# Development of a computational biomechanics model of the glomerulus to assess risk of mechanical stress-induced glomerular injury in conditions of reduced afferent arteriole vasoconstrictive response.

> **NIH NIH F31** · TULANE UNIVERSITY OF LOUISIANA · 2020 · $29,520

## Abstract

Project Summary/Abstract
A reduction in the vasoconstrictive responsiveness of the afferent arteriole relative to perfusion pressure is
implicated in the progression of glomerular injury in diabetes, some forms of hypertension and chronic kidney
diseases involving the loss of functional nephrons. A reduction of the vasoconstrictive responsiveness of the
afferent arteriole raises afferent blood flow and intraglomerular pressure which is believed to increase
mechanical stress (cyclic stretch and fluid flow shear stress) on the glomerular cells. In response to cyclic
stretch, mesangial cells increase deposition of extracellular matrix (ECM) components and podocytes may
detach from the glomerular capillary. In response to increased shear stress, vascular endothelial cells increase
production of inflammatory markers. These results collectively indicate that a reduced vasoconstrictive
responsiveness of the afferent arteriole relative to perfusion pressure causes injury of glomerular cells by
increasing shear stress on and cyclic stretch of the glomerular capillary walls. Although mechanical stress-
induced glomerular injury is a generally accepted concept in kidney disease research, the actual magnitudes of
mechanical stress, in particular shear stress and hoop stress resulting from an attenuated afferent arteriole
vasoconstrictive response, are unknown. The overall aim of this proposal is to use multiscale mathematical
modeling to estimate the magnitudes of shear stress and capillary wall stretch in the glomerular capillary
network as a result of decreased afferent arteriole vasoconstrictive responsiveness. We will develop a
“glomerular network model” that calculates flows through the capillaries of an actual, anatomically-accurate
glomerular microvascular network. A feedback model of afferent arteriole resistance will be integrated with the
glomerular network model to represent the complex dynamics of renal autoregulation in our model.
Additionally, we will develop a computational fluid dynamics (CFD) model of a single glomerular capillary,
taking into account the dynamics arising from elastic red blood cell structures flowing in a permeable channel.
Taking a multiscale mathematical modeling approach, for each capillary segment of the glomerular network
model, output of the glomerular network model will be mapped to parameters in the CFD capillary model to
calculate shear stresses on the vessel walls. The mechanical stresses calculated using this approach will be
compared to experimental parameters of previous cell studies to determine the risk of glomerular cell injury
with and without the pathological hemodynamic conditions arising from reduction in afferent arteriole
vasoconstrictive responsiveness. This work will serve as a basis for a glomerular injury risk index in
pathological renal hemodynamic conditions and will inform the design of “glomerulus-on-a-chip”
microphysiological systems. Mechanical forces are known to crucially affect the efficac...

## Key facts

- **NIH application ID:** 9924241
- **Project number:** 5F31DK121445-02
- **Recipient organization:** TULANE UNIVERSITY OF LOUISIANA
- **Principal Investigator:** Owen Richfield
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $29,520
- **Award type:** 5
- **Project period:** 2019-05-10 → 2021-06-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9924241

## Citation

> US National Institutes of Health, RePORTER application 9924241, Development of a computational biomechanics model of the glomerulus to assess risk of mechanical stress-induced glomerular injury in conditions of reduced afferent arteriole vasoconstrictive response. (5F31DK121445-02). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9924241. Licensed CC0.

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