A Novel Approach for Assessing Outcomes of Type 1 Diabetes Prevention Trials Over a Fixed Time Interval

We evaluated whether a binary metabolic endpoint for change (∆) from baseline to 1-year post-randomization could be useful in type 1 diabetes (T1D) prevention trials. Using 2hr-OGTT data from the abatacept prevention trial and similar participants in the observational TrialNet Pathway to Prevention (PTP) Study we assessed ∆metabolic measures, plotted glucose and C-peptide response curves, and categorized vectors for ∆ from baseline to 1 year as metabolic treatment failure vs. success. Analyses were validated using the teplizumab prevention study. PTP participants with ∆glucose>0 and ∆C-peptide<0 from baseline to 1 year were at substantially higher risk for stage 3 T1D than those with ∆glucose<0 and ∆C-peptide>0 (p<0.0001). Based on this, we compared placebo vs. treatment groups in both trials for failure (∆glucose>0 with ∆C-peptide<0) vs. success (∆glucose<0 with ∆C-peptide>0) after 1 year. In individuals with stage 1 T1D, measures showed a favorable metabolic impact of abatacept after 12 months of treatment. An analytic approach using a binary metabolic endpoint of failure vs. success at a fixed time interval appears to detect treatment effects at least as well as standard primary endpoints with shorter follow-up.