Identification of potential biomarkers for coronary slow flow using untargeted metabolomics.

Background: Coronary slow flow (CSF) is associated with poor cardiovascular prognosis. However, its pathogenesis is unclear. This study aimed to identify potential characteristic biomarkers in patients with CSF using untargeted metabolomics.

Methods: We prospectively enrolled 30 patients with CSF, 30 with coronary artery disease (CAD), and 30 with normal coronary arteries (NCA), all of whom were age-matched, according to the results of coronary angiography. Serum metabolomics were analyzed using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Differentially expressed metabolites were identified through orthogonal partial least squares-discriminant analysis (OPLS-DA) combined with univariate fold-change and VIP value analysis. Pathway enrichment of these metabolites was performed using the KEGG database, and ROC curves were plotted to assess the diagnostic value of the metabolites in CSF patients.

Results: Compared to the CAD and NCA groups, 256 metabolites showed specific expression in CSF, with 18 meeting stringent screening criteria (VIP > 1, FC ≥ 2, or FC ≤ 0.5, and P < 0.05). Seven metabolites demonstrated high diagnostic value for CSF: inositol 1,3,4-trisphosphate (AUC: 1.0), Cer (d24:1/18:0 (2OH)) (AUC: 0.984), Creosol (AUC: 0.976), Chaps (AUC: 0.904), Arg-Thr-Lys-Arg (AUC: 0.929), Ser-Tyr-Arg (AUC: 0.912), and Methyl Indole-3-Acetate (AUC: 0.909). Pathway analysis highlighted the HIF-1 signaling pathway as the most significant metabolic pathway.

Conclusions: We identified seven metabolites that may serve as serum biomarkers for predicting and diagnosing CSF through untargeted metabolomics. The HIF-1 signaling pathway appears to be crucial in the development of CSF.