Automatic computation of the glycemic index: data driven analysis of the glucose standard

TL;DR

This study uses a mathematical model to analyze glucose response data from healthy subjects, enabling classification based on glycemic peak timing and advancing simulation-based GI estimation.

Contribution

It introduces a data-driven simulation approach that captures individual variability in postprandial glucose response using a physiology-based model.

Findings

Subjects classified into three groups by glycemic peak timing

Model captures inter-individual variability in glucose dynamics

Simulation approach advances GI estimation methods

Abstract

The Glycemic Index (GI) is a tool for classifying carbohydrates based on their impact on postprandial glycemia, useful for diabetes prevention and management. This study applies a mathematical model for a data driven simulation of the glycemic response following glucose ingestion. The analysis is performed on a dataset of 35 healthy subjects undergone a standard 50 g oral glucose test. The results reveal a direct correlation between glucose response profiles and parameters describing glucose absorption, enabling the classification of subjects into three groups based on the timing of their glycemic peak: <30 min, 30-50 min, >50 min. These findings highlight the ability of a physiology-based mathematical model to capture inter-individual variability in postprandial glucose dynamics and represent a step toward simulation-based approaches for GI estimation.