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The prevalence and characteristics of metabolic syndrome according to different definitions in China: a nationwide cross-sectional study, 2012–2015

BMC Public Health volume 22, Article number: 1869 (2022) Cite this article

Abstract

Background

Metabolic syndrome (MetS) is characterized by a cluster of signs of metabolic disturbance and has caused a huge burden on the health system. The study aims to explore the prevalence and characteristics of MetS defined by different criteria in the Chinese population.

Methods

Using the data of the China Hypertension Survey (CHS), a nationally representative cross-sectional study from October 2012 to December 2015, a total of 28,717 participants aged 35 years and above were included in the analysis. The MetS definitions of the International Diabetes Federation (IDF), the updated US National Cholesterol Education Program Adult Treatment Panel III (the revised ATP III), and the Joint Committee for Developing Chinese Guidelines (JCDCG) on Prevention and Treatment of Dyslipidemia in Adults were used. Multivariable logistic regression was used to identify factors associated with MetS.

Results

The prevalence of MetS diagnosed according to the definitions of IDF, the revised ATP III, and JCCDS was 26.4%, 32.3%, and 21.5%, respectively. The MetS prevalence in men was lower than in women by IDF definition (22.2% vs. 30.3%) and by the revised ATP III definition (29.2% vs. 35.4%), but the opposite was true by JCDCG (24.4%vs 18.5%) definition. The consistency between the three definitions for men and the revised ATP III definition and IDF definition for women was relatively good, with kappa values ranging from 0.77 to 0.89, but the consistency between the JCDCG definition and IDF definition (kappa = 0.58) and revised ATP III definition (kappa = 0.58) was poor. Multivariable logistic regression showed that although the impact and correlation intensity varied with gender and definition, area, age, education, smoking, alcohol use, and family history of cardiovascular disease were factors related to MetS.

Conclusions

The prevalence and characteristics of the MetS vary with the definition used in the Chinese population. The three MetS definitions are more consistent in men but relatively poor in women. On the other hand, even if estimated according to the definition of the lowest prevalence, MetS is common in China.

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Background

MetS is a syndrome clustering, including fat metabolism disorder, obesity, diabetes, insulin resistance, and other risk factors, increasing cardiovascular diseases (CVDs) [1]. Convincing evidence shows that metabolic syndrome (MetS) has been a growing public health problem worldwide. The prevalence of MetS is high and is expected to continue rising in developed and developing countries [2,3,4]. Exploring the characteristics and prevalence of metabolic syndrome may provide important public health implications for preventing and managing CVDs.

In the past few decades, several international organizations had provided the definitions of MetS. The World Health Organization (WHO) 1998 first attempted to put forward a diagnostic criterion of metabolic syndrome [5] the US National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) proposed diagnostic criteria of 5 components in 2001 to facilitate clinical diagnosis of high-risk individuals [6], the American Heart Association/National Heart, Lung, and Blood Institute updated the ATP III definition in 2005 (the revised ATP III) [7], and International Diabetes Federation (IDF) recommended a new definition in 2006 [8]. In China, the Joint Committee for Developing Chinese Guidelines (JCDCG) on Prevention and Treatment of Dyslipidemia in Adults suggested a Chinese definition for MetS in 2016 [9].

Depending on the definition used, estimates of the prevalence of MetS vary worldwide [10,11,12], and there is a clear difference. In recent studies, the MetS was prevalent in 24.6% of men and 23.8% of women in China according to ATP III criteria [13], 21.8% of men and 45.6% of women in Iran in 2021 according to IDF definition [14], 32.8% of men and 36.6% women according to ATP III criteria in 2011–2012 in the United States [15]. Using various criteria, the prevalence in China ranged from 9.82% to 48.8% [13, 16, 17], which led to confusion and a lack of comparability among studies. Therefore, it is necessary to report and compare the prevalence of MetS by different criteria, which may be helpful for researchers to understand MetS better and formulate a more scientific definition.

Although many epidemiological studies on MetS were conducted on the Chinese population in recent years, there is little national information on the prevalence of different MetS definitions. In the WHO definition, insulin resistance is regarded as a prerequisite, which limits its use [5]. Therefore, in this study, we will use the data of the China Hypertension Survey (CHS) to explore the prevalence and characteristics of MetS according to IDF, the revised ATP III, and JCDCG criteria.

Methods

Design and study population

The CHS was a cross-sectional study conducted between October 2012 and December 2015, and the study design was published previously [18, 19]. Briefly, A nationally representative sample of the general Chinese population across all 31 provinces in mainland China was obtained using a stratified multistage random sampling method. In this sub-study, 262 sampled urban cities and rural counties in the CHS were stratified into eastern, central, and western regions according to geographical location and economic level, and 16 cities and 17 counties were selected with a simple random sampling method, including 7 cities and 7 counties from the eastern regions, 6 cities and 6 counties from the central regions, and 3 cities and 4 counties from the western regions. Then, at least three communities or villages were randomly selected from each city or county. To meet the designed sample size of 35,000 participants aged ≥ 35 years and take nonresponses into account, 56,000 subjects were randomly selected from the eligible sites. Finally, 34,994 participants completed the survey, with an overall response rate of 62.5%. After excluding the pregnant or lactating (n = 163) women and the subjects with incomplete demographic data (n = 925) and laboratory tests(n = 5189), 28,717 subjects aged ≥ 35 years were included in the final analysis. The comparison of the characteristics of the subjects participating in the study and those not participating in the analysis can be found in Appendix Table 1.

