Comparisons of Metabolic Measures to Predict T1D vs Detect a Preventive Treatment Effect in High-Risk Individuals
Authors' conclusion
The capacity of a metabolic endpoint to detect a treatment effect does not necessarily correspond to its accuracy for predicting T1D. However, combined glucose and C-peptide endpoints appear to be effective for both predicting diabetes and detecting a response to immunotherapy. These findings suggest that combined glucose and C-peptide endpoints should be incorporated into the design of future T1D prevention trials.
Study context
- Population
- Participants in the teplizumab prevention trial (14-day IV teplizumab vs placebo) and Pathway to Prevention participants selected with the same inclusion criteria
- Comparison
- Glucose-based vs C-peptide-based vs combined glucose and C-peptide metabolic endpoints (6-month changes)
- Primary endpoint
- Detection of treatment effect (t-values, placebo vs teplizumab) and prediction of T1D (chi-square, proportional hazards regression)
- Follow-up
- 6-month changes
Methodological detailsmulticenter
- Multicenter
Primary result
As reported in the abstract
6 months
- Glucose-based endpoints predicted diabetes better than C-peptide-based endpoints
- C-peptide-based endpoints were better at detecting a teplizumab effect
- Combined glucose and C-peptide measures were more balanced than glucose alone or C-peptide alone for predicting diabetes and detecting a teplizumab effect
Original publication
Comparisons of Metabolic Measures to Predict T1D vs Detect a Preventive Treatment Effect in High-Risk Individuals. J Clin Endocrinol Metab. Published 24 January 2024. doi:10.
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