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Analyzing postprandial metabolomics data using multiway models: a simulation study.
Li, Lu; Yan, Shi; Bakker, Barbara M; Hoefsloot, Huub; Chawes, Bo; Horner, David; Rasmussen, Morten A; Smilde, Age K; Acar, Evrim.
Afiliación
  • Li L; Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway. lu@simula.no.
  • Yan S; Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
  • Bakker BM; Laboratory 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.
  • Hoefsloot H; Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
  • Chawes B; Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
  • Horner D; Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
  • Rasmussen MA; Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
  • Smilde AK; Department of Food Science, University of Copenhagen, Copenhagen, Denmark.
  • Acar E; Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
BMC Bioinformatics ; 25(1): 94, 2024 Mar 04.
Article en En | MEDLINE | ID: mdl-38438850
ABSTRACT

BACKGROUND:

Analysis 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.

RESULTS:

We 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.

CONCLUSIONS:

Our 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Metabolómica / Medicina Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Metabolómica / Medicina Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido