Evidence map›Paper›PMID 39066850›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2024

Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.

Lu Li, Shi Yan, David Horner, Morten A Rasmussen, Age K Smilde, Evrim Acar

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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 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Lu LiDepartment of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway. lu@simula.no.
Shi YanDepartment of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
David HornerCopenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
Morten A RasmussenCopenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
Age K SmildeDepartment of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
Evrim AcarDepartment of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway. evrim@simula.no.

Funding

Research Council of Norway 300489
6 · The paper itself

Abstract

introductionLongitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications.

objectivesThrough joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health.

methodsWe jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC

resultsThe proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state.

conclusionThe CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications-when markers are present in both fasting and dynamic states.

Indexed as

BiomarkersFastingMetabolomicsPostprandial PeriodAdultFemaleHumansMaleMiddle AgedBiomarkersCoupled matrix and tensor factorizationsData fusionDynamic biomarkersPostprandial metabolomics dataTensor factorizations

Identifiers

PMID39066850

What OpenQuestion holds

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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.