| Title: | Decoding sexually dimorphic proteomic landscapes in the context of aging and mortality |
| Journal: | Communications Medicine |
| Published: | 29 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41023390/ |
| DOI: | https://doi.org/10.1038/s43856-025-01113-0 |
| URL: | https://www.nature.com/articles/s43856-025-01113-0.pdf |
Publication 19375
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Abstract
BackgroundAging-associated changes are major contributors to the onset and progression of chronic diseases. Different aging clocks have been developed to assess biological aging, demonstrating their utility in predicting mortality, diagnosing disease, and evaluating the efficacy of antiaging interventions. However, the protein profile underlying the accelerating or decelerating rates of aging, hidden behind aging clocks, remains poorly understood.MethodBased on the UK Biobank (n = 53, 013; age range 39-71 years; 53.9% men and 46.1% women), we built a proteomic-based aging clock, ProteAge, and assessed its performance in predicting all-cause mortality. Sex-specific aging trajectories and aging rate-associated proteins (ARPs) were identified.ResultsProteAge reveals distinct aging trajectories for males and females, with females exhibiting more nonlinear changes in the aging rate than males do. We identify hundreds of accelerating and decelerating aging rate proteins (ARPs) in both sexes. Given the critical role of mortality prediction in aging and longevity research, we identify a subset of mortality-aging-associated proteins among ARPs, with a total of 1 protein in females but 172 in males. Furthermore, the protective and risk factors in both sexes are identified based on these ARPs.ConclusionsThese findings highlight sexually-dimorphic proteomic changes associated with aging and mortality, offering insights into the biological mechanisms underlying aging and longevity.</p>
10 Authors
- Zhihao Jin
- Bingying Du
- Xuehao Jiao
- Zhengsheng Gu
- Tianren Wang
- Li Cao
- Xiaoying Bi
- Lei Yuan
- Bo Peng
- Yanxia Rao
1 Application
| Application ID | Title |
|---|---|
| 99709 | Risk factor and causal association detection in common diseases |
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