Abstract
Background
Metabolic syndrome (MetS) is a cluster of risk factors that increase the risk of cardiovascular disease and type 2 diabetes. We aim to investigate the prevalence of MetS based on four prevalent criteria and its associated factors among Tehran’s adult residents.
Methods
We utilized the data from the enrollment phase of the Tehran Cohort Study (TeCS). We included the demographic, anthropometric, and biomedical data of 8,232 adult Tehran residents. MetS was defined using four criteria: World Health Organization (WHO), Adult Treatment Panel III (ATP III), International Diabetes Federation (IDF), and Joint Interim Statement (JIS). Sex and age-adjusted prevalence estimates and associated factors were analyzed using regression models.
Results
The Participants’ mean age was 53.7 ± 12.72 years, and 54% were women. The sex and age-adjusted prevalence of MetS varied depending on the diagnostic criteria used, with the highest rates reported using the JIS criteria (59.3%), followed by the IDF criteria (46.8%), ATP-III (27.1%), and WHO criteria (11.0%). The prevalence generally increased with age across all criteria in both females and males. Females had a higher prevalence than males, except for the WHO and JIS criteria. Factors associated with higher odds of MetS included older age, female gender, lower education level, and low physical activity level. Tobacco use and opium use were associated with lower odds of MetS based on IDF and JIS criteria.
Conclusion
MetS is highly prevalent among Tehran adults, with notable variation based on diagnostic criteria. Preventive efforts should address modifiable lifestyle factors.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40200-025-01708-3.
Keywords: Metabolic syndrome, MetS, Epidemiology, Tehran cohort study, Iran
Introduction
Metabolic syndrome (MetS) is a pathological condition characterized by obesity, insulin resistance, hypertension, glucose intolerance, and dyslipidemia, which increase the risk of various non-communicable diseases, including cardiovascular disease and type 2 diabetes [1, 2]. Various criteria and definitions have been applied to diagnose MetS [2–4]. However, it is widely recognized that the diagnosis typically requires the presence of at least three of the following conditions: increased waist circumference (WC), elevated TG levels, reduced high-density lipoprotein (HDL) cholesterol, high blood pressure, and elevated fasting blood sugar (FBS) [5].
The prevalence of MetS is increasing globally, leading to significantly higher cardiovascular mortality and morbidity rates compared to those without MetS in both Western and Asian populations [6]. In 2020, a global estimate found a prevalence of 3% in children and 5% in adolescents, equating to approximately 25 million children and 35 million adolescents affected by MetS [7]. This rising trend is evident in both developed and developing countries, with MetS prevalence estimated to be as high as 25% among Middle Eastern nations [8]. Notably, among Middle Eastern countries, Iran exhibits the highest rate of MetS prevalence in adolescents at 9% and the second highest rate in children at 8.8% compared to 44 other countries [7]. Moreover, In 2013, the prevalence of MetS in Tehran, Iran’s largest city, ranged from 31.3 to 41.8%, depending on the diagnostic criteria applied [9].
Furthermore, evaluating MetS components in adults leads to the identification of those at risk of the syndrome; therefore, early interventions can be implemented to reduce the risk of developing MetS and associated disorders. Although MetS components, including obesity and hypertension, are becoming more prevalent in Tehran, few epidemiological studies have been conducted to determine the precise prevalence of MetS [10, 11]. However, these studies were limited by various factors, such as the limited sample size of a particular district or participants of a specific age group. Therefore, we investigated the prevalence of MetS based on the different criteria among adult Tehran residents using data obtained from the Tehran Cohort Study (TeCS).
Methods
Study design and participants
The data for this study were derived from the enrolment phase of the Tehran Cohort Study (TeCS), an ongoing population-based prospective study of Tehran residents. The details of the study design have been previously published [12]. Briefly, during the period between March 2016 and March 2019, a total of ten thousand households from 22 districts in Tehran were chosen utilizing a systematic sampling method. The inclusion criteria required participants to be permanent residents of Tehran, have at least one household member over 35 years old, and be 15 years or older. A total of 4215 households (42.1%) took part in the study, leading to 8296 cardiovascular assessments from the participating families, and their personal, clinical, and para-clinical information was gathered. Exclusions were made for individuals who were unable to participate, were not permanent residents of Tehran, had moved to Tehran within the last year, or had immigrated from Tehran during the study. Patients who met the criteria were invited to visit the Tehran Heart Center for recruitment, while home visits were arranged for those who could not attend in person. For this study, 64 patients were excluded due to a lack of information on the various components essential for defining MetS.
The study protocol was approved by the Research Deputy and Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.MEDICINE.REC.1399.074). Each participant provided written informed consent before enrollment in the study.
Data collection and measurements
Demographic data, including sex, age, tobacco use (never, former smoker, and current smoker), opium use, alcohol use, physical activity level (classified as low, moderate, and high), marital status (single, married, and other), ethnicity (Fars, Azari, and other ethnicities), and education, were retrieved for this study using the TeCS database. Additionally, anthropometric characteristics like weight, height, WC, hip circumference, body mass index (BMI), and waist-to-hip ratio (WHR) were recorded.
