| Title: | Combination of biological aging and genetic risk helps identify at-risk population of thoracic aortic aneurysms insight from a prospective cohort study of UK Biobank. |
| Journal: | International Journal of Surgery |
| Published: | 30 Jun 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40576428/ |
| DOI: | https://doi.org/10.1097/js9.0000000000002898 |
Publication 16506
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Abstract
BACKGROUND: Both age and genetic risk are associated with risk of thoracic aortic aneurysms (TAA). This study seeks to explore the association between phenotypic age acceleration (PhenoAgeAccel), a novel biomarker of aging, and risk of TAA, as well as to conduct risk stratification for TAA based on PhenoAgeAccel and genetic risk.</p>
METHODS: A total of 406,750 participants from the prospective cohort of UK Biobank were included. PhenoAgeAccel was calculated based on chronological age and 9 biomarkers. Associations between PhenoAgeAccel, genetic risk and incident TAA were explored based on cox proportional hazards models. Additionally, mediation analyses were conducted to explore whether PhenoAgeAccel mediated the pathogenic process of risk factors for TAA.</p>
RESULTS: PhenoAgeAccel was significantly associated with increased risk of TAA (hazard ratio [HR]: 1.31; 95% confidence intervals [CI]: 1.15-1.48). Compared to biologically younger participants with low genetic risk, biologically older participants with high genetic risk had a 3.73 (95% CI: 2.70-5.16) folds risk of TAA. PhenoAgeAccel exhibited a significant additive interaction with genetic susceptibility. Mediation analyses revealed that PhenoAgeAccel mediates the association between various risk factors and the progression of TAA.</p>
CONCLUSION: PhenoAgeAccel has the potential to serve as a novel aging biomarker for identifying high-risk populations of TAA. The combination of PhenoAgeAccel and genetic risk can further improve TAA risk stratification, thereby informing the formulation of screening strategy and primary prevention for TAA.</p>
8 Authors
- Jiatang Xu
- Zhensheng Hu
- Hongze Liu
- Yangfan Su
- Runnan Shen
- Chaoyu Xie
- Yi Zhou
- 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