Evaluation of the effect of baseline predictors on the incidence of metabolic syndrome: a population-based cohort study

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Our aims were to estimate the incidence rates and to assess the risk factors of MetS among an adult population in content of a prospective cohort study. Methods This population-based cohort study conducted on 10009 adult individuals in southwest Iran. Among the participants of cohort baseline, a total number of 1,781 at risk individuals remained for follow-up, after putting aside the exclusions. Incidence rates (IRs) of metabolic syndrome were calculated by dividing the number of new events, occurred during the follow-up period by the person-years at risk. The effects of various explanatory variables on these incidence rates were assessed using Poisson regression models. Results The overall incidence rate of MetS was 102.23 per 1,000 person-years, with a higher rate in females (122.86) compared to males (83.66) (p-value < 0.001). In the multiple Poisson regression model, higher age, being female, marital status (married-widowed-divorced), higher BMI, having a history of cardiovascular diseases (CVDs) and a family history of diabetes significantly increased MetS incidence (all p-values < 0.05). Conclusion: Targeted public health strategies and lifestyle interventions are crucial for reducing metabolic syndrome incidence, particularly among high-risk groups such as women, older adults, and individuals with cardiovascular history. incidence metabolic syndrome cohort study Iran Figures Figure 1 Introduction Metabolic syndrome (MetS) is characterized by a combination of abdominal obesity, glucose intolerance, hypertriglyceridemia, low high-density lipoprotein (HDL), cholesterol, and hypertension [ 1 , 2 ]. This condition is rapidly increasing worldwide, affecting both developed and developing countries [ 3 , 4 ]. In Iran, more than 30% of the population was predicted to be affected by MetS [ 5 ]. A study investigating the prevalence of within the Hoveyzeh Cohort Study (HCS) reported 39.1% in baseline phase [ 6 ]. A rapid increase in the incidence of MetS in the United States has been documented from 1980 to 2012, rising from 25–34.25% respectively, and a significant increase in incidence has also been reported in China [ 7 ]. Research on the incidence of metabolic syndrome in Iran are limited. A study conducted in Zahedan (southeastern Iran) indicated that the incidence of metabolic syndrome varied from 17.21–27.18%, depending on the criteria used [ 8 ]. A community-based study from central Iran, which enrolled individuals participating in the Yazd Healthy Heart Program (YHHP), estimated the incidence of MetS at 56.1 per 1,000 person-years for men and 58.7 per 1,000 person-years for women, using a modified version of the NCEP-ATP III criteria [ 9 ]. Another study conducted in Tehran reported an incidence of 55 per 1,000 person-years using the Joint Interim Statement criteria [ 10 ]. Additionally, metabolic syndrome imposes significant costs on the healthcare system and generally reduces the quality of life [ 11 ]. MetS is a cluster of conditions associated with abdominal obesity and increased risk of cardiovascular disease (CVD) and mortality [ 12 ], type 2 diabetes [ 13 ], Fatty liver disease, steatohepatitis and cirrhosis. Moreover, MetS has been identified as a risk factor for a variety of diseases, including gastrointestinal, breast, and prostate cancers, polycystic ovary syndrome, and even psychological disorders [ 14 – 17 ]. Additionally, MetS is a leading cause of mortality worldwide. Some studies have reported a twofold increase in overall mortality among patients with MetS compared to the control group [ 18 ]. Despite the significance of metabolic syndrome and its components in the incidence of common non-communicable diseases(NCDs), as well as the growing burden on the healthcare system, research on the incidence of this syndrome in Iran remains limited. Furthermore, since the factors contributing to metabolic syndrome can vary across different regions and demographic and socioeconomic groups, this study aims to estimate the 5-year incidence of metabolic syndrome and identify the factors influencing it in the Hoveyzeh cohort population. Method Study design and participants The Hoveyzeh Cohort Study (HCS) is a population-based cohort study designed to assess NCDs in southwest Iran. In enrollment phase of this prospective study, 10,009 adults (age 35–70 years) recruited from May 2016 to August 2018 [ 19 ]. The re-assessment phases of this cohort aim to identify and document potential changes in the nature and extent of contact, exposures, and risk factors for NCDs among the study participants. It will be implemented every 5 years after the enrollment phase. The first round of re-assessment phases was held in 2022. During this phase, a 30 percent representative sample of the participants from the enrollment phase Invited to the cohort center and were re-evaluated. The inclusion criteria for the re-evaluation study were carefully selected from the study cohort that was accepted and enrolled during the initial phase in a multi-stage random manner. First, the proportion of urban and rural populations in the initial cohort sample size was determined and stratified the two categories. Subsequently, 3,019 individuals out of 10,009 were selected based on systematic random sampling method from each list of the urban and rural participants. The median follow-up period was 3.98 years. After excluding participants who had metabolic syndrome at baseline (n = 1181), those with missing laboratory information (n = 8) and pregnant participants (n = 49), there were 1781 participants remaining for the analysis (Fig. 1 ). The implementation process of the study during the re-assessment phase generally included registration, obtaining informed consent, recording the demographic information of the subjects, taking anthropometric measurements, collecting biological samples, and administering medical, general, and nutritional questionnaires. The supervisor and quality control officer were presented at the cohort center daily to continuously address any potential issues related to the implementation of the study and data collection. Definition of Metabolic Syndrome (MetS) NCEP-ATP III criteria were used to diagnose MetS, which include: 1) abdominal obesity (waist circumference ≥ 102 cm in men and ≥ 88 cm in women), 2) elevated serum triglycerides (≥ 150 mg/dL) or the use of medications for hypertriglyceridemia, 3) low serum high-density lipoprotein (HDL) cholesterol (< 40 mg/dL in men and < 50 mg/dL in women) or the use of drug treatment for low HDL cholesterol, 4) high blood pressure (≥ 130/85 mmHg) or the use of antihypertensive medications, and 5) elevated fasting plasma glucose (FPG) (≥ 100 mg/dL) or the use of medications for hyperglycemia. The presence of at least 3 out of the 5 criteria listed above constitutes a diagnosis of MetS(1). Collection of Biological Samples Individuals attending the study had been fasting for about 10 to 12 h on the day of enrollment. A 15 mL blood sample was obtained from each participant, consisting of a 9 mL EDTA tube and a 6 mL clot tube. After centrifugation of the clot tube, a serum sample (λ 500) was separated and transported to the laboratory for biochemical analysis. The laboratory procedures and quality control measures of the Hoveyzeh Cohort Study have been previously published [ 6 ]. Covariates Demographic factors included sex (male and female), age groups (35–44, 45–54, 55–64, and 65–70 years), marital status (single, married, widowed, divorced), and education level (illiterate, primary school, secondary school, high school diploma, and university). Residential areas were classified as either urban or rural. Physical activity was evaluated using the Metabolic Equivalent Task (MET), with scores divided into quartiles (Q1-Q4). The wealth index is calculated by considering various household factors, such as ownership of assets including televisions, bicycles, cars, and computers. These wealth scores are then categorized into five quintiles, ranging from the poorest to the richest. Body Mass Index (BMI) is classified into categories (underweight, normal, overweight, and obese). Smoking status and alcohol consumption were recorded as either yes or no. Sleep duration is categorized as follows: ≤6 hours, 7 hours, 8 hours, and ≥ 9 hours. Additionally, a history of CVDs and a family history of diabetes are noted as either yes or no. Statistical analysis Descriptive statistics were calculated using the mean and standard deviation for quantitative variables, while incidence and percentage were utilized for categorical variables. Crude incidence rates (IRs) and their corresponding 95% confidence intervals (95% CIs) were calculated by dividing the number of metabolic syndrome events by the person-years at risk for the entire sample. Time at risk was defined as the number of years from the first visit in the baseline phase to the second visit in the reassessment phase for individuals who did not have metabolic syndrome. Generalized linear Poisson models were used to estimate the effects of explanatory variables on incidence rates. All reported p-values were derived from two-tailed tests and were compared to a significance level of 0.05. All analyses were conducted using Stata software, version 15. Results A total of 1,781 individuals