Introduction

Metabolic Syndrome (MetS) is one of the most common non-communicable disorders worldwide [1]. It is characterized by a set of metabolic disorders that increase the risk of cardiovascular disease (CVD) and Type 2 Diabetes Mellitus (T2DM). Individuals with MetS are between five and three times more likely to develop T2DM and CVD than those without the problem [1]. The prevalence of MetS in the world's adult population is estimated to be between 20–25% [2]. This is influenced by definition used and by factors such as age, sex, race/ethnicity, lifestyle, genetic and environmental factors, endocrine dysfunction, specific medications, and oxidative stress [3].

There are various definitions and criteria for the diagnosis of MetS, including the World Health Organization (WHO), The National Cholesterol Education Program Adult Treatment III (NCEP ATP III), and the International Diabetes Federation (IDF). The major components such as obesity, hypertension, and dyslipidemia are similar in these definitions, although they differ in details and criteria [4]. While glucose tolerance and insulin resistance are essential elements of the WHO definition, clinicians prefer more straightforward definitions such as NCEP ATP III and IDF. Both definitions focus on central obesity rather than insulin resistance [4].

Iran is one of the countries with a high prevalence of MetS in the world [5]. A meta-analysis study found that high levels of triglycerides and low levels of high-density lipoprotein (HDL-C) were the leading causes of the high prevalence of MetS in adults in Iran [6]. Similar to various studies, a population study in Iran showed that the prevalence of MetS depends on the ethnicity of participants [7]. As the population ages, the prevalence of MetS and related diseases increases. The Tehran Lipid and Glucose study found that the prevalence of MetS in the age group of 60–69 years was six times higher than in the age group of 20–29 years [8].

Studies have shown that gender-related factors cause gender inequality in the prevalence of MetS [9]. In adults under 50 years of age, the prevalence of MetS is higher in men than in women, but this ratio is reversed after menopause. Hormonal changes, decreased HDL-c levels, and increased abdominal fat in menopause is associated with increased insulin resistance [10]. Recent studies confirm that the prevalence of MetS is lower in men than women [11]. Recently a meta analysis including 69 studies conducted in different regions of Iran showed that the prevalence of MetS is significantly higher among women than men (34.8% vs. 25.7%) [12]. Although the epidemiology of the MetS has been described in the general population, this would be the first large population-based study in older adults in Northeast of Iran to look at sex differences in details. The current study extends our understanding of this under-researched topic by looking at gender differences in non-Western middle-income countries. This study could also really add to our knowledge and be of immense practical use as it means that policymakers and healthcare practitioners could devise gender-specific (rather than gender blind) information/interventions to reduce Mets. Therefore, the aim of the study was to assess the MetS prevalence and its correlates by gender in older adults based on a large population-based study in Northeast of Iran; Nayshabur Longitudinal Study on Ageing (NeLSA).

Materials and methods

Study population

The PERSIAN Elderly Cohort is an ageing component of the Prospective Epidemiological Research Studies in Iran (PERSIAN) cohort [13]. It was conducted in 4 sites, including Neyshabur (Razavi Khorasan province, Northeast of Iran), Guilan (Northern Iran), Tabriz (Northwest of Iran), and Ardakan (central Iran) to include people from different ethnicity and geographical areas. The current study included people aged 50 -94 in Neyshabur, Northeast of Iran, during 2016–2018 (NeLSA). Participants were selected through stratified random sampling from the list of people registered with six health centers. A total of 9220 people met the eligibility criteria minimum 3-year residency in Neyshabur, Iranian citizens, without dementia, major depression, and disabilities, limiting their ability to participate in the study), of whom a total of 7462 individuals (4831 households) provided the written consent to participate in the study. The participation rate was 81%”.Due to the two MetS criteria comparability, the study was restricted to 7256 participants, all of whom had waist circumference measurements recorded. Standard questionnaires were used to collect information such as demographics, current smoking, marital status, income status, and education level, physical activity, history of chronic disease, and medication use. Trained individuals conducted face-to-face interviews.. Blood pressure and anthropometrics were measured and glucose and lipid profile were also done.

