Trends and Implications of Metabolic Syndrome in Korea, 2007-2022: A Nationwide Study

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Methods A cross-sectional study of data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2022 was conducted. The study included 87,397 subjects. MetS was defined according to the National Cholesterol Education Program-Third Adult Treatment Panel (NCEP-ATP) III and the Korean Society for the Study of Obesity (KOSSO) criteria. Results MetS prevalence increased from 22.8% in 2007 to 28.6% in 2022 but showed sex differences, with males increasing (24.5–36.8%) and females decreasing (20.6–19.5%). Among the components of MetS, hyperglycemia and abdominal obesity showed the most significant increases (1.51-fold and 1.29-fold, respectively). While hyperglycemia increased in all age groups, abdominal obesity increased most in males aged 30–39 (1.98-fold) and 19–29 (1.81-fold). Low high-density lipoprotein cholesterol (HDL-C) was the only component that decreased (0.62-fold) and was more prevalent among females. In the sub-analysis of those aged 65 and older, MetS increased in both males and females but was more prevalent in females. Individuals with MetS had higher rates of current smoking, heavy drinking, physical inactivity, and carbohydrate consumption. Conclusions The prevalence of MetS is gradually increasing in Korea, and hyperglycemia and abdominal obesity are rapidly increasing, especially in younger males. Although the prevalence of MetS in females is decreasing due to changes in the social environment, continuous efforts are needed for postmenopausal females. Targeted health policies and interventions should be established. Health sciences/Endocrinology Health sciences/Medical research Metabolic syndrome Prevalence Sex characteristics Health policy Figures Figure 1 Figure 2 Figure 3 Introduction Metabolic syndrome (MetS) is a cluster of conditions, including obesity, hypertension, insulin resistance, and dyslipidemia, which significantly increase the risk of chronic diseases such as cardiovascular disease and type 2 diabetes mellitus. 1 A prospective cohort study involving 11,512 participants found that MetS increased all-cause mortality by 1.4 times in both men and women and increased cardiovascular disease mortality by 2.3 times in men and 2.8 times in women. 2 With the spread of the Western lifestyle, MetS has become a global epidemic. Modern sedentary lifestyles, physical inactivity, and high-calorie-low fiber fast food consumption are major factors to MetS. 3 In the United States, the prevalence of MetS reached 36.9% from 2015 to 2016, increasing with age. 4 Given the rapidly aging population in South Korea 5 , intensified efforts to manage MetS are necessary. Effective intervention requires understanding MetS characteristics across different age and sex groups. Based on the Korea National Health and Nutrition Examination Survey (KNHANES), the prevalence of MetS in South Korea has increased from 24.9% in 1998 to 31.3% in 2007. 6 By 2020, it reached 33.2%, indicating that approximately one in three individuals had MetS. 7 However, recent studies in Korea lack detailed stratification by sex, age, and behavioral habits and need to be updated. Therefore, in-depth analyses are necessary to understand the effects of societal changes, dietary shifts, and other overlooked factors. Accordingly, this study not only presents evidence on the recent trend in MetS based on the data from KNHANES from 2007 to 2022 but also provides evidence for sex, age, habits such as smoking, drinking, and physical activity, energy intake, and carbohydrate, protein, and fat intake. The focus was on nutritional factors such as protein and fat intake according to well-defined health behavior definitions 8 . Therefore, we aimed to provide valuable insights to support the formulation of Korea’s healthcare strategy. Results Prevalence of metabolic syndrome from 2007 to 2022 Supplementary Table 1 shows the secular trends of age-standardized values of MetS and components. The age-standardized prevalence of MetS in the population increased from 22.8% in 2007 to 28.6% in 2022. Regarding sex-specific changes, the prevalence of MetS in males increased from 24.5–36.8%, while in females, it decreased from 20.6–19.5%, highlighting a sex disparity. Among the components of MetS, high WC and high glucose levels consistently increased. High WC rose from 26.0% in 2007 to 33.6% in 2022, and high glucose from 21.1–31.9%. In males, high WC increased from 25.7–42.9%, and high glucose from 25.7–39.8%. However, in females, high WC fluctuated but recently decreased from 25.9% in 2020 to 23.2% in 2022, while high glucose increased from 16.5–23.6% over the study period, indicating sex-specific differences. After decreasing between 2009 and 2014, the prevalence of high BP remained stable, ranging from 29.4–33.7%. High TG showed an upward trend, from 30.6–34.6%. Although sex disparities existed in high BP and high TG, these differences were less significant compared to other components of MetS. Low HDL-C consistently decreased from 43.4–27.2% and was more prevalent among females. The decline was more significant in females, reducing sex disparity, with males showing a higher prevalence by 2022. (Fig. 1 ). Prevalence of metabolic syndrome by age and sex The prevalence of MetS increased across all age groups, especially the over 70 group, from 51.2% in 2007–2009 to 64.3% in 2022. For females, the prevalence of MetS increased only in those aged 70 and older (from 59.2–71.2%), while it decreased in all other age groups. (Supplementary Table 2). High WC increased the most in males aged 30–39, with a 1.98-fold increase, followed by males aged 19–29, with a 1.81-fold increase. The prevalence of high BP increased only in the over-70 group. High glucose increased in all groups, with the most changes in those aged 19–29 and 30–39 (1.66-fold and 1.55-fold, respectively). The prevalence of high TG remained relatively stable in younger adults while increasing 1.48-fold in those aged 70 and older. Low HDL-C levels decreased across all groups except for males aged 70 and older, with the most change observed in younger females. (Fig. 2 ). In 2022, among younger adults, the prevalence of MetS components was highest for high WC, followed by high TG. For those aged over 60, high BP and high glucose had the highest prevalence rates, while high WC was the least prevalent. When analyzed by gender, low HDL-C was one of the most prevalent components among females across all age groups. (Fig. 3 ). Subgroup analysis of the prevalence of metabolic syndrome in older adults In the sub-analysis results of those aged 65 and older, the prevalence of MetS increased from 50.2% in 2007 to 62.0% in 2022. It increased from 37.8–55.3% in males and 59.2–67.4% in females, narrowing the sex disparity from 21.4–12.1%. High WC decreased from 43.8% in 2007 to 34.7% in 2014 but increased to 48.5% in 2022 while recently reducing since 2022. High BP prevalence increased from 66.5–73.1%, with a narrowed sex disparity by 2022. High glucose and high TG steadily increased, from 41.6% in 2007 to 59.9% in 2022 and 40.4% in 2007 to 55.6% in 2022, respectively. High glucose was the only component that was more prevalent in males. Low HDL-C in females decreased over the study period, while that of males increased. (Table 1 ). Table 1 Prevalence of Metabolic syndrome in individuals aged 65 and older, 2007–2022 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Total Unweighted n 652 1,383 1,490 1,175 1,331 1,280 971 1,093 1,231 1,475 1,475 1,506 1,609 1,578 1,681 1,539 MetS* 50.2 50.2 51.8 53.9 49.3 50.1 53.0 50.5 58.0 58.9 54.9 59.8 61.1 60.8 63.1 62.0 High WC † 43.8 39.3 36.2 39.1 38.5 34.8 34.9 34.7 46.0 43.9 38.7 42.7 51.9 52.9 50.0 48.5 High BP ‡ 66.5 66.5 74.4 76.5 72.5 71.9 66.6 67.1 72.9 75.2 69.5 74.1 72.5 71.7 71.3 73.1 High glucose § 41.6 45.6 47.3 47.5 42.8 48.3 55.1 49.3 53.1 55.9 56.4 57.0 57.4 58.1 59.7 59.9 High TG ‖ 40.4 38.4 40.4 43.6 40.0 42.9 44.6 45.6 47.0 47.8 47.2 49.8 49.7 51.0 49.9 55.6 Low HDL-C ¶ 57.6 59.7 59.8 60.8 53.4 55.4 56.0 55.7 57.4 56.6 57.8 61.5 57.1 60.4 59.1 56.0 Male Unweighted n 276 545 631 540 579 550 436 503 557 659 648 642 687 678 727 693 MetS 37.8 39.9 40.2 40.8 40.3 38.9 42.2 42.9 49.0 51.1 46.8 52.1 52.3 54.3 57.9 55.3 High WC 25.0 31.0 26.3 28.4 33.1 27.4 26.0 29.1 38.9 32.6 32.1 39.6 47.4 50.1 48.1 42.5 High BP 62.9 61.5 71.3 69.3 66.5 65.3 64.2 62.4 68.9 73.2 66.3 70.2 67.6 69.0 68.0 72.7 High glucose 42.6 45.5 49.0 47.1 43.1 50.5 56.6 52.9 55.0 57.8 57.9 59.1 63.1 61.9 65.0 62.7 High TG 38.2 32.4 37.1 37.5 35.1 35.2 38.3 41.6 40.7 45.4 42.4 44.5 42.9 45.8 45.6 49.0 Low HDL-C 37.0 44.4 40.7 45.0 37.8 37.5 37.9 37.9 41.8 43.6 43.9 46.0 41.6 49.3 46.1 43.7 Female Unweighted n 376 838 859 635 752 730 535 590 674 816 827 864 922 900 954 846 MetS 59.2 57.6 60.2 63.6 55.9 58.3 61.6 56.7 65.0 64.8 61.1 65.7 67.7 65.8 67.2 67.4 High WC 57.2 45.1 43.4 47.0 42.5 40.2 42.1 39.3 51.5 52.4 43.8 45.1 55.3 55.1 51.6 53.3 High BP 69.1 70.1 76.7 81.8 76.9 76.7 68.5 70.9 76.0 76.7 71.9 77.0 76.2 73.8 74.0 73.5 High glucose 40.8 45.6 46.2 47.8 42.5 46.8 53.8 46.4 51.7 54.5 55.2 55.4 53.2 55.2 55.5 57.6 High TG 41.9 42.7 42.8 48.2 43.7 48.6 49.7 48.8 51.8 49.6 51.0 53.9 54.7 55.0 53.4 60.8 Low HDL-C 72.4 70.7 73.7 72.5 65.0 68.4 