Enhanced Post-Prandial Glycemic Response Prediction in Type 2 Diabetes with Microbiome Data and Deep Learning

Nutritional intervention can improve glycemic control for type 2 diabetes mellitus (T2DM), and thus accurately predicting post-prandial glycemic responses (PPGRs) to each meal is essential. PPGRs can vary significantly between individuals, even when consuming the same foods, due to the diverse and complex nature of individual characteristics. However, to date, system-scale studies investigating the variability of PPGRs in people living with T2DM are scarce. This research collected meal logs, continuous glucose monitoring records, clinicodemographic profiles, and gut microbiota data comprising over 2,000 real-life meals across 88 individuals with T2DM, revealing causal relationships in the diet-microbiome-PPGR interplay. Furthermore, we developed a multimodal deep learning predictive PPGR model that integrates heterogeneous input data. The proposed model achieves R of 0.62 and 0.66 for 2- and 4-h PPGR prediction, respectively, significantly surpassing the perfor-mance of the carbohydrate single predictor and state-of-the-art machine learning algorithms. This model substantially improved the prediction in the subgroup of low responders to carbohydrates, a traditionally challenging population for accurate prediction using carbohydrate-based methods. This advancement empowers personalized PPGR prediction, laying the foundation for precision nutrition and better glycemic management for individuals with T2DM.