Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/109057
Title: Phenolic and microbial-targeted metabolomics to discovering and evaluating wine intake biomarkers in human urine and plasma
Author: Urpí Sardà, Mireia
Boto Ordóñez, María
Queipo-Ortuño, María Isabel
Tulipani, Sara
Corella, Dolores
Estruch Riba, Ramon
Tinahones, Francisco J.
Andrés Lacueva, Ma. Cristina
Keywords: Marcadors bioquímics
Metabòlits
Vi
Dietoteràpia
Orina
Plasma sanguini
Biochemical markers
Metabolites
Wine
Diet therapy
Urine
Blood plasma
Issue Date: 30-Apr-2015
Publisher: Wiley-VCH
Abstract: The 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.
Note: Versió postprint del document publicat a: https://doi.org/10.1002/elps.201400506
It is part of: Electrophoresis, 2015, vol. 36, num. 18, p. 2259-2268
Related resource: https://doi.org/10.1002/elps.201400506
URI: http://hdl.handle.net/2445/109057
ISSN: 0173-0835
Appears in Collections:Articles publicats en revistes (Nutrició, Ciències de l'Alimentació i Gastronomia)

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