| Title: | Environmental and genetic predictors of human cardiovascular ageing |
| Journal: | Nature Communications |
| Published: | 21 Aug 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37604819/ |
| DOI: | https://doi.org/10.1038/s41467-023-40566-6 |
| URL: | https://www.nature.com/articles/s41467-023-40566-6.pdf |
| Citations: | 10 (10 in last 2 years) as of 8 Aug 2024 |
Publication 9732
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Abstract
Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.</p>
18 Authors
- Mit Shah
- Marco H. de A. Inácio
- Chang Lu
- Pierre-Raphaël Schiratti
- Sean L. Zheng
- Adam Clement
- Antonio de Marvao
- Wenjia Bai
- Andrew P. King
- James S. Ware
- Martin R. Wilkins
- Johanna Mielke
- Eren Elci
- Ivan Kryukov
- Kathryn A. McGurk
- Christian Bender
- Daniel F. Freitag
- Declan P. O'Regan
1 Application
| Application ID | Title |
|---|---|
| 40616 | Machine learning discovery of genotype-phenotype associations in cardiovascular science. |
Enabling scientific discoveries that improve human health