Aortic stiffness is a strong predictor of cardiovascular events, yet its genetic basis remains poorly understood. Data presented at the European Society of Cardiology 2025 leveraged deep learning and large-scale genome-wide association studies to elucidate the genetic architecture of aortic stiffness. 

Cardiac magnetic resonance images from 45,789 UK Biobank participants of European ancestry were analyzed using a pre-trained neural network to quantify ascending aorta diameter. A pressure-independent stiffness measure (β0) was derived and subjected to Genome-Wide Association Study (GWAS). Seventeen independent loci were identified, with putative effector genes including ELN, LTBP4, HAS2, ULK4, ARHGAP24, SVIL, ARHGAP22, CDH13, and SMG6. Pathway enrichment analyses implicated elastin fiber formation, extracellular matrix assembly, and regulation of cellular component organization. 

These findings highlight biological mechanisms underlying aortic stiffness and provide a foundation for mechanistic studies and potential precision therapies targeting cardiovascular disease risk.