Evaluation of acute kidney injury by urinary tissue inhibitor metalloproteinases-2 and insulin-like growth factor-binding protein 7 after pediatric cardiac surgery

Biomarkers for acute kidney injury in children - where are we now?

PURPOSE OF REVIEW

Review the literature over the last 2 years on commonly evaluated biomarkers of acute kidney injury (AKI) and highlight the findings of these biomarkers.

RECENT FINDINGS

Among several studied AKI biomarkers, urine neutrophil gelatinase-associated lipocalin (NGAL) and the combination of urine tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) have been recently studied most frequently as diagnostic biomarkers of AKI and for AKI risk stratification. Urine NGAL has continued to show good discriminative value to predict and diagnose AKI in childhood. Urine TIMP-2∗IGFBP7 can provide modest improvement to clinical models of AKI.

SUMMARY

Prior research supports that AKI biomarkers may identify AKI at an earlier time point and indicate clinically meaningful tubular injury. More effort should be made to understand if AKI biomarkers can guide treatments and improve outcomes.

Potential of Urine Biomarkers CHI3L1, NGAL, TIMP-2, IGFBP7, and Combinations as Complementary Diagnostic Tools for Acute Kidney Injury after Pediatric Cardiac Surgery: A Prospective Cohort Study

Acute kidney injury (AKI) is common after pediatric cardiac surgery (CS). Several urine biomarkers have been validated to detect AKI earlier. The objective of this study was to evaluate urine CHI3L1, NGAL, TIMP-2, IGFBP7, and NephroCheck® as predictors for AKI ≥ 1 in pediatric CS after 48 h and AKI ≥ 2 after 12 h. Pediatric patients (age < 18 year; body weight ≥ 2 kg) requiring CS were prospectively included. Urine CHI3L1, NGAL, TIMP-2, IGFBP7, and NephroCheck® were measured during surgery and intensive care unit (ICU) stay and corrected for urine dilution. One hundred and one pediatric patients were included. AKI ≥ 1 within 48 h after ICU admission occurred in 62.4% and AKI ≥ 2 within 12 h in 30.7%. All damage biomarkers predicted AKI ≥ 1 within 48 h after ICU admission, when corrected for urine dilution: CHI3L1 (AUC-ROC: 0.642 (95% CI, 0.535–0.741)), NGAL (0.765 (0.664–0.848)), TIMP-2 (0.778 (0.662–0.868)), IGFBP7 (0.796 (0.682–0.883)), NephroCheck® (0.734 (0.614–0.832)). Similarly, AKI ≥ 2 within 12 h was predicted by all damage biomarkers when corrected for urine dilution: uCHI3L1 (AUC-ROC: 0.686 (95% CI, 0.580–0.780)), NGAL (0.714 (0.609–0.804)), TIMP-2 (0.830 (0.722–0.909)), IGFBP7 (0.834 (0.725–0.912)), NephroCheck® (0.774 (0.658–0.865)). After pediatric cardiac surgery, the damage biomarkers urine CHI3L1, NGAL, TIMP-2, IGFBP7, and NephroCheck® reliably predict AKI after correction for urine dilution.

Identification of hub genes associated with acute kidney injury induced by renal ischemia–reperfusion injury in mice

Background: Acute kidney injury (AKI) is a severe clinical syndrome, and ischemia–reperfusion injury is an important cause of acute kidney injury. The aim of the present study was to investigate the related genes and pathways in the mouse model of acute kidney injury induced by ischemia–reperfusion injury (IRI-AKI).

Method: Two public datasets (GSE39548 and GSE131288) originating from the NCBI Gene Expression Omnibus (GEO) database were analyzed using the R software limma package, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) and gene set enrichment analysis (GSEA) were performed using the differentially expressed genes. Furthermore, a protein-protein interaction (PPI) network was constructed to investigate hub genes, and transcription factor (TF)–hub gene and miRNA–hub gene networks were constructed. Drugs and molecular compounds that could interact with hub genes were predicted using the DGIdb.

Result: A total of 323 common differentially expressed genes were identified in the renal ischemia–reperfusion injury group compared with the control group. Among these, 260 differentially expressed genes were upregulated and 66 differentially expressed genes were downregulated. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes analysis results showed that these common differentially expressed genes were enriched in positive regulation of cytokine production, muscle tissue development, and other biological processes, indicating that they were involved in mitogen-activated protein kinase (MAPK), PI3K-Akt, TNF, apoptosis, and Epstein–Barr virus infection signaling pathways. Protein-protein interaction analysis showed 10 hub genes, namely, Jun, Stat3, MYC, Cdkn1a, Hif1a, FOS, Atf3, Mdm2, Egr1, and Ddit3. Using the STRUST database, starBase database, and DGIdb database, it was predicted that 34 transcription factors, 161 mi-RNAs, and 299 drugs or molecular compounds might interact with hub genes.

Conclusion: Our findings may provide novel potential biomarkers and insights into the pathogenesis of ischemia–reperfusion injury–acute kidney injury through a comprehensive analysis of Gene Expression Omnibus data, which may provide a reliable basis for early diagnosis and treatment of ischemia–reperfusion injury–acute kidney injury.

Filtering Down to Risks and Solutions: Risk Factors and Stratification After Pediatric Cardiac Surgery

Acute kidney injury after cardiac surgery (CS-AKI) is common in neonatal and pediatric populations and is a risk factor for poor outcomes, such as mortality and increased hospital resource utilization. This review presents a summary of CS-AKI risk factors, integration of biomarkers, and the need to improve risk stratification for targeting future clinical trials. To date, studies examining CS-AKI risk factors cannot be generalized easily owing to variability in patient age, surgical complexity or population, AKI definition, and center-specific practices. However, certain risk associations, such as younger age at surgery, history of prematurity, cardiopulmonary bypass time, and surgical complexity, have been identified across multiple, but not all, studies. CS-AKI appears to have different severity and duration phenotypes, and serum creatinine is limited in its ability to identify CS-AKI early and predict CS-AKI course. Treatment strategies are largely supportive, and efforts are ongoing to use biomarkers and clinical features to risk-stratify patients, which in turn may facilitate differential CS-AKI phenotyping and management with supportive care bundles, clinical decision support techniques, and modulation of modifiable risk factors.