Abstract Identifying serum biomarkers that accurately reflect the progression of coronary artery disease (CAD) remains a major challenge. Integrative proteomic and metabolomic profiling can provide novel insights into disease pathogenesis and improve clinical prediction. We conducted a four-phase study. In the discovery phase, serum from 40 patients (controls, stable CAD, and acute coronary syndrome (ACS)) was analyzed using data-independent acquisition (DIA) proteomics and liquid chromatography-tandem mass spectrometry (LC–MS)/gas chromatography–mass spectrometry (GC–MS) metabolomics to identify differentially expressed proteins (DEPs) and metabolites. In the verification phase, selected DEPs were validated by parallel reaction monitoring (PRM) in an independent 40-patient cohort. In the derivation phase, six validated proteins were measured by ELISA in 207 angina patients to assess their association with coronary obstruction (≥ 50% stenosis). In the validation phase, a support vector machine (SVM) model incorporating clinical risk factors and these biomarkers was developed in the derivation cohort and tested in an independent 97-patient cohort. Model performance was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis. Coronary obstruction is defined as ≥ 50% luminal diameter stenosis in at least one major coronary artery on angiography. Proteomic analysis identified 97 DEPs, and metabolomic profiling revealed 322 DEMs (including 289 from LC–MS and 33 from GC–MS analyses). Seven proteins showed consistent changes in both DIA and PRM validation. Among these, thrombospondin-1 (TSP-1) was significantly upregulated in stable CAD compared with controls, while serum amyloid A1 (SAA1) was markedly elevated in ACS compared with stable CAD. In an independent angina cohort ( n = 207), serum levels of TSP-1 and SAA1 were significantly higher in patients with coronary obstruction. Multivariate logistic regression adjusted for conventional cardiovascular risk factors (including age, sex, homocysteine, and other clinical variables) demonstrated that TSP-1 remained independently associated with coronary artery occlusion (OR = 1.424, 95% CI 1.057–1.918, P = 0.020). A support vector machine (SVM) model incorporating conventional clinical risk factors was constructed, and the addition of TSP-1 and SAA1 significantly improved diagnostic performance for CAD severity (AUC = 0.919 in derivation, 0.992 in validation). In conclusion, our dual-omics approach identified novel biomarkers and pathways in CAD progression. The SVM-based prediction model offers a promising non-invasive tool for early CAD detection, potentially reducing unnecessary invasive procedures.
Wang et al. (Fri,) studied this question.