18F-NaF PET-CT Imaging and Blood Biomarker Analysis as Tools to Predict Patients at Risk of Developing Severe Aortic Valve Calcification
Description: Calcific aortic valve degeneration (CAVD) is the most common heart valve disease worldwide and is increasing exponentially in our aging population. Over time and with aging, the normal aortic valve undergoes structural changes leading to its thickening while maintaining normal function as assessed by echocardiography. This condition is known as aortic valve sclerosis (ASc). Epidemiologically and clinically, we know that within the population of patients with ASc, two clearly differentiated groups exist: one that will progress towards CAVD and another that will remain stable over time. Aortic Stenosis (AS) is the late-stage manifestation of CAVD, characterized by severe thickening and calcification of the aortic valve leading to pressure and flow changes measured by echocardiography. ASc is highly prevalent in the population; however, it remains unclear which ASc patients will progress towards the development of AS. The following proposal will provide the possibility of creating a new window for clinical observation to study CAVD. The currently available tools are unable to detect early, sub-clinical phases of the disease, or stratify patients, segregating the ones that will remain stable throughout time versus the ones that will progress to more severe stages of the disease. This project will help restage the early phase of aortic valve degeneration, ASc, providing the framework necessary, using novel imaging and molecular biology tools, to open the way for the potential development of non-invasive therapies to alter the progression of CAVD in the future.
Amount: CA$55,000
Funding type: fellowship
Funder scheme: CIHR Fellowship
Start date: 2021-10-01
End date: 2022-11-11
Investigator: Lara Gharibeh
Institution: University of Ottawa Heart Institute
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Funded outputs count: 0
Funder award ID: 176492_1
OpenAlex ID: G3518844836