Fasting blood samples were taken to determine baseline laboratory parameters, including HDL, total cholesterol (TCH), TG, and FBS. A trained nurse measured Blood pressure on the left arm using a digital sphygmomanometer (M6 Comfort Omron, Omron Healthcare, Kyoto, Japan) in a standard setting. If the first measurement was recorded above 140/90 mm Hg, a subsequent measurement was taken on the same arm following a five-minute rest period.
Definition of variables
The classifications of tobacco use, opium and alcohol consumption, physical activity level, glycemic status, blood pressure categories, and preexisting comorbidities, including chronic kidney disease (CKD), have been comprehensively defined in our previously published studies [13–15]. The most common criteria for defining MetS are the World Health Organization (WHO) [16], Adult Treatment Panel III (ATP III) [4], International Diabetes Foundation (IDF) [17], and Joint Interim Statement JIS [18]. A summary of the four criteria used to define MetS is presented in Table S1. All these criteria are based on the presence of a combination of risk factors associated with cardiovascular disease and diabetes, including obesity, high blood pressure, elevated levels of glucose, and dyslipidemia.
Statistical analyses
Continuous variables were described as mean with standard deviation (SD) and were compared between MetS-positive and MetS-negative groups using an independent t-test. Categorical variables were expressed as frequencies with percentages and compared between the two groups using the chi-square test. The age- and sex-weighted prevalence, along with the associated 95% confidence interval (CI), was provided for all four different definition criteria of MetS using the Tehran population from the 2016 census, utilizing the direct standardization method. Additionally, the prevalence of MetS was reported as stratified by age and sex groups. Finally, the association between demographic characteristics and metabolic complications was demonstrated for each MetS definition type using multivariable logistic regression analysis. Adjusted odds ratios (aORs) and 95% CIs were reported for this analysis. All statistical analyses were performed using IBM SPSS Statistics for Windows, version 23 (Armonk, NY: IBM Corp.). Moreover, the geographic distribution of MetS based on four criteria was visualized across Tehran using the first three digits of participants postal codes. Maps were created in Stata version 14.2 (StataCorp, College Station, TX, USA) using the ‘’’shp2dta’’’ and ‘’’spmap’’’ commands. Our previously published study protocol [12] contains detailed geographical references, including district boundaries and scale bars in the Tehran map.
Results
Total prevalence and sex-specific differences of metabolic syndrome
A total of 8232 people from different Tehran districts participated in this study. The sex- and age-adjusted prevalence of MetS showed considerable variation depending on the diagnostic criteria used, with the lowest prevalence observed using the WHO criteria (11.0%) and the highest using the JIS criteria (59.3%). According to both ATP III and IDF definitions, females exhibited a significantly higher prevalence of MetS compared to males. Specifically, under the ATP III criteria, 32.4% of females were affected, compared to 21.3% of males (P < 0.001). Similarly, the IDF definition indicated a prevalence of 50.7% among females, which exceeded the 42.8% observed in males (P < 0.001). In comparison, no statistically significant difference was observed between sexes when applying the WHO and JIS criteria. Overall, across all four criteria, the prevalence of MetS was higher in females than in males. The detailed prevalence of MetS, based on different definitions, is presented in Table 1.
Table 1.
Age- and sex-adjusted prevalence of metabolic syndrome based on four diagnostic criteria; age-adjusted values shown separately for each gender
| Diagnostic criteria | Total | Female | Male | P-value* |
|---|---|---|---|---|
| WHO | 11.0% (9.7, 12.4) | 11.6% (9.8, 13.6) | 10.4% (8.5, 12.5) | 0.381 |
| ATP-III | 27.1% (25.1, 29.2) | 32.4% (29.6, 35.3) | 21.3% (18.6, 24.2) | < 0.001 |
| IDF | 46.8% (44.6, 49.1) | 50.7% (47.8, 53.6) | 42.8% (39.5, 46.2) | < 0.001 |
| JIS | 59.3% (57.1, 61.5) | 59.9% (57.0, 62.7) | 59.1% (55.7, 62.4) | 0.208 |
Abbreviations: ATP III National Cholesterol Education Program Adult Treatment Panel III, IDF International Diabetes Federation, JIS Joint Interim Statement, WHO World Health Organization
* P-values less than 0.05 were considered statistically significant
Baseline characteristics of the study population
Across all definitions, individuals with MetS were significantly older (P < 0.001), with the most pronounced age difference observed under the WHO criteria (62 vs. 52.6 years). Significant sex disparities emerged under the ATP-III and IDF criteria, where females exhibited a higher prevalence of MetS compared to males (63.6% vs. 36.4% and 58% vs. 42%, respectively; P < 0.001), whereas the the WHO and JIS criteria demonstrated minimal sex differences. The prevalence of MetS increased substantially with age, particularly among participants aged 75 years or older. Additionally, an inverse relationship was observed between education level and metabolic syndrome prevalence across all diagnostic criteria (P < 0.001). Within each diagnostic group, lower educational years (illiteracy and primary education) were more frequent among MetS-positive individuals, regardless of the total number of study participants. Conversely, higher education (more than 12 years) was less common in MetS-positive groups. In the case of lifestyle factors, low physical activity levels were more prevalent among individuals with MetS (P < 0.001), whereas current tobacco and alcohol use were inversely correlated with MetS prevalence (P < 0.001). Additionally, the prevalence of chronic kidney disease was notably higher among participants with MetS, particularly under the WHO criteria (2.9% vs. 0.6%; P < 0.001). Table 2 provides a comparison of the baseline characteristics of participants with and without MetS based on the four different definition criteria.
