Ke Xu1, Hui Juan Zhu1, Shi Chen1, Lu Chen1, Xin Wang2, Li Yuan Zhang2, Li Pan3, Li Wang3, Kui Feng3, Ke Wang4, Fen Dong5, Ding Ming Wang6, Yang Wen Yu6, Hui Pan1, Guang Liang Shan3. 1. Key Laboratory of Endocrinology of National Health and Family Planning Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China. 2. Graduate School, Hebei North University, Zhangjiakou 075000, Hebei, China. 3. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100730, China. 4. National Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, China. 5. Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing 100029, China. 6. Institute for Infection, Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, Guizhou, China.
Abstract
OBJECTIVE: To investigate the prevalence and possible factors influencing metabolic syndrome in people from Guizhou Province and to explore the predictive value of the fat-to-muscle ratio in diagnosing metabolic syndrome. METHODS: A multistage stratified sampling method was used in this cross-sectional study of 20-80 years old Han and Bouyei populations from Guizhou Province, southwestern China, from October-December 2012. The study included 4,553 cases of metabolic syndrome, that was defined according to 2005 International Diabetes Federation criteria. The receiver operating characteristic curve was used for determining the sensitivity, specificity, and predictive ability of the fat-to-muscle ratio for the diagnosis of metabolic syndrome. RESULTS: The age-standardized prevalence of metabolic syndrome was 11.38% (men: 9.76%; women: 12.72%) for Han and 4.78% (men: 4.43%; women: 5.30%) for Bouyei populations. In Guizhou Province, the cut-off value for the men fat-to-muscle ratio was 0.34, the area under the curve was 0.95, and the sensitivity and specificity were 0.94 and 0.85, respectively. The cut-off value for the women fat-to-muscle ratio was 0.55, the area under the curve was 0.91, and the sensitivity and specificity were 0.93 and 0.79, respectively. CONCLUSION: The fat-to-muscle ratio is highly predictive of metabolic syndrome in Guizhou Province, and a useful reference indicator.
OBJECTIVE: To investigate the prevalence and possible factors influencing metabolic syndrome in people from Guizhou Province and to explore the predictive value of the fat-to-muscle ratio in diagnosing metabolic syndrome. METHODS: A multistage stratified sampling method was used in this cross-sectional study of 20-80 years old Han and Bouyei populations from Guizhou Province, southwestern China, from October-December 2012. The study included 4,553 cases of metabolic syndrome, that was defined according to 2005 International Diabetes Federation criteria. The receiver operating characteristic curve was used for determining the sensitivity, specificity, and predictive ability of the fat-to-muscle ratio for the diagnosis of metabolic syndrome. RESULTS: The age-standardized prevalence of metabolic syndrome was 11.38% (men: 9.76%; women: 12.72%) for Han and 4.78% (men: 4.43%; women: 5.30%) for Bouyei populations. In Guizhou Province, the cut-off value for the men fat-to-muscle ratio was 0.34, the area under the curve was 0.95, and the sensitivity and specificity were 0.94 and 0.85, respectively. The cut-off value for the women fat-to-muscle ratio was 0.55, the area under the curve was 0.91, and the sensitivity and specificity were 0.93 and 0.79, respectively. CONCLUSION: The fat-to-muscle ratio is highly predictive of metabolic syndrome in Guizhou Province, and a useful reference indicator.
Authors: Jorge Cazorla-González; Sergi García-Retortillo; Mariano Gacto-Sánchez; Gerard Muñoz-Castro; Juan Serrano-Ferrer; Blanca Román-Viñas; Abel López-Bermejo; Raquel Font-Lladó; Anna Prats-Puig Journal: Int J Environ Res Public Health Date: 2022-05-03 Impact factor: 4.614