Diez Benavente, E;
Jimenez-Lopez, F;
Attia, ZI;
Malyutina, S;
Kudryavtsev, A;
Ryabikov, A;
Friedman, PA;
... Leon, DA; + view all
(2021)
Studying accelerated cardiovascular ageing in Russian adults through a novel deep-learning ECG biomarker [version 1; peer review: awaiting peer review].
Wellcome Open Research
, 6
, Article 12. 10.12688/wellcomeopenres.16499.1.
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Abstract
Background: A non-invasive, easy-to-access marker of accelerated cardiac ageing would provide novel insights into the mechanisms and aetiology of cardiovascular disease (CVD) as well as contribute to risk stratification of those who have not had a heart or circulatory event. Our hypothesis is that differences between an ECG-predicted and chronologic age of participants (δage) would reflect accelerated or decelerated cardiovascular ageing. Methods: A convolutional neural network model trained on over 700,000 ECGs from the Mayo Clinic in the U.S.A was used to predict the age of 4,542 participants in the Know Your Heart study conducted in two cities in Russia (2015-2018). Thereafter, δage was used in linear regression models to assess associations with known CVD risk factors and markers of cardiac abnormalities. / Results: The biomarker δage (mean: +5.32 years) was strongly and positively associated with established risk factors for CVD: blood pressure, body mass index (BMI), total cholesterol and smoking. Additionally, δage had strong independent positive associations with markers of structural cardiac abnormalities: N-terminal pro b-type natriuretic peptide (NT-proBNP), high sensitivity cardiac troponin T (hs-cTnT) and pulse wave velocity, a valid marker of vascular ageing. / Conclusion: The difference between the ECG-age obtained from a convolutional neural network and chronologic age (δage) contains information about the level of exposure of an individual to established CVD risk factors and to markers of cardiac damage in a way that is consistent with it being a biomarker of accelerated cardiovascular (vascular) ageing. Further research is needed to explore whether these associations are seen in populations with different risks of CVD events, and to better understand the underlying mechanisms involved.
| Type: | Article |
|---|---|
| Title: | Studying accelerated cardiovascular ageing in Russian adults through a novel deep-learning ECG biomarker [version 1; peer review: awaiting peer review] |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.12688/wellcomeopenres.16499.1 |
| Publisher version: | https://doi.org/10.12688/wellcomeopenres.16499.1 |
| Language: | English |
| Additional information: | Copyright © 2021 Diez Benavente E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Cardiovascular disease, Biomarkers, Aging, Machine Learning, Russian Federation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10120782 |
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