Type 2 Diabetes Genetic Risk and Type 1 Diabetes Heterogeneity and Progression

Abstract

Insulin secretion varies widely in preclinical type 1 diabetes. To understand the pathogenesis of this metabolic heterogeneity, we asked whether genetic predisposition to type 2 diabetes, quantified by a type 2 diabetes genetic risk score (T2D-GRS), modulates β-cell function and disease progression in individuals at risk of type 1 diabetes. We analyzed 4,324 islet autoantibody–positive TrialNet Pathway to Prevention participants with genome-wide genotyping and oral glucose tolerance testing. Both T2D-GRS and the type 1 diabetes genetic risk score 2 (T1D-GRS2) differed significantly across five previously described groups defined by C-peptide area under the curve (AUC; a measure of insulin secretion). The highest C-peptide AUC group, compared with the lowest, had significantly higher T2D-GRS, lower T1D-GRS2, higher BMI z-score, greater insulin resistance, older age, and lower prevalence of male participants; multiple islet autoantibody positivity; and IA-2 or insulin autoantibody positivity. Progression to clinical (stage 3) type 1 diabetes was significantly associated with T1D-GRS2 across all groups and with T2D-GRS in all but the lowest C-peptide AUC group. In conclusion, type 2 diabetes genetic burden shapes metabolic heterogeneity and accelerates progression in preclinical type 1 diabetes. These results support the evaluation of type 2 diabetes–related mechanisms as targets to improve the prediction and prevention of type 1 diabetes. Article highlights: Heterogeneity in β-cell function is a barrier to precision medicine in type 1 diabetes. We asked whether type 2 diabetes-associated genes influence insulin secretion and progression to clinical type 1 diabetes in autoantibody-positive individuals. A type 2 diabetes genetic risk score was associated with higher C-peptide area under the curve (AUC) and increased clinical type 1 diabetes risk in all but the lowest C-peptide AUC subgroup. Addressing type 2 diabetes mechanisms could improve type 1 diabetes prediction and prevention.