Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles
Authors' conclusion
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.
Study context
- Population
- Genetically susceptible prospectively observed children positive for ≥1 islet autoantibody (IAA, GAD antibody (GADA) or IA-2A), T1DI study
- Participants
- 1,845 participants
- Exposure
- Five autoantibody-profile clusters
- Outcome
- Risk of progression from first positive autoantibody to T1D
- Follow-up
- 5 years
Methodological detailsunsupervised hierarchical clustering, survival analysis
- Unsupervised hierarchical clustering
- Survival analysis
Main finding
As reported in the abstract
Risk from first positive autoantibody to T1D
Observational data: associations, not effects.
Further findings
| Outcome | Values |
|---|---|
| Risk of T1D · 10 years | Early IAA then GADA then IA-2A 89.9% (95% CI 81.9–95.4) |
Original publication
Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles. Diabetes Care. Published 11 June 2024. doi:10.
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