Coefficient of Variation to Assess the Reproducibility of Meal-Induced Glycemic Responses (Preprint)

Abstract

BACKGROUND Managing Type 1 Diabetes (T1D) requires maintaining target blood glucose levels through precise diet and insulin dosing. Predicting postprandial glycemic responses (PPGRs) based solely on carbohydrate content is limited by factors like meal composition, individual physiology, and lifestyle. Continuous glucose monitors (CGMs) provide insights into these responses, revealing significant individual variability. OBJECTIVE The statistical clustering method propsed here balances the number of clusters formed and the glycemic variability of the PPGRs within each cluster to offer a clustering technique on which treatment decisions could be based. METHODS Blood glucose (BG) data from the OhioT1DM dataset were to assess clustering of PPGR based on the CV of glucose. Clustering was performed using statistical clustering, with each PPGR isolated into 48 data points per event. A CV threshold of <36% was used to define clinically similar clusters. This aimed to cluster PPGRs with minimal glycemic variability. The approach aims to enhance precision in analyzing postprandial glycemic dynamics, assessing cluster cohesion via standard deviation and CV within meal categories. RESULTS The analysis revealed a reproducible set of PPGR-clusters specific to meal types and individuals (2.4±1.8 for breakfast, 2.7±0.9 for lunch, 3.1±1.0 for dinner), with the number of clusters varying across participants and meals in the dataset. Carbohydrate intake alone did not affect cluster formation, suggesting a complex relationship between meal composition and PPGR variability. However, certain individuals showed significant associations between carbohydrate intake and cluster formation for specific meals. CONCLUSIONS The meal-based glycemic clustering algorithm provides a promising framework for predicting PPGRs in people living with T1D (PwT1D), independent of carbohydrate intake. It emphasizes the need for personalized prediction models to optimize TIR and enhance diabetes management. Efforts to refine treatment strategies are crucial in reducing T1D-related complications.