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<p>Sepsis-associated acute kidney injury (SA-AKI) is a severe complication in critically ill patients, with a complex pathogenesis involving in cell cycle arrest, microcirculatory dysfunction, and inflammation. Current diagnostic strategies remain suboptimal. Therefore, this study aimed to evaluate pathophysiology-based biomarkers and develop an improved predictive model for SA-AKI. The prospective observational study was conducted,&#xa0;enrolling 26 healthy individuals and 96 sepsis patients from Peking University Third Hospital. Clinical and laboratory data were collected, and patients were monitored for AKI development within 72&#xa0;h. Further,&#xa0;sepsis patients were categorized into SA-noAKI (<i>n</i> = 46) and SA-AKI (<i>n</i> = 50) groups. Novel biomarkers, including tissue inhibitor of metalloproteinase-2 (TIMP-2), insulin-like growth factor-binding protein-7 (IGFBP-7), and angiopoietin-2 (Ang-2), were measured in all participants. Among these sepsis patients, the SA-AKI incidence was 52.08% (50/96). Compared to SA-noAKI, the SA-AKI group had significantly higher levels of TIMP-2 (93.55 [79.36, 119.56] ng/mL), IGFBP-7 (27.8 [21.44, 37.29] ng/mL), TIMP-2×IGFBP-7 (2.91 [1.90, 3.55] (ng/mL)²/1000), and Ang-2 (10.61 [5.79, 14.57] ng/mL) (<i>P</i> &lt; 0.05). Accordingly,&#xa0;logistic regression identified TIMP-2×IGFBP-7 (<i>OR</i> = 2.71), Ang-2 (<i>OR</i> = 1.19), and PCT (<i>OR</i> = 1.05) as independent risk factors. The ROC curve of the predictive model demonstrated superior early-stage accuracy (AUC = 0.898), which remained stable during internal validation (AUC = 0.899). Meanwhile, the nomogram exhibited that this model was characterized with excellent discrimination, calibration, and clinical performance. In general,&#xa0; TIMP-2×IGFBP-7, Ang-2 and PCT were the independent risk factors for SA-AKI, and the novel model based on the three indicators provided a more accurate and sensitive strategy for the early prediction of SA-AKI.</p>

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Biomarkers of cell cycle arrest, microcirculation dysfunction, and inflammation in the prediction of SA-AKI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月7日

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  • Qian Zhang,
  • Boxin Yang,
  • Xiaodan Li,
  • Yang Zhao,
  • Shuo Yang,
  • Qingbian Ma,
  • Liyan Cui

摘要

Sepsis-associated acute kidney injury (SA-AKI) is a severe complication in critically ill patients, with a complex pathogenesis involving in cell cycle arrest, microcirculatory dysfunction, and inflammation. Current diagnostic strategies remain suboptimal. Therefore, this study aimed to evaluate pathophysiology-based biomarkers and develop an improved predictive model for SA-AKI. The prospective observational study was conducted, enrolling 26 healthy individuals and 96 sepsis patients from Peking University Third Hospital. Clinical and laboratory data were collected, and patients were monitored for AKI development within 72 h. Further, sepsis patients were categorized into SA-noAKI (n = 46) and SA-AKI (n = 50) groups. Novel biomarkers, including tissue inhibitor of metalloproteinase-2 (TIMP-2), insulin-like growth factor-binding protein-7 (IGFBP-7), and angiopoietin-2 (Ang-2), were measured in all participants. Among these sepsis patients, the SA-AKI incidence was 52.08% (50/96). Compared to SA-noAKI, the SA-AKI group had significantly higher levels of TIMP-2 (93.55 [79.36, 119.56] ng/mL), IGFBP-7 (27.8 [21.44, 37.29] ng/mL), TIMP-2×IGFBP-7 (2.91 [1.90, 3.55] (ng/mL)²/1000), and Ang-2 (10.61 [5.79, 14.57] ng/mL) (P < 0.05). Accordingly, logistic regression identified TIMP-2×IGFBP-7 (OR = 2.71), Ang-2 (OR = 1.19), and PCT (OR = 1.05) as independent risk factors. The ROC curve of the predictive model demonstrated superior early-stage accuracy (AUC = 0.898), which remained stable during internal validation (AUC = 0.899). Meanwhile, the nomogram exhibited that this model was characterized with excellent discrimination, calibration, and clinical performance. In general,  TIMP-2×IGFBP-7, Ang-2 and PCT were the independent risk factors for SA-AKI, and the novel model based on the three indicators provided a more accurate and sensitive strategy for the early prediction of SA-AKI.