Table 2.
Baseline characteristics of participants with and without MetS based on the four different definition criteria
| Characteristic* | Total | WHO − | WHO + | P-value** | ATP-III − | ATP-III + | P-value | IDF − | IDF + | P-value | JIS − | JIS + | P-value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age, years | 53.7 (12.72) | 52.6 (12.53) | 62 (10.9) | < 0.001 | 51.6 (12.54) | 58.4 (11.66) | < 0.001 | 50.2 (12.24) | 57.2 (12.07) | < 0.001 | 49.6 (12.16) | 56.4 (12.33) | < 0.001 | |
| BMI, kg/m2 | 28 (4.82) | 27.7 (4.75) | 29.8 (4.96) | < 0.001 | 26.7 (4.28) | 30.9 (4.73) | < 0.001 | 26.1 (4.29) | 29.8 (4.59) | < 0.001 | 25.9 (4.36) | 29.3 (4.62) | < 0.001 | |
| Hip | 105.2 (9.88) | 104.8 (9.64) | 107.9 (11.08) | < 0.001 | 102.9 (8.98) | 110.6 (9.91) | < 0.001 | 101.7 (9.12) | 108.6 (9.45) | < 0.001 | 101.6 (9.26) | 107.5 (9.61) | < 0.001 | |
| Waist | 96.3 (11.75) | 95.4 (11.59) | 102.5 (10.98) | < 0.001 | 92.9 (10.86) | 104.2 (10) | < 0.001 | 90.8 (10.91) | 101.7 (9.98) | < 0.001 | 90.1 (10.96) | 100.2 (10.56) | < 0.001 | |
| Waist to Hip ratio | 0.9 (0.07) | 0.9 (0.07) | 1 (0.06) | < 0.001 | 0.9 (0.07) | 0.9 (0.06) | < 0.001 | 0.9 (0.07) | 0.9 (0.07) | < 0.001 | 0.9 (0.07) | 0.9 (0.07) | < 0.001 | |
| Age, Category, years | ||||||||||||||
| 35–44 | 2319 (28.2) | 2254 (31.3) | 65 (6.4) | < 0.001 | 1987 (35) | 296 (12.7) | < 0.001 | 1610 (39.8) | 647 (16.4) | < 0.001 | 1278 (41.6) | 962 (19.5) | < 0.001 | |
| 45–54 | 2194 (26.7) | 2017 (28) | 177 (17.3) | 1575 (27.7) | 574 (24.7) | 1116 (27.6) | 1017 (25.8) | 852 (27.7) | 1285 (26) | |||||
| 55–64 | 1950 (23.7) | 1599 (22.2) | 351 (34.4) | 1146 (20.2) | 749 (32.2) | 746 (18.5) | 1152 (29.2) | 529 (17.2) | 1378 (27.9) | |||||
| 65–74 | 1210 (14.7) | 918 (12.7) | 292 (28.6) | 668 (11.8) | 486 (20.9) | 388 (9.6) | 779 (19.8) | 285 (9.3) | 903 (18.3) | |||||
| ≥75 | 559 (6.8) | 423 (5.9) | 136 (13.3) | 309 (5.4) | 223 (9.6) | 183 (4.5) | 349 (8.8) | 127 (4.1) | 418 (8.5) | |||||
| Sex | ||||||||||||||
| Female | 4442 (54) | 3878 (53.8) | 564 (55.2) | 0.381 | 2841 (50) | 1480 (63.6) | < 0.001 | 2015 (49.8) | 2286 (58) | < 0.001 | 1639 (53.4) | 2711 (54.8) | 0.208 | |
| Male | 3790 (46) | 3333 (46.2) | 457 (44.8) | 2844 (50) | 848 (36.4) | 2028 (50.2) | 1658 (42) | 1432 (46.6) | 2235 (45.2) | |||||
| Marital Status | ||||||||||||||
| Single | 64 (0.8) | 54 (0.7) | 10 (1) | < 0.001 | 42 (0.7) | 22 (0.9) | < 0.001 | 29 (0.7) | 29 (0.7) | < 0.001 | 22 (0.7) | 40 (0.8) | 0.037 | |
| Married | 7967 (96.8) | 6962 (96.6) | 1005 (98.4) | 5471 (96.4) | 2271 (97.6) | 3887 (96.2) | 3848 (97.6) | 2957 (96.4) | 4803 (97.1) | |||||
| Divorced | 198 (2.4) | 192 (2.7) | 6 (0.6) | 160 (2.8) | 34 (1.5) | 125 (3.1) | 66 (1.7) | 90 (2.9) | 101 (2) | |||||
| Education, Category, years | ||||||||||||||
| Illiterate | 579 (7) | 436 (6.1) | 143 (14) | < 0.001 | 277 (4.9) | 268 (11.5) | 0.001 | 156 (3.9) | 390 (9.9) | < 0.001 | 114 (3.7) | 452 (9.1) | < 0.001 | |
| 1–5 | 836 (10.2) | 681 (9.5) | 155 (15.2) | 469 (8.3) | 333 (14.3) | 305 (7.6) | 497 (12.6) | 214 (7) | 598 (12.1) | |||||
| 6–12 | 4285 (52.1) | 3762 (52.2) | 523 (51.2) | 2916 (51.4) | 1257 (54) | 2043 (50.6) | 2117 (53.7) | 1559 (50.8) | 2609 (52.8) | |||||
| >12 | 2526 (30.7) | 2326 (32.3) | 200 (19.6) | 2009 (35.4) | 468 (20.1) | 1535 (38) | 938 (23.8) | 1181 (38.5) | 1283 (26) | |||||
