X Chen1, Z Wu1, Y Chen2, X Wang3, J Zhu3, N Wang1, Q Jiang1, C Fu4. 1. School of Public Health, Key Laboratory of Public Health Safety and Pudong Institute of Preventive Medicine, Fudan University, Shanghai, 200032, China. 2. School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada. 3. Deqing County Center of Disease Prevention and Control, Huzhou, Zhejiang, China. 4. School of Public Health, Key Laboratory of Public Health Safety and Pudong Institute of Preventive Medicine, Fudan University, Shanghai, 200032, China. fcw@fudan.edu.cn.
Abstract
OBJECTIVE: Risk score (RS) model is a cost-effective tool to identify adults who are at high risk for diabetes. This study was to develop an RS model of type 2 diabetes (T2DM) prediction specifically for rural Chinese adults. METHODS: A prospective whole cohort study (n = 28,251) and a sub-cohort study (n = 3043) were conducted from 2006-2014 and 2006-2008 to 2015 in rural Deqing, China. All participants were free of T2DM at baseline. Incident T2DM cases were identified through electronic health records, self-reported and fasting plasma glucose testing for the sub-cohort, respectively. RS models were constructed with coefficients (β) of Cox regression. Receiver-operating characteristic curves were plotted and the area under the curve (AUC) reflected the discriminating accuracy of an RS model. RESULTS: By 2015, the incidence of T2DM was 3.3 and 7.7 per 1000 person-years in the whole cohort and the sub-cohort, respectively. Based on data from the whole cohort, the non-invasive RS model included age (4 points), overweight (2 points), obesity (4 points), family history of T2DM (3 points), meat diet (3 points), and hypertension (2 points). The plus-fasting plasma glucose (FPG) model added impaired fasting glucose (4 points). The AUC was 0.705 with a positive predictive value of 2.5% for the non-invasive model, and for the plus-FPG model the AUC was 0.754 with a positive predictive value of 2.5%. These models performed better as compared with 12 existing RS models for the study population. CONCLUSIONS: Our non-invasive RS model can be used to identify individuals who are at high risk of T2DM as a simple, fast, and cost-effective tool for rural Chinese adults.
OBJECTIVE: Risk score (RS) model is a cost-effective tool to identify adults who are at high risk for diabetes. This study was to develop an RS model of type 2 diabetes (T2DM) prediction specifically for rural Chinese adults. METHODS: A prospective whole cohort study (n = 28,251) and a sub-cohort study (n = 3043) were conducted from 2006-2014 and 2006-2008 to 2015 in rural Deqing, China. All participants were free of T2DM at baseline. Incident T2DM cases were identified through electronic health records, self-reported and fasting plasma glucose testing for the sub-cohort, respectively. RS models were constructed with coefficients (β) of Cox regression. Receiver-operating characteristic curves were plotted and the area under the curve (AUC) reflected the discriminating accuracy of an RS model. RESULTS: By 2015, the incidence of T2DM was 3.3 and 7.7 per 1000 person-years in the whole cohort and the sub-cohort, respectively. Based on data from the whole cohort, the non-invasive RS model included age (4 points), overweight (2 points), obesity (4 points), family history of T2DM (3 points), meat diet (3 points), and hypertension (2 points). The plus-fasting plasma glucose (FPG) model added impaired fasting glucose (4 points). The AUC was 0.705 with a positive predictive value of 2.5% for the non-invasive model, and for the plus-FPG model the AUC was 0.754 with a positive predictive value of 2.5%. These models performed better as compared with 12 existing RS models for the study population. CONCLUSIONS: Our non-invasive RS model can be used to identify individuals who are at high risk of T2DM as a simple, fast, and cost-effective tool for rural Chinese adults.
Entities:
Keywords:
Cohort study; Risk score; Rural China; Type 2 diabetes
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