were evaluated in this analysis. The mean age of the participants was 47.71 years (± 8.64), with ages ranging from 35 to 70 years. Of the participants, 907 (50.9%) were male. The median follow-up duration was 3.98 years, and the study participants were followed for a total of 6,548.176 person-years. During the follow-up period, 670 new cases of MetS were diagnosed, resulting in an overall incidence rate of 102.23 (95% CI: 94.72–110.37) per 1,000 person-years. In other words, approximately 10.2% of at-risk participants develop MetS annually. Table 1 presents the incidence rates of metabolic syndrome based on demographic, socioeconomic, and lifestyle risk factors. The results indicate that the incidence rates of MetS vary significantly across various demographic, socioeconomic, and lifestyle factors. The findings indicate that females, older participants, widowed individuals, those with lower education levels, urban residents, obese individuals, people with a history of CVDs, and participants with a family history of diabetes had higher incidence rates of MetS. Table 1 Incidence rates of metabolic syndrome by demographic, socioeconomic, and risk factors of the condition. Variables Total (no) Events (no) Person-time at risk (years) Incidence rate per 1000 person-yeas (95% CI) Overall Incidence rates 1781 670 6548.176 102.23 (94.72–110.37) Sex Male 907 287 3430.692 83.66(75.84–92.09) Female 874 383 3117.484 122.86(107.29-139.97) Age groups (years) 35–44 765 261 2868.775 90.98(77.13-106.52) 45–54 608 247 2209.190 111.81(94.33-131.47) 55–64 329 135 1179.716 114.43(90.65-142.34) ≥ 65 79 27 290.493 92.95(52.33-149.74) Marital Status Single 56 11 221.407 49.68(19.52-102.88) Married 1624 607 5978.514 101.53(91.23-112.64) Widowed 71 35 248.768 140.69(86.98-214.35) Divorced 30 17 99.486 92.95(52.33-149.74) Education level Illiterate 1016 415 3744.580 110.82(97.31-125.63) Primary school 321 116 1196.395 96.96(75.33-122.66) Secondary school 123 42 443.356 94.73(61.28-129.33) High school 160 54 562.645 95.98(65.65-135.05) University 161 43 601.20 71.52(46.53-104.74) Residence area Urban 1136 421 4087.530 102.99(93.39-113.32) Rural 645 249 2460.645 101.19(89.01-114.57) Physical activity (MET) Q1 391 146 1423.86 102.54(87–120) Q2 407 164 1435.851 114.22(98–132) Q3 436 166 1614.53 102.82(83.42-125.22) Q4 547 194 2073.934 93.54(77.15-112.28) Wealth status Poorest 329 130 1240.312 104.81(82.64-130.91) Poor 388 138 1461.73 94.41(74.99-117.16) Moderate 340 126 1232.685 102.22(80.28-128.11) Rich 320 115 1179.432 97.5(75.67-123.47) Richest 404 161 1434.016 112.27(90.79-137.14) BMI Underweight 30 4 134.458 29.75(11.8-63.74) Normal 518 112 2112.237 53.02(45.28–61.78) Overweight 712 259 2623.042 98.74(89.05-109.24) Obese 521 295 1678.438 175.76(159.56-193.23) Smoker Yes 388 131 1463.849 89.49(70.63-111.68) No 1393 539 5084.326 106.01(98–115) Alcohol consumption Yes 32 11 119.384 92.14(36.19–190.8) No 1749 659 6428.792 102.5(92.51-113.25) Sleep duration (hours) ≤ 6 446 147 1656.675 88.73(77.33-101.42) 7 400 160 1443.466 110.84(97.16-126.01) 8 454 176 1673.518 105.17(92.76-118.85) ≥ 9 481 187 1774.517 105.38(93.30-118.67) History of CVDs Yes 197 92 682.897 134.72 (113.15-159.42) No 1584 578 5865.278 98.55(91.98-105.47) Family History of Diabetes Yes 884 360 3170.851 113.53 (104.03-123.72) No 889 309 3346.137 92.35 (84.02-101.31) Female participants showed a higher incidence rate of MetS at 122.86 per 1,000 person-years compared to males, who had an incidence rate of 83.66 per 1,000 person-years. The incidence rates generally increased with age; however, the highest incidence rate was observed among middle-aged individuals (ages 55–64), while there was a slight decrease in the incidence for those aged 65 and older. Widowed had a notably higher incidence rate of 140.69 per 1,000 person-years, particularly in comparison to singles, who had an incidence rate of 49.68 per 1,000 person-years. Overall, the incidence rates of MetS decreased with increasing educational level, so the highest incidence rate found among illiterate participants 110.82 per 1,000 person-years, while the lowest rate was observed in individuals with a university education, at 71.52 per 1,000 person-years. Urban residents showed a similar incidence rate of 102.99 per 1,000 person-years compared to those living in rural areas, who had an incidence rate of 101.19 per 1,000 person-years. Physical activity did not show a clear trend; however, participants with high levels of physical activity (Q4) had the lowest incidence rate of MetS at 93.54 per 1,000 person-years. Conversely, the category with the highest incidence rate was the richest, at 114.22 per 1,000 person-years, although no obvious trend was observed. Additionally, the incidence rates of MetS increased as BMI rose, indicating a strong and direct association between BMI and MetS. Non-smokers had a higher incidence rate of MetS (106.01 per 1,000 person-years) compared to smokers (89.49 per 1,000 person-years). However, the results indicated a lower incidence for individuals who consume alcohol (92.14 per 1,000 person-years) compared to non-drinkers (102.5 per 1,000 person-years), this difference was not statistically significant. Sleep duration did not demonstrate a clear association with the incidence rates of MetS. Individuals with a history of CVDs had a significantly higher incidence rate of MetS (134.72 per 1,000 person-years) compared to those without a history of CVDs (98.55 per 1,000 person-years). Furthermore, participants with a family history of diabetes showed a higher incidence rate of MetS (113.53 per 1,000 person-years) compared to those without a family history of diabetes (92.35 per 1,000 person-years) (Table 1 ). The univariate and multiple Poisson regression analyses were utilized to estimate crude and adjusted rate ratios, respectively. In the univariate regression analysis, several assessed variables including sex, age, marital status, education, BMI, history of CVDs, and family history of diabetes were significantly associated with the incidence of MetS (P < 0.05). In contrast, no significant associations were observed between area of residence, physical activity, wealth score, smoking, alcohol consumption, and sleep duration with the incidence of the condition (P > 0.05). In the next step, to control for potential confounding factors, all assessed variables with a p-value < 0.25 in the univariate analysis were simultaneously included in the multiple Poisson regression model. The results from the multiple Poisson regression analysis indicated that the adjusted rate ratio (RR) was 1.26 (95% CI: 1.02–1.55) with a p-value of 0.031, suggesting that females had a 26% higher incidence of MetS compared to males. Additionally, the findings demonstrated that the incidence of MetS increased with age, particularly among middle-aged participants. Specifically, compared to the reference group of individuals aged 35–39, those in the 55–64 age group had a 44% higher incidence of MetS [RR = 1.44 (95% CI: 1.14–1.82); p = 0.002]. However, the increase in the incidence rate of MetS for individuals aged 65 years and older compared to the 35–39 age group was not statistically significant [RR = 1.28 (95% CI: 0.84–1.96); p = 0.252]. Married [RR = 1.88 (95% CI: 1.03–3.43)]., widowed [RR = 2.08 (95% CI: 1.04–4.14)]., and divorced [RR = 2.38 (95% CI: 1.11–5.13)] individuals have a higher risk of MetS compared to singles. Higher BMI significantly increases the risk of MetS, particularly in the overweight [RR= (3.82(95% CI: 1.42–10.31)] and obese [RR= (95% CI: 6.46(95% CI: 2.38–17.46)] categories. Additionally, having a history of CVDs and a family history of diabetes are also associated with an increased risk of MetS, with RR values of [RR = 1.28(95% CI: 1.02–1.60)] and [RR = 1.21(95% CI: 1.04–1.37)], respectively. Table 2 The crude and adjusted rate ratios of the assessed factors for Metabolic syndrome and their 95% confidence intervals using the Poisson regression model Variables Crude RR p-value Adjusted RR P-value Sex Male 1 < 0.001 1 0.031 Female 1.47(1.26–1.71) 1.26 (1.02–1.55) Age groups (years) 35–44 1 1 45–54 1.22(1.03–1.46) 0.02 1.25(1.04–1.50) 0.015 55–64 1.26(1.02–1.55) 0.03 1.44(1.14–1.82) 0.002 ≥ 65 1.02(0.69–1.52) 0.916 1.28(0.84–1.96) 0.252 Marital Status Single 1 1 Married 2.04(1.13–3.70) 0.019 1.88 (1.03–3.43) 0.041 Widowed 2.83(1.44–5.58) 0.003 2.08 (1.04–4.14) 0.037 Divorced 3.44(1.61–7.34) 0.001 2.38 (1.11–5.13) 0.026 Educational levels Illiterate 1 1 Primary school 0.87(0.71–1.07) 0.203 0.92 (0.74–1.15) 0.472 Secondary school 0.85(0.62–1.17) 0.333 0.93 (0.66–1.31) 0.698 High school 0.86(0.65–1.15) 0.320 1.02 (0.74– 1.39) 0.901 University 0.64(0.47–0.88) 0.006 0.76 (0.53– 1.09) 0.139 Residence area Urban 1 0.825 - - Rural 0.98(0.83–1.15) - Physical activity (MET) Q1 1.09(0.88–1.36) 0.402 1.04 (0.82–1.31) 0.758 Q2 1.22(0.99–1.50) 0.06 1.08 (0.87–1.34) 0.507 Q3 1.09(0.89–1.35) 0.371 0.98 (0.79–1.22) 0.877 Q4 1 1 Wealth status Poorest 1 - - Poor 0.90(0.70–1.39) 0.392 - - Moderate 0.97(0.76–1.25) 0.841 - - Rich 0.93(0.72–1.19) 0.572 - - Richest 1.07(0.85–1.35) 0.560 - - BMI Underweight 1 1 Normal 1.78(0.66–4.83) 0.256 2.01(0.73–5.47) 0.171 Overweight 3.32(1.24–8.91) 0.017 3.82(1.42–10.31) 0.008 Obese 5.90(2.21–15.85) < 0.001 6.46(2.38–17.46) < 0.001 Smoker Yes 1 0.082 1 0.822 No 1.18(0.98–1.43) 0.98(0.78–1.21) Alcohol consumption Yes 0.89(0.49–1.63) 0.726 - - No 1 - Sleep duration (years) ≤ 6 1 1 7 1.25(0.99–1.56) 