Incidence of metabolic syndrome and determinants of its progression in Southern Iran: A 5-year longitudinal follow-up study
Marzieh Bakhshayeshkaram1, Sayed Taghi Heydari2, Behnam Honarvar3, Parisa Keshani4, Jamshid Roozbeh5, Mohammad Hossein Dabbaghmanesh6, Kamran Bagheri Lankarani7
1 Shiraz Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
2 Department of Biostatistics, Shiraz Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
3 Department of Public and Community Medicine, Shiraz Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
4 Department of Nutrition, Shiraz HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
5 Department of Internal Medicine, Nephrologist, Shiraz Nephro-Urology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
6 Department of Internal Medicine, Endocrinologist, Shiraz Endocrinology and Metabolism Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
7 Department of Internal Medicine, Gastroenterologist, Shiraz Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
|Date of Submission||01-Dec-2019|
|Date of Decision||18-Apr-2020|
|Date of Acceptance||09-Jul-2020|
|Date of Web Publication||26-Nov-2020|
Prof. Kamran Bagheri Lankarani
Department of Internal Medicine, Gastroenterologist, Shiraz Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz
Source of Support: None, Conflict of Interest: None
Background: Metabolic syndrome (MetS) is a cluster of conditions increasing the risk of serious diseases. This study aimed to define the predictors of MetS incident in a community-based cohort in Southern Iran, during a mean follow-up period of 5.1 years. Materials and Methods: During the mean follow-up period of 5.1 years, a cohort study was conducted on 819 Iranian adults aged ≥18 years at baseline and followed to determine the incidence and predictors of MetS progression in Shiraz, a main urban region in the southern part of Iran. The International Diabetes Federation Guideline was used to detect the MetS. Multiple Cox's proportional hazards models were also used to estimate the predictors of new-onset MetS. Results: The prevalence of MetS was 25.9% at baseline, and the overall incidence of subsequent MetS was 5.45% (95% confidence interval [CI]: 4.47–6.59). The incidence of MetS was significantly higher in women (7.12% [95% CI: 5.52–9.05]) than in men (3.92% [95% CI: 2.80–5.34]). Moreover, it increased by 5.02 (95% CI, 3.75–6.58) among individuals who had one metabolic component and by 12.65 (95% CI, 9.72–16.18) for those who had three or more components (P < 0001). The incidence of MetS was also analyzed using the multiple Cox's proportional hazards model for potential risk factors, and it was revealed that female gender (hazard ratio [HR] 2.45; 95% CI: 1.33, 4.50; P = 0.004), higher body mass index (HR 3.13; 95% CI: 126.96.36.199; P = 0.012), increased abdominal obesity (HR 1.45; 95% CI 0.85, 2.46; P = 0.045), smoking (HR 4.79; 95% CI 2.09, 10.97; P < 0.001), and lower high-density lipoprotein (HR 0.53; 95% CI: 0.29, 1.00;P = 0.044) significantly predicted the onset of MetS at baseline; however, age, systolic and diastolic blood pressure, serum uric acid, fasting blood glucose, cholesterol, triglyceride and creatinine, estimated glomerular filtration rate, marital status, level of education, and level of physical activity did not independently predict the onset of MetS when other covariates were considered. Conclusion: This study showed the high-incidence rates of MetS in males and females residing in Southern Iran. Therefore, the prevention through community-based lifestyle modification should be implemented to reduce the burden of MetS and its complications.
