Many factors can lead to an increase in the prevalence of metabolic syndrome (MetS) in different populations. Using an advanced structural equation model (SEM), this study is aimed to determine the most important risk factors of MetS, as a continuous latent variable, using a large number of males and females. We also aimed to evaluate the interrelations among the associated factors involved in the development of MetS. This study used data derived from the Fasa PERSIAN cohort study, a branch of the PERSIAN cohort study, for participants aged 35 to 70 years with 10,138 males and females. SEM was used to evaluate the direct and indirect effects, as well as gender effects of influencing factors. Results from the SEM showed that in females most changes in MetS are described by waist circumference (WC), followed by hypertension (HP) and triglyceride (TG), while in males most changes in MetS are described by WC, followed by TG then fasting blood glucose (FBG). Results from the SEM confirmed the gender effects of social status on MetS, mediated by sleep and controlled by age, BMI, ethnicity and physical activity. This study also shows that the integration of TG and WC within genders could be useful as a screening criterion for MetS in our study population.
Metabolic syndrome (MetS) is manifested by insulin resistance, central obesity, dyslipidemia, and hypertension and the diagnosis of MetS is based on the co-occurrence of these risk factors1 The presence of the MetS can significantly increase the risk of atherosclerotic cardiovascular disease (ASCVD) and mortality due to this disease as well as the risk of type 2 diabetes2,3,4. Moreover, MetS is associated with the risk of certain types of cancers, such as pancreatic, colorectal, and breast cancer5,6,7,8.
With the worldwide increasing prevalence of sedentary lifestyle and consequently increasing of obesity9, MetS has become a growing public health challenge, with an estimated prevalence of 10–40%10. In Iran, according to several systematic review and meta-analysis as well as based on different definitions, the overall prevalence of MetS is estimated approximately 33.7% indicating the relatively high prevalence of this disease in the Iranian population11.Although genetic predisposition plays an important role in the regulation of body weight and other metabolic parameters, susceptible individuals are also influenced by physical, social, economic and cultural factors as well12.
MetS is usually considered as a binary variable when assessed according to commonly used definitions. For example, according to the US National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria, the presence of MetS is defined as having three or more abnormal components of MetS; However, discarding information on how close each MetS component was to the threshold might result in large information loss; Therefore, using a method which enables us evaluate MetS as a continuous latent or a construct variable, which considers the combination of the multiple dimensions of MetS seems more appropriate than using a single observed binary variable for MetS. In this regard, we used the advanced Structural Equation Model (SEM) as a theory-driven data analytical approach. SEM is an efficient technique to evaluate the hypothesized simultaneous direct and indirect relationships among a set of variables, including those that cannot be directly measure, as MetS in this study13. Thus, the main purpose of the present study was to use SEM to model MetS risk factors considering the five components as continuous variables that represent the concept of the MetS as a latent continuous variable rather than a binary one we performed the analysis for males and female participants separately to determine which components of MetS are regarded as the most important ones in this Iranian rural population using a large number of males and females. We also evaluated the interrelations among the associated factors involved in the development of MetS employing SEM.
Origin of data
The data used in this study was driven from the baseline survey of the Fasa PERSIAN cohort study (FPCA). Fasa PERSIAN cohort study was a part of the Prospective Epidemiological Research Study in IRAN (PERSIAN) cohort study and was conducted in 2014 as a large epidemiological study on a random sample from the general population14,15. FPCS was conducted according to the Declaration of Helsinki, and the regional and national research ethics committees of Fasa University of Medical Sciences (FUMS) has approved the study protocol (Ethic code IR.FUMS.RES.1394.3). Written informed consent was obtained from all participants before taking part in the study for examination and interview. In addition, the participants were able to withdraw from the study at any time if they wished to do so.
The first phase of PERSIAN population-based cohort was conducted from Oct. 2014 to Sep 2016. A rural region known as Sheshdeh, the suburb of Fasa city and its 24 satellite villages with a total population of 41,000 was chosen for the cohort study and 11,097 adult subjects aged 35 to 70 were recruited into the study.
Metabolic syndrome assessment and Measurements
MetS components, defined by National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III)16 were measured as follows: (1) Waist circumference (WC) was measured in the standing position at the middle between the lower rib and the iliac crest without any pressure to the body surface; (2) Blood pressure (BP) was measured twice after at least 5 min of rest by using right arm in a sitting position. The average of the two measurements was recorded for both systolic and diastolic BP; (3) Fasting blood glucose (FBG); (4) high-density lipoproteins (HDL)- cholesterol (KIMIA Kit, code 890,303, Iran) and triglycerides (TG) were measured by standard method in a central, certified laboratory using Pars Azmoon kits (Pars Azmoon Co., Tehran, Iran). According to joint NCEP ATP III criteria and Iranian-specific cut-off for abdominal obesity17 the cut point for MetS indicator variables were considered as follows (1) WC ≥ 89 cm in males and ≥ 86 cm in females; (2) TG ≥ 150 mg/dL; (3) low HDL-cholesterol (< 40 mg/dL in males and < 50 mg/dL in females); (4) high blood pressure (BP ≥ 130/85 mm Hg) or on antihypertensive medication; and (5) FG ≥ 100 mg/dL or on antidiabetic medication.
Physical activity (PA)
The intensity of physical activity was determined using the International Physical Activity Questionnaire (IPAQ). This questionnaire, consisting of 20 items, can measure routine physical activities of rural Iranians. The amount of each activity in hours and minutes was determined; the total physical activity score was calculated based on formula of MET coefficient of activity duration14.
Sleep duration and quality
Sleep duration was measured as a continuous variable through the subtraction of two questions (“when do you usually go to bed?” and “when do you usually get up in the morning?”). The sleep duration was calculated in hours by subtracting wake-up time from bedtime and then a mean of night sleep duration per 24 h was calculated according to the participants’ answers. Data on sleep quality was collected through a binary variable asking the participants about the presence of any of the sleep disorders listed (use of sleep medication, limb movements during sleep and the difference between sleeping and waking time with the desired time)14.
In this study, the social status of each person was assessed by considering their level of education and employment status. Social status is defined as a latent variable resulting from education and employment status in our model.
A descriptive analysis of the main characteristics of interest was reported as frequencies, means, and standard deviations. Two independent sample test was used to t test for differences in general characteristics across different gender, while, Chi-square test was used to evaluate the distribution of qualitative variables between males and females.
