Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young

Background: Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects. Methods: The 10-factor weighted risk model (HLA, PTPN22, INS, IL2RA, ERBB3, ORMDL3, BACH2, IL27, GLIS3, RNLS), 3-factor model (HLA, PTPN22, INS), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941). Results: Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P =.00006; FDR: P =.0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P =.03; FDR, P =.01). In GP, the restricted 3-factor model was superior to HLA (P =.03), but not different from the 10-factor model (P =.22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P =.12) and performed worse than the 10-factor model (P =.02). Conclusions: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups.