TY - JOUR

T1 - Lipid Profiles and Heart Failure Risk

T2 - Results From Two Prospective Studies

AU - Wittenbecher, Clemens

AU - Eichelmann, Fabian

AU - Toledo, Estefanía

AU - Guasch-Ferré, Marta

AU - Ruiz-Canela, Miguel

AU - Li, Jun

AU - Arós, Fernando

AU - Lee, Chih Hao

AU - Liang, Liming

AU - Salas-Salvadó, Jordi

AU - Clish, Clary B.

AU - Schulze, Matthias B.

AU - Martínez-González, Miguel Ángel

AU - Hu, Frank B.

N1 - Publisher Copyright: © 2020 American Heart Association, Inc.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - biomarker

KW - heart failure

KW - lipidomics

KW - lipids

KW - metabolism

U2 - 10.1161/CIRCRESAHA.120.317883

DO - 10.1161/CIRCRESAHA.120.317883

M3 - Journal article

C2 - 33272114

AN - SCOPUS:85102018619

VL - 128

SP - 309

EP - 320

JO - Circulation Research

JF - Circulation Research

SN - 0009-7330

IS - 3

ER -