Plasma Metabolites Associated with Frequent Red Wine Consumption: A Metabolomics Approach within the PREDIMED Study

• Published in: Molecular nutrition & food research Vol. 63; no. 17; pp. e1900140 - n/a
• Main Authors: Hernández‐Alonso, Pablo, Papandreou, Christopher, Bulló, Mònica, Ruiz‐Canela, Miguel, Dennis, Courtney, Deik, Amy, Wang, Dong D., Guasch‐Ferré, Marta, Yu, Edward, Toledo, Estefanía, Razquin, Cristina, Corella, Dolores, Estruch, Ramon, Ros, Emilio, Fitó, Montserrat, Arós, Fernando, Fiol, Miquel, Serra‐Majem, Lluís, Liang, Liming, Clish, Clary B., Martínez‐González, Miguel A, Hu, Frank B, Salas‐Salvadó, Jordi
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.09.2019
• Subjects: Aged; Alkaloids; Area Under Curve; Blood - metabolism; Consumers; Consumption; Cross-Sectional Studies; data collection; Diet; Eating; Elastic analysis; Female; glass; graphs; Humans; LC‐MS; lipidomics; Lipids; liquid chromatography; Male; mass spectrometry; metabolism; Metabolites; Metabolomics; Metabolomics - methods; Middle Aged; Organic acids; organic acids and salts; red wine; red wines; Regression analysis; Regression coefficients; Regression models; Sensitivity and Specificity; Smoking; Wine; LC-MS; red wine; metabolomics; metabolites; lipidomics
• ISSN: 1613-4125; 1613-4133; 1613-4133
• DOI: 10.1002/mnfr.201900140

Scope The relationship between red wine (RW) consumption and metabolism is poorly understood. It is aimed to assess the systemic metabolomic profiles in relation to frequent RW consumption as well as the ability of a set of metabolites to discriminate RW consumers. Methods and results A cross‐sectional analysis of 1157 participants is carried out. Subjects are divided as non‐RW consumers versus RW consumers (>1 glass per day RW [100 mL per day]). Plasma metabolomics analysis is performed using LC–MS. Associations between 386 identified metabolites and RW consumption are assessed using elastic net regression analysis taking into consideration baseline significant covariates. Ten‐cross‐validation (CV) is performed and receiver operating characteristic curves are constructed in each of the validation datasets based on weighted models. A subset of 13 metabolites is consistently selected and RW consumers versus nonconsumers are discriminated. Based on the multi‐metabolite model weighted with the regression coefficients of metabolites, the area under the curve is 0.83 (95% CI: 0.80–0.86). These metabolites mainly consisted of lipid species, some organic acids, and alkaloids. Conclusions A multi‐metabolite model identified in a Mediterranean population appears useful to discriminate between frequent RW consumers and nonconsumers. Further studies are needed to assess the contribution of these metabolites in health and disease. The relationship between red wine (RW) consumption and metabolism is poorly understood. The aim of this study is to assess the circulating metabolomic profiles in relation to frequent RW consumption as well as the ability of a set of metabolites to discriminate RW consumers from nonconsumers. The validated 13‐multi‐metabolite model accurately discriminates those who frequently consume RW from non‐RW consumers.