Objectives
Postprandial glucose responses vary widely between individuals with type 2 diabetes, making effective dietary management difficult. Recent machine learning approaches have improved the prediction of glucose responses to meals, but few frameworks translate these predictions into optimized meal recommendations. This study aimed to develop a predictive modeling framework capable of forecasting individualized postprandial glucose trajectories and enabling personalized optimization of meal composition to improve glycemic control.