Classifying individuals into a dietary pattern based on metabolomic data

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dc.check.date2022-04-17
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisher.en
dc.contributor.authorPrendiville, Orla
dc.contributor.authorWalton, Janette
dc.contributor.authorFlynn, Albert
dc.contributor.authorNugent, Anne P.
dc.contributor.authorMcNulty, Breige A.
dc.contributor.authorBrennan, Lorraine
dc.contributor.funderHorizon 2020en
dc.contributor.funderEuropean Research Councilen
dc.date.accessioned2021-05-10T10:29:35Z
dc.date.available2021-05-10T10:29:35Z
dc.date.issued2021-04-17
dc.date.updated2021-04-22T11:34:04Z
dc.description.abstractScope: The objectives are to develop a metabolomic‐based model capable of classifying individuals into dietary patterns and to investigate the reproducibility of the model. Methods and Results: K‐means cluster analysis is employed to derive dietary patterns using metabolomic data. Differences across the dietary patterns are examined using nutrient biomarkers. The model is used to assign individuals to a dietary pattern in an independent cohort, A‐DIET Confirm (n = 175) at four time points. The stability of participants to a dietary pattern is assessed. Four dietary patterns are derived: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns has lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4, p < 0.001). The dietary patterns are replicated in A‐DIET Confirm, with good reproducibility across four time points. The stability of participants’ dietary pattern membership ranged from 25.0% to 61.5%. Conclusion: The multivariate model classifies individuals into dietary patterns based on metabolomic data. In an independent cohort, the model classifies individuals into dietary patterns at multiple time points furthering the potential of such an approach for nutrition research.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPrendiville, O., Walton, J., Flynn, A., Nugent, A. P., McNulty, B. A. and Brennan, L. (2021) 'Classifying individuals into a dietary pattern based on metabolomic data', Molecular Nutrition and Food Research. doi: 10.1002/mnfr.202001183en
dc.identifier.doi10.1002/mnfr.202001183en
dc.identifier.eissn1613-4133
dc.identifier.issn1613-4125
dc.identifier.journaltitleMolecular Nutrition and Food Researchen
dc.identifier.urihttps://hdl.handle.net/10468/11262
dc.language.isoenen
dc.publisherJohn Wiley & Sons, Inc.en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::ERC::ERC-COG/647783/EU/Metabolomics based biomarkers of dietary intake- new tools for nutrition research/A-DIETen
dc.rights© 2021, Wiley‐VCH GmbH. This is the peer reviewed version of the following article: Prendiville, O., Walton, J., Flynn, A., Nugent, A. P., McNulty, B. A. and Brennan, L. (2021) 'Classifying individuals into a dietary pattern based on metabolomic data', Molecular Nutrition and Food Research, doi: 10.1002/mnfr.202001183, which has been published in final form at https://doi.org/10.1002/mnfr.202001183. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.en
dc.subjectBiomarkersen
dc.subjectDietary patternsen
dc.subjectMetabolomicsen
dc.subjectReproducibilityen
dc.titleClassifying individuals into a dietary pattern based on metabolomic dataen
dc.typeArticle (peer-reviewed)en
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