Keywords: Incidence, metabolic syndrome, risk factors
|How to cite this article:|
Bakhshayeshkaram M, Heydari ST, Honarvar B, Keshani P, Roozbeh J, Dabbaghmanesh MH, Lankarani KB. Incidence of metabolic syndrome and determinants of its progression in Southern Iran: A 5-year longitudinal follow-up study. J Res Med Sci 2020;25:103
|How to cite this URL:|
Bakhshayeshkaram M, Heydari ST, Honarvar B, Keshani P, Roozbeh J, Dabbaghmanesh MH, Lankarani KB. Incidence of metabolic syndrome and determinants of its progression in Southern Iran: A 5-year longitudinal follow-up study. J Res Med Sci [serial online] 2020 [cited 2021 Jul 28];25:103. Available from: https://www.jmsjournal.net/text.asp?2020/25/1/103/301552
| Introduction|| |
Metabolic syndrome (MetS) is a collection of inter-connected metabolic abnormalities such as glucose metabolism, lipid metabolism, elevated blood pressure (BP), and central obesity. MetS increases the risk of serious diseases such as Type-2 diabetes, cardiovascular diseases, and eventually all-cause mortality. It is also associated with nonalcoholic steatohepatitis, reproductive diseases, and certain types of cancers. Numerous definitions and diagnostic criteria have been used to identify MetS. However, regardless of the used criteria, it is well acknowledged that the prevalence of MetS is increasing in epidemic extents in both developed and developing countries. The prevalence of MetS in the adult population is estimated to be 20%–25%. The specific cause of MetS is not clear; however, it is considered as a combination of genetic, metabolic, and some environmental factors. The pathophysiology of MetS is complex, with insulin resistance and disorder in lipid metabolism playing a central role in the pathogenesis. The prevalence of MetS varies considerably worldwide due to the differences in genetics, lifestyle factors, and socioeconomic status. The prevalence of MetS is high in the Middle East, and urgent measures are required to decrease its complications. Recent studies have shown that increasing economic development in developing countries has mainly contributed to the increasing prevalence of obesity, Type-2 diabetes, and cardiovascular diseases. Several cross-sectional studies on the prevalence of MetS have been published in this field.,, Cigarette smoking is proved to play a role in the emergence of various MetS components. The existing data from epidemiological studies on this issue are inconsistent and controversial. Some observational studies have reported an independent association between MetS and chronic kidney disease. Hyperuricemia could deteriorate the insulin resistance. Sequentially, insulin resistance is thought to play a pivotal role in MetS. Recent reports have revealed that raised uric acid may be a predictor for MetS in different individuals; however, the results of these studies are controversial. We sought to determine the association between serum uric acid levels and MetS prevalence and incidence. Better understanding of the incidence of MetS and determinants of its progression in a prospective study would result in a better evaluation of populations at higher risk to implement effective preventive strategies among the population in Southern Iran.
| Materials and Methods|| |
Setting and study design
This community-based prospective study was an ongoing population-based longitudinal study carried out in Shiraz, the capital of Fars province, Southern Iran, under the auspices of health policy research center, Shiraz University of Medical Sciences, Shiraz, Iran. According to the national census 2016, the overall population of Shiraz was estimated to be 1869001 persons. The study procedures were performed in two phases from November 2011 to September 2012 and from October 2016 to November 2017.
Sample size and data collection
The sample size was determined using the following formula:
However, considering the loss rate of 35%, the final sample size was 819 persons.
The participants took part in two phases. In the first phase, the participants were selected by a stratified multistage probability sampling method, with selections made from the sampling units based on the geographical area, gender, and age groups from seven municipality regions in Shiraz. The participants were selected using the cluster random sampling. The probability proportional to the size sampling methodology was used on the home addresses, postal zip codes, and municipality regions to select the study population from each municipality. Individuals aged ≥18 years were selected and invited by phone calls to participate in the study. The exclusion criteria were non-Iranian nationality, pregnancy, or baby delivery within the previous 6 months. The cohort of 819 residents who participated in the baseline survey was re-assessed after a mean period of 5.1 years. The data from the second phase of the study were obtained in the same as well. The relevant period was considered to start on the date when the baseline examination was performed until either the onset of MetS, death, or the end of the follow-up period. The study proposal was approved by the Ethics Committee of Shiraz University of Medical Sciences (No. 397433), and written informed consent was obtained from each participant. Among 819 participants meeting the criteria for the follow-up study, 577 (70.5%) persons returned for follow-up examinations during October 2016 to November 2017. During the 5.1 year follow-up period, 11 persons died, and 231 persons quitted the study. In this regard, 180 individuals had a change in address or had migrated and were no further accessible, and 51 persons refused to participate even after repeated attempts. [Figure 1] depicts the composition of the study population.
