Newswise — Aortic stenosis is a narrowing of the aortic valve which connects the heart to the rest of the body. It affects millions of people and can be fatal if left untreated. At present, there are no effective medical therapies to prevent or slow its progression. The only effective treatments are surgery or percutaneous valve replacement when the valve has become severely narrowed.

Recently, common genetic variants have been linked to the clinical diagnosis of aortic stenosis in population genetic studies.

To gain insight into the genetic underpinnings that lead to the initial development of aortic stenosis, a team of researchers from UC San Francisco and the Broad Institute of MIT and Harvard conducted a study analyzing genetic associations with aortic valve measurements in a healthy population.

Their research appears December 19 in Nature Genetics.

Since severe aortic stenosis on a population level is rare, the researchers used continuous measurements of aortic valve function to find genetic signals. For this, they conducted a large-scale genetic analysis of three deep learning-derived measurements of aortic valve function in almost 60,000 healthy participants from the UK Biobank. The measurements included peak velocity, mean gradient, and aortic valve area (AVA) from MRI’s and genome-wide association studies (GWAS). They used these three deep learning-derived measurements of aortic valve function from MRI to identify 61 distinct genetic loci associated with these traits.

They additionally conducted a meta-analysis of aortic stenosis diagnoses in over 40,000 cases and 1.5 million controls from multiple biobanks, identifying 91 loci. Finally, they conducted a multi-trait analysis incorporating both continuous aortic valve measures in the healthy population and the disease-based GWAS, identifying 166 genetic loci (134 for aortic valve function and 134 for aortic stenosis).

“In this study, we analyzed aortic valve function and disease diagnoses to comprehensively evaluate the common genetic basis for aortic stenosis,” said study senior author James Pirruccello, MD, a cardiologist and assistant professor of Medicine in the UCSF Division of Cardiology. “Our findings suggest that risk for aortic stenosis is conferred at least in part through the same genetic mechanisms that drive normal variation in aortic valve function in the healthy population.”

The genetic correlation between these measures in healthy people and the aortic stenosis GWAS meta-analysis was substantial: on a scale from 0-1 (0 being no relationship and 1 being in perfect agreement), the correlation with aortic stenosis was 0.64 for the gradient-based measures and 0.50 for AVA.

“Using deep learning to measure normal variation in aortic valve function helped us to identify 134 loci associated with aortic stenosis risk and 166 with aortic valve stenosis or function,” said Shinwan Kany, MD, visiting scientist at the Broad Institute of MIT and Harvard. “We observed strong associations between aortic stenosis risk and coronary artery disease, lipoprotein biology, and phosphate handling, suggesting future avenues for research to prevent the development or progression of aortic stenosis.” 

The authors – which include collaborators at Mass General Brigham, Institute for Molecular Medicine Finland, and Beth Israel Deaconess Medical Center – note that clinical validation is needed before any implementation of programs to manipulate cholesterol or phosphate levels for aortic stenosis prevention.

Still, Pirruccello adds: “These findings demonstrate the power of jointly analyzing cardiovascular structure and function and their downstream disease outcomes.”

Other UCSF Authors: Jeffrey E. Olgin and Sammy Elmariah

Additional Authors: Joel T. Rämö, Cody Hou, Sean J. Jurgens, Shaan Khurshid, Victor Nauffal, Jonathan W. Cunningham, Emily S. Lau, Satoshi Koyama, FinnGen, Jennifer E. Ho, Aarno Palotie, Mark E. Lindsay, and Patrick T. Ellinor.

Funding: This work was supported by NIH K08HL159346 and NIH R01HL178603 to Pirruccello. Kany was supported by the Walter Benjamin Fellowship from the Deutsche Forschungsgemeinschaft (521832260). Rämö was supported by a research fellowship from the Sigrid Jusélius Foundation. Jurgens is supported by the Junior Clinical Scientist Fellowship from the Dutch Heart Foundation (grant no. 03-007-2022-0035). Elmariah receives research grants from the National Institutes of Health (R01 HL151838) and the Patient-Centered Outcomes Research Institute. Dr. Palotie was supported by the Academy of Finland Centre of Excellence in Complex Disease Genetics (grant no. 312074 and 336824). This work was supported by the Fondation Leducq (14CVD01), and by grants from the National Institutes of Health to Ellinor (1RO1HL092577, K24HL105780) and Ho (R01HL168889, R01HL160003, K24HL153669). Nauffal was supported by the Sperling Family Fellowship. Khurshid was supported by grants from NIH (K23HL169839) and the American Heart Association (23CDA1050571). Lau was supported by grants from NIH (K23-HL159243) and the American Heart Association (853922). This work was supported by a grant from the American Heart Association Strategically Focused Research Networks to Ellinor. Lindsay was supported by the Fredman Fellowship for Aortic Disease and the Toomey Fund for Aortic Dissection Research.

Disclosures: Ho has received past research support from Bayer AG focused on machine-learning and cardiovascular disease. Lau has received previous honoraria and consulting fees from Roche Diagnostics and Astellas Pharma. Khurshid receives sponsored research support from Bayer AG. Elmariah is supported by a grant from Edwards Lifesciences, Abbott Vascular, and Medtronic; has received consulting fees from Edwards Lifesciences; and holds equity in Prospect Health. Ellinor is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. Ellinor has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. Remaining authors report no disclosures.

About UCSF Health: UCSF Health is recognized worldwide for its innovative patient care, reflecting the latest medical knowledge, advanced technologies and pioneering research. It includes the flagship UCSF Medical Center, which is a top-ranked specialty hospital, as well as UCSF Benioff Children’s Hospitals, with campuses in San Francisco and Oakland; two community hospitals, UCSF Health St. Mary’s and UCSF Health Saint Francis; Langley Porter Psychiatric Hospital and Clinics; UCSF Benioff Children’s Physicians; and the UCSF Faculty Practice. These hospitals serve as the academic medical center of the University of California, San Francisco, which is world-renowned for its graduate-level health sciences education and biomedical research. UCSF Health has affiliations with hospitals and health organizations throughout the Bay Area. Visit http://www.ucsfhealth.org/. Follow UCSF Health on Facebook, Threads or LinkedIn.

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