Enhancing Type 1 Diabetes Immunological Risk Prediction with Continuous Glucose Monitoring and Genetic Profiling.

Abstract

Background: Early identification of individuals at high risk for type 1 diabetes (T1D) is essential for timely intervention. Islet autoantibodies (AB) and continuous glucose monitoring (CGM) reveal early signs of glycemic dysregulation, while T1D genetic risk scores (GRS) further improve disease prediction. We use CGM data and T1D GRS to develop an AB classifier (1 AB vs. ≥2 AB) and predict early T1D risk. Methods: Thirty-nine AB-positive (18 with 1 and 21 with ≥2 AB) healthy relatives of T1D (mean age 22.1 ± 11.1 years, HbA1c 5.3 ± 0.3%, body mass index 24.1 ± 5.8 kg/m²) were enrolled in a National Institutes of Health's (NIH) TrialNet ancillary study. Participants wore CGMs for a week and consumed three standardized liquid mixed meals (SLMM). Post-SLMM CGM glycemic features and T1D GRS were used in a linear support vector machine (SVM) model with recursive feature elimination (RFE) for AB classification, evaluated via fivefold cross-validation using the receiver operating characteristic and precision-recall area under the curve (AUC-ROC/PR). Results: Significant differences between the AB groups were observed in the post-SLMM percent time of glucose >180 mg/dL and GRS (P = 0.020 and P = 0.001, respectively). An SVM model with two RFE-selected features (T1D GRS and incremental AUC) achieved the best performance, classifying 1 versus ≥2 AB individuals with an AUC-ROC of 0.93 (95% confidence interval [CI]: 0.83-1.00) and AUC-PR of 0.89 (95% CI: 0.71-0.99), compared with AUC-ROC of 0.80 (95% CI: 0.46-1.00) and AUC-PR of 0.82 (95% CI: 0.71-0.93) using all features. Conclusions: A machine learning approach combining a 1-week CGM home test and T1D GRS reliably assesses T1D immunological risk, enabling early intervention.