Risk models for predicting diabetes and prediabetes in the first-degree Relatives (FDRs) of patients with type2 diabetic and its comparison with the Finnish Diabetes Risk Score (FINDRIC)

Abstract

Introduction: The prevalence of diabetes in Iranian adults is 9.5% with high morbidity and mortality. Here, 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. Methods: A total of n = 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, HbA1c, area under oral glucose tolerance curve (OGTT-AUC) and the lipid profile were included. The best prediabetes risk model included HbA1c, OGTT-AUC, systolic blood pressure, and the lipid profile. The predictive efficiencies of models were higher than FPG and HbA1c; however, the predictive efficiency of multivariable models were comparable to OGTT-AUC alone. Additionally, diabetes models and OGTT-AUC showed better performance than FINDRISC.Discussion: 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.