Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information

Abstract:Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually reported dietary intake, limiting their scalability in free-living settings. We propose a multimodal framework that replaces manual dietary logging with image-derived macronutrient estimates and integrates them with clinical variables and gut microbiome information for personalized PPGR prediction. The framework jointly performs image-based macronutrient estimation and glucose prediction, while an attention-based prediction module models interactions between dietary and host-specific information. We evaluate the proposed approach on a real-world dataset comprising meal images, continuous glucose monitoring, clinical variables, and gut microbiome profiles. The proposed model outperforms existing PPGR baselines using image-derived nutritional inputs and approaches the performance of methods that rely on manually reported macronutrients despite using automatically estimated nutritional information. These results demonstrate that combining image-derived nutrition with complementary clinical and gut microbiome information provides a practical foundation for scalable personalized PPGR prediction.

11 pages (9 text + 2 references). This is a paper first submitted to MICCAI 2026 MultiTab workshop prior to peer review. The final revised version will be published in Springer LNCS proceedings after the MICCAI 2026 conference

Artificial Intelligence (cs.AI)

arXiv:2609.24453 [cs.AI]

(or arXiv:2609.24453v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.24453 arXiv-issued DOI via DataCite (pending registration)