Beyond Scalar Metrics: Functional Data Analysis of Postprandial Continuous Glucose Monitoring in the AEGIS Study

TL;DR

This study applies multilevel functional data analysis to continuous glucose monitoring data, revealing detailed temporal patterns and dietary effects on postprandial glucose responses that scalar metrics overlook.

Contribution

It introduces a hierarchical FDA approach for analyzing CGM trajectories, extending the r-square metric, and demonstrates its ability to uncover nuanced glucose response dynamics.

Findings

Dietary effects vary across the 6-hour postprandial window.

Fats reduce early glucose rises within 50 minutes.

Responses differ between normoglycemic and prediabetic individuals.

Abstract

Postprandial glucose collected through continuous glucose monitoring (CGM) provides critical information for assessing metabolic capacity and guiding dietary recommendations. Traditional approaches summarize these data into scalar measures, such as 2-hour AUC or peak glucose, potentially overlooking temporal dynamics. We propose analyzing entire CGM trajectories using multilevel functional data analysis (FDA), which accounts for the smooth, hierarchical nature of glucose responses. Applying these methods to AEGIS study participants without diabetes, we illustrate how FDA characterizes variability in postprandial responses and links dietary/patient characteristics to glucose dynamics. We further extend the r-square metric to hierarchical functional models to quantify explanatory power. Our results show that dietary effects vary across the 6-hour postprandial window-for example, fiber blunts responses after 90 minutes, while fats reduce early rises within 50 minutes. Moreover, metabolic responses differ between normoglycemic and prediabetic individuals. These findings demonstrate that functional approaches reveal temporal and stratified insights into postprandial glucose regulation that scalar methods cannot capture.