[SS06] Data-based dynamical modeling of complex biological systems

[SS06-05] Modelling glucose dynamics from continuous glucose monitoring data

*Yong Wang¹ (1. Chinese Academy of Sciences (China))

Keywords:

Glucose dynamics

Capturing glucose dynamics including the rigorous fasting glucose homeostasis and postprandial glucose adaptation is central to the diagnosis, subtyping, early warning, lifestyle intervention, and treatment for type 2 diabetes (T2D). Recently, continuous glucose monitoring (CGM) technology has revolutionized fields to track real-time blood glucose levels and trends, and facilitated safe and effective decision making for diabetes management. We will introduce our recent efforts in developing an attention-based deep learning model, CGMformer, pretrained on a large-scale and diverse corpus of CGM data from a nationwide multi-center study in China to enable context-specific predictions and clinical applications to individuals. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states. We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task (AUROC = 0.914 for type 2 diabetes (T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics, CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling and helps personalized dietary recommendations.