Definition of MetS

In this analysis, NCEP ATP III and IDF criteria were used to identify MetS. According to the modified NCEP ATP III criteria (2005), having at least three of the following five criteria indicates metabolic disorder: 1) Waist Circumference (WC) ≥ 102 cm in men or ≥ 88 cm in women); 2) High- Density Lipoprotein Cholesterol (HDL-C) < 40 mg/dl in men or < 50 mg/dl in women; 3) Triglycerides (TG) ≥ 150 mg/dl; 4) Blood Pressure (BP) > 130/85 mmHg; 5) fasting plasma glucose ≥ 100 mg/dl, and pharmacologic treatment to control hyperglycemia, dyslipidemia and hypertension [4]. Abdominal obesity is a key criterion for diagnosing MetS based on the IDF definition, with at least two of the other four indices similar to the NCEP ATP III criteria, and pharmacologic treatment for dyslipidemia, and hypertension or previously diagnosed T2DM [14, 15]. According to NCEP ATP III criteria and IDF definition, the WC cut-off points for Middle Eastern men and women are 94 and 80 cm, respectively[15]. It is noteworthy that the National Iranian Committee on Obesity considers WC cut-off of ≥ 90 cm as diagnosis criteria of MetS in Iranian adults of both sexes [5, 16]. Thus, in the current study, WC cut-off of ≥ 90 cm was used for MetS diagnosis. Australian findings showed that Body Mass Index (BMI) > 30 kg/m2 is likely to be associated with increased waist circumference [15].

Anthropometric measures and blood pressure

Weight, BMI. and anthropometric parameters were measured using an InBody 770 connected to a BSM 370. BMI (kg/m2) was categorised as healthy weight (< 25), overweight (25–29.9), obese (≥ 30) based on WHO-defined standard cut-off points. WC was determined, in duplicate, at the midpoint between the lowest costal ridge and upper border of the iliac crest. BP was measured twice in a sitting position on both arms. Systolic and diastolic BP were measured by a nurse using a calibrated mercury sphygmomanometer (Riester diplomat, Australia). At least half an hour before measuring BP; coffee, alcohol, drugs, stimulant drinks, smoking, and heavy activity was banned.

Other epidemiological and clinical variables

The age was categorised as 50–59 years, 60–69 years. and 70 years and over for better presentation in the tables. Marital status was considered as two groups: 1) married/living with a partner; and 2) divorced/separated/single/widow. Socioeconomic status indicators included years of education categorized as 0, 1–8, ≥ 9 years. The Financial situation was based on the respondent’s subjective evaluation of the adequacy of their income, (e.g. I don’t have a problem, it is enough for basic needs, and it is not enough for basic needs). Smoking status was based on whether respondents identified themselves as regular smokers or not. The Physical Activity Scale for the Elderly (PASE) was used to estimate the level of physical activity. It has been validated in previous studies in Iran [17]. The PASE is a brief and specific instrument that has been designed to estimate physical activity for older adults for a one-week period. The frequency and time spent in a variety of activities including leisure time activities (walking, light activities, moderate, or strenuous intensity and muscle-conditioning activities) as well as work-related activities (in paid or volunteer work) and household activities such as light house-work, yard work, and caring for others were also recorded. After considering the weight according to the PASE Administration and Scoring Instruction Manual for each activity, the final PASE score for the week was calculated based on the sum of all activities, and the mean score was presented. The data were separated into tertile to categorize physical activity levels as high (≥ 106.5), medium (40.3–106.5), or low (< 40.3).

Hypertension was defined as: Self-report history of hypertension and/or using hypertension drugs and/or systolic blood pressure (SYS) >  = 140 mmHg and/or diastolic blood pressure (DYS) >  = 90 in a blood test. Diabetes was defined as Self-report history of diabetes and/or using diabetes drugs and/or FBS >  = 126 in the blood test. Medical conditions were based on clinical assessments by a physician and the participant’s response to the question ‘Has a doctor ever told you that you have any of the following health problems? This study includes a list of different chronic diseases including gastrointestinal conditions, heart diseases, neurological diseases, musculoskeletal and endocrine conditions, respiratory diseases, and cancers. These were coded as (zero, one, two or more conditions. Participants had been asked to bring all medical records, laboratory results and medications that they were using on the interview day; they all checked by a general practitioner to verify the self-reported medical conditions as well.

Blood sampling and related tests

Venous blood samples were taken from 7:00 to 9:00 AM after 8–12 h of fasting. Blood tubes were centrifuged for 15 min at 3000 rpm to separate serum. Serum concentrations of glucose, and HDL-C were measured by enzymatic reactions using a chemistry analyzer (BT1500, Italy; Pars Azmun kits, Iran).