| Ethnicity | ||||||||||||||
| Fars | 4011 (48.8) | 3457 (48) | 554 (54.3) | < 0.001 | 2704 (47.7) | 1193 (51.3) | 0.004 | 1912 (47.3) | 1972 (50) | 0.009 | 1456 (47.4) | 2450 (49.6) | 0.014 | |
| Azari | 2440 (29.7) | 2144 (29.8) | 296 (29) | 1703 (30) | 683 (29.4) | 1207 (29.9) | 1174 (29.8) | 902 (29.4) | 1481 (30) | |||||
| Other | 1775 (21.6) | 1604 (22.3) | 171 (16.7) | 1263 (22.3) | 450 (19.3) | 921 (22.8) | 795 (20.2) | 711 (23.2) | 1010 (20.4) | |||||
| BMI, Category, kg/m2 | ||||||||||||||
| <25 | 2293 (28.1) | 2132 (29.8) | 161 (16) | < 0.001 | 2069 (36.7) | 183 (7.9) | < 0.001 | 1758 (43.7) | 493 (12.6) | < 0.001 | 1412 (46.2) | 809 (16.5) | < 0.001 | |
| 25-29.99 | 3409 (41.7) | 3015 (42.1) | 394 (39.1) | 2454 (43.5) | 876 (37.9) | 1600 (39.8) | 1717 (43.8) | 1157 (37.8) | 2167 (44.2) | |||||
| ≥30 | 2472 (30.2) | 2019 (28.2) | 453 (44.9) | 1121 (19.9) | 1250 (54.1) | 667 (16.6) | 1712 (43.7) | 488 (16) | 1927 (39.3) | |||||
| Chronic kidney disease | 71 (0.9) | 41 (0.6) | 30 (2.9) | < 0.001 | 32 (0.6) | 36 (1.5) | < 0.001 | 28 (0.7) | 39 (1) | 0.147 | 13 (0.4) | 56 (1.1) | 0.001 | |
| Tobacco use | ||||||||||||||
| Never | 6303 (76.6) | 5489 (76.2) | 814 (79.7) | < 0.001 | 4232 (74.7) | 1890 (81.2) | < 0.001 | 2971 (73.6) | 3142 (79.7) | < 0.001 | 2285 (74.5) | 3865 (78.2) | < 0.001 | |
| Quitted | 334 (4.1) | 272 (3.8) | 62 (6.1) | 208 (3.7) | 111 (4.8) | 128 (3.2) | 189 (4.8) | 78 (2.5) | 237 (4.8) | |||||
| Current | 1590 (19.3) | 1445 (20.1) | 145 (14.2) | 1229 (21.7) | 327 (14) | 939 (23.3) | 613 (15.5) | 705 (23) | 841 (17) | |||||
| Opium use | 434 (5.3) | 386 (5.4) | 48 (4.7) | 0.384 | 326 (5.8) | 100 (4.3) | 0.008 | 240 (6) | 185 (4.7) | 0.012 | 159 (5.2) | 264 (5.4) | 0.771 | |
| Alcohol use | 737 (9) | 673 (9.4) | 64 (6.3) | 0.001 | 577 (10.2) | 145 (6.3) | < 0.001 | 426 (10.6) | 292 (7.4) | < 0.001 | 319 (10.4) | 396 (8) | < 0.001 | |
| Physical Activity Level, Category | ||||||||||||||
| Low | 1434 (17.6) | 1139 (15.9) | 295 (29.1) | < 0.001 | 794 (14.1) | 582 (25.2) | < 0.001 | 530 (13.2) | 845 (21.6) | < 0.001 | 380 (12.5) | 1024 (20.9) | < 0.001 | |
| Intermediate | 4738 (58) | 4153 (58.1) | 585 (57.7) | 3243 (57.6) | 1371 (59.4) | 2304 (57.4) | 2300 (58.8) | 1740 (57) | 2878 (58.7) | |||||
| High | 1996 (24.4) | 1862 (26) | 134 (13.2) | 1594 (28.3) | 355 (15.4) | 1178 (29.4) | 769 (19.6) | 930 (30.5) | 1001 (20.4) | |||||
Abbreviations: BMI Body mass index
* Values are displayed as Mean (SD) and number (%) for continuous and categorical variables, respectively
** P-values indicate the difference between the alcohol-user and non-user groups, and a P-value less than 0.05 was considered statistically significant
A clear age-related increase in MetS prevalence was observed in both sexes across all definitions, with the JIS criteria consistently identifying the highest prevalence and the WHO criteria the lowest (Fig. 1). Females demonstrated higher prevalence rates than males, particularly in older age groups, under both the ATP-III and IDF definitions. In the ≥ 75 age group, the prevalence reached 83.9% in females and 71.0% in males, according to the JIS criteria, while in the 65–74 age group, it was 82.3% and 69.8% in females and males, respectively. The most pronounced increase among females occurred between the 45–54 and 55–64 age groups.
