# A Computational Analysis of Hemodynamics in Patients with Renal Artery Fibromuscular Dysplasia

> **NIH NIH F32** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $81,256

## Abstract

PROJECT SUMMARY/ABSTRACT
Fibromuscular Dysplasia (FMD) is a non-atherosclerotic disease of medium and small sized arteries. It most
commonly affects the renal arteries of women ages 20 – 60 years old, overlapping with the demographics and
symptoms caused by hypertensive disorders of pregnancy (HDoP). We have previously observed associations
between renovascular hypertension caused by renal artery FMD and preeclampsia among women with history
of pregnancy. However, a large cohort of these patients observed with overlapping diagnoses were not
diagnosed with FMD until later in life. Literature shows a decline in cure rates for patients with FMD treated
with angioplasty that correlates with age - from up to 65% in patients with an average age of 30 to as low as
20% in patients with an average age of 55. To better determine treatment efficacy in renal FMD it is crucial to
understand the renal hemodynamic properties of FMD, FMD vs ARAS, and HDOP processes. Currently, there
is a lack of understanding about the relative contribution of large vessel versus small vessel disease in FMD,
as well as the differences between FMD, FMD in HDoP, and ARAS patients that could explain the distinct
clinical outcomes between these groups. Furthermore, there is also a lack of understanding on the potential
impact of large vessel disease on small vessel disease over time.
 The goal of this proposal is to investigate the impact of large vessel and small vessel disease
on FMD versus ARAS patients through hemodynamic indices such as FFR, and IMR via image-based
computational fluid dynamics (CFD) and machine learning. Our aim is to ascertain how these indices
can enhance diagnostic value and inform patient treatment selection. Our hypotheses are: First, the
microvascular burden in FMD and ARAS results in increased IMR, thereby undermining the therapeutic
outcomes of renovascular interventions. Second, hyperemia secondary to the surge in intravascular
volume during pregnancy leads to more pronounced renal FMD artery stenosis, unveiling the clinical
manifestations of FMD. To explore these two hypotheses our project is organized into 2 aims:
- Aim 1: To characterize the hemodynamics effects of renal FMD using CFD and machine
learning.
 o Sub Aim 1: This analysis will be repeated within a subset of renal FMD patients with HDoP.
- Aim 2: To evaluate and compare the pre- and post-intervention shifts in hemodynamics for renal
 FMD compared to ARAS patients.

## Key facts

- **NIH application ID:** 10995919
- **Project number:** 1F32HL176189-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Luciano Delbono
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $81,256
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10995919

## Citation

> US National Institutes of Health, RePORTER application 10995919, A Computational Analysis of Hemodynamics in Patients with Renal Artery Fibromuscular Dysplasia (1F32HL176189-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10995919. Licensed CC0.

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