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  • Genetic Predictors of Aortic Stenosis and Aortic Aneurysm Defined in New UK Biobank Analysis

    Genetic signatures that could predispose people to aortic stenosis (AS), and which could be most relevant to predicting thoracic aortic aneurysm, have been defined by researchers on a large UK population-based biobank.

    The findings were published Monday online ahead of the Aug. 2 issue of the Journal of the American College of Cardiology, with authors led by Mahan Nekoui, MD, and James P. Pirruccello, MD, both from Massachusetts General Hospital and the Broad Institute.

    Aortic aneurysm is associated with a risk of aortic dissection, which in turn is linked with sudden cardiac death, the researchers said. Approximately 50% of patients with a type A dissection of the ascending aorta die before arriving at a hospital.

    “Therefore, understanding the epidemiological and genetic contributions to ascending aortic risk may be important to the development of preventive strategies to avoid sudden cardiac death,” the researchers noted.

    The researchers previously used “deep learning” – a human imitative method based on machine learning and artificial intelligence – to evaluate the dimensions of the thoracic aorta in 4.6 million cardiac magnetic resonance (CMR) images from the UK Biobank.

    Using short-axis images, they also conducted genome-wide association studies in up to 39,688 individuals, identifying 82 loci associated with ascending thoracic aortic diameter and 47 loci with descending thoracic aortic diameter.

    “These results contributed to an understanding of the genetic basis of the diameter of the thoracic aorta; however, the short-axis view used in our prior work limited measurement of ascending and descending aorta diameters to a single location,” said the researchers.

    “As the proximal aorta is known to be spatially complex, consisting of unique anatomic subregions with distinct embryologic origins, we sought to study the structure in greater detail.”

    The current study, therefore, included a “fine-grained” evaluation, computing the diameters of the left ventricular outflow tract (LVOT), aortic root and six locations of ascending order in 2.3 million CMR images from 43,317 UK Biobank participants using deep learning architecture.

    For each diameter, a genome-wide association study was carried out including 33,870 participants, generating a polygenic score, which was then investigated for links to disease incidence. The participants included 18,403 women and 15,467 men, with an overall mean body mass index of 26.2 kg/m2, a mean systolic blood pressure of 139 mmHg and diastolic of 78.7 mmHg, mean LVOT diameter of 24.8 mm, and mean aortic root diameter of 30.2 mm.

    The goal was to elucidate genetic risk and association with ascending aortic disease.

    Genetic signatures

    A total of 79 loci were significantly associated with at least one diameter. Of these, 35 were novel, and most were associated with one or two diameters, the researchers said.

    “A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm,” they added (n = 427,016; mean hazard ratio [HR]: 1.42 per standard deviation [SD]; 95% confidence interval [CI]: 1.34-1.50; P = 6.67 x 10–21).

    The researchers referred to their extracted diameters of the LVOT, aortic root and serial diameters of the ascending aorta in order of most proximal to most distal: LVOT, aortic root, and aorta 0 through 5. Measurements of aorta were serially defined in order of distance from the sinotubular junction.

    A polygenic score predicting a smaller aortic root of the four most proximal diameters (namely LVOT, aortic root, aorta 0 and aorta 1) was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 x 10–6).

    “For both aneurysm and stenosis, future work, including validation in more ancestrally diverse study populations, is warranted to determine whether a model incorporating a polygenic score and clinical risk factors might identify high-risk, asymptomatic individuals who would benefit from screening via thoracic imaging,” the researchers concluded.

    Real-world implications

    The integration of machine learning with genetic and clinical data reveals new biology of the ascending aorta, James R. Priest, MD, of the Stanford University School of Medicine, said in an accompanying editorial.

    “Overall this work yields exciting real-world implications for both research and clinical care, delineating a plethora of new loci related to aortic structure and function, and relating these loci to clinically important outcomes that are difficult to predict,” he added.

    “These data will be an important tool for reaching beyond simple epidemiologic associations to uncover causal relationships between risk factors and thoracic aortic disease by means of mendelian randomization.”


    Nekoui M, Pirruccello JP, Di Achille P, et al. Spatially Distinct Genetic Determinants of Aortic Dimensions Influence Risks of Aneurysm and Stenosis. J Am Coll Cardiol 2022;80:486-497.

    Priest JR. Leveraging Machine Learning for Translational Genetics of Cardiovascular Imaging. J Am Coll Cardiol 2022;80:498-499.

    Image Credit: Pitchy –

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