Objectives:
On November 17th, 2022, teplizumab became the first disease-modifying therapy approved by the US Food and Drug Administration to delay the onset of type 1 diabetes (T1D), representing an important milestone for patients. However, this study took over eight years to complete, highlighting a need to improve the ability to identify individuals likely to progress to T1D during a clinical trial of shorter duration.
Methods:
Complete patient-level data was aggregated from two clinical trials and three observational studies that enrolled participants at-risk for T1D to enable development of an accelerated failure time (AFT) model for predicting T1D diagnosis from start of follow-up. Data was supplied from the NIDDK CR, a program of the National Institute of Diabetes and Digestive and Kidney Diseases (TEDDY, TN01, TN10), Institute for Diabetes Research (BABYDIET) and University of Colorado (DAISY) studies. Several parametric distributions were tested, and the most appropriate distribution was selected based on the Akaike information criterion (AIC) and visual inspection of standard diagnostic plots. A univariate AFT model analysis based on p-value (p < 0.05) followed by a multivariate analysis based on reduction in AIC was performed to assess the statistical significance of the covariates. Covariates tested included baseline age, sex, first degree relative, high-risk human leukocyte antigen (HLA) genotypes, and the time-varying presence of islet autoantibodies (GADA, IAA, IA2, and ZnT8).
Results:
There were 9598 individuals with complete covariate information and a median follow-up time of 3.9 years (maximum follow-up time: 29.5 years). The Gompertz distribution was found to be the most appropriate distribution. Time-varying islet autoantibodies, high-risk HLA genotypes and first-degree relative greatly improved the model as demonstrated by a further reduction in AIC.
Conclusions:
This model provides additional evidence for islet AAs as an enrichment biomarker for identifying individuals at risk of developing T1D. Future analyses will include the incorporation of longitudinal glycemic measures as a predictor for T1D diagnosis to enable patient enrichment in T1D prevention studies.

**Objectives:**
On November 17th, 2022, teplizumab became the first disease-modifying therapy approved by the US Food and Drug Administration to delay the onset of type 1 diabetes (T1D), representing an important milestone for patients. However, this study took over eight years to complete, highlighting a need to improve the ability to identify individuals likely to progress to T1D during a clinical trial of shorter duration.
**Methods:**
Complete patient-level data was aggregated from two clinical trials and three observational studies that enrolled participants at-risk for T1D to enable development of an accelerated failure time (AFT) model for predicting T1D diagnosis from start of follow-up. Data was supplied from the NIDDK CR, a program of the National Institute of Diabetes and Digestive and Kidney Diseases (TEDDY, TN01, TN10), Institute for Diabetes Research (BABYDIET) and University of Colorado (DAISY) studies. Several parametric distributions were tested, and the most appropriate distribution was selected based on the Akaike information criterion (AIC) and visual inspection of standard diagnostic plots. A univariate AFT model analysis based on p-value (p < 0.05) followed by a multivariate analysis based on reduction in AIC was performed to assess the statistical significance of the covariates. Covariates tested included baseline age, sex, first degree relative, high-risk human leukocyte antigen (HLA) genotypes, and the time-varying presence of islet autoantibodies (GADA, IAA, IA2, and ZnT8).
**Results:**
There were 9598 individuals with complete covariate information and a median follow-up time of 3.9 years (maximum follow-up time: 29.5 years). The Gompertz distribution was found to be the most appropriate distribution. Time-varying islet autoantibodies, high-risk HLA genotypes and first-degree relative greatly improved the model as demonstrated by a further reduction in AIC.
**Conclusions:**
This model provides additional evidence for islet AAs as an enrichment biomarker for identifying individuals at risk of developing T1D. Future analyses will include the incorporation of longitudinal glycemic measures as a predictor for T1D diagnosis to enable patient enrichment in T1D prevention studies.

Predicting Onset of Type 1 Diabetes Using Time-Varying Islet Autoantibodies: A Pooled Analysis of 9598 High-Risk Individuals

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