Metabolomic Profiling of Cholesterol Efflux Capacity in a Multiethnic Population: Insights From MESA

Author:

Hunter Wynn G.1ORCID,Smith Alexander G.2,Pinto Rui C.23ORCID,Saldanha Suzanne1,Gangwar Anamika1,Pahlavani Mandana1,Deodhar Sneha1,Wilkins John4ORCID,Pandey Ambarish1ORCID,Herrington David5,Greenland Philip4ORCID,Tzoulaki Ioanna267ORCID,Rohatgi Anand1ORCID

Affiliation:

1. Division of Cardiology, Department of Medicine, University of Texas Southwestern School of Medicine, Dallas (W.G.H., S.S., A.G., M.P., S.D., A.P., A.R.).

2. MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health (A.G.S., R.C.P., I.T.), Imperial College London, United Kingdom.

3. UK Dementia Research Institute (R.C.P), Imperial College London, United Kingdom.

4. Division of Cardiology, Department of Medicine, and Department of Preventive Medicine, Feinberg School of Medicine, Chicago, IL (J.W., P.G.).

5. Department of Medicine, Wake Forest School of Medicine, Winston-Salem, NC (D.H.).

6. BHF Centre of Excellence (I.T.), Imperial College London, United Kingdom.

7. Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Greece (I.T.).

Abstract

BACKGROUND: Impaired cholesterol efflux capacity (CEC) is a novel lipid metabolism trait associated with atherosclerotic cardiovascular disease. Mechanisms underlying CEC variation are unknown. We evaluated associations of circulating metabolites with CEC to advance understanding of metabolic pathways involved in cholesterol efflux regulation. METHODS: Participants enrolled in the MESA (Multi-Ethnic Study of Atherosclerosis) who underwent nuclear magnetic resonance metabolome profiling and CEC measurement (N=3543) at baseline were included. Metabolite associations with CEC were evaluated using standard linear regression analyses. Repeated ElasticNet and multilayer perceptron regression were used to assess metabolite profile predictive performance for CEC. Features important for CEC prediction were identified using Shapley Additive Explanations values. RESULTS: Greater CEC was significantly associated with metabolite clusters composed of the largest-sized particle subclasses of VLDL (very-low-density lipoprotein) and HDL (high-density lipoprotein), as well as their constituent apo A 1 , apo A 2 , phospholipid, and cholesterol components (β=0.072–0.081; P <0.001). Metabolite profiles had poor accuracy for predicting in vitro CEC in linear and nonlinear analyses (R 2 <0.02; Spearman ρ<0.18). The most important feature for CEC prediction was race, with Black participants having significantly lower CEC compared with other races. CONCLUSIONS: We identified independent associations among CEC, the largest-sized particle subclasses of VLDL and HDL, and their constituent apolipoproteins and lipids. A large proportion of variation in CEC remained unexplained by metabolites and traditional clinical risk factors, supporting further investigation into genomic, proteomic, and phospholipidomic determinants of CEC.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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