Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease.

ABSTRACT: Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statisti

SUBMITTER: Mouskeftara T 

PROVIDER: S-EPMC11301671 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature