Subtyping and Prediction of the Glucose Based on the Hybrid Modeling of Deep Learning and Dietary Stimulated Glucose Metabolism Dynamical Model.

Diabetes represents a significant global health challenge, underscoring the need for enhanced methodologies in glycemic monitoring and risk assessment. Static biomarkers, including fasting plasma glucose and glycated hemoglobin, may have limitations in capturing the temporal variations and individual heterogeneities inherent in glucose regulation. Continuous glucose monitoring (CGM) offers a high-resolution approach that may facilitate timely metabolic categorization. However, there is a notable scarcity of modeling frameworks that integrate CGM data while maintaining both interpretability and predictive accuracy. This study proposes an integrated analytical framework that amalgamates CGM data-driven classification of glucose response with a hybrid modeling approach that combines dynamical systems and deep learning methodologies for personalized glucose regulation assessments. A cohort of 44 adults was enrolled and followed up on an individual basis for 7 to 14 days, and typical daily glucose profiles were derived through dynamic time warping. K-shape clustering analysis was employed to identify significant glucose response subtypes, differentiating participants into three categories: health, prediabetes, and diabetes based on CGM-derived indicators. We subsequently developed an integrated hybrid model that synergizes a dietary stimulation glucose-insulin dynamical model with a long short-term memory residual network. This hybrid model demonstrated substantially improved accuracy, achieving a root mean square error (RMSE) of 6.61 and a coefficient of determination ( R 2 ) of 0.91. The glucose-insulin dynamical model effectively elucidates the physiological mechanisms underlying postprandial glucose-insulin regulation. The findings of this study emphasize the utility of CGM-derived glucose phenotyping and hybrid predictive models as viable tools for individualized metabolic risk assessment. Additionally, they contribute to the early identification of dysglycemia and establish a practical framework for precision glycemic management.