Poverty and InequalitySexual and Reproductive HealthFamily, Maternal & Child HealthMethodology

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

TitleRisk score model of type 2 diabetes prediction for rural Chinese adults: the Rural Deqing Cohort Study
Publication TypeJournal Article
Year of Publication2017
AuthorsChen, X, Wu, Z, Chen, Y, Wang, X, Zhu, J, Wang, N, Jiang, Q, Fu, C
JournalJournal of Endocrinological Investigation
Type of ArticleArticle
ISBN Number03914097 (ISSN)
Keywordscohort study, Risk score, Rural China, type 2 diabetes

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).