| Title: | Biological aging predicts mortality in Parkinson's patients: evidence from UK Biobank |
| Journal: | npj Parkinson's Disease |
| Published: | 21 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41565714/ |
| DOI: | https://doi.org/10.1038/s41531-026-01268-0 |
| URL: | https://www.nature.com/articles/s41531-026-01268-0_reference.pdf |
Publication 18591
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Accelerated biological aging serves as a risk factor for age-related diseases, its role in the prognosis of PD remains ambiguous. This study investigates the association between biological aging and the mortality in PD patients. Data were sourced from the UK Biobank. Independent prognostic factors for mortality in PD patients were assessed by Cox regression model, and a nomogram was developed to predict the survival of PD patients. A total of 569 PD patients were enrolled in this study. Phenotypic age (PhenoAge) and PhenoAge acceleration (PhenoAgeAccel) were found to affect the survival in PD patients. Independent risk factors for PD mortality included age, male gender, smoke, underweight, depressive mood, low-density lipoprotein, and higher genetic susceptibility. The nomogram constructed based on PhenoAge showed robust prediction performance for mortality in PD patients. PhenoAge emerges as a pivotal PD mortality predictor, enabling the identification of individuals experiencing accelerated biological aging and implementing targeted interventions.</p>
10 Authors
- Qing-Qing Duan
- Wei-Ming Su
- Kang-Fu Yin
- Sheng-Yi He
- Ru-Yin Liu
- Xiang-Jin Wen
- Xiao-Jing Gu
- Ting Chen
- Bei Cao
- Yong-Ping Chen
1 Application
| Application ID | Title |
|---|---|
| 194434 | Integrating multimodal data from the UK Biobank to improve precision medicine in brain disorders |
Enabling scientific discoveries that improve human health