Article Impact Level: HIGH Data Quality: STRONG Summary of Nature Cardiovascular Research, 4(5), 624–636. https://doi.org/10.1038/s44161-025-00650-0 Dr. Shuang Qian et al.
Points
- Researchers created over 3,800 cardiac digital twins using MRI and ECG data to study how sex, age, and obesity influence heart function and disease development.
- The study found that sex-related differences in QRS duration are mainly due to anatomical size, while conduction velocity remained similar across sexes but was affected by age and obesity.
- Longer QTc intervals in obese females were linked to increased potassium conductance, a pattern confirmed in patients with ischemic heart disease.
- Digital twins revealed how lifestyle and mental health factors influence electrical heart properties, offering new insights into cardiovascular disease risk and outcomes.
- These personalized heart models pave the way for targeted, individualized treatments and may help link genetic variations to cardiac function in future research.
Summary
A recent study led by researchers from King’s College London, Imperial College London, and The Alan Turing Institute introduced the concept of cardiac digital twins (CDTs), personalized in silico models of the human heart, to investigate how various factors influence heart function and disease. The researchers constructed 3,461 CDTs from the UK Biobank and 359 from an ischemic heart disease (IHD) cohort, using cardiac magnetic resonance imaging and electrocardiograms (ECGs). These digital models revealed that sex-specific differences in QRS duration were primarily explained by myocardial anatomy. In contrast, myocardial conduction velocity (CV) remained similar across sexes, though it was affected by age and obesity. Specifically, longer QTc intervals in obese females were linked to increased delayed rectifier potassium conductance (GKrKs), a finding validated in the IHD cohort. These results suggest that myocardial tissue remodeling due to age and obesity impacts the electrical properties of the heart.
Creating these digital twins allowed identify how lifestyle, age, and sex affect the heart’s electrical properties and contribute to cardiovascular disease (CVD) risk. For instance, the study found that differences in ECG readings between men and women were primarily attributable to heart size rather than differences in myocardial conduction. CV and GKrKs were also shown to correlate with cardiac function, lifestyle factors, and mental health phenotypes, highlighting their role in adverse clinical outcomes. The study demonstrated that CDTs can offer valuable insights into the biology of CVD across diverse populations, providing a clearer understanding of the complex interactions between the heart’s anatomy and its electrical properties.
By creating over 3,800 anatomically accurate digital hearts, the research team opened the door to more personalized care in treating heart conditions. These CDTs help understand heart function across different populations and pave the way for tailoring treatments based on individual risk factors. The team hopes to extend this research to explore how genetic variations influence heart function to provide even more precise and personalized treatments for patients in the future.
Link to the article: https://www.nature.com/articles/s44161-025-00650-0
References Qian, S., Ugurlu, D., Fairweather, E., Toso, L. D., Deng, Y., Strocchi, M., Cicci, L., Jones, R. E., Zaidi, H., Prasad, S., Halliday, B. P., Hammersley, D., Liu, X., Plank, G., Vigmond, E., Razavi, R., Young, A., Lamata, P., Bishop, M., & Niederer, S. (2025). Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization. Nature Cardiovascular Research, 4(5), 624–636. https://doi.org/10.1038/s44161-025-00650-0
