Phenolic and microbial-targeted metabolomics to discovering and evaluating wine intake biomarkers in human urine and plasma

dc.contributor.authorUrpí Sardà, Mireia
dc.contributor.authorBoto Ordóñez, María
dc.contributor.authorQueipo Ortuño, María Isabel
dc.contributor.authorTulipani, Sara
dc.contributor.authorCorella Piquer, Dolores
dc.contributor.authorEstruch Riba, Ramon
dc.contributor.authorTinahones, Francisco J.
dc.contributor.authorAndrés Lacueva, Ma. Cristina
dc.date.accessioned2017-03-28T15:27:33Z
dc.date.available2017-03-28T15:27:33Z
dc.date.issued2015-04-30
dc.date.updated2017-03-28T15:27:33Z
dc.description.abstractThe discovery of biomarkers of intake in nutritional epidemiological studies is essential in establishing an association between dietary intake (considering their bioavailability) and diet-related risk factors for diseases. The aim is to study urine and plasma phenolic and microbial profile by targeted metabolomics approach in a wine intervention clinical trial for discovering and evaluating food intake biomarkers. High-risk male volunteers (n = 36) were included in a randomized, crossover intervention clinical trial. After a washout period, subjects received red wine or gin, or dealcoholized red wine over four weeks. Fasting plasma and 24-h urine were collected at baseline and after each intervention period. A targeted metabolomic analysis of 70 host and microbial phenolic metabolites was performed using ultra performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). Metabolites were subjected to stepwise logistic regression to establish prediction models and received operation curves were performed to evaluate biomarkers. Prediction models based mainly on gallic acid metabolites, obtained sensitivity, specificity and area under the curve (AUC) for the training and validation sets of between 91 and 98% for urine and between 74 and 91% for plasma. Resveratrol, ethylgallate and gallic acid metabolite groups in urine samples also resulted in being good predictors of wine intake (AUC>87%). However, lower values for metabolites were obtained in plasma samples. The highest correlations between fasting plasma and urine were obtained for the prediction model score (r = 0.6, P<0.001), followed by gallic acid metabolites (r = 0.5-0.6, P<0.001). This study provides new insights into the discovery of food biomarkers in different biological samples.
dc.format.extent10 p.
dc.format.mimetypeapplication/pdf
dc.identifier.idgrec654181
dc.identifier.issn0173-0835
dc.identifier.pmid25929678
dc.identifier.urihttps://hdl.handle.net/2445/109057
dc.language.isoeng
dc.publisherWiley-VCH
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1002/elps.201400506
dc.relation.ispartofElectrophoresis, 2015, vol. 36, num. 18, p. 2259-2268
dc.relation.urihttps://doi.org/10.1002/elps.201400506
dc.rights(c) Wiley-VCH, 2015
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.sourceArticles publicats en revistes (Nutrició, Ciències de l'Alimentació i Gastronomia)
dc.subject.classificationMarcadors bioquímics
dc.subject.classificationMetabòlits
dc.subject.classificationVi
dc.subject.classificationDietoteràpia
dc.subject.classificationOrina
dc.subject.classificationPlasma sanguini
dc.subject.otherBiochemical markers
dc.subject.otherMetabolites
dc.subject.otherWine
dc.subject.otherDiet therapy
dc.subject.otherUrine
dc.subject.otherBlood plasma
dc.titlePhenolic and microbial-targeted metabolomics to discovering and evaluating wine intake biomarkers in human urine and plasma
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion

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