Abstract
Aims: Biological age derived from 12-lead electrocardiograms (ECGs) using deep learning has emerged as a promising marker of physiological ageing. However, its relationship with cognitive performance remains poorly understood. To investigate the association between ECG-derived ageing and cognitive performance in two large population-based cohorts.
Methods and results: We analysed data from the UK Biobank (UKB) and the Framingham Heart Study (FHS). A deep learning model estimated ECG-based biological age (ECG-age) from ECG waveforms. We calculated the difference between ECG-age and chronological age (Δage), which was used to classify participants into ageing groups: accelerated ageing, normal ageing, and decelerated ageing. Cognitive performance was measured with standardized neuropsychological tests, which were grouped into six cognitive domains and a global cognitive score. Multivariable linear regression models were used to examine the associations of Δage and ageing groups with cognitive performance. Among 59 213 UKB participants (mean age 64.7 ± 7.8 years; 51.7% women) and 6534 FHS participants (mean age 59.5 ± 14.5 years; 55.7% women), the mean absolute error between ECG-age and chronological age was 4.7 and 7.5 years, respectively. In both cohorts, higher Δage was associated with lower global cognitive performance (UKB: β = -0.02, 95% CI: -0.03, -0.02; FHS: β = -0.04, 95% CI: -0.06, -0.02) and poorer performance across multiple cognitive domains.
Conclusion: Electrocardiogram-derived age acceleration is associated with poorer cognitive performance across two independent cohorts. Electrocardiogram-based ageing metrics may serve as scalable, low-cost digital markers to identify individuals at elevated risk for cognitive decline.