| Sumario: | Background: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with increased mortality in critical patients. The early detection of SA-AKI is crucial for clinical intervention. This study aims to integrate multiple metabolomics data related to SA-AKI to identify and validate novel metabolic markers. Methods: Real-time glomerular filtration rate (RT-GFR) measurement was adopted to establish SA-AKI mice. Untargeted metabolomics sequencing was performed on SA-AKI mice renal tissue (Control—LPS-8 h—LPS-24 h, N = 4) and urine samples (Control group vs. LPS-24 h group, N = 6). Time series analysis and random forest algorithm were employed to identify key metabolic molecule. Subsequently, renal spatiotemporal metabolomics was used to explore the specific distribution of key molecule. Eventually, a clinical cohort (20 healthy volunteers vs. 30 sepsis patients vs. 45 SA-AKI patients) urine quantitative metabolomic analysis was carried out to validate it as a biomarker and construct a diagnostic model via logistic regression (LR). Results: Forty-two key renal metabolites and top fifty urinary metabolites were determined through multidimensional metabolomics study of SA-AKI mice. Urinary 3-Methylhistidine (3-MH) was charactered as a potential biomarker. The distribution of 3-MH increased in collecting ducts through renal spatiotemporal metabolomics sequencing. Then, we recruited 95 urine samples to validate its diagnostic performance (AUC = 0.86, 95% CI 0.77–0.95) and its role as an independent predictive factor for SA-AKI (OR = 0.21, 95% CI: 0.05–0.84, p < 0.05). Ultimately, a diagnostic model combined urinary 3-MH with clinical variables was constructed to identify SA-AKI (AUC = 0.89, 95% CI 0.74–1.00). Conclusions: We proposed that urinary 3-Methylhistidine has potential diagnostic value for SA-AKI screening. Future studies will focus on its performance in other clinical populations to comprehensively evaluate its diagnostic role.
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