The primary outcome for the current study was evaluating the risk factors affecting MetS using SEM. SEM allowed us to assess multiple relationships across variables and measure MetS which cannot be directly observed. According to previous studies and based on the univariate analyses in the prior steps, 13 observed variables were selected to construct the hypothesized theory-driven model. In our model, the latent continuous variable, MetS, was loaded by incorporating the HP, WC, HDL, TG, and GL. Additionally, the sleep latent variable was represented by sleep duration and sleep quality indicators. Lastly, the social status latent variable was measured by education and employment status indicators. Confirmatory factor analysis (CFA) of MetS, social status, and sleep components was carried out. SEM was used to test the direct and indirect effects social status on MetS, mediated by sleep, controlled by confounding factors including age, BMI, ethnicity and physical activity (MET) (Fig. 1).
Since the MetS is different between males and females, the model was compared in both groups using multiple-group analysis to determine whether the estimates vary by gender. The method of estimation was Maximum Likelihood (ML) based on the covariance matrix. The model’s goodness of fit was examined using seven fit indices including relative chi-square (× 2/df), range 2 to 5, the comparative fit index (CFI) > 0.90, the goodness-of-fit index (GFI) > 0.90, and the root mean square error of approximation (RMSEA) < 0.07. Values for the adjusted goodness-of-fit (AGFI) of ≥ 0.90 and ≥ 0.95 indicate acceptable and good fit, respectively. Values for the Normed-fit index (NIF) of ≥ 0.95 represent good fit, whereas Non-Normed Fit Index (NNFI) values > 0.80 represent good fit18.
Characteristics of subjects
Data was cleaned up and validated. Participation rate was almost 94% and declining to participate was due to unwillingness, lack of confidence and feeling unwell. The response rate was 99%. The analysis included data from 10,138 individuals (5558 (54.8%) females and 4580 (45.2%) males) with mean (SD) age of 48.2 (9.6) years. The average sleep hours were 7.9 ± 4.0 per day from which 6.5 ± 1.7 h (85% of participants) were night sleep. In addition, poor sleep quality was seen in 55.8% of participants.
Demographic, social status and health characteristics of the subjects, for both female and male, were significant differences in all MetS components between males and females (Table 1). Females had a significantly higher mean sleep duration and lower physical activity than males. There were 1337 (29.2%) males with three or more of the five possible MetS components and 2100 (37.8%) females.
Structural equation modelling
The correlation coefficients among the manifest variables for examining the association between associated variables and MetS were estimated in females and males. All of the MetS components were statistically correlated with social status variable, sleep, age, BMI, ethnicity and physical activity.
The CFA of Fig. 1 resulted in a good model fit. The selected indicators loaded significantly on MetS, social status, and sleep components. Most changes in MetS are described by the WC index in both female and male; after WC, HP and TG were the two most important MetS components in females and TG and GL in males. In the male group 57.8% and in the female group 45.3% changes in the MetS are explained by the WC index. Most changes in social status, and sleep components are described by employment status and quality of sleep respectively in both gender (Table 2).
Multiple-group SEM revealed significant differences between males and females in path coefficients (p < 0.05).
In both male and female, the model fits the data well \((\chi 2/df\) = 3.6, CFI = 0.92, GFI = 0.92, RMSEA = 0.060 [0.054–0.67]) (Table 3). Based on the results, social status, controlled by factors such as age, BMI, physical activity, and ethnicity, has a direct negative impact on the occurrence of MetS in both male and female. Additionally, sleep acts as a mediator, with a significant negative effect on MetS which leads to a significant positive indirect effect of social status on the development of MetS (in both male [direct effect = − 0.212, P = 0.014; indirect effect = 0.004, P = 0.002 and total effect = − 0.208, P = 0.011] and female group [direct effect = − 0.625, P = 0.028; indirect effect = 0.004, P = 0.002 and total effect = 0.621, P < 0.001]).
Based on the results, in both male and female, age, ethnicity, and BMI have a significant positive effect on the occurrence of MetS while physical activity shows a significant negative effect. In both groups, BMI has the greatest effect on the occurrence of the MetS (the path coefficient is 0.915 for males and 0.631 for females). The effect of BMI on the incidence of MetS in males is more than females. In males, age, social status, sleep, and physical activity, respectively, have the greatest role in explaining the variance of MetS after BMI. However, in females, social activity, age, sleep, and physical activity, respectively, have the greatest role in explaining the variance of MetS (Table 3).
The main purpose of this study was to use a structural equation model (SEM) to firstly estimate and identify the impact of different MetS components separately for females and males considering MetS as a latent continuous variable rather than a binary variable, and secondly to evaluate the interrelations among the associated factors involved in the development of MetS separately for females and males. Therefore, we created the theory-based path-diagram and used the SEM to test the hypothesized relationships proposed in the theory-based path-diagram.
The prevalence of metabolic syndrome has also been evaluated in this study. This prevalence was 28.3% (32.1% of females and 24.7% of males) that indicated a higher prevalence of MetS in females. The prevalence of MetS has been different in different studies, which might be due to the application of different definitions for this syndrome. In a meta-analysis of 125 Iranian studies (105 cross-sectional and 20 cohort studies) the pooled prevalence of MetS was 27% based on the International Diabetes Federation (IDF) criteria and the ATP III criteria and MetS were more frequent in females (34%) than in males (22%)19; several other studies and meta-analysis11,20,21,22,23,24,25,26 have also reported a higher prevalence of MetS in females. Some studies have also shown that the prevalence of MetS is higher in females after their fifth decade of life and also after menopause22,24,27,28. For example, a large-scale study of 36 cohorts (the MOnica, Risk, Genetics, Archiving and Monograph—MORGAM project) reported the prevalence of MetS increased among adults from age group 19–39 years to 60–78 years two-fold in males and five-fold in females29. Considering the average age of the female population in this study) 48.3 ± 9.6(, the following factors may have an impact on the higher prevalence of metabolic syndrome in female participants of this study. According to the articles hormonal differences between males and females play an important role in the development of metabolic syndrome30,31. According to studies, female estrogen levels decline during menopause, although estrogen has many beneficial effects on the body, including improving insulin sensitivity, reducing inflammation, and promoting the breakdown of fats30,31,32, but a decline in estrogen levels can lead to an increase visceral abdominal fat, which is associated with insulin resistance, elevated free fatty acids, increase in WC which can significantly affect blood preacher and increased hepatic lipase activity33 and also due to physiological changes during this time studies have also shown that while females tend to gain subcutaneous adipose tissue with age regardless of menopausal status, the increase in visceral adipose tissue only occurs after menopause and is associated with a decline in 17β-estradiol34,35 which may increase their risk of developing metabolic syndrome. In addition, considering the rurality of our study area, they may face socio-economic challenges such as lower income levels, higher poverty rates, and limited educational opportunities especially for females that contributing to a higher prevalence of metabolic syndrome.
