Pablo Hernández-Alonso1,2,3,4, Nerea Becerra-Tomás1,2,3, Christopher Papandreou1,2,3, Mònica Bulló1,2,3, Marta Guasch-Ferré1,3,5, Estefanía Toledo3,6,7, Miguel Ruiz-Canela3,6,7, Clary B Clish8, Dolores Corella3,9, Courtney Dennis8, Amy Deik8, Dong D Wang5, Cristina Razquin3,6,7, Jean-Philippe Drouin-Chartier5,10,11, Ramon Estruch3,12, Emilio Ros3,13, Montserrat Fitó3,14, Fernando Arós3,15, Miquel Fiol3,16, Lluís Serra-Majem3,17, Liming Liang18, Miguel A Martínez-González3,6,7,5, Frank B Hu8,18,19, Jordi Salas-Salvadó1,2,3. 1. Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Reus, 43201, Spain. 2. Institut d'Investigació Pere Virgili (IISPV), Reus, 43003, Spain. 3. Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, 28029, Spain. 4. Unidad de Gestión Clínica de Endocrinología y Nutrición del Hospital Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, 29010, Spain. 5. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA. 6. University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, 31008, Spain. 7. Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, 31008, Spain. 8. Broad Institute of MIT and Harvard University, Cambridge, MA, 02142, USA. 9. Department of Preventive Medicine, University of Valencia, Valencia, 46020, Spain. 10. Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, G1V 0A6, Canada. 11. Faculté de Pharmacie, Université Laval, Québec, G1V 0A6, Canada. 12. Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, 08036, Spain. 13. Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, 08036, Spain. 14. Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, 08003, Spain. 15. Department of Cardiology, University Hospital of Alava, Vitoria, 01009, Spain. 16. Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, 07122, Spain. 17. Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, Las Palmas, 35001, Spain. 18. Departments of Epidemiology and Statistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA. 19. Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
Abstract
SCOPE: The plasma metabolomics profiles of protein intake have been rarely investigated. The aim is to identify the distinct plasma metabolomics profiles associated with overall intakes of protein as well as with intakes from animal and plant protein sources. METHODS AND RESULTS: A cross-sectional analysis using data from 1833 participants at high risk of cardiovascular disease is conducted. Associations between 385 identified metabolites and the intake of total, animal protein (AP), and plant protein (PP), and plant-to-animal ratio (PR) are assessed using elastic net continuous regression analyses. A double 10-cross-validation (CV) procedure is used and Pearson correlations coefficients between multi-metabolite weighted models and reported protein intake in each pair of training-validation datasets are calculated. A wide set of metabolites is consistently associated with each protein source evaluated. These metabolites mainly consisted of amino acids and their derivatives, acylcarnitines, different organic acids, and lipid species. Few metabolites overlapped among protein sources (i.e., C14:0 SM, C20:4 carnitine, GABA, and allantoin) but none of them toward the same direction. Regarding AP and PP approaches, C20:4 carnitine and dimethylglycine are positively associated with PP but negatively associated with AP. However, allantoin, C14:0 SM, C38:7 PE plasmalogen, GABA, metronidazole, and trigonelline (N-methylnicotinate) behave contrarily. Ten-CV Pearson correlation coefficients between self-reported protein intake and plasma metabolomics profiles range from 0.21 for PR to 0.32 for total protein. CONCLUSIONS: Different sets of metabolites are associated with total, animal, and plant protein intake. Further studies are needed to assess the contribution of these metabolites in protein biomarkers' discovery and prediction of cardiometabolic alterations.
SCOPE: The plasma metabolomics profiles of protein intake have been rarely investigated. The aim is to identify the distinct plasma metabolomics profiles associated with overall intakes of protein as well as with intakes from animal and plant protein sources. METHODS AND RESULTS: A cross-sectional analysis using data from 1833 participants at high risk of cardiovascular disease is conducted. Associations between 385 identified metabolites and the intake of total, animal protein (AP), and plant protein (PP), and plant-to-animal ratio (PR) are assessed using elastic net continuous regression analyses. A double 10-cross-validation (CV) procedure is used and Pearson correlations coefficients between multi-metabolite weighted models and reported protein intake in each pair of training-validation datasets are calculated. A wide set of metabolites is consistently associated with each protein source evaluated. These metabolites mainly consisted of amino acids and their derivatives, acylcarnitines, different organic acids, and lipid species. Few metabolites overlapped among protein sources (i.e., C14:0 SM, C20:4 carnitine, GABA, and allantoin) but none of them toward the same direction. Regarding AP and PP approaches, C20:4 carnitine and dimethylglycine are positively associated with PP but negatively associated with AP. However, allantoin, C14:0 SM, C38:7 PE plasmalogen, GABA, metronidazole, and trigonelline (N-methylnicotinate) behave contrarily. Ten-CV Pearson correlation coefficients between self-reported protein intake and plasma metabolomics profiles range from 0.21 for PR to 0.32 for total protein. CONCLUSIONS: Different sets of metabolites are associated with total, animal, and plant protein intake. Further studies are needed to assess the contribution of these metabolites in protein biomarkers' discovery and prediction of cardiometabolic alterations.
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