Abstract

Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalised physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We developed an automatic pipeline for generating finite element biventricular heart models from cardiac magnetic resonance images (CMRs) in the UK biobank. Using this pipeline, each digital twin can be created in only 8 minutes on a standard desktop, compatible with clinical time scales and enabling large-scale virtual population-based studies. We also made a cohort of representative healthy hearts (n=1388), categorised by sex, age and BMI , out of 54,000 hearts in the UK biobank. The pipeline and the cohort will be made open to the public soon. Using this pipeline, we have constructed an initial cohort of 3464 CDTs and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG).  We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health. Co-authors: Devran Ugurlu, Elliot Fairweather, Marina Strocchi, Laura Dal Toso, Yu Deng, Gernot Plank, Edward Vigmond, Reza Razavi, Alistair Young, Pablo Lamata, Martin Bishop, Steven Niederer