In contrast, a recently published study of the American population (aged ≥ 20 years), using a US nationally representative sample of 17,048 individuals, showed that the overall prevalence of MetS was 35.1% in males and 34.3% in females; this result showed that MetS prevalence was not significantly different among American males and females36.
Following the main purpose of this study, results from the SEM showed that in females most changes in MetS are described by WC, followed by HP and TG, while in males most changes in MetS are described by WC, followed by TG then GL. The results of this study indicate that 45.3% and 57.8% changes in the MetS are explained by the WC index in females and males respectively. Accordingly, WC has the highest factor loading indicating that WC is the most important component of MetS in both female and male. This could be explained by the fact that WC is closely associated with insulin resistance due to its correlation with visceral fat quantity. In centrally obese adults proinflammatory adipocytokines and excess fatty acids are released into the portal circulation by visceral adipocytes and leads to increasing hepatic steatosis and insulin resistance37. In general, therefore, in a multi-ethnic country like Iran it is necessary to have a country-specific or even ethnic-specific WC cut-off points.
According to the MetS variance explained, another important component of MetS in both female and male was TG. There is evidence indicating the important role of TG in insulin resistance38,39,40; for that reason, a considerable amount of literature has investigated on hypertriglyceridemia waist phenotype as a simple and cost-effective measurement for screening MetS and MetS-related diseases40,41,42.
In this study in both female and male, WC index has the highest impact among the MetS components while in another study focusing on Iranian urban residents, hypertension explained the most changes of MetS43. This different result could be due to the rural lifestyle and environmental conditions in the current study. Urbanization is associated with decreased physical activity44 and higher intake of processed food45, increased risk of chronic disease (type 2 diabetes, hypertension and the MetS)44,46,47,48 and psychosocial stress48,49. As rural areas are less affected by artificial environmental conditions, the MetS components’ factor loadings are more likely to reflect the natural physiological relationships in our recruiting participants.
Results from the SEM confirmed the gender effects of social status on MetS, mediated by sleep and controlled by age, BMI, ethnicity and physical activity. This demonstrated that after controlling for age, BMI, ethnicity and physical activity, individuals (both male and female) with a higher level of social status (those employed with higher education level) were at lower risk for MetS. This association was mediated by sleep and could be explained by the fact that stricter schedule and regularity for sleeping hours in participants who are employed could lead to lower risk of for MetS because sleep–wake cycle and circadian system, which is linked to both sleep and metabolism, is less frequently disturbed in employed participants than unemployed ones50,51.
Current study also demonstrated an inverse correlation between sleep (sleeping hours and sleep quality) with the prevalence of MetS. Several epidemiological studies have investigated the association between sleeping hours and MetS, with contradictory results. Some of them have reported that short sleep duration was significantly associated with the risk of MetS52,53,54,55,56, while conversely several studies showed that long sleep duration was associated with MetS57,58. Furthermore, some studies have reported a U-shaped association between sleep duration and MetS as both short and long sleeping hours were associated with the risk of MetS58,59. A study conducted on the Fasa PERSIAN cohort study reported a correlation between short sleep duration and higher prevalence of MetS components such as CVD, and hypertension and indicated an inverse correlation between sleep duration, age, and BMI60. Several pathophysiologic mechanisms link short sleep duration and MetS. Experimental studies have shown that61,62,63, sleep restriction can reduce leptin and increase ghrelin levels, leading to increased hunger and appetite specifically for carbohydrate-dense food63. The deficiency in melatonin secretion, as in shift-work associated with reductions in blood melatonin levels64 .Recent studies indicates that melatonin has potential benefits for cardiovascular health and metabolic syndrome by influencing on blood pressure regulation, lipid metabolism, and glucose homeostasis64,65. In addition, chronic sleep insufficiency can cause an increase in both in advanced glycation end products (AGEs)66 and pro-inflammatory cytokines, both of which can contribute to an increase in insulin resistance67. In general, human studies has shown the chronic sleep insufficiency can cause an imbalance of adrenalin, cortisol and glucose metabolism and cause increase in appetite, adiposity, weight gain, inflammation and endothelial damage68,69.
One of the notable findings from the present study was that an increased level of BMI leads to an increase in MetS occurrence. Previous studies also have demonstrated similar findings with this study. According to these studies, MetS occurs most commonly in individuals with a BMI higher than 30 kg/m270,71. Obesity has been shown to be a strong predictor of the occurrence of MetS by predisposing to diabetes, hypertension, and hyperlipidemia72,73,74,75. Also, our results demonstrated that men with higher level of BMI are more susceptible to MetS compared to females. This could be due to sex differences in central obesity distribution. females are more prone to peripheral adiposity; central obesity is more common in males33,76. Centrally accumulation of body fat is associated with increased the development of type 2 diabetes and cardiovascular mortality76,77,78. Visceral fat produces free fatty acids and a variety of factors known as adipokines. These adipokines include hormones (e.g. leptin and adiponectin) and inflammatory cytokines (e.g. tumor necrosis factor α (TNFα), interleukin (IL)-6, omentin and visfatin) and other proteins (e.g. plasminogen activator inhibitor (PAI)-1, angiotensinogen, resistin and apelin)79. The inflammatory mediators and FFA is directly delivered to the liver via the portal vein and may contribute to the health problems associated with adiposity such as dyslipidemia, insulin resistance and atherosclerosis79,80,81,82,83. Considering WC as a good surrogate marker of visceral fat accumulation beside higher visceral fat accumulation in males33, could explain our results indicating that 57.8% of MetS changes in males are explained by the WC index while this impact rate is 45.3% in the female’s group.
Current study indicated that individuals with a higher social status (higher education and being employed) were less susceptible to the incidence of MetS. Previous studies have also reported an inverse association between social condition and developing MetS84,85,86,87,88,89. According to those studies, the prevalence of MetS is higher among the unemployed and even housewives, and they also reported an inverse relationship between education level and MetS. These results showed that higher level of education can lead to greater awareness of a healthy lifestyle. Individuals with higher education have also a better economic situation. Higher level of income can lead to an increase in quality of life and prevent the prevalence of MetS risk factors. This contrary relationship between social status and the prevalence of MetS among the Iranian population has also been reported in previous studies90,91. Another finding from this study was that females with lower social status are more susceptible to increase prevalence of MetS compared with males, which was consistent with previous studies92,93. This could be explained by the fact that in rural areas males with lower social status are more likely to have manual occupations which reduces the risk of MetS.