Fig. 1.
The prevalence of MetS across age and sex groups based on four diagnostic criteria (WHO, ATP-III, IDF, and JIS)
Geographical distribution of metabolic syndrome in Tehran
The geographical distribution of MetS in Tehran, based on the four diagnostic criteria (WHO, ATP-III, IDF, and JIS), reveals distinct spatial patterns and prevalence rates across the city. The JIS criteria (Fig. 2D) showed the highest overall prevalence, with a widespread distribution across Tehran, particularly concentrated in the central and southern districts. The IDF criteria (Fig. 2, B) were followed, highlighting similar high-prevalence regions, especially in the southeast. The ATP-III criteria (Fig. 2, C) demonstrated a moderate prevalence, with notable concentrations in central districts, while the WHO criteria (Fig. 2, A) depicted the lowest prevalence, with high-prevalence clusters primarily located in the southern parts of the city. In contrast, the northern districts consistently exhibited the lowest prevalence across all definitions, especially under the WHO and ATP-III criteria. Among all criteria, the highest prevalence was observed under the JIS definition in central Tehran, while the lowest prevalence was recorded under the WHO definition in the northern districts.
Fig. 2.
Geographic distribution of the prevalence of metabolic syndrome based on Tehran postal regions according to: A World Health Organization (WHO), B International Diabetes Federation (IDF), C Adult Treatment Panel (ATP-III), and D Joint interim statement (JIS) definitions
Determinants associated with metabolic syndrome
Age was a significant determinant, with participants aged 65–74 years showing the highest odds of MetS under the WHO criteria (OR: 8.99; 95% CI: 6.72–12.05; P < 0.001), followed by the ATP-III (OR: 4.09; 95% CI: 3.41–4.91; P < 0.001), IDF (OR: 4.20; 95% CI: 3.57–4.95; P < 0.001), and JIS (OR: 3.34; 95% CI: 2.83–3.95; P < 0.001) definitions. Significant sex differences were observed under the ATP-III and IDF criteria, where males had lower odds of MetS compared to females (ATP-III: OR: 0.61; 95% CI: 0.54–0.68; P < 0.001 and IDF: OR: 0.77; 95% CI: 0.69–0.86; P < 0.001), while no significant differences were noted under the WHO and JIS criteria. Education level was inversely associated with MetS across all definitions; individuals with more than 12 years of education had significantly reduced odds, particularly under the IDF criteria (OR: 0.52; 95% CI: 0.42–0.65; P < 0.001) and ATP-III criteria (OR: 0.55; 95% CI: 0.44–0.69; P < 0.001). CKD was also a significant determinant, with the highest odds observed under the WHO criteria (OR: 2.93; 95% CI: 1.77–4.86; P < 0.001) and the ATP-III criteria (OR: 1.77; 95% CI: 1.06–2.94; P = 0.028). Moreover, high physical activity level was significantly protective across all criteria, with the greatest effect under the WHO definition (OR: 0.41; 95% CI: 0.32–0.52; P < 0.001), while current tobacco use was inversely associated with MetS under the IDF and JIS definitions (both OR: 0.78; 95% CI: 0.68–0.89; P < 0.001). Additionally, opium use was associated with lower odds of MetS under the WHO (OR: 0.65; 95% CI: 0.46–0.92; P = 0.016), ATP-III (OR: 0.73; 95% CI: 0.56–0.94; P = 0.017), and IDF (OR: 0.72; 95% CI: 0.57–0.90; P = 0.004) criteria. Table 3 presents the determinants associated with MetS using multivariable analysis.
Table 3.
