Metabolomics Profiling of Epicardial Adipose Tissue: MESA and the Rotterdam Study

BACKGROUND: 

Excess epicardial adipose tissue (EAT) has been associated with cardiovascular diseases such as atrial fibrillation, coronary artery disease, and heart failure. The metabolomic signature of EAT is not well studied.

METHODS: 

Untargeted 1H nuclear magnetic resonance metabolomics profiling of serum was performed (1-dimensional nuclear magnetic resonance, Carr-Purcell-Meiboom-Gill Echo Train Acquisition, lipidomics) and EAT was measured with computed tomography in MESA (Multi-Ethnic Study of Atherosclerosis; N=3936) and the Rotterdam study (N=465). Associations between fasting serum metabolites and EAT volume were assessed using cross-sectional linear regression of individual-level data in MESA and validated in Rotterdam.

RESULTS: 

A total of 23 571 metabolomic spectral variables were evaluated. In MESA, after adjustment for age, sex, and race and ethnicity, 38 metabolites were positively and 19 metabolites negatively associated with EAT at a false discovery rate P<0.01. Several metabolites were replicated in Rotterdam, including 1,5-anhydrosorbitol and N-acetyl (glycoproteins) that were positively associated with EAT and trimethylamine (phospholipids) that were inversely associated with EAT. Branched-chain amino acids (leucine, isoleucine, and valine) and 3-hydroxybutyrate were also associated with EAT in the Rotterdam study. In MESA, apolipoprotein B and very-low-density and intermediate-density lipoprotein fractions were positively associated with EAT and the majority of high-density lipoprotein subclasses were inversely associated with EAT. Associations were partially attenuated in MESA and fully attenuated in Rotterdam after further adjustment for health and socioeconomic factors.

CONCLUSIONS: 

From >20 000 metabolomic features, 1,5-anhydrosorbitol, glycoproteins, phospholipids, and atherogenic dyslipidemia markers emerged as significant markers of EAT. Further investigation is warranted to determine whether nuclear magnetic resonance-based metabolic profiling can improve EAT detection with implications for cardiometabolic health.