Evidence map›Paper›PMID 32067420›Full record

ArticleJournal of cachexia, sarcopenia and muscle2020

An epigenetic clock for human skeletal muscle.

Sarah Voisin, Nicholas R Harvey, Larisa M Haupt, Lyn R Griffiths, Kevin J Ashton, Vernon G Coffey, Thomas M Doering, Jamie-Lee M Thompson, Christian Benedict, Jonathan Cedernaes and 6 more

Open access · goldAbstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 5 pooled it
6.3field-weighted citation impact, top 2% of its field
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

67 citing papers in PubMed, 5 syntheses or guidelines pooled it, 117 citations in OpenAlex.

  1. Pooled it
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  6. Review
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  9. Embracing non-linearity in human ageing.Nature reviews. Genetics · 2026
    Review
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  17. Does Time Tick Faster in Cerebral Palsy? Accelerated Aging as a Framework for Skeletal Muscle Dysfunction.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Review
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7 more citing papers are in PubMed but not listed here.

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

16 authors at 9 institutions in 6 countries.

Sarah VoisinInstitute for Health and Sport, Victoria University, Melbourne, Australia.ORCID 0000-0002-4074-7083
Nicholas R HarveyFaculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
Larisa M HauptGenomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia.
Lyn R GriffithsGenomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia.
Kevin J AshtonFaculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
Vernon G CoffeyFaculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
Thomas M DoeringFaculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
Jamie-Lee M ThompsonFaculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
Christian BenedictSleep Research Laboratory, Department of Neuroscience, Uppsala University, Uppsala, Sweden.
Jonathan CedernaesDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.
Malene E LindholmDepartment of Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
Jeffrey M CraigCentre for Molecular and Medical Research, Deakin University, Geelong, Australia.
David S RowlandsSchool of Sport, Exercise and Nutrition, Massey University, Wellington, New Zealand.
Adam P SharplesDepartment of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway.
Steve HorvathDepartment of Human Genetics and Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Nir EynonInstitute for Health and Sport, Victoria University, Melbourne, Australia.
Bond University · AUQueensland University of Technology · AUUppsala University · SEVictoria University · AULiverpool John Moores University · GBMassey University · NZRoyal Children's Hospital · AUStanford Medicine · USUniversity of California, Los Angeles · US

Funding

Validation and optimization of epigenetic clocksU01AG060908 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI RITZ, BEATE R. · 2018 to 2022
$3.4M
NIA NIH HHS U01 AG060908
6 · The paper itself

Abstract

backgroundAgeing is associated with DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns across tissues. However, the construction of the original pan-tissue epigenetic clock did not include skeletal muscle samples and hence exhibited a strong deviation between DNA methylation and chronological age in this tissue.

methodsTo address this, we developed a more accurate, muscle-specific epigenetic clock based on the genome-wide DNA methylation data of 682 skeletal muscle samples from 12 independent datasets (18-89 years old, 22% women, 99% Caucasian), all generated with Illumina HumanMethylation (HM) arrays (HM27, HM450, or HMEPIC). We also took advantage of the large number of samples to conduct an epigenome-wide association study of age-associated DNA methylation patterns in skeletal muscle.

resultsThe newly developed clock uses 200 cytosine-phosphate-guanine dinucleotides to estimate chronological age in skeletal muscle, 16 of which are in common with the 353 cytosine-phosphate-guanine dinucleotides of the pan-tissue clock. The muscle clock outperformed the pan-tissue clock, with a median error of only 4.6 years across datasets (vs. 13.1 years for the pan-tissue clock, P < 0.0001) and an average correlation of ρ = 0.62 between actual and predicted age across datasets (vs. ρ = 0.51 for the pan-tissue clock). Lastly, we identified 180 differentially methylated regions with age in skeletal muscle at a false discovery rate < 0.005. However, gene set enrichment analysis did not reveal any enrichment for gene ontologies.

conclusionsWe have developed a muscle-specific epigenetic clock that predicts age with better accuracy than the pan-tissue clock. We implemented the muscle clock in an r package called Muscle Epigenetic Age Test available on Bioconductor to estimate epigenetic age in skeletal muscle samples. This clock may prove valuable in assessing the impact of environmental factors, such as exercise and diet, on muscle-specific biological ageing processes.

Indexed as

AdolescentAdultAgedAged, 80 and overEpigenomicsFemaleHumansMaleMiddle AgedMuscle, SkeletalYoung AdultAgeingBiological ageDNA methylationEpigenetic ageEpigenetic clockSkeletal muscle

Identifiers

PMID32067420
PMCPMC7432573
OpenAlexW3007728540

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.