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.