Background: Currently, the relationship between circulating lipids and abdominal aortic aneurysm (AAA) is unclear. We conducted a lipidomic analysis to identify serum lipids associated with AAA presence. Secondary analyses assessed the ability of models incorporating lipidomic features to improve stratification of patient groups with and without AAA beyond traditional risk factors.
Methods and results: Serum lipids were profiled via liquid chromatography tandem mass spectrometry analysis of serum from 161 patients with AAA and 168 controls with peripheral artery disease. Binary logistic regression was used to identify AAA-associated lipids. Classification models were created based on a combination of (1) traditional risk factors only or (2) lipidomic features and traditional risk factors. Model performance was assessed using receiver operator characteristic curves. Three diacylglycerols and 7 triacylglycerols were associated with AAA. Combining lipidomic features with traditional risk factors significantly improved stratification of AAA and peripheral artery disease groups when compared with traditional risk factors alone (mean area under the receiver operator characteristic curve [95% confidence interval], 0.760 [0.756-0.763] and 0.719 [0.716-0.723], respectively; P<0.05).
Conclusions: A group of linoleic acid containing triacylglycerols and diacylglycerols were significantly associated with AAA presence. Inclusion of lipidomic features in multivariate analyses significantly improved prediction of AAA presence when compared with traditional risk factors alone.
Keywords: aortic aneurysm, abdominal; biomarkers; lipids; peripheral arterial disease.