Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study
Matabuena, Marcos Sartini, Joseph Gude, Francisco
Matabuena, Marcos
Sartini, Joseph
Gude, Francisco
Supervisor
Department
Epidemiology
Embargo End Date
Type
Journal article
Date
2026
License
Language
English
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Research Projects
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Journal Issue
Abstract
Purpose
Postprandial glucose, collected through continuous glucose monitoring (CGM), has established clinical relevance in assessing metabolic capacity and informing diet prescriptions. However, most studies of postprandial glucose summarize these data into scalar values, such as 2-hour area under the curve (AUC) or 2-hour peak glucose. We propose analyzing the full CGM time-series trajectories to provide more detailed insights. Given the smooth dynamics of glucose metabolism, the resulting data are inherently functional, with hierarchical structure when there are multiple time series per participant.
Methods
We consider multilevel functional data analysis (FDA) techniques to analyze postprandial CGM trajectories, applying these methods to data from participants without diabetes in the AEGIS study. The AEGIS study collected meal timing and nutrient composition during periods the participants wore CGM devices. We illustrate the utility of FDA methods to characterize postprandial CGM variability and to explore the associations between dietary/patient characteristics and CGM over the postprandial period. We introduce an extension of the R-squared (
) metric to hierarchical functional models to quantify variability explained in this context.
Results
The FDA models indicate that, for many nutrients, the effect of dietary composition varies throughout the 6-hour post-prandial temporal window. For example, fiber blunts the postprandial glucose response 90 minutes after the meal, while fats reduce the response during the first 50 minutes. In addition, metabolic responses to dietary intake differ between normoglycemic and prediabetic individuals as expected.
Conclusion
Analyzing postprandial glucose responses with functional methods yields temporal insights that traditional scalar approaches cannot capture. Stratifying the analysis by glycemic status (normoglycemic vs. prediabetes) also provides novel findings.
Data availability
Citation
M. Matabuena, J. Sartini, and F. Gude, “Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study,” BMC Medical Research Methodology 2026, Jan. 2026, doi: 10.1186/S12874-025-02748-2
Source
BMC Medical Research Methodology
Conference
Keywords
Continuous glucose monitoring, Postprandial glucose, Functional data analysis, Glucose metabolism, Hierarchical modeling
Subjects
Source
Publisher
Springer Nature