Identifying epigenetic aging moderators using the epigenetic pacemaker

Colin Farrell, Chanyue Hu, Kalsuda Lapborisuth, Kyle Pu, Sagi Snir, Matteo Pellegrini

Research output: Contribution to journalArticlepeer-review


Epigenetic clocks are DNA methylation-based chronological age prediction models that are commonly employed to study age-related biology. The difference between the predicted and observed age is often interpreted as a form of biological age acceleration, and many studies have measured the impact of environmental and disease-associated factors on epigenetic age. Most epigenetic clocks are fit using approaches that minimize the error between the predicted and observed chronological age, and as a result, they may not accurately model the impact of factors that moderate the relationship between the actual and epigenetic age. Here, we compare epigenetic clocks that are constructed using penalized regression methods to an evolutionary framework of epigenetic aging with the epigenetic pacemaker (EPM), which directly models DNA methylation as a function of a time-dependent epigenetic state. In simulations, we show that the value of the epigenetic state is impacted by factors such as age, sex, and cell-type composition. Next, in a dataset aggregated from previous studies, we show that the epigenetic state is also moderated by sex and the cell type. Finally, we demonstrate that the epigenetic state is also moderated by toxins in a study on polybrominated biphenyl exposure. Thus, we find that the pacemaker provides a robust framework for the study of factors that impact epigenetic age acceleration and that the effect of these factors may be obscured in traditional clocks based on linear regression models.

Original languageEnglish
Article number1308680
JournalFrontiers in Bioinformatics
StatePublished - 2023

Bibliographical note

Publisher Copyright:
Copyright © 2024 Farrell, Hu, Lapborisuth, Pu, Snir and Pellegrini.


  • DNA methylation
  • aging
  • epigenetic
  • epigenetic clock
  • epigenome

ASJC Scopus subject areas

  • Computational Mathematics
  • Structural Biology
  • Biochemistry
  • Biotechnology
  • Statistics and Probability


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