0.051 1.25(0.99–1.58) 0.054 8 1.18(0.95–1.48) 0.128 1.15(0.91–1.45) 0.233 ≥ 9 1.19(0.95–1.47) 0.119 1.17(0.92–1.48) 0.192 History of CVDs Yes 1.37(1.23–1.51) 0.005 1.28(1.02–1.60) 0.031 No 1 1 Family History of Diabetes Yes 1.23(1.056–1.432) 0.008 1.21(1.04–1.37) 0.014 No 1 1 *P < 0.05 significant for the Poisson regression model RR: rate ratios Discussion The present study aimed to investigate the incidence of metabolic syndrome and its associated factors in the population aged 35 to 70 years participating in the Hoveyzeh Cohort Study (HCS). The incidence of MetS was found to be 102.23 per 1,000 person-years, which corresponds to an approximate annual incidence of 10.2% among at risk participants. The findings indicated that age, gender, marital status, BMI, history of CVDs, and family history of Diabetes predictors for the incidence of MetS. Our study indicated an overall incidence rate of 102.23 (95% CI: 94.72 -110.37) per 1,000 person-years. In a study conducted in south-east of Iran, the incidence rate of MetS was reported 54.59 per 1,000 person-years. [ 20 ]. Additionally, a systematic review estimated that the overall pooled incidence rate among the general population in Iran was 97.96 (95% CI: 75.98, 131.48) per 1000 [ 21 ]. In a cohort study conducted in Tehran, the incidence rate was reported 550.9/10000 person/years. This significant difference may be attributed to variations in population characteristics, lifestyle factors, or access to healthcare, all of which can influence the development of metabolic syndrome. The incidence of metabolic syndrome was higher in our population, which may be partly due to the higher average age of participants in the present study compared to other studies. The results showed that women were 26% more likely to develop MetS compared to men. Some previous studies supported our findings [ 22 ], while others had different results [ 23 – 25 ]. In a Portuguese cohort, the incidence rate was 47.2 per 1,000 person-years, with no significant gender difference [ 26 ]. The discrepancies seen in studies regarding the relationship between sex and the development of metabolic syndrome stem from a complex interplay of hormonal influences, variations in body composition, age-related factors, socioeconomic status, lifestyle choices like physical activity and ethnic differences [ 27 ]. The results indicate that age is a significant risk factor for the development of metabolic syndrome. Other studies also suggest that age plays a crucial role in the development and progression of metabolic disorders. Aging linked to a decrease in metabolic rate and changes in body composition, such as increased fat mass and decreased muscle mass. These changes contribute to insulin resistance and higher levels of inflammation, both of which are risk factors for MetS [ 28 , 29 ]. Additionally, as individuals age increase, they tend to engage in less physical activity and make dietary changes. These can lead to enhance weight and obesity, that in turn, increase the risk of developing MetS [ 28 , 30 ]. Furthermore in women, specially post-menopausal women, hormonal changes play a significantly role in the increased risk of MetS due to weight redistribution and enhanced fat accumulation around the abdomen [ 30 ]. In this study the incidence of MetS was higher in married, widowed, and divorced individuals compared to single individuals. Widowed individuals, especially women, were consistently at higher risk of developing MetS compared with their married counterparts. A Korean study noted that the odds ratio (OR) for metabolic syndrome was significantly increased in widowed women, even after adjusting for socioeconomic factors and health behaviors, indicating a direct and strong association between widowhood and risk of metabolic syndrome [ 31 ]. The connection between marital status and MetS is not clear for single people. Some research indicates singles might have a lower risk than married individuals, influenced by lifestyle factors. The impact of divorce on MetS risk is also unclear, with mixed findings on health changes post-divorce [ 32 ]. The incidence of metabolic syndrome was significantly higher in patients with CVDs. Studies have shown that patients with CVDs are at greater risk of developing MetS due to several interconnected factors. These risk factors include hypertension [ 33 ], hyperglycemia (increased blood sugar) [ 34 ], dyslipidemia, and abdominal obesity [ 35 ], all of which are common in this population. Lifestyle factors such as a sedentary lifestyle, poor diet, and smoking worsen these risks [ 34 , 35 ]. Additionally, age and gender play a role in this relationship, with older adults and postmenopausal women being more vulnerable [ 36 , 37 ]. Limitation and Strengths Our study has several strengths. First, we utilized a cohort study design, which allowed us to collect longitudinal data. This enabled us to observe the development of MetS over time and establish temporal relationships between risk factors and outcomes. Second, our study included large populations, increasing the statistical power of our results and reducing the possibility of random error. This led to more reliable incidence estimates. Third, the comprehensive data collection in the Hoveyzeh cohort study allowed us to examine associations between various health measures, lifestyle factors, and the full range of the association between multiple risk factors for metabolic syndrome. Fourth, we used standardized definitions for diagnosing of metabolic syndrome and accurately measured of its components using standard laboratory and anthropometric devices. This increased the comparability and validity of our findings across different studies. However, our study had limitations. First, the study population was limited to middle-aged and elderly individuals. This demographic characteristic may have limited the generalizability of the results. Second, the outcome was measured only in the re-assessment phase (approximately 5 years after the enrollment phase) and not during the follow-up period, so the exact timing of the outcome is not known. Conclusion Analysis of MetS incidence among 1,781 participants revealed that approximately 10.2% of at-risk individuals develop MetS annually, with significant variations based on gender, age, marital status, education level, and health history. Notably, females exhibit a higher incidence rate than males, and the risk increases with age, particularly peaking among those aged 55–64. Additionally, widowed individuals and those with lower educational attainment show higher rates of MetS, while a history of cardiovascular diseases is significantly associated with increased incidence. These findings highlight the need for targeted public health strategies focused on high-risk groups, including women, older adults, and individuals with lower education or cardiovascular histories. Promoting healthy lifestyle choices may also play a crucial role in reducing MetS risk. Overall, the study emphasizes the importance of tailored interventions and further research to effectively address the burden of MetS and its associated health complications within these populations. Abbreviations MetS Metabolic syndrome IRs Incidence rates BMI Body mass index CVDs Cardiovascular diseases HDL High-density lipoprotein HCS Hoveyzeh Cohort Study NCDs Non-communicable diseases RR Rate ratio 95% CIs Confidence intervals Declarations Acknowledgments This article is extracted from the general doctoral thesis of Seyyedeh Maryam Hashemi (Grant number U-02072). This study was approval by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences. We would like to express the participants and staff of the Hoveyzeh Cohort Study Center who assisted us in conducting this study. Competing interests The authors declare that there are no competing interests related to this manuscript. CRediT authorship contribution statement Bahman Cheraghian and Seyed Jalal Hashemi: conceptualization, project administration, writing- review editing. Zahra Rahimi formal analysis, methodology, writing- review editing. Seyedeh Maryam Hashemi project administration, and writing-original draft. Alireza Jahanshahi supervision. All authors reviewed the manuscript. Funding sources This work was supported by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences (Grant number U-02072). The Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences as our funding body played no role in the design of the study, collection, analysis, and interpretation of data and in writing the manuscript. Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.HGOLESTAN.REC.1402.040). Consent to Publish declaration Not applicable References Saely CH, et al. Adult Treatment Panel III 2001 but not International Diabetes Federation 2005 criteria of the metabolic syndrome predict clinical cardiovascular events in subjects who underwent coronary angiography. Diabetes Care. 2006;29(4):901–7. 10.2337/diacare.29.04.06.dc05-2011 . Grundy SM, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation. 2005;112(17):2735–52. 10.1161/CIRCULATIONAHA.105.169404 . Estes C, et al. Modeling nafld disease burden in china, france, germany, italy, japan, spain, united kingdom, and united states for the period 2016–2030. 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Social determinants of health in non-communicable diseases: case studies from Japan. Springer Nat. 2020. 