Xu Han1, Jing Wang1, Yaru Li1, Hua Hu1, Xiulou Li2, Jing Yuan1, Ping Yao1, Xiaoping Miao1, Sheng Wei1, Youjie Wang1, Yuan Liang1, Xiaomin Zhang1, Huan Guo1, An Pan1, Handong Yang2, Tangchun Wu1, Meian He3. 1. Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China. 2. Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China. 3. Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China. hemeian@hotmail.com.
Abstract
AIMS: The aim of this study was to develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and older Chinese population. METHODS: This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort. Participants were recruited in 2008 and were followed until October 2013. Incident diabetes was defined as self-reported clinician diagnosed diabetes, fasting glucose ≥7.0 mmol/l, or the use of insulin or oral hypoglycemic agent. A total of 1390 incident diabetic cases were diagnosed during the follow-up period. β-Coefficients were derived from Cox proportional hazard regression model and were used to calculate the risk score. RESULTS: The diabetes risk score includes BMI, fasting glucose, hypertension, hyperlipidemia, current smoking status, and family history of diabetes. The β-coefficients of these variables ranged from 0.139 to 1.914, and the optimal cutoff value was 1.5. The diabetes risk score was calculated by multiplying the β-coefficients of the significant variables by 10 and rounding to the nearest integer. The score ranges from 0 to 36. The area under the receiver operating curve of the score was 0.751. At the optimal cutoff value of 15, the sensitivity and specificity were 65.6 and 72.9%, respectively. Based upon these risk factors, this model had the highest discrimination compared with several commonly used diabetes prediction models. CONCLUSIONS: The newly established diabetes risk score with six parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and older Chinese population.
AIMS: The aim of this study was to develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and older Chinese population. METHODS: This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort. Participants were recruited in 2008 and were followed until October 2013. Incident diabetes was defined as self-reported clinician diagnosed diabetes, fasting glucose ≥7.0 mmol/l, or the use of insulin or oral hypoglycemic agent. A total of 1390 incident diabetic cases were diagnosed during the follow-up period. β-Coefficients were derived from Cox proportional hazard regression model and were used to calculate the risk score. RESULTS: The diabetes risk score includes BMI, fasting glucose, hypertension, hyperlipidemia, current smoking status, and family history of diabetes. The β-coefficients of these variables ranged from 0.139 to 1.914, and the optimal cutoff value was 1.5. The diabetes risk score was calculated by multiplying the β-coefficients of the significant variables by 10 and rounding to the nearest integer. The score ranges from 0 to 36. The area under the receiver operating curve of the score was 0.751. At the optimal cutoff value of 15, the sensitivity and specificity were 65.6 and 72.9%, respectively. Based upon these risk factors, this model had the highest discrimination compared with several commonly used diabetes prediction models. CONCLUSIONS: The newly established diabetes risk score with six parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and older Chinese population.
Authors: Weinan Dong; Will Ho Gi Cheng; Emily Tsui Yee Tse; Yuqi Mi; Carlos King Ho Wong; Eric Ho Man Tang; Esther Yee Tak Yu; Weng Yee Chin; Laura Elizabeth Bedford; Welchie Wai Kit Ko; David Vai Kiong Chao; Kathryn Choon Beng Tan; Cindy Lo Kuen Lam Journal: BMJ Open Date: 2022-05-24 Impact factor: 3.006