One of the strongest predictors of MetS is age29,94,95. The large increase in the prevalence of MetS in older adults can be explained by a lifetime accumulation of risk factors95,96,97 consistent whit these studies our findings show that MetS risk factors typically develop or worsen with advancing age in both genders, but it influences females more strongly. The present findings seem to be consistent with K. Vishram study using 36 cohorts from the MORGAN-Project with 69,094 participants which reported that aging can increase MetS prevalence by 5 and 2 times in females and males, respectively98. A meta-analysis that was conducted on the Iranian population reported that in the age group of older than 40 years (compared to those under 30) the frequency of the MetS was significantly higher in the female group11. This, gender differences in older females can be due to sex hormone changes and increased blood pressure (BP) in postmenopausal females, which can eventually lead to cardiovascular disease98,99.
Our results indicated that hypertension is the second most important MetS component in females whereas the fourth in males. This could be explained by the fact that with ageing the rate of hypertension increases faster in females than males100. The incidence of hypertension is reported to be higher in postmenopausal females than in premenopausal females101,102. After menopause, the WC also increases, which can significantly affect BP level102. In centrally obese hypertensive females and after menopause accumulated visceral fat is reported to be associated with higher blood pressure levels and insulin resistance103. Several mechanisms have been identified to explain how obesity is associated with hypertension. Renin–angiotensin–aldosterone system (RAAS), the sympathetic nervous system (SNS), endothelial dysfunction, hyperinsulinemia, adipokine imbalance, and increased inflammatory cytokines, increase in intraabdominal pressure, glomerular and tubular effects103,104,105. Some of these mechanisms (e.g. renin angiotensin system) are differently regulated in males and females106. Another possible explanation for stronger impact of hypertension component of MetS in females than males might be that postmenopause females are more salt sensitive in their blood pressure regulation than premenopausal females107.
In addition, our result indicated the effect of ethnicity on the MetS risk factor. Previous studies have examined the prevalence of MetS in different ethnicities and found that this prevalence varies among different ethnicities108,109. Despite differences in diagnostic criteria, age, and gender of subjects, many studies have investigated the prevalence of MetS in different ethnic groups in Iran as well110,111,112,113,114,115,116,117,118,119,120,121,122,123,124. According to these articles, the prevalence of MetS varies among different Iranian ethnic groups. Among these, the highest prevalence of MetS has been reported in studies such as Khorasan Razavi and Bushehr studies125,126. In contrast, the lowest rat e has been reported in Isfahan and Khorasan studies26,123. In general, in a meta-analysis of 69 studies, Kalan Farmanfarma et al. reported that the prevalence of MetS was higher in southern (Boshehr and Hormozgan) and northern (Mazandaran) Iran than elsewhere11. Based on the current study, which was conducted on the population of the Fasa PERSIAN cohort study (including three ethnicities, Persian, Turkish, and Arab), it was found that ethnicity plays an important role in the risk of developing this syndrome. So that risk of metabolic syndrome was higher in Arab and Turkish people compared to Persian ones. Based on local research in this area it was found that the lifestyles and eating habits of the three ethnic groups of Persia and Turkish and Arab are clearly different. Arab and Turkish ethnicities in this region have sweet and high-fat diet as well as high-calorie lifestyles, which can lead to increased risk factors for metabolic syndrome.
Another variable that had a significant relationship with MetS in this study was MET score as a measure for total physical activity. According to our results 62% of the males and 79% of the females had moderate physical activity, and 20% and 37.5% of them had high physical activity respectively. The studies indicated that physical activity is helpful to reduce the MetS risk factors86,127,128,129. But some of them have shown that only a high level of physical activity is associated with a lower risk of MetS130,131. Physical activity can reduce the MetS risk factors such as high blood pressure, low HDL-C, elevated fasting glucose, and abdominal adipose tissue accumulation127,132,133,134,135,136,137. Consistent with these studies, the present study also showed an inverse correlation between MET that the prevalence of MetS, so that with increased physical activity, the prevalence of this syndrome decreases.
This study has some limitations; firstly, the collected data in the field of sleep and physical activity, and social status were based on self-reported by the individuals. Secondly, this study was a cross-sectional study and we used one-time measurement points that adding new follow-up can increase the value of the study. Thirdly Sleep quality measured in this study was not based on global sleep quality protocols and was measured according to three questions (mentioned before) which showed each of these three factors increased the risk of MetS, therefore, the results are not comparable to other studies.
In conclusion, the results of this study, which were performed on a large population of Fasa PERSIAN cohort study using SEM, indicate that WC has the highest factor loading and is identified as the most important component of MetS explaining 45.3% and 57.8% of changes in the MetS in females and males respectively. In general, therefore, in a multi-ethnic country like Iran it is necessary to have a country-specific or even ethnic-specific WC cut-off points. Results from the SEM also showed that in females most changes in MetS are described by WC component, followed by HP and TG, while in males most changes in MetS are described by WC, followed by TG then GL. Therefore, the integration of TG and WC within genders could be useful as a screening criteria for MetS in our study population. Results from the SEM also confirmed the gender effects of social status on MetS, is mediated by sleep and controlled by age, BMI, ethnicity and physical activity. Age, sex, ethnicity, sleep, SES, and physical activity are also found to be associated with the prevalence of MetS.
The datasets used and/or analyzed during the current study available from the corresponding author on request.
Alberti, K. et al. Joint scientific statement. Circulation 120, 1640–1645 (2009).
Hu, G. et al. Prevalence of the metabolic syndrome and its relation to all-cause and cardiovascular mortality in nondiabetic European men and women. Arch. Intern. Med. 164, 1066–1076 (2004).
Mottillo, S. et al. The metabolic syndrome and cardiovascular risk: A systematic review and meta-analysis. J. Am. Coll. Cardiol. 56, 1113–1132 (2010).
Grundy, S., American heart association; national heart, lung, and blood institute. Diagnosis and management of the metabolic syndrome: An American heart association/national heart, lung, and blood institute scientific statement. Circulation 112, 2735–2752 (2005).
Uzunlulu, M., Caklili, O. T. & Oguz, A. Association between metabolic syndrome and cancer. Ann. Nutr. Metab. 68, 173–179 (2016).
Lee, J. et al. The relationship between metabolic syndrome and the incidence of colorectal cancer. Environ. Health Prev. Med. 25, 1–14 (2020).
Park, S. K. et al. Metabolic syndrome, metabolic components, and their relation to the risk of pancreatic cancer. Cancer 126, 1979–1986 (2020).
Wani, B. et al. Metabolic syndrome and breast cancer risk. Indian J. Med. Paediatr. Oncol. Off. J. Indian Soc. Med. Paediatr. Oncol. 38, 434 (2017).
Ng, M. et al. 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 384, 766–781 (2014).
Grundy, S. M. Metabolic syndrome pandemic. Arterioscler. Thromb. Vasc. Biol. 28, 629–636 (2008).
Farmanfarma, K. K. et al. Prevalence of metabolic syndrome in Iran: A meta-analysis of 69 studies. Diabetes Metab. Syndr. 13, 792–799 (2019).
