BOSTON – An abnormally enlarged aorta—also called aortic aneurysm—can tear or rupture and cause sudden cardiac death. Unfortunately, patients often show no signs or symptoms before the aorta, which carries blood from the heart to the rest of the body, fails. A team led by investigators at Massachusetts General Hospital (MGH) recently used a type of artificial intelligence called deep learning to uncover insights into the genetic basis for variation in the aorta’s size. In addition to identifying at-risk individuals, the findings may point to new preventive and therapeutic targets.
The research, which is published in Nature Genetics, relied on data from the UK Biobank, a study that performed multiple magnetic resonance imaging tests of the heart and aorta in more than 40,000 people. “There were no aortic measurements provided by the UK Biobank, and we wanted to read the aortic diameter in all of the images collected,” explains lead author James Pirruccello, MD, a cardiologist at MGH and an instructor in medicine at Harvard Medical School. “That is very hard for a human to do because it would take a long time, which motivated our use of deep learning models to do this process at a large scale.”
The researchers trained deep learning models to evaluate the dimensions of the ascending and descending sections of the aorta in 4.6 million cardiac images. They then analyzed the study participants’ genes to identify variations in 82 genetic regions (or loci) linked to the diameter of the ascending aorta and 47 linked to the diameter of the descending aorta. Some of the loci were near genes with known associations with aortic disease.
“When we added up the genetic variants into what’s called a polygenic score, people with a higher score were more likely to be diagnosed with aortic aneurysm by a doctor,” says Pirruccello. “This suggests that, after further development and testing, such a score might one day be useful to help us identify people at high risk of an aneurysm. The genetic loci that we discovered also offer a useful starting point for trying to identify new drug targets for aortic enlargement.”
Pirruccello adds that the findings also provide supportive evidence that deep learning and other machine learning methods can help accelerate scientific analyses of complex biomedical data such as imaging results.
This work was supported by Leducq, the National Institutes of Health, the American Heart Association, the John S. LaDue Memorial Fellowship, a Sarnoff Cardiovascular Research Foundation Scholar Award, the Burroughs Wellcome Fund, the Fredman Fellowship for Aortic Disease, the Toomey Fund for Aortic Dissection Research, Bayer AG, and the Susan Eid Tumor Heterogeneity Initiative.
About the Massachusetts General Hospital
Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The Mass General Research Institute conducts the largest hospital-based research program in the nation, with annual research operations of more than $1 billion and comprises more than 9,500 researchers working across more than 30 institutes, centers and departments. In August 2021, Mass General was named #5 in the U.S. News & World Report list of “America’s Best Hospitals.”
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Deep learning enables genetic analysis of the human thoracic aorta
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J.P.P. and A.G.B. have served as consultants for Maze Therapeutics. A.-D.A. and C.M.S. are employees of Bayer US LLC (a subsidiary of Bayer AG), and may own stock in Bayer AG. D.J. is supported by grants from Genentech, Eisai, EMD Serono, Takeda, Amgen, Celgene, Placon Therapeutics, Syros, Petra Pharma, InventisBio, Infinity Pharmaceuticals and Novartis. D.J. has also received personal fees from Genentech, Eisai, EMD Serono, Ipsen, Syros, Relay Therapeutics, MapKure, Vibliome, Petra Pharma and Novartis. A.A.P. is employed as a Venture Partner at GV; he is also supported by a grant from Bayer AG to the Broad Institute focused on machine learning for clinical trial design. J.E.H. is supported by a grant from Bayer AG focused on machine learning and cardiovascular disease and a research grant from Gilead Sciences. J.E.H. has received research supplies from EcoNugenics. P.B. is supported by grants from Bayer AG and IBM applying machine learning in cardiovascular disease. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. P.T.E. has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim and Fitbit, and has consulted for Bristol Myers Squibb/Pfizer and Bayer AG, and participates in a research collaboration with IBM. The Broad Institute has filed for a patent on an invention from P.T.E., M.E.L. and J.P.P. related to a genetic risk predictor for aortic disease.