya C., Riaz A., Farooq M., Nawaz I., Wilkes A., Li Y.. **Potential mechanisms of abiotic stress tolerance in crop plants induced by thiourea**. *Front. Plant Sci.* (2019) **10** 1336. PMID: 31736993
7. Hasanuzzaman M., Nahar K., Alam M.M., Roychowdhury R., Fujita M.. **Physiological, biochemical and molecular mechanisms of heat stress tolerance in plants**. *Int. J. Mol. Sci.* (2013) **14**. DOI: 10.3390/ijms14059643
8. Suwa R., Hakata H., Hara H., El-Shemy H.A., Adu-Gyamfi J.J., Nguyen N.T., Fujita K.. **High temperature effects on photosynthate partitioning and sugar metabolism during ear expansion in maize (Zea mays L.) genotypes**. *Plant Physiol. Biochem.* (2010) **48** 124-130. DOI: 10.1016/j.plaphy.2009.12.010
9. Gunawardena K., Kumarendran B., Ebenezer R., Gunasingha M.S., Pathmeswaran A., De Silva N.. **Soil-transmitted hel-minth infections among plantation sector schoolchildren in Sri Lanka: Prevalence after ten years of preventive chemo-therapy**. *PLoS Negl. Trop. Dis.* (2011) **9**
10. Edreira J.I.R., Otegui M.E.. **Heat stress in temperate and tropical maize hybrids: Differences in crop growth, biomass partitioning and reserves use**. *Field Crop. Res.* (2012) **130** 87-98. DOI: 10.1016/j.fcr.2012.02.009
11. Krasensky J., Jonak C.. **Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory net-works**. *J. Exp. Bot.* (2012) **4** 1593-1608. DOI: 10.1093/jxb/err460
12. Semenov M.A., Halford N.G.. **Identifying target traits and molecular mechanisms for wheat breeding under a changing climate**. *J. Exp. Bot.* (2009) **10** 2791-2804. DOI: 10.1093/jxb/erp164
13. Hama J.R., Omer R.A., Rashid R.S.M., Mohammad N.E.A.M., Thoss V.. **The diversity of phenolic compounds along defatted kernel, green husk and leaves of walnut (Juglans regia L.)**. *Anal. Chem. Lett.* (2016) **6** 35-46. DOI: 10.1080/22297928.2016.1152912
14. Chen S.S., Spiro M.. **Study of microwave extraction of essential oil constituents from plant materials**. *J. Microw. Power Electromagn. Energy* (1994) **4** 231-241. DOI: 10.1080/08327823.1994.11688251
15. Los D.A., Mironov K.S., Allakhverdiev S.I.. **Regulatory role of membrane fluidity in gene expression and physiological functions**. *Photosynth. Res.* (2013) **116** 489-509. DOI: 10.1007/s11120-013-9823-4
16. Rajashekar C.B.. **Cold Response and Freezing Tolerance in Plants**. *Plant Environment Interactions* (2000) 321-341
17. De Palma M., Grillo S., Massarelli I., Costa A., Balogh G., Vigh L., Leone A.. **Regulation of desaturase gene expression, changes in membrane lipid composition and freezing tolerance in potato plants**. *Mol. Breed.* (2008) **21** 15-26. DOI: 10.1007/s11032-007-9105-y
18. Janská A., Maršík P., Zelenková S., Ovesná J.. **Cold stress and acclimation—What is important for metabolic adjustment?**. *Plant Biol.* (2010) **12** 395-405. DOI: 10.1111/j.1438-8677.2009.00299.x
19. Yordanova R., Popova L.. **Effect of exogenous treatment with salicylic acid on photosynthetic activity and antioxidant capacity of chilled wheat plants**. *Gen. Appl. Plant Physiol.* (2007) **33** 155-170
20. Hassan M.A., Xiang C., Farooq M., Muhammad N., Yan Z., Yuanyuan K., Bruno A.K., Lele Z., Jincal L.. **Cold stress in wheat: Plant acclimation responses and management strategies**. *Front. Plant Sci.* (2021) **12** 676884. DOI: 10.3389/fpls.2021.676884
