Metabolic signatures and machine learning identify gut-liver-heart axis dysfunction as a potential link to major adverse cardiovascular events in coronary artery disease

Abstract

Traditional risk factors do not fully account for the residual cardiometabolic risk of major adverse cardiovascular events (MACE) in coronary artery disease (CAD). We aimed to identify circulating metabolic signatures associated with MACE susceptibility and uncover potential pathobiological mechanisms underlying the gut-liver-heart axis. In this retrospective case-control study, untargeted high-performance liquid chromatography-mass spectrometry (HPLC-MS) was performed on fasting serum from 200 patients with CAD and MACE, 200 with CAD without MACE, and 400 matched non-CAD controls. Metabolomics data were processed using univariate analysis, multivariate analysis, and eXtreme Gradient Boosting (XGBoost) machine learning. Pathway enrichment was conducted using metabolite set enrichment analysis. Circulating fibroblast growth factor 19 (FGF19) was quantified via enzyme-linked immunosorbent assay to validate enterohepatic endocrine disruption. The MACE cohort exhibited a pronounced cardiometabolic phenotype, characterized by significantly highest rates of diabetes, hypertension, and dyslipidemia (p < 0.01). The XGBoost model robustly discriminated patients with CAD and MACE from non-CAD controls (area under the curve [AUC] = 0.984) and from patients with CAD without MACE (AUC = 0.932). Pathway analysis revealed marked dysregulation of linoleic acid metabolism and peroxisome proliferator-activated receptor (PPAR) signaling (p < 0.05). Specifically, pro-inflammatory oxidized linoleic acid metabolites, including 9- and 13-hydroxyoctadecadienoic acid (HODE)—which drive plaque instability—were significantly elevated in the MACE cohort. Furthermore, atheroprotective primary bile acids were significantly depleted in patients with CAD (p < 0.001). This depletion was accompanied by an elevated serum FGF19 level (p = 0.003), reflecting a potential disruption of the gut-liver-heart endocrine axis. In conclusion, dysregulated linoleic acid oxidation, altered PPAR signaling, and disturbed primary bile acid-FGF19 metabolism may represent key metabolic pathways associated with MACE susceptibility. Integrating these gut-liver-heart axis signatures into machine learning models holds significant promise for refining cardiovascular risk stratification and guiding targeted preventive interventions.