Applicability of epigenetic age models to next-generation methylation arrays.

Genome Med

Breast Cancer Clinical Research Unit, Centro Nacional de Investigaciones Oncológicas-CNIO, Melchor Fernández Almagro, 3, Madrid, 28029, Spain.

Published: October 2024

AI Article Synopsis

  • Epigenetic clocks estimate biological age based on DNA methylation, but older models may not work well with new technology like the EPICv2 array, which could render them obsolete.
  • In this study, researchers evaluated four existing epigenetic clocks against the new EPICv2 array and created a more effective model that works on multiple platforms, validated with thousands of samples.
  • The new model showed significant improvements, providing accurate age predictions and effectively detecting changes in epigenetic age related to cancer treatments and interventions, highlighting the need to update older models for better compatibility.

Article Abstract

Background: Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.

Methods: We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.

Results: The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.

Conclusion: Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11460231PMC
http://dx.doi.org/10.1186/s13073-024-01387-4DOI Listing

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