Personalized Forecasting of Glycemic Control in Type 1 and 2 Diabetes Using Foundational AI and Machine Learning Models

Background: Accurate week-ahead forecasts of continuous glucose monitoring (CGM) derived metrics could enable proactive diabetes management, but relative performance of modern tabular learning approaches is incompletely defined. Methods: We trained and internally validated four regression models (CatBoost, XGBoost, AutoGluon, tabPFN) to predict six weekahead CGM metrics (TIR, TITR, TAR, TBR, CV, MAGE, and related quantiles) using 4,622 case-weeks from two cohorts (T1DM n=3,389; T2DM n=1,233). Performance was assessed with mean absolute error (MAE) and mean absolute relative difference (MARD); quantile classification was summarized via confusion-matrix heatmaps. Results: Across T1DM and T2DM, all models produced broadly comparable performance for most targets. For T1DM, MARD for TIR, TITR, TAR and MAGE ranged 8.5 to 16.5% while TBR showed large MARD (mean ~48%) despite low MAE. AutoGluon and tabPFN showed lower MAE than XGBoost for several targets (e.g., TITR: p