Written informed consent was obtained from each participant. The Ethics Committee of Fuwai Hospital (Beijing, China) approved this study.

Data collection

All study investigators and staff members were trained according to the study protocol. A standardized questionnaire developed by the coordinating center, Fuwai Hospital, was administered to obtain information on demographic characteristics factors, such as age, area, education level, smoking status and alcohol use, and family history of cardiovascular disease (CVD). Smoking status was defined as participants who had smoked at least 20 packs of cigarettes in their lifetime and currently smoked cigarettes. Alcohol use was defined as consuming at least one alcoholic beverage per week in the past month. Family history of cardiovascular disease (CVD) referred to that at least one of the parents and siblings had a history of hypertension, dyslipidemia, diabetes, coronary heart disease, or stroke.

Anthropometry data (weight, height, and waist circumference) and blood pressure were measured at the local medical centers. Fasting blood samples were collected in the morning after 10-12 h fasting and were processed properly and refrigerated immediately. Serum glucose, triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) were determined by automatic biochemical analyzer (Beckman Coulter AU 680). The serum glucose was measured by the hexokinase method, serum TG by GPO-POD method, and HDL-C by automated homogeneous direct measurement method. All samples were analyzed in the central laboratory. Body mass index (BMI) was classified according to the recommendations of Working Group of Obesity in China, < 18.5 kg/m2 (underweight), 18.5–23.9 kg/m2 (normal range), 24–27.9 kg/m2 (overweight), ≥ 28 kg/m2 (obesity) [20].

Diagnosing standard

According to the IDF definition, MetS was defined as central obesity (WC ≥ 90 cm for Chinese men and ≥ 80 cm for Chinese women) along with two or more of the following abnormalities: (1) Elevated triglyceride (TG) > 1.7 mmol/L or receipt of specific treatment for this lipid abnormality; (2) High-density lipoproteins cholesterol (HDL-C) level of 1.03 mmol/L in men and 1.29 mmol/L in women or receipt of specific treatment for this lipid abnormality; (3) Systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg or receipt of treatment of previously diagnosed hypertension; (4) Fasting plasma glucose (FPG) level of 5.6 mmol/L or previously diagnosed type 2 diabetes [8].

According to the revised ATP III definition, MetS was defined as if there were more than three or more of the following abnormalities: (1) Central obesity (WC \(\ge\) 90 cm for men and \(\ge\) 80 cm for women); (2) Elevated triglyceride level \(\ge\) 1.7 mmol/L or on drug treatment for elevated triglycerides; (3) Reduced HDL-C < 40 mg/dL (1.03 mmol/L) in men; < 50 mg/dL (1.3 mmol/L) in women or receipt of drug treatment for reduced HDL-C; (4) Systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg or receipt of treatment of previously diagnosed hypertension; (5) Elevated plasma glucose (FPG) ≥ 5.6 mmol/dL or receipt of drug treatment for elevated glucose [7].

According to the JCDCG definition, MetS was defined as if there were three or more of the following abnormalities: (1) Central obesity (WC \(\ge\) 90 cm for men and \(\ge\) 85 cm for women); (2) Elevated triglyceride level \(\ge\) 1.7 mmol/L) or receipt of specific treatment for this lipid abnormality; (3) Reduced HDL-C level (< 1.0 mmol/l) or specific treatment for this lipid abnormality; (4) Systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg or current treatment for hypertension or previously diagnosed hypertension; (5) Elevated fasting plasma glucose level (FPG \(\ge\) 6.1 mmol/L or 2 h postprandial PG ≥ 7.8 mmol/L) or previously diagnosed diabetes mellitus [9].

Statistical analysis

The study population was sampled with the multilevel, stratified sampling design based on sex, area, and province [19]. Survey weights were computed based on the study design and 2010 Chinese census data and included oversampling for specific age subgroups, nonresponse, and other demographics between the sample and the total population. Differential probabilities of selection were adjusted, and the complex sampling design was used to enhance the representativeness of the survey sample population.

All data analyses were conducted using R version 4.1.1(http://www.r-project.org). The normality of the data was assessed by the Kolmogorov–Smirnov test. Means for continuous variables and percentages and proportions for categorical variables were used for summarizing. The Student t-test and Rao-Scott χ2 test were used to assess the differences across groups for continuous and categorical variables. Venn diagrams and kappa value ( poor, kappa ≤ 0.20; fair, kappa = 0.21–0.40; moderate, kappa = 0.41–0.60; substantial, kappa = 0.61–0.80; very good, kappa > 0.80) were used to assess disparity and agreement of three definitions. Univariate analysis was conducted to identify variables potentially associated with any defined MetS, and variables with P < 0.10 were included in the multivariable logistic regression. The 95% confidence intervals (CIs) were calculated for Odds ratios (OR). All tests were two-tailed, and a value of P < 0.05 was considered statistically significant.

