Clemens Wittenbecher1,2,3, Fabian Eichelmann2,3, Estefanía Toledo4,5,6, Marta Guasch-Ferré1,7, Miguel Ruiz-Canela4,5,6, Jun Li1, Fernando Arós6,8, Chih-Hao Lee1,9, Liming Liang10,11, Jordi Salas-Salvadó6,12,13, Clary B Clish14, Matthias B Schulze2,3,15, Miguel Ángel Martínez-González1,4,5,6, Frank B Hu1,7,10. 1. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA (C.W., M.G.-F., J.L., C.-H.L., M.A.M.-G., F.B.H.). 2. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (C.W., F.E., M.B.S.). 3. German Center for Diabetes Research (DZD), Neuherberg, Germany (C.W., F.E., M.B.S.). 4. Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain (E.T., M.R.-C., M.A.M.-G.). 5. IdiSNA (Instituto de investigación Sanitaria de Navarra), Pamplona, Spain (E.T., M.R.-C., M.A.M.-G.). 6. CIBER Fisiopatología de la Obesidad y Nutricion (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain (E.T., M.R.-C., F.A., J.S.-S., M.A.M.-G.). 7. Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA (M.G.-F., F.B.H.). 8. Department of Cardiology, University Hospital of Alava, Vitoria, Spain (F.A.). 9. Department of Molecular Metabolism (C.-H.L.), Harvard T.H. Chan School of Public Health, Boston, MA. 10. Department of Epidemiology (L.L., F.B.H.), Harvard T.H. Chan School of Public Health, Boston, MA. 11. Department of Biostatistics (L.L.), Harvard T.H. Chan School of Public Health, Boston, MA. 12. Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició, Reus, Spain (J.S.-S.). 13. Institut d'Investigació Sanitària Pere Virgili (IISPV), University Hospital of Sant Joan de Reus, Nutrition Unit, Reus, Spain (J.S.-S.). 14. Broad Institute of MIT and Harvard, Cambridge, MA (C.B.C.). 15. Institute of Nutritional Science, University of Potsdam, Germany (M.B.S.).
Abstract
RATIONALE: Altered lipid metabolism has been implicated in heart failure (HF) development, but no prospective studies have examined comprehensive lipidomics data and subsequent risk of HF. OBJECTIVE: We aimed to link single lipid metabolites and lipidomics networks to the risk of developing HF. METHODS AND RESULTS: Discovery analyses were based on 216 targeted lipids in a case-control study (331 incident HF cases and 507 controls, matched by age, sex, and study center), nested within the PREDIMED (Prevención con Dieta Mediterránea) study. Associations of single lipids were examined in conditional logistic regression models. Furthermore, lipidomics networks were linked to HF risk in a multistep workflow, including machine learning-based identification of the HF-related network clusters, and regression-based discovery of the HF-related lipid patterns within these clusters. If available, significant findings were externally validated in a subsample of the EPIC-Potsdam cohort (2414 at-risk participants, including 87 incident HF cases). After confounder-adjustments, 2 lipids were significantly associated with HF risk in both cohorts: CER (ceramide) 16:0 (relative risk [RR] per SD in PREDIMED, 1.28 [95% CI, 1.13-1.47]) and phosphatidylcholine 32_0 (RR per SD in PREDIMED, 1.23 [95% CI, 1.08-1.41]). Additionally, lipid patterns in several network clusters were associated with HF risk in PREDIMED. Adjusted for standard risk factors, an internally cross-validated score based on the significant HF-related lipids that were identified in the network analysis in PREDIMED was associated with a higher HF risk (20 lipids, RR per SD, 2.33 [95% CI, 1.93%-2.81%). Moreover, a lipid score restricted to the externally available lipids was significantly associated with HF incidence in both cohorts (6 lipids, RRs per SD, 1.30 [95% CI, 1.14-1.47] in PREDIMED, and 1.46 [95% CI, 1.17-1.82] in EPIC-Potsdam). CONCLUSIONS: Our study identified and validated 2 lipid metabolites and several lipidomics patterns as potential novel biomarkers of HF risk. Lipid profiling may capture preclinical molecular alterations that predispose for incident HF. Registration: URL: https://www.isrctn.com/ISRCTN35739639; Unique identifier: ISRCTN35739639.
RATIONALE: Altered lipid metabolism has been implicated in heart failure (HF) development, but no prospective studies have examined comprehensive lipidomics data and subsequent risk of HF. OBJECTIVE: We aimed to link single lipid metabolites and lipidomics networks to the risk of developing HF. METHODS AND RESULTS: Discovery analyses were based on 216 targeted lipids in a case-control study (331 incident HF cases and 507 controls, matched by age, sex, and study center), nested within the PREDIMED (Prevención con Dieta Mediterránea) study. Associations of single lipids were examined in conditional logistic regression models. Furthermore, lipidomics networks were linked to HF risk in a multistep workflow, including machine learning-based identification of the HF-related network clusters, and regression-based discovery of the HF-related lipid patterns within these clusters. If available, significant findings were externally validated in a subsample of the EPIC-Potsdam cohort (2414 at-risk participants, including 87 incident HF cases). After confounder-adjustments, 2 lipids were significantly associated with HF risk in both cohorts: CER (ceramide) 16:0 (relative risk [RR] per SD in PREDIMED, 1.28 [95% CI, 1.13-1.47]) and phosphatidylcholine 32_0 (RR per SD in PREDIMED, 1.23 [95% CI, 1.08-1.41]). Additionally, lipid patterns in several network clusters were associated with HF risk in PREDIMED. Adjusted for standard risk factors, an internally cross-validated score based on the significant HF-related lipids that were identified in the network analysis in PREDIMED was associated with a higher HF risk (20 lipids, RR per SD, 2.33 [95% CI, 1.93%-2.81%). Moreover, a lipid score restricted to the externally available lipids was significantly associated with HF incidence in both cohorts (6 lipids, RRs per SD, 1.30 [95% CI, 1.14-1.47] in PREDIMED, and 1.46 [95% CI, 1.17-1.82] in EPIC-Potsdam). CONCLUSIONS: Our study identified and validated 2 lipid metabolites and several lipidomics patterns as potential novel biomarkers of HF risk. Lipid profiling may capture preclinical molecular alterations that predispose for incident HF. Registration: URL: https://www.isrctn.com/ISRCTN35739639; Unique identifier: ISRCTN35739639.
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