Statistical analysis

For continuous and categorical variables, descriptive statistical analyses were performed. Data were expressed as mean (SD) and median (interquartile range) for continuous variables due to the asymmetric nature of the variables. For categorical variables, numbers and percentages are presented. We used Neyshabur census data of residents 50 years and up to calculate the overall age-adjusted prevalence of MetS. Except for the overall prevalence of MetS, Because participants in the Neyshabur longitudinal study on Ageing were selected through stratified random sampling from residents of 50 years and up. Except for the overall prevalence of MetS, all prevalence in the current analysis is crude. The normality of distribution of continuous variables was checked by plots (histograms and Q-Q plots) and tested by a one-sample Kolmogorov–Smirnov test. Mann–Whitney tests for non-normally distributed variables and the Chi-square tests were used to examine differences between participants in terms of categorical variables. The prevalence of individual components of metabolic syndrome is described using sex-specific criteria. Multivariable logistic regression was used to identify risk factors associated with metabolic syndrome for both definitions (ATP and the IDF) by sex, including age categories, marital status, income status, education levels, smoking, physical activity, other medical conditions, and BMI). Crude and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were calculated. The agreement between the definitions of ATP and IDF was assessed with kappa statistics. The agreement level was rated as poor with К ≤ 0.20, fair with К = 0.21 to 0.40, moderate with К = 0.41to 0.60, remarkable with К = 0.61 to 0.80, and excellent with К ˃0.80. All statistical analyses were performed using R version 4.0.2 for Windows. A two-sided P-value of < 0.05 was considered statistically significant.

Result

There were 3383 men and 3873 women aged ≥ 50 years evaluated in this study. The overall median age of the population was 59.3 (interquartile range (IQR) = 54.9–65.5) years, whereas the median age of men and women were 60.3 and women were 60.3 (IQR = 55.9–66.8) and 58.4 (IQR = 54.2–64.4), respectively. The categorical baseline characteristics of the study subjects according to the status of the MetS are shown in Table 1. In general, the frequency of MetS was higher in older people, women, illiterate, those with higher BMI and less physical activity, and those who were living alone or had hypertension, diabetes, or other medical conditions compared to those without MetS. However, the prevalence of MetS was lower in those in less income category and smokers.,All variables were significantly different between the two groups (P < 0.05). According to the ATP and IDF definitions, the overall crude and age-adjusted prevalence of the MetS were similar ( 45.8% and 47.1%, respectively). The prevalence of the MetS in men and women was 36.8% and 53.5% by the ATP definition, 33.1% and 59.4% by the IDF definition, respectively. The Prevalence of individual components of MetS in older adults according to NCEP ATP III and IDF criteria was presented in Fig. 1. As shown in Fig. 1, by both the ATP and the IDF definitions, prevalence of individual components in women was higher than men (prevalence of abnormal obesity in women was more than twice). Tables 2 and 3 illustrate the multivariable logistic regression findings for MetS indicators for both criteria (ATP and the IDF) by gender. In the fully adjusted model, according to both definitions, those with MetS were more likely to be in the age range of 70 years and above, have at least one disease, and a higher BMI.

Table 1 Socio-demographic and clinical profile of the study participants according to the status of Metabolic Syndrome (MetS), Neyshabur Longitudinal Study on Ageing
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Fig. 1
figure 1

Prevalence of individual components of Metabolic Syndrome in older adults according different definitions; The National Cholesterol Education Program Adult Treatment III (NCEP ATP III), and the International Diabetes Federation (IDF) criteria, Neyshabur Longitudinal Study on Ageing

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Table 2 Factors related to the prevalence of metabolic syndrome in men and women aged 50 years and up according to the National Cholesterol Education Program Adult Treatment III (NCEP ATP III) definition, Neyshabur Longitudinal Study on Aging
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Table 3 Factors related to the prevalence of metabolic syndrome in men and women aged 50 years and up according to the International Diabetes Federation (IDF)definition, Neyshabur Longitudinal Study on Aging
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After fully adjusting, according to ATP criteria, other gender‐specific metabolic syndrome risk factors in men included smoking, income, physical activity, whereas in women, those who had a higher education level were less likely to have MetS. According to IDF criteria, other gender‐specific metabolic syndrome risk factors in men included physical activity, and in women, it was education level.

