Abstract 37: Metabolomic Signatures of Metabolic Risk Factors for Atherosclerotic Cardiovascular Disease

Abstract

Background: Multiple cardiovascular disease (CVD) risk factors cluster in the same individuals and their concurrence is used to diagnose metabolic syndrome (MetSyn), which carries substantial risk for CVD. We hypothesized that MetSyn is associated with multiple metabolomic derangements.

Methods: As part of the SABRe CVD initiative, a multi-project investigation of biomarkers of CVD and its risk factors, we designed a 2x2x2 factorial study of MetSyn risk factors that included 650 individuals from the Framingham Heart Study (out of a total N of ∼3200) assigned to 8 unique groups of approximately 81 individuals each, sampled from high vs. low strata of BMI, lipids, and glucose. We conducted gas chromatography-mass spectroscopy (GC/MS) on plasma samples from 650 eligible individuals. General linear modeling was used to identify biomarkers that differed across all 8 groups or differed in their main effects on individual risk factors.

Results: Characteristics of the study sample (mean±SD), according to group assignment, are presented in the Table. GC/MS characterized 149 metabolites; of these 18 differed across all groups at P<5x10^-8 and 36 differed at P<0.00001. The top 3 most highly significant metabolites across all groups were glucose (P=2x10^-40), glutamic acid (P=4x10^-26), and sphingomyelins (lowest P=8x10^-25). The top 3 most highly significant main effects of metabolites for BMI were: glutamic acid (P=2x10^-18), sitosterol (P=2x10^-10), and uric acid (P=3x10^-10), for dyslipidemia: sphingomyelins (lowest P=1x10^-27), glutamic acid (P=5x10^-20), and lactic acid (P=7x10^-12), and for dysglycemia: glucose (P=1x10^-42), fructose (P=3x10^-7), and 2-hydroxybutanoic acid (P=6x10^-7).

Conclusions: Metabolomic profiling identified multiple biomarker signatures of MetSyn and its major metabolic risk factors. These novel findings warrant external replication. Understanding the pathways represented by our results may help to unravel the molecular derangements contributing to MetSyn and its constituent risk factors. This knowledge may identify therapeutic targets for the prevention and treatment of CVD.