Evidence map›Paper›PMID 42156953›Full record

ArticleNature aging2026

A unifying model of stem cell dynamics explains age-related methylation patterns across mammals.

Samuel J C Crofts, Caleb M Grenko, Riccardo E Marioni, Eric Latorre-Crespo, Tamir Chandra

Abstract read
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In one paragraph

Article in Nature aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Samuel J C CroftsRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0001-7496-082X
Caleb M GrenkoRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Riccardo E MarioniInstitute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0003-4430-4260
Eric Latorre-Crespo *Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK. elatorre@crm.cat.
Tamir Chandra *Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA. chandra.tamir@mayo.edu.ORCID http://orcid.org/0000-0002-7935-317X

Funding

European Hematology Association (EHA) BCG-202209-02649RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) UKRI1941RCUK | Medical Research Council (MRC) MC_UU_00009/2Wellcome Trust (Wellcome) 226831/Z/22/Z
6 · The paper itself

Abstract

DNA methylation changes are reliable biomarkers of aging, but the driving mechanisms remain poorly understood. Here we present SCARLET (Stem Cells and Age-ReLated Epigenetic Trajectories), a parsimonious mathematical model that describes how methylation changes in blood arise and propagate through hematopoietic stem cell divisions. Using a large human cohort, we demonstrate that seemingly distinct age-related methylation patterns can be explained by a unifying mechanistic model. We show that SCARLET captures known drivers of epigenetic aging, with accelerated individuals showing reduced ratios of stem cell pool size to division rate (N/s). Applying SCARLET to methylation data from 11 mammalian species reveals that N/s scales with maximum lifespan, suggesting that evolutionary adjustments to stem cell dynamics, rather than epigenetic maintenance efficiency, drive the previously observed relationship between methylation rates and lifespan. Our findings provide a quantitative framework for understanding epigenetic aging and suggest that stem cell dynamics may be a key driver of aging across mammals.

Indexed as

AgingDNA MethylationModels, TheoreticalStem CellsAnimalsEpigenesis, GeneticHematopoietic Stem CellsHumansLongevityMammals

Identifiers

PMID42156953

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.