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Clock Work: Deconstructing the Epigenetic Clock Signals in Aging, Disease, and Reprogramming

Morgan E. LevineiD, Albert Higgins‐CheniD, Kyra ThrushiD, Christopher J. MinteeriD, Peter Niimi

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Abstract

Epigenetic clocks have come to be regarded as powerful tools for estimating aging. However, a major drawback in their application is our lack of mechanistic understanding. We hypothesize that uncovering the underlying biology is difficult due to the fact that epigenetic clocks are multifactorial composites: They are comprised of disparate parts, each with their own causal mechanism and functional consequences. Thus, only by deconstructing epigenetic clock signals will it be possible to glean biological insight. Here we clustered 5,717 clock CpGs into twelve distinct modules using multi-tissue and in-vitro datasets. We show that epigenetic clocks are comprised of different proportions of modules, which may explain their discordance when simultaneously applied in a given study. We also observe that epigenetic reprogramming does not reset the entire clock and instead the observed rejuvenation is driven by a subset of modules. Overall, two modules stand-out in terms of their unique features. The first is one of the most responsive to epigenetic reprogramming; is the strongest predictor of all-cause mortality; and shows increases with in vitro passaging up until senescence burden begins to emerge. The light-second module is moderately responsive to reprogramming; is very accelerated in tumor vs. normal tissues; and tracks with passaging in vitro even as population doublings decelerate. Overall, we show that clock deconstruction can identify unique DNAm alterations and facilitate our mechanistic understanding of epigenetic clocks.

Abstract summary

Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract · CC BY-NC-ND 4.0

Clustering 5,717 clock CpGs into twelve modules shows that different epigenetic clocks mix those modules in different proportions, and that reprogramming rejuvenates only some of them.

Epigenetic clocks are regarded as powerful tools for estimating ageing but, as the authors argue, resist mechanistic interpretation because each is a composite of parts with separate causal mechanisms and consequences. Splitting the clock CpGs into modules supplies a vocabulary for why clocks disagree with one another and for which components actually move under reprogramming.

Key results

  1. SuggestedClock CpGs separate into twelve modules, and individual clocks draw on these modules in different proportions, which the authors put forward as a reason clocks disagree when applied to the same study.

    5,717 clock CpGs were clustered into twelve modules using multi-tissue and in-vitro datasets; the per-clock module proportions and any measure of clock discordance are not given numerically in the abstract. · Abstract

  2. SuggestedEpigenetic reprogramming does not reset the whole clock: the rejuvenation observed comes from a subset of modules rather than a uniform change across them.

    Reported as an observation across the module decomposition, without the number of responsive modules, effect sizes or the reprogramming system used being stated. · Abstract

  3. SuggestedTwo modules behave distinctively: one is among the most reprogramming-responsive, predicts all-cause mortality best, and rises with passaging until senescence burden appears; the other responds moderately, is strongly accelerated in tumour versus normal tissue, and keeps tracking passaging as doublings slow.

    Both profiles are described qualitatively in the abstract; no hazard ratios, tumour/normal differences, passage numbers or module identities are reported. · Abstract

Methods, data and reproducibility

CpGs used by existing epigenetic clocks were clustered into twelve modules from multi-tissue and in-vitro DNA methylation datasets. Each clock was then described by the mix of modules it draws on, and the modules themselves were characterised by response to epigenetic reprogramming, association with all-cause mortality, behaviour across in vitro passaging and senescence, and tumour versus normal tissue differences.

  • multi-tissue and in-vitro DNA methylation datasets used to cluster clock CpGs · n = 5,717 clock CpGs grouped into twelve modules
  • in vitro passaging series spanning the emergence of senescence burden
  • epigenetic reprogramming data, and tumour versus normal tissue comparisons
Limitations (2)
  • No dataset names, cohort sizes, accessions or clustering details appear in the available text, so neither the module definitions nor their stability can be examined. · noted in this summary
  • Every module characterisation is an association reported without effect sizes or statistics; the mortality prediction in particular is not quantified or validated in an independent cohort here. · noted in this summary

Read this if you use DNA methylation clocks and want to know why two clocks can disagree on the same samples, or which part of the clock signal moves under reprogramming.

Citations
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