Evidence map›Paper›PMID 38438850›Full record

ArticleBMC bioinformatics2024

Analyzing postprandial metabolomics data using multiway models: a simulation study.

Lu Li, Shi Yan, Barbara M Bakker, Huub Hoefsloot, Bo Chawes, David Horner, Morten A Rasmussen, Age K Smilde, Evrim Acar

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In one paragraph

Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Characterizing human postprandial metabolic response using multiway data analysis.Metabolomics : Official journal of the Metabolomic Society · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 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.
Barbara M BakkerLaboratory of Pediatrics, Section Systems Medicine and Metabolic Signalling, Center for Liver, Digestive and Metabolic Disease, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Huub HoefslootSwammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
Bo ChawesCopenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
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

Norges Forskningsråd 300489Novo Nordisk Fonden NNF19OC0057934
6 · The paper itself

Abstract

backgroundAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data.

resultsWe introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups.

conclusionsOur experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

Indexed as

MedicineMetabolomicsComputer SimulationData AnalysisHealth StatusHumansCANDECOMP/PARAFAC (CP)Meal challenge testPostprandial metabolomics dataPrincipal component analysis (PCA)Tensor factorizations (multiway data analysis)Time-resolved metabolomics dataWhole-body metabolic model

Identifiers

PMID38438850
PMCPMC10913623

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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.