70.6 70.2 69.5 66.4 68.5 73.2 68.8 68.9 69.4 65.7 Values are expressed as percentages. Abbreviations: MetS, metabolic syndrome; WC, waist circumference; BP, blood pressure; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol. * Metabolic syndrome was defined as those meeting three or more of the following criteria: † High WC was defined as waist circumference ≥90 cm in male or ≥ 85 cm in female, ‡ High BP was defined as BP ≥ 130/85 mmHg or taking antihypertensive drugs, § High glucose was defined as fasting plasma glucose ≥ 100 mg/dL or taking diabetes mellitus drugs, ‖ High TG was defined as TG ≥ 150 mg/dL or taking dyslipidemia drugs, ¶ Low HDL-C was defined as HDL < 40 mg/dL in male, < 50 mg/dL in female, or taking dyslipidemia drugs. Prevalence of metabolic syndrome according to health behaviors The prevalence of current smoking was higher in the MetS group compared to the non-MetS group (21.0% vs 17.4%, p < 0.0001). Similarly, heavy drinking was more prevalent in the MetS group (15.8% vs 11.2%, p < 0.0001). Physical activity was lower in the MetS group compared to the non-MetS group (39.3% vs 50.0%, p < 0.0001). These trends were more pronounced in males. (Supplementary Table 3). The energy intake percentage from carbohydrates was higher in people with MetS. Total energy intake showed no statistically significant difference between people with and without MetS in the general population. (Table 2 ). Table 2 Energy Intake according to the presence of metabolic syndrome, 2019–2022 Variables Metabolic Syndrome P value No Yes Total energy intake, kcal/day Total 1900.9 ± 11 1873 ± 13.9 0.0908 Male 2239.8 ± 16.7 2165.2 ± 19.6 0.0026 Female 1607.3 ± 10 1491.8 ± 13.1 < .0001 Energy intake percentage from macronutrients Carbohydrates, % Total 59.4 ± 0.2 63.4 ± 0.2 < .0001 Male 59 ± 0.2 61.5 ± 0.3 < .0001 Female 59.7 ± 0.2 65.7 ± 0.2 < .0001 Protein, % Total 16 ± 0.1 15.4 ± 0.1 < .0001 Male 16.4 ± 0.1 16 ± 0.1 0.0007 Female 15.6 ± 0.1 14.7 ± 0.1 < .0001 Fat, % Total 24.6 ± 0.1 21.2 ± 0.2 < .0001 Male 24.6 ± 0.2 22.5 ± 0.2 < .0001 Female 24.7 ± 0.1 19.5 ± 0.2 < .0001 Values are expressed as mean ± standard error. Discussion MetS has steadily increased over the past 15 years, especially in males. A notable sex difference in trends was observed in females under 70, which showed a decreasing trend. The increase in MetS was primarily driven by increases in hyperglycemia and abdominal obesity, particularly among younger males. Among the components of MetS, only low HDL-C decreased significantly in younger females. Conversely, Mets were more prevalent in older adults than females, narrowing the sex difference. The prevalence of MetS increased with age in both males and females. Among individuals aged 70 and older, the prevalence of MetS increased by 20.0% over the study period, resulting in approximately 7 out of 10 individuals having MetS in 2022. There was a sharp rise in the prevalence among males between the age group 19–29 and 30–39 and among females between the age group 40–49 and 50–59. The difference between sexes implies that social, environmental, and lifestyle changes due to involvement in social activities and marriage are major factors influencing MetS in males, while hormonal changes due to menopause are significant in females. 9,10 Contrary to our findings, the analysis of MetS prevalence in the United States from 2011 to 2016 didn’t exhibit a disparity between sex (35.1% for men and 34.3% for women, p = 0.47) 4 . In China, MetS was much more prevalent in women, likely due to higher rates of abdominal obesity and low HDL-C compared to men. 11 A study analyzing trends in the prevalence of MetS components in the U.S. from 2007 to 2014 revealed that high WC among women was more prevalent than in men (p < 0.05) and showed a sharp increasing trend (p-trend = 0.009) 12 . Moreover, in most countries, obesity prevalence and average BMI tend to be higher in males than in females of all ages. 13 This is believed to stem from the biological differences in female’s fat storage capacity as an adaptation for childbearing. 10 Therefore, the relatively low abdominal obesity, as reflected by high WC, among Korean females is likely to be influenced more by sociocultural factors than biological reasons. These factors may include increased female participation in social activities, a higher average age of marriage, lower birth rates, and a social preference for slim females. For older females, factors such as decreased social activities due to changes in employment status and reduced social pressure to maintain a slender physique, in addition to hormonal changes due to menopause, may have played a role, resulting in a distinct pattern among older adults. 14 These results indicate that while the prevalence of MetS is gradually decreasing in females, there is a critical need for ongoing health management efforts, particularly for postmenopausal females. Meanwhile, our study's unique pattern of high WC prevalence highlights the necessity of managing abdominal obesity, particularly in younger males. 9 Abdominal obesity in young adulthood can contribute to metabolic burden, potentially increasing susceptibility to obesity-related metabolic diseases such as coronary heart disease and diabetes as these individuals age. 15 Additionally, together with obesity, prediabetes is a key mechanism leading to MetS. 1 In 2020, the prevalence of prediabetes among Korean adults reached 39.3%, with diabetes mellitus increasing in young adults. However, diabetes management outcomes remained inadequate. 16 Similar to abdominal obesity, earlier onset of diabetes results in adverse long-term consequences, including complications and quality of life. 17 Therefore, comprehensive measures targeting weight reduction blood glucose management in young adults should be made to reduce future health burdens at both individual and national levels. Given the high internet usage rates among younger populations, digital health interventions based on information and communication technology (ICT) can be efficient health management tools. 18 Since no single medication for treating MetS exists, lifestyle modifications such as healthy eating habits and weight reduction are crucial therapeutic strategies. 3 ICT interventions hold strong potential for supporting these lifestyle changes. 19,20 Smoking, heavy alcohol consumption, physical inactivity, and poor dietary choices are well-known unhealthy lifestyle factors. As these are modifiable factors that can reduce the risk of chronic diseases, understanding their epidemics is essential for effective public health management. 21 The relationship between these unhealthy lifestyle factors and the presence of MetS was more pronounced in males. This phenomenon could be attributed to two factors. First, females generally have lower smoking rates 22 and monthly binge drinking rates 23 compared to males, so the impact of these behaviors on MetS may be relatively less significant. Second, females with MetS may have become more health-conscious and adopted healthier lifestyles after diagnosis. It can be inferred that increasing physical activity and addressing smoking and drinking, particularly in males, are important areas of focus. Carbohydrates directly and indirectly influence our metabolic state, affecting conditions such as dyslipidemia and MetS 24 . According to a study based on the 2008–2011 KNHANES, a high carbohydrate intake was associated with a higher prevalence of MetS in males. In females, a high carbohydrate and low-fat intake was related to a higher prevalence of MetS 25 . Although carbohydrate consumption decreased between 2001 and 2020 7 , our analysis demonstrated that carbohydrate consumption is still significantly associated with the presence of MetS. This suggests that diet composition, particularly the proportion of carbohydrates, is more critical than total energy intake. There are some limitations to this study. First, as this is a cross-sectional study, it is difficult to determine whether behavior changes led to a higher prevalence of the condition or if the higher prevalence led individuals to adopt certain habits. Second, other socioenvironmental factors influencing MetS prevalence may exist. Third, health behavior data were self-reported, resulting in memory decay bias and recall bias. Only the presence or absence of behavioral habits was reflected; therefore, the effect of degree may not be considered. Fourth, the comparison only involved the average energy intake percentage from each macronutrient between those with and without MetS. The proportion of individuals consuming more than the recommended intake was not considered. Despite these limitations, this study remains significant as it conducted research analyzing nationally representative data, carrying out statistical analyses based on detailed age and sex stratification. In conclusion, the prevalence of MetS in South Korea has steadily increased over the past 15 years. Fasting hyperglycemia and abdominal obesity have risen rapidly, and health disparity between sexes has been exacerbated. It is crucial to implement lifestyle modifications, including balanced eating habits. This study provides a better understanding of MetS trends. It offers valuable insights for formulating health policies and management strategies for the Korean population and other populations with similar socioeconomic status. Specifically, targeted solutions regarding the characteristics according to age and sex should be established. Methods The data analyzed in this study were obtained from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2022. The KNHANES is a nationally representative survey provided by the Korea Centers for Disease Control and Prevention (KCDC) for national health promotion, disease prevention, and comparative health data analysis. 