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Background <p>Cardiovascular disease remains a major source of morbidity and mortality, and population imaging studies have yielded insights into disease etiology and risk.</p> Methods <p>In this study, we segment the heart, aorta, and vena cava from abdominal magnetic resonance imaging (MRI) scans using deep learning. We generate six image-derived phenotypes (IDP): heart volume, four aortic and one vena cava cross-sectional areas (CSA), from 44,541 UK Biobank participants, and explore their associations with disease outcomes, as well as genetic and environmental factors.</p> Results <p>Here we show concordance between our IDPs and related IDPs from cardiac magnetic resonance imaging, the current gold standard, and replicate previous findings related to sex differences and age-related changes in heart and vessel dimensions. We identify a significant association between infrarenal descending aorta CSA and incident abdominal aortic aneurysm, and between heart volume and several cardiovascular disorders. In a genome-wide association study, we identify 72 associations at 59 loci (15 novel). We derive a polygenic risk score for each trait and demonstrated an association with thoracic aneurysm diagnosis, pointing to a potential screening opportunity. We demonstrate substantial genetic correlation with cardiovascular traits including aneurysms, varicose veins, dysrhythmia, and cardiac failure. Finally, heritability enrichment analysis implicates vascular tissue in the heritability of these traits.</p> Conclusions <p>This study illustrates the value of non-specific abdominal MRI for exploring cardiovascular disease risk in cohort studies, and identifies novel genetic associations with vascular dimensions.</p>

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Cardiovascular measures from abdominal MRI provide insights into abdominal vessel genetic architecture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Nicolas Basty,
  • Elena P. Sorokin,
  • Marjola Thanaj,
  • Brandon Whitcher,
  • Yi Liu,
  • Jimmy D. Bell,
  • E. Louise Thomas,
  • Madeleine Cule

摘要

Background

Cardiovascular disease remains a major source of morbidity and mortality, and population imaging studies have yielded insights into disease etiology and risk.

Methods

In this study, we segment the heart, aorta, and vena cava from abdominal magnetic resonance imaging (MRI) scans using deep learning. We generate six image-derived phenotypes (IDP): heart volume, four aortic and one vena cava cross-sectional areas (CSA), from 44,541 UK Biobank participants, and explore their associations with disease outcomes, as well as genetic and environmental factors.

Results

Here we show concordance between our IDPs and related IDPs from cardiac magnetic resonance imaging, the current gold standard, and replicate previous findings related to sex differences and age-related changes in heart and vessel dimensions. We identify a significant association between infrarenal descending aorta CSA and incident abdominal aortic aneurysm, and between heart volume and several cardiovascular disorders. In a genome-wide association study, we identify 72 associations at 59 loci (15 novel). We derive a polygenic risk score for each trait and demonstrated an association with thoracic aneurysm diagnosis, pointing to a potential screening opportunity. We demonstrate substantial genetic correlation with cardiovascular traits including aneurysms, varicose veins, dysrhythmia, and cardiac failure. Finally, heritability enrichment analysis implicates vascular tissue in the heritability of these traits.

Conclusions

This study illustrates the value of non-specific abdominal MRI for exploring cardiovascular disease risk in cohort studies, and identifies novel genetic associations with vascular dimensions.