Multimodal large language models and mechanistic modeling for glucose forecasting in type 1 diabetes patients.

Abstract

Management of type 1 Diabetes remains a significant challenge as blood glucose levels can fluctuate dramatically and are highly individual. We introduce an innovative approach that combines multimodal Large Language models (mLLMs), mechanistic modeling of individual glucose metabolism and machine learning (ML) for forecasting blood glucose levels. This study uses the D1NAMO dataset (6 patients with meal images) to demonstrate mLLM integration for glucose prediction. An mLLM (Pixtral Large) was employed to estimate macronutrients from meal images, providing automated meal analysis without manual food logging. We compare three distinct approaches: (1) Baseline using only glucose dynamics and basic insulin features, (2) LastMeal providing additional information about the last meal ingested by the patient, and (3) Bézier incorporating mechanistically modeled temporal features using optimized cubic Bézier curves to model temporal impacts of individual macronutrients on blood glucose. The modeled feature impacts served as input features for a LightGBM model. We also validate the mechanistic modeling component on the AZT1D dataset (24 patients with structured carbohydrate and correction insulin logs). The Bézier approach achieved the best performance across both datasets: D1NAMO RMSE of 15.06 at 30 min and 28.15 at 60 min; AZT1D RMSE of 16.61 at 30 min and 24.58 at 60 min. One-way ANOVA revealed statistically significant differences across prediction horizons of 45 to 120 min for the AZT1D dataset. Patient-specific Bézier curves revealed distinct metabolic response patterns: simple sugars peaked at 0.74 h, complex sugars at 3.07 h, and proteins at 4.36 h post-ingestion. Feature importance analysis showed temporal evolution from glucose change dominance to macronutrient prominence at longer horizons. Patient-specific modeling uncovered individual metabolic signatures with varying nutritional sensitivity and circadian influences. This study demonstrates the potential of combining mLLMs with mechanistic modeling for personalized diabetes management. The optimized Bézier curve approach provides superior temporal mapping while patient-specific models reveal individual metabolic signatures essential for personalized care.