26 The research protocol was approved by the Institutional Review Boards of the KDCA (2007-02CON-04-P, 2008-04EXP-01-C, 2009-01CON-03–2 C, 2010-02CON-21-C, 2011-02CON-06-C, 2012-01EXP-01–2 C, 2013-07CON-03–4 C, 2013-12EXP-035 C, 2018-01-03-P-A, 2018-01-03-C-A, 2018-01-03–2 C-A, 2018-01-03–5 C-A, 2018-01-03–4 C-A). All participants provided written informed consent before participation. Additionally, KNHANES data are publicly accessible and serve as a valuable resource for various epidemiological studies. The principles of the Declaration of Helsinki conducted this study. The study was approved by the Institutional Review Board of the Kyunghee University Hospital (No. 2024-07-021). Informed consent was waived. Out of 126,446 subjects from KNHANES (2007–2022), 27,005 individuals under 19 were excluded. Among the remaining 99,441 subjects, 12,044 individuals were excluded due to missing data for defining MetS. Thus, the final study population consisted of 87,397 subjects. Participants missing data on waist circumference, systolic blood pressure (BP), diastolic BP, fasting plasma glucose, triglyceride, or high-density lipoprotein cholesterol (HDL-C) were considered to lack a complete definition of MetS. This study used the definition provided by the National Cholesterol Education Program-Third Adult Treatment Panel (NCEP-ATP) III, with waist circumference criteria modified according to the cutoffs established by the Korean Society for the Study of Obesity (KOSSO). 27,28 Subjects were defined as MetS if they met three or more of the following criteria: Waist circumference (WC) ≥ 90 cm in males or ≥ 85 cm in females BP ≥ 130/85 mmHg or taking antihypertensive drugs Fasting plasma glucose ≥ 100 mg/dL or taking diabetes mellitus drugs Triglyceride (TG) ≥ 150 mg/dL or taking dyslipidemia drugs HDL-C < 40 mg/dL in males, < 50 mg/dL in females, or taking dyslipidemia drugs Individuals self-reported health behaviors such as smoking, drinking, and physical activity. Each definition is followed by. Current smoking was defined as those currently smoking and having smoked more than 5 packs (100 cigarettes) throughout their lifetime. Heavy drinking was defined as those who drank more than twice per week and consumed more than 7 glasses of alcohol per occasion for males and 5 glasses for females within the past year. The physical activity group was defined as those who spent more than 150 minutes on moderate physical activity, 75 minutes on vigorous physical activity, or an equivalent combination. (vigorous physical activity 1 minute = moderate physical activity 2 min) Total energy intake was calculated as the sum of calories consumed daily. The energy intake percentage from each macronutrient was calculated using the following formulas 8 ; Carbohydrates: \(\:\left(\frac{\text{C}\text{a}\text{r}\text{b}\text{o}\text{h}\text{y}\text{d}\text{r}\text{a}\text{t}\text{e}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4}{\text{C}\text{a}\text{r}\text{b}\text{o}\text{h}\text{y}\text{d}\text{r}\text{a}\text{t}\text{e}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4+\text{P}\text{r}\text{o}\text{t}\text{e}\text{i}\text{n}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4+\text{F}\text{a}\text{t}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:9\:}\right)\times\:100\) Proteins: \(\:\left(\frac{\text{P}\text{r}\text{o}\text{t}\text{e}\text{i}\text{n}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4}{\text{C}\text{a}\text{r}\text{b}\text{o}\text{h}\text{y}\text{d}\text{r}\text{a}\text{t}\text{e}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4+\text{P}\text{r}\text{o}\text{t}\text{e}\text{i}\text{n}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4+\text{F}\text{a}\text{t}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:9\:}\right)\times\:100\) Fats: \(\:\left(\frac{\text{F}\text{a}\text{t}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:9}{\text{C}\text{a}\text{r}\text{b}\text{o}\text{h}\text{y}\text{d}\text{r}\text{a}\text{t}\text{e}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4+\text{P}\text{r}\text{o}\text{t}\text{e}\text{i}\text{n}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:4+\text{F}\text{a}\text{t}\:\text{i}\text{n}\text{t}\text{a}\text{k}\text{e}\:\text{p}\text{e}\text{r}\:\text{d}\text{a}\text{y}\left(\text{g}\right)\times\:9\:}\right)\times\:100\) Changes in the prevalence of MetS were calculated using a direct standardization method based on the 2005 projected population data from the Korean Statistical Information Service (KOSIS). To assess the potential influence of age and sex on the transition of MetS prevalence, stratified analysis was performed, with age groups categorized as 19–29, 30–39, 40–49, 50–59, 60–69, and ≥ 70 years. Considering the high prevalence of MetS in the elderly, subgroup analysis was additionally conducted for individuals aged 65 and older. Health behavior status and energy intake of each sex were compared according to the presence of MetS. Statistical significance was defined as p < 0.05. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Declarations Conflicts of Interest The authors declare no conflict of interest. Author Contribution Study concept and design: SH, RSY, HK, KS; acquisition of data: all authors; analysis and interpretation of data: all authors; drafting of the manuscript: KY, CY and LK-N; critical revision of the manuscript: all authors; statistical analysis: LK-N and HK; and study supervision: SYR and KS. Acknowledgement We thank the Korean Society for the Study of Obesity and the Korea Centers for Disease Control and Prevention for their support. Data Availability The data are available upon request. The study protocol and statistical code are available from KDH ( [email protected] ). The dataset, available from the Korea National Health and Nutrition Examination Survey by the Korea Disease Control and Prevention Agency, can be accessed via the following link (https://knhanes.kdca.go.kr/knhanes/main.do). References Grundy, S. M. Pre-Diabetes, Metabolic Syndrome, and Cardiovascular Risk. Journal of the American College of Cardiology 59 , 635-643, doi:https://doi.org/10.1016/j.jacc.2011.08.080 (2012). Hu, G. et al. Prevalence of the Metabolic Syndrome and Its Relation to All-Cause and Cardiovascular Mortality in Nondiabetic European Men and Women. Archives of Internal Medicine 164 , 1066-1076, doi:10.1001/archinte.164.10.1066 (2004). Saklayen, M. G. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep 20 , 12, doi:10.1007/s11906-018-0812-z (2018). Hirode, G. & Wong, R. J. Trends in the Prevalence of Metabolic Syndrome in the United States, 2011-2016. Jama 323 , 2526-2528, doi:10.1001/jama.2020.4501 (2020). Statistics Korea. 2023 Statistics on the Aged. 2023. https://kostat.go.kr/board.es?mid=a20101000000&bid=11759&act=view&list_no=427605&tag=&nPage=13&ref_bid=11707,11708,11709,11711,11712, 11713,11715,11716,11717,11718,11719,11721,11722,11723,11724,11725,11726,12071,11727,11728,11729,11730,11731,11732,11733,11734,11735,12051,11786,11736,11737,11738,11739, 11740,11741,11742,11743,11744,11745,11746,11747,11748,11749,11773,11774,11750,11751,11752,11754,11755,11756,11757,11758,11759,11760,11761,11762,12050,11763,11764, 11765,11766,11767,11768,11769,11770,11771,11772&keyField=&keyWord= Lim, S. et al. Increasing prevalence of metabolic syndrome in Korea: the Korean National Health and Nutrition Examination Survey for 1998-2007. Diabetes Care 34 , 1323-1328, doi:10.2337/dc10-2109 (2011). Park, D. et al. 20-Year Trends in Metabolic Syndrome Among Korean Adults From 2001 to 2020. JACC: Asia 3 , 491-502, doi:https://doi.org/10.1016/j.jacasi.2023.02.007 (2023). Jung, C. H. et al. Diabetes Fact Sheets in Korea, 2020: An Appraisal of Current Status. Diabetes Metab J 45 , 1-10, doi:10.4093/dmj.2020.0254 (2021). Jeong, S.-M. et al. 2023 Obesity Fact Sheet: Prevalence of Obesity and Abdominal Obesity in Adults, Adolescents, and Children in Korea from 2012 to 2021. JOMES 33 , 27-35, doi:10.7570/jomes24012 (2024). Mauvais-Jarvis, F. Sex differences in metabolic homeostasis, diabetes, and obesity. Biol Sex Differ 6 , 14, doi:10.1186/s13293-015-0033-y (2015). Gu, D. et al. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet 365 , 1398-1405, doi:10.1016/s0140-6736(05)66375-1 (2005). Shin, D., Kongpakpaisarn, K. & Bohra, C. Trends in the prevalence of metabolic syndrome and its components in the United States 2007-2014. Int J Cardiol 259 , 216-219, doi:10.1016/j.ijcard.2018.01.139 (2018). Jaacks, L. M. et al. