High-Throughput Quantification of Circulating Metabolites Improves Prediction of Subclinical Atherosclerosis

Study Questions:

What is the predictive ability of metabolite quantification by nuclear magnetic resonance (NMR) for detection of subclinical atherosclerosis in comparison to conventional lipid testing?


Circulating lipids, lipoprotein subclasses, and small molecules were assayed by NMR for 1,595 individuals ages 24-39 years from the population-based Cardiovascular Risk in Young Finns Study. Carotid intima–media thickness (IMT), a marker of subclinical atherosclerosis, was measured in 2001 and 2007. Baseline conventional risk factors and systemic metabolites were used to predict 6-year incidence of high IMT (≥90th percentile) or plaque.


The best prediction of high IMT was achieved when total and high-density lipoprotein (HDL) cholesterol were replaced by NMR-determined low-density lipoprotein (LDL) cholesterol and medium HDL, docosahexaenoic acid, and tyrosine in prediction models with risk factors from the Framingham risk score. The extended prediction model improved risk stratification beyond established risk factors alone (area under the receiver operating characteristic curve 0.764 vs. 0.737, p = 0.02, and net reclassification index 17.6%, p = 0.0008). Higher docosahexaenoic acid levels were associated with decreased risk for incident high IMT (odds ratio [OR], 0.74; 95% confidence interval [CI], 0.67-0.98; p = 0.007). Tyrosine (OR, 1.33; 95% CI, 1.10-1.60; p = 0.003) and glutamine (OR, 1.38; CI, 1.13-1.68; p = 0.001) levels were associated with 6-year incident high IMT independent of lipid measures. Furthermore, these amino acids were cross-sectionally associated with carotid IMT and the presence of angiographically ascertained coronary artery disease in independent populations.


The authors concluded that high-throughput metabolite quantification, with new systemic biomarkers, improved risk stratification for subclinical atherosclerosis in comparison to conventional lipids.


This study suggests that high-throughput metabolite quantification by NMR improves risk stratification for subclinical atherosclerosis in comparison to conventional lipid testing, as evidenced by increased discrimination and improved reclassification. Furthermore, the cost of the high-throughput quantification is comparable to that of conventional lipid testing, and could potentially represent a cost-effective approach for early cardiovascular risk assessment. More accurate risk assessment at an early phase of atherosclerosis development obtainable with these biomarkers has potential to benefit individualized treatment strategies and prevention of cardiovascular events, but this strategy needs additional testing and validation.

Clinical Topics: Dyslipidemia, Noninvasive Imaging, Lipid Metabolism, Nonstatins, Echocardiography/Ultrasound, Magnetic Resonance Imaging

Keywords: Odds Ratio, Coronary Artery Disease, Atherosclerosis, Plaque, Atherosclerotic, Carotid Intima-Media Thickness, Lipoproteins, Risk Factors, Magnetic Resonance Imaging, Tyrosine, Cholesterol, Glutamine, Biological Markers, Docosahexaenoic Acids, Cardiovascular Diseases, Confidence Intervals, ROC Curve, Risk Assessment, Magnetic Resonance Spectroscopy

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