Metabolite-based Analysis of High-Risk Factors on Occurrence of AKI in Patients with Acute Myocardial Infarction

Abstract

Current methods lack high‑risk identification for acute kidney injury (AKI) after acute myocardial infarction (AMI). This study aimed to develop a metabolic‑biomarker‑based predictive system. 124 AMI patients (July 2023–October 2024) were enrolled prospectively. Logistic regression, ROC curves, and Pearson correlation were used to assess predictive values. Post-PCI kidney injury incidence was 19.39% ( n = 19). The injury group showed higher LVEF, FFA, and Killip ≥ 2 rates ( P < 0.05), but lower 5-MTP and UMOD ( P < 0.05). FFA, 5-MTP, and UMOD were independent risk factors ( P < 0.05), with combined AUC = 0.931 (superior to single markers, P < 0.05). BUN, UA, SCr, and eGFR correlated strongly with these metabolites ( P < 0.05). LVEF, 5‑MTP, and UMOD are key metabolic indicators for early AKI risk identification. The integrated “biomarker+nursing” pathway improves early warning and outcomes in AMI patients.