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol 7 , 231-240, doi:10.1016/s2213-8587(19)30026-9 (2019). Rhee, S. Y., Park, S. W., Kim, D. J. & Woo, J. Gender disparity in the secular trends for obesity prevalence in Korea: analyses based on the KNHANES 1998-2009. Korean J Intern Med 28 , 29-34, doi:10.3904/kjim.2013.28.1.29 (2013). Reis, J. P. et al. Association between duration of overall and abdominal obesity beginning in young adulthood and coronary artery calcification in middle age. Jama 310 , 280-288, doi:10.1001/jama.2013.7833 (2013). Bae, J. H. et al. Diabetes Fact Sheet in Korea 2021. Diabetes Metab J 46 , 417-426, doi:10.4093/dmj.2022.0106 (2022). Lascar, N. et al. Type 2 diabetes in adolescents and young adults. Lancet Diabetes Endocrinol 6 , 69-80, doi:10.1016/s2213-8587(17)30186-9 (2018). Rhee, S. Y., Kim, C., Shin, D. W. & Steinhubl, S. R. Present and Future of Digital Health in Diabetes and Metabolic Disease. Diabetes Metab J 44 , 819-827, doi:10.4093/dmj.2020.0088 (2020). Chatterjee, A., Prinz, A., Gerdes, M. & Martinez, S. Digital Interventions on Healthy Lifestyle Management: Systematic Review. J Med Internet Res 23 , e26931, doi:10.2196/26931 (2021). Kim S, Rhee SY, Lee S, Committee of IT convergence Treatment of Metabolic Syndrome the Korean Society for the Study of Obesity. Effectiveness of Information and Communications Technology-Based Interventions for Obesity and Metabolic Syndrome. JOMES 2022; 31(3): 201-7. Nyberg, S. T. et al. Association of Healthy Lifestyle With Years Lived Without Major Chronic Diseases. JAMA Internal Medicine 180 , 760-768, doi:10.1001/jamainternmed.2020.0618 (2020). Korea Disease Control and Prevention Agency. National Health and Nutrition Examination Survey, trends in average daily smoking amount of current smokers. https://kosis.kr/statHtml/statHtml.do?orgId=177&tblId=DT_11702_N003&conn_path=I2 Korea Disease Control and Prevention Agency. National Health and Nutrition Examination Survey, trends in monthly binge drinking rate. https://kosis.kr/statHtml/statHtml.do?orgId=177&tblId=DT_11702_N234&conn_path=I2 Volek, J. S., Fernandez, M. L., Feinman, R. D. & Phinney, S. D. Dietary carbohydrate restriction induces a unique metabolic state positively affecting atherogenic dyslipidemia, fatty acid partitioning, and metabolic syndrome. Prog Lipid Res 47 , 307-318, doi:10.1016/j.plipres.2008.02.003 (2008). Kwon, Y. J., Lee, H. S. & Lee, J. W. Association of carbohydrate and fat intake with metabolic syndrome. Clin Nutr 37 , 746-751, doi:10.1016/j.clnu.2017.06.022 (2018). Kweon, S. et al. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol 43 , 69-77, doi:10.1093/ije/dyt228 (2014). Huang, P. L. A comprehensive definition for metabolic syndrome. Dis Model Mech 2 , 231-237, doi:10.1242/dmm.001180 (2009). Lee, S. Y. et al. Appropriate waist circumference cutoff points for central obesity in Korean adults. Diabetes Res Clin Pract 75 , 72-80, doi:10.1016/j.diabres.2006.04.013 (2007). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 26 Mar, 2025 Editor assigned by journal 23 Feb, 2025 Editor invited by journal 11 Feb, 2025 Submission checks completed at journal 11 Feb, 2025 First submitted to journal 07 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5979489","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":414279601,"identity":"0f68320d-2dbb-4245-8976-a8c7c9f096b8","order_by":0,"name":"Yaelim Kim","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Yaelim","middleName":"","lastName":"Kim","suffix":""},{"id":414279603,"identity":"95805262-4e00-47e2-b00e-46d08d9de134","order_by":1,"name":"Yeonghun Choi","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Yeonghun","middleName":"","lastName":"Choi","suffix":""},{"id":414279604,"identity":"1a42fea8-5fb5-4070-8d4f-e254e3ed7b05","order_by":2,"name":"Kyu-Na Lee","email":"","orcid":"","institution":"Catholic University","correspondingAuthor":false,"prefix":"","firstName":"Kyu-Na","middleName":"","lastName":"Lee","suffix":""},{"id":414279606,"identity":"6aaac6d2-7d05-4502-b75b-838a6131ee77","order_by":3,"name":"Hyunji Sang","email":"","orcid":"","institution":"Kyung Hee University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Hyunji","middleName":"","lastName":"Sang","suffix":""},{"id":414279607,"identity":"db4af213-d483-4051-b084-a99b86379069","order_by":4,"name":"Kyungdo Han","email":"","orcid":"","institution":"Soongsil University","correspondingAuthor":false,"prefix":"","firstName":"Kyungdo","middleName":"","lastName":"Han","suffix":""},{"id":414279608,"identity":"6912f032-147f-49e0-926c-240de90764f0","order_by":5,"name":"Sunyoung Kim","email":"","orcid":"","institution":"Kyung Hee University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Sunyoung","middleName":"","lastName":"Kim","suffix":""},{"id":414279609,"identity":"232502fd-3486-4c08-ac84-e69ad98a6079","order_by":6,"name":"Sang Youl Rhee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACAyCW+PjHJgHMSyggUovkzIY0qBYQl40ILdK8DYcTEFxCWszZzz68zbvjfB7/7O7EDw8MGPL45Rvwa7HsSTe2nHvmdrHEnbObJYAOK5ZsI+SwA2lsEm/Ybic23MjdANKSuOEYIS3nn7FJ8LCdS5x/I3fzD5CW/QS13Ehjk+RtO5C44UbuNogthLxvOeMZs+WMM8mJG4FaLBIMJBJnHEvAr8WcP43xxocKu8R5QIfd/FFhk9jffICANWhAgjTlo2AUjIJRMAqwAwD9tkjLbfwUVwAAAABJRU5ErkJggg==","orcid":"","institution":"Kyung Hee University Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Sang","middleName":"Youl","lastName":"Rhee","suffix":""}],"badges":[],"createdAt":"2025-02-07 09:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5979489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5979489/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04193-z","type":"published","date":"2025-07-01T15:58:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76167486,"identity":"861b5de3-f7cf-4020-8d7e-97f4e4f61a69","added_by":"auto","created_at":"2025-02-13 04:44:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":254984,"visible":true,"origin":"","legend":"\u003cp\u003eSecular trends of metabolic syndrome (A) and its components; high waist circumference (B), high blood pressure (C), high glucose (D), high triglyceride (E), and low HDL(high-density lipoprotein) cholesterol (F), between 2007 and 2022. The grey, blue, and orange lines represent the prevalence of total population, male, and female, respectively. Direct standardization was performed based on the 2005 projected population.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5979489/v1/299b1024837ce4c3def81c7f.png"},{"id":76167481,"identity":"d12d4f3e-2fcf-469b-888d-a04e6aa14fea","added_by":"auto","created_at":"2025-02-13 04:44:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":298852,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence ratios of metabolic syndrome and its components between 2022 and 2007.\u003c/p\u003e\n\u003cp\u003eEach cell represents the prevalence ratio of metabolic syndrome and respective components; high waist circumference, high blood pressure, high glucose, high triglyceride, and low HDL(high-density lipoprotein), for a specific age group and sex in 2022 compared to 2007. The color scale indicates the relative increase (red) or decrease (blue) in prevalence. Maximum prevalence ratio, ratio of 1.00 and minimum prevalence ratio was depicted as red, white and blue, respectively. HDL, high-density lipoprotein.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5979489/v1/be7f302526f95eb1ebdd1f48.png"},{"id":76167780,"identity":"ecb9dabc-2283-471d-bba2-58af909d7f68","added_by":"auto","created_at":"2025-02-13 04:52:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171751,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific prevalence of metabolic syndrome and its components by sex in 2022.\u003c/p\u003e\n\u003cp\u003eGrey lines represent the overall prevalence trend of metabolic syndrome in 2022 across age groups. Components of metabolic syndrome are represented as grouped bar charts for each age group.\u003c/p\u003e\n\u003cp\u003eWC=waist circumference, BP=blood pressure, TG=triglyceride, HDL-C=high-density lipoprotein cholesterol, MetS=metabolic syndrome.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5979489/v1/d937f70ce9b9bb841ce4d1bd.png"},{"id":86179509,"identity":"7df74c8c-5ea2-4812-a7f7-462831b88673","added_by":"auto","created_at":"2025-07-07 16:17:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1554396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5979489/v1/74c52adc-f46b-46b4-8b37-0e35c22b8559.pdf"},{"id":76167483,"identity":"982a5c4b-9f30-4139-a98b-83aa14e19543","added_by":"auto","created_at":"2025-02-13 04:44:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46445,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5979489/v1/b1acfd1d3991763d1246454e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends and Implications of Metabolic Syndrome in Korea, 2007-2022: A Nationwide Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is a cluster of conditions, including obesity, hypertension, insulin resistance, and dyslipidemia, which significantly increase the risk of chronic diseases such as cardiovascular disease and type 2 diabetes mellitus.\u003csup\u003e1\u003c/sup\u003e A prospective cohort study involving 11,512 participants found that MetS increased all-cause mortality by 1.4 times in both men and women and increased cardiovascular disease mortality by 2.3 times in men and 2.8 times in women.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWith the spread of the Western lifestyle, MetS has become a global epidemic. Modern sedentary lifestyles, physical inactivity, and high-calorie-low fiber fast food consumption are major factors to MetS.