Multivariable analysis of risk factors associated with MetS among Tehran cohort study participants
| WHO | ATP-III | IDF | JIS | |||||
|---|---|---|---|---|---|---|---|---|
| Characteristic | OR (95% CI) | P-value* | OR (95% CI) | P-value | OR (95% CI) | P-value | OR (95% CI) | P-value |
| Age category, year | ||||||||
| 35–44 | ref | − | ref | − | ref | − | ref | − |
| 45–54 | 2.83 (2.11, 3.8) | < 0.001 | 2.34 (2, 2.75) | < 0.001 | 2.16 (1.9, 2.45) | < 0.001 | 1.89 (1.67, 2.14) | < 0.001 |
| 55–64 | 6.73 (5.09, 8.9) | < 0.001 | 3.9 (3.32, 4.58) | < 0.001 | 3.47 (3.03, 3.98) | < 0.001 | 3.07 (2.68, 3.52) | < 0.001 |
| 65–74 | 8.99 (6.72, 12.05) | < 0.001 | 4.09 (3.41, 4.91) | < 0.001 | 4.2 (3.57, 4.95) | < 0.001 | 3.34 (2.83, 3.95) | < 0.001 |
| ≥75 | 6.85 (4.87, 9.63) | < 0.001 | 3.27 (2.58, 4.15) | < 0.001 | 3.39 (2.71, 4.24) | < 0.001 | 2.87 (2.26, 3.63) | < 0.001 |
| Sex | ||||||||
| Male | 1.04 (0.89, 1.21) | 0.648 | 0.61 (0.54, 0.68) | < 0.001 | 0.77 (0.69, 0.86) | < 0.001 | 1.04 (0.94, 1.16) | 0.449 |
| Marital Status | ||||||||
| Single | ref | − | ref | − | ref | − | ref | − |
| Married | 0.72 (0.35, 1.47) | 0.366 | 0.91 (0.51, 1.61) | 0.733 | 1.21 (0.69, 2.11) | 0.511 | 1.08 (0.62, 1.87) | 0.786 |
| Other | 0.19 (0.07, 0.58) | 0.003 | 0.49 (0.25, 0.98) | 0.043 | 0.72 (0.38, 1.36) | 0.308 | 0.9 (0.48, 1.68) | 0.732 |
| Education | ||||||||
| Illiterate | ref | − | ref | − | ref | − | ref | − |
| 1–5 | 0.96 (0.73, 1.26) | 0.768 | 1 (0.79, 1.26) | 0.975 | 0.88 (0.69, 1.12) | 0.299 | 0.95 (0.72, 1.24) | 0.696 |
| 6–12 | 0.85 (0.67, 1.08) | 0.180 | 0.81 (0.67, 1) | 0.046 | 0.74 (0.6, 0.92) | 0.007 | 0.73 (0.58, 0.92) | 0.008 |
| >12 | 0.68 (0.52, 0.9) | 0.006 | 0.55 (0.44, 0.69) | < 0.001 | 0.52 (0.42, 0.65) | < 0.001 | 0.55 (0.43, 0.7) | < 0.001 |
| Ethnicity | ||||||||
| Fars | ref | − | ref | − | ref | − | ref | − |
| Azari | 0.87 (0.74, 1.02) | 0.090 | 0.88 (0.78, 0.99) | 0.040 | 0.94 (0.84, 1.05) | 0.259 | 0.97 (0.87, 1.09) | 0.648 |
| Other | 0.78 (0.65, 0.95) | 0.012 | 0.92 (0.8, 1.05) | 0.204 | 0.95 (0.84, 1.07) | 0.364 | 0.95 (0.84, 1.07) | 0.367 |
| Chronic kidney disease | 2.93 (1.77, 4.86) | < 0.001 | 1.77 (1.06, 2.94) | 0.028 | 0.81 (0.48, 1.35) | 0.408 | 1.56 (0.83, 2.92) | 0.167 |
| Tobacco use | ||||||||
| Never | ref | − | ref | − | ref | − | ref | − |
| Quitted | 1.22 (0.89, 1.67) | 0.223 | 1.32 (1.02, 1.72) | 0.037 | 1.31 (1.02, 1.68) | 0.038 | 1.49 (1.12, 1.98) | 0.006 |
| Yes | 0.86 (0.69, 1.07) | 0.166 | 0.86 (0.73, 1.01) | 0.062 | 0.78 (0.68, 0.89) | < 0.001 | 0.78 (0.68, 0.89) | < 0.001 |
| Opium use | 0.65 (0.46, 0.92) | 0.016 | 0.73 (0.56, 0.94) | 0.017 | 0.72 (0.57, 0.9) | 0.004 | 0.83 (0.66, 1.05) | 0.113 |
| Alcohol use | 0.94 (0.7, 1.28) | 0.713 | 0.99 (0.79, 1.23) | 0.917 | 1.05 (0.87, 1.26) | 0.606 | 1.03 (0.86, 1.23) | 0.770 |
| Physical Activity | ||||||||
| Low | ref | − | ref | − | ref | < 0.001 | ref | − |
| Intermediate | 0.72 (0.61, 0.85) | < 0.001 | 0.7 (0.61, 0.8) | < 0.001 | 0.76 (0.67, 0.87) | < 0.001 | 0.75 (0.65, 0.86) | < 0.001 |
| High | 0.41 (0.32, 0.52) | < 0.001 | 0.42 (0.35, 0.5) | < 0.001 | 0.55 (0.47, 0.64) | < 0.001 | 0.52 (0.44, 0.61) | < 0.001 |
Abbreviations: BMI Body mass index, OR Odds ratio, CI confidence intervals
* P-values less than 0.05 were considered statistically significant
Discussion
This study aimed to investigate the prevalence of MetS and its related risk factors using different definitions across Tehran citizens. The results showed that the overall sex- and age-adjusted prevalence of MetS among Tehran adults was highest for the JIS criteria (59.3%), followed by the IDF criteria (46.8%), the ATP III criteria (27.1%), and lowest with the WHO criteria (11.0%). The rate of MetS varies widely across different regions and populations, depending on the criteria and cut-off points used to define MetS. However, the global MetS prevalence ranged from 12.5 to 31.4%, depending on the definition used [19]. The prevalence of MetS in the current study, except for the WHO criteria, was higher than the estimated 25% prevalence among Middle Eastern nations [8]. Various studies conducted in different countries and settings have reported varying prevalence rates of MetS. For instance, in India, the overall prevalence rate of MetS was 30.0%, while it was 28.8% in Pakistan and 22.0% in China [20–22].
The discrepancy between the results of different studies could be attributed to the difference in abdominal obesity and WC in different populations, as well as the variation in the criteria and cut-off points used to define MetS.
