Urinary 3-methylhistidine as a potential biomarker for sepsis-associated acute kidney injury: multidimensional metabolomics analysis in mice and human.
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 nove...
| Publicado en: | Annals of Intensive Care Vol. 15; no. 1; pp. 1 - 18 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
8/26/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187773853&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187773853 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21105820 ESHV jtl: Annals of Intensive Care issn: 21105820 maglogo: N pubinfo: dt: 8/26/2025 vid: 15 iid: 1 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 187773853 10.1186/s13613-025-01550-z 187773853 ppf: 1 ppct: 17 formats: tig: atl: Urinary 3-methylhistidine as a potential biomarker for sepsis-associated acute kidney injury: multidimensional metabolomics analysis in mice and human. aug: au: Wang, Xibo Huang, Pengfei Luo, Yinghao Xin, Yu Li, Yue Shen, Lifeng Liu, Yanqi Zhou, Yang Zhang, Yuxin Zhang, Qianqian Wang, Dawei Luan, Feiyu Zhang, Weiting Yuan, Mengyao Liu, Yuhan Liu, Fengye Zhang, Nan Wu, Jinyuan Wu, Tao Wang, Xuan affil: https://ror.org/05vy2sc54 Departments of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, 150001, Harbin, Heilongjiang, China sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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