\u003csup\u003e3\u003c/sup\u003e In the United States, the prevalence of MetS reached 36.9% from 2015 to 2016, increasing with age.\u003csup\u003e4\u003c/sup\u003e Given the rapidly aging population in South Korea\u003csup\u003e5\u003c/sup\u003e, intensified efforts to manage MetS are necessary. Effective intervention requires understanding MetS characteristics across different age and sex groups.\u003c/p\u003e \u003cp\u003eBased on the Korea National Health and Nutrition Examination Survey (KNHANES), the prevalence of MetS in South Korea has increased from 24.9% in 1998 to 31.3% in 2007.\u003csup\u003e6\u003c/sup\u003e By 2020, it reached 33.2%, indicating that approximately one in three individuals had MetS.\u003csup\u003e7\u003c/sup\u003e However, recent studies in Korea lack detailed stratification by sex, age, and behavioral habits and need to be updated. Therefore, in-depth analyses are necessary to understand the effects of societal changes, dietary shifts, and other overlooked factors.\u003c/p\u003e \u003cp\u003eAccordingly, this study not only presents evidence on the recent trend in MetS based on the data from KNHANES from 2007 to 2022 but also provides evidence for sex, age, habits such as smoking, drinking, and physical activity, energy intake, and carbohydrate, protein, and fat intake. The focus was on nutritional factors such as protein and fat intake according to well-defined health behavior definitions\u003csup\u003e8\u003c/sup\u003e. Therefore, we aimed to provide valuable insights to support the formulation of Korea\u0026rsquo;s healthcare strategy.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of metabolic syndrome from 2007 to 2022\u003c/h2\u003e \u003cp\u003eSupplementary Table\u0026nbsp;1 shows the secular trends of age-standardized values of MetS and components. The age-standardized prevalence of MetS in the population increased from 22.8% in 2007 to 28.6% in 2022. Regarding sex-specific changes, the prevalence of MetS in males increased from 24.5\u0026ndash;36.8%, while in females, it decreased from 20.6\u0026ndash;19.5%, highlighting a sex disparity.\u003c/p\u003e \u003cp\u003eAmong the components of MetS, high WC and high glucose levels consistently increased. High WC rose from 26.0% in 2007 to 33.6% in 2022, and high glucose from 21.1\u0026ndash;31.9%. In males, high WC increased from 25.7\u0026ndash;42.9%, and high glucose from 25.7\u0026ndash;39.8%. However, in females, high WC fluctuated but recently decreased from 25.9% in 2020 to 23.2% in 2022, while high glucose increased from 16.5\u0026ndash;23.6% over the study period, indicating sex-specific differences. After decreasing between 2009 and 2014, the prevalence of high BP remained stable, ranging from 29.4\u0026ndash;33.7%. High TG showed an upward trend, from 30.6\u0026ndash;34.6%. Although sex disparities existed in high BP and high TG, these differences were less significant compared to other components of MetS. Low HDL-C consistently decreased from 43.4\u0026ndash;27.2% and was more prevalent among females. The decline was more significant in females, reducing sex disparity, with males showing a higher prevalence by 2022. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence of metabolic syndrome by age and sex\u003c/h3\u003e\n\u003cp\u003eThe prevalence of MetS increased across all age groups, especially the over 70 group, from 51.2% in 2007\u0026ndash;2009 to 64.3% in 2022. For females, the prevalence of MetS increased only in those aged 70 and older (from 59.2\u0026ndash;71.2%), while it decreased in all other age groups. (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eHigh WC increased the most in males aged 30\u0026ndash;39, with a 1.98-fold increase, followed by males aged 19\u0026ndash;29, with a 1.81-fold increase. The prevalence of high BP increased only in the over-70 group. High glucose increased in all groups, with the most changes in those aged 19\u0026ndash;29 and 30\u0026ndash;39 (1.66-fold and 1.55-fold, respectively). The prevalence of high TG remained relatively stable in younger adults while increasing 1.48-fold in those aged 70 and older. Low HDL-C levels decreased across all groups except for males aged 70 and older, with the most change observed in younger females. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2022, among younger adults, the prevalence of MetS components was highest for high WC, followed by high TG. For those aged over 60, high BP and high glucose had the highest prevalence rates, while high WC was the least prevalent. When analyzed by gender, low HDL-C was one of the most prevalent components among females across all age groups. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSubgroup analysis of the prevalence of metabolic syndrome in older adults\u003c/h3\u003e\n\u003cp\u003eIn the sub-analysis results of those aged 65 and older, the prevalence of MetS increased from 50.2% in 2007 to 62.0% in 2022. It increased from 37.8\u0026ndash;55.3% in males and 59.2\u0026ndash;67.4% in females, narrowing the sex disparity from 21.4\u0026ndash;12.1%.\u003c/p\u003e \u003cp\u003eHigh WC decreased from 43.8% in 2007 to 34.7% in 2014 but increased to 48.5% in 2022 while recently reducing since 2022. High BP prevalence increased from 66.5\u0026ndash;73.1%, with a narrowed sex disparity by 2022. High glucose and high TG steadily increased, from 41.6% in 2007 to 59.9% in 2022 and 40.4% in 2007 to 55.6% in 2022, respectively. High glucose was the only component that was more prevalent in males. Low HDL-C in females decreased over the study period, while that of males increased. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003ePrevalence of Metabolic syndrome in individuals aged 65 and older, 2007\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1,609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1,578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1,681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1,539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetS*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh WC\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh BP\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e67.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e72.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e69.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh glucose\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh TG\u003csup\u003e‖\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow HDL-C\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e56.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e57.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e73.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e69.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e46.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e52.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e73.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e69.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e73.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eValues are expressed as percentages.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eAbbreviations: MetS, metabolic syndrome; WC, waist circumference; BP, blood pressure; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eMetabolic syndrome was defined as those meeting three or more of the following criteria:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eHigh WC was defined as waist circumference \u0026ge;90 cm in male or \u0026ge; 85 cm in female,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eHigh BP was defined as BP \u0026ge; 130/85 mmHg or taking antihypertensive drugs,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eHigh glucose was defined as fasting plasma glucose \u0026ge; 100 mg/dL or taking diabetes mellitus drugs,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e‖\u003c/sup\u003eHigh TG was defined as TG \u0026ge; 150 mg/dL or taking dyslipidemia drugs,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026para;\u003c/sup\u003eLow HDL-C was defined as HDL \u0026lt; 40 mg/dL in male, \u0026lt; 50 mg/dL in female, or taking dyslipidemia drugs.\u003c/p\u003e\n\u003ch3\u003ePrevalence of metabolic syndrome according to health behaviors\u003c/h3\u003e\n\u003cp\u003eThe prevalence of current smoking was higher in the MetS group compared to the non-MetS group (21.0% vs 17.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, heavy drinking was more prevalent in the MetS group (15.8% vs 11.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Physical activity was lower in the MetS group compared to the non-MetS group (39.3% vs 50.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These trends were more pronounced in males. (Supplementary Table\u0026nbsp;3). The energy intake percentage from carbohydrates was higher in people with MetS. Total energy intake showed no statistically significant difference between people with and without MetS in the general population. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eEnergy Intake according to the presence of metabolic syndrome, 2019\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal energy intake, kcal/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1900.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1873\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2239.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2165.