|Figure 1: Flow diagram showing the process of participants attending the 5-year follow-up study|
Click here to view
Clinical and nutritional assessments
A standard questionnaire addressing demographic characteristics, level of education, medical history, and health-related behaviors was administered by the trained research staff. Past smokers were defined as those who had abstained from smoking for > 3 months at the examination time. The participants' heights and weights were assessed while the participants were wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters (kg/m2). After a 15-min rest in the sitting position, the BP was recorded using a standardized mercury sphygmomanometer as an average of two consecutive readings. The dietary intake from a 24-h food recall was assessed by the food frequency questionnaire. Data entry and interviews were performed by a trained dietitian, and the participants' intake was analyzed in terms of energy, macronutrients, and micronutrients contents by the Nutritionist-4 software (First Databank Inc., Hearst Corp., San Bruno, CA, USA).
Blood sampling and laboratory measurements
Blood samples were collected after an overnight fasting and were then analyzed. The intra- and inter-assay coefficients of variation were 2.1% and 2.2% for FBS, 0.8% and 3.1% for total cholesterol, 0.9% and 2.1% for triglyceride (TG), and 2.1% and 3.4 for high-density lipoprotein cholesterol (HDL-C), respectively. The assessments were performed using the enzyme or radioimmunoassay methods. Serum creatinine (Cr) was measured by Jaffe's kinetic method. The intra- and inter-assay coefficients of variation were 2.4 and 3.1%, respectively. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.
MetS was defined using the International Diabetes Federation Guideline. Individuals with central obesity defined as waist circumference ≥ 94 cm for men and ≥80 cm for women plus any two of the following four factors were defined as having MetS: (1) Raised TG level: ≥150 mg/dL (1.7 mmol/L) or specific treatment for this lipid abnormality; (2) Reduced HDL-C: <40 mg/dL in males and <50 mg/dL in females, or specific treatment for this lipid abnormality; (3) Raised BP: Systolic BP ≥130 or diastolic BP ≥85 mm Hg, or treatment of previously diagnosed hypertension; (4) Raised fasting plasma glucose ≥100 mg/dL, or previously diagnosed Type 2 diabetes; and BMI was categorized according to the World Health Organization's guidelines. The active individuals were recommended to do physical activity based on the CDC/ACSM guidelines of either ≥30 min of moderate-intensity physical activity on ≥5 days/week or ≥20 min of vigorous-intensity physical activity on ≥3 days/week.
Mean (standard deviation [SD]) or frequency (percentage) values of the baseline characteristics were achieved. The baseline characteristics of follow-ups and non-follow-ups (those without any follow-up data) are shown as mean (SD) or frequency (%). A comparison between two groups was performed using the statistical methods such as Student's t-test, Chi-squared test, analysis of variance or Kruskal–Wallis tests for normally or not normally distributed continuous variables, respectively. The progression rates were estimated as the number of cases, who developed MetS per 100 person-years of follow-up. The incidence rates were calculated by dividing the number of events by the years at risk for the whole population. To facilitate interpretations, the progression rates are reported in terms of percentage per year. The relevant period date for the incident cases of MetS was defined as the date of completion of the baseline examination until the date when the MetS was diagnosed for the first time, the date of the last completed follow-up, death, or end of follow-up in the second phase. Hazard ratio (HR) was determined based on the incidence rates and adjusted HR with 95% CI using Cox-proportional hazard regression analysis, which simultaneously is adjusted for other covariates. According to this analysis, age, fasting blood glucose, systolic and diastolic BP, BMI, waist-to-hip ratio (WHR), cholesterol, TG and creatinine, eGFR and uric acid, gender, marital status, cigarette smoking, level of education, and physical activity were considered as dichotomous variables. For the risk factors with more than two categories, the first category was considered as the reference group. All the statistical significance analyses were two-tailed, the confidence intervals (CIs) were set at 95%, and P < 0.05 was considered statistically significant. Statistical analysis was performed using the Statistical Package for the Social Sciences software (SPSS, version 20; SPSS Inc., Chicago, Illinois, USA).
