Characterizing human postprandial metabolic response using multiway data analysis

Shi Yan; Lu Li; David Horner; Parvaneh Ebrahimi; Bo Chawes; Lars O Dragsted; Morten A Rasmussen; Age K Smilde; Evrim Acar

doi:10.1007/s11306-024-02109-y

Characterizing human postprandial metabolic response using multiway data analysis

Metabolomics. 2024 May 9;20(3):50. doi: 10.1007/s11306-024-02109-y.

Authors

Shi Yan¹, Lu Li¹, David Horner², Parvaneh Ebrahimi^{2

3}, Bo Chawes², Lars O Dragsted⁴, Morten A Rasmussen^{2

3}, Age K Smilde^{1

5}, Evrim Acar⁶

Affiliations

¹ Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
² COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
³ Department of Food Science, University of Copenhagen, Copenhagen, Denmark.
⁴ Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark.
⁵ Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
⁶ Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway. evrim@simula.no.

Abstract

Introduction: Analysis 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.

Objectives: Our 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.

Methods: We 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.

Results: Our 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.

Conclusion: The 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; Challenge tests; Dynamic metabolomics data; Tensor factorizations.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Data Analysis
Fasting / metabolism
Female
Humans
Magnetic Resonance Spectroscopy / methods
Male
Metabolome / physiology
Metabolomics* / methods
Middle Aged
Postprandial Period* / physiology
Principal Component Analysis

Abstract

Publication types

MeSH terms

Grants and funding