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Risk score model of type 2 diabetes prediction for rural Chinese adults: the Rural Deqing Cohort Study

Chen, X.; Wu, Z.; Chen, Y.; Wang, X.; Zhu, J.; Wang, N.; Jiang, Q.; Fu, C.

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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. © 2017, Italian Society of Endocrinology (SIE).

Automatic Tags

Cohort study; Risk score; Rural China

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