21. Paśko P., Tyszka-Czochara M., Galanty A., Gdula-Argasińska J., Żmudzki P., Bartoń H., Zagrodzki P., Gorinstein S.. **Comparative study of predominant phytochemical compounds and proapoptotic potential of broccoli sprouts and florets**. *Plant Foods Hum. Nutr.* (2018) **73** 95-100. DOI: 10.1007/s11130-018-0665-2
22. Razis A.F.A., Noor N.M.. **Cruciferous vegetables: Dietary phytochemicals for cancer prevention**. *Asian Pac. J. Cancer Prev.* (2013) **14** 1565-1570. DOI: 10.7314/APJCP.2013.14.3.1565
23. Bhandari S.R., Kwak J.H.. **Chemical composition and antioxidant activity in different tissues of brassica vegetables**. *Molecules* (2015) **20**. DOI: 10.3390/molecules20011228
24. Bachiega P., Salgado J.M., de Carvalho J.E., Ruiz A.L.T.G., Schwarz K., Tezotto T., Morzelle M.C.. **Antioxidant and an-tiproliferative activities in different maturation stages of broccoli (Brassica oleracea italica) biofortified with selenium**. *Food Chem.* (2016) **190** 771-776. DOI: 10.1016/j.foodchem.2015.06.024
25. Iglesias–Acosta M., Martinez–Bellesta C., Teruel J.A., Carvajal M.. **The response of broccoli plants to high temperature and possible role of root aquaporins**. *Environ. Exp. Bot.* (2010) **68** 83-90. DOI: 10.1016/j.envexpbot.2009.10.007
26. Liebelt D.J., Jordan J.T., Doherty C.J.. **Only a matter of time: The impact of daily and seasonal rhythms on phytochemicals**. *Phytochem. Rev.* (2019) **18** 1409-1433. DOI: 10.1007/s11101-019-09617-z
27. Šola I., Vujčić Bok V., Dujmović M., Rusak G.. **Developmentally-related changes in phenolic and L-ascorbic acid content and antioxidant capacity of Chinese cabbage sprouts**. *Food Sci. Technol.* (2020) **57** 702-712. DOI: 10.1007/s13197-019-04103-y
28. Kumar S., Yadav A., Yadav M., Yadav J.P.. **Effect of climate change on phytochemical diversity, total phenolic content and in vitro antioxidant activity of Aloe vera (L.) Burm. f**. *BMC Res. Notes* (2017) **10** 60. DOI: 10.1186/s13104-017-2385-3
29. Watanabe M., Ayugase J.. **Effect of low temperature on flavonoids, oxygen radical absorbance capacity values, and major components of winter sweet spinach (Spinacia oleracea L.)**. *J. Sci. Food Agric.* (2014) **95** 2095-2104. DOI: 10.1002/jsfa.6925
30. Rauf A., Imran M., Abu-Izneid T., Patel S., Pan X., Naz S., Suleria H.A.R.. **Proanthocyanidins: A comprehensive review**. *Biomed. Pharmacother.* (2019) **116** 108999. DOI: 10.1016/j.biopha.2019.108999
31. Harding S.A.. **Condensed tannins: Arbiters of abiotic stress tolerance?**. *Tree Physiol.* (2019) **39** 341-344. DOI: 10.1093/treephys/tpz005
32. Gourlay G., Hawkins B.J., Albert A., Schnitzler J.-P., Constabel C.P.. **Condensed tannins as antioxidants that protect poplar against oxidative stress from drought and UV-B**. *Plant Cell Environ.* (2022) **45** 362-377. DOI: 10.1111/pce.14242
33. Top S.M., Preston C.M., Dukes J.S., Tharayil N.. **Climate influences the content and chemical composition of foliar tannins in green and senesced tissues of Quercus rubra**. *Front. Plant Sci.* (2017) **8** 217. DOI: 10.3389/fpls.2017.00423
34. Poudel P.R., Koyama K., Goto-Yamamoto N.. **Evaluating the influence of temperature on proanthocyanidin biosynthesis in developing grape berries (Vitis vinifera L.)**. *Mol. Biol. Rep.* (2020) **47** 3501-3510. DOI: 10.1007/s11033-020-05440-4