10.1007/978-981-15-1831-7 . Fu CE, et al. The prognostic value of including non-alcoholic fatty liver disease in the definition of metabolic syndrome. Aliment Pharmacol Ther. 2023;57(9):979–87. 10.1111/apt.17397 . Hamaguchi M, et al. Identification of individuals with non-alcoholic fatty liver disease by the diagnostic criteria for the metabolic syndrome. World J gastroenterology: WJG. 2012;18(13):1508. 10.3748/wjg.v18.i13.1508 . Mandrelle K, et al. Prevalence of metabolic syndrome in women with polycystic ovary syndrome attending an infertility clinic in a tertiary care hospital in south India. J Hum reproductive Sci. 2012;5(1):26–31. 10.4103/0974-1208.97791 . Braun S, Bitton-Worms K, LeRoith D. The link between the metabolic syndrome and cancer. Int J Biol Sci. 2011;7(7):1003. 10.7150/ijbs.7.1003 . Li Z, et al. The cohort study on prediction of incidence of all-cause mortality by metabolic syndrome. PLoS ONE. 2016;11(5):e0154990. 10.1371/journal.pone.0154990 . Cheraghian B, et al. Cohort profile: The Hoveyzeh Cohort Study (HCS): A prospective population-based study on non-communicable diseases in an Arab community of Southwest Iran. Med J Islamic Repub Iran. 2020;34:141. 10.34171/mjiri.34.141 . Bakhshayeshkaram M, et al. 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. 10.4103/jrms.JRMS_884_19 . 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. 2020;11(1):64. 10.4103/ijpvm.IJPVM_489_18 . Kang Y, Kim J. Soft drink consumption is associated with increased incidence of the metabolic syndrome only in women. Br J Nutr. 2017;117(2):315–24. 10.1017/S0007114517000046 . Alipour P, ROLE OF SEX AND GENDER IN DEVELOPMENT OF METABOLIC SYNDROME, et al. A PROSPECTIVE COHORT STUDY. Can J Cardiol. 2022. 10.1016/j.cjca.2022.08.046 . Alipour P, et al. Role of sex and gender-related variables in development of metabolic syndrome: A prospective cohort study. Eur J Intern Med. 2023;121:63–75. 10.1016/j.ejim.2023.10.006 . Dev R, et al. Impact of sex and gender on metabolic syndrome in adults: a retrospective cohort study from the Canadian Primary Care Sentinel Surveillance Network. Can J Diabetes. 2024;48(1):36–43. e2. Santos AC, Severo M, Barros H. Incidence and risk factors for the metabolic syndrome in an urban South European population. Prev Med. 2010;50(3):99–105. 10.1016/j.ypmed.2009.11.011 . Nouri-Keshtkar M, et al. Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling. Sci Rep. 2023;13(1):16007. 10.1038/s41598-023-40485-y . Roos V, et al. Metabolic Syndrome Development During Aging with Special Reference to Obesity Without the Metabolic Syndrome. Metab Syndr Relat Disord. 2017;15(1):36–43. 10.1089/met.2016.0082 . Devrajani T, et al. Relationship between aging and control of metabolic syndrome with telomere shortening: a cross-sectional study. Sci Rep. 2023;13(1):17878. 10.1038/s41598-023-44715-1 . Bae C-Y, et al. Biological age and lifestyle in the diagnosis of metabolic syndrome: the NHIS health screening data, 2014–2015. Sci Rep. 2021;11(1):444. 10.1038/s41598-020-79256-4 . Jung Y-A, et al. Relationship between marital status and metabolic syndrome in Korean middle-aged women: the sixth Korea National Health and Nutrition Examination Survey (2013–2014). Korean J family Med. 2018;39(5):307. 10.4082/kjfm.17.0020 . Hosseinpour-Niazi S, et al. Association of marital status and marital transition with metabolic syndrome: tehran lipid and glucose study. Int J Endocrinol metabolism. 2014;12(4). 10.5812/ijem.18980 . Oğuz A, et al. Risk of cardiovascular events in patients with metabolic syndrome: Results of a population-based prospective cohort study (PURE Turkey). Anatol J Cardiol. 2020;24(3):192–200. 10.14744/AnatolJCardiol.2020.27227 . Rocha E. Metabolic syndrome and cardiovascular risk. Rev Port Cardiol. 2019;38(5):333–5. 10.1016/j.repc.2019.06.003 . Guembe MJ, et al. Risk for cardiovascular disease associated with metabolic syndrome and its components: a 13-year prospective study in the RIVANA cohort. Cardiovasc Diabetol. 2020;19(1):195. 10.1186/s12933-020-01166-6 . Lind L, et al. A longitudinal study over 40 years to study the metabolic syndrome as a risk factor for cardiovascular diseases. Sci Rep. 2021;11(1):2978. 10.1038/s41598-021-82398-8 . Li X, et al. Impact of metabolic syndrome and it's components on prognosis in patients with cardiovascular diseases: a meta-analysis. Front Cardiovasc Med. 2021;8:704145. 10.3389/fcvm.2021.704145 . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6420781","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451788929,"identity":"2ece5925-7c5f-4063-aaf3-f837183a61a0","order_by":0,"name":"Seyedeh Maryam Hashemi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seyedeh","middleName":"Maryam","lastName":"Hashemi","suffix":""},{"id":451788930,"identity":"56d44ae5-1251-44c7-a277-2c9007953ee3","order_by":1,"name":"Seyed Jalal Hashemi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Jalal","lastName":"Hashemi","suffix":""},{"id":451788931,"identity":"61cb7e18-3da9-425b-a91f-8ab2b21c2d45","order_by":2,"name":"Zahra Rahimi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Rahimi","suffix":""},{"id":451788932,"identity":"d2c1e804-c2ba-4fc5-bfaa-801f961195d9","order_by":3,"name":"Alireza Jahanshahi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Jahanshahi","suffix":""},{"id":451788933,"identity":"8f1568c0-333b-4d8a-9c70-0fc0c4581b0c","order_by":4,"name":"Bahman Cheraghian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACCTB5gIeBgfnAgQQDG5gIUVrYEh98qEgjXgsQ8xgbzjhzmLAW/tnNxz78+HVHhn92g5k0b9v5xP7ZzQcfMNTYROO05M6x5Jm9fc94JO4cSANquZ04AyhiwHAsLbcBhxYDiRxjBt6ewzwMNxKOgbU03Mgxk2BsOIxHS/5nxr9ALfI3EtuAWs4lziesJYeZmefHYR6DG8nMQO8fSNxASIvEjTRjZtmGwzyGN9IYgYGcbLzxRlqyQQIev/DPSH7M+ObPYXu5G/kfgFFpJzvvRvLBBx9qbHBqAQPGNgTbEawyAZ9yMPiDYNoTVDwKRsEoGAUjDgAASsRlxk1R5h0AAAAASUVORK5CYII=","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Bahman","middleName":"","lastName":"Cheraghian","suffix":""}],"badges":[],"createdAt":"2025-04-10 13:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6420781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6420781/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82300844,"identity":"c7cbfea3-bb6d-4865-a9b6-52e3aceff63d","added_by":"auto","created_at":"2025-05-08 20:51:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64674,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study participants\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6420781/v1/caca37864d2fd93954cfbd39.png"},{"id":82301573,"identity":"8f27457a-1c49-43b7-8a45-653b2bac3b81","added_by":"auto","created_at":"2025-05-08 21:07:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1124367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6420781/v1/930fd926-916f-4786-89bc-00068df3e9d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the effect of baseline predictors on the incidence of metabolic syndrome: a population-based cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is characterized by a combination of abdominal obesity, glucose intolerance, hypertriglyceridemia, low high-density lipoprotein (HDL), cholesterol, and hypertension [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This condition is rapidly increasing worldwide, affecting both developed and developing countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Iran, more than 30% of the population was predicted to be affected by MetS [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A study investigating the prevalence of within the Hoveyzeh Cohort Study (HCS) reported 39.1% in baseline phase [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA rapid increase in the incidence of MetS in the United States has been documented from 1980 to 2012, rising from 25\u0026ndash;34.25% respectively, and a significant increase in incidence has also been reported in China [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Research on the incidence of metabolic syndrome in Iran are limited. A study conducted in Zahedan (southeastern Iran) indicated that the incidence of metabolic syndrome varied from 17.21\u0026ndash;27.18%, depending on the criteria used [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A community-based study from central Iran, which enrolled individuals participating in the Yazd Healthy Heart Program (YHHP), estimated the incidence of MetS at 56.1 per 1,000 person-years for men and 58.7 per 1,000 person-years for women, using a modified version of the NCEP-ATP III criteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Another study conducted in Tehran reported an incidence of 55 per 1,000 person-years using the Joint Interim Statement criteria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, metabolic syndrome imposes significant costs on the healthcare system and generally reduces the quality of life [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetS is a cluster of conditions associated with abdominal obesity and increased risk of cardiovascular disease (CVD) and mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], type 2 diabetes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], Fatty liver disease, steatohepatitis and cirrhosis. Moreover, MetS has been identified as a risk factor for a variety of diseases, including gastrointestinal, breast, and prostate cancers, polycystic ovary syndrome, and even psychological disorders [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, MetS is a leading cause of mortality worldwide. Some studies have reported a twofold increase in overall mortality among patients with MetS compared to the control group [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the significance of metabolic syndrome and its components in the incidence of common non-communicable diseases(NCDs), as well as the growing burden on the healthcare system, research on the incidence of this syndrome in Iran remains limited. Furthermore, since the factors contributing to metabolic syndrome can vary across different regions and demographic and socioeconomic groups, this study aims to estimate the 5-year incidence of metabolic syndrome and identify the factors influencing it in the Hoveyzeh cohort population.