Lili Liu1, Bixia Gao1, Jinwei Wang1, Chao Yang1, Shouling Wu2, Yuntao Wu2, Shuohua Chen3, Qiuyun Li4, Huifen Zhang5, Guodong Wang2, Min Chen1, Rodica Pop-Busui6, Ming-Hui Zhao7, Luxia Zhang8. 1. Renal Division, Department of Medicine, Peking University First Hospital, Institute of Nephrology, Peking University, Key Laboratory of Renal Disease, National Health and Family Planning Commission of the People's Republic of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Ministry of Education, Beijing 100034, China. 2. Department of Cardiology, Kailuan General Hospital Affiliated to North China University of science and technology, Tangshan 063000, China. 3. Department of Health Care Center, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, 063000, China. 4. Department of Endocrinology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, 063000, China. 5. Department of Laboratory, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, 063000, China. 6. Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI, United States. 7. Renal Division, Department of Medicine, Peking University First Hospital, Institute of Nephrology, Peking University, Key Laboratory of Renal Disease, National Health and Family Planning Commission of the People's Republic of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Ministry of Education, Beijing 100034, China; Peking-Tsinghua Center for Life Sciences, Beijing, China. 8. Renal Division, Department of Medicine, Peking University First Hospital, Institute of Nephrology, Peking University, Key Laboratory of Renal Disease, National Health and Family Planning Commission of the People's Republic of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Ministry of Education, Beijing 100034, China; Peking University, Center for Data Science in Health and Medicine, Beijing, China. Electronic address: zhanglx@bjmu.edu.cn.
Abstract
OBJECTIVE: To assess the joint association of body mass index (BMI) and central obesity with cardiovascular events and all-cause mortality in prediabetic population. METHODS: Altogether 18,703 participants with prediabetes completed follow-up between June 2006 and December 2015 were included in the analyses. Prediabetes was defined as fasting plasma glucose level 5.6-6.9mmol/L, and without history of diabetes or currently use of hypoglycemic agents. Participants were classified according to the baseline status of BMI as well as the absence/presence of central obesity. We examined these associations in men and women separately. RESULTS: The mean age was 51.5±11.1 years, and 85.6% (N=16,002) were male. During a median follow up of 9.0 (interquartile range 8.7-9.2) years, 848 and 88 major CV events occurred in men and women, respectively. Besides, 1111 men and 89 women died. Compared with men of BMI 22-23.9kg/m2 and without central obesity, the risk of CV events was increased among men with higher BMI and central obesity (HR 1.32 (95% CI: 1.05-1.67) for BMI 24-27.9kg/m2 and 1.31 (1.03-1.66) for BMI≥28kg/m2, respectively); and the risk of all-cause mortality was the lowest among men of BMI 24-27.9kg/m2 but without central obesity (0.75, 0.61-0.92). We found no such association in women. CONCLUSION: Among men with prediabetes, both BMI and waist circumference should be included when evaluating the risks of major CV events and mortality. Measurement of adiposity constitutes a simple and cost-effective strategy to identify those at high-risk population in prediabetes.
OBJECTIVE: To assess the joint association of body mass index (BMI) and central obesity with cardiovascular events and all-cause mortality in prediabetic population. METHODS: Altogether 18,703 participants with prediabetes completed follow-up between June 2006 and December 2015 were included in the analyses. Prediabetes was defined as fasting plasma glucose level 5.6-6.9mmol/L, and without history of diabetes or currently use of hypoglycemic agents. Participants were classified according to the baseline status of BMI as well as the absence/presence of central obesity. We examined these associations in men and women separately. RESULTS: The mean age was 51.5±11.1 years, and 85.6% (N=16,002) were male. During a median follow up of 9.0 (interquartile range 8.7-9.2) years, 848 and 88 major CV events occurred in men and women, respectively. Besides, 1111 men and 89 women died. Compared with men of BMI 22-23.9kg/m2 and without central obesity, the risk of CV events was increased among men with higher BMI and central obesity (HR 1.32 (95% CI: 1.05-1.67) for BMI 24-27.9kg/m2 and 1.31 (1.03-1.66) for BMI≥28kg/m2, respectively); and the risk of all-cause mortality was the lowest among men of BMI 24-27.9kg/m2 but without central obesity (0.75, 0.61-0.92). We found no such association in women. CONCLUSION: Among men with prediabetes, both BMI and waist circumference should be included when evaluating the risks of major CV events and mortality. Measurement of adiposity constitutes a simple and cost-effective strategy to identify those at high-risk population in prediabetes.