| Results|| |
Characteristics of the participants
Among 819 participants, 58.5% of the participants were women, and 41.5% were men. The mean (SD) age of the participants was 43.0 (14.0) years. [Table 1] shows the baseline characteristics of the individuals after stratification according to follow-up status. The participants at the follow-up phase differed significantly from the participants with no follow-up regarding some baseline characteristics such as age, fasting glucose, waist circumference, TG, weight, marital status, and BMI. However, the follow-up sample was similar to the baseline sample.
|Table 1: The characteristics of the baseline variables of the followed up versus nonfollow-up participants|
Click here to view
Prevalence and incidence
During the study, the overall prevalence of MetS increased from 25.9% in 2012 to 28.6% in 2017. Out of 405 individuals with no MetS in the first stage, 107 (26.4%) (40 men and 67 women) developed MetS in 1960 (1020 men and 940 women) person-years of 5.1 year follow-up.
The overall incidence of subsequent MetS was 5.45% (95% CI: 4.47–6.59) per year. The incidence of MetS among women (men, 3.92%;[95% CI: 2.80–5.34]) was significantly higher, compared to women (7.12% [95% CI: 5.52–9.05] ;P < 0001). The incidence of MetS showed a significant increase with an increase in the number of MetS components at the baseline. The incidence of MetS increased by 5.02 (95% CI, 3.75–6.58) for individuals who had a metabolic component and by 12.65 (95% CI, 9.72–16.18) for those who had three or more components (P < 0001).
[Table 2] shows the compares the baseline characteristics of the participants with and without MetS. The participants who developed MetS were older and female with higher BMI, WC, HC, WHR, TG, and uric acid and higher percentage of obesity at the baseline; however, they had lower eGFR compared to the participants free of MetS at the end of follow-up phase (P < 0.05 for all measures). [Table 3] shows the HR of MetS with regard to age, gender, MetS components, hypertension, diabetes, abdominal obesity, and other potential risk factors for Mets such as baseline eGFR, uric acid, smoking, physical activity, marital status, and level of education. A univariate analysis showed that female gender, age, increased total cholesterol, TG, uric acid, BMI, and abdominal obesity were significantly associated with the risk of developing MetS. The incidence of MetS was also analyzed using multiple models for the potential risk factors of MetS. After adjusting the covariates, Cox's proportional hazards model showed that female gender (HR 2.45; 95% CI: 1.33,4.50), higher BMI (HR 3.13; 95% CI: 188.8.131.52), increased WHR (HR 1.45; 95% CI: 0.85,2.46), smoking 4.79; 95% CI: 2.09,10.97), and lower HDL (HR 0.53; 95% CI: 0.29,1.00) at the baseline significantly predicted the onset of MetS; however, the other variables were not significant.
