Abstract 15009: Plasma Lipidomic Analysis for Prediction of Unstable Coronary Artery Disease
Introduction: Worldwide, 19 million people will die from a sudden coronary event annually as a Results of disruption of an atherosclerotic plaque. Currently there is no means to predict plaque instability in coronary artery disease (CAD). Traditional risk factors such as age, sex, smoking status, diabetes, hypertension and standard blood lipids fail to adequately identify plaque instability. Lipids play a pivotal role in the progression of CAD and novel lipid species offer the potential to act as biomarkers for plaque instability.
Methods: Modified ceramide and modified phosphatidylcholine species were shown to distinguish stable and unstable CAD. These newly identified biomarkers were measured together with known plasma lipids, including sphingolipids, acylglycerols, and phospholipids, to establish a plasma lipid profile using electrospray-ionisation tandem mass spectrometry. Plasma lipid profiles were determined for 202 participants (control, n=60, stable CAD, n=61, unstable CAD, n=81).
Results: From a total of 331 lipid species, 160 were different between control and CAD groups (p<0.01) while 37 were different between stable and unstable CAD groups (p<0.01). Multivariate analysis using a statistical machine learning approach combined with recursive feature elimination and multiple cross-validation iterations was applied for the creation of prediction models. Comparison of models with varying number of features showed that models with only 8 lipids were sufficient to provide optimal discrimination between stable and unstable cohorts (AUC=0.75) while 16 lipids were sufficient to discriminate control from CAD patients (AUC=0.94).
Conclusions: Plasma lipid profiles can provide improved discrimination, beyond conventional risk factors, between stable and unstable CAD.
- © 2010 by American Heart Association, Inc.