Hai Cheng Zhou1, Ya Xin Lai1, Zhong Yan Shan1, Wei Ping Jia2, Wen Ying Yang3, Ju Ming Lu4, Jian Ping Weng5, Li Nong Ji6, Jie Liu7, Hao Ming Tian8, Qiu He Ji9, Da Long Zhu10, Li Chen11, Xiao Hui Guo12, Zhi Gang Zhao13, Qiang Li14, Zhi Guang Zhou15, Jia Pu Ge16, Guang Liang Shan17. 1. Department of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning, China. 2. Department of Endocrinology and Metabolism, The Sixth Affiliated People's Hospital of Shanghai Jiaotong University, Shanghai 200233, China. 3. Department of Endocrinology and Metabolism, China-Japan Friendship Hospital, Beijing 100029, China. 4. Department of Endocrinology and Metabolism, Chinese People's Liberation Army General Hospital, Beijing 100853, China. 5. Department of Endocrinology and Metabolism, Sun Yat-sen University Third Hospital, Guangzhou 510630, Guangdong, China. 6. Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China. 7. Department of Endocrinology and Metabolism, Shanxi Provincial People's Hospital, Taiyuan 030012, Shanxi, China. 8. Department of Endocrinology and Metabolism, Sichuan University West China Hospital, Chengdu 610041, Sichuan, China. 9. Department of Endocrinology and Metabolism, Fourth Military Medical University Xijing Hospital, Xi'an 710032, Shaanxi, China. 10. Department of Endocrinology and Metabolism, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu, China. 11. Department of Endocrinology and Metabolism, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China. 12. Department of Endocrinology and Metabolism, Peking University First Hospital, Beijing 100034, China. 13. Department of Endocrinology and Metabolism, Henan Provincial People's Hospital, Zhengzhou 450003, Henan, China. 14. Department of Endocrinology and Metabolism, Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China. 15. Department of Endocrinology and Metabolism, Xiangya Second Hospital of Central South University, Changsha 410008, Hunan, China. 16. Department of Endocrinology and Metabolism, Xinjiang Uygur Autonomous Region Hospital, Urumqi 830001, Xinjiang, China. 17. Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China.
Abstract
OBJECTIVE: To study the effectiveness of waist circumference cut-off values in predicting the prevalence of metabolic syndrome (MetS) and risk factors in adults in China. METHODS: A cross-sectional survey was condcuted in 14 provinces (autonomous region, municipality) in China. A total of 47,325 adults aged⋝20 years were selected by multistage stratified sampling, and questionnaire survey and physical and clinical examination were conducted among them. MetS was defined according to the International Diabetes Federation (IDF) criteria and modified IDF criteria. RESULTS: The age-standardized prevalence of MetS was 24.2% (22.1% in men and 25.8% in women) and 19.5% (22.1% in men and 18.0% in women) according to the IDF criteria and modified IDF criteria respectively. The age-standardized prevalence of pre-MetS was 8.1% (8.6% in men and 7.8% in women) according to the modified IDF criteria. The prevalence of MetS was higher in urban residents than rural residents and in northern China residents than in southern China residents. The prevalence of central obesity was about 30% in both men and women according to the ethnicity-specific cut-off values of waist circumference for central obesity (90 cm for men and 85 cm for women). Multivariate regression analysis revealed no significant difference in risk factors between the two MetS definitions. CONCLUSION: Using both the modified IDF criteria and ethnicity-specific cut-off values of waist circumference can provide more useful information about the prevalence of MetS in China. Conclusion Using both the modified IDF criteria and ethnicity-specific cut-off values of waist circumference can provide more useful information about the prevalence of MetS in China.
OBJECTIVE: To study the effectiveness of waist circumference cut-off values in predicting the prevalence of metabolic syndrome (MetS) and risk factors in adults in China. METHODS: A cross-sectional survey was condcuted in 14 provinces (autonomous region, municipality) in China. A total of 47,325 adults aged⋝20 years were selected by multistage stratified sampling, and questionnaire survey and physical and clinical examination were conducted among them. MetS was defined according to the International Diabetes Federation (IDF) criteria and modified IDF criteria. RESULTS: The age-standardized prevalence of MetS was 24.2% (22.1% in men and 25.8% in women) and 19.5% (22.1% in men and 18.0% in women) according to the IDF criteria and modified IDF criteria respectively. The age-standardized prevalence of pre-MetS was 8.1% (8.6% in men and 7.8% in women) according to the modified IDF criteria. The prevalence of MetS was higher in urban residents than rural residents and in northern China residents than in southern China residents. The prevalence of central obesity was about 30% in both men and women according to the ethnicity-specific cut-off values of waist circumference for central obesity (90 cm for men and 85 cm for women). Multivariate regression analysis revealed no significant difference in risk factors between the two MetS definitions. CONCLUSION: Using both the modified IDF criteria and ethnicity-specific cut-off values of waist circumference can provide more useful information about the prevalence of MetS in China. Conclusion Using both the modified IDF criteria and ethnicity-specific cut-off values of waist circumference can provide more useful information about the prevalence of MetS in China.
Authors: Lin Zhang; Jin-Long Li; Li-Li Zhang; Lei-Lei Guo; Hong Li; Wenzhu Yan; Dan Li Journal: Medicine (Baltimore) Date: 2019-03 Impact factor: 1.817
Authors: Ri Li; Wenchen Li; Zhijun Lun; Huiping Zhang; Zhi Sun; Joseph Sam Kanu; Shuang Qiu; Yi Cheng; Yawen Liu Journal: BMC Public Health Date: 2016-04-01 Impact factor: 3.295