Pablo Hernández-Alonso1,2, Christopher Papandreou1,2, Mònica Bulló1,2, Miguel Ruiz-Canela2,3,4, Courtney Dennis5, Amy Deik5, Dong D Wang6, Marta Guasch-Ferré1,2,6, Edward Yu6, Estefanía Toledo2,3,4, Cristina Razquin2,3,4, Dolores Corella2,7, Ramon Estruch2,8, Emilio Ros2,9, Montserrat Fitó2,10, Fernando Arós2,11, Miquel Fiol2,12, Lluís Serra-Majem2,13, Liming Liang14, Clary B Clish5, Miguel A Martínez-González2,3,4,6, Frank B Hu5,14,15, Jordi Salas-Salvadó1,2. 1. Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201, Reus, Spain. 2. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029, Madrid, Spain. 3. University of Navarra, Department of Preventive Medicine and Public Health, 31008, Pamplona, Spain. 4. Navarra Institute for Health Research (IdisNA), 31008, Pamplona, Spain. 5. Broad Institute of MIT and Harvard University, 02142, Cambridge, MA, USA. 6. Department of Nutrition, Harvard T.H. Chan School of Public Health, 02115, Boston, MA, USA. 7. Department of Preventive Medicine, University of Valencia, 46020, Valencia, Spain. 8. Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDI- BAPS), Hospital Clinic, University of Barcelona, 08036, Barcelona, Spain. 9. Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDI- BAPS), Hospital Clinic, University of Barcelona, 08036, Barcelona, Spain. 10. Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, 08003, Barcelona, Spain. 11. Department of Cardiology, University Hospital of Alava, 01009, Vitoria, Spain. 12. Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, 07122, Palma de Mallorca, Spain. 13. Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, 35001, Las Palmas, Spain. 14. Departments of Epidemiology and Statistics, Harvard T.H. Chan School of Public Health, 02115, Boston, MA, USA. 15. Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 02115, Boston, MA, USA.
Abstract
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.
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.
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