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1607.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1491.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEnergy intake percentage from macronutrients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrates, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e65.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetS has steadily increased over the past 15 years, especially in males. A notable sex difference in trends was observed in females under 70, which showed a decreasing trend. The increase in MetS was primarily driven by increases in hyperglycemia and abdominal obesity, particularly among younger males. Among the components of MetS, only low HDL-C decreased significantly in younger females. Conversely, Mets were more prevalent in older adults than females, narrowing the sex difference.\u003c/p\u003e \u003cp\u003eThe prevalence of MetS increased with age in both males and females. Among individuals aged 70 and older, the prevalence of MetS increased by 20.0% over the study period, resulting in approximately 7 out of 10 individuals having MetS in 2022. There was a sharp rise in the prevalence among males between the age group 19–29 and 30–39 and among females between the age group 40–49 and 50–59. The difference between sexes implies that social, environmental, and lifestyle changes due to involvement in social activities and marriage are major factors influencing MetS in males, while hormonal changes due to menopause are significant in females.\u003csup\u003e9,10\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eContrary to our findings, the analysis of MetS prevalence in the United States from 2011 to 2016 didn’t exhibit a disparity between sex (35.1% for men and 34.3% for women, p = 0.47)\u003csup\u003e4\u003c/sup\u003e. In China, MetS was much more prevalent in women, likely due to higher rates of abdominal obesity and low HDL-C compared to men.\u003csup\u003e11\u003c/sup\u003e A study analyzing trends in the prevalence of MetS components in the U.S. from 2007 to 2014 revealed that high WC among women was more prevalent than in men (p \u0026lt; 0.05) and showed a sharp increasing trend (p-trend = 0.009)\u003csup\u003e12\u003c/sup\u003e. Moreover, in most countries, obesity prevalence and average BMI tend to be higher in males than in females of all ages.\u003csup\u003e13\u003c/sup\u003e This is believed to stem from the biological differences in female’s fat storage capacity as an adaptation for childbearing.\u003csup\u003e10\u003c/sup\u003e Therefore, the relatively low abdominal obesity, as reflected by high WC, among Korean females is likely to be influenced more by sociocultural factors than biological reasons. These factors may include increased female participation in social activities, a higher average age of marriage, lower birth rates, and a social preference for slim females. For older females, factors such as decreased social activities due to changes in employment status and reduced social pressure to maintain a slender physique, in addition to hormonal changes due to menopause, may have played a role, resulting in a distinct pattern among older adults.\u003csup\u003e14\u003c/sup\u003e These results indicate that while the prevalence of MetS is gradually decreasing in females, there is a critical need for ongoing health management efforts, particularly for postmenopausal females.\u003c/p\u003e \u003cp\u003eMeanwhile, our study's unique pattern of high WC prevalence highlights the necessity of managing abdominal obesity, particularly in younger males.\u003csup\u003e9\u003c/sup\u003e Abdominal obesity in young adulthood can contribute to metabolic burden, potentially increasing susceptibility to obesity-related metabolic diseases such as coronary heart disease and diabetes as these individuals age.\u003csup\u003e15\u003c/sup\u003e Additionally, together with obesity, prediabetes is a key mechanism leading to MetS.\u003csup\u003e1\u003c/sup\u003e In 2020, the prevalence of prediabetes among Korean adults reached 39.3%, with diabetes mellitus increasing in young adults. However, diabetes management outcomes remained inadequate.\u003csup\u003e16\u003c/sup\u003e Similar to abdominal obesity, earlier onset of diabetes results in adverse long-term consequences, including complications and quality of life.\u003csup\u003e17\u003c/sup\u003e Therefore, comprehensive measures targeting weight reduction blood glucose management in young adults should be made to reduce future health burdens at both individual and national levels.\u003c/p\u003e \u003cp\u003eGiven the high internet usage rates among younger populations, digital health interventions based on information and communication technology (ICT) can be efficient health management tools.\u003csup\u003e18\u003c/sup\u003e Since no single medication for treating MetS exists, lifestyle modifications such as healthy eating habits and weight reduction are crucial therapeutic strategies.\u003csup\u003e3\u003c/sup\u003e ICT interventions hold strong potential for supporting these lifestyle changes.\u003csup\u003e19,20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSmoking, heavy alcohol consumption, physical inactivity, and poor dietary choices are well-known unhealthy lifestyle factors. As these are modifiable factors that can reduce the risk of chronic diseases, understanding their epidemics is essential for effective public health management.\u003csup\u003e21\u003c/sup\u003e The relationship between these unhealthy lifestyle factors and the presence of MetS was more pronounced in males.\u003c/p\u003e \u003cp\u003eThis phenomenon could be attributed to two factors. First, females generally have lower smoking rates\u003csup\u003e22\u003c/sup\u003e and monthly binge drinking rates\u003csup\u003e23\u003c/sup\u003e compared to males, so the impact of these behaviors on MetS may be relatively less significant. Second, females with MetS may have become more health-conscious and adopted healthier lifestyles after diagnosis. It can be inferred that increasing physical activity and addressing smoking and drinking, particularly in males, are important areas of focus.\u003c/p\u003e \u003cp\u003eCarbohydrates directly and indirectly influence our metabolic state, affecting conditions such as dyslipidemia and MetS\u003csup\u003e24\u003c/sup\u003e. According to a study based on the 2008–2011 KNHANES, a high carbohydrate intake was associated with a higher prevalence of MetS in males. In females, a high carbohydrate and low-fat intake was related to a higher prevalence of MetS\u003csup\u003e25\u003c/sup\u003e. Although carbohydrate consumption decreased between 2001 and 2020\u003csup\u003e7\u003c/sup\u003e, our analysis demonstrated that carbohydrate consumption is still significantly associated with the presence of MetS. This suggests that diet composition, particularly the proportion of carbohydrates, is more critical than total energy intake.\u003c/p\u003e \u003cp\u003eThere are some limitations to this study. First, as this is a cross-sectional study, it is difficult to determine whether behavior changes led to a higher prevalence of the condition or if the higher prevalence led individuals to adopt certain habits. Second, other socioenvironmental factors influencing MetS prevalence may exist. Third, health behavior data were self-reported, resulting in memory decay bias and recall bias. Only the presence or absence of behavioral habits was reflected; therefore, the effect of degree may not be considered. Fourth, the comparison only involved the average energy intake percentage from each macronutrient between those with and without MetS. The proportion of individuals consuming more than the recommended intake was not considered. Despite these limitations, this study remains significant as it conducted research analyzing nationally representative data, carrying out statistical analyses based on detailed age and sex stratification.\u003c/p\u003e \u003cp\u003eIn conclusion, the prevalence of MetS in South Korea has steadily increased over the past 15 years. Fasting hyperglycemia and abdominal obesity have risen rapidly, and health disparity between sexes has been exacerbated. It is crucial to implement lifestyle modifications, including balanced eating habits. This study provides a better understanding of MetS trends. It offers valuable insights for formulating health policies and management strategies for the Korean population and other populations with similar socioeconomic status. Specifically, targeted solutions regarding the characteristics according to age and sex should be established.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eThe data analyzed in this study were obtained from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2022. The KNHANES is a nationally representative survey provided by the Korea Centers for Disease Control and Prevention (KCDC) for national health promotion, disease prevention, and comparative health data analysis.