Researchers are presenting their findings at the annual meeting of the Radiological Society of North America (RSNA) next week. They found the first-of-its-kind biomarker of chronic stress observable through regular imaging using a deep learning AI model.
According to the American Psychological Association, long-term stress can have an adverse effect on one’s physical and mental health, leading to a number of issues such as anxiety, sleeplessness, muscle soreness, elevated blood pressure, and a compromised immune system. Chronic stress has been linked in studies to the emergence of serious conditions like obesity, depression, and heart disease.
A deep learning model was created and trained by the study’s lead author, Elena Ghotbi, M.D., a postdoctoral research fellow at Johns Hopkins University School of Medicine in Baltimore, Maryland, to quantify the volume of the adrenal glands on already-existing CT scans.
In the United States alone, tens of millions of chest CT scans are carried out annually.
“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 stated. “This AI-driven biomarker has the potential to enhance cardiovascular risk stratification and guide preventive care without additional testing or radiation.”
Senior author Shadpour Demehri, M.D., professor of radiology at Johns Hopkins, said chronic stress is a prevalent condition or complaint that many adults deal with on a daily basis.
“For the first time, we can ‘see’ the long-term burden of stress inside the body, using a scan that patients already get every day in hospitals across the country. Until now, we haven’t had a way to measure and quantify the cumulative effects of chronic stress, other than questionnaires, surrogate serum markers like chronic inflammation, and cortisol measurement, which is very cumbersome to obtain,” Dr. Demehri stated.
Adrenal volume functions as a biological barometer of chronic stress, in contrast to individual cortisol readings, which offer a brief picture of stress levels.
The Multi-Ethnic Study of Atherosclerosis, a comprehensive study that combined chest CT scans, validated stress questionnaires, cortisol measurements, and markers of allostatic load—the cumulative physiological and psychological effects of chronic stress on the body—provided the researchers with data on 2,842 participants (mean age 69.3; 51% women). It was the best, and probably the only, cohort for creating an imaging biomarker of chronic stress due to the uncommon combination of imaging, biochemical, and psychosocial data.
ITo segment and determine the volume of the adrenal glands, the researchers applied their deep learning model to previous CT scans. TThe formula for calculating the Adrenal Volume Index (AVI) is the volume in cubic centimeters (cm³) divided by the height in square meters (m²). OOver the course of two days, salivary cortisol levels were measured eight times each day. Body mass index, creatinine, hemoglobin, albumin, glucose, white blood count, heart rate, and blood pressure were used to calculate allostatic load.
Allostatic load, cortisol, and psychosocial stress markers, such as depression and perceived stress questionnaires, were compared statistically with AVI. The researchers discovered a correlation between AI-derived AVI and circulating cortisol levels, validated stress questionnaires, and potential negative cardiovascular effects.
Greater cortisol, peak cortisol, and allostatic load were linked to higher AVI. AVI was higher in those who felt a lot of stress than in those who didn’t. A larger left ventricular mass index was likewise linked to AVI. A higher risk of cardiac failure and death was associated with every 1 cm³/m² increase in AVI.
“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.”
“For over three decades, we’ve known that chronic stress can wear down the body across multiple systems,” stated Teresa E. Seeman, Ph.D., study co-author and professor of epidemiology at UCLA and a pioneering researcher in stress and health. “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 said that by linking an easily measurable imaging feature with multiple validated indicators of stress and downstream disease, this research introduces an entirely new, practical way to quantify chronic stress.
“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,” Dr. Demehri stated. “Secondly, it is a physiologically sound measure of adrenal volume, which is part of the chronic stress physiologic cascade.”
The researchers said the imaging biomarker could be used in a variety of diseases that are associated with chronic stress in middle-aged and older adults.
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