ArticleAging cell2025
Proteomic Signatures of Epigenetic Age in African Green Monkey Cerebrospinal Fluid and Plasma.
Article in Aging cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The Roles of EDA2R in Ageing and Disease.Aging cell · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Strategies to slow the aging process or mitigate its consequences on health rely on the validation of minimally-invasive biomarkers of aging that can be used to track aging and test the effectiveness of antiaging interventions. Study of aging in a nonhuman primate species offers a robust translational approach to achieving these aims, avoiding wide differences in genetics and environmental exposures that confound human aging studies. As epigenetic age appears to predict biological aging, biomarkers linked to epigenetic aging should be especially valuable in identifying individual differences in aging progression and documenting the impact of antiaging strategies. Proteins are the final effectors in most signaling pathways, indicating that alteration in levels of circulating proteins potentially offers an informative and valuable quantitative index of aging. Accordingly, a proteomic analysis was conducted on matching CSF and plasma samples collected from a large group of African green monkeys, with epigenetic ages ranging from young to old as determined by differential methylation of blood DNA. In addition to analyzing the data with linear statistical models, a gradient boosting machine learning technique was employed to identify not only individual CSF and plasma proteins that correlated with aging progression but also groups of proteins that could be used as predictors of global aging and of specialized aspects of aging such as inflammation. Overall, this study identified new CSF and plasma protein targets for understanding aging biology, together with identifying biomarkers to track changes in the rate of biological aging in a translationally relevant nonhuman primate model.
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Registered trials
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