35. Chen Z., Cuin T.A., Zhou M., Twomey A., Naidu B.P., Shabala S.. **Compatible solute accumulation and stress-mitigating effects in barley genotypes contrasting in their salt tolerance**. *J. Exp. Bot.* (2007) **58** 4245-4255. DOI: 10.1093/jxb/erm284
36. Sami F., Yusuf M., Faizan M., Faraz A., Hayat S.. **Role of sugars under abiotic stress**. *Plant Physiol. Biochem.* (2016) **109** 54-61. DOI: 10.1016/j.plaphy.2016.09.005
37. Rosa M., Prado C., Podazza G., Interdonato R., González J.A., Hilal M., Prado F.E.. **Soluble sugars—Metabolism, sensing and abiotic stress. A complex network in the life of plants**. *Plant Signal. Behav.* (2009) **4** 388-393. DOI: 10.4161/psb.4.5.8294
38. Harsh A., Sharma Y.K., Joshi U., Rampuria S., Singh G., Kumar S., Sharma R.. **Effect of short-term heat stress on total sugars, proline and some antioxidant enzymes in moth bean (Vigna aconitifolia)**. *Ann. Agric. Sci.* (2016) **61** 57-64. DOI: 10.1016/j.aoas.2016.02.001
39. D’Souza M., Devaraj V.R.. **Induction of thermotolerance through heat acclimation in lablab bean (Dolichos lablab)**. *Afr. J. Biotechnol.* (2013) **12** 5695-5704
40. Tarkowski L.P., Van den Ende W.. **Cold tolerance triggered by soluble sugars: A multifaceted countermeasure**. *Front. Plant Sci.* (2015) **6** 203. DOI: 10.3389/fpls.2015.00203
41. Yuanyuan M., Yali Z., Jiang L., Hongbo S.. **Roles of plant soluble sugars and their responses to plant cold stress**. *Afr. J. Biotechnol.* (2009) **8** 2004-2010
42. Becker T.M., Juvik J.A.. **The role of glucosinolate hydrolysis products from Brassica vegetable consumption in inducing antioxidant activity and reducing cancer incidence**. *Diseases* (2016) **4**. DOI: 10.3390/diseases4020022
43. Martínez-Ballesta M.d.C., Moreno D.A., Carvajal M.. **The physiological importance of glucosinolates on plant response to abiotic stress in Brassica**. *Int. J. Mol. Sci.* (2013) **14**. DOI: 10.3390/ijms140611607
44. Zhile L., Kuanhong W., Jing Y., Biao Z., Zhujun Z.. **The importance of glucosinolates on plant response to abiotic stress in Brassicaceae family**. *Acta Hortic. Sin.* (2022) **49** 200-220
45. Oerlemans K., Barrett D.M., Suades C.B., Verkerk R., Dekker M.. **Thermal degradation of glucosinolates in red cabbage**. *Food Chem.* (2006) **95** 19-29. DOI: 10.1016/j.foodchem.2004.12.013
46. Steindal A.L.H., Rødven R., Hansen E., Mølmann J.. **Effects of photoperiod, growth temperature and cold acclimatisation on glucosinolates, sugars and fatty acids in kale**. *Food Chem.* (2015) **174** 44-51. DOI: 10.1016/j.foodchem.2014.10.129
47. Ljubej V., Karalija E., Salopek-Sondi B., Šamec D.. **Effects of short-term exposure to low temperatures on proline, pigments, and phytochemicals level in kale (Brassica oleracea var. acephala)**. *Horticulturae* (2021) **7**. DOI: 10.3390/horticulturae7100341
48. Awasthi R., Bhandari K., Nayyar H.. **Temperature stress and redox homestasis in agricultural crops**. *Front. Environ. Sci.* (2015) **3** 11. DOI: 10.3389/fenvs.2015.00011
49. Šola I., Stić P., Dujmović M., Rusak G.. **Effect of flooding and drought on the content of phenolics, sugars, photosynthetic pigments and vitamin C, and antioxidant potential of young Chinese cabbage**. *Eur. Food Res. Technol.* (2021) **247** 1913-1920. DOI: 10.1007/s00217-021-03759-1
50. Zafar S.A., Hameed A., Khan S., Ashraf M.. **Heat shock induced morpho-physiological response in indica rice (**. *Pak. J. Bot.* (2017) **49** 453-463
