Lipidomics-based deep learning algorithms can enhance prediction of obstructive coronary artery disease

Abstract

Abstract Background Lipidomics emerge as a promising research field with the potential to help in personalized risk stratification and also improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). The existing technological challenges to detect diverse yet structurally similar lipids and their isomers, as well as the need for novel statistical and computational tools to handle the high-dimensional lipidome may be responsible for the reduced clinical translation of the very first lipidomics research outcomes. Methods To that end, this post-hoc analysis of the prospective CorLipid trial aimed to investigate the predictive capability of a lipidomics panel for obstructive CAD risk through an extreme gradient boosting (XGBoost) machine learning (ML) approach. The lipid profiles of 146 individuals with suspected CAD were investigated through liquid chromatography-mass spectrometry. Fasting blood samples were drawn prior to invasive coronary angiography execution. Obstructive CAD was defined as SYNTAX Score (SS) >0 compared to non-obstructive CAD (SS=0). Results Study participants (75.3% male, mean age: 61 ±10.5 years old) were divided into two categories based on the existence of obstructive CAD (54.8% with SS>0 and 45.2% with SS=0). In total, 517 lipid species were identified, from which 290 lipid species were finally quantified in participants’ serum [glycerophospholipids (52.1%), glycerolipids (28.6%) and sphingolipids (19.3%)]. The levels of glycerophospholipid, sphingolipid and glycerolipid classes were significantly different in patients with obstructive CAD. Finally, a ML XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, triacylglycerols, galectin-3, glucose, LDL and LDH) as totally sensitive (100% sensitivity, 62.1% specificity and 100% negative predictive value) for the prediction of obstructive CAD. Conclusions These findings provide molecular insights into the role of dysregulated lipid metabolism in the development and progression of CAD while validating the existing body of evidence from similar research studies. Further (ML-based) investigation of lipid metabolism could hold promises for novel therapeutic strategies and improvement of the existing risk stratification schemes.Lipidomics panel importance