Result

Characteristics of the study population

A total of 13,035(45.4%) men and 15,682(54.6%) women aged ≥ 35 years old were included in this survey. The characteristics of the participants are shown in Table 1. Overall, the mean age was 52.0 years (51.5 years for men and 52.4 years for women), and the range of age was 35 to 107 years. Most (65.8%) people lived in rural areas, and 40.6% were located in eastern China, 81.4% were educated in middle school or below, and 12.8% of participants had a CVD family history. In men, 48.3% were current smokers, and 37.9% had alcohol use, whereas the corresponding proportions were only 2.6% and 2.7% in women. Compared to women, men had a higher level of WC, TG, blood pressure, fasting plasma glucose, and lower levels of HDL-C.

Table1 Characteristics of the study population
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Prevalence and presence of MetS in different definitions

Table 2 shows the prevalence of MetS with IDF, the revised ATP III, and JCDCG criteria. The prevalence of MetS in the overall population was 26.4% (22.2% in men and 30.3% in women) by IDF criteria, 32.3% (29.2% in men and 35.4% in women) by revised ATP III definition, 21.5% (24.4% in men and 18.5% in women) by JCDCG criteria. Despite some subtle differences, the relationship between various factors and MetS according to the three definitions were very similar. Regardless of the definition used, living in urban areas, having a family history of CVD, or having a higher BMI was significantly associated with a higher prevalence of MetS in the overall population and in both men and women. The prevalence of MetS reached its highest in the age group of 55–64 years in the total population and 45–54 years in men, and the prevalence decreased with age regardless of the definition used. In women over 55 years of age, the MetS prevalence maintained a high level. Regardless of the definition used, higher education levels were associated with a higher prevalence of MetS in men. In contrast, higher education levels were associated with a lower prevalence of MetS in women. The difference was statistically significant in the overall population only when the JCDCG definition was used and significant in women when IDF and the revised ATP III definitions were used. For smoking, there was a significant association between smoking and MetS in the overall population, but not in men and women. Regardless of the definition used, alcohol use was associated with a lower prevalence of MetS in women, whereas when using the IDF and the revised ATP III, alcohol use was associated with a higher prevalence of MetS in men. In contrast, in the overall population, alcohol use was only significantly associated with MetS as defined by the JCDCG.

Table 2 The Prevalence of MetS defined by different definitions
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Agreement on the various definitions of the metabolic syndrome

The consistency and differences between diagnoses using IDF, the revised ATP III, and JCDCG criteria are shown in Fig. 1 and Table 3. In individuals with MetS diagnosed according to at least one definition, 64.4% of men and 46.6% of women were diagnosable by all definitions, and above 90% of people diagnosed with MetS according to two or three definitions. The JCDCG definition was the strictest, especially for women, only 52.1% of women were diagnosed with MetS. Table 3 shows the kappa values between any two definitions for men and women. The test showed good consistency between any two definitions in men and between the revised ATP III and IDF in women, with kappa values ranging from 0.77 to 0.89. JCDCG was moderately consistent with IDF (kappa = 0.58) and the revised ATP III (kappa = 0.58) in women.

Fig. 1
figure 1

Venn diagrams showing the agreement and disparity in the diagnosis of the metabolic syndrome defined by IDF, the revised ATP III and JCDCG criteria among those 3879 men and 6288 women who qualified for the diagnosis of the metabolic syndrome by at least one of these definitions. Abbreviations: IDF: International Diabetes Federation; the revised ATP III: the American Heart Association/National Heart, Lung, and Blood Institute updated the ATP III; JCDCG: the Joint Committee for Developing Chinese Guidelines

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Table 3 The agreement between the various definitions of the MetS
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Multivariable logistic regression analysis of factors related to MetS

Table 4 shows the factors associated with MetS in men and women according to different definitions. The results showed that area, age, education, smoking, alcohol use, and family history of cardiovascular disease were related to MetS, but the effects and correlation intensity of these factors varied with gender and definition. Living in urban areas and having a family history of CVD was significantly associated with the high prevalence of MetS in both men and women under all MetS definitions, although there were slight differences in OR values. Among men, 75 years and older were significantly associated with a lower prevalence of MetS, and college education and above were significantly associated with a higher prevalence of MetS according to all three definitions. However, among women, regardless of the definition used, all groups aged 45 and above were significantly associated with a higher prevalence of MetS, and college education and above and alcohol use were significantly associated with a lower prevalence of MetS. Living in the eastern region was significantly associated with a higher prevalence of MetS in men but not in women. Smoking was significantly associated with a lower prevalence of MetS defined by the revised ATP III and JCDCG criteria but not associated with MetS by IDF in men and according to all three definitions in women. In men, alcohol use was only significantly associated with a higher prevalence of MetS defined by IDF criteria. In women, alcohol use was associated with a lower prevalence of MetS defined by all three definitions.

