Metabolite-based Analysis of High-Risk Factors on Occurrence of AKI in Patients with Acute Myocardial Infarction
Year: 2026
Type: article
Study design: Observational Study
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.
Institution: Northern Jiangsu People's Hospital
Language: English
Topic: Acute Kidney Injury Research
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,390
OpenAlex ID: W7214701764