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Los Angeles, CA – February 5, 2025 – A groundbreaking study presented at the American Stroke Association’s International Stroke Conference 2025 suggests that electrocardiogram (ECG) tests, when paired with artificial intelligence (AI), could one day detect premature aging and cognitive decline. The research, conducted by a team at UMass Chan Medical School, highlights the potential of AI models to analyze ECG data for more than just heart health—it could reveal insights into overall biological age and cognitive performance.
ECG tests, which measure the electrical activity of the heart, could provide vital clues about the state of a person’s biological age. The study, led by Bernard Ofosuhene, B.A., a clinical research coordinator at UMass Chan Medical School, focused on the relationship between ECG-age (a measure of biological age based on ECG data) and cognitive decline. The deep neural network (DNN) AI model designed for this purpose uses ECG data to predict how a person’s biological age compares to their chronological age and can identify those at risk for cognitive decline.
“ECG-age offers a reflection of the functional status of the heart and possibly the whole organism at the tissue level,” said Ofosuhene. “This can provide valuable insights into aging and health status, beyond just the heart.”
The study, conducted using data from more than 63,000 participants in the UK Biobank, analyzed the participants’ ECG data and cognitive performance. Cognitive tests were aligned with ECG testing to ensure accurate analysis. The participants were divided into three groups based on their ECG-age: normal aging, accelerated ECG-aging (older than their chronological age), and decelerated ECG-aging (younger than their chronological age).
The results were telling. Those who had accelerated ECG-aging (appearing older biologically) performed worse on six out of eight cognitive tests, while those with decelerated ECG-aging (appearing younger biologically) performed better on six out of eight cognitive tests, compared to the normal aging group.
“This finding is crucial for detecting early signs of cognitive decline,” Ofosuhene said. “The ECG data already available for stroke treatment could serve as an early warning system for cognitive issues, potentially leading to earlier diagnosis and interventions.”
However, the study is not without its limitations. It focused on participants aged 43 to 85 from the UK Biobank, raising questions about whether the findings can be generalized to other age groups or populations. Furthermore, the cross-sectional nature of the study—where all data were collected at a single point in time—means it does not offer insights into how cognitive function changes over time.
The research team aims to explore the role of gender in the relationship between ECG-age and cognitive performance in future studies. They also plan to extend the analysis to more diverse populations, as the majority of the UK Biobank participants are of European descent.
Dr. Fernando D. Testai, chair of the American Heart Association’s 2024 scientific statement on “Cardiac Contributions to Brain Health,” praised the study for emphasizing the link between heart and brain health. He believes that if the findings are validated, ECG data could be used as a faster, more accessible tool for assessing cognitive health—particularly in rural areas or regions without neuropsychiatric specialists.
The future possibilities are vast. With wearable ECG devices becoming more widespread, the combination of AI and ECG could lead to quicker, more objective cognitive assessments, potentially transforming how cognitive decline is detected and treated.
Disclaimer: This study is based on preliminary data, and the findings are not yet conclusive. Further research, including long-term studies and diverse population samples, is necessary to confirm the potential of ECG data in predicting cognitive decline.
