Acute kidney injury (AKI) is a major global public health problems, as it causes high morbidity and serious injury to renal function. However, the etiology for AKI is not very clear. In this study, a serum metabolite profile analysis was performed to identify potential biomarkers for gentamicin-induced AKI and to investigate the mechanism of action of Amomum compactum (AC) used for treatment. A metabonomics approach by ultra-performance liquid chromatography together with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was applied to perform the analysis. Back propagation (BP) neural network models were established for classifying data from the control, model, and AC-treated groups. Accuracy rate for classification was 91.7% in positive ion mode and 87.5% in negative ion mode. By orthogonal partial least squares discriminant analysis (OPLS-DA), 29 metabolites were identified as potential biomarkers of gentamicin-induced AKI. Most of them are related to phospholipid metabolism. After treatment with AC, the levels of sphingomyelin, sphingosine, phytosphingosine, and arachidonic acid were restored to normal. The results indicate that AC plays a protective role in rats with gentamicin-induced AKI via regulation of the phospholipid metabolic pathway. In this work, early biomarkers of AKI has been identified and underlying therapeutic mechanism of AC has been understood, therefore, AC can be further investigated and tested for clinical application.
Keywords: Acute kidney injury; Amomum compactum; BP neural network algorithm; Gentamicin; Metabonomics; UPLC–MS.
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