arXiv:2606. 10120v1 Announce Type: cross Abstract: Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful.
By Asiful Arefeen, Carol Johnston, Hassan Ghasemzadeh
arXiv:2503. 19158v3 Announce Type: replace Abstract: Type 1 Diabetes (T1D) management is a complex task due to many variability factors.
By Stefano De Carli, Nicola Licini, Davide Previtali, Fabio Previdi, Antonio Ferramosca
arXiv:2606. 12699v1 Announce Type: cross Abstract: Type 2 Diabetes (T2D) poses an increasing global health threat, demanding effective glycemic assessment to support personalized and improved diabetes care.
By Yifan Gao, Yanmin Gong, Yun Shi, Yuanxiong Guo
arXiv:2607. 19006v1 Announce Type: new Abstract: Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely therapeutic interventions.
By Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli, Paolo Napoletano
The paper examines how the representation of physiological data affects the performance of large language models (LLMs) in predicting post‑meal blood glucose events for people with type 1 diabetes. Using the OhioT1DM dataset, the authors compare zero‑shot and few‑shot prompt‑based LLMs across 30, 60, and 90‑minute horizons, varying the textual encoding of glucose readings, derived descriptors, and contextual variables such as insulin, meals, carbs, and activity. Results show that while conventional supervised models excel at hyperglycemia prediction, certain prompt‑based LLM configurations outperform them for hypoglycemia, and that the way data is presented to the model is a key determinant of success, with added context not consistently improving outcomes.
The study introduces a multimodal framework that predicts postprandial glycemic response (PPGR) by combining image-derived macronutrient estimates with clinical variables and gut microbiome data. It jointly performs macronutrient estimation from meal images and glucose prediction, using an attention-based module to model interactions between dietary and host-specific information. Evaluated on a real-world dataset, the model outperforms existing PPGR baselines that use image-derived inputs and nearly matches methods relying on manually reported macronutrients.
By Varvara Kondratyeva, Kamilia Zaripova, Nassir Navab, Azade Farshad