Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass

Shaan Khurshid, Julieta Lazarte, James P. Pirruccello, Lu-Chen Weng, Seung Hoan Choi, Amelia W. Hall, Xin Wang, Samuel F. Friedman, Victor Nauffal, Kiran J. Biddinger, Krishna G. Aragam, Puneet Batra, Jennifer E. Ho, Anthony A. Philippakis, Patrick T. Ellinor and Steven A. Lubitz ()
Additional contact information
Shaan Khurshid: Massachusetts General Hospital
Julieta Lazarte: Massachusetts General Hospital
James P. Pirruccello: Massachusetts General Hospital
Lu-Chen Weng: Massachusetts General Hospital
Seung Hoan Choi: Massachusetts General Hospital
Amelia W. Hall: Broad Institute of Harvard and the Massachusetts Institute of Technology
Xin Wang: Massachusetts General Hospital
Samuel F. Friedman: Broad Institute of Harvard and the Massachusetts Institute of Technology
Victor Nauffal: Brigham and Women’s Hospital
Kiran J. Biddinger: Massachusetts General Hospital
Krishna G. Aragam: Massachusetts General Hospital
Puneet Batra: Broad Institute of Harvard and the Massachusetts Institute of Technology
Jennifer E. Ho: Broad Institute of Harvard and the Massachusetts Institute of Technology
Anthony A. Philippakis: Broad Institute of Harvard and the Massachusetts Institute of Technology
Patrick T. Ellinor: Massachusetts General Hospital
Steven A. Lubitz: Massachusetts General Hospital

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-023-37173-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37173-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-37173-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().