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

We evaluated whether a binary metabolic end point for change (Δ) from baseline to 1-year postrandomization could be useful in type 1 diabetes (T1D) prevention trials. Using 2-h oral glucose tolerance testing data from the stage 1 participants in the recent abatacept prevention trial and similar participants in the observational TrialNet Pathway to Prevention (PTP) study, we assessed Δmetabolic measures, plotted glucose and C-peptide response curves, and categorized vectors for Δ from baseline to 1 year as metabolic treatment failure versus success. Analyses were validated using the teplizumab prevention study. PTP participants with Δglucose >0 and ΔC-peptide <0 from baseline to 1 year were at substantially higher risk for stage 3 T1D than those with Δglucose <0 and ΔC-peptide >0 (P < 0.0001). Based on this, we compared placebo versus treatment groups in both trials for failure (Δglucose >0 with ΔC-peptide <0) versus success (Δglucose <0 with ΔC-peptide >0) after 1 year. Using this end point, a favorable metabolic impact of abatacept was found after 12 months of treatment. An analytic approach using a binary metabolic end point of failure versus success at a fixed time interval appears to detect treatment effects at least as well as standard primary end points with shorter follow-up.

Challenges in time to event type 1 diabetes (T1D) prevention trial design can yield negative results even for treatments that may actually improve disease pathology. We evaluated whether a binary metabolic end point for 12-month change from baseline to 1 year postrandomization could be useful in T1D prevention trials. This approach detected treatment effects at least as well as standard primary end points with shorter follow-up. Fixed interval metabolic end points should be used in combination with traditional T1D end points to better understand treatment effects of preventive agents.