| Title: | Associations of biological ageing and genetic risk with incident abdominal aortic aneurysm |
| Journal: | Communications Medicine |
| Published: | 9 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41514073/ |
| DOI: | https://doi.org/10.1038/s43856-025-01373-w |
| URL: | https://www.nature.com/articles/s43856-025-01373-w_reference.pdf |
Publication 18671
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Abstract
BackgroundAbdominal aortic aneurysm (AAA) is a degenerative cardiovascular disorder prevalent with ageing. While accelerated biological ageing contributes to age-related diseases, its specific role in AAA risk remains unclear. This study investigates the relationships between biological ageing and risk of incident AAA and genetic predisposition to the disease.MethodsThis retrospective study used UK Biobank data from 350,483 participants without AAA at baseline. Biological age was assessed using Klemera-Doubal Method (KDMAge) and phenotypic age (PhenoAge) algorithms. Accelerated biological ageing was determined through residual analysis against chronological age, with values above 0 indicating accelerated ageing. Cox proportional hazards models evaluated the associations of biological age accelerations with AAA risk. Polygenic risk scores were calculated to evaluate genetic predisposition to AAA. We also examined the interactions between biological age accelerations and genetic predisposition on the risk of AAA.ResultsHere we show that participants with accelerated biological ageing have an elevated risk of AAA onset compared to those without, with hazard ratios (HRs) of 1.29 (95% CI 1.17-1.42) for KDMAge and 1.63 (95% CI 1.47-1.81) for PhenoAge. For joint associations, participants with accelerated biological ageing and high genetic risk have the highest risk of incident AAA (KDMAge: HR 2.15, 95% CI 1.81-2.54; PhenoAge: HR 2.72, 95% CI 2.26-3.28). There is a significant additive interaction between high genetic risk and accelerated biological ageing of PhenoAge.ConclusionsAccelerated biological ageing is significantly associated with an increased risk of AAA incidence, suggesting its potential as a focal point for risk assessment and targeted intervention development.</p>
7 Authors
- Chen Yao
- Guochang You
- Runnan Shen
- Kangjie Wang
- Yunhao Sun
- Xiong Chen
- Kai Huang
1 Application
| Application ID | Title |
|---|---|
| 100739 | Artificial intelligence for using multi-modal data to improve the identification and prediction of diseases in the UK Biobank |
Enabling scientific discoveries that improve human health