What is it about?
Electrocardiograms (ECGs) are among the most widely used tools in cardiac diagnostics, capturing the electrical potential on the skin’s surface that reflects the heart’s electrical activity. This signal is influenced by the heart’s orientation and position within the chest—factors often overlooked during ECG interpretation. In this study, we examined the anatomical axis as the heart’s orientation and the electrical axis as a 3D spatial representation of the heart’s electrical activity, exploring their interactions in both healthy individuals and those with hypertension. Using cardiac magnetic resonance imaging and ECGs from about 39,000 UK Biobank participants, we investigated how factors such as body mass index, age, sex, and hypertension affect these axes and their coupling.
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Why is it important?
We found that increased BMI, male sex, and hypertension are associated with a more horizontal heart orientation and electrical axis, emphasising complex anatomical-electrical interaction. Our findings also show association with health outcomes, pointing out the importance of accounting for patient-specific variations and suggest that considering these factors could lead to more accurate and personalised ECG interpretation.
Perspectives
The project was a team effort built on the vast UK Biobank dataset. Drawing on population‑level information shows how big‑data resources can guide and inform care tailored to each person. By examining how BMI, age, sex and high blood pressure alter the heart’s orientation and its electrical signals, the study underlines that patients are not identical and that ECG readings should reflect those differences, not an "average" template. Analysing large health datasets reveals patterns and potential new biomarkers that may help cater to more patient-centric diagnosis & prognosis.
Mohammad Kayyali
King's College London
Read the Original
This page is a summary of: Anatomical-electrical coupling of cardiac axes: Definitions and population variability for advancing personalised ECG interpretation, PLoS Computational Biology, July 2025, PLOS,
DOI: 10.1371/journal.pcbi.1013161.
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