Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.

Journal: Nature cardiovascular research
Published Date: May 16, 2025

Abstract

Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) are personalized physics-constrained and physiology-constrained in silico representations, uncovering multi-scale insights tied to these mechanisms. In this study, we constructed 3,461 CDTs from the UK Biobank and another 359 from an ischemic heart disease (IHD) cohort, using cardiac magnetic resonance images and electrocardiograms. We show here that sex-specific differences in QRS duration were fully explained by myocardial anatomy while their myocardial conduction velocity (CV) remains similar across sexes but changes with age and obesity, indicating myocardial tissue remodeling. Longer QTc intervals in obese females were attributed to larger delayed rectifier potassium conductance . These findings were validated in the IHD cohort. Moreover, CV and were associated with cardiac function, lifestyle and mental health phenotypes, and CV was also linked with adverse clinical outcomes. Our study demonstrates how CDT development at scale reveals biological insights across populations.

Authors

  • Shuang Qian
    School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Devran Ugurlu
  • Elliot Fairweather
    Department of Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Laura Dal Toso
    Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland.
  • Yu Deng
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, People's Republic of China.
  • Marina Strocchi
    Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Ludovica Cicci
    MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.
  • Richard E Jones
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Hassan Zaidi
    Department of Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Sanjay Prasad
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Brian P Halliday
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Daniel Hammersley
    Department of Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Xingchi Liu
    Department of Thoracic Surgery, General Hospital of Shenyang Military Command, Shenyang 110016, China.
  • Gernot Plank
    Medical University of Graz, Graz 8036, Austria.
  • Edward Vigmond
    IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, France (E.V.).
  • Reza Razavi
  • Alistair Young
  • Pablo Lamata
    Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's Health Partners, King's College of London, 3rd Floor Lambeth Wing, St Thomas' Hospital, SE1 7EH, London.
  • Martin Bishop
    Department of Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Steven Niederer
    Department of Biomedical Engineering, King's College London, United Kingdom (C.C., S.N.).

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