Metabolomics based early warning model for acute kidney injury risk in patients exposed to diquat

Acute kidney injury (AKI) is one of the most important indications of severe clinical symptoms in patients with diquat poisoning and is closely related to poor prognosis. However, current studies have rarely focused on early warnings of diquat-related AKI, which is not conducive to the treatment of patients with early clinical diquat poisoning. In this study, untargeted plasma metabolomics was employed to reveal the differences between diquat-poisoned patients with and without AKI, as well as between patients and healthy volunteers. The results showed that 48 metabolites were significantly changed in the patients, among which 3-hydroxybutyrylcarnitine, SAICAR, dodecanoic acid, and tetrahydrofolyl-[Glu](2) could be used to effectively differentiate the above three groups. Based on the ratios of the first two metabolites and the ratios of the last two metabolites, a decision tree model for the early warning of diquat-induced AKI was established with an accuracy rate of 88.7 %. This model provides great support for accurate clinical diagnosis and intervention regarding the AKI risk of diquat-exposed patients.