51. Kumar Tewari A., Tripathy B.C.. **Temperature-stress-induced impairment of chlorophyll biosynthetic reactions in cucumber and wheat**. *Plant Physiol.* (1998) **17** 851-858. DOI: 10.1104/pp.117.3.851
52. Efeoğlu B., Terzioğlu S.. **Photosynthetic responses of two wheat varieties to high temperature**. *EurAsian J. Biosci.* (2009) **3** 97-106. DOI: 10.5053/ejobios.2009.3.0.13
53. Martinez M.A., Flors V., Heil M., Mauch-Mani B., Pieterse C.M., Pozo M.J., Conrath U.. **Recognizing plant defense priming**. *Trends Plant Sci.* (2016) **10** 818-822. DOI: 10.1016/j.tplants.2016.07.009
54. Ferreira P.S., Victorelli F.D., Fonseca-Santos B., Chorilli M.. **A review of analytical methods for p-coumaric acid in plant-based products, beverages, and biological matrices**. *Crit. Rev. Anal. Chem.* (2019) **49** 21-31. DOI: 10.1080/10408347.2018.1459173
55. Eom S.H., Ahn M.A., Kim E., Lee H.J., Lee J.H., Wi S.H., Hyun T.K.. **Plant response to cold stress: Cold stress changes antioxidant metabolism in heading type Kimchi cabbage (Brassica rapa L. ssp. pekinensis)**. *Antioxidants* (2022) **4**. DOI: 10.3390/antiox11040700
56. Laddomada B., Blanco A., Mita G., D’Amico L., Singh R.P., Ammar K., Guzmán C.. **Drought and heat stress impacts on phenolic acids accumulation in durum wheat cultivars**. *Foods* (2021) **9**. DOI: 10.3390/foods10092142
57. Alhaithloul H.A., Galal F.H., Seufi A.M.. **Effect of extreme temperature changes on phenolic, flavonoid contents and antioxidant activity of tomato seedlings (Solanum lycopersicum L.)**. *Peer J.* (2021) **9** 11193. DOI: 10.7717/peerj.11193
58. Wang J., Yuan B., Huang B.. **Differential heat-induced changes in phenolic acids associated with genotypic variations in heat tolerance for hard fescue**. *Crop Sci.* (2019) **59** 667-674. DOI: 10.2135/cropsci2018.01.0063
59. Król A., Amarowicz R., Weidner S.. **Changes in the composition of phenolic compounds and antioxidant properties of grapevine roots and leaves (Vitis vinifera L.) under continuous of long-term drought stress**. *Acta Physiol. Plant.* (2014) **36** 1491-1499. DOI: 10.1007/s11738-014-1526-8
60. Adisakwattana S., Sookkongwaree K., Roengsumran S., Petsom A., Ngamrojnavanich N., Chavasiri W., Deesamer S., Yibchok-anuna S.. **Structure–activity relationships of trans-cinnamic acid derivatives on α-glucosidase inhibition**. *Bioorganic Med. Chem. Lett.* (2004) **14** 2893-2896. DOI: 10.1016/j.bmcl.2004.03.037
61. Park S.-H., Kim D.-S., Park S.-H., Shin J.-W., Youn S.-W., Park K.-C.. **Inhibitory effect of p-coumaric acid by Rhodiola sachalinensis on melanin synthesis in B16F10 cells**. *Pharmazie* (2008) **63** 290-295. PMID: 18468389
62. Pei K., Ou J., Huang J., Ou S.. **p-Coumaric acid and its conjugates: Dietary sources, pharmacokinetic properties and biological activities**. *J. Sci. Food Agric.* (2016) **96** 2952-2962. DOI: 10.1002/jsfa.7578
63. Šola I., Stipaničev M., Vujčić V., Mitić B., Huđek A., Rusak G.. **Comparative analysis of native Crocus taxa as a great source of flavonoids with high antioxidant activity**. *Plant Foods Hum. Nutr.* (2018) **73** 189-195. DOI: 10.1007/s11130-018-0674-1
64. Chambial S., Dwivedi S., Shukla K.K., John P.J., Sharma P.. **Vitamin C in disease prevention and cure: An overview**. *Indian J. Clin. Biochem.* (2013) **28** 314-328. DOI: 10.1007/s12291-013-0375-3