In this study, the prevalence of MetS was highest when using the criteria described by the JIS and lowest when using the WHO criteria. One potential explanation for the increased incidence of MetS identified by the JIS criteria, as opposed to alternative criteria, may be due to the JIS’s use of lower WC cut-off points, resulting in a greater number of individuals being identified as having central obesity. Additionally, the absence of a mandatory requirement for abdominal obesity in the JIS definition could further contribute to this disparity [2]. In addition, the prevalence of MetS was slightly higher when using the IDF classification than when using the WHO and ATP-III classifications. The IDF criteria give greater significance to central obesity when identifying MetS and recommend lower WC thresholds, which is similar to the definition provided by the JIS criteria [23].
In this study, males and females had a similar likelihood of being diagnosed with MetS, as defined by the WHO and JIS. However, when using the definitions provided by the ATP III and IDF, male participants were found to have a lower likelihood of being diagnosed with MetS, which is consistent with previous studies [24, 25]. One plausible explanation is that males with a WC of less than 90 cm may meet the criteria for MetS according to the JIS guidelines but not the IDF criteria.
Another study conducted in the European population supports our findings, indicating that MetS is more commonly observed in women than in men [26]. Additionally, in African populations, the prevalence of MetS is higher in females (36.9%) than in males (26.7%) [27]. However, a study conducted in another Middle Eastern country, Qatar, reported contrasting results, revealing that MetS was 1.33 times more widespread in men than in women [28]. According to a prospective study, men with a normal WC are still at a high risk of cardiovascular disease mortality owing to the presence of multiple risk factors [29]. While WC is an important factor in MetS, it is essential to note that central obesity is not a mandatory requirement for diagnosing MetS according to the IDF criteria. It has been suggested that the definition proposed by the IDF may be considered more appropriate for identifying individuals who exhibit insulin resistance and are at an elevated risk of developing type 2 diabetes [30].
Our findings showed that the prevalence of MetS was positively associated with age, which is consistent with previous studies [27]. MetS is more commonly observed in older adults because of the higher incidence of hypertension and diabetes with advancing age [31, 32]. In brief, a cascade of loss of elasticity in the blood vessels, impaired blood flow, abdominal lipid accumulation, and the resulting insulin resistance and hypertriglyceridemia can contribute to the progression of MetS with advancing age [33]. In detail, with advancing age, individuals are more likely to meet multiple diagnostic criteria due to sustained influences, such as sedentary behavior, dietary imbalances, adiposity, and subclinical metabolic dysfunctions, which progressively manifest [34, 35]. This lifelong accumulation of metabolic abnormalities likely contributes to the age-dependent rise in MetS prevalence observed in epidemiological studies [36].
As women reach the age of approximately 50 years, they experience menopause and related metabolic changes, which could lead to an increase in hypertension and cardiovascular disease owing to the loss of protective effects of female hormones [37, 38]. Postmenopausal women are at an elevated risk of developing central obesity and insulin resistance, which can ultimately lead to the onset of metabolic syndrome [39].
Based on the ATP-III and IDF criteria, Our investigation found that MetS is most common in the male population aged 65–74. Additionally, we observed that the prevalence of MetS, based on these two criteria, tended to decrease in individuals aged 75 years or older. This shift in sex difference among older adults could be attributed to the higher mortality rate of men with metabolic diseases before they reach 75 years of age [38, 40].
Our study revealed that individuals with a higher level of education had a lower likelihood of developing MetS, which is consistent with the results of other studies [41, 42]. However, some studies have found no significant association between education level and the prevalence of MetS [43, 44]. Higher estimates of MetS among those with lower educational levels might be attributed to their inadequate knowledge of risk factors and preventive measures [45]. Individuals with lower socioeconomic status often have unhealthy lifestyles and poor dietary habits, which further increase the risk of developing MetS [46].
