ROCHESTER, Minn. — Mayo Clinic research identified a powerful new way to improve the prediction of a patient's long-term cardiovascular disease risk by enhancing a routinely performed imaging test with artificial intelligence (AI). Heart disease develops over time and remains the leading cause of death worldwide, so identifying risk early is critical to preventing heart attack, stroke and other serious outcomes.
The study highlights the growing role of AI in helping experts uncover new insights from existing medical data. Findings were presented at the 2026 American College of Cardiology Scientific Session with simultaneous publication in the American Journal of Preventive Cardiology .
The study followed nearly 12,000 adults for approximately 16 years. Investigators applied AI to participants' standard coronary artery calcium scans to measure fat surrounding the heart. They compared the predictive value of this measurement with and in combination with two standard risk assessment approaches: the American Heart Association PREVENT equation , which incorporates traditional factors such as age, sex, blood pressure, cholesterol, diabetes and other variables, and the coronary artery calcium score, which measures calcified plaque in coronary arteries.
The findings show that the volume of heart fat could be used independently to predict cardiovascular events. It significantly improved the overall accuracy of long-term risk prediction when combined with the coronary artery calcium score and the PREVENT equation, especially among patients in low-risk categories.
"Pericardial fat has been recognized as a marker of cardiovascular risk, but this study shows how we can now measure it automatically and use it to meaningfully improve risk prediction, especially in patients at borderline or intermediate risk where clinical decisions are often less clear," says Zahra Esmaeili, first author and researcher in the Department of Cardiovascular Medicine at Mayo Clinic. "This opens the door to more personalized prevention strategies."
Key findings:
- Nearly 10% of participants developed cardiovascular disease during follow-up.
- Higher fat volume around the heart was independently associated with increased risk of cardiovascular events, even after accounting for traditional risk factors and coronary calcium scores.
- Participants with the highest coronary fat volume had elevated risk across all coronary calcium levels.
- Adding coronary fat measurements improved the accuracy of predicting cardiovascular events beyond established models.
Coronary artery calcium scoring is widely used to assess cardiovascular risk. This study shows that additional information can be extracted from the same scan without extra testing or cost.
"Because this measurement comes from imaging that many patients are already receiving, it represents a practical and scalable way to enhance cardiovascular risk assessment," says senior author Francisco Lopez-Jimenez, M.D. , a preventive cardiologist and co-director of the AI in Cardiology program at Mayo Clinic. "It could help clinicians intervene earlier and more effectively."
Researchers note that further studies will help determine how best to incorporate coronary fat measurement into routine clinical care and whether it can guide treatment decisions.
The manuscript, Deep Learning–Derived Pericardial Adipose Tissue by ECG-Gated Computed Tomography Predicts Cardiovascular Events Beyond Coronary Calcium, and a complete list of authors is published in the American Journal of Preventive Cardiology .
