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Abstract: PO0418

Can Urinary Biomarkers at AKI Predict Progression to CKD?

Session Information

Category: Acute Kidney Injury

  • 103 AKI: Mechanisms

Authors

  • Charlton, Jennifer R., University of Virginia School of Medicine, Charlottesville, Virginia, United States
  • Li, Teng, Arizona State University, Tempe, Arizona, United States
  • Xu, Yanzhe, Arizona State University, Tempe, Arizona, United States
  • Wu, Teresa, Arizona State University, Tempe, Arizona, United States
  • Deronde, Kimberly, University of Virginia School of Medicine, Charlottesville, Virginia, United States
  • Bennett, Kevin M., Washington University in St Louis, St Louis, Missouri, United States
Background

Acute kidney injury (AKI) can cause permanent structural changes and progressive chronic kidney disease (CKD). If kidney function normalizes after AKI, it is difficult to distinguish who will progress to CKD. We evaluated urinary biomarkers from mice at the time of AKI and correlated them to a range of structural features derived from histopathology and cationic ferritin enhanced-MRI (CFE-MRI) in the kidney later in life. We investigated whether these biomarkers at AKI could predict future progression to CKD.

Methods

Adult male mice were injected with folic acid (AKI) or NaHCO3 (controls), (n=8/grp) and urine was collected after 4 days. Biomarkers were measured using the Cytokine Array Q1000 (Ray BioTech). Mice received CF 12 wks after AKI and kidneys were imaged ex vivo using a 7T MRI. Structural metrics were derived by CFE-MRI (Nglom and aVglom) and histology (proximal tubule (PT) content, atubular glomeruli (ATG), and scarred area).

Results

We performed a univariate analysis comparing the AKI and controls. Using hierarchical edge bundling, there were 19 connections between the urinary biomarkers and structural metrics at 12 wks and 7 connections in the control group (Fig 1A). EGF, OPG, TARC and TNF RII (correlation>0.8, Fig 1B) were correlated to structural metrics in AKI and only IGFBP-6 was correlated to the structural metrics in controls. We developed predictive models using the 13 urinary markers in Fig 1B. The best model predicted mean aVglom (r2=0.67), %ATG (r2=0.50) and PT content (r2=0.47).

Conclusion

Urinary biomarkers, alone or in combination, may provide noninvasive predictive markers for progression to CKD after AKI. We identified 13 urinary biomarkers that appear to predict structural changes in the kidney 12 weeks after AKI and may serve as candidate biomarkers to predict outcomes in patients.

Funding

  • NIDDK Support