Use of novel structural features to identify urinary biomarkers during acute kidney injury that predict progression to chronic kidney disease

Jennifer R Charlton; Teng Li; Teresa Wu; Kimberly deRonde; Yanzhe Xu; Edwin J Baldelomar; Kevin M Bennett

doi:10.1186/s12882-023-03196-0

Use of novel structural features to identify urinary biomarkers during acute kidney injury that predict progression to chronic kidney disease

BMC Nephrol. 2023 Jun 19;24(1):178. doi: 10.1186/s12882-023-03196-0.

Authors

Jennifer R Charlton¹, Teng Li², Teresa Wu³, Kimberly deRonde⁴, Yanzhe Xu³, Edwin J Baldelomar⁵, Kevin M Bennett⁵

Affiliations

¹ School of Medicine, Department of Pediatrics, Division Nephrology, University of Virginia, Box 800386, Charlottesville, VA, 22903, USA. jrc6n@virginia.edu.
² Department of Computer Science, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
³ Department of Industrial Engineering, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
⁴ School of Medicine, Department of Pediatrics, Division Nephrology, University of Virginia, Box 800386, Charlottesville, VA, 22903, USA.
⁵ Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis MO, USA.

Abstract

Background: A significant barrier to biomarker development in the field of acute kidney injury (AKI) is the use of kidney function to identify candidates. Progress in imaging technology makes it possible to detect early structural changes prior to a decline in kidney function. Early identification of those who will advance to chronic kidney disease (CKD) would allow for the initiation of interventions to halt progression. The goal of this study was to use a structural phenotype defined by magnetic resonance imaging and histology to advance biomarker discovery during the transition from AKI to CKD.

Methods: Urine was collected and analyzed from adult C57Bl/6 male mice at four days and 12 weeks after folic acid-induced AKI. Mice were euthanized 12 weeks after AKI and structural metrics were obtained from cationic ferritin-enhanced-MRI (CFE-MRI) and histologic assessment. The fraction of proximal tubules, number of atubular glomeruli (ATG), and area of scarring were measured histologically. The correlation between the urinary biomarkers at the AKI or CKD and CFE-MRI derived features was determined, alone or in combination with the histologic features, using principal components.

Results: Using principal components derived from structural features, twelve urinary proteins were identified at the time of AKI that predicted structural changes 12 weeks after injury. The raw and normalized urinary concentrations of IGFBP-3 and TNFRII strongly correlated to the structural findings from histology and CFE-MRI. Urinary fractalkine concentration at the time of CKD correlated with structural findings of CKD.

Conclusions: We have used structural features to identify several candidate urinary proteins that predict whole kidney pathologic features during the transition from AKI to CKD, including IGFBP-3, TNFRII, and fractalkine. In future work, these biomarkers must be corroborated in patient cohorts to determine their suitability to predict CKD after AKI.

Keywords: Biomarker; CFE-MRI; Folic acid nephropathy; Nephron number.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Acute Kidney Injury* / pathology
Animals
Biomarkers / metabolism
Chemokine CX3CL1 / metabolism
Insulin-Like Growth Factor Binding Protein 3
Male
Mice
Renal Insufficiency, Chronic* / diagnostic imaging
Renal Insufficiency, Chronic* / pathology

Substances

Insulin-Like Growth Factor Binding Protein 3
Chemokine CX3CL1
Biomarkers

Abstract

Publication types

MeSH terms

Substances

Grants and funding