|Table 2: The baseline characteristics between participants who did and did not develop metabolic syndrome|
Click here to view
|Table 3: Hazard ratio and 95% confidence intervals of potential risk factors in relation to metabolic syndrome incidence|
Click here to view
| Discussion|| |
During the last three decades, the prevalence of MetS increased worldwide. The incidence and prevalence rates of MetS show remarkable differences among the general populations in various studies from different countries. In this cohort study, the incidence of MetS was 5.45% (3.92% in men and 7.12% in women). This rate is close to the reported incidence rate of MetS (5.5%) in the northern region of Iran. However, in comparison to our study, the incidence rate of the reported value was significantly higher among men than women (7.49% vs. 4.33%). Similar to this study, in a cohort study conducted the central part of Iran, the incidence rate of MetS was reported to be 5.65%. The study showed that the incidence rates of MetS among males and females were 5.6% and 5.8%, respectively. These values were larger than the value in our study on males and smaller than that among females. In a study conducted in Korea by Hwang et al., the incidence rates of MetS during a 5-year follow-up were 3% among males and 4.6% among females, respectively. Accordingly, the incidence of MetS in the present study was higher, compared to the study in Korea. In a study conducted in an urban south European population as Santos' cohort study, the incidence rate of MetS (4.72%) was close to the reported value in the present study; however, the incidence rates were similar among men and women. The results of the present study showed that the incidence of MetS was higher than that of the Coronary Artery Risk Development in Young Adults (CARDIA) (1%). This might be explained with regard to the age distribution of the study population. The participants in the CARDIA study aged 18–30 years. The incidence rates of MetS increased in China throughout the past decade. The incidence of MetS increased from 8% to 10.6% in the urban regions and from 4.9% to 5.3% in the rural regions. The differences in the incidence of MetS between studies conducted in Iran's regions and other studies may be attributed to different inclusion criteria, different age groups, and different prevalence rates of MetS components. The present study showed that the incidence of MetS was higher in females than in males.
The reason of such a gender variance in MetS has not yet been determined; however, some studies have proposed that female sex hormones may contribute to the changes in glucose tolerance and all MetS components. Moreover, metabolic changes associated with menopause might explain the increased prevalence of MetS in women. More stringent cutoffs employed for waist circumference and HDL among women partly explain this variation. The present study revealed that higher age, BMI, WHR, TG, and lower HDL at baseline significantly increased the incidence of MetS. In this study, the incidence of MetS increased with age. The result was similar to a 5-year follow-up study in Korea. The adoption of a more sedentary lifestyle and dietary changes that occur with urbanization and westernization following the socioeconomic rise in developing countries, seem to be the main factors leading to obesity and the MetS pandemic. Many studies have indicated that obesity is closely associated with hypertension, Type 2 diabetes, and dyslipidemia. In obesity, the serum concentrations of leptin and resistin increase, whereas adiponectin decreases. The increased production of leptin and resistin and the decreased secretion of adiponectin increase the risk of developing the MetS components. Insulin resistance enhances hypertriglyceridemia and low HDL-C. In this study, no association was detected between eGFR and the incidence of MetS. Consistent with our findings, some studies report that MetS is not significantly associated with reduced eGFR. In humans, the uric acid is the end compound of purines catabolism. The overproduction of uric acid is observed to play an emerging role in human disease. Some studies have found a positive relationship between serum uric acid levels and the prevalence of MetS. However, it is not yet clear whether the increase in uric acid level is an independent risk factor or just a biomarker in the development and progression of MetS. Greater awareness and identification of the MetS trend in this region would facilitate the prioritization and implementation of interventions to optimize the risk factors that could have a positive effect on MetS and treat these metabolic risk factors. To the best of our knowledge, this is the first study assessing the relationship between uric acid and the incidence of MetS in Iranian population. Our study demonstrated that elevated uric acid was positively associated with the incidence of MetS in univariate analysis, whereas the increased uric acid concentration was not independently associated with the incidence of MetS. There are several arguments suggesting that uric acid may not be a real risk factor for metabolic diseases. There is ample evidence indicating that even acutely raising uric acid concentrations improves the endothelial function. The improvement in the endothelial function is supposed to be due to the potentials of the uric acid to function as an antioxidant. In addition, other studies have documented that uric acid levels are significantly enhanced in individuals with abdominal obesity, low HDL-C, and hypertension. Accordingly, hyperuricemia can be considered as an insulin resistance marker as such some studies have shown that decreased insulin resistance by some interventions such as diets or medications decreases the uric acid levels.
The present study showed that smoking in our population was associated with the incidence of MetS. A remarkable association between abdominal obesity and smoking with the increased incidence of MetS in the Iranian population highlights the significance of implementing lifestyle intervention programs to promote healthy eating habits, physical activities, and awareness of the risk of abdominal obesity and smoking.
