title: (1)H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors.
abstract: Moderate wine consumption is associated with health-promoting activities. An H-NMR-based metabolomic approach was used to identify urinary metabolomic differences of moderate wine intake in the setting of a prospective, randomized, crossover, and controlled trial. Sixty-one male volunteers with high cardiovascular risk factors followed three dietary interventions (28 days): dealcoholized red wine (RWD) (272mL/day, polyphenol control), alcoholized red wine (RWA) (272mL/day) and gin (GIN) (100mL/day, alcohol control). After each period, 24-h urine samples were collected and analyzed by (1) H-NMR. According to the results of a one-way ANOVA, significant markers were grouped in four categories: alcohol-related markers (ethanol); gin-related markers; wine-related markers; and gut microbiota markers (hippurate and 4-hydroxphenylacetic acid). Wine metabolites were classified into two groups; first, metabolites of food metabolome: tartrate (RWA and RWD), ethanol, and mannitol (RWA); and second, biomarkers that relates to endogenous modifications after wine consumption, comprising branched-chain amino acid (BCAA) metabolite (3-methyl-oxovalerate). Additionally, a possible interaction between alcohol and gut-related biomarkers has been identified. To our knowledge, this is the first time that this approach has been applied in a nutritional intervention with red wine. The results show the capacity of this approach to obtain a comprehensive metabolome picture including food metabolome and endogenous biomarkers of moderate wine intake.
authors.affiliation: Nutrition and Food Science Department, XaRTA, INSA, Pharmacy Faculty, University of Barcelona, Barcelona, Spain.
authors.forename: Rosa
authors.initials: R
authors.lastname: Vázquez-Fresno
authors.forename: Rafael
authors.initials: R
authors.lastname: Llorach
authors.forename: Francesca
authors.initials: F
authors.lastname: Alcaro
authors.forename: Miguel Ángel
authors.initials: MÁ
authors.lastname: Rodríguez
authors.forename: Maria
authors.initials: M
authors.lastname: Vinaixa
authors.forename: Gemma
authors.initials: G
authors.lastname: Chiva-Blanch
authors.forename: Ramon
authors.initials: R
authors.lastname: Estruch
authors.forename: Xavier
authors.initials: X
authors.lastname: Correig
authors.forename: Cristina
authors.initials: C
authors.lastname: Andrés-Lacueva
mesh_terms: D000428:Alcohol Drinking / Q000378:metabolism* / Q000652:urine; D000704:Analysis of Variance; D015415:Biomarkers / Q000378:metabolism / Q000652:urine; D002318:Cardiovascular Diseases / Q000378:metabolism* / Q000652:urine; D018592:Cross-Over Studies; D000431:Ethanol / Q000008:administration & dosage / Q000378:metabolism; D005419:Flavonoids / Q000008:administration & dosage / Q000378:metabolism; D006801:Humans; D008297:Male; D055442:Metabolome / Q000187:drug effects*; D055432:Metabolomics; D054892:Metagenome; D008875:Middle Aged; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D010636:Phenols / Q000008:administration & dosage / Q000378:metabolism; D011446:Prospective Studies; D012307:Risk Factors; D014920:Wine
chemical_list: D015415:Biomarkers; D005419:Flavonoids; D010636:Phenols; D000431:Ethanol