Table 4 Factors related to MetS defined by IDF, Revised ATP III, and JCDCG definitions
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Discussion

This study aimed to investigate the prevalence and characteristics of MetS with different definitions across China. The results showed that the overall prevalence of MetS among Chinese populations aged ≥ 35 years according to the definition of IDF, the revised ATP III, and JCDCG was 26.4%, 32.3%, and 21.5%, respectively. The MetS was less prevalent in men than women according to IDF definition (22.2% vs 30.3%) and the revised ATP III (29.2% vs 35.4%) definition, but the opposite was true according to JCDCG definition (24.4%vs 18.5%). The result also showed that JCDCG definition was not in good agreement with IDF and the revised ATP III in women. In addition, the study indicated that area, age, education, smoking, alcohol use, and family history of CVD were related to MetS, but the impact and strength of the association of these factors varied by gender and definition.

The study explored the prevalence and characteristics of MetS with different MetS definitions across China. The prevalence of MetS varied greatly, with the lowest being defined by JCDCG (21.5%) and the highest being defined by ATP III (32.3%), the latter was about 1.5 times of the former. Even if estimated according to the definition of the lowest prevalence, MetS was common in the Chinese adults. Therefore, it is necessary to take targeted intervention measures to reduce the burden of MetS in China. Multivariate logistic regression showed that although the impact and correlation intensity varied by gender and definition, region, age, education, smoking, alcohol consumption, and family history of CVD were factors associated with MetS. An in-depth study of the relationship between these factors and MetS may help to understand the causes of MetS and help to control MetS.

Consistency and difference analysis showed that there was a great overlap between the three definitions. Among individuals with MetS diagnosed according to at least one definition, 64.4% of men and 46.6% of women could be diagnosed by all definitions. This may explain why the influence and correlation intensity of the factors associated with MetS varied by definition, but the difference was not large. The consistency tests showed that the consistency between any two definitions of men and the revised ATP III definition and IDF definition of women was relatively good, while the consistency between JCDCG and IDF definition (kappa 0.58) and the revised ATP III (kappa 0.58) was relatively poor in women. Moreover, the results showed that the MetS prevalence was higher in men than in women with IDF and the revised ATP III definition, but lower in men than in women with the JCDCG definition. This phenomenon may be caused by the strictest central obesity standard (WC \(\ge\) 85 cm for women). Understanding the differences among the definitions may helpful to correctly analyze the differences in prevalence among different definitions. Some studies have shown that the revised ATP III definition was the best predictor of cardiovascular disease [21, 22]. The current study is a cross-sectional study, and it is impossible to compare the advantages and disadvantages of different definitions. To solve this problem, more longitudinal studies may be needed. The prevalence of MetS in our population lies well within the data previously obtained in China [13, 21, 23]. In a nationwide studies of people over 45 years old, the prevalence of MetS was 34.8%, 39.7%, and 25.6%, according to IDF, the revised ATP III, JCDCG criteria, respectively [21]. The participants in that study were older than those in our study. In a survey of people aged 18 years and older, the prevalence of MetS according to the revised ATP III definition was 24.2%, much lower than the 32.3% we obtained when using the same definition [13]. However, the prevalence of MetS in the 45–54, 55–64, and ≥ 65 years age groups in that study was 32.12%, 36.97%, 37.81%, respectively. In our study, the MetS prevalence in the 45–54, 55–64, 65–64, and ≥ 75 years age groups was 33.4%, 39.4%, 37.9%, and 38.1%, respectively (Table 2). The numbers are very close.

It has been seen that the prevalence of MetS was closely related to age and gender [24]. In our study, the prevalence of MetS in the total population peaked at the age of 55–64 years, which is close to Wu’s study, which peaked at the age of 60–69 years [25]. In addition to age, gender cannot be ignored. In our study, the prevalence of MetS in women over 45 years old remained at a high level (Table 2), and the odds ratio of women over 45 years old reached around 3 (Table 4). Menopause may explain this phenomenon, for menopause generally occurs around the age of 50 [26]. The loss of heart and kidney protection of female hormones with age may lead to the sharp increase in hypertension and cardiovascular disease in postmenopausal women [27]. The prevalence of MetS in our study reached its highest in men aged 45–54 years and then decreased, becoming a protective factor over 65 years. This marked reversal of gender difference in older adults may be partly attributable to the men prone to metabolic disease who had died before the age of 75 or refused to participate in this study [27, 28]. The characteristics of MetS vary by sex, suggesting that reasonable comparisons should be made by sex.