One of the strengths of the present study is that it was a population-based study covering a wide age range of participants, representative sampling methodology, and the use of standardized data collection protocols. Furthermore, the incidence of MetS was estimated in accordance with the most frequently used definitions. Selection bias was minimized since this study was a continuous survey of randomly selected individuals. The use of population-based sample would also enhance the likelihood of generalizability.
The study was considered the most recent data of the potential risk factors available in our region and provided decision-makers with valuable information regarding the health promotion areas that should be reinforced. The measures aimed at improving adherence to preventive recommendations for obesity. Adequate measures are required with a special focus on dietary recommendations, increased physical activity, and smoking cessation.
The present study had several limitations. First, 29.5% loss in the follow-up phase might have caused selection bias. However, in further analysis, the follow-up sample was similar to the baseline sample in terms of the frequency distributions of the participants' baseline characteristics, including age, gender, level of education, and behaviors (i.e., smoking and physical activity) as such they might have had some slight effects on the present findings due to the loss to follow-up. Researchers in a cohort study on the Korean population also reported that they had 30.4% subject attrition in the follow-up phase. Regardless of these limitations, the major strength of this study includes a prospective cohort study on the general population in one of the urban region in Iran. To the best of our knowledge, this study describes the relationship between uric acid concentration and the incidence of MetS among Iranian population for the first time. In addition, given that many factors influence the progression of MetS, a variety of risk factors, in terms of type and number, used in the present work, can serve as a significant advantage.
| Conclusion|| |
In conclusion, this study provides information about the incidence of MetS in an urban region in southern Iran as men, 3.92%; women, 7.12%. Cox's proportional hazards model showed that female gender, increased TG, BMI, abdominal obesity, and smoking at baseline significantly predicted the onset of MetS after the mean period of 5.1 years. These data further confirmed the need for future research, public health, and clinical collaboration against MetS in the Iranian population.
This work was financially supported and approved by vice chancellor for research of Shiraz University of Medical Sciences (Grant: 12389 with the ethical code of 397433). We would like to thank the vice chancellor for research of Shiraz University of Medical Sciences for financial support also Center for Development of Clinical Research of Nemazee Hospital and Dr. Nasrin Shokrpour for editorial assistance.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Akbarzadeh M, Naderi T, Dabbaghmanesh MH. The glucose metabolism disorder and dyslipidemia among girls with different phenotype polycystic ovary syndrome. J Res Med Sci 2019;24:72.
] [Full text]
Ebrahimi H, Emamian MH, Khosravi A, Hashemi H, Fotouhi A. Comparison of the accuracy of three diagnostic criteria and estimating the prevalence of metabolic syndrome: A latent class analysis. J Res Med Sci 2019;24:108.
] [Full text]
O'Neill S, O'Driscoll L. Metabolic syndrome: A closer look at the growing epidemic and its associated pathologies. Obes Rev 2015;16:1-2.
Alberti KG, Zimmet P, Shaw J. Metabolic syndrome-a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 2006;23:469-80.
DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care 1991;14:173-94.
Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: Findings from the third National Health and Nutrition Examination Survey. JAMA 2002;287:356-9.
Ansarimoghaddam A, Adineh HA, Zareban I, Iranpour S, HosseinZadeh A, Kh F. Prevalence of metabolic syndrome in Middle-East countries: Meta-analysis of cross-sectional studies. Diabetes Metab Syndr 2018;12:195-201.
Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills AP, Misra A. Prevalence and trends of metabolic syndrome among adults in the asia-pacific region: A systematic review. BMC Public Health 2017;17:101.
Hadaegh F, Hasheminia M, Lotfaliany M, Mohebi R, Azizi F, Tohidi M. Incidence of metabolic syndrome over 9 years follow-up; the importance of sex differences in the role of insulin resistance and other risk factors. PLoS One 2013;8:e76304.
Rabaeus M, Salen P, de Lorgeril M. Is it smoking or related lifestyle variables that increase metabolic syndrome risk? BMC Med 2013;11:196.
