4.6 Article

Metabolomics Profiling of Visceral Adipose Tissue: Results From MESA and the NEO Study

Journal

Publisher

WILEY
DOI: 10.1161/JAHA.118.010810

Keywords

adipose tissue; cohort; metabolite; metabolomics; obesity; visceral adipose tissue

Funding

  1. National Institutes of Health (NIH) [K23 DK106520]
  2. University of Texas Southwestern
  3. National Heart, Lung, and Blood Institute (NHLBI) [HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169]
  4. National Center for Advancing Translational Sciences [UL1-TR-000040, UL1-TR-001079, UL1-TR-001420]
  5. NHLBI [R01-HL088451]
  6. European Union [654241, 305422]
  7. Leiden University Medical Center
  8. Leiden University
  9. Netherlands Cardiovascular Research Initiative
  10. Dutch Heart Foundation [CVON2014-02 ENERGISE]
  11. Dutch Science Organization (ZonMW-VENI) [916.14.023]

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Background-Identifying associations between serum metabolites and visceral adipose tissue (VAT) could provide novel biomarkers of VAT and insights into the pathogenesis of obesity-related diseases. We aimed to discover and replicate metabolites reflecting pathways related to VAT. Methods and Results-Associations between fasting serum metabolites and VAT area (by computed tomography or magnetic resonance imaging) were assessed with cross-sectional linear regression of individual-level data from participants in MESA (Multi-Ethnic Study of Atherosclerosis; discovery, N=1103) and the NEO (Netherlands Epidemiology of Obesity) study (replication, N=2537). Untargeted H-1 nuclear magnetic resonance metabolomics profiling of serum was performed in MESA, and metabolites were replicated in the NEO study using targeted H-1 nuclear magnetic resonance spectroscopy. A total of 30 590 metabolomic spectral variables were evaluated. After adjustment for age, sex, race/ethnicity, socioeconomic status, smoking, physical activity, glucose/lipid-lowering medication, and body mass index, 2104 variables representing 24 nonlipid and 49 lipid/lipoprotein subclass metabolites remained significantly associated with VAT (P=4.88 x 10(-20)-1.16 x 10(-3)). These included conventional metabolites, amino acids, acetylglycoproteins, intermediates of glucose and hepatic metabolism, organic acids, and subclasses of apolipoproteins, cholesterol, phospholipids, and triglycerides. Metabolites mapped to 31 biochemical pathways, including amino acid substrate use/metabolism and glycolysis/gluconeogenesis. In the replication cohort, acetylglycoproteins, branched-chain amino acids, lactate, glutamine (inversely), and atherogenic lipids remained associated with VAT (P=1.90 x 10(-35)-8.46 x 10(-7)), with most associations remaining after additional adjustment for surrogates of VAT (glucose level, waist circumference, and serum triglycerides), reflecting novel independent associations. Conclusions-We identified and replicated a metabolite panel associated with VAT in 2 community-based cohorts. These findings persisted after adjustment for body mass index and appear to define a metabolic signature of visceral adiposity.

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