In addition to age and gender, our study showed there were some other factors associated with MetS. The present study revealed that individuals living in urban areas had a higher risk of MetS, in line with some other studies [25, 29]. The reason for this phenomenon may be that, in China, compared to rural areas, in economically developed urban areas with rapid industrialization, animal food and fast food with high fat and purine content increased dramatically, while grain consumption was the opposite [30]. Our results also indicated that there were gender differences in the association between education and MetS, with a positive association for women and negative for men. This was consistent with a study conducted by the Korea National Health and Nutrition Examination Surveys [31]. One possible explanation was that more educated women might have a favorable opportunity to get more nutrition knowledge and prefer healthy food consumption patterns [32]. And men with higher education are more likely to consume high-calorie foods and alcohol, while avoiding physically demanding tasks [31]. It is worth noting that a family history of CVD was an independent risk factor for MetS in our study, suggesting that more attention should be paid to individuals with a CVD family history [33]. There was a significant negative correlation between smoking and MetS defined by the revised ATP III and JCDCG definition in men. Although the association between smoking and MetS was not significant in women, its OR value was smaller than that in men, which may be due to the small number of women smoking and insufficient test power. This phenomenon is contrary to the general conclusion that smokers had higher insulin resistance and a higher risk of fatal coronary artery disease than non-smokers [34]. One possible explanation is that some smokers weigh less than non-smokers because of the effects of nicotine on metabolism [35]. Interestingly, we found an arguable result that alcohol use was a protective factor for women and a risk factor for men, which was also reported in Sampson’s study [36]. Men have higher drinking rates and tend to consume large amount of alcohol. Heavy drinking, especially > 30 g/day in men, is often accompanied by an increase in energy intake and changes in the concentration of steroid hormones that may cause central fat storage, which will aggravate elevated blood pressure, elevated plasma glucose, and central obesity [37]. Women drink less often and in lower amounts. Some studies have shown that drinking small amounts of alcohol may have cardiovascular protective effects [38]. However, the protective effect of drinking small amounts of alcohol remains controversial and needs further study [39].

Our study has some strength. Firstly, the sample size of the current study was large and the study population was randomly selected from the whole country by stratified and multistage sampling, and the sample was nationally representative. This allowed us to estimate the prevalence of MetS across the country and to explore the impact of different definitions on the prevalence of MetS in China. Secondly, strict quality control ensured the high quality of data and reliability of the findings. The uniform research protocol and measuring instruments, strict training and examination, and the centralized detection of blood glucose and lipids in the central laboratory ensure the accuracy and comparability of the data. Thirdly, we used different definitions in the same group of people to explore the prevalence and characteristics of MetS, which enables us to have a comprehensive understanding of the prevalence and characteristics of MetS and is also convenient to compare with the data of other regions and population. The limitations of this study need to be recognized. Firstly, we only compared the revised ATP III, IDF, and JCDCG definitions due to the lack of some indicators, such as the data of insulin resistance. Secondly, we explored some related factors of MetS, but we cannot claim causality because of the cross-sectional design. Thirdly, in this study, we explored the related factors of MetS, some variables which may affect MetS were not included in our study, such as physical activity and dietary patterns. In addition, due to funding and other reasons, the investigation lasted for a long time, and some related factors may have changed.

Conclusions

In summary, the prevalence and characteristics of metabolic syndrome vary according to the definition used in the Chinese population. The three MetS definitions of IDF, the revised ATP III, and JCDCG are in relatively good agreement in men, but the differences between JCDCG and IDF and between JCDCG and the revised ATP III are large in women. On the other hand, even if estimated according to the definition of the lowest prevalence. MetS is common in the Chinese adults. It is necessary to explore the causes of the difference in the prevalence of MetS in different populations and take targeted intervention measures in China.

Availability of data and materials

The dataset analyzed during the current study is available from the corresponding author on reasonable request.

Abbreviations

MetS:

Metabolic syndrome

WHO:

The World Health Organization

EGIR:

The European Group for the study of Insulin Resistance

NCEP:

The National Cholesterol Education Program Adult Treatment Panel III

IDF:

The International Diabetes Federation

Revised ATP III:

The American Heart Association/National Heart, Lung, and Blood Institute updated the ATP III

JCDCG:

The Joint Committee for Developing Chinese Guideline

WC:

Waist circumference

TG:

Triglyceride

HDL-C:

High-density lipoproteins cholesterol

BP:

Blood pressure

FPG:

Fasting plasma glucose

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Acknowledgements

We thank all the colleagues involved in the China Hypertension Survey. The authors are grateful to OMRON Corporation, Kyoto, Japan, for providing the blood pressure monitor (HBP-1300) and body fat and weight measurement device (V- body HBF-371); Henan Huanan Medical Science & Technology Co., Ltd, China, for providing digital ECG device (GY- 5000); and Microlife, Taipei, Taiwan, for providing the automated ABI device (Watch BP Office device). Finally, the authors are thankful to BUCHANG PHARMA, Xian, China; Kinglian Technology, Guangzhou, China; Merck Serono; Pfizer, China; and Essen Technology (Beijing) Company Limited for their financial support for the project.

