L-Carnitine, A new biomarker screened based on untargeted metabolomics, predict cardiac surgery-associated acute kidney injury: A prospective cohort study

Abstract Background: This study aims to investigate the diagnostic significance of L-carnitine (LC) for the early detection of acute kidney injury associated with cardiac surgery (CSA-AKI). Methods: We collected clinical data and serum samples from 27 patients admitted to the Intensive Care Unit (ICU) of Nanjing Medical University Affiliated Nanjing Hospital between February 2024 and March 2024. Of these, 13 patients belonged to the CSA-AKI group, while 14 were in the non-CSA-AKI group. An untargeted metabolomic analysis was conducted, which identified LC as a differential metabolite. In addition, clinical data and serum samples were prospectively collected from patients undergoing cardiac surgery at Nanjing Medical University Affiliated Nanjing Hospital between May 2024 and July 2024. Serum samples were taken preoperatively (immediately upon entering the operating room) and postoperatively (immediately upon ICU admission). The concentrations of blood urea nitrogen (BUN), serum creatinine (Scr), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and LC were assessed. Multivariate logistic regression analysis was used to find independent risk variables for CSA-AKI. Predictive performance of the biomarkers, the clinical model, and their combination were evaluated using the area under the receiver operating characteristic curve (AUC). Results: 170 patients in all who satisfied the inclusion requirements for cardiac surgery were included in the study. The incidence of CSA-AKI was 27.06%. Multivariate logistic regression analysis indicated that preoperative heart failure, vasopressor-inotropic score, and postoperative partial pressure of oxygen were independent risk factors for the development of CSA-AKI. Serum biomarker analysis showed significant differences in BUN, Scr, NGAL, and LC levels before and after cardiac surgery. After surgery, LC levels in patients with CSA-AKI were considerably lower than those in patients without CSA-AKI. Postoperative LC had a predictive ability with an AUC of 0.777 (95%CI: 0.697-0.857, P < 0.001). Incorporating postoperative LC into the clinical model can greatly enhance the model's predictive performance. Conclusion: Postoperative LC can effectively predict the occurrence of CSA-AKI, and when combined with the clinical prediction model, it demonstrates improved predictive performance for CSA-AKI.