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

arXiv: https://arxiv.org/abs/2609.24453 · HTML: https://mlsanity.com/paper/2609.24453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food](https://mlsanity.com/paper/2103.03375/) (2021)
- [DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion](https://mlsanity.com/paper/2310.11702/) (2023)
- [Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes](https://mlsanity.com/paper/2511.03986/) (2025)
- [STSM-FiLM: A FiLM-Conditioned Neural Architecture for Time-Scale Modification of Speech](https://mlsanity.com/paper/2510.02672/) (2025)

## Abstract (arXiv)

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.