China Hypertension Survey

Linfeng Zhang1, Zengwu Wang1, Xin Wang1, Zuo Chen1, Lan Shao1, Ye Tian1, Liqun Hu3, Hongqi Li3, Qi Zhang3, Guang Yan3, Fangfang Zhu4, Xianghua Fang5, Chunxiu Wang5, Shaochen Guan5, Xiaoguang Wu5, Hongjun Liu5, Chengbei Hou5, Han Lei6, Wei Huang6, Nan Zhang6, Ge Li7, Lihong Mu7, Xiaojun Tang7, Ying Han8, Huajun Wang8, Dongjie Lin8, Liangdi Xie8, Daixi Lin9, Jing Yu10, Xiaowei Zhang10, Wei Liang10, Heng Yu10, Qiongying Wang10, Lan Yang11, Yingqing Feng12, Yuqing Huang12, Peixi Wang13, Jiaji Wang13, Harry HX Wang14, Songtao Tang15, Tangwei Liu16, Rongjie Huang16, Zhiyuan Jiang16, Haichan Qin16, Guoqin Liu17, Zhijun Liu17, Wenbo Rao17, Zhen Chen17, Yalin Chu17, Fang Wu17, Haitao Li18, Jianlin Ma18, Tao Chen18, Ming Wu19, Jixin Sun20, Yajing Cao20, Yuhuan Liu20, Zhikun Zhang21, Yanmei Liu22, Dejin Dong23, Guangrong Li24, Hong Guo25, Lihang Dong25, Haiyu Zhang25, Fengyu Sun25, Xingbo Gu25, Ye Tian25, Kaijuan Wang26, Chunhua Song26, Peng Wang26, Hua Ye26, Wei Nie27, Shuying Liang27, Congxin Huang28, Fang Chen28, Yan Zhang28, Heng Zhou28, Jing Xie28, Jianfang Liu28, Hong Yuan29, Chengxian Guo29, Yuelong Huang30, Biyun Chen30, Xingsheng Zhao31, Wenshuai He31, Xia Wen31, Yanan Lu31, Xiangqing Kong32, Ming Gui32, Wenhua Xu32, Yan Lu32, Jun Huang32, Min Pan33, Jinyi Zhou34, Ming Wu34, Xiaoshu Cheng35, Huihui Bao35, Xiao Huang35, Kui Hong35, Juxiang Li35, Ping Li35, Bin Liu36, Junduo Wu36, Longbo Li36, Yunpeng Yu36, Yihang Liu36, Chao Qi36, Jun Na37, Li Liu37, Yanxia Li37, Guowei Pan37, Degang Dong38, Peng Qu38, Jinbao Ma39, Juan Hu40, Fu Zhao41, Jianning Yue42, Minru Zhou42, Zhihua Xu42, Xiaoping Li42, Qiongyue Sha42, Fuchang Ma42, Qiuhong Chen43, Huiping Bian43, Jianjun Mu44, Tongshuai Guo44, Keyu Ren44, Chao Chu44, Zhendong Liu45, Hua Zhang45, Yutao Diao45, Shangwen Sun45, Yingxin Zhao45, Junbo Ge46, Jingmin Zhou46, Xuejuan Jin46, Jun Zhou46, Bao Li47, Lijun Zhu47, Yuean Zhang47, Gang Wang47, Zhihan Hao48, Li Cai49, Zhou Liu49, Zhengping Yong49, Shaoping Wan50, Zhenshan Jiao51, Yuqiang Fan51, Hui Gao52, Wei Wang52, Qingkui Li53, Xiaomei Zhou53, Yundai Chen54, Bin Feng54, Qinglei Zhu54, Sansan Zhou54, Nanfang Li55, Ling Zhou55, Delian Zhang55, Jing Hong55, Tao Guo56, Min Zhang56, Yize Xiao57, Xuefeng Guang58, Xinhua Tang59, Jing Yan59, Xiaoling Xu59, Li Yang59, Aimin Jiang59, Wei Yu59.

1. Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, China

3. Anhui Provincial Hospital, Hefei, Anhui, China.

4. Anhui Institute of Cardiovascular Disease, Hefei, Anhui, China.

5. Xuanwu Hospital, Capital Medical University, Beijing, China.

6. First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

7. Chongqing Medical University, Chongqing, China.

8. First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.

9. Fujian medical university, Fuzhou, Fujian, China.

10. Lanzhou University Second Hospital, Lanzhou, Gansu, China.

11. Maternal and Child Care Service Centre, Lanzhou, Gansu, China.

12. Guangdong General Hospital, Guangzhou, Guangdong, China.

13. Guangzhou Medical University, Guangzhou, Guangdong, China.

14. Sun Yat-Sen University, Guangzhou, Guangdong, China.

15. Community Health Services Center of Liaobu, Dongguan, Guangdong, China.

16. First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China

17. Zunyi Medical University, Zunyi, Gouzhou, China.

18. Hainan General Hospital, Haikou, Hainan, China.

19. Health and Family Planning Commission of Hainan, Haikou, Hainan, China.

20. Center for Disease Prevention and Control of Hebei, Shijiazhuang, Hebei, China.

21. Center for Disease Prevention and Control of Tangshan, Tangshan, Hebei, China.

22. Center for Disease Prevention and Control of Langfang, Langfang, Hebei, China.

23. Center for Disease Prevention and Control of Xingtai, Xingtai, Hebei, China.

24. Center for Disease Prevention and Control of Dingzhou, Dingzhou, Hebei, China.

25. First Affiliated Hospital of Harbin Medical University, Haerbin, Heilongjiang, China.

26. Zhengzhou University, Zhengzhou, Henan, China.

27. Henan Academy of Medical Sciences, Zhengzhou, Henan, China.

28. Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.