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThe Hoveyzeh Cohort Study (HCS) is a population-based cohort study designed to assess NCDs in southwest Iran. In enrollment phase of this prospective study, 10,009 adults (age 35\u0026ndash;70 years) recruited from May 2016 to August 2018 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The re-assessment phases of this cohort aim to identify and document potential changes in the nature and extent of contact, exposures, and risk factors for NCDs among the study participants. It will be implemented every 5 years after the enrollment phase. The first round of re-assessment phases was held in 2022. During this phase, a 30 percent representative sample of the participants from the enrollment phase Invited to the cohort center and were re-evaluated.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for the re-evaluation study were carefully selected from the study cohort that was accepted and enrolled during the initial phase in a multi-stage random manner. First, the proportion of urban and rural populations in the initial cohort sample size was determined and stratified the two categories. Subsequently, 3,019 individuals out of 10,009 were selected based on systematic random sampling method from each list of the urban and rural participants. The median follow-up period was 3.98 years. After excluding participants who had metabolic syndrome at baseline (n\u0026thinsp;=\u0026thinsp;1181), those with missing laboratory information (n\u0026thinsp;=\u0026thinsp;8) and pregnant participants (n\u0026thinsp;=\u0026thinsp;49), there were 1781 participants remaining for the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The implementation process of the study during the re-assessment phase generally included registration, obtaining informed consent, recording the demographic information of the subjects, taking anthropometric measurements, collecting biological samples, and administering medical, general, and nutritional questionnaires. The supervisor and quality control officer were presented at the cohort center daily to continuously address any potential issues related to the implementation of the study and data collection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of Metabolic Syndrome (MetS)\u003c/h3\u003e\n\u003cp\u003eNCEP-ATP III criteria were used to diagnose MetS, which include: 1) abdominal obesity (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;102 cm in men and \u0026ge;\u0026thinsp;88 cm in women), 2) elevated serum triglycerides (\u0026ge;\u0026thinsp;150 mg/dL) or the use of medications for hypertriglyceridemia, 3) low serum high-density lipoprotein (HDL) cholesterol (\u0026lt;\u0026thinsp;40 mg/dL in men and \u0026lt;\u0026thinsp;50 mg/dL in women) or the use of drug treatment for low HDL cholesterol, 4) high blood pressure (\u0026ge;\u0026thinsp;130/85 mmHg) or the use of antihypertensive medications, and 5) elevated fasting plasma glucose (FPG) (\u0026ge;\u0026thinsp;100 mg/dL) or the use of medications for hyperglycemia. The presence of at least 3 out of the 5 criteria listed above constitutes a diagnosis of MetS(1).\u003c/p\u003e\n\u003ch3\u003eCollection of Biological Samples\u003c/h3\u003e\n\u003cp\u003eIndividuals attending the study had been fasting for about 10 to 12 h on the day of enrollment. A 15 mL blood sample was obtained from each participant, consisting of a 9 mL EDTA tube and a 6 mL clot tube. After centrifugation of the clot tube, a serum sample (λ 500) was separated and transported to the laboratory for biochemical analysis. The laboratory procedures and quality control measures of the Hoveyzeh Cohort Study have been previously published [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic factors included sex (male and female), age groups (35\u0026ndash;44, 45\u0026ndash;54, 55\u0026ndash;64, and 65\u0026ndash;70 years), marital status (single, married, widowed, divorced), and education level (illiterate, primary school, secondary school, high school diploma, and university). Residential areas were classified as either urban or rural. Physical activity was evaluated using the Metabolic Equivalent Task (MET), with scores divided into quartiles (Q1-Q4). The wealth index is calculated by considering various household factors, such as ownership of assets including televisions, bicycles, cars, and computers. These wealth scores are then categorized into five quintiles, ranging from the poorest to the richest. Body Mass Index (BMI) is classified into categories (underweight, normal, overweight, and obese). Smoking status and alcohol consumption were recorded as either yes or no. Sleep duration is categorized as follows: \u0026le;6 hours, 7 hours, 8 hours, and \u0026ge;\u0026thinsp;9 hours. Additionally, a history of CVDs and a family history of diabetes are noted as either yes or no.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were calculated using the mean and standard deviation for quantitative variables, while incidence and percentage were utilized for categorical variables. Crude incidence rates (IRs) and their corresponding 95% confidence intervals (95% CIs) were calculated by dividing the number of metabolic syndrome events by the person-years at risk for the entire sample. Time at risk was defined as the number of years from the first visit in the baseline phase to the second visit in the reassessment phase for individuals who did not have metabolic syndrome. Generalized linear Poisson models were used to estimate the effects of explanatory variables on incidence rates. All reported p-values were derived from two-tailed tests and were compared to a significance level of 0.05. All analyses were conducted using Stata software, version 15.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,781 individuals were evaluated in this analysis. The mean age of the participants was 47.71 years (\u0026plusmn;\u0026thinsp;8.64), with ages ranging from 35 to 70 years. Of the participants, 907 (50.9%) were male. The median follow-up duration was 3.98 years, and the study participants were followed for a total of 6,548.176 person-years. During the follow-up period, 670 new cases of MetS were diagnosed, resulting in an overall incidence rate of 102.23 (95% CI: 94.72\u0026ndash;110.37) per 1,000 person-years. In other words, approximately 10.2% of at-risk participants develop MetS annually.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the incidence rates of metabolic syndrome based on demographic, socioeconomic, and lifestyle risk factors. The results indicate that the incidence rates of MetS vary significantly across various demographic, socioeconomic, and lifestyle factors. The findings indicate that females, older participants, widowed individuals, those with lower education levels, urban residents, obese individuals, people with a history of CVDs, and participants with a family history of diabetes had higher incidence rates of MetS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence rates of metabolic syndrome by demographic, socioeconomic, and risk factors of the condition.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal (no)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003cp\u003e(no)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerson-time\u003c/p\u003e \u003cp\u003eat risk (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncidence rate\u003c/p\u003e \u003cp\u003eper 1000 person-yeas (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Incidence rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6548.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.23 (94.72\u0026ndash;110.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3430.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.66(75.84\u0026ndash;92.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3117.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122.86(107.29-139.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e35\u0026ndash;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2868.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.98(77.13-106.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e45\u0026ndash;54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2209.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e111.81(94.33-131.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e55\u0026ndash;64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1179.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114.43(90.65-142.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e290.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.95(52.33-149.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSingle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e221.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.68(19.52-102.