Lipid Profiles and Heart Failure Risk: Results from Two Prospective Studies

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 heart failure. 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 study. Associations of single lipids were examined in conditional logistic regression models. Furthermore, lipidomics networks were linked to HF risk in a multi-step 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, two lipids were significantly associated with HF risk in both cohorts: ceramide 16:0 (HR per SD in PREDIMED 1.28, 95%CI 1.13, 1.47) and phosphatidylcholine 32_0 (HR 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, HR 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, HRs 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 two 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.