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Lipid Profiles and Heart Failure Risk: Results From Two Prospective Studies.
Wittenbecher, C, Eichelmann, F, Toledo, E, Guasch-Ferré, M, Ruiz-Canela, M, Li, J, Arós, F, Lee, CH, Liang, L, Salas-Salvadó, J, et al
Circulation research. 2021;(3):309-320
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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.
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Lipid metabolic networks, Mediterranean diet and cardiovascular disease in the PREDIMED trial.
Wang, DD, Zheng, Y, Toledo, E, Razquin, C, Ruiz-Canela, M, Guasch-Ferré, M, Yu, E, Corella, D, Gómez-Gracia, E, Fiol, M, et al
International journal of epidemiology. 2018;(6):1830-1845
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Abstract
BACKGROUND Perturbed lipid metabolic pathways may play important roles in the development of cardiovascular disease (CVD). However, existing epidemiological studies have focused more on discovering individual lipid metabolites for CVD risk prediction rather than assessing metabolic pathways. METHODS This study included a subcohort of 787 participants and all 230 incident CVD cases from the PREDIMED trial. Applying a network-based analytical method, we identified lipid subnetworks and clusters from a global network of 200 lipid metabolites and linked these subnetworks/clusters to CVD risk. RESULTS Lipid metabolites with more double bonds clustered within one subnetwork, whereas lipid metabolites with fewer double bonds clustered within other subnetworks. We identified 10 lipid clusters that were divergently associated with CVD risk. The hazard ratios [HRs, 95% confidence interval (CI)] of CVD per a 1-standard deviation (SD) increment in cluster score were 1.39 (1.17-1.66) for the hydroxylated phosphatidylcholine (HPC) cluster and 1.24 (1.11-1.37) for a cluster that included diglycerides and a monoglyceride with stearic acyl chain. Every 1-SD increase in the score of cluster that included highly unsaturated phospholipids and cholesterol esters was associated with an HR for CVD of 0.81 (95% CI, 0.67-0.98). Despite a suggestion that MedDiet modified the association between a subnetwork that included most lipids with a high degree of unsaturation and CVD, changes in lipid subnetworks/clusters during the first-year follow-up were not significantly different between intervention groups. CONCLUSIONS The degree of unsaturation was a major determinant of the architecture of lipid metabolic network. Lipid clusters that strongly predicted CVD risk, such as the HPC cluster, warrant further functional investigations.