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5978.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101.53(91.23-112.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWidowed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e248.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e140.69(86.98-214.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDivorced\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.95(52.33-149.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIlliterate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3744.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110.82(97.31-125.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1196.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.96(75.33-122.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSecondary school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e443.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.73(61.28-129.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e562.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.98(65.65-135.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUniversity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e601.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.52(46.53-104.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4087.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.99(93.39-113.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2460.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101.19(89.01-114.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePhysical activity (MET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1423.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.54(87\u0026ndash;120)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1435.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114.22(98\u0026ndash;132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1614.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.82(83.42-125.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2073.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.54(77.15-112.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWealth status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoorest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1240.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e104.81(82.64-130.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1461.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.41(74.99-117.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1232.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.22(80.28-128.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRich\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1179.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.5(75.67-123.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRichest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1434.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112.27(90.79-137.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnderweight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.75(11.8-63.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2112.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.02(45.28\u0026ndash;61.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2623.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.74(89.05-109.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eObese\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1678.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e175.76(159.56-193.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1463.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.49(70.63-111.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5084.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106.01(98\u0026ndash;115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e119.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.14(36.19\u0026ndash;190.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6428.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.5(92.51-113.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSleep duration (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026le;\u0026thinsp;6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1656.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.73(77.33-101.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1443.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110.84(97.16-126.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1673.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105.17(92.76-118.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1774.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105.38(93.30-118.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHistory of CVDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e682.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134.72 (113.15-159.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5865.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.55(91.98-105.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily History of Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3170.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e113.53 (104.03-123.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3346.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.35 (84.02-101.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFemale participants showed a higher incidence rate of MetS at 122.86 per 1,000 person-years compared to males, who had an incidence rate of 83.66 per 1,000 person-years. The incidence rates generally increased with age; however, the highest incidence rate was observed among middle-aged individuals (ages 55\u0026ndash;64), while there was a slight decrease in the incidence for those aged 65 and older. Widowed had a notably higher incidence rate of 140.69 per 1,000 person-years, particularly in comparison to singles, who had an incidence rate of 49.68 per 1,000 person-years. Overall, the incidence rates of MetS decreased with increasing educational level, so the highest incidence rate found among illiterate participants 110.82 per 1,000 person-years, while the lowest rate was observed in individuals with a university education, at 71.52 per 1,000 person-years. Urban residents showed a similar incidence rate of 102.99 per 1,000 person-years compared to those living in rural areas, who had an incidence rate of 101.19 per 1,000 person-years. Physical activity did not show a clear trend; however, participants with high levels of physical activity (Q4) had the lowest incidence rate of MetS at 93.54 per 1,000 person-years. Conversely, the category with the highest incidence rate was the richest, at 114.22 per 1,000 person-years, although no obvious trend was observed. Additionally, the incidence rates of MetS increased as BMI rose, indicating a strong and direct association between BMI and MetS. Non-smokers had a higher incidence rate of MetS (106.01 per 1,000 person-years) compared to smokers (89.49 per 1,000 person-years). However, the results indicated a lower incidence for individuals who consume alcohol (92.14 per 1,000 person-years) compared to non-drinkers (102.5 per 1,000 person-years), this difference was not statistically significant. Sleep duration did not demonstrate a clear association with the incidence rates of MetS. Individuals with a history of CVDs had a significantly higher incidence rate of MetS (134.72 per 1,000 person-years) compared to those without a history of CVDs (98.55 per 1,000 person-years). Furthermore, participants with a family history of diabetes showed a higher incidence rate of MetS (113.53 per 1,000 person-years) compared to those without a family history of diabetes (92.35 per 1,000 person-years) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe univariate and multiple Poisson regression analyses were utilized to estimate crude and adjusted rate ratios, respectively. In the univariate regression analysis, several assessed variables including sex, age, marital status, education, BMI, history of CVDs, and family history of diabetes were significantly associated with the incidence of MetS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, no significant associations were observed between area of residence, physical activity, wealth score, smoking, alcohol consumption, and sleep duration with the incidence of the condition (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn the next step, to control for potential confounding factors, all assessed variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in the univariate analysis were simultaneously included in the multiple Poisson regression model. The results from the multiple Poisson regression analysis indicated that the adjusted rate ratio (RR) was 1.26 (95% CI: 1.02\u0026ndash;1.55) with a p-value of 0.031, suggesting that females had a 26% higher incidence of MetS compared to males. Additionally, the findings demonstrated that the incidence of MetS increased with age, particularly among middle-aged participants. Specifically, compared to the reference group of individuals aged 35\u0026ndash;39, those in the 55\u0026ndash;64 age group had a 44% higher incidence of MetS [RR\u0026thinsp;=\u0026thinsp;1.44 (95% CI: 1.14\u0026ndash;1.82); p\u0026thinsp;=\u0026thinsp;0.002]. However, the increase in the incidence rate of MetS for individuals aged 65 years and older compared to the 35\u0026ndash;39 age group was not statistically significant [RR\u0026thinsp;=\u0026thinsp;1.28 (95% CI: 0.84\u0026ndash;1.96); p\u0026thinsp;=\u0026thinsp;0.252].