29. Third Xiangya Hospital, Central South University, Changsha, Hunan, China.

30. Center for Disease Control and Prevention of Hunan, Changsha, Hunan, China.

31. Inner Mongolia people's hospital, Hohhot, Inner Mongolia, China.

32. First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

33. Affiliated Hospital of Nantong University, Nanjing, Jiangsu, China.

34. Center for Disease Control and Prevention of Jiangsu, Nanjing, Jiangsu, China.

35. Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.

36. Second Hospital of Jilin University, Changchun, Jilin, China.

37. Center for Disease Prevention and Control of Liaoning, Shenyang, Liaoning, China.

38. Health and Family Planning Commission of Liaoning, Shenyang, Liaoning, China.

39. Health and Family Planning Commission of Ning Xia Hui Autonomous Region, Yinchuan, Ningxia, China.

40. Center for Disease Control and Prevention of Ning Xia Hui Autonomous Region, Yinchuan, Ningxia, China.

41. Health Supervision Institute of Xixia District in Yinchuan, Ning Xia Hui Autonomous Region, Yinchuan, Ningxia, China.

42. Qing Hai Center for Disease Control and Prevention, Xining, Qinghai, China.

43. Qinghai Cardio-Cerebrovascular Disease Special Hospital, Xining, Qinghai, China.

44. First Affiliated Hospital of Xi’an Jiaotong University, Xian, Shaanxi, China.

45. Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, Shandong, China.

46. Zhongshan Hospital, Fudan University, Shanghai, China.

47. Shanxi Cardiovascular Hospital, Taiyuan, Shanxi, China.

48. Wuxiang County People's Hospital, Wuxiang, Shanxi, China.

49. Jianhong Tao, Yijia Tang, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.

50. Sichuan Cancer Hospital, Chengdu, Sichuan, China.

51. Tianjin Academy of Traditional Chinese Medicine, Tianjin, China.

52. Tianjin Municipal Commission of Health and Family Planning, Tianjin, China.

53. Tianjin Medical University, Tianjin, China.

54. Chinese People’s Liberation Army General Hospital, Lasha, Tibet, China.

55. People's Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang, China.

56. First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.

57. Center for Disease Prevention and Control of Yunnan, Kunming, Yunnan, China.

58. Affiliated Yan'an Hospital of Kunming Medical University, Kunming, Yunnan, China.

59. Zhejiang Hospital, Hangzhou, Zhejiang, China.

Financial disclosure

No financial disclosures were reported by the authors of this paper.

Funding

The study was supported by the Chinese Academy of Medical Science (CAMS) Innovation Fund for Medical Sciences (grant number 2017-I2M-1–004), China National Science & Technology Pillar Program (2011BAI11B01), Special Research Fund for Public Welfare Projects of National Health and Family Planning Commission, China (201,402,002), and the National Natural Science Foundation of China(81,973,117), the surveillance of Cardiovascular Disease and its risk factors in Chinese residents.

Author information

Authors and Affiliations

  1. Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, No. 15 (Lin), Fengcunxili, Mentougou District, Beijing, 102308, China

    Yilin Huang, Linfeng Zhang, Zengwu Wang, Xin Wang, Zuo Chen, Lan Shao, Ye Tian, Congying Zheng, Lu Chen, Haoqi Zhou, Xue Cao & Yixin Tian

  2. Department of Cardiology, National Center for Cardiovascular Disease, National Clinical Research Center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, No. 167, Beilishilu, Xicheng District, Beijing, 100037, China

    Runlin Gao

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  1. Yilin Huang
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  2. Linfeng Zhang
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  4. Xin Wang
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Consortia

