ArticleMetabolomics : Official journal of the Metabolomic Society2024
Characterizing human postprandial metabolic response using multiway data analysis.
Article in Metabolomics : Official journal of the Metabolomic Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Extracting host-specific developmental signatures from longitudinal microbiome data.PLoS computational biology · 2026Article
- Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models.Metabolites · 2024Article
- Sample Preparation for Metabolomic Analysis in Exercise Physiology.Biomolecules · 2024Review
- Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.Metabolomics : Official journal of the Metabolomic Society · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
introductionAnalysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time.
objectivesOur goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles.
methodsWe analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC
resultsOur analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state.
conclusionThe CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.
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Registered trials
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