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

2026-09-21

AI Summary

本文提出一种多模态框架，通过餐食图片估算宏量营养素，并结合临床变量和肠道微生物信息预测个体餐后血糖反应，以替代手动饮食记录。

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.

Problem

• Postprandial Glycemic Response
• Personalized Nutrition
• Type 2 Diabetes Management
• Manual Dietary Logging

Innovation

• multimodal framework
• image-based macronutrient estimation
• attention-based prediction module
• personalized PPGR prediction