for the China Hypertension Survey investigators

  • Linfeng Zhang
  • , Zengwu Wang
  • , Xin Wang
  • , Zuo Chen
  • , Lan Shao
  • , Ye Tian
  • , Liqun Hu
  • , Hongqi Li
  • , Qi Zhang
  • , Guang Yan
  • , Fangfang Zhu
  • , Xianghua Fang
  • , Chunxiu Wang
  • , Shaochen Guan
  • , Xiaoguang Wu
  • , Hongjun Liu
  • , Chengbei Hou
  • , Han Lei
  • , Wei Huang
  • , Nan Zhang
  • , Ge Li
  • , Lihong Mu
  • , Xiaojun Tang
  • , Ying Han
  • , Huajun Wang
  • , Dongjie Lin
  • , Liangdi Xie
  • , Daixi Lin
  • , Jing Yu
  • , Xiaowei Zhang
  • , Wei Liang
  • , Heng Yu
  • , Qiongying Wang
  • , Lan Yang
  • , Yingqing Feng
  • , Yuqing Huang
  • , Peixi Wang
  • , Jiaji Wang
  • , Harry HX Wang
  • , Songtao Tang
  • , Tangwei Liu
  • , Rongjie Huang
  • , Zhiyuan Jiang
  • , Haichan Qin
  • , Guoqin Liu
  • , Zhijun Liu
  • , Wenbo Rao
  • , Zhen Chen
  • , Yalin Chu
  • , Fang Wu
  • , Haitao Li
  • , Jianlin Ma
  • , Tao Chen
  • , Ming Wu
  • , Jixin Sun
  • , Yajing Cao
  • , Yuhuan Liu
  • , Zhikun Zhang
  • , Yanmei Liu
  • , Dejin Dong
  • , Guangrong Li
  • , Hong Guo
  • , Lihang Dong
  • , Haiyu Zhang
  • , Fengyu Sun
  • , Xingbo Gu
  • , Kaijuan Wang
  • , Chunhua Song
  • , Peng Wang
  • , Hua Ye
  • , Wei Nie
  • , Shuying Liang
  • , Congxin Huang
  • , Fang Chen
  • , Yan Zhang
  • , Heng Zhou
  • , Jing Xie
  • , Jianfang Liu
  • , Hong Yuan
  • , Chengxian Guo
  • , Yuelong Huang
  • , Biyun Chen
  • , Xingsheng Zhao
  • , Wenshuai He
  • , Xia Wen
  • , Yanan Lu
  • , Xiangqing Kong
  • , Ming Gui
  • , Wenhua Xu
  • , Yan Lu
  • , Jun Huang
  • , Min Pan
  • , Jinyi Zhou
  • , Ming Wu
  • , Xiaoshu Cheng
  • , Huihui Bao
  • , Xiao Huang
  • , Kui Hong
  • , Juxiang Li
  • , Ping Li
  • , Bin Liu
  • , Junduo Wu
  • , Longbo Li
  • , Yunpeng Yu
  • , Yihang Liu
  • , Chao Qi
  • , Jun Na
  • , Li Liu
  • , Yanxia Li
  • , Guowei Pan
  • , Degang Dong
  • , Peng Qu
  • , Jinbao Ma
  • , Juan Hu
  • , Fu Zhao
  • , Jianning Yue
  • , Minru Zhou
  • , Zhihua Xu
  • , Xiaoping Li
  • , Qiongyue Sha
  • , Fuchang Ma
  • , Qiuhong Chen
  • , Huiping Bian
  • , Jianjun Mu
  • , Tongshuai Guo
  • , Keyu Ren
  • , Chao Chu
  • , Zhendong Liu
  • , Hua Zhang
  • , Yutao Diao
  • , Shangwen Sun
  • , Yingxin Zhao
  • , Junbo Ge
  • , Jingmin Zhou
  • , Xuejuan Jin
  • , Jun Zhou
  • , Bao Li
  • , Lijun Zhu
  • , Yuean Zhang
  • , Gang Wang
  • , Zhihan Hao
  • , Li Cai
  • , Zhou Liu
  • , Zhengping Yong
  • , Shaoping Wan
  • , Zhenshan Jiao
  • , Yuqiang Fan
  • , Hui Gao
  • , Wei Wang
  • , Qingkui Li
  • , Xiaomei Zhou
  • , Yundai Chen
  • , Bin Feng
  • , Qinglei Zhu
  • , Sansan Zhou
  • , Nanfang Li
  • , Ling Zhou
  • , Delian Zhang
  • , Jing Hong
  • , Tao Guo
  • , Min Zhang
  • , Yize Xiao
  • , Xuefeng Guang
  • , Xinhua Tang
  • , Jing Yan
  • , Xiaoling Xu
  • , Li Yang
  • , Aimin Jiang
  •  & Wei Yu

Contributions

YH prepared the draft manuscript. LZ designed the concept of the study and statistically analyzed the data. XW, ZC, LS, YT and CZ effectively worked for the data collection. LC, XC, HZ and YT provided guidance on the study design and editing. RG and ZW critically reviewed/edited the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Linfeng Zhang or Zengwu Wang.

Ethics declarations

Ethics approval and consent to participate

Written informed consent was obtained from each participant before data collection. This study (No. 2011BAI11B01) was approved by the ethics committee of Fuwai Hospital, Beijing, China. All procedures were in accordance with the 1964 Helsinki Declaration.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Appendix Table 1.

Characteristics of the subjects included and excluded in the analysis (age ≥ 35 years old).

Additional file 2: Supplemental Text 2.

Weights calculation in the Study.

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Huang, Y., Zhang, L., Wang, Z. et al. The prevalence and characteristics of metabolic syndrome according to different definitions in China: a nationwide cross-sectional study, 2012–2015. BMC Public Health 22, 1869 (2022). https://doi.org/10.1186/s12889-022-14263-w

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