A non-invasive risk score for predicting incident diabetes among rural Chinese people: A village-based cohort study.

Abstract

ObjectiveTo develop a new non-invasive risk score for predicting incident diabetes in a rural Chinese population.MethodsData from the Handan Eye Study conducted from 2006–2013 were utilized as part of this analysis. The present study utilized data generated from 4132 participants who were ≥30 years of age. A non-invasive risk model was derived using two-thirds of the sample cohort (selected randomly) using stepwise logistic regression. The model was subsequently validated using data from individuals from the final third of the sample cohort. In addition, a simple point system for incident diabetes was generated according to the procedures described in the Framingham Study. Incident diabetes was defined as follows: (1) fasting plasma glucose (FPG) ≥ 7.0 mmol/L; or (2) hemoglobin A1c (HbA1c) ≥ 6.5%; or (3) self-reported diagnosis of diabetes or use of anti-diabetic medications during the follow-up period.ResultsThe simple non-invasive risk score included age (8 points), Body mass index (BMI) (3 points), waist circumference (WC) (7 points), and family history of diabetes (9 points). The score ranged from 0 to 27 and the area under the receiver operating curve (AUC) of the score was 0.686 in the validation sample. At the optimal cutoff value (which was 9), the sensitivity and specificity were 74.32% and 58.82%, respectively.ConclusionsUsing information based upon age, BMI, WC, and family history of diabetes, we developed a simple new non-invasive risk score for predicting diabetes onset in a rural Chinese population, using information from individuals aged 30 years of age and older. The new risk score proved to be more optimal in the prediction of incident diabetes than most of the existing risk scores developed in Western and Asian countries. This score system will aid in the identification of individuals who are at risk of developing incident diabetes in rural China.