Bianchi, C. et al. Optimizing management of metabolic syndrome to reduce risk: Focus on life-style. Intern. Emerg. Med. 3, 87–98 (2008).
Mueller, R. O. Structural Equation Modeling (Routledge, 2018).
Farjam, M. et al. A cohort study protocol to analyze the predisposing factors to common chronic non-communicable diseases in rural areas: Fasa Cohort Study. BMC Public Health 16, 1–8 (2016).
Poustchi, H. et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): Rationale, objectives, and design. Am. J. Epidemiol. 187, 647–655 (2018).
Lorenzo, C. et al. The National Cholesterol Education Program-Adult Treatment Panel III, International Diabetes Federation, and World Health Organization definitions of the metabolic syndrome as predictors of incident cardiovascular disease and diabetes. Diabetes Care 30, 8–13 (2007).
Gozashti, M. H., Najmeasadat, F., Mohadeseh, S. & Najafipour, H. Determination of most suitable cut off point of waist circumference for diagnosis of metabolic syndrome in Kerman. Diabetes Metab. Syndr. 8, 8–12 (2014).
Hooper, D., Coughlan, J. & Mullen, M. R. Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods 6, 53–60 (2008).
Fatahi, A., Doosti-Irani, A. & Cheraghi, Z. Prevalence and incidence of metabolic syndrome in Iran: A systematic review and meta-analysis. Int. J. Prev. Med. 11, 64. https://doi.org/10.4103/ijpvm.IJPVM_489_18 (2020).
Ambachew, S. et al. The prevalence of metabolic syndrome in ethiopian population: A systematic review and meta-analysis. J. Obes. 2020, 2701309. https://doi.org/10.1155/2020/2701309 (2020).
Mokhayeri, Y., Riahi, S. M., Rahimzadeh, S., Pourhoseingholi, M. A. & Hashemi-Nazari, S. S. Metabolic syndrome prevalence in the Iranian adult’s general population and its trend: A systematic review and meta-analysis of observational studies. Diabetes Metab. Syndr. 12, 441–453. https://doi.org/10.1016/j.dsx.2017.12.023 (2018).
Li, R. et al. Prevalence of metabolic syndrome in Mainland China: A meta-analysis of published studies. BMC Public Health 16, 296. https://doi.org/10.1186/s12889-016-2870-y (2016).
Jaspers Faijer-Westerink, H., Kengne, A. P., Meeks, K. A. C. & Agyemang, C. Prevalence of metabolic syndrome in sub-Saharan Africa: A systematic review and meta-analysis. Nutr. Metab. Cardiovasc. Dis. NMCD 30, 547–565. https://doi.org/10.1016/j.numecd.2019.12.012 (2020).
Krishnamoorthy, Y. et al. Prevalence of metabolic syndrome among adult population in India: A systematic review and meta-analysis. PLoS One 15, e0240971. https://doi.org/10.1371/journal.pone.0240971 (2020).
Chowdhury, M. Z. I. et al. Prevalence of metabolic syndrome in Bangladesh: A systematic review and meta-analysis of the studies. BMC Public Health 18, 308. https://doi.org/10.1186/s12889-018-5209-z (2018).
Mazloomzadeh, S., Khazaghi, Z. R. & Mousavinasab, N. The prevalence of metabolic syndrome in Iran: A systematic review and meta-analysis. Iran. J. Public Health 47, 473 (2018).
Pucci, G. et al. Sex- and gender-related prevalence, cardiovascular risk and therapeutic approach in metabolic syndrome: A review of the literature. Pharmacol. Res. 120, 34–42. https://doi.org/10.1016/j.phrs.2017.03.008 (2017).
Ebtekar, F., Dalvand, S. & Gheshlagh, R. G. The prevalence of metabolic syndrome in postmenopausal women: A systematic review and meta-analysis in Iran. Diabetes Metab. Syndr. 12, 955–960. https://doi.org/10.1016/j.dsx.2018.06.002 (2018).
Vishram, J. K. et al. Impact of age and gender on the prevalence and prognostic importance of the metabolic syndrome and its components in Europeans. The MORGAM Prospective Cohort Project. PloS One 9, e107294 (2014).
Pucci, G. et al. Sex-and gender-related prevalence, cardiovascular risk and therapeutic approach in metabolic syndrome: A review of the literature. Pharmacol. Res. 120, 34–42 (2017).
Chedraui, P. & Pérez-López, F. Metabolic syndrome during female midlife: What are the risks?. Climacteric 22, 127–132 (2019).
Ko, S.-H. & Kim, H.-S. Menopause-associated lipid metabolic disorders and foods beneficial for postmenopausal women. Nutrients 12, 202 (2020).
Pradhan, A. D. Sex differences in the metabolic syndrome: Implications for cardiovascular health in women. Clin. Chem. 60, 44–52 (2014).
Esmailidehaj, M. et al. 17β-Estradiol improves insulin signalling and insulin resistance in the aged female hearts: Role of inflammatory and anti-inflammatory cytokines. Life Sci. 253, 117673 (2020).
Lovejoy, J. C., Champagne, C., De Jonge, L., Xie, H. & Smith, S. Increased visceral fat and decreased energy expenditure during the menopausal transition. Int. J. Obes. 32, 949–958 (2008).
Hirode, G. & Wong, R. J. Trends in the prevalence of metabolic syndrome in the United States, 2011–2016. JAMA 323, 2526–2528. https://doi.org/10.1001/jama.2020.4501 (2020).
Kojta, I., Chacińska, M. & Błachnio-Zabielska, A. Obesity, bioactive lipids, and adipose tissue inflammation in insulin resistance. Nutrients 12, 1305 (2020).
Kahn, D. et al. Subcellular localisation and composition of intramuscular triacylglycerol influence insulin sensitivity in humans. Diabetologia 64, 168–180 (2021).
Pan, D. et al. Skeletal muscle triglyceride levels are inversely related to insulin action. Diabetes 46, 983–988 (1997).
He, S. et al. Hypertriglyceridemic waist might be an alternative to metabolic syndrome for predicting future diabetes mellitus. PLoS One 8, e73292 (2013).
Liu, P. J., Lou, H. P. & Zhu, Y. N. Screening for metabolic syndrome using an integrated continuous index consisting of waist circumference and triglyceride: A preliminary cross-sectional study. Diabetes Metab. Syndr. Obes. Targets Ther. 13, 2899 (2020).
Lemieux, I. et al. Hypertriglyceridemic waist: A useful screening phenotype in preventive cardiology?. Can. J. Cardiol. 23, 23 B-31 B (2007).
