BACKGROUND: This study is aimed at developing and evaluating a diabetes risk score (DRS) to predict incident diabetes and screen for undiagnosed diabetes and abnormal glucose tolerance in the Chinese population. METHODS: Three DRS instruments were respectively developed and validated based on the data collected from a 10-year longitudinal health checkup-based population of 1,851 individuals without diabetes at baseline. The efficiency on glucose abnormality screening was evaluated based on the testing of a cross-sectional sample of 699 individuals without known diabetes. RESULTS: The DRS consisting of age, hypertension, history of high blood glucose, body mass index, fasting plasma glucose, serum triglycerides, and serum high-density lipoprotein-cholesterol had the best prediction properties (area under curve [AUC] = 0.734 [95% confidence interval 0.702-0.766] and 0.759 [0.686-0.831] in exploratory and validation cohorts, respectively). The DRS had a sensitivity of 64.5% and 72.9%, respectively, and a specificity of 71.6% and 63.9%, respectively, with an optimal cutoff of 4. AUCs were 0.828 (0.797-0.860) and 0.909 (0.884-0.933) for detecting abnormal glucose tolerance and diabetes, respectively, through cross-sectional screening. Performance of the oral glucose tolerance test (OGTT) in selected subjects with DRS ≥ 4 led to the identification of 76.2% cases of abnormal glucose tolerance and 100% cases of diabetes, while avoiding an OGTT in 52.8% of the study group. CONCLUSIONS: The DRS instrument including age, hypertension, history of high blood glucose, body mass index, fasting plasma glucose, triglycerides, and high-density lipoprotein-cholesterol is practical and effective in predicting incident diabetes and screening glucose abnormality in the Chinese population.
BACKGROUND: This study is aimed at developing and evaluating a diabetes risk score (DRS) to predict incident diabetes and screen for undiagnosed diabetes and abnormal glucose tolerance in the Chinese population. METHODS: Three DRS instruments were respectively developed and validated based on the data collected from a 10-year longitudinal health checkup-based population of 1,851 individuals without diabetes at baseline. The efficiency on glucose abnormality screening was evaluated based on the testing of a cross-sectional sample of 699 individuals without known diabetes. RESULTS: The DRS consisting of age, hypertension, history of high blood glucose, body mass index, fasting plasma glucose, serum triglycerides, and serum high-density lipoprotein-cholesterol had the best prediction properties (area under curve [AUC] = 0.734 [95% confidence interval 0.702-0.766] and 0.759 [0.686-0.831] in exploratory and validation cohorts, respectively). The DRS had a sensitivity of 64.5% and 72.9%, respectively, and a specificity of 71.6% and 63.9%, respectively, with an optimal cutoff of 4. AUCs were 0.828 (0.797-0.860) and 0.909 (0.884-0.933) for detecting abnormal glucose tolerance and diabetes, respectively, through cross-sectional screening. Performance of the oral glucose tolerance test (OGTT) in selected subjects with DRS ≥ 4 led to the identification of 76.2% cases of abnormal glucose tolerance and 100% cases of diabetes, while avoiding an OGTT in 52.8% of the study group. CONCLUSIONS: The DRS instrument including age, hypertension, history of high blood glucose, body mass index, fasting plasma glucose, triglycerides, and high-density lipoprotein-cholesterol is practical and effective in predicting incident diabetes and screening glucose abnormality in the Chinese population.
Authors: Katya L Masconi; Tandi E Matsha; Justin B Echouffo-Tcheugui; Rajiv T Erasmus; Andre P Kengne Journal: EPMA J Date: 2015-03-11 Impact factor: 6.543
Authors: Le Zheng; Yue Wang; Shiying Hao; Andrew Y Shin; Bo Jin; Anh D Ngo; Medina S Jackson-Browne; Daniel J Feller; Tianyun Fu; Karena Zhang; Xin Zhou; Chunqing Zhu; Dorothy Dai; Yunxian Yu; Gang Zheng; Yu-Ming Li; Doff B McElhinney; Devore S Culver; Shaun T Alfreds; Frank Stearns; Karl G Sylvester; Eric Widen; Xuefeng Bruce Ling Journal: JMIR Med Inform Date: 2016-11-11