| Title: | Plasma-based brain age as a potential biomarker for cognitive health and risk of brain-related disorders |
| Journal: | Communications Medicine |
| Published: | 29 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41318807/ |
| DOI: | https://doi.org/10.1038/s43856-025-01268-w |
| URL: | https://www.nature.com/articles/s43856-025-01268-w_reference.pdf |
Publication 16821
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Abstract
BackgroundUnderstanding brain aging is essential for identifying early markers of cognitive decline. This study aimed to develop plasma-based biomarkers of brain aging and examine their associations with cognitive function.MethodsWe used data from 53,005 UK Biobank participants (2006-2010) with available Olink proteomics data (mean age 57 ± 8 years, 54% women). Protein levels were used to estimate brain age, organismal age, and conventional proteomic age using Least Absolute Shrinkage and Selection Operator (LASSO) regression. We assessed the relationships between these biological ages and cognitive function using multivariable-adjusted linear regression models, and their association with incident Alzheimer's disease (AD) and stroke using Cox proportional hazards models. Findings were validated in 2066 participants from the Framingham Heart Study (FHS) Offspring cohort (Exam 8, 2002-2005, mean age 67 ± 9 years, 55% women), also using the Olink platform.ResultsAccelerated brain aging is significantly associated with poorer cognitive performance, whereas organismal and conventional proteomic ages are not. All three biological age measures are linked to increased risk of AD and stroke, but brain aging shows the strongest association (hazard ratio (HR) for AD: 1.79 [95% confidence interval (CI): 1.66-1.93]; HR for stroke: 1.25 [95% CI: 1.17-1.33]). In the FHS validation, brain aging is associated with lower performance in cognitive domains such as attention and visual memory, and with increased risk of AD (HR: 1.64 [95% CI: 1.37-1.97]).ConclusionsPlasma-based biomarkers of brain aging may offer a promising tool for monitoring cognitive health and identifying individuals at risk for age-related diseases.</p>
6 Authors
- Biqi Wang
- Huitong Ding
- Derek Qi
- Mayra S. Tisminetzky
- Joanne M. Murabito
- Honghuang Lin
1 Application
| Application ID | Title |
|---|---|
| 76269 | Multimodal machine learning study of neurodegenerative diseases |
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