Comparisons of Metabolic Measures to Predict T1D vs. Detect a Preventive Treatment Effect in High-Risk Individuals.

Abstract

Metabolic measures are frequently used to predict T1D and to understand effects of disease-modifying therapies. Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D. Six-month changes in metabolic endpoints were assessed for: 1) detecting treatment effects by comparing placebo and treatment arms from the randomized controlled teplizumab prevention trial and 2) predicting T1D in the TrialNet Pathway to Prevention natural history study. Multicenter clinical trial network. 14-day intravenous teplizumab infusion. T-values from t tests for detecting a treatment effect were compared to Chi-square values from proportional hazards regression for predicting T1D for each metabolic measure. Participants in the teplizumab prevention trial and participants in the Pathway to Prevention study selected with the same inclusion criteria used for the teplizumab trial were studied. Six-month changes in glucose-based endpoints predicted diabetes better than C-peptide-based endpoints, yet the latter were better at detecting a teplizumab effect. Combined measures of glucose and C-peptide were more balanced than measures of glucose alone or C-peptide alone for predicting diabetes and detecting a teplizumab effect. 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.