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

Marcos Matabuena, Joseph Sartini, Francisco Gude

Abstract readPreprint

In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

Updated by
Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study.2026

5 · Who and what money

Authors and funding

3 authors.

Marcos MatabuenaBiostatistics Dept., Harvard University, 677 Huntington Ave, Boston, 02115, MA, United States.

Joseph SartiniBiostatistics Dept., Johns Hopkins University, 615 N Wolfe St, Baltimore, 21205, MD, United States.

Francisco GudeDept. of Medicine, Universidad de Santiago de Compostela, Praza do Obradoiro, Santiago de Compostela, 15705, Spain.

Funding

CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026

$17.5M

NHLBI NIH HHS T32 HL007024

6 · The paper itself

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

Indexed as

Continuous glucose monitoringFunctional data analysisGlucose metabolismHierarchical modelingPostprandial glucose

Identifiers

PMID38827463

PMCPMC11142320

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.