Sudden cardiac death (SCD) presents diagnostic challenges in distinguishing hyperacute ischemic heart disease (IHD) from drug-related fatalities. This pilot study leverages untargeted lipidomics to identify myocardial lipid biomarkers, analyzing heart tissue from six forensic cases (three hyperacute IHD and three drug deaths) via UHPLC-Q-TOF mass spectrometry. Data preprocessing (normalization, transformation, scaling) and multivariate analyses (PCA, PLS-DA) revealed distinct lipid profiles. Three lipids—PC 16:0_16:2, SM 34:1;3O, and PC O-40:5_C—were significantly upregulated in hyperacute IHD (FDR < 0.05), linked to glycerophospholipid metabolism and autophagy dysregulation. Machine learning models (SVM, random forest) achieved 66.7% accuracy in classifying etiology, with triacylglycerols and sphingomyelins as key discriminators. Toxic deaths showed elevated phosphatidylinositols (e.g., PI 38:4) and hexosylceramides. Despite the limited sample size, this work highlights lipidomic potential to complement traditional autopsies in SCD diagnostics. Findings implicate membrane remodeling and sphingolipid signaling in hyperacute IHD pathogenesis. Future studies with expanded cohorts are crucial to validate biomarkers and elucidate mechanisms
