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Background <p>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.</p> Methods <p>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&#xa0;h—LPS-24&#xa0;h, N = 4) and urine samples (Control group <i>vs.</i> LPS-24&#xa0;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 <i>vs.</i> 30 sepsis patients <i>vs.</i> 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).</p> Results <p>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%&#xa0;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,<i> p</i> &lt; 0.05). Ultimately, a diagnostic model combined urinary 3-MH with clinical variables was constructed to identify SA-AKI (AUC = 0.89, 95%&#xa0;CI 0.74–1.00).</p> Conclusions <p>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.</p>

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Urinary 3-methylhistidine as a potential biomarker for sepsis-associated acute kidney injury: multidimensional metabolomics analysis in mice and human

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Xibo Wang,
  • Pengfei Huang,
  • Yinghao Luo,
  • Yu Xin,
  • Yue Li,
  • Lifeng Shen,
  • Yanqi Liu,
  • Yang Zhou,
  • Yuxin Zhang,
  • Qianqian Zhang,
  • Dawei Wang,
  • Feiyu Luan,
  • Weiting Zhang,
  • Mengyao Yuan,
  • Yuhan Liu,
  • Fengye Liu,
  • Nan Zhang,
  • Jinyuan Wu,
  • Tao Wu,
  • Xuan Wang,
  • Yuping Bai,
  • Mingyan Zhao,
  • Changsong Wang,
  • Kaijiang Yu

摘要

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.