65. Figueroa-Méndez R., Rivas-Arancibia S.. **Vitamin C in health and disease: Its role in the metabolism of cells and redox state in the brain**. *Front. Physiol.* (2015) **6** 397. DOI: 10.3389/fphys.2015.00397
66. Gegotek A., Skrzydlewska E.. **Antioxidative and anti-Inflammatory activity of ascorbic acid**. *Antioxidants* (2022) **11**. DOI: 10.3390/antiox11101993
67. Chen K., Zhang M., Zhu H., Huang M., Zhu Q., Tang D., Han X., Li J., Sun J., Fu J.. **Ascorbic acid alleviates damage from heat stress in the photosystem II of tall fescue in both the photochemical and thermal phases**. *Front. Plant Sci.* (2017) **8** 1373. DOI: 10.3389/fpls.2017.01373
68. Njoku P.C., Ayuk A.A., Okoye C.V.. **Temperature effects on vitamin C content in citrus fruits**. *Pak. J. Nutr.* (2011) **10** 1168-1169. DOI: 10.3923/pjn.2011.1168.1169
69. Cuvas-Limon R.B., Ferreira-Santos P., Cruz M., Teixeira J.A., Belmares R., Nobre C.. **Effect of gastrointestinal digestion on the bioaccessibility of phenolic compounds and antioxidant activity of fermented Aloe vera juices**. *Antioxidants* (2022) **11**. DOI: 10.3390/antiox11122479
70. Zhang Q., Xing B., Sun M., Zhou B., Ren G., Qin P.. **Changes in bio-accessibility, polyphenol profile and antioxidants of quinoa and djulis sprouts during in vitro simulated gastrointestinal digestion**. *Food Sci. Nutr.* (2020) **8** 4232-4241. DOI: 10.1002/fsn3.1718
71. Suzuki N., Mittler R.. **Reactive oxygen species and temperature stresses: A delicate balance between signaling and destruction**. *Physiol. Plant.* (2006) **126** 45-51. DOI: 10.1111/j.0031-9317.2005.00582.x
72. Devireddy A.R., Tschaplinski T.J., Tuskan G.A., Muchero W., Chen J.-G.. **Role of reactive oxygen species and hormones in plant responses to temperature changes**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22168843
73. Pucciariello C., Banti V., Perata P.. **ROS signaling as common element in low oxygen and heat stresses**. *Plant Physiol. Biochem.* (2012) **59** 3-10. DOI: 10.1016/j.plaphy.2012.02.016
74. Sachdev S., Ansari S.A., Ansari M.I., Fujita M., Hasanuzzaman M.. **Abiotic stress and reactive oxygen species: Generation, signaling, and defense mechanisms**. *Antioxidants* (2021) **10**. DOI: 10.3390/antiox10020277
75. Shah P., Modi H.A.. **Comparative study of DPPH, ABTS and FRAP assays for determination of antioxidant activity**. *Int. J. Res. Appl. Sci. Eng. Technol.* (2015) **3** 2321-9653
76. Floegel A., Kim D.O., Chung S.J., Koo S.I., Chun O.K.. **Comparison of ABTS/DPPH assays to measure antioxidant capacity in popular antioxidant-rich US foods**. *J. Food Compos. Anal.* (2011) **24** 1043-1048. DOI: 10.1016/j.jfca.2011.01.008
77. Soengas P., Rodríguez V.M., Velasco P., Cartea M.E.. **Effect of temperature stress on antioxidant defenses in Brassica oleracea**. *ACS Omega* (2019) **5** 5237-5243. DOI: 10.1021/acsomega.8b00242
78. Mugaranja K.P., Kulal A.. **Investigation of effective natural inhibitors for starch hydrolysing enzymes from Simaroubaceae plants by molecular docking analysis and comparison with in-vitro studies**. *Heliyon* (2022) **8** e09360. DOI: 10.1016/j.heliyon.2022.e09360
79. Kicel A., Magiera A., Skrzywanek M., Malczuk M., Olszewska M.A.. **The inhibition of α-glucosidase, α-amylase and protein glycation by phenolic extracts of Cotoneaster bullatus, Cotoneaster zabelii, and Cotoneaster integerrimus leaves and fruits: Focus on anti-hyperglycemic activity and kinetic parameters**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27207081
