MealGluco: Physiologically Structured Postprandial Glucose Trajectory Forecasting with Online Personalization

Predicting postprandial glucose trajectories is important for proactive metabolic health management but remains underexplored. This task is challenging because a model must capture long-horizon glucose dynamics, integrate the effects of the current meal with the evolving pre-meal glucose state, and account for substantial inter-individual variability. To address these challenges, we propose MealGluco, a physiologically structured model that predicts the complete, high-resolution postprandial glucose trajectory from glucose history and meal composition. MealGluco represents the meal-induced response using a peak-normalized Gamma function, captures background glucose dynamics through a separate state pathway, and recursively updates two participant-specific coordinates to enable lightweight online personalization without retraining the shared network. Across three datasets and five evaluation metrics, MealGluco achieves a mean rank of 1.33 among seven evaluated models. Its online personalization reduces trajectory prediction error relative to the non-personalized model while matching the performance of participant-specific fine-tuning at orders-of-magnitude lower computational and storage costs. MealGluco also consistently outperforms the strongest baseline in cross-dataset evaluation, demonstrating improved generalization across populations and devices.