AI Model Could Use ECG Tests to Detect Premature Aging and Cognitive Decline

Researchers at UMass Chan Medical School (Worcester, MA, USA) have developed an AI model, referred to as a deep neural network (DNN), designed to estimate individuals’ biological age—reflecting the age of their body’s cells and tissues—using ECG data. Unlike chronological age, which counts the number of years a person has lived, ECG-age provides an assessment of the heart’s functional health and potentially the overall condition of the body at the tissue level, offering valuable insights into aging and health. Previous studies have shown that ECG-age can predict the likelihood of heart disease and mortality. However, prior to this new study, the link between ECG-age and cognitive decline was not well understood.

The researchers analyzed data from over 63,000 participants in the UK Biobank, a large-scale ongoing study that includes more than 500,000 volunteers from the UK, all of whom enrolled between the ages of 40 and 69. Participants completed a variety of cognitive tests, which were analyzed in relation to their ECG testing, ensuring the cognitive data reflected the participants' mental status at the same time their ECG-age was calculated. Based on comparisons between actual ages and ECG-age, participants were categorized into three groups: normal aging, accelerated ECG-aging (those with an ECG-age older than their chronological age), and decelerated ECG-aging (those with an ECG-age younger than their chronological age). The findings revealed that individuals in the accelerated ECG-aging group performed worse on 6 of the 8 cognitive tests, while those in the decelerated ECG-aging group performed better on 6 of the 8 cognitive tests, compared to those with normal aging based on their ECG-age.

“There is a lot of ECG-data available for stroke treatment and I encourage health care professionals to use this data to look for signs of cognitive decline. Doing so may help with early diagnosis and timely intervention,” said Bernard Ofosuhene, B.A., lead author of the study and clinical research coordinator in the department of medicine at the UMass Chan Medical School.