Baseball-Shaped Hearts: A New Indicator for Atrial Fibrillation and Heart Muscle Disease?

by Lars Hundley

New research from the Smidt Heart Institute at Cedars-Sinai has revealed a connection between the shape of one’s heart and the risk of developing atrial fibrillation and cardiomyopathy, a type of heart muscle disease. The study, published in the peer-reviewed medical journal Med, used deep learning and advanced imaging analysis to examine the genetics of heart structure.

Investigators found that individuals with spherical hearts, resembling a baseball, were 31% more likely to develop atrial fibrillation and 24% more likely to develop cardiomyopathy than those with elongated, Valentine heart-shaped hearts. The findings were based on the analysis of cardiac MRI images from 38,897 healthy individuals from the UK Biobank.

Cardiologist David Ouyang, MD, a researcher at the Smidt Heart Institute, said, “By looking at the genetics of sphericity, we found four genes associated with cardiomyopathy: PLN, ANGPT1, PDZRN3, and HLA DR/DQ. The first three of these genes were also associated with a greater risk of developing atrial fibrillation.”

Atrial fibrillation, the most common type of abnormal heart rhythm disorder, greatly increases the risk of stroke and is projected to affect 12.1 million people in the U.S. by 2030. Cardiomyopathy affects as many as 1 in every 500 adults and makes it harder for the heart to pump blood to the rest of the body, potentially leading to heart failure.

Christine M. Albert, MD, MPH, chair of the Department of Cardiology in the Smidt Heart Institute and a study author, emphasized that the shape of one’s heart changes over time, especially after major cardiac events like heart attacks. “A change in the heart’s shape may be a first sign of disease,” she said.

The study highlights the potential of cardiac imaging in diagnosing and preventing many conditions. Ouyang pointed out that large biobanks with cardiac imaging data now offer an opportunity to define variations in cardiac structure and function that were previously impossible to analyze using traditional approaches. “Deep learning and computer vision also allow for faster, more comprehensive cardiac measures that may help to identify genetic variations affecting a heart—up to years or even decades before any obvious heart disease develops,” he added.