AI Breakthrough: Imaging Reveals ‘Biological Barometer’ of Chronic Stress, Predicting Heart Failure Risk
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A new deep learning model has identified the first-of-its-kind imaging biomarker for chronic stress, detectable through routine CT scans, offering a potential pathway to earlier cardiovascular risk assessment and preventative care. Researchers presenting their findings next week at the annual meeting of the radiological Society of North America (RSNA) say this breakthrough could revolutionize how clinicians understand and address the pervasive health impacts of long-term stress.
Chronic stress is widely recognized for its detrimental effects on both physical and psychological well-being, contributing to conditions like anxiety, insomnia, muscle pain, high blood pressure, and a weakened immune system, according to the American Psychological Association. Mounting evidence also links chronic stress to the growth of serious illnesses, including heart disease, depression, and obesity.
The study,led by Elena Ghotbi,M.D., a postdoctoral research fellow at Johns Hopkins university School of Medicine in Baltimore, Maryland, focused on leveraging existing medical imaging data. Dr. Ghotbi and her team developed and trained a deep learning model to precisely measure adrenal gland volume on standard chest CT scans.
“Our approach leverages widely available imaging data and opens the door to large-scale evaluations of the biological impact of chronic stress across a range of conditions using existing chest CT scans,” Dr. Ghotbi explained. “This AI-driven biomarker has the potential to enhance cardiovascular risk stratification and guide preventive care without additional testing or radiation.”
Seeing the Invisible Burden of Stress
Currently, assessing chronic stress relies heavily on subjective measures like questionnaires, or cumbersome biochemical tests such as cortisol measurements. A senior researcher noted that these methods often prove inadequate for capturing the full scope of chronic stress’s impact. The new AI-derived adrenal volume index (AVI) offers a more objective and quantifiable assessment.
The study involved over 400 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Researchers found that AVI was consistently associated with elevated cortisol levels, increased allostatic load, and a greater risk of heart failure and mortality.Participants reporting high levels of perceived stress exhibited significantly higher AVI compared to those with low stress. Moreover,AVI correlated with a higher left ventricular mass index.
“With up to 10-year follow-up data on our participants,we were able to correlate AI-derived AVI with clinically meaningful and relevant outcomes,” Dr.Ghotbi stated. “This is the very first imaging marker of chronic stress that has been validated and shown to have an independent impact on a cardiovascular outcome, namely, heart failure.”
A Paradigm Shift in Understanding Stress and Health
Teresa E. Seeman, Ph.D., a study co-author and professor of epidemiology at UCLA, emphasized the significance of this work. “For over three decades, we’ve known that chronic stress can wear down the body across multiple systems,” she said. “What makes this work so exciting is that it links a routinely obtained imaging feature, adrenal volume, with validated biological and psychological measures of stress and shows that it independently predicts a major clinical outcome. It’s a true step forward in operationalizing the cumulative impact of stress on health.”
Dr. Demehri highlighted the practical implications of the discovery. “The key significance of this work is that this biomarker is obtainable from CTs that are performed widely in the United States for various reasons,” he explained. “Secondly, it is indeed a physiologically sound measure of adrenal volume, which is part of the chronic stress physiologic cascade.”
The researchers believe this imaging biomarker could prove valuable in managing a range of diseases linked to chronic stress, especially in middle-aged and older adults.
The research team included Roham Hadidchi, Seyedhouman Seyedekrami, Quincy A. Hathaway, M.D., Ph.D., Michael Bancks, Nikhil Subhas, Matthew J. Budoff, M.D., David A. Bluemke, M.D., Ph.D., R. Graham Barr and Joao A.C. Lima, M.D.
Source: Radiological Society of North America.