On the other hand, in men, smoking had an associated reduction in odds of MetS. There was an interaction between smoking and BMI. Therefore, a composition variable was defined to include both smoking and BMI in the model (Table 4). Accordingly, based on the ATP criteria among men, nonsmokers with an abnormal level of BMI were significantly four times more (AOR 4.74, 95% CI: 3.84–5.86, p-value < 0.001) likely to have MetS compared to those with a normal level of BMI. Moreover, smokers with an abnormal level of BMI were four times more (AOR 4.17, 95% CI: 3.08–5.63, p-value < 0.001) likely to have MetS compared to nonsmokers with a normal level of BMI. Besides, smokers with a normal level of BMI were 52% (AOR 0.48, 95% CI: 0.31–0.74, p-value < 0.001) less likely to have MetS than nonsmokers with normal BMI levels. The same pattern was true for women. In addition, in ATP III criteria and women’s results, nonsmokers with an abnormal level of BMI were significantly three times more likely to have MetS compared to those with a normal level of BMI (AOR 2.99, 95% CI: 2.46–3.63, p-value < 0.001). Smokers with an abnormal level of BMI were 63% more likely to have MetS than to nonsmokers with a normal level of BMI (AOR 1.63, 95% CI: 0.83–3.20, p-value = 0.157).

Table 4 Association between smoking and metabolic syndrome by gender; interaction between smoking and BMI, Neyshabur Longitudinal Study on Ageing
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The degree of agreement (kappa statistic) between ATPIII and IDF definition was nearly perfect (Kappa = 0.79, 95% CI 0.72–0.80, p-value < 0.001).

Discussion

The present study investigated the associations between gender and the prevalence of MetS components in adults 50 years and up based on IDF and NCEP: ATP III definitions. Our findings showed that the overall prevalence of MetS by the ATP III and IDF definitions was 45.76% and 47.13%, respectively. There was no significant difference between IDF and ATP III diagnosis for individuals with MetS.

Iran in the Middle- East has been reported as a country with a high prevalence of MetS [18, 19]. A meta-analysis of cross-sectional studies on the population of the Middle- East reported that the overall prevalence of MetS in Iran is 6–42%,.[18]. Various Iranian studies indicate a high prevalence of MetS in adults, suggesting an increased risk of this syndrome with increasing age. In a meta-analysis study in Iran, the overall prevalence of MetS was 30.4%; this value increases to 51.7% in those over 60 years of age [12]. In another meta-analysis study in Iran, the prevalence of MetS was 36.9% for ATP III and 34.6% for IDF in adults [19].

In our study, the prevalence of MetS was higher in women than in men, 53.6% vs 36.8% by the definition of ATP III and 59.4% vs. 33.1% by IDF. Furthermore, the prevalence of all components of MetS among women was higher (Fig. 1). These results were in line with the other studies carried out in Iran. A longitudinal study in people aged 40–70 found that the prevalence of MetS and its components was more in women than in men [20]. The Tehran Lipid and Glucose Study showed that MetS prevalence increased with age in both sexes with a significantly higher prevalence in the age group above 50 years, especially in women (Men; ≥ 33.9%, and women; ≥ 64.1%) [21]. The findings of a meta-analysis study in Iran showed that MetS prevalence was higher in women than men; 35.4% versus 24.1% by the ATPIII definition, and 36.0% versus 29.9% by the IDF definition (32).

Based on ATP III and IDF criteria, our results revealed a higher prevalence of all the MetS components in women than in men. The highest prevalence among the components was low HDL-C and high WC. The prevalence of other components, including high TG level, high BP, and elevated BS levels, were also higher in women than in men. The gender-specific differences in the prevalence of MetS components were reported in several previous studies [22,23,24,25,26]. Although, the findings of these studies are not consistent in terms of gender differences characteristics. For instance, an analysis of the Turkish and Kurdish populations showed the most common components of MetS were hypertension, abdominal obesity, elevated FBS, and TG but not low HDL-C in both sexes [27]. In Chain, it was revealed that older adult women had higher TG, lower HDL-C, and larger WC than older men [25]. In another study in older adults in Turkey, it has been shown that the prevalence of systolic BP, WC, and HDL-C in women is more than the prevalence in men [28]. In addition, the prevalence of TG and BP was higher in Korean women than in men aged 60 and over, whereas in individuals with 20 to 59 years old, the prevalence of TG and BP in men was higher than in women [23]. It seems that differences in age [23, 29], genetic traits [30], dietary habits [31, 32], level of physical activity [22], and socioeconomic status [33] in the study populations are possible explanations for inconsistent findings regarding gender difference in various studies.

