Abstract
Acute myocardial infarction (AMI) is a leading cause of cardiovascular mortality. Current diagnostic dilemma suffers from limited sensitivity, insufficiently timely and effective specificity. To address this dilemma, Raman spectroscopy, a rapid and non-invasive technique with significant potential for plasma metabolic profiling, although AMI involves rapid metabolic alterations, diagnostic approaches based on plasma metabolites remain underexplored. In murine AMI model induced by coronary artery ligation, we acquired Raman spectra from ultrafiltered plasma samples 8 h post-surgery. We identified eight distinct Raman peaks, assigned to amino acids, lipids, and nucleic acids, which collectively differentiated AMI group (n = 27) from the Sham group (n = 27). To ensure optimal accuracy, we employed five different algorithms including SVM, LR, RF, LDA and PLS-DA to analyze the Raman spectra, both RF and LDA achieved the highest accuracy of 79.6%, specificity of 85.2%, and sensitivity of 74.1%. Furthermore, metabolomic analysis revealed significant down-regulation of most lipid classes, aligning with the downregulation observed in the Raman peaks at 2885 cm⁻¹ and 2940 cm⁻¹. These results demonstrate a high concordance between plasma metabolic profiling via Raman spectroscopy and MS analysis. The integration of Raman spectroscopy with machine learning has remarkable potential for the early and accurate diagnosis of AMI, offering a promising approach for clinical translation.