In recent decades, interest in nitrated fatty acids (NO2-FAs) has grown due to their role as endogenous signaling molecules involved in health and disease. As a result, their metabolic profiling has gained increasing attention. For metabolite analysis, GC-MS/MS offers greater sensitivity and robustness than LC-MS/MS, with more reliable annotation libraries. This study investigates metabolic dysregulation in cardiovascular disease (CVD) patients by using advanced metabolomics. A novel GC-MS/MS method for profiling NO2-FAs was developed, showing improved precision using 17-BrHDA as an internal standard compared to previous HDA-based methods. It is also the first report of alkylation and silylation derivatization of 17-BrHDA, demonstrating superior GC-MS sensitivity for pentafluorobenzyl-alkylated fatty acids over their silylated counterparts in positive ion mode. Untargeted metabolomics was applied to plasma samples from acute myocardial infarction (AMI) patients and healthy controls using both derivatization techniques. Multivariate analysis (PCA and PLS-DA) revealed distinct metabolic profiles. Key metabolites, identified based on VIP scores, were annotated via the Human Metabolome Database and literature. Findings highlight the complementary nature of both derivatization approaches for comprehensive plasma metabolome analysis. Notably, NO2-OA levels were significantly elevated (p < 0.01) in AMI patients, indicating its possibility to be utilized as a cardiovascular biomarker. This study represents the first use of alkylation derivatization in untargeted metabolomics for AMI and introduces a highly sensitive GC-MS/MS method with an innovative internal standard and optimized derivatization for cardiovascular biomarker discovery. The method demonstrates the potential to discriminate between groups of patients and healthy subjects.
Elshoura et al. (Thu,) studied this question.