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Predicting Islet Cell Autoimmunity and Type 1 Diabetes: An 8-Year TEDDY Study Progress Report
Jeffrey P Krischer1, Xiang Liu2, Kendra Vehik2
1Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL jeffrey.krischer@epi.usf.edu.
Diabetes Care
|April 11, 2019
Summary
The Environmental Determinants of Diabetes in the Young (TEDDY) study assessed risk factors for islet autoimmunity (IA) and type 1 diabetes (T1D). While individual factors had limited predictive power, their combination significantly improved the prediction of IA and T1D in children.
Area of Science:
- Pediatric Endocrinology
- Immunology
- Genetics
- Epidemiology
Background:
- Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of insulin-producing beta cells.
- Identifying early risk factors for T1D development is crucial for prevention and intervention strategies.
- The Environmental Determinants of Diabetes in the Young (TEDDY) study has identified several potential risk factors.
Purpose of the Study:
- To evaluate the predictive capability of TEDDY-identified risk factors for islet autoimmunity (IA) and T1D.
- To assess the ability of these factors to predict the type of autoantibody appearing first.
- To determine the collective and individual predictive power of these risk factors.
Main Methods:
- Longitudinal study of 7,777 children from birth to a median of 9.1 years.
- Monitoring for the development of islet autoantibodies and progression to T1D.
- Application of time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves for predictive modeling.
Main Results:
- HLA genotype (DR3/4) was the strongest predictor for IA, while PTPN22 polymorphism rs2476601 predicted first insulin autoantibodies (IAA).
- One-year weight was the best predictor for first GAD autoantibodies (GADA).
- Multivariate models showed substantial predictive ability: AUCs ranged from 0.678 to 0.707 for IA and antibody subtypes, and 0.706 for T1D prediction.
Conclusions:
- Individual TEDDY risk factors have limited predictive power for IA and T1D.
- Combining multiple risk factors significantly enhances the prediction of IA and T1D.
- Prediction modeling statistics are valuable for assessing risk in a time-until-event framework.
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