Exploratory Analysis of Continuous Glucose Monitoring Patterns in First-Degree Relatives with Dysglycemia at Risk of Progression to Stage 3 Type 1 Diabetes

INTRODUCTION: Continuous glucose monitoring (CGM) can characterize subtle glucose changes in early-stage type 1 diabetes (T1D). We assessed whether CGM metrics predict Stage 3 T1D in first-degree relatives (FDRs) with positive islet autoantibodies (IAbs) and dysglycemia participating in INNODIA. METHODS: We analyzed IAb-positive FDRs with dysglycemia and available CGM data. CGM metrics were analyzed longitudinally and in a restricted analysis limited to the last assessment before Stage 3 T1D in progressors. CGM metrics were compared using linear mixed models. Predictive performance of single and combined CGM metric models was evaluated using receiver operating characteristic curve analyses. RESULTS: = 0.001). Time >140 mg/dL yielded an area under the curve (AUC) of 0.83 (95% confidence interval [CI] 0.66-1.00). Combining time >140 mg/dL with time-specific percentages >180 and <70 mg/dL increased AUC to 0.93 (95% CI 0.84-1.00). A model incorporating time between 70 and 140 mg/dL, mean glucose, early morning <70 mg/dL, and MAGE achieved 100% sensitivity and 86% specificity. CONCLUSIONS: CGM may help identify FDRs with IAbs and dysglycemia at higher risk of progressing to Stage 3 T1D. Combining CGM metrics improved prediction compared with time >140 mg/dL alone.