Cardiovascular ageing is a process that begins early in life and leads to a progressive change in
structure and decline in function due to accumulated damage across diverse cell types, tissues and
organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and
end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here
we use machine learning approaches to quantify cardiovascular age from image-derived traits of
vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by
cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers
of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights
into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets
to attenuate age-related processes.