Kidney Week

Abstract: SA-PO0932

MRI-Measured Renal Blood Flow as a Biomarker for Function

Session Information

• Pathology: Updates and Insights
  November 08, 2025 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Pathology and Lab Medicine

• 1800 Pathology and Lab Medicine

Authors

• Powell, Emily, The University of Texas at Austin Cockrell School of Engineering, Austin, Texas, United States

Emily Powell

• Margain, Corina, The University of Texas at Austin Cockrell School of Engineering, Austin, Texas, United States

Corina Margain

• Leyva, Victoria L, The University of Texas at Austin College of Liberal Arts, Austin, Texas, United States

Victoria L Leyva

• Clark, Alexandra Leigh, The University of Texas at Austin College of Liberal Arts, Austin, Texas, United States

Alexandra Leigh Clark, PhD

• Bush, Adam, The University of Texas at Austin Cockrell School of Engineering, Austin, Texas, United States

Adam Bush, PhD

Background

Existing methods for assessment of kidney function are imperfect. Serum creatinine-based estimated GFR is widely used and accessible but is inaccurate and confounded by many factors. Conversely, clinical standard measured GFR techniques are cumbersome, time-intensive, require infusions, and often depend upon nephrotoxic or ionizing radiation. A fast, reliable, accessible, and non-contrast measure of kidney function would be transformative, especially for quantifying normo- and hyperfiltrating patients.
Physiologically, GFR = Renal Plasma Flow* Filtration Fraction (FF) = RBF* (1-Hematocrit)* FF. We therefore sought to test whether MRI-measured renal blood flow (RBF) could be used as such a technique.

Methods

We imaged 65 adults (19-84 years old) without known kidney disease (eGFR: 91.51 +/- 19.65 mL/min/1.73^2) on a 3T Siemens Vida Scanner. Vitals were recorded, and eGFR was estimated using CDK-EPI 2021. Phase contrast MRI was used to measure RBF of all left and right renal arteries and summed. Images were processed in MATLAB, and statistical analysis was completed using JMP.

Results

Univariate predictors of eGFR were age (p<.0001, r²=0.486), RBF (p<.0001, r²=0.303), and systolic BP (p=.033, r²=0.071). Univariate predictors of RBF were age (p<.0001, r²=0.350), eGFR (p<.0001, r²=0.303), systolic BP (p=.002, r²=0.144), and diastolic BP (p=.027, r²=0.078). On multivariate analysis, age was the only significant predictor of eGFR (r²=0.483). When age was locked out of the analysis, RBF was the predominate predictor of eGFR (r²=0.288).

Conclusion

The association of RBF with eGFR, BP, and age shows that RBF is the strongest determinant of function in an otherwise healthy population without disease. Future work should explore the use of MRI RBF as a biomarker of kidney function, measured GFR, and kidney outcomes.

eGFR and RBF plotted against age (top row) and systolic blood pressure (bottom row).

Funding

• NIDDK Support

Digital Object Identifier (DOI)

• doi: 10.1681/ASN.2025d5e19098