\u003c/p\u003e \u003cp\u003eMarried [RR\u0026thinsp;=\u0026thinsp;1.88 (95% CI: 1.03\u0026ndash;3.43)]., widowed [RR\u0026thinsp;=\u0026thinsp;2.08 (95% CI: 1.04\u0026ndash;4.14)]., and divorced [RR\u0026thinsp;=\u0026thinsp;2.38 (95% CI: 1.11\u0026ndash;5.13)] individuals have a higher risk of MetS compared to singles. Higher BMI significantly increases the risk of MetS, particularly in the overweight [RR= (3.82(95% CI: 1.42\u0026ndash;10.31)] and obese [RR= (95% CI: 6.46(95% CI: 2.38\u0026ndash;17.46)] categories. Additionally, having a history of CVDs and a family history of diabetes are also associated with an increased risk of MetS, with RR values of [RR\u0026thinsp;=\u0026thinsp;1.28(95% CI: 1.02\u0026ndash;1.60)] and [RR\u0026thinsp;=\u0026thinsp;1.21(95% CI: 1.04\u0026ndash;1.37)], respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe crude and adjusted rate ratios of the assessed factors for Metabolic syndrome and their 95% confidence intervals using the Poisson regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude RR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted RR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47(1.26\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26 (1.02\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22(1.03\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25(1.04\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26(1.02\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44(1.14\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02(0.69\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28(0.84\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04(1.13\u0026ndash;3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88 (1.03\u0026ndash;3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83(1.44\u0026ndash;5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08 (1.04\u0026ndash;4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.44(1.61\u0026ndash;7.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38 (1.11\u0026ndash;5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducational levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87(0.71\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.74\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85(0.62\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93 (0.66\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86(0.65\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.74\u0026ndash; 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64(0.47\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76 (0.53\u0026ndash; 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003cp\u003earea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98(0.83\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePhysical activity (MET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.88\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04 (0.82\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22(0.99\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.87\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.89\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.79\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWealth status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90(0.70\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97(0.76\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93(0.72\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07(0.85\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnderweight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78(0.66\u0026ndash;4.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01(0.73\u0026ndash;5.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.32(1.24\u0026ndash;8.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.82(1.42\u0026ndash;10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eObese\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.90(2.21\u0026ndash;15.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.46(2.38\u0026ndash;17.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18(0.98\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.78\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.49\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25(0.99\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25(0.99\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18(0.95\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15(0.91\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19(0.95\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17(0.92\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHistory of CVDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37(1.23\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28(1.02\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily History of Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23(1.056\u0026ndash;1.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21(1.04\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant for the Poisson regression model\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eRR: rate ratios\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to investigate the incidence of metabolic syndrome and its associated factors in the population aged 35 to 70 years participating in the Hoveyzeh Cohort Study (HCS). The incidence of MetS was found to be 102.23 per 1,000 person-years, which corresponds to an approximate annual incidence of 10.2% among at risk participants. The findings indicated that age, gender, marital status, BMI, history of CVDs, and family history of Diabetes predictors for the incidence of MetS.\u003c/p\u003e \u003cp\u003eOur study indicated an overall incidence rate of 102.23 (95% CI: 94.72 -110.37) per 1,000 person-years. In a study conducted in south-east of Iran, the incidence rate of MetS was reported 54.59 per 1,000 person-years. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, a systematic review estimated that the overall pooled incidence rate among the general population in Iran was 97.96 (95% CI: 75.98, 131.48) per 1000 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In a cohort study conducted in Tehran, the incidence rate was reported 550.9/10000 person/years. This significant difference may be attributed to variations in population characteristics, lifestyle factors, or access to healthcare, all of which can influence the development of metabolic syndrome. The incidence of metabolic syndrome was higher in our population, which may be partly due to the higher average age of participants in the present study compared to other studies.\u003c/p\u003e \u003cp\u003eThe results showed that women were 26% more likely to develop MetS compared to men. Some previous studies supported our findings [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], while others had different results [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In a Portuguese cohort, the incidence rate was 47.2 per 1,000 person-years, with no significant gender difference [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The discrepancies seen in studies regarding the relationship between sex and the development of metabolic syndrome stem from a complex interplay of hormonal influences, variations in body composition, age-related factors, socioeconomic status, lifestyle choices like physical activity and ethnic differences [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results indicate that age is a significant risk factor for the development of metabolic syndrome. Other studies also suggest that age plays a crucial role in the development and progression of metabolic disorders. Aging linked to a decrease in metabolic rate and changes in body composition, such as increased fat mass and decreased muscle mass. These changes contribute to insulin resistance and higher levels of inflammation, both of which are risk factors for MetS [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, as individuals age increase, they tend to engage in less physical activity and make dietary changes. These can lead to enhance weight and obesity, that in turn, increase the risk of developing MetS [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore in women, specially post-menopausal women, hormonal changes play a significantly role in the increased risk of MetS due to weight redistribution and enhanced fat accumulation around the abdomen [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study the incidence of MetS was higher in married, widowed, and divorced individuals compared to single individuals. Widowed individuals, especially women, were consistently at higher risk of developing MetS compared with their married counterparts. A Korean study noted that the odds ratio (OR) for metabolic syndrome was significantly increased in widowed women, even after adjusting for socioeconomic factors and health behaviors, indicating a direct and strong association between widowhood and risk of metabolic syndrome [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The connection between marital status and MetS is not clear for single people. Some research indicates singles might have a lower risk than married individuals, influenced by lifestyle factors. The impact of divorce on MetS risk is also unclear, with mixed findings