Amiri, P. et al. Factors affecting gender differences in the association between health-related quality of life and metabolic syndrome components: Tehran Lipid and Glucose Study. PLoS One 10, e0143167 (2015).
Allender, S. et al. Level of urbanization and noncommunicable disease risk factors in Tamil Nadu, India. Bull. World Health Organ. 88, 297–304. https://doi.org/10.2471/blt.09.065847 (2010).
Pandey, B., Reba, M., Joshi, P. K. & Seto, K. C. Urbanization and food consumption in India. Sci. Rep. 10, 17241. https://doi.org/10.1038/s41598-020-73313-8 (2020).
Allender, S., Wickramasinghe, K., Goldacre, M., Matthews, D. & Katulanda, P. Quantifying urbanization as a risk factor for noncommunicable disease. J. Urban Health Bull. N. Y. Acad. Med. 88, 906–918. https://doi.org/10.1007/s11524-011-9586-1 (2011).
Sobngwi, E. et al. Exposure over the life course to an urban environment and its relation with obesity, diabetes, and hypertension in rural and urban Cameroon. Int. J. Epidemiol. 33, 769–776. https://doi.org/10.1093/ije/dyh044 (2004).
Ventimiglia, I. & Seedat, S. Current evidence on urbanicity and the impact of neighbourhoods on anxiety and stress-related disorders. Curr. Opin. Psychiatry 32, 248–253. https://doi.org/10.1097/yco.0000000000000496 (2019).
Ushakov, A. V., Ivanchenko, V. S. & Gagarina, A. A. Psychological stress in pathogenesis of essential hypertension. Curr. Hypertens. Rev. 12, 203–214. https://doi.org/10.2174/1573402112666161230121622 (2016).
Zohal, M. et al. Association of sleep quality components and wake time with metabolic syndrome: The Qazvin Metabolic diseases study (QMDS), Iran. Diabetes Metab. Syndr. Clin. Res. Rev. 11, S377–S380 (2017).
Huang, W. et al. Circadian rhythms, sleep, and metabolism. J. Clin. Investig. 121, 2133–2141 (2011).
Jennings, J. R., Muldoon, M. F., Hall, M., Buysse, D. J. & Manuck, S. B. Self-reported sleep quality is associated with the metabolic syndrome. Sleep 30, 219–223 (2007).
Najafian, J., Toghianifar, N., Mohammadifard, N. & Nouri, F. Association between sleep duration and metabolic syndrome in a population-based study: Isfahan Healthy Heart Program. J. Res. Med. Sci. 16, 801 (2011).
Choi, J.-K. et al. Association between short sleep duration and high incidence of metabolic syndrome in midlife women. Tohoku J. Exp. Med. 225, 187–193 (2011).
Kobayashi, D., Takahashi, O., Deshpande, G. A., Shimbo, T. & Fukui, T. Relation between metabolic syndrome and sleep duration in Japan: A large scale cross-sectional study. Intern. Med. 50, 103–107 (2011).
Wu, M.-C. et al. Short sleep duration associated with a higher prevalence of metabolic syndrome in an apparently healthy population. Prev. Med. 55, 305–309 (2012).
Arora, T. et al. Self-reported long total sleep duration is associated with metabolic syndrome: The Guangzhou Biobank Cohort Study. Diabetes Care 34, 2317–2319 (2011).
Santos, A.-C., Ebrahim, S. & Barros, H. Alcohol intake, smoking, sleeping hours, physical activity and the metabolic syndrome. Prev. Med. 44, 328–334 (2007).
Fan, L. et al. Non-linear relationship between sleep duration and metabolic syndrome: A population-based study. Medicine 99, e18753 (2020).
Yazdanpanah, M. H. et al. Short sleep is associated with higher prevalence and increased predicted risk of cardiovascular diseases in an Iranian population: Fasa PERSIAN Cohort Study. Sci. Rep. 10, 1–13 (2020).
Taheri, S., Lin, L., Austin, D., Young, T. & Mignot, E. Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med. 1, e62 (2004).
Van Cauter, E., Spiegel, K., Tasali, E. & Leproult, R. Metabolic consequences of sleep and sleep loss. Sleep Med. 9, S23–S28 (2008).
Spiegel, K., Tasali, E., Penev, P. & Cauter, E. V. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann. Intern. Med. 141, 846–850 (2004).
Cipolla-Neto, J., Amaral, F., Afeche, S. C., Tan, D.-X. & Reiter, R. Melatonin, energy metabolism, and obesity: A review. J. Pineal Res. 56, 371–381 (2014).
Imenshahidi, M., Karimi, G. & Hosseinzadeh, H. Effects of melatonin on cardiovascular risk factors and metabolic syndrome: A comprehensive review. Naunyn Schmiedebergs Arch. Pharmacol. 393, 521–536 (2020).
Xu, J.-X. et al. Serum advanced glycation end products are associated with insulin resistance in male nondiabetic patients with obstructive sleep apnea. Sleep Breath. 19, 827–833 (2015).
Leger, D., Bayon, V. & de Sanctis, A. The role of sleep in the regulation of body weight. Mol. Cell. Endocrinol. 418, 101–107 (2015).
Crispim, C. A. et al. The influence of sleep and sleep loss upon food intake and metabolism. Nutr. Res. Rev. 20, 195–212 (2007).
Zhu, B., Shi, C., Park, C. G., Zhao, X. & Reutrakul, S. Effects of sleep restriction on metabolism-related parameters in healthy adults: A comprehensive review and meta-analysis of randomized controlled trials. Sleep Med. Rev. 45, 18–30 (2019).
Ervin, R. B. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index; United States, 2003–2006 (2009).
Park, Y.-W. et al. The metabolic syndrome: Prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994. Arch. Intern. Med. 163, 427–436 (2003).
Hajian-Tilaki, K., Heidari, B., Hajian-Tilaki, A., Firouzjahi, A. & Bagherzadeh, M. The discriminatory performance of body mass index, waist circumference, waist-to-hip ratio and waist-to-height ratio for detection of metabolic syndrome and their optimal cutoffs among Iranian adults. J. Res. Health Sci. 14, 276–281 (2014).
Kelishadi, R. Childhood overweight, obesity, and the metabolic syndrome in developing countries. Epidemiol. Rev. 29, 62–76 (2007).
Wang, F. et al. Waist circumference, body mass index and waist to hip ratio for prediction of the metabolic syndrome in Chinese. Nutr. Metab. Cardiovasc. Dis. 19, 542–547 (2009).
Azizi, F. et al. Appropriate definition of metabolic syndrome among Iranian adults: Report of the Iranian National Committee of Obesity. Arch. Iran. Med. 13, 426–428 (2010).
