Multilevel functional data analysis modeling of human glucose response to meal intake

2024-05-23

arXiv.org

AI Summary

Traditional scalar metrics (e.g., 2-h AUC, peak glucose) inadequately characterize the full postprandial glycemic dynamics. To address this, we propose a multilevel functional mixed-effects model that jointly models continuous glucose monitoring (CGM) time-series trajectories with time-varying covariates—including nutrient intake (particularly lipids) and metabolic state—while incorporating subject-specific random effects to account for inter-individual heterogeneity. We further introduce a novel functional mixed R² to quantify the overall explanatory power of functional covariates on glycemic curves. Our key contribution is the first demonstration that lipid intake induces a *specific perturbation* in postprandial glycemic dynamics—not merely in steady-state metrics—among individuals with prediabetes, significantly distinguishing their response from that of healthy controls. This advances postprandial metabolic assessment by enhancing temporal resolution and improving the statistical interpretability and clinical utility of personalized dietary interventions.

Technology Category

• Machine Learning: Kernel Methods
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients’ meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.

Problem

• Analyzing entire CGM trajectories using functional data analysis
• Characterizing variability in postprandial glucose responses
• Linking dietary and patient characteristics to glucose dynamics

Innovation

• Multilevel functional data analysis for CGM trajectories
• Extended r-square metric for hierarchical functional models
• Temporal and stratified insights into glucose regulation