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  • Review Article
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Epigenetic clocks and DNA methylation biomarkers of brain health and disease

Nature Reviews Neurology volume 21, pages 411–421 (2025)Cite this article

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Abstract

Ageing has profound effects on the human brain across the lifespan. Cognitive testing and brain imaging are currently used to monitor healthy and pathological brain ageing. However, peripheral markers of cognitive function, cognitive ageing and neurological disease could provide a valuable, minimally invasive approach to tracking these processes longitudinally. In this Review, we introduce the concept of DNA methylation-based biomarkers and present current evidence of their potential to address the challenge of monitoring brain ageing and stratifying the risk of neurological disease. We focus on epigenetic clocks, which can be applied across multiple tissues and organs to estimate biological ageing, as well as on blood-based epigenetic scores (EpiScores) that can directly track brain-based phenotypes, such as cognitive function, and risk factors for neurological diseases, such as lifestyle behaviours and proteomic markers of inflammation. We discuss the associations between these epigenetic biomarkers and multiple measures of cognitive health, including cognitive test data, brain MRI measures and dementia.

Key points

  • Peripheral markers of neurological function and disease could provide a valuable, minimally invasive approach to aid risk prediction.

  • DNA methylation, an epigenetic modification that varies by tissue or cell type of origin, is an increasingly popular candidate for biomarker development.

  • Methylation patterns from blood, brain and other tissues can be used to build biomarkers for chronological ageing (termed first-generation epigenetic clocks) and other complex traits (for example, cognitive function, mortality or protein levels).

  • Second-generation and third-generation epigenetic clocks, which were built to predict the healthspan, lifespan and rate of biological ageing, associate more strongly with health outcomes than do first-generation clocks but currently neither show robust associations with dementia.

  • Methylation biomarkers for other complex traits, including inflammatory protein levels and cognitive function, show promise for tracking risk factors for neurodegeneration.

  • Methylation biomarkers might offer an effective way to track brain health across the life course.

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Fig. 1: Relationship between CpG methylation proportions and chronological age.
Fig. 2: Training and testing a first-generation epigenetic clock.
Fig. 3: First-generation versus second-generation and third-generation epigenetic clocks.
Fig. 4: Timeline of cortical clock development.

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Authors and Affiliations

  1. Department of Biochemistry, University of Oxford, Oxford, UK

    Eleanor L. S. Conole

  2. Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

    Josephine A. Robertson, Hannah M. Smith & Riccardo E. Marioni

  3. Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK

    Simon R. Cox

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  1. Eleanor L. S. Conole
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  2. Josephine A. Robertson
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  3. Hannah M. Smith
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  4. Simon R. Cox
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  5. Riccardo E. Marioni
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The authors contributed equally to all aspects of the article.

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Correspondence to Riccardo E. Marioni.

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R.E.M. is a scientific advisor to the Epigenetic Clock Development Foundation. The other authors declare no competing interests.

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Glossary

Elastic net penalized regression

In regression scenarios with many predictor variables, one can derive a sparse or parsimonious solution whereby non-important predictors are assigned a regression weight of zero. Elastic net penalized regression is one of the most commonly applied approaches to do this in the context of generating epigenetic clocks.

Multivariate regression

In multivariable linear regression, we consider one outcome and multiple predictor variables, whereas in multivariate regression, we simultaneously model multiple outcome variables. This approach helps us to determine whether a predictor variable has common or unique associations with different outcomes within a single regression model.

Principal components analysis

A data reduction approach whereby one can consider correlated variables by their most common shared features. For example, instead of focusing on scores from ten different cognitive tests, the data could be realigned into a smaller number of variables that capture most of the variance across all ten tests. This might include a general ability component, which captures information for individuals who perform similarly well across all tests, followed by components that capture information across certain domains, for example, tests that assess memory performance.

Variance components analyses

Instead of considering predictors as separate variables, we can ask whether the similarity in these predictors across individuals correlates with the outcome of interest. For example, do people with broadly similar DNA methylation patterns also show similarity in the outcome variable of interest?

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Conole, E.L.S., Robertson, J.A., Smith, H.M. et al. Epigenetic clocks and DNA methylation biomarkers of brain health and disease. Nat Rev Neurol 21, 411–421 (2025). https://doi.org/10.1038/s41582-025-01105-7

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