Urinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study

dc.contributor.authorMadrid Gambín, Francisco Javier
dc.contributor.authorLlorach, Rafael
dc.contributor.authorVázquez Fresno, Rosa
dc.contributor.authorUrpí Sardà, Mireia
dc.contributor.authorAlmanza Aguilera, Enrique
dc.contributor.authorGarcia Aloy, Mar
dc.contributor.authorEstruch Riba, Ramon
dc.contributor.authorCorella Piquer, Dolores
dc.contributor.authorAndrés Lacueva, Ma. Cristina
dc.date.accessioned2017-03-29T13:51:26Z
dc.date.available2018-01-09T23:01:46Z
dc.date.issued2017-01-09
dc.date.updated2017-03-29T13:51:26Z
dc.description.abstractLittle is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils and beans), spot urine samples from a subcohort from the PREDIMED study were stratified, using a validated food frequency questionnaire. Non-pulse consumers (≤ 4 g/day of pulse intake) and habitual pulse consumers (≥ 25 g/day of pulse intake) were analysed using a 1H-NMR metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine and 3-methylhistidine. This model was evaluated by receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to pulse exposure highlighted new candidates for biomarkers of pulse consumption, the role of choline metabolism and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings.
dc.format.mimetypeapplication/pdf
dc.identifier.idgrec666373
dc.identifier.issn1535-3893
dc.identifier.pmid28067528
dc.identifier.urihttps://hdl.handle.net/2445/109123
dc.language.isoeng
dc.publisherAmerican Chemical Society
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1021/acs.jproteome.6b00860
dc.relation.ispartofJournal of Proteome Research, 2017, vol.
dc.relation.urihttps://doi.org/10.1021/acs.jproteome.6b00860
dc.rights(c) American Chemical Society , 2017
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.sourceArticles publicats en revistes (Nutrició, Ciències de l'Alimentació i Gastronomia)
dc.subject.classificationLlegums
dc.subject.classificationMetabòlits
dc.subject.classificationRessonància magnètica nuclear
dc.subject.classificationOrina
dc.subject.classificationMarcadors bioquímics
dc.subject.otherLegumes
dc.subject.otherMetabolites
dc.subject.otherNuclear magnetic resonance
dc.subject.otherUrine
dc.subject.otherBiochemical markers
dc.titleUrinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion

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