Background/Objectives: Restenosis following coronary artery bypass grafting (CABG) remains a major long-term complication that adversely affects patient prognosis. Although prior studies have investigated clinical features, imaging parameters, and circulating biomarkers for restenosis risk stratification, the metabolic mechanisms underlying long-term restenosis—particularly those reflecting both the local cardiac microenvironment and systemic circulation—remain poorly defined. Therefore, this study aims to identify restenosis-associated metabolic alterations and develop a risk prediction model based on integrated targeted metabolomic profiling of pericardial fluid (PF) and serum in patients undergoing isolated CABG. Methods: Patients undergoing isolated CABG were prospectively enrolled. Paired PF and serum samples were collected during surgery or the perioperative period for targeted metabolomic analysis. Differential metabolite (DM) analysis was performed between patients with and without restenosis. Key metabolites were selected to construct a restenosis risk prediction model, which was subsequently evaluated in training and validation cohorts. Results: Compared with patients without restenosis, those who developed restenosis exhibited two key differential metabolites identified in PF and serum: 7α-Hydroxy-4-cholesten-3-one and Phenoxyacetic acid (PAA). A logistic regression-based prediction model incorporating these metabolites was developed and evaluated using receiver operating characteristic (ROC) analysis, integrated discrimination improvement (IDI), and decision curve analysis (DCA). The model demonstrated robust predictive performance in both training and validation cohorts. Kaplan–Meier survival analysis further revealed that higher model scores were significantly associated with an increased risk of long-term restenosis in the training cohort (HR = 1.44, p = 0.047) and validation cohort (HR = 1.83, p = 0.012). Conclusions: This study provides the first evidence that integrated metabolomic signatures derived from PF and serum are associated with long-term restenosis after CABG. By capturing complementary metabolic information from the local cardiac microenvironment and systemic circulation, this integrated approach enhances current understanding of restenosis biology and supports the potential clinical utility of targeted metabolomics for long-term restenosis risk prediction following CABG.
Zhou et al. (2026) studied this question.