New research suggests that electrocardiogram (ECG) tests, which measure the heart's electrical activity, may one day be paired with artificial intelligence (AI) models to detect premature aging and cognitive decline.
The study found that individuals with accelerated ECG ages scored lower on cognitive tests compared to those with normal aging. Preliminary findings will be presented at the American Stroke Association's international stroke conference in Los Angeles on Wednesday. These results remain preliminary until published in a peer-reviewed journal.
"Unlike chronological age, which is based on years lived, ECG age reflects the functional state of the heart and potentially the entire body at the tissue level, providing insights into aging and health status," said lead author Bernard Ofosuhene, a clinical research coordinator at UMass Chan Medical School in Worcester, Massachusetts.
Each heartbeat emits an electrical impulse or wave. An ECG, also known as EKG, translates these impulses into wavy lines on paper, allowing doctors to identify heart problems. For this study, researchers developed an AI model to predict a person's biological age using their ECG data, which reflects how cells and tissues may deteriorate.
Previous studies have shown that ECG age can help predict heart disease and mortality, but little was known about its relationship with cognitive decline. Researchers note that since ECGs are widely used in heart and stroke diagnostics, this new tool could help identify individuals at risk for cognitive decline.
In the study, researchers analyzed data from 63,800 participants in the UK Biobank, an ongoing study involving over 500,000 volunteers in the UK. Participants, aged 40 to 69 at enrollment from 2006 to 2010, underwent ECGs and took a battery of cognitive tests between August 2023 and July 2024. The AI model was then applied to determine their ECG age.
According to the AI model, 15,563 participants were found to be experiencing normal aging, 24,671 were undergoing accelerated aging, and 23,566 were experiencing decelerated aging. Compared to the normal aging group, the accelerated aging group showed a significant decline in cognitive performance, while those with slower ECG aging performed better on tests.
Based on ECG age, those who appeared younger than their chronological age outperformed their peers in six of eight cognitive tests, while individuals with older ECG ages performed worse in six tests.
"There is a wealth of ECG data available for stroke treatment, and I encourage healthcare professionals to use this data to look for signs of cognitive decline," said Ofosuhene. "Doing so may aid in early diagnosis and timely intervention."
Future studies will investigate whether gender influences the link between ECG age and cognitive performance. Additionally, as most UK Biobank participants are of white European descent, Ofosuhene's team aims to determine if their findings can be replicated in more diverse populations.
This study adds to a growing body of research recognizing "the strong connection between heart and brain health," said Dr. Fernando D. Testai in a press release. Testai, a professor of neurology and rehabilitation at the University of Illinois College of Medicine in Chicago, led the writing committee for a 2024 American Heart Association scientific statement on the link between common cardiovascular diseases and cognitive decline.
Testai, who did not participate in the study, stated that using ECG data to assess cognitive ability may seem "futuristic," but validating the study could yield significant outcomes. ECG data collected in a doctor's office or remotely via wearables could help evaluate cognition at home or in rural areas lacking neuropsychiatric specialists, he noted. The use of data and AI could also be faster and more objective than traditional assessments.
"However, one important question remains," Testai said. "Can ECG data predict future cognitive decline? Answering this could lead to valuable treatments."