open access publication

Article, 2024

Characterizing human postprandial metabolic response using multiway data analysis

Metabolomics, ISSN 1573-3882, 1573-3890, Volume 20, 3, Page 50, 10.1007/s11306-024-02109-y

Contributors

Yan, Shi [1] Li, Lu [1] Horner, David Lyle George [2] Ebrahimi, Parvaneh 0000-0002-9194-2073 [2] Chawes, Bo Lund Krogsgaard 0000-0001-6846-6243 [2] Dragsted, Lars Ove 0000-0003-0609-6317 [2] Rasmussen, Morten Arendt 0000-0001-7431-5206 [2] Smilde, Age K 0000-0002-3052-4644 [1] [3] Acar, Evrim 0000-0002-3737-292X (Corresponding author) [1]

Affiliations

  1. [1] Simula Metropolitan Center for Digital Engineering
    2. [NORA names: Norway; Europe, Non-EU; Nordic; OECD];
  2. [2] University of Copenhagen
    3. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
  3. [3] University of Amsterdam
    4. [NORA names: Netherlands; Europe, EU; OECD]

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 COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences.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.

Keywords

CANDECOMP/PARAFAC, CP model, ConclusionThe, MethodsWe, Nightingale, ObjectivesOur, ObjectivesOur goal, ResultsOur, ResultsOur analysis, analysis, analysis method, approach, behavior, body, body mass index, challenge test, challenges, changes, clusters, clusters of metabolites, cohort, component analysis, comprehensive picture, comprehensive summary, data, data analysis, data analysis methods, data summaries, differences, dynamic behavior, dynamic markers, dynamic patterns, dynamic state, dynamics, effective approach, fasting, females, goal, group, group differences, human metabolism, i., index, individuals, lipoprotein, male, markers, markers of phenotype, mass index, meal, meal challenge, meal challenge test, measured metabolites, measurements of plasma samples, metabolic response, metabolism, metabolite groups, metabolites, metabolomics data, method, model, multiway data analysis, panel, patterns, phenotype, picture, plasma samples, postprandial data, postprandial dynamics, postprandial metabolic response, postprandial response, postprandial state, principal component analysis, profile, response, responses of individuals, samples, similarity, state, state data, static patterns, stratification, subject group differences, subject stratification, subjects, summary, temporal profile, test, traditional data analysis methods, univariate, univariate approach

Funders

  • The Research Council of Norway
  • Novo Nordisk Foundation

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