New risk models for predicting diabetes and prediabetes in the first-degree relatives of patients with type 2 diabetes and a comparison with the FINDRIC

Abstract Background We aimed to develop a risk model, monitoring the FDRs of patients with type 2 diabetes, who have normal glucose tolerance, to predict the onset of developing diabetes and prediabetes. In this study, 1765 FDRs of patients with type 2 diabetes mellitus, who had normal glucose tolerance, were subjected to statistical analysis. Diabetes risk factors including anthropometric indices, physical activity, fast plasma glucose, plasma glucose concentrations two-hour after oral glucose administration, glycosylated hemoglobin, blood pressure, and lipid profile at the baseline were considered as independent variables. Kaplan-Meier, log Rank test, univariate, and multivariable proportional hazard Cox regression were conducted. The optimal cut point for risk score was created according to receiver operating characteristic curve (ROC) analysis. Results The best diabetes predictability was achieved by a model in which waist to hip ratio (WHR), HbA1c, OGTT and the lipid profile were included. The best prediabetes risk model included HbA1c, systolic blood pressure, the lipid profile, and the oral glucose tolerance test (OGTT). These multivariable risk models were compared with FPG, HbA1c, and OGTT. The predictive efficiencies of models were higher than FPG and HbA1c; however the best predictive model of the current study showed comparable predictive efficiency to OGTT-AUC. Additionally, both diabetes models showed better performance than FINDRISC. Conclusion We recommend regular tests for FDRs of patients with type 2 diabetes to predict the risk of diabetes and prediabetes by using the OGTT-AUC. As a health check assessment tool, our diabetes models showed a more precise predictor compared to FINDRISC in our population.