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Updated: Jun 8, 2026

Isolation of Mouse Interstitial Valve Cells to Study the Calcification of the Aortic Valve In Vitro
Published on: May 10, 2021
Insights into the use of biomarkers in calcific aortic valve disease
Erik Beckmann1, Juan B Grau, Rachana Sainger
1Department of Surgery, Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia 19036, USA.
Abstract:
Calcific aortic valve disease (CAVD) is the most common acquired valvular disorder in developed countries. CAVD ranges from mild thickening of the valve, known as aortic valve sclerosis (AVSc), to severe impairment of the valve motion, which is termed aortic valve stenosis (AVS). The prevalence of CAVD is nearing epidemic status: its preceding stage, in which there is aortic sclerosis without obstruction of the left ventricular outflow, is present in almost 30% of adults aged over 65 years. As there is no existing medical therapy to treat or slow the progression of CAVD, surgery for advanced disease represents the only available treatment. Aortic valve replacement is the second most frequently performed cardiac surgical procedure after coronary artery bypass grafting, and consequently CAVD represents a major societal and economic burden. The pathophysiological development of CAVD is incompletely defined. At the present time, the major methods for its diagnosis are clinical examination, echocardiography, and cardiac catheterization. Yet, due to the multiple biological pathways leading to CAVD, there are many potential biomarkers that might be suitable for deriving clinically useful information regarding the presence, severity, progression, and prognosis of CAVD. Although at the present time the available data do not permit recommendations for clinicians, they do support a paradigm of screening patients based on multiple biomarkers to provide the information necessary to optimize future therapeutic interventions. This review summarizes the results of several studies investigating the value of potential biomarkers that have been used to predict the severity, progression, and prognosis of CAVD.
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