Menopause can be one of the reasons for the difference in the prevalence of MetS in women over 50 years of age compared to men. It has been found that MetS can occur in 40% of postmenopausal women due to obesity and weight gain [34]. Evidence suggests that reducing estrogen levels is associated with an increased risk of MetS during menopause. Reduced estrogen is associated with increased central fat, weight gain, insulin resistance, and increased levels of total cholesterol, LDL-C, TG, and decreased HDL-C[35, 36]. A study in Iran showed that the prevalence of MetS in postmenopausal women was 53.5% compared to premenopausal women (18.3%) [37]. One of the reasons for the high prevalence rate of MetS in our study population was a large percentage of postmenopausal women. One study in the elderly women population showed that the prevalence of MetS was 45.7%; they found WC to be the most appropriate indicator for MetS detection in this group [38]. Abdominal obesity is an index of dysfunctional adipose tissue, which is crucial in clinical diagnosis. Adipose tissue plays a prominent role in insulin resistance and MetS by producing and secreting various biological molecules called adipokines. One study in older adults showed that the prevalence of MetS is associated with high levels of adipokines, including leptin, IL-6, TNF-α and low adiponectin levels [39]. It is known that MetS is associated with glucose intolerance, hypertension, and dyslipidemia. This study also found that FBS, TG, and BP levels increased with increasing abdominal obesity. Also, the use of anti-diabetic, anti-hypertensive, and anti-hyperlipidaemia drugs was higher in women than in men (Women: 14.8%, and 40.4, 19.5%; men: 11.3%, 29.2%, and 14.2%, respectively).

Our results also show that the odds of MetS are affected by some conditions such as income status, current smoking, education level, physical activity, medical conditions, and BMI. Among these conditions, the effect of BMI is more significant than other factors. So, the abnormality in BMI (≥ 25) versus normal BMI (< 25) increased the odds of MetS more than 9 and 3,5 times in men and women, respectively. These results are inconsistent with other studies reporting the significant effects of BMI on odds of MetS [40,41,42]. In this regard, our results about the effect of smoking on the odds of MetS should be considered with caution. Although numerous studies have been indicated that smoking has an inductive effect on MetS [43,44,45], our results show that smoking has a protective role against MetS. Onat et al. reported that the protective role of smoking against MetS is due to the fact that smoking has a “protective” effect against abdominal obesity in both sexes, independent of waist circumference [46]. Compelling evidence implicated that smokers tended to have lower BMI than non-smokers, which is due to the fact that nicotine could suppress appetite by acting on the brain, and extend inter-meal interval; thus less food intake resulted in weight loss [47]. Although our result is consistent with the study performed by Onat [46], it seems that the protective effect of smoking is more due to the interaction between smoking and normal BMI, not to only smoking. Thus, it can be concluded that BMI is a strong determining factor that removes the inductive effect of smoking on MetS odds in smoker subjects with normal BMI. This effect might be due to the low number of smokers in this study and that most of them have normal BMI. Therefore, more studies on the high population of older smokers are necessary to reveal the pure effect of smoking on MetS.

There are few epidemiologic studies regarding sex- specific MetS prevalence in older adults. In the current study, a large number of men and women aged over 50 years in a cohort study were analysed. Our study was the first cohort study in Iran to reveal the prevalence of MetS in older subjects 50 years and older. Given the relatively high sample size, this study could provide a good estimation of the prevalence of MetS in older adults and provide insights for the management and reduction of MetS in older adults targeting women specifically. However, because of the cross-sectional design of the current study, we do not permit to evaluation of the cause-effect relationship between MetS and its components. In addition, few subjects of patients suffering from chronic illness were eliminated from the study and, few subjects not accepted our invitation for contribution to the study.

Conclusion

The current study was done based on the data from 7256 older individuals (50 years old and up) with MetS to explore the sex-specific prevalence of MetS and its components according to IDF and the NCEP ATP III definitions. Our results showed that the prevalence of MetS in older adults increased in both sexes, the prevalence of almost double in women. An almost perfect agreement was found between the IDF and the NCEP ATP III definitions in the present study. Our study also revealed that women had a higher prevalence of all the components of MetS contributed to the definition of MetS than did men. The findings showed that lowering HDL and increasing abdominal obesity could be good diagnostic factors for examining the prevalence of MetS in women over the age of 50. Sex-specific public health strategies and management policies for preventing and management of MetS and its components among the older adult population should be developed to reduce social and medical burden.