\u003csup\u003e26\u003c/sup\u003e The research protocol was approved by the Institutional Review Boards of the KDCA (2007-02CON-04-P, 2008-04EXP-01-C, 2009-01CON-03–2 C, 2010-02CON-21-C, 2011-02CON-06-C, 2012-01EXP-01–2 C, 2013-07CON-03–4 C, 2013-12EXP-035 C, 2018-01-03-P-A, 2018-01-03-C-A, 2018-01-03–2 C-A, 2018-01-03–5 C-A, 2018-01-03–4 C-A). All participants provided written informed consent before participation. Additionally, KNHANES data are publicly accessible and serve as a valuable resource for various epidemiological studies. The principles of the Declaration of Helsinki conducted this study. The study was approved by the Institutional Review Board of the Kyunghee University Hospital (No. 2024-07-021). Informed consent was waived.\u003c/p\u003e\u003cp\u003eOut of 126,446 subjects from KNHANES (2007–2022), 27,005 individuals under 19 were excluded. Among the remaining 99,441 subjects, 12,044 individuals were excluded due to missing data for defining MetS. Thus, the final study population consisted of 87,397 subjects. Participants missing data on waist circumference, systolic blood pressure (BP), diastolic BP, fasting plasma glucose, triglyceride, or high-density lipoprotein cholesterol (HDL-C) were considered to lack a complete definition of MetS.\u003c/p\u003e\u003cp\u003eThis study used the definition provided by the National Cholesterol Education Program-Third Adult Treatment Panel (NCEP-ATP) III, with waist circumference criteria modified according to the cutoffs established by the Korean Society for the Study of Obesity (KOSSO).\u003csup\u003e27,28\u003c/sup\u003e Subjects were defined as MetS if they met three or more of the following criteria:\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWaist circumference (WC) ≥ 90 cm in males or ≥ 85 cm in females\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBP ≥ 130/85 mmHg or taking antihypertensive drugs\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFasting plasma glucose ≥ 100 mg/dL or taking diabetes mellitus drugs\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTriglyceride (TG) ≥ 150 mg/dL or taking dyslipidemia drugs\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHDL-C \u0026lt; 40 mg/dL in males, \u0026lt; 50 mg/dL in females, or taking dyslipidemia drugs\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003eIndividuals self-reported health behaviors such as smoking, drinking, and physical activity. Each definition is followed by.\u003c/p\u003e\u003cp\u003eCurrent smoking was defined as those currently smoking and having smoked more than 5 packs (100 cigarettes) throughout their lifetime. Heavy drinking was defined as those who drank more than twice per week and consumed more than 7 glasses of alcohol per occasion for males and 5 glasses for females within the past year. The physical activity group was defined as those who spent more than 150 minutes on moderate physical activity, 75 minutes on vigorous physical activity, or an equivalent combination. (vigorous physical activity 1 minute = moderate physical activity 2 min) Total energy intake was calculated as the sum of calories consumed daily. The energy intake percentage from each macronutrient was calculated using the following formulas\u003csup\u003e8\u003c/sup\u003e;\u003c/p\u003e\u003cp\u003eCarbohydrates: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{\\text{C}\\text{a}\\text{r}\\text{b}\\text{o}\\text{h}\\text{y}\\text{d}\\text{r}\\text{a}\\text{t}\\text{e}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4}{\\text{C}\\text{a}\\text{r}\\text{b}\\text{o}\\text{h}\\text{y}\\text{d}\\text{r}\\text{a}\\text{t}\\text{e}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4+\\text{P}\\text{r}\\text{o}\\text{t}\\text{e}\\text{i}\\text{n}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4+\\text{F}\\text{a}\\text{t}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:9\\:}\\right)\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eProteins: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{\\text{P}\\text{r}\\text{o}\\text{t}\\text{e}\\text{i}\\text{n}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4}{\\text{C}\\text{a}\\text{r}\\text{b}\\text{o}\\text{h}\\text{y}\\text{d}\\text{r}\\text{a}\\text{t}\\text{e}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4+\\text{P}\\text{r}\\text{o}\\text{t}\\text{e}\\text{i}\\text{n}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4+\\text{F}\\text{a}\\text{t}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:9\\:}\\right)\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFats: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{\\text{F}\\text{a}\\text{t}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:9}{\\text{C}\\text{a}\\text{r}\\text{b}\\text{o}\\text{h}\\text{y}\\text{d}\\text{r}\\text{a}\\text{t}\\text{e}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4+\\text{P}\\text{r}\\text{o}\\text{t}\\text{e}\\text{i}\\text{n}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:4+\\text{F}\\text{a}\\text{t}\\:\\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{a}\\text{y}\\left(\\text{g}\\right)\\times\\:9\\:}\\right)\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eChanges in the prevalence of MetS were calculated using a direct standardization method based on the 2005 projected population data from the Korean Statistical Information Service (KOSIS). To assess the potential influence of age and sex on the transition of MetS prevalence, stratified analysis was performed, with age groups categorized as 19–29, 30–39, 40–49, 50–59, 60–69, and ≥ 70 years. Considering the high prevalence of MetS in the elderly, subgroup analysis was additionally conducted for individuals aged 65 and older. Health behavior status and energy intake of each sex were compared according to the presence of MetS. Statistical significance was defined as p \u0026lt; 0.05. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy concept and design: SH, RSY, HK, KS; acquisition of data: all authors; analysis and interpretation of data: all authors; drafting of the manuscript: KY, CY and LK-N; critical revision of the manuscript: all authors; statistical analysis: LK-N and HK; and study supervision: SYR and KS.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Korean Society for the Study of Obesity and the Korea Centers for Disease Control and Prevention for their support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available upon request. The study protocol and statistical code are available from KDH ([email protected]). The dataset, available from the Korea National Health and Nutrition Examination Survey by the Korea Disease Control and Prevention Agency, can be accessed via the following link (https://knhanes.kdca.go.kr/knhanes/main.do).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrundy, S. M. Pre-Diabetes, Metabolic Syndrome, and Cardiovascular Risk. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 635-643, doi:https://doi.org/10.1016/j.jacc.2011.08.080 (2012).\u003c/li\u003e\n\u003cli\u003eHu, G.\u003cem\u003e et al.\u003c/em\u003e Prevalence of the Metabolic Syndrome and Its Relation to All-Cause and Cardiovascular Mortality in Nondiabetic European Men and Women. \u003cem\u003eArchives of Internal Medicine\u003c/em\u003e \u003cstrong\u003e164\u003c/strong\u003e, 1066-1076, doi:10.1001/archinte.164.10.1066 (2004).\u003c/li\u003e\n\u003cli\u003eSaklayen, M. G. The Global Epidemic of the Metabolic Syndrome. \u003cem\u003eCurr Hypertens Rep\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 12, doi:10.1007/s11906-018-0812-z (2018).\u003c/li\u003e\n\u003cli\u003eHirode, G. \u0026amp; Wong, R. J. Trends in the Prevalence of Metabolic Syndrome in the United States, 2011-2016. \u003cem\u003eJama\u003c/em\u003e \u003cstrong\u003e323\u003c/strong\u003e, 2526-2528, doi:10.1001/jama.2020.4501 (2020).\u003c/li\u003e\n\u003cli\u003eStatistics Korea. 2023 Statistics on the Aged. 2023. https://kostat.go.kr/board.es?mid=a20101000000\u0026amp;bid=11759\u0026amp;act=view\u0026amp;list_no=427605\u0026amp;tag=\u0026amp;nPage=13\u0026amp;ref_bid=11707,11708,11709,11711,11712,\u003cbr\u003e11713,11715,11716,11717,11718,11719,11721,11722,11723,11724,11725,11726,12071,11727,11728,11729,11730,11731,11732,11733,11734,11735,12051,11786,11736,11737,11738,11739,\u003cbr\u003e11740,11741,11742,11743,11744,11745,11746,11747,11748,11749,11773,11774,11750,11751,11752,11754,11755,11756,11757,11758,11759,11760,11761,11762,12050,11763,11764,\u003cbr\u003e11765,11766,11767,11768,11769,11770,11771,11772\u0026amp;keyField=\u0026amp;keyWord= \u003c/li\u003e\n\u003cli\u003eLim, S.\u003cem\u003e et al.\u003c/em\u003e Increasing prevalence of metabolic syndrome in Korea: the Korean National Health and Nutrition Examination Survey for 1998-2007. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 1323-1328, doi:10.2337/dc10-2109 (2011).\u003c/li\u003e\n\u003cli\u003ePark, D.\u003cem\u003e et al.\u003c/em\u003e 20-Year Trends in Metabolic Syndrome Among Korean Adults From 2001 to 2020. \u003cem\u003eJACC: Asia\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 491-502, doi:https://doi.org/10.1016/j.jacasi.2023.02.007 (2023).\u003c/li\u003e\n\u003cli\u003eJung, C. H.\u003cem\u003e et al.\u003c/em\u003e Diabetes Fact Sheets in Korea, 2020: An Appraisal of Current Status. \u003cem\u003eDiabetes Metab J\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 1-10, doi:10.4093/dmj.2020.0254 (2021).\u003c/li\u003e\n\u003cli\u003eJeong, S.-M.\u003cem\u003e et al.\u003c/em\u003e 2023 Obesity Fact Sheet: Prevalence of Obesity and Abdominal Obesity in Adults, Adolescents, and Children in Korea from 2012 to 2021. \u003cem\u003eJOMES\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 27-35, doi:10.7570/jomes24012 (2024).