Fried, S. K., Lee, M. J. & Karastergiou, K. Shaping fat distribution: New insights into the molecular determinants of depot-and sex-dependent adipose biology. Obesity 23, 1345–1352 (2015).
Engin, A. The definition and prevalence of obesity and metabolic syndrome. Obes. Lipotox. 960, 1–17 (2017).
Matsuzawa, Y., Funahashi, T. & Nakamura, T. The concept of metabolic syndrome: Contribution of visceral fat accumulation and its molecular mechanism. J. Atheroscler. Thromb. 18, 1107070401–1107070401 (2011).
Ridker, P. M. Inflammatory biomarkers and risks of myocardial infarction, stroke, diabetes, and total mortality: Implications for longevity. Nutr. Rev. 65, S253–S259 (2007).
Wajchenberg, B. L. Subcutaneous and visceral adipose tissue: Their relation to the metabolic syndrome. Endocr. Rev. 21, 697–738 (2000).
Shulman, G. I. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N. Engl. J. Med. 371, 1131–1141 (2014).
Vatner, D. F. et al. Insulin-independent regulation of hepatic triglyceride synthesis by fatty acids. Proc. Natl. Acad. Sci. 112, 1143–1148 (2015).
Medina-Urrutia, A. et al. Role of adiponectin and free fatty acids on the association between abdominal visceral fat and insulin resistance. Cardiovasc. Diabetol. 14, 1–8 (2015).
Alkerwi, A. et al. Dietary, behavioural and socio-economic determinants of the metabolic syndrome among adults in Luxembourg: findings from the ORISCAV-LUX study. Public Health Nutr. 15, 849–859 (2012).
Santos, A. C., Ebrahim, S. & Barros, H. Gender, socio-economic status and metabolic syndrome in middle-aged and old adults. BMC Public Health 8, 62 (2008).
Adedoyin, R. A., Afolabi, A., Adegoke, O. O., Akintomide, A. O. & Awotidebe, T. O. Relationship between socioeconomic status and metabolic syndrome among Nigerian adults. Diabetes Metab. Syndr. 7, 91–94 (2013).
Lidfeldt, J. et al. Socio-demographic and psychosocial factors are associated with features of the metabolic syndrome. The Women’s Health in the Lund Area (WHILA) study. Diabetes Obes. Metab. 5, 106–112 (2003).
Goyal, A. et al. Attained educational level and incident atherothrombotic events in low-and middle-income compared with high-income countries. Circulation 122, 1167–1175 (2010).
Winkleby, M. A., Jatulis, D. E., Frank, E. & Fortmann, S. P. Socioeconomic status and health: How education, income, and occupation contribute to risk factors for cardiovascular disease. Am. J. Public Health 82, 816–820 (1992).
Hajian-Tilaki, K. Metabolic syndrome and its associated risk factors in Iranian adults: A systematic review. Casp. J. Intern. Med. 6, 51 (2015).
Gharipour, M. et al. Socioeconomic determinants and metabolic syndrome: Results from the Isfahan Healthy Heart Program. Acta Biomed. 87, 291–198 (2017).
Wu, H. F. et al. Age, gender, and socioeconomic gradients in metabolic syndrome: Biomarker evidence from a large sample in Taiwan, 2005–2013. Ann. Epidemiol. 27, 315-322.e312 (2017).
Ying, X. et al. Sex differences in the Association between socioeconomic status and metabolic syndrome in Rural China: An updated cross-sectional study (2020).
Devers, M. C., Campbell, S. & Simmons, D. Influence of age on the prevalence and components of the metabolic syndrome and the association with cardiovascular disease. BMJ Open Diabetes Res. Care 4, e000195 (2016).
Sumner, A. D., Sardi, G. L. & Reed, J. F. III. Components of the metabolic syndrome differ between young and old adults in the US population. J. Clin. Hypertens. 14, 502–506 (2012).
Assmann, G. et al. Harmonizing the definition of the metabolic syndrome: Comparison of the criteria of the Adult Treatment Panel III and the International Diabetes Federation in United States American and European populations. Am. J. Cardiol. 99, 541–548 (2007).
Hildrum, B., Mykletun, A., Hole, T., Midthjell, K. & Dahl, A. A. Age-specific prevalence of the metabolic syndrome defined by the International Diabetes Federation and the National Cholesterol Education Program: The Norwegian HUNT 2 study. BMC Public Health 7, 220 (2007).
Vishram, J. K. et al. Impact of age and gender on the prevalence and prognostic importance of the metabolic syndrome and its components in Europeans. The MORGAM Prospective Cohort Project. PLoS One 9, e107294. https://doi.org/10.1371/journal.pone.0107294 (2014).
Lawlor, D., Smith, G. D. & Ebrahim, S. Does the new International Diabetes Federation definition of the metabolic syndrome predict CHD any more strongly than older definitions? Findings from the British Women’s Heart and Health Study. Diabetologia 49, 41–48 (2006).
Gasse, C. et al. Assessing hypertension management in the community: Trends of prevalence, detection, treatment, and control of hypertension in the MONICA Project, Augsburg 1984–1995. J. Hum. Hypertens. 15, 27–36 (2001).
Mehndiratta, N., Sharma, S., Sharma, R. K. & Grover, S. A prospective study on the incidence of metabolic syndrome in premenopausal and postmenopausal women. J. Mid-life Health 11, 17 (2020).
Ben Ali, S. et al. Postmenopausal hypertension, abdominal obesity, apolipoprotein and insulin resistance. Clin. Exp. Hypertens. 38, 370–374 (2016).
Faria, A. N., Filho, F. F. R., Ferreira, S. R. G. & Zanella, M. T. Impact of visceral fat on blood pressure and insulin sensitivity in hypertensive obese women. Obes. Res. 10, 1203–1206 (2002).
Kang, Y. S. Obesity associated hypertension: New insights into mechanism. Electrolytes Blood Press. 11, 46–52 (2013).
Regitz-Zagrosek, V., Lehmkuhl, E. & Weickert, M. O. Gender differences in the metabolic syndrome and their role for cardiovascular disease. Clin. Res. Cardiol. 95, 147–147 (2006).
Nickenig, G. Should angiotensin II receptor blockers and statins be combined?. Circulation 110, 1013–1020 (2004).
Pechère-Bertschi, A. & Burnier, M. Female sex hormones, salt, and blood pressure regulation. Am. J. Hypertens. 17, 994–1001 (2004).
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 287, 356–359 (2002).
Berlie, H. D., Herman, W. H., Brown, M. B., Hammad, A. & Jaber, L. A. Quality of diabetes care in Arab Americans. Diabetes Res. Clin. Pract. 79, 249–255 (2008).
