Methylation clocks for evaluation of anti-aging interventions

Methylation clocks have found their way into the community of aging research as a way to test anti-aging interventions without having to wait for mortality statistics. But methylation is a primary means of epigenetic control, and presumably has evolved under strong selection. Hence, if methylation patterns change consistently at late ages it must mean one of two things. Either (1) the body is evolved to destroy itself (with inflammation, autoimmunity, etc.), and the observed methylation changes are a means to this end; or (2) the body detects accumulated damage, and is ramping up repair mechanisms in a campaign to rescue itself. My thesis herein is that both Type 1 and Type 2 changes are occurring, but that only Type 1 changes are useful in constructing methylation clocks to evaluate anti-aging interventions. This is because a therapy that sets back Type 1 changes to an earlier age state has stopped the body from destroying itself; but a therapy that sets back Type 2 changes has stopped the body from repairing itself. Thus, a major challenge before the community of epigenetic clock developers is to distinguish Type 2 from Type 1. The existence of Type 1 epigenetic changes is in conflict with conventional Darwinian thinking, and this has prompted some researchers to explore the possibility that Type 1 changes might be a form of stochastic epigenetic drift. I argue herein that what seems like directed epigenetic change really is directed epigenetic change. Of five recent articles on "stochastic methylation clocks," only one (from the Conboy lab) is based on truly stochastic changes. Using the Conboy methodology and a methylation database, I construct a measure of true methylation drift, and show that its correlation with age is too low to be useful.

Authors

Publication Details

Journal
Aging
Published
2025-05-05
DOI
https://doi.org/10.18632/aging.206245
Citations
1
Primary Topic
Epigenetics and DNA Methylation
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Methylation clocks for evaluation of anti-aging interventions

Josh Mitteldorf
1 citations
Aging
Epigenetics and DNA Methylation
0.81
article

Methylation clocks for evaluation of anti-aging interventions

Josh Mitteldorf
article en
1 citations

Abstract

Methylation clocks have found their way into the community of aging research as a way to test anti-aging interventions without having to wait for mortality statistics. But methylation is a primary means of epigenetic control, and presumably has evolved under strong selection. Hence, if methylation patterns change consistently at late ages it must mean one of two things. Either (1) the body is evolved to destroy itself (with inflammation, autoimmunity, etc.), and the observed methylation changes are a means to this end; or (2) the body detects accumulated damage, and is ramping up repair mechanisms in a campaign to rescue itself. My thesis herein is that both Type 1 and Type 2 changes are occurring, but that only Type 1 changes are useful in constructing methylation clocks to evaluate anti-aging interventions. This is because a therapy that sets back Type 1 changes to an earlier age state has stopped the body from destroying itself; but a therapy that sets back Type 2 changes has stopped the body from repairing itself. Thus, a major challenge before the community of epigenetic clock developers is to distinguish Type 2 from Type 1. The existence of Type 1 epigenetic changes is in conflict with conventional Darwinian thinking, and this has prompted some researchers to explore the possibility that Type 1 changes might be a form of stochastic epigenetic drift. I argue herein that what seems like directed epigenetic change really is directed epigenetic change. Of five recent articles on "stochastic methylation clocks," only one (from the Conboy lab) is based on truly stochastic changes. Using the Conboy methodology and a methylation database, I construct a measure of true methylation drift, and show that its correlation with age is too low to be useful.

AgingVol. 17(5)
Zero hunger
Openalex Percentile: Top 28%
Epigenetics and DNA Methylation
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.