211-OR: Analysis of Longitudinal Autoantibody Profiles and the Progression Rates to Type 1 Diabetes

Clinical type 1 diabetes (T1D) is preceded by asymptomatic islet-autoimmunity (IA) with presence of islet autoantibodies (AAb). However, both AAb profiles (clusters) during IA phase and progression rate to T1D are heterogeneous. It is unclear if AAb profiles affect the progression to T1D similarly in different countries. We analyzed data from 3 prospective birth cohorts, DAISY (U.S.), DiPiS (Sweden), and DIPP (Finland), to address this question. We developed a similarity algorithm that considers subject-level temporal IA profiles (based on 4 AAbs - IAA, GADA, IA2A, and ZnT8A positivity or negativity), age at which AAb developed, and imbalance in AAb positivity by weighting positive AAb matches higher than negative ones. Using this subject-level similarity measure, we performed hierarchical clustering of AAb positive subjects having at least 3 visits. We used a log-rank test to identify the number of clusters in 1125 DIPP subjects (329 T1D cases), 309 DAISY subjects (70 cases), and 226 DiPiS subjects (33 cases). Each dataset was individually clustered. We identified three clusters in each dataset associated with different rate of progression from seroconversion to T1D - slow progressors (SP), moderate progressors (MP) and fast progressors (FP). These were distributed as follows: DIPP (654 SP with 3% 10-year T1D risk, 356 MP with 60% risk, 115 FP with 96% risk); DAISY (215 SP with 6% 10-year risk, 92 MP with 62% risk, 2 FP with 100% risk); and DiPiS (160 SP with 3% 10-year risk, 62 MP with 58% risk, 4 FP with 100% risk). Finally, we tested the generalizability of the clusters by 1) learning a clustering from one data set “learned clusters,” 2) classifying subjects from another (independent) data set with the “learned clusters,” and 3) measuring how often the subjects were assigned to the same cluster (SP, MP, FP) as the “internal” clustering. The average accuracy of cluster transfer was high (mean = 87.9%, sd = 1.1%) suggesting that the IA profile predicts progression rate to T1D independently of the population.