Azak, S., Mohebbi, I., Salarilak, S. & Gharaaghaji Asl, R. Prevalence of metabolic syndrome and related risk factors in professional drivers in west Azerbaijan province, 2010–2012. Urmia Med. J. 26, 775–784 (2015).
Sharifi, F., Mousavinasab, S., Saeini, M. & Dinmohammadi, M. Prevalence of metabolic syndrome in an adult urban population of the west of Iran. Exp. Diabetes Res. 2009, 1–5 (2009).
Azizi, F., Salehi, P., Etemadi, A. & Zahedi-Asl, S. Prevalence of metabolic syndrome in an urban population: Tehran Lipid and Glucose Study. Diabetes Res. Clin. Pract. 61, 29–37 (2003).
Esteghamati, A. et al. Leptin cut-off values for determination of metabolic syndrome: Third national surveillance of risk factors of non-communicable diseases in Iran (SuRFNCD-2007). Endocrine 40, 117–123 (2011).
Kabir, A., Pourshams, A., Khoshnia, M. & Malekzadeh, F. Normal limit for serum alanine aminotransferase level and distribution of metabolic factors in old population of Kalaleh, Iran. Hepat. Mon. https://doi.org/10.5812/hepatmon.10640 (2013).
Ostovaneh, M. R. et al. Prevalence of metabolic syndrome in Amol and Zahedan, Iran: A population based study. Arch. Iran. Med. 17, 0–0 (2014).
Amirkalali, B. et al. Prevalence of non-alcoholic fatty liver disease and its predictors in north of Iran. Iran. J. Public Health 43, 1275 (2014).
Sarrafzadegan, N. et al. Metabolic syndrome: An emerging public health problem in Iranian women: Isfahan Healthy Heart program. Int. J. Cardiol. 131, 90–96 (2008).
Jahandideh, D., Dabbaghmanesh, M., Fattahi, M. & Omrani, G. The prevalence of metabolic syndrome and its components among adults in a rural community, Fars, Iran. Int. Cardiovasc. Res. J. 9, 94–99 (2017).
Babai, M. A. et al. Defining a BMI cut-off point for the Iranian population: The Shiraz Heart Study. PLoS One 11, e0160639 (2016).
Yousefzadeh, G. & Sheikhvatan, M. Age and gender differences in the clustering of metabolic syndrome combinations: A prospective cohort research from the Kerman Coronary Artery Disease Risk Study (KERCADRS). Diabetes Metab. Syndr. 9, 337–342 (2015).
Sadrbafoghi, S. et al. Prevalence and criteria of metabolic syndrome in an urban population: Yazd Healthy Heart Project. Tehran Univ. Med. J. TUMS Publ. 64, 90–96 (2006).
Azimi-Nezhad, M. et al. Anthropometric indices of obesity and the prediction of cardiovascular risk factors in an Iranian population. Sci. World J. 9, 424–430 (2009).
Khayyatzadeh, S. S. et al. Nutrient patterns and their relationship to metabolic syndrome in Iranian adults. Eur. J. Clin. Investig. 46, 840–852 (2016).
Ghorbani, R., Eskandarian, R., Rashidy-Pour, A., Khamseh, M. E. & Malek, M. Prevalence of metabolic syndrome according to ATPIII and IDF criteria in the Iranian population. Koomesh 14, 65–75 (2012).
Movahed, A. et al. Reduced serum osteocalcin concentrations are associated with type 2 diabetes mellitus and the metabolic syndrome components in postmenopausal women: The crosstalk between bone and energy metabolism. J. Bone Miner. Metab. 30, 683–691 (2012).
Ebrahimi, M. et al. Metabolic syndrome may not be a good predictor of coronary artery disease in the Iranian population: Population-specific definitions are required. Sci. World J. 9, 86–96 (2009).
Carroll, S. & Dudfield, M. What is the relationship between exercise and metabolic abnormalities?. Sports Med. 34, 371–418 (2004).
Lakka, T. A. & Laaksonen, D. E. Physical activity in prevention and treatment of the metabolic syndrome. Appl. Physiol. Nutr. Metab. 32, 76–88 (2007).
Wu, S., Fisher-Hoch, S. P., Reininger, B. & McCormick, J. B. Recommended levels of physical activity are associated with reduced risk of the metabolic syndrome in Mexican-Americans. PLoS One 11, e0152896 (2016).
Xu, F. et al. The Association between physical activity and metabolic syndrome in older adults with obesity. J. Frailty Aging 8, 27–32 (2019).
He, D. et al. Association between leisure time physical activity and metabolic syndrome: a meta-analysis of prospective cohort studies. Endocrine 46(2), 231–240. https://doi.org/10.1007/s12020-013-0110-0 (2014).
Lee, J., Kim, Y. & Jeon, J. Y. Association between physical activity and the prevalence of metabolic syndrome: From the Korean National Health and Nutrition Examination Survey, 1999–2012. Springerplus 5, 1870 (2016).
Rennie, K., McCarthy, N., Yazdgerdi, S., Marmot, M. & Brunner, E. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int. J. Epidemiol. 32, 600–606 (2003).
Yu, Z. et al. Associations of physical activity with inflammatory factors, adipocytokines, and metabolic syndrome in middle-aged and older chinese people. Circulation https://doi.org/10.1161/CIRCULATIONAHA.108.833574 (2009).
Ross, R. et al. Reduction in obesity and related comorbid conditions after diet-induced weight loss or exercise-induced weight loss in men: A randomized, controlled trial. Ann. Intern. Med. 133, 92–103 (2000).
Torjesen, P. et al. Lifestyle changes may reverse development of the insulin resistance syndrome. The Oslo diet and exercise study: A randomized trial. Diabetes Care 20, 26–31 (1997).
Hagberg, J. M., Park, J.-J. & Brown, M. D. The role of exercise training in the treatment of hypertension. Sports Med. 30, 193–206 (2000).
The authors appreciate all people that patiently contributed to this study; Royan Institute and Fasa University of Medical Sciences for financial supports of this work. In addition, this work is partially based upon research supported by the South Africa National Research Foundation (NRF) and South Africa Medical Research Council (SAMRC) (South Africa DST-NRF-SAMRC SARChI Research Chair in Biostatistics-Professor Ding-Geng Chen, Grant number 114613). Opinions expressed and conclusions arrived at are those of the author and are not necessarily to be attributed to the NRF and SAMRC. We would also like to thank members of methodology and applied biostatistics (MetABio) research core of Royan Institute for their support and Parsa Farjam for his assistance.
The authors declare no competing interests.
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Nouri-Keshtkar, M., Shojaei Shahrokhabadi, M., Ghaheri, A. et al. Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling. Sci Rep 13, 16007 (2023). https://doi.org/10.1038/s41598-023-40485-y