Different from the popular heart symbol, the anatomical heart looks more similar to a strawberry - elongated with a wide top and a narrow tip. The heart does undergo aging-related structural changes which may make it more susceptible to disease but does heart shape matter for young, healthy individuals?
New research published in Med - Cell Press used artificial intelligence (AI) to analyze whether heart shape can be used to predict progression to heart disease. Vukadinovic et.al used deep learning analysis on MRI images from around 39,000 participants of the UK Biobank - a large-scale study collecting health information from half a million volunteers.
The study measured the ratio between the short and long axis of the heart, which they refer to as the sphericity index. A lower index means an elongated heart while a higher sphericity index means a round-shaped heart.
Analysis showed that a round heart is associated with a 47% increased risk of cardiomyopathy and 20% increased risk of atrial fibrillation (AF), independent of other clinical factors. If factors were only minimally adjusted, sphericity index was associated with cardiomyopathy, AF and heart failure, but not cardiac arrest.
Cardiomyopathy is a disease that can make heart muscle thick, stiff or scarred - making it difficult for the heart to pump blood throughout the body. As the disease progresses, the heart gets weaker eventually leading to complications like arryhthmias, heart valve disease and heart failure; with serious cases needing an open-heart surgery.
Cardiomyopathy can affect anyone of any age or sex and will often have no symptoms until the heart's condition has worsen making it important to identify high-risk individuals early.
This type of research is still on its initial stages and more validation is needed before heart sphericity can be used for clinical diagnosis. But it is an important step in developing methods to identify high-risk individuals before they develop heart disease.
Reference:
Vukadinovic, M., Kwan, A. C., Yuan, V., Salerno, M., Lee, D. C., Albert, C. M., Cheng, S., Li, D., Ouyang, D., & Clarke, S. L. (2023). Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes. Med. https://doi.org/10.1016/j.medj.2023.02.009
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