80. Gironés-Vilaplana A., Baenas N., Villaño D., Speisky H., Garcia-Viguera C., Moreno A.A.. **Evaluation of Latin-American fruits rich in phytochemicals with biological effects**. *J. Funct. Foods* (2014) **7** 599-608. DOI: 10.1016/j.jff.2013.12.025
81. Podsędek A., Majewska I., Redzynia M., Sosnowska D., Koziołkiewicz M.. **In vitro inhibitory effect on digestive enzymes and antioxidant potential of commonly consumed fruits**. *J. Agric. Food Chem.* (2014) **62** 4610-4617. DOI: 10.1021/jf5008264
82. Zakłos-Szyda M., Majewska I., Redzynia M., Koziołkiewicz M.. **Antidiabetic effect of polyphenolic extracts from selected edible plants as α-amylase, α-glucosidase and PTP1B Inhibitors, and β pancreatic cells cytoprotective agents—A comparative study**. *Curr. Top. Med. Chem.* (2015) **15** 2431-2444. DOI: 10.2174/1568026615666150619143051
83. Kawada Y., Miura M., Gomyo T.. **Inhibitory effect of vegetables, fruits and herbs on α-glucosidase in an immobilized enzyme assay system**. *Food Sci. Technol.* (2006) **12** 275-277. DOI: 10.3136/fstr.12.275
84. Fabroni S., Ballistreri G., Amenta M., Romeo F.V., Rapisarda P.. **Screening of the anthocyanin profile and in vitro pancreatic lipase inhibition by anthocyanin-containing extracts of fruits, vegetables, legumes and cereals**. *J. Sci. Food Agric.* (2016) **14** 4713-4723. DOI: 10.1002/jsfa.7708
85. de Souza P.M., de Sales P.M., Simeoni L.A., Silva E.C., Silveira D., Magalhães P.D.. **Inhibitory activity of α-amylase and α-glucosidase by plant extracts from the Brazilian Cerrado**. *Planta Med.* (2012) **78** 393-399. DOI: 10.1055/s-0031-1280404
86. Sales P.M., Souza P.M., Simeoni L.A., Silveira D.. **α-Amylase inhibitors: A review of raw material and isolated compounds from plant source**. *J. Pharm. Sci.* (2012) **15** 141-183. DOI: 10.18433/J35S3K
87. Tundis R., Loizzo M.R., Menichini F.. **Natural products as alpha-amylase and alpha-glucosidase inhibitors and their hypoglycaemic potential in the treatment of diabetes: An update**. *Mini-Rev. Med. Chem.* (2010) **10** 315-331. DOI: 10.2174/138955710791331007
88. Baenas N., Piegholdt S., Schloesser A., Moreno D.A., García-Viguera C., Rimbach G., Wagner A.E.. **Metabolic activity of radish sprouts derived isothiocyanates in Drosophila melanogaster**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17020251
89. Mollica A., Stefanucci A., Zengin G., Locatelli M., Macedonio G., Orlando G., Ferrante C., Menghini L., Recinella L., Leone S.. **Polyphenolic composition, enzyme inhibitory effects ex-vivo and in-vivo studies on two Brassicaceae of north-central Italy**. *Biomed. Pharmacother.* (2018) **107** 129-138. DOI: 10.1016/j.biopha.2018.07.169
90. Assefa S.T., Yang E.Y., Chae S.Y., Song M., Lee J., Cho M.C., Jang S.. **Alpha glucosidase inhibitory activities of plants with focus on common vegetables**. *Plants* (2020) **9**. DOI: 10.3390/plants9010002
91. Akil L., Ahmad H.A.. **Relationships between obesity and cardiovascular diseases in four southern states and Colorado**. *J. Health Care Poor Underserved* (2017) **22** 61-72. DOI: 10.1353/hpu.2011.0166
92. Ballinger A., Peikin S.. **Orlistat: Its current status as an anti-obesity drug**. *Eur. J. Pharmacol.* (2002) **440** 109-117. DOI: 10.1016/S0014-2999(02)01422-X