\u003c/li\u003e\n\u003cli\u003eMauvais-Jarvis, F. Sex differences in metabolic homeostasis, diabetes, and obesity. \u003cem\u003eBiol Sex Differ\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 14, doi:10.1186/s13293-015-0033-y (2015).\u003c/li\u003e\n\u003cli\u003eGu, D.\u003cem\u003e et al.\u003c/em\u003e Prevalence of the metabolic syndrome and overweight among adults in China. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e365\u003c/strong\u003e, 1398-1405, doi:10.1016/s0140-6736(05)66375-1 (2005).\u003c/li\u003e\n\u003cli\u003eShin, D., Kongpakpaisarn, K. \u0026amp; Bohra, C. Trends in the prevalence of metabolic syndrome and its components in the United States 2007-2014. \u003cem\u003eInt J Cardiol\u003c/em\u003e \u003cstrong\u003e259\u003c/strong\u003e, 216-219, doi:10.1016/j.ijcard.2018.01.139 (2018).\u003c/li\u003e\n\u003cli\u003eJaacks, L. M.\u003cem\u003e et al.\u003c/em\u003e The obesity transition: stages of the global epidemic. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 231-240, doi:10.1016/s2213-8587(19)30026-9 (2019).\u003c/li\u003e\n\u003cli\u003eRhee, S. Y., Park, S. W., Kim, D. J. \u0026amp; Woo, J. Gender disparity in the secular trends for obesity prevalence in Korea: analyses based on the KNHANES 1998-2009. \u003cem\u003eKorean J Intern Med\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 29-34, doi:10.3904/kjim.2013.28.1.29 (2013).\u003c/li\u003e\n\u003cli\u003eReis, J. P.\u003cem\u003e et al.\u003c/em\u003e Association between duration of overall and abdominal obesity beginning in young adulthood and coronary artery calcification in middle age. \u003cem\u003eJama\u003c/em\u003e \u003cstrong\u003e310\u003c/strong\u003e, 280-288, doi:10.1001/jama.2013.7833 (2013).\u003c/li\u003e\n\u003cli\u003eBae, J. H.\u003cem\u003e et al.\u003c/em\u003e Diabetes Fact Sheet in Korea 2021. \u003cem\u003eDiabetes Metab J\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 417-426, doi:10.4093/dmj.2022.0106 (2022).\u003c/li\u003e\n\u003cli\u003eLascar, N.\u003cem\u003e et al.\u003c/em\u003e Type 2 diabetes in adolescents and young adults. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 69-80, doi:10.1016/s2213-8587(17)30186-9 (2018).\u003c/li\u003e\n\u003cli\u003eRhee, S. Y., Kim, C., Shin, D. W. \u0026amp; Steinhubl, S. R. Present and Future of Digital Health in Diabetes and Metabolic Disease. \u003cem\u003eDiabetes Metab J\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 819-827, doi:10.4093/dmj.2020.0088 (2020).\u003c/li\u003e\n\u003cli\u003eChatterjee, A., Prinz, A., Gerdes, M. \u0026amp; Martinez, S. Digital Interventions on Healthy Lifestyle Management: Systematic Review. \u003cem\u003eJ Med Internet Res\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e26931, doi:10.2196/26931 (2021).\u003c/li\u003e\n\u003cli\u003eKim S, Rhee SY, Lee S, Committee of IT convergence Treatment of Metabolic Syndrome the Korean Society for the Study of Obesity. Effectiveness of Information and Communications Technology-Based Interventions for Obesity and Metabolic Syndrome. JOMES 2022; 31(3): 201-7.\u003c/li\u003e\n\u003cli\u003eNyberg, S. T.\u003cem\u003e et al.\u003c/em\u003e Association of Healthy Lifestyle With Years Lived Without Major Chronic Diseases. \u003cem\u003eJAMA Internal Medicine\u003c/em\u003e \u003cstrong\u003e180\u003c/strong\u003e, 760-768, doi:10.1001/jamainternmed.2020.0618 (2020).\u003c/li\u003e\n\u003cli\u003eKorea Disease Control and Prevention Agency. National Health and Nutrition Examination Survey, trends in average daily smoking amount of current smokers. https://kosis.kr/statHtml/statHtml.do?orgId=177\u0026amp;tblId=DT_11702_N003\u0026amp;conn_path=I2 \u003c/li\u003e\n\u003cli\u003eKorea Disease Control and Prevention Agency. National Health and Nutrition Examination Survey, trends in monthly binge drinking rate. https://kosis.kr/statHtml/statHtml.do?orgId=177\u0026amp;tblId=DT_11702_N234\u0026amp;conn_path=I2 \u003c/li\u003e\n\u003cli\u003eVolek, J. S., Fernandez, M. L., Feinman, R. D. \u0026amp; Phinney, S. D. Dietary carbohydrate restriction induces a unique metabolic state positively affecting atherogenic dyslipidemia, fatty acid partitioning, and metabolic syndrome. \u003cem\u003eProg Lipid Res\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 307-318, doi:10.1016/j.plipres.2008.02.003 (2008).\u003c/li\u003e\n\u003cli\u003eKwon, Y. J., Lee, H. S. \u0026amp; Lee, J. W. Association of carbohydrate and fat intake with metabolic syndrome. \u003cem\u003eClin Nutr\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 746-751, doi:10.1016/j.clnu.2017.06.022 (2018).\u003c/li\u003e\n\u003cli\u003eKweon, S.\u003cem\u003e et al.\u003c/em\u003e Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). \u003cem\u003eInt J Epidemiol\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 69-77, doi:10.1093/ije/dyt228 (2014).\u003c/li\u003e\n\u003cli\u003eHuang, P. L. A comprehensive definition for metabolic syndrome. \u003cem\u003eDis Model Mech\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 231-237, doi:10.1242/dmm.001180 (2009).\u003c/li\u003e\n\u003cli\u003eLee, S. Y.\u003cem\u003e et al.\u003c/em\u003e Appropriate waist circumference cutoff points for central obesity in Korean adults. \u003cem\u003eDiabetes Res Clin Pract\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 72-80, doi:10.1016/j.diabres.2006.04.013 (2007).\u003c/li\u003e\n\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, Prevalence, Sex characteristics, Health policy","lastPublishedDoi":"10.21203/rs.3.rs-5979489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5979489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study analyzed the prevalence of metabolic syndrome (MetS) among Korean adults aged 19 and older over the past 15 years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study of data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2022 was conducted. The study included 87,397 subjects. MetS was defined according to the National Cholesterol Education Program-Third Adult Treatment Panel (NCEP-ATP) III and the Korean Society for the Study of Obesity (KOSSO) criteria.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMetS prevalence increased from 22.8% in 2007 to 28.6% in 2022 but showed sex differences, with males increasing (24.5\u0026ndash;36.8%) and females decreasing (20.6\u0026ndash;19.5%). Among the components of MetS, hyperglycemia and abdominal obesity showed the most significant increases (1.51-fold and 1.29-fold, respectively). While hyperglycemia increased in all age groups, abdominal obesity increased most in males aged 30\u0026ndash;39 (1.98-fold) and 19\u0026ndash;29 (1.81-fold). Low high-density lipoprotein cholesterol (HDL-C) was the only component that decreased (0.62-fold) and was more prevalent among females. In the sub-analysis of those aged 65 and older, MetS increased in both males and females but was more prevalent in females. Individuals with MetS had higher rates of current smoking, heavy drinking, physical inactivity, and carbohydrate consumption.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe prevalence of MetS is gradually increasing in Korea, and hyperglycemia and abdominal obesity are rapidly increasing, especially in younger males. Although the prevalence of MetS in females is decreasing due to changes in the social environment, continuous efforts are needed for postmenopausal females. Targeted health policies and interventions should be established.\u003c/p\u003e","manuscriptTitle":"Trends and Implications of Metabolic Syndrome in Korea, 2007-2022: A Nationwide Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 04:44:41","doi":"10.21203/rs.3.rs-5979489/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-10T09:19:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T15:24:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T04:48:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286900458819412384812126596106414459750","date":"2025-03-31T23:51:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148271839652374384551107576007341143588","date":"2025-03-28T15:57:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-26T07:37:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-23T09:42:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-02-11T14:21:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-11T09:29:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-07T08:53:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4776090d-936c-4a20-a43d-6024d15a29a7","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44176962,"name":"Health sciences/Endocrinology"},{"id":44176963,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-07-07T16:08:11+00:00","versionOfRecord":{"articleIdentity":"rs-5979489","link":"https://doi.org/10.1038/s41598-025-04193-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:58:35","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-02-13 04:44:41","video":"","vorDoi":"10.1038/s41598-025-04193-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-04193-z","workflowStages":[]},"version":"v1","identity":"rs-5979489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5979489","identity":"rs-5979489","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}