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A Quantitative Detection Method for MicroRNAs in the Kidney of an Ischemic Kidney Injury Mouse Model
Published on: September 11, 2020
Research progress on biomarkers for acute kidney injury in children
Wenqin Jin1,2, Qing Ye3, Dongqing Cheng4
1School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
Insights
New biomarkers offer earlier detection of pediatric acute kidney injury (AKI) across various causes like surgery, sepsis, and drugs. Integrating multiple markers and functional tests improves risk stratification and clinical management.
Area of Science:
- Nephrology
- Pediatric Critical Care
- Biomarker Research
Background:
- Pediatric acute kidney injury (AKI) diagnosis is delayed by traditional markers.
- Current criteria fail to capture diverse AKI etiologies and injury patterns.
- Advances in biomarker research since 2020 offer new diagnostic potential.
Purpose of the Study:
- Summarize recent pediatric AKI biomarker advances post-ADQI 2020 consensus.
- Review biomarker evidence in cardiac surgery-associated AKI (CS-AKI), sepsis-associated AKI (SA-AKI), and nephrotoxic drug-induced AKI (NT-AKI).
- Explore biomarker applications in risk stratification and understanding AKI pathophysiology.
Main Methods:
- Literature review of pediatric AKI biomarker studies since 2020.
- Analysis of biomarker performance in specific clinical scenarios (CS-AKI, SA-AKI, NT-AKI).
- Mechanistic review of AKI pathways: oxygenation-perfusion, immune dysregulation, tubular-mitochondrial injury.
Main Results:
- CS-AKI: Early detection with uNGAL, followed by IL-18, L-FABP, KIM-1; [TIMP-2]×[IGFBP7] and exosomal miRNA for risk stratification.
- SA-AKI: suPAR, endothelial markers (syndecan-1, Angpt-2/sTM/Tie-2), urinary DKK3, and complement Ba aid early risk stratification.
- NT-AKI: uNGAL has high negative predictive value; uKIM-1, uCysC, uOPN, and combinations detect subclinical tubular injury.
Conclusions:
- Single biomarkers are insufficient for heterogeneous pediatric AKI.
- Multi-biomarker panels and functional assessments are crucial.
- Future research should focus on AI-driven, scenario-stratified diagnostic pathways for clinical integration.
Abstract:
Pediatric acute kidney injury (AKI) often presents insidiously and progresses rapidly. Traditional diagnostic criteria based on serum creatinine and urine output are markedly delayed and insufficient to capture injury patterns across different etiologies. This paper aims to summarize recent advances in pediatric AKI biomarker research since the release of the ADQI 23 (2020) consensus. Focusing on three major clinical scenarios-cardiac surgery, sepsis, and nephrotoxic drugs-it reviews early biomarker evidence and explores their potential applications in risk stratification. At the mechanistic level, this paper outlines key pathological pathways in pediatric AKI progression: oxygenation-perfusion imbalance after cardiac surgery, endothelium-immune dysregulation driven by sepsis, and tubular-mitochondrial injury associated with nephrotoxic exposure. In CS-AKI, uNGAL shows the earliest elevation within hours after cardiopulmonary bypass, followed by sequential changes in IL-18, L-FABP, and KIM-1. [TIMP-2] × [IGFBP7] and exosomal miRNA aid in identifying high-risk or severe AKI. In SA-AKI, suPAR and glycocalyx/endothelial injury markers (e.g., syndecan-1, Angpt-2/sTM/Tie-2), combined with urinary DKK3 and complement Ba, can be used for early risk stratification and predicting poor outcomes. In NT-AKI, uNGAL has high negative predictive value for excluding severe AKI, while uKIM-1, uCysC, uOPN, and multi-biomarker combinations can indicate subclinical tubular injury earlier after drug exposure. Overall, single biomarkers struggle to cover AKI heterogeneity. Future efforts should integrate functional dynamic assessments (e.g., FST, RRI), scenario-based multi-biomarker combinations, and AI dynamic models to propose evidence-based, scenario-stratified identification pathways. These will serve as structured references for prospective studies and clinical workflow optimization.
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