93. Seyedan A., Alshawsh M.A., Alshagga M.A., Koosha S., Mohamed Z.. **Medicinal plants and their inhibitory activities against pancreatic lipase: A review. Evid.-Based Complement**. *Altern. Med.* (2015) **2015** 973143
94. Elsbaey M., Amen Y., Nakagawa T., Shimizu K.. **White poplar: Targeted isolation of pancreatic lipase inhibitors**. *Ind. Crop. Prod.* (2019) **141** 111778. DOI: 10.1016/j.indcrop.2019.111778
95. Verrillo M., Savy D., Cangemi S., Savarese C., Cozzolino V., Piccolo A.. **Valorization of lignins from energy crops and agro-industrial byproducts as antioxidant and antibacterial materials**. *J. Sci. Food Agric.* (2021) **102** 2885-2892. DOI: 10.1002/jsfa.11629
96. Šola I., Vujčić Bok V., Pinterić M., Auer S., Ludwig-Müller J., Rusak G.. **Improving the phytochemical profile and bi-oactivity of Chinese cabbage sprouts by interspecific transfer of metabolites**. *Food Res. Int.* (2020) **137** 109726. DOI: 10.1016/j.foodres.2020.109726
97. Zhishen J., Mengcheng T., Jianming W.. **The determination of flavonoid contents in mulberry and their scavenging effects on superoxide radicals**. *Food Chem.* (1999) **64** 555-559. DOI: 10.1016/S0308-8146(98)00102-2
98. Šamec D., Bogović M., Vincek D., Martinčić J., Salopek-Sondi B.. **Assessing the authenticity of the white cabbage (Brassica oleracea var. capitata f. alba) cv. ‘Varaždinski’ by molecular and phytochemical markers**. *Food Res. Int.* (2014) **60** 266-272. DOI: 10.1016/j.foodres.2013.07.015
99. Mawlong I., Sujith Kumar M.S., Gurung B., Singh K.H., Singh D.. **A simple spectrophotometric method for estimating total glucosinolates in mustard de-oiled cake**. *Int. J. Food Prop.* (2017) **20** 3274-3281. DOI: 10.1080/10942912.2017.1286353
100. Linić I., Mlinarić S., Brkljačić L., Pavlović I., Smolko A., Salopek-Sondi B.. **Ferulic acid and salicylic acid foliar treatments reduce short-term salt stress in chinese cabbage by increasing phenolic compounds accumulation and photosynthetic performance**. *Plants* (2021) **11**. DOI: 10.3390/plants10112346
101. Junglee S., Urban L., Sallanon H., Lopez-Lauri F.. **Optimized assay for hydrogen peroxide determination in plant tissue using potassium iodide**. *Am. J. Anal. Chem.* (2014) **5** 730-736. DOI: 10.4236/ajac.2014.511081
102. Sumanta N., Haque C.I., Nishika J., Suprakash R.. **Spectrophotometric analysis of chlorophills and carotenoids from commonly grown Fren species by using various extracting solvents**. *Res. J. Chem. Sci.* (2014) **4** 63-69
103. Rusak G., Šola I., Vujčić Bok V.. **Matcha and Sencha green tea extracts with regard to their phenolics pattern and antioxidant and antidiabetic activity during in vitro digestion**. *J. Food Sci. Technol.* (2021) **58** 3568-3578. DOI: 10.1007/s13197-021-05086-5
104. Spínola V., Llorent-Martínez E.J., Castilho P.C.. **Inhibition of α-amylase, α-glucosidase and pancreatic lipase by phenolic compounds of Rumex maderensis (Madeira sorrel). Influence of simulated gastrointestinal digestion on hyper-glycaemia-related damage linked with aldose reductase activity and protein glycation**. *LWT* (2020) **118** 108727
105. Wuensch K.L., Evans J.D.. **Straightforward statistics for the behavioral sciences**. *J. Am. Stat. Assoc.* (1996) **91** 1750. DOI: 10.2307/2291607
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