The 12-lead electrocardiogram as a biomarker of biological age

Adetola O. Ladejobi, Jose R. Medina-Inojosa, Michal Shelly Cohen, Zachi I. Attia, Christopher G. Scott, Nathan K. Lebrasseur, Bernard J. Gersh, Peter A. Noseworthy, Paul A. Friedman, Suraj Kapa, Francisco Lopez-Jimenez

Research output: Contribution to journalArticlepeer-review


Background: We have demonstrated that a neural network is able to predict a person's age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results: We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P < 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion: The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing.

Original languageEnglish (US)
Pages (from-to)379-389
Number of pages11
JournalEuropean Heart Journal - Digital Health
Issue number3
StatePublished - Sep 1 2021


  • Artificial intelligence
  • Biological ageing
  • ECG age
  • Mortality

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine


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