Bakhshayeshkaram M, Roozbeh J, Heidari ST, Honarvar B, Dabbaghmanesh MH, B Lankarani K. Relationships between various components of metabolic syndrome and chronic kidney disease in Shiraz, Iran. Int J Endocrinol Metab 2019;17:e81822.
Goli P, Riahi R, Daniali SS, Pourmirzaei M, Kelishadi R. Association of serum uric acid concentration with components of pediatric metabolic syndrome: A systematic review and meta-analysis. J Res Med Sci 2020;25:43. [Full text]
Wang HJ, Shi LZ, Liu CF, Liu SM, Shi ST. Association between uric acid and metabolic syndrome in elderly Women. Open Med (Wars) 2018;13:172-7.
Karimi F, 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 Cardio Res J 2017;9:e11402.
Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd
, Feldman HI, et al
. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604-12.
Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C, et al
. Physical activity and public health. A recommendation from the centers for disease control and prevention and the American college of sports medicine. JAMA 1995;273:402-7.
Dabbaghmanesh MH, Naderi T, Akbarzadeh M, Tabatabaee H. Metabolic syndrome in Iranian adolescents with polycystic ovary syndrome. Int J Adolesc Med Health 2017;31:4.
Sarebanhassanabadi M, Jalil Mirhosseini S, Mirzaei M, Namayandeh S M, Soltani M H, Pedarzadeh A, et al
. The Incidence of metabolic syndrome and the most powerful components as predictors of metabolic syndrome in central Iran: A 10-year follow-up in a cohort study. Iran Red Crescent Med J 2017;19:e14934.
Hwang JH, Kam S, Shin JY, Kim JY, Lee KE, Kwon GH, et al
. Incidence of metabolic syndrome and relative importance of five components as a predictor of metabolic syndrome: 5-year follow-up study in Korea. J Korean Med Sci 2013;28:1768-73.
Santos AC, Severo M, Barros H. Incidence and risk factors for the metabolic syndrome in an urban South European population. Prev Med 2010;50:99-105.
Carnethon MR, Loria CM, Hill JO, Sidney S, Savage PJ, Liu K, et al
. Risk factors for the metabolic syndrome: The coronary artery risk development in young adults (CARDIA) study, 1985-2001. Diabetes Care 2004;27:2707-15.
Wang Y, Mi J, Shan XY, Wang QJ, Ge KY. Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. Int J Obes (Lond) 2007;31:177-88.
Faulkner JL, Belin de Chantemèle EJ. Sex hormones, aging and cardiometabolic syndrome. Biol Sex Differ 2019;10:30.
Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of Type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018;14:88-98.
Darbandi M, Darbandi S, Owji AA, Mokarram P, Mobarhan MG, Fardaei M, et al
. Auricular or body acupuncture: Which one is more effective in reducing abdominal fat mass in Iranian men with obesity: A randomized clinical trial. J Diabetes Metab Disord 2014;13:92.
Tabrizi R, Tamtaji OR, Lankarani KB, Akbari M, Dadgostar E, Dabbaghmanesh MH, et al
. The effects of resveratrol intake on weight loss: A systematic review and meta-analysis of randomized controlled trials. Crit Rev Food Sci Nutr 2020;60:375-90.
Bakhshayeshkaram M, Lankarani KB, Mirhosseini N, Tabrizi R, Akbari M, Dabbaghmanesh MH, et al
. The effects of coenzyme Q10 supplementation on metabolic profiles of patients with chronic kidney disease: A systematic review and meta-analysis of randomized controlled trials. Curr Pharm Des 2018;24:3710-23.
Schrauben SJ, Jepson C, Hsu JY, Wilson FP, Zhang X, Lash JP, et al
. Insulin resistance and chronic kidney disease progression, cardiovascular events, and death: Findings from the chronic renal insufficiency cohort study. BMC Nephrol 2019;20:60.
Karimi F, Dabbaghmanesh MH, Omrani GR. Association between serum uric acid and bone health in adolescents. Osteoporos Int 2019;30:2057-64.
[Table 1], [Table 2], [Table 3]