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.
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.