Yuxin Fan1,2, Ruodan Wang1, Li Ding1, Zhaowei Meng3, Qing Zhang4, Yun Shen2,5, Gang Hu2, Ming Liu1. 1. Department of Endocrinology and Metabolism, Tianjin Medical University General Hospital, Tianjin, China. 2. Pennington Biomedical Research Center, Baton Rouge, LA, USA. 3. Department of Nuclear Medicine, Tianjin Medical University General Hospital, Tianjin, China. 4. Department of Health Management, Tianjin Medical University General Hospital, Tianjin, China. 5. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Six People's Hospital, Shanghai, China.
Abstract
BACKGROUND: The associations of different adiposity indicators and short-term adiposity change with diabetes risk are not fully elucidated. OBJECTIVE: We aimed to assess the independent and joint effects of different baseline adiposity indicators and short-term body adiposity change on the risk of type 2 diabetes. METHODS: We prospectively followed 10,419 Chinese adults aged 20-80 y in 2008-2012. Incident diabetes was diagnosed based on fasting glucose, 2-h glucose, or glycated hemoglobin (HbA1c) after an oral glucose tolerance test using the American Diabetes Association standard. Cox proportional hazard regression models were used to assess the associations of adiposity indicators and adiposity change with diabetes risk. RESULTS: During a mean follow-up of 2.8 y, we identified 805 type 2 diabetes cases. Baseline BMI, waist circumference, and waist-height ratio (WHtR) were all positively associated with diabetes risk. The area under the curve was significantly greater for waist circumference (0.624) and WHtR (0.627) than for BMI (0.608) (P <0.05). Compared with subjects with stable adiposity levels (±2 kg or ± 3 cm in changes in body weight or waist circumference) from baseline to Year 1, those subjects with the most weight gain or the most waist circumference gain had a 1.53-fold or 1.37-fold greater risk of diabetes; those with the most weight loss had a 46% lower risk of diabetes. Furthermore, regardless of baseline weight status, weight or waist circumference change in the first year was associated with diabetes risk. CONCLUSION: Abdominal adiposity indicators, waist circumference and its change, are more strongly associated with the risk of type 2 diabetes than general adiposity indicators, BMI, and changes in body weight among Chinese adults.
BACKGROUND: The associations of different adiposity indicators and short-term adiposity change with diabetes risk are not fully elucidated. OBJECTIVE: We aimed to assess the independent and joint effects of different baseline adiposity indicators and short-term body adiposity change on the risk of type 2 diabetes. METHODS: We prospectively followed 10,419 Chinese adults aged 20-80 y in 2008-2012. Incident diabetes was diagnosed based on fasting glucose, 2-h glucose, or glycated hemoglobin (HbA1c) after an oral glucose tolerance test using the American Diabetes Association standard. Cox proportional hazard regression models were used to assess the associations of adiposity indicators and adiposity change with diabetes risk. RESULTS: During a mean follow-up of 2.8 y, we identified 805 type 2 diabetes cases. Baseline BMI, waist circumference, and waist-height ratio (WHtR) were all positively associated with diabetes risk. The area under the curve was significantly greater for waist circumference (0.624) and WHtR (0.627) than for BMI (0.608) (P <0.05). Compared with subjects with stable adiposity levels (±2 kg or ± 3 cm in changes in body weight or waist circumference) from baseline to Year 1, those subjects with the most weight gain or the most waist circumference gain had a 1.53-fold or 1.37-fold greater risk of diabetes; those with the most weight loss had a 46% lower risk of diabetes. Furthermore, regardless of baseline weight status, weight or waist circumference change in the first year was associated with diabetes risk. CONCLUSION: Abdominal adiposity indicators, waist circumference and its change, are more strongly associated with the risk of type 2 diabetes than general adiposity indicators, BMI, and changes in body weight among Chinese adults.
Authors: Jing Yi Xiao; Wei Sen Zhang; Chao Qiang Jiang; Ya Li Jin; Feng Zhu; Kar Keung Cheng; Tai Hing Lam; Lin Xu Journal: BMC Public Health Date: 2022-01-10 Impact factor: 3.295
Authors: Caleigh M Sawicki; Alice H Lichtenstein; Gail T Rogers; Paul F Jacques; Jiantao Ma; Edward Saltzman; Nicola M McKeown Journal: Nutrients Date: 2021-03-19 Impact factor: 5.717