Seeking a Unique Lipid Signature Predicting Cardiovascular Disease Risk

There is now overwhelming evidence that alterations in lipid metabolism underlie the pathogenesis of atherosclerotic cardiovascular disease (CVD). However, research in this area has focused primarily on abundant plasma lipids such as cholesterol, triglycerides, and oxidized phospholipids that are largely carried on circulating lipoproteins.1,2 Circulating levels of abundant lipids can provide valuable information for CVD risk stratification, yet the majority of CVD risk cannot be explained by traditional lipid risk factors.3 Over the last decade, there have been rapid advances in mass spectrometry–based methods to comprehensively annotate the entire “lipidome,” which includes a large variety of lipid molecular species such as non–lipoprotein-associated lipids and less abundant signaling lipids that affect disease. This new field of lipidomics provides a powerful platform for the identification of potential lipid biomarker signatures predicting disease risk but also provides clues into novel lipid metabolic pathways that may be directly involved in the pathogenesis of disease. At an amazingly rapid pace (Figure 1), lipidomic approaches have yielded valuable information linking specific lipid types with structural and signaling roles in the development of cancer,4 obesity,5 diabetes mellitus,6 hypertension,7 Alzheimer disease,8 infectious diseases,9 and CVD.10 Undoubtedly, lipidomic approaches provide a powerful discovery platform that has promise both to identify new biomarkers of chronic disease and to provide clues into unrecognized lipid metabolic pathways with potential mechanistic links to disease pathogenesis and untapped therapeutic potential.Figure 1. The growth of lipidomic technology over the last decade. The histogram represents the number of publications using lipidomics as a discovery platform. The numbers were obtained by using “lipidomics” as a search term in the PubMed database.Article see p 1821In this issue of Circulation , Stegemann and colleagues11 apply a targeted lipidomic approach to population-based cohorts with documented CVD events. …