Multi-Metric Subgroup Analysis for Glucose Forecasting in Type 1 Diabetes

Type 1 Diabetes (T1D) management remains challenging due to the complexity of glucose regulation, demanding accurate and reliable short-term glucose prediction models. This study investigates the use of population-based and personalized predictive models to enhance glucose prediction in T1D patients. We develop an XGBoost-based model, optimized using Bayesian methods, to predict glucose concentration at 15, 30, and 60-minute prediction horizons. The model was tested across various patient subgroups categorized by glucose patterns, glucose variability (GV), and other clinical factors. Results show that the population-based model consistently outperformed personalized models, achieving RMSE values of 17.39 mg/dL, 27.28 mg/dL, and 40.69 mg/dL at 15, 30, and 60 minutes, respectively. Subgroups with higher GV exhibited poorer prediction accuracy. This study highlights the potential of combining population-based models with subgroup-specific optimizations to improve glycemic control in T1D patients. Accurate glucose prediction is crucial for improving glycemic control and reducing risks, ultimately enhancing patient outcomes.Clinical relevance- This study shows that population-based models can enhance glucose prediction in Τ1D, helping clinicians optimize insulin therapy and improve glycemic control.