Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study

Abstract

Background: The bulk of cardiovascular disease risk is not explained by traditional risk factors. Recent advances in mass spectrometry allow the identification and quantification of hundreds of lipid species. Molecular lipid profiling by mass spectrometry may improve cardiovascular risk prediction.

Methods and results: Lipids were extracted from 685 plasma samples of the prospective population-based Bruneck Study (baseline evaluation in 2000). One hundred thirty-five lipid species from 8 different lipid classes were profiled by shotgun lipidomics with the use of a triple-quadrupole mass spectrometer. Levels of individual species of cholesterol esters (CEs), lysophosphatidylcholines, phosphatidylcholines, phosphatidylethanolamines (PEs), sphingomyelins, and triacylglycerols (TAGs) were associated with cardiovascular disease over a 10-year observation period (2000-2010, 90 incident events). Among the lipid species with the strongest predictive value were TAGs and CEs with a low carbon number and double-bond content, including TAG(54:2) and CE(16:1), as well as PE(36:5) (P=5.1 × 10⁻⁷, 2.2 × 10⁻⁴, and 2.5 × 10⁻³, respectively). Consideration of these 3 lipid species on top of traditional risk factors resulted in improved risk discrimination and classification for cardiovascular disease (cross-validated ΔC index, 0.0210 [95% confidence interval, 0.0010-0.0422]; integrated discrimination improvement, 0.0212 [95% confidence interval, 0.0031-0.0406]; and continuous net reclassification index, 0.398 [95% confidence interval, 0.175-0.619]). A similar shift in the plasma fatty acid composition was associated with cardiovascular disease in the UK Twin Registry (n=1453, 45 cases).

Conclusions: This study applied mass spectrometry-based lipidomics profiling to population-based cohorts and identified molecular lipid signatures for cardiovascular disease. Molecular lipid species constitute promising new biomarkers that outperform the conventional biochemical measurements of lipid classes currently used in clinics.

Keywords: biomarkers; cardiovascular diseases; lipids; mass spectrometry; metabolomics.