Cristina Razquin1, Liming Liang2, Estefanía Toledo1, Clary B Clish3, Miguel Ruiz-Canela1, Yan Zheng4, Dong D Wang4, Dolores Corella5, Olga Castaner6, Emilio Ros7, Fernando Aros8, Enrique Gomez-Gracia9, Miquel Fiol10, José Manuel Santos-Lozano11, Marta Guasch-Ferre12, Lluis Serra-Majem13, Aleix Sala-Vila7, Pilar Buil-Cosiales14, Monica Bullo15, Montserrat Fito6, Olga Portoles5, Ramon Estruch16, Jordi Salas-Salvado15, Frank B Hu17, Miguel A Martinez-Gonzalez18. 1. University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain. 2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 3. Broad Institute of MIT and Harvard University, Cambridge, MA, USA. 4. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 5. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain. 6. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain. 7. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomediques August Pi Sunyer (IDI- BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 8. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Department of Cardiology, University Hospital of Alava, Vitoria, Spain. 9. Department of Preventive Medicine, University of Malaga, Malaga, Spain. 10. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Instituto de Investigación Sanitaria de Palma (IdISPa), Palma de Mallorca, Spain. 11. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla, Centro de Salud Universitario San Pablo, Sevilla, Spain. 12. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain. 13. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, University of Las Palmas de Gran Canaria, Las Palmas, Spain. 14. IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Osasunbidea, Servicio Navarro de Salud, Primary Health Care, Pamplona, Spain. 15. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain. 16. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 17. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA, USA. 18. University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: mamartinez@unav.es.
Abstract
BACKGROUND: The study of the plasma lipidome may help to better characterize molecular mechanisms underlying cardiovascular disease. The identification of new lipid biomarkers could provide future targets for prevention and innovative therapeutic approaches. In the frame of the PREDIMED trial, our aim was to examine the associations of baseline lipidome patterns or their changes with the risk of clinical CVD events. METHODS: We included 983 participants in our case-cohort study. The end-point was the incidence of major CVD during 4.8years of median follow-up. We repeatedly measured 202 plasma known lipid metabolites at baseline and after 1-year of intervention. Principal component analysis was used to identify lipidome factors. Among the 15 identified factors, 7 were significantly associated with CVD. Considering common patterns among factors, lipids were grouped (summed) into scores. RESULTS: After adjustment for traditional CVD risk factors, scores of baseline polyunsaturated phosphatidylcholines (PC)/lysoPC/PC-plasmalogens and polyunsaturated cholesterol esters (CE) showed inverse associations with CVD (p=0.036 and 0.012, respectively); whereas scores of monoacylglycerols (MAGs)/diacylglycerols (DAGs) and short triacylglycerols (TAGs) showed a direct association with CVD (p=0.026 and 0.037, respectively). Baseline phosphatidylethanolamines (PEs) and their 1-y changes tended to be associated with higher CVD risk (p=0.066 and 0.081, respectively). We did not find a significant effect of the intervention with the Mediterranean Diet on these scores. CONCLUSIONS: Our study suggests that polyunsaturated PCs and CEs may confer protection against CVD. In contrast, MAGs, DAGs, TAGs and PEs appeared to be associated with higher CVD risk.
RCT Entities:
BACKGROUND: The study of the plasma lipidome may help to better characterize molecular mechanisms underlying cardiovascular disease. The identification of new lipid biomarkers could provide future targets for prevention and innovative therapeutic approaches. In the frame of the PREDIMED trial, our aim was to examine the associations of baseline lipidome patterns or their changes with the risk of clinical CVD events. METHODS: We included 983 participants in our case-cohort study. The end-point was the incidence of major CVD during 4.8years of median follow-up. We repeatedly measured 202 plasma known lipid metabolites at baseline and after 1-year of intervention. Principal component analysis was used to identify lipidome factors. Among the 15 identified factors, 7 were significantly associated with CVD. Considering common patterns among factors, lipids were grouped (summed) into scores. RESULTS: After adjustment for traditional CVD risk factors, scores of baseline polyunsaturated phosphatidylcholines (PC)/lysoPC/PC-plasmalogens and polyunsaturated cholesterol esters (CE) showed inverse associations with CVD (p=0.036 and 0.012, respectively); whereas scores of monoacylglycerols (MAGs)/diacylglycerols (DAGs) and short triacylglycerols (TAGs) showed a direct association with CVD (p=0.026 and 0.037, respectively). Baseline phosphatidylethanolamines (PEs) and their 1-y changes tended to be associated with higher CVD risk (p=0.066 and 0.081, respectively). We did not find a significant effect of the intervention with the Mediterranean Diet on these scores. CONCLUSIONS: Our study suggests that polyunsaturated PCs and CEs may confer protection against CVD. In contrast, MAGs, DAGs, TAGs and PEs appeared to be associated with higher CVD risk.
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