Metabolomic signatures associated with the risk of secondary cardiovascular events in patients post-myocardial infarction

Patients who experience myocardial infarction (MI) are at a higher risk of subsequent major adverse cardiovascular events (MACE), highlighting the need for better risk-assessment strategies. Traditional clinical markers fail to fully capture the evolving biochemical shifts associated with negative cardiovascular outcomes. This longitudinal pilot study investigated metabolic alterations in post-myocardial infarction (MI) patients using targeted longitudinal metabolomics profiling with LC-MS/MS (Multiple Reaction Monitoring (MRM) approach). From 351 enrolled patients with First-time MI, a nested sub-cohort of 24 patients (12 who developed MACE and 12 who did not) was selected. The parent study was registered with the Clinical Trials Registry of India (CTRI/2018/03/012695). Plasma samples were collected at five time points from hospital admission. At admission, 16 metabolites showed nominal differences between the MACE and non-MACE groups, with oleic acid levels being lower in patients who subsequently experienced MACE. Longitudinal analysis revealed significant changes in 22 metabolites, indicating variations in amino acid, purine, and lipid metabolism, particularly involving branched-chain amino acids (BCAAs), oleic acid, arginine derivatives, and nucleobases such as hypoxanthine and uric acid. Arginine-related pathways showed exploratory alterations consistent with biologically plausible links to nitric oxide bioavailability, vascular inflammation, and endothelial dysfunction. Integrative analysis revealed significant correlations between metabolite levels and clinical parameters, such as triglyceride levels, blood pressure, and diabetes, emphasizing the relationship between metabolic dysregulation and cardiovascular disease. These exploratory findings suggest that dynamic plasma metabolite profiles may provide preliminary insights into MACE-associated metabolic alterations after MI; however, they require validation in larger, time-matched cohorts before being considered biomarkers or predictors. Future studies with larger sample sizes and more frequent sampling are required to validate these findings.