Background: Aortic structure impacts cardiovascular health through multiple mechanisms. Aortic structural degeneration occurs with aging, increasing left ventricular afterload and promoting increased arterial pulsatility and target organ damage. Despite the impact of aortic structure on cardiovascular health, three-dimensional (3D) aortic geometry has not been comprehensively characterized in large populations.
Methods: We segmented the complete thoracic aorta using a deep learning architecture and used morphological image operations to extract aortic geometric phenotypes (AGPs, including diameter, length, curvature, and tortuosity) across multiple subsegments of the thoracic aorta. We deployed our segmentation approach on imaging scans from 54,241 participants in the UK Biobank and 8,456 participants in the Penn Medicine Biobank. Age-related structural remodeling was quantified on a reference cohort of normative participants. The genetic architecture of three-dimensional aortic geometry was quantified using genome-wide association studies, followed by gene-level analysis and drug-gene interactions.
Results: Aging was associated with various 3D-geometric changes, reflecting structural aortic degeneration, including decreased arch unfolding, descending aortic lengthening and luminal dilation across multiple subsegments of the thoracic aorta. Male aortas exhibited increased length and diameters compared to female aortas across all age ranges, whereas female aortas exhibited increased curvature compared with males. We identified 209 novel genetic loci associated with various 3D-AGPs. 357 significant gene-level associations were uncovered, with Fibrillin-2 gene polymorphisms being identified as key determinants of aortic arch structure. Drug-gene interaction analysis identified 25 cardiovascular drugs potentially interacting with aortic geometric loci.
Conclusion: Our analysis identified key patterns of aortic structural degeneration linked to aging. We present the first GWAS results for multiple 3D-structural parameters of the aorta, including length, curvature, and tortuosity. Additionally, we confirm various loci associated with aortic diameter. These results expand the genetic loci associated aortic structure and will provide crucial insights into the joint interplay between aging, genetics and cardiovascular structure.
Keywords: 3D aortic structure; Deep learning; GWAS; aortic aging.