on health changes post-divorce [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe incidence of metabolic syndrome was significantly higher in patients with CVDs. Studies have shown that patients with CVDs are at greater risk of developing MetS due to several interconnected factors. These risk factors include hypertension [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], hyperglycemia (increased blood sugar) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], dyslipidemia, and abdominal obesity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], all of which are common in this population. Lifestyle factors such as a sedentary lifestyle, poor diet, and smoking worsen these risks [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, age and gender play a role in this relationship, with older adults and postmenopausal women being more vulnerable [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eLimitation and Strengths\u003c/h3\u003e\n\u003cp\u003eOur study has several strengths. First, we utilized a cohort study design, which allowed us to collect longitudinal data. This enabled us to observe the development of MetS over time and establish temporal relationships between risk factors and outcomes. Second, our study included large populations, increasing the statistical power of our results and reducing the possibility of random error. This led to more reliable incidence estimates. Third, the comprehensive data collection in the Hoveyzeh cohort study allowed us to examine associations between various health measures, lifestyle factors, and the full range of the association between multiple risk factors for metabolic syndrome. Fourth, we used standardized definitions for diagnosing of metabolic syndrome and accurately measured of its components using standard laboratory and anthropometric devices. This increased the comparability and validity of our findings across different studies. However, our study had limitations. First, the study population was limited to middle-aged and elderly individuals. This demographic characteristic may have limited the generalizability of the results. Second, the outcome was measured only in the re-assessment phase (approximately 5 years after the enrollment phase) and not during the follow-up period, so the exact timing of the outcome is not known.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAnalysis of MetS incidence among 1,781 participants revealed that approximately 10.2% of at-risk individuals develop MetS annually, with significant variations based on gender, age, marital status, education level, and health history. Notably, females exhibit a higher incidence rate than males, and the risk increases with age, particularly peaking among those aged 55\u0026ndash;64. Additionally, widowed individuals and those with lower educational attainment show higher rates of MetS, while a history of cardiovascular diseases is significantly associated with increased incidence. These findings highlight the need for targeted public health strategies focused on high-risk groups, including women, older adults, and individuals with lower education or cardiovascular histories. Promoting healthy lifestyle choices may also play a crucial role in reducing MetS risk. Overall, the study emphasizes the importance of tailored interventions and further research to effectively address the burden of MetS and its associated health complications within these populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMetS Metabolic syndrome\u003c/p\u003e\u003cp\u003eIRs Incidence rates\u003c/p\u003e\u003cp\u003eBMI Body mass index\u003c/p\u003e\u003cp\u003eCVDs Cardiovascular diseases\u003c/p\u003e\u003cp\u003eHDL High-density lipoprotein\u003c/p\u003e\u003cp\u003eHCS Hoveyzeh Cohort Study\u003c/p\u003e\u003cp\u003eNCDs Non-communicable diseases\u003c/p\u003e\u003cp\u003eRR Rate ratio\u003c/p\u003e\u003cp\u003e95% CIs Confidence intervals\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis article is extracted from the general doctoral thesis of Seyyedeh Maryam Hashemi\u0026nbsp;(Grant number U-02072).\u0026nbsp;This study was approval by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences.\u0026nbsp;We would like to express the participants and staff of the Hoveyzeh Cohort Study Center who assisted us in conducting this study.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there are no competing interests related to this manuscript.\u003c/p\u003e\n\u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e\n\u003cp\u003eBahman Cheraghian and Seyed Jalal Hashemi: conceptualization, project administration, writing- review editing. Zahra Rahimi formal analysis, methodology, writing- review editing. \u0026nbsp;Seyedeh Maryam Hashemi project administration, and writing-original draft. Alireza Jahanshahi supervision. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding sources\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences (Grant number U-02072). \u0026nbsp;The Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences as our funding body played no role in the design of the study, collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.HGOLESTAN.REC.1402.040).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent to Publish declaration\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaely CH, et al. Adult Treatment Panel III 2001 but not International Diabetes Federation 2005 criteria of the metabolic syndrome predict clinical cardiovascular events in subjects who underwent coronary angiography. Diabetes Care. 2006;29(4):901\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diacare.29.04.06.dc05-2011\u003c/span\u003e\u003cspan address=\"10.2337/diacare.29.04.06.dc05-2011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrundy SM, et al. 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Front Cardiovasc Med. 2021;8:704145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2021.704145\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2021.704145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"incidence, metabolic syndrome, cohort study, Iran","lastPublishedDoi":"10.21203/rs.3.rs-6420781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6420781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic syndrome (MetS) is a common indicator of non-communicable diseases (NCDs) and mortality, causing health problems in most societies. Our aims were to estimate the incidence rates and to assess the risk factors of MetS among an adult population in content of a prospective cohort study.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis population-based cohort study conducted on 10009 adult individuals in southwest Iran. Among the participants of cohort baseline, a total number of 1,781 at risk individuals remained for follow-up, after putting aside the exclusions. Incidence rates (IRs) of metabolic syndrome were calculated by dividing the number of new events, occurred during the follow-up period by the person-years at risk. The effects of various explanatory variables on these incidence rates were assessed using Poisson regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall incidence rate of MetS was 102.23 per 1,000 person-years, with a higher rate in females (122.86) compared to males (83.66) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the multiple Poisson regression model, higher age, being female, marital status (married-widowed-divorced), higher BMI, having a history of cardiovascular diseases (CVDs) and a family history of diabetes significantly increased MetS incidence (all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eTargeted public health strategies and lifestyle interventions are crucial for reducing metabolic syndrome incidence, particularly among high-risk groups such as women, older adults, and individuals with cardiovascular history.\u003c/p\u003e","manuscriptTitle":"Evaluation of the effect of baseline predictors on the incidence of metabolic syndrome: a population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 20:51:09","doi":"10.21203/rs.3.rs-6420781/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T17:22:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T15:57:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T05:41:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67627513093768097450000267523391976049","date":"2025-07-06T14:03:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T10:27:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233368318966155527646612799639416662235","date":"2025-07-06T10:27:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78076647927925719494935078420129326195","date":"2025-07-05T22:54:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21589729042509484659583710369850260075","date":"2025-05-08T12:32:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T19:10:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-22T18:42:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-22T08:59:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-04-10T13:47:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab413f90-71d7-4759-9b93-44b688dcf35f","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T00:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 20:51:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6420781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6420781","identity":"rs-6420781","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}