Researchers have found that subtle changes in fat tissue visible on routine chest CT scans can signal a nearly 20-fold higher risk of developing heart failure years later.
The study, based on data from over 72,000 adults in the United Kingdom with no prior heart failure or heart attack, tracked participants for four to five years. During that period, several thousand developed heart failure.
Analysis revealed a clear pattern: the more abnormal the structure of fat tissue, the greater the risk. Individuals in the highest-risk group based on this radiomic signature had almost 20 times the likelihood of developing heart failure compared to those in the lowest-risk group.
Even minor deviations in fat tissue composition were linked to significantly increased probabilities, suggesting the method detects early biological shifts invisible to traditional measures like body-mass index or total fat volume.
How the method improves on existing risk models
Established risk factors such as age, hypertension, and coronary artery disease remain important but do not fully explain who will develop heart failure. The new approach adds value by capturing biological changes in fat tissue that standard diagnostics overlook.
Incorporating these imaging-derived insights significantly improved prediction accuracy, indicating that fat tissue structure could serve as a meaningful supplement to current risk-assessment tools.
Why routine chest scans could become dual-purpose tools
A key advantage is that the method uses existing CT scan data. Many people undergo chest CTs for reasons unrelated to heart risk, such as evaluating lung symptoms or other chest conditions.
These same images could potentially be reanalyzed to estimate long-term heart failure risk without requiring additional scans, radiation exposure, or cost.
Researchers suggest this could enable earlier, more targeted prevention strategies, though the technique remains primarily a research tool for now.
What does the radiomic pattern actually measure?
The pattern reflects structural changes in fat tissue that are not visible through conventional metrics like BMI or fat volume, indicating early biological shifts associated with higher heart failure risk.
Could this be used in clinical practice today?
Not yet; the method is still a research approach. Further validation would be needed before it could be integrated into routine clinical workflows for risk prediction.
