A targeted LC-MS/MS analysis of circulatory lipid profile to highlight biomarkers for patient stratification according to coronary artery disease severity

Traditional risk factors play a pivotal role in predicting coronary artery disease (CAD) presence/severity and correlation with standard bio-humoral parameters may contribute to improve predictive models of coronary heart disease (CHD). Recently, the use of molecular markers has been proposed as a tool for an adequate risk assessment of CHD [1] and discovery of new potential biomarkers is considered useful for early diagnosis and prognosis of CAD. The study of plasma lipidomics through mass spectrometry is gaining an increasing clinical relevance and lipids are emerging as promising biomarkers of plaque development, composition and vulnerability in patients with CAD [2-7]. In the frame of the "SMARTool Project" we developed a targeted LC-MS/MS method to perform fast, single run evaluation of lipid fingerprint in CAD patients. Our method allowed quantification of 152 lipid species distributed among 9 lipid classes. Principal component analysis evidenced a remarkable separation between obstructive and minimal/absent coronary disease (> 50% vs < 30% degree of stenosis of major vessels). Nine lipids belonging to triglycerides (TG), phosphatidylethanolamine (PE) and Ceramides (Cer), were found significantly upregulated in obstructive patients (TG(48:1), TG(50:2), TG(52:2), PE(34:1), PE(34:2), PE(36:3), PE(36:4), PE(38:5), Cer(d18:1/23:0)). Among them, TG(48:1), PE(36:4) and PE(38:5) [3,5] have been already reported as prognostic markers of future adverse cardiovascular outcomes. Our results suggest that this LC-MS/MS based plasma lipid profile represents a source of potentially useful biomarkers for CAD patient stratification according to disease severity, thus paving the way for future clinical applications. Acknowledgments This work is partially funded by the European Commission: Project SMARTool, "Simulation Modeling of coronary ARTery disease: a tool for clinical decision support -- SMARTool" [GA number: 689068]. References [1] T. Infante et al., Am J Transl Res. 9, 3148 (2017). [2] P. J. Meikle et al., Arterioscler. Thromb. Vasc. Biol. 31, 2723 (2011). [3] C. Fernandez et al., Plos One 8, 1 (2013). [4] C. Stegemann et al., Circulation 129, 1821 (2014). [5] A. H. Ellims et al., Eur. Heart J.-Card. Img. 15, 908 (2014). [6] J. M. Cheng et al., Atherosclerosis 243, 560 (2015). [7] K. Paapstel et al., Nutr. Metab. Cardiovasc. Dis. 28, 44 (2018).