We observed a significant inverse association between physical activity levels and the risk of MetS among participants, which is in accordance with previous observations [47]. Our results indicate that engaging in high levels of physical activity is associated with a lower likelihood of developing MetS than engaging in intermediate or low levels of physical activity. One notable study conducted by Lee et al. revealed that individuals who engaged in vigorous physical activity six times per week had the lowest prevalence of metabolic syndrome [48]. Our study revealed a lower prevalence of MetS among tobacco users, as defined by the IDF and JIS. This finding contradicts the popular concept that smoking is associated with higher levels of insulin resistance, hyperinsulinemia, and dyslipidemia than in non-smokers [49]. Several studies have reported that smoking is a significant risk factor for the development of MetS and its components [50]. It is possible that the metabolic effects of nicotine result in lower body weight in some smokers, which could contribute to this phenomenon [51]. In addition, a study conducted on Chinese men found no significant correlation between MetS prevalence and smoking status. However, after examining daily cigarette consumption among current smokers and adjusting for other risk factors, the study found that current heavy smokers (consuming ≥ 40 cigarettes per day) had a significantly higher risk of developing MetS than non-smokers [52]. Another potential explanation could be that individuals who smoked may have died earlier [53], leading to their exclusion from this cross-sectional study. Lastly, in the case of our findings it should be considered due to the cross-sectional design limitations, the observed association may reflect reverse causation, wherein individuals diagnosed with hypertension or other components of MetS are more likely to reduce or quit smoking following clinical advice, while those without such diagnoses may be less inclined to change their smoking behavior [54]. Although our findings show a lower prevalence of MetS among opium users, no causal inference can be made due to the cross-sectional study design. According to a recent report from the Fasa PERSIAN Cohort Study, opium consumption is negatively associated with MetS [55], which is in line with our results. As opposed to our findings, Jamali et al. also reported significantly higher opium consumption in lifetime among subjects with MetS [56]. In addition, Yousefzadeh et al.‘s study demonstrated that opium use is associated with a higher prevalence of MetS, likely due to its effects on serum glucose, TG levels, and blood pressure [57]. Nonetheless, the existing literature on opium use remains limited, highlighting the need for further research to better understand its effects. However, it should be considered that survival bias may have influenced the observed inverse association. Individuals who experienced severe metabolic consequences from prolonged opium or tobacco use may have already died due to cardiovascular or related comorbidities, resulting in a remaining sample of users who appear metabolically healthier than the overall population of users [58]. Additionally, both opium and tobacco have been linked to metabolic disturbances and increased mortality risk in previous investigations [59–61], suggesting that the observed association may be a result of study design limitations rather than a true protective effect. Moreover, in the case of alcohol consumption, our study did not find any significant association with the risk of MetS. However, studies on the association between alcohol consumption and MetS risk have shown conflicting results [62]. For instance, a study conducted in China found that moderate alcohol consumption was associated with a lower risk of MetS compared to those who never consumed alcohol [44]. This finding is consistent with that of another study conducted in Spain [63].
The varying prevalence rates of MetS across different diagnostic criteria highlight the importance of selecting the most appropriate method for clinical practice and screening in Iran. According to our findings, diagnostic criteria incorporating abdominal obesity, such as the IDF and JIS, are more predictive of MetS, particularly in regions like Iran, where abdominal obesity is a significant concern [64]. However, in settings with limited resources, criteria based on more accessible measurements, including the ATP III and WHO definitions, may be more feasible [65]. As a result, selecting the most appropriate MetS diagnostic criteria should take into account both the accuracy with which they can identify high-risk individuals, as well as the feasibility of implementing them within Iran’s healthcare system. This includes evaluating the availability of necessary resources, such as laboratory testing and trained personnel. Additionally, it ensures that the criteria align with Iran’s national health priorities.
Finally, considering the burden and associated diseases of MetS, policymakers and health-related organizations must continue to regularly evaluate and control the risk factors of MetS. In addition, regarding the results of our study and similar studies, targeted strategies must be employed to improve the detection of MetS and its determinants in the population, particularly among high-risk and vulnerable groups. Additional investigations are also necessary to determine the impact of social determinants on the development and progression of MetS.
Strengths and limitations
Although this study is the first large-scale epidemiological investigation of MetS in a representative sample from all geographical districts of Tehran utilizing four established diagnostic criteria, certain limitations should be acknowledged. Our study’s cross-sectional design only offers a snapshot of the situation and does not establish causal relationships between variables. Furthermore, the observed differences in MetS prevalence across diagnostic definitions underscore the critical role of definition choice and the need for further research to evaluate their predictive value for clinical outcomes. Additionally, the study’s enrollment was restricted to individuals aged 35 years and above in TeCS, which may limit the representativeness of our findings for the general population because data on younger individuals are not available.
Conclusion
The findings of this study highlight the substantial burden of MetS among the adult population of Tehran, with prevalence varying widely based on the diagnostic criteria used, ranging from 59.3% (using the JIS criteria) to 11% (using the WHO criteria). The current study also identified several sociodemographic and Lifestyle factors associated with MetS, such as female gender, age, educational and physical activity level, and tobacco and opium use, emphasizing the requirement for targeted interventions and preventive measures. It should be considered that early screening and management of MetS components could help reduce the risk of developing associated non-communicable diseases, such as cardiovascular disease and type 2 diabetes, in the population.
Supplementary Information
Below is the link to the electronic supplementary material.
(DOCX 20.4 KB)
Acknowledgements
We acknowledge the cooperation of the staff and participants in the TeCS study.
Author contributions
HG and AH: study concept, data collecting, and drafting the initial manuscript and final approval; AJ: Data cleaning, interpretation, analysis, revision critically, and final approval; NH: drafting the initial manuscript, revision critically, and final approval; AS: study design and management, supervision, data cleaning, interpretation; revised the study critically and final approval; MH and MM: drafting the initial manuscript, revised the study critically, and final approval; FA and VAA: revised the study critically and final approval; SaSa, MB, AK: supervision, study management, revision, and final approval. All authors reviewed and approved the final manuscript.
Funding
This study was financially supported by the Iranian Ministry of Health and the Tehran Heart Center.
Data availability
All the data generated or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This project was approved by the Tehran Heart Center’s review board and the ethical committee of the Tehran University of Medical Sciences (ID: IR.TUMS.MEDICINE.REC.1399.074). All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all study participants before enrollment.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hamed Ghoshouni MD, MSc and Amirhossein Heidari MD contributed equally to this work.
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Associated Data
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Data Availability Statement
All the data generated or analyzed during the current study are available from the corresponding author upon reasonable request.