Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles

Objective. To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes

Research Design and Methods: Type 1 Diabetes Intelligence (T1DI) study combined data from 1845 genetically susceptible, prospectively followed children who were positive for at least one islet autoantibody against insulin, glutamic acid decarboxylase or insulinoma antigen-2 (IAA, GADA, IA-2A). Using a novel similarity algorithm that considers individual’s temporal autoantibody profile, age at autoantibody appearance, and variation in positivity of autoantibody types, we performed unsupervised hierarchical clustering analysis. Progression rates to diabetes were analyzed via survival analysis.

Results: We identified five main clusters of individuals with distinct autoantibody profiles characterized by seroconversion age and sequence of appearance of the three autoantibodies. The highest 5-year risk from first positive autoantibody to type 1 diabetes (69.9%; 95% CI 60.0-79.2) was observed in children who first develop IAA in early life (median age 1.6yr) followed by GADA (1.9yr) and then IA-2A (2.1yr). Their 10-year risk was 89.9% (95% CI 81.9-95.4). High 5-year risk was also found in children with persistent IAA and GADA (39.1%), or children with persistent GADA and IA-2A (30.9%). Lower 5-year risk (10.5%) was observed in children with late appearance of persistent GADA (6.1yr). The lowest 5-year diabetes risk (1.6%) was associated with positivity for single, often reverting autoantibody.

Conclusions: The novel clustering algorithm identified children with distinct islet autoantibody profiles and progression rates to diabetes. These results are useful for prediction, selection of individuals for prevention trials, and studies investigating various pathways to type 1 diabetes.