Cristina Razquin1,2,3, Estefanía Toledo1,2,3, Clary B Clish4, Miguel Ruiz-Canela1,2,3, Courtney Dennis4, Dolores Corella3,5, Christopher Papandreou3,6, Emilio Ros3,7, Ramon Estruch3,8, Marta Guasch-Ferré3,6,9, Enrique Gómez-Gracia3,10, Montserrat Fitó3,11, Edward Yu9, José Lapetra3,12, Dong Wang9, Dora Romaguera3,13, Liming Liang14,15, Angel Alonso-Gómez3,16, Amy Deik4, Mónica Bullo3,6, Lluis Serra-Majem3,17, Jordi Salas-Salvadó3,6, Frank B Hu9,18, Miguel A Martínez-González19,2,3,9. 1. Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain. 2. IdiSNA, Navarra Institute for Health Research, Pamplona, Spain. 3. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain. 4. Broad Institute of MIT and Harvard University, Cambridge, MA. 5. Department of Preventive Medicine, University of Valencia, Valencia, Spain. 6. Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain. 7. Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomediques August Pi Sunyer (IDI-BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 8. Department of Internal Medicine, Institut d'Investigacions Biomediques August Pi Sunyer (IDI-BAPS), Barcelona, Spain. 9. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA. 10. Department of Preventive Medicine, University of Malaga, Malaga, Spain. 11. Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain. 12. Research Unit, Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain. 13. Instituto de Investigación Sanitaria Illes Balears (IdISBa), University Hospital of Son Espases, Palma de Mallorca, Spain. 14. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA. 15. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA. 16. Department of Cardiology, University Hospital of Alava, Vitoria, Spain. 17. Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, and Service of Preventive Medicine, Complejo Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canary Health Service, Las Palmas de Gran Canaria, Spain. 18. Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. 19. Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain mamartinez@unav.es.
Abstract
OBJECTIVE: Specific lipid molecular changes leading to type 2 diabetes (T2D) are largely unknown. We assessed lipidome factors associated with future occurrence of T2D in a population at high cardiovascular risk. RESEARCH DESIGN AND METHODS: We conducted a case-cohort study nested within the PREDIMED trial, with 250 incident T2D cases diagnosed during 3.8 years of median follow-up, and a random sample of 692 participants (639 noncases and 53 overlapping cases) without T2D at baseline. We repeatedly measured 207 plasma known lipid metabolites at baseline and after 1 year of follow-up. We built combined factors of lipid species using principal component analysis and assessed the association between these lipid factors (or their 1-year changes) and T2D incidence. RESULTS: Baseline lysophosphatidylcholines and lysophosphatidylethanolamines (lysophospholipids [LPs]), phosphatidylcholine-plasmalogens (PC-PLs), sphingomyelins (SMs), and cholesterol esters (CEs) were inversely associated with risk of T2D (multivariable-adjusted P for linear trend ≤0.001 for all). Baseline triacylglycerols (TAGs), diacylglycerols (DAGs), and phosphatidylethanolamines (PEs) were positively associated with T2D risk (multivariable-adjusted P for linear trend <0.001 for all). One-year changes in these lipids showed associations in similar directions but were not significant after adjustment for baseline levels. TAGs with odd-chain fatty acids showed inverse associations with T2D after adjusting for total TAGs. CONCLUSIONS: Two plasma lipid profiles made up of different lipid classes were found to be associated with T2D in participants at high cardiovascular risk. A profile including LPs, PC-PLs, SMs, and CEs was associated with lower T2D risk. Another profile composed of TAGs, DAGs, and PEs was associated with higher T2D risk.
OBJECTIVE: Specific lipid molecular changes leading to type 2 diabetes (T2D) are largely unknown. We assessed lipidome factors associated with future occurrence of T2D in a population at high cardiovascular risk. RESEARCH DESIGN AND METHODS: We conducted a case-cohort study nested within the PREDIMED trial, with 250 incident T2D cases diagnosed during 3.8 years of median follow-up, and a random sample of 692 participants (639 noncases and 53 overlapping cases) without T2D at baseline. We repeatedly measured 207 plasma known lipid metabolites at baseline and after 1 year of follow-up. We built combined factors of lipid species using principal component analysis and assessed the association between these lipid factors (or their 1-year changes) and T2D incidence. RESULTS: Baseline lysophosphatidylcholines and lysophosphatidylethanolamines (lysophospholipids [LPs]), phosphatidylcholine-plasmalogens (PC-PLs), sphingomyelins (SMs), and cholesterol esters (CEs) were inversely associated with risk of T2D (multivariable-adjusted P for linear trend ≤0.001 for all). Baseline triacylglycerols (TAGs), diacylglycerols (DAGs), and phosphatidylethanolamines (PEs) were positively associated with T2D risk (multivariable-adjusted P for linear trend <0.001 for all). One-year changes in these lipids showed associations in similar directions but were not significant after adjustment for baseline levels. TAGs with odd-chain fatty acids showed inverse associations with T2D after adjusting for total TAGs. CONCLUSIONS: Two plasma lipid profiles made up of different lipid classes were found to be associated with T2D in participants at high cardiovascular risk. A profile including LPs, PC-PLs, SMs, and CEs was associated with lower T2D risk. Another profile composed of TAGs, DAGs, and PEs was associated with higher T2D risk.
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