Evidence map›Paper›PMID 30482885›Full record

ArticleAging2018

Cell and tissue type independent age-associated DNA methylation changes are not rare but common.

Tianyu Zhu, Shijie C Zheng, Dirk S Paul, Steve Horvath, Andrew E Teschendorff

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Aging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 4 pooled it
–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

43 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  15. A Sex-Specific Minimal CpG-Based Model for Biological Aging UsingInternational journal of molecular sciences · 2025
    Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. On epigenetic stochasticity, entropy and cancer risk.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Review
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.

Tianyu ZhuCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Shijie C ZhengCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Dirk S PaulCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK.
Steve HorvathDepartment of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, CA 90095, USA.
Andrew E TeschendorffCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Funding

Validation and optimization of epigenetic clocksU01AG060908 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI RITZ, BEATE R. · 2018 to 2022
$3.4M
ARACHIDONATE METABOLISM IN GLOMERULONEPHRITIS/VASCULITISK08HL001313 · NHLBI · WASHINGTON UNIVERSITY · PI LEFKOWITH, JAMES B · 1985 to 1988
–
British Heart Foundation RG/13/13/30194Medical Research Council MR/L003120/1NIA NIH HHS U01 AG060908
6 · The paper itself

Abstract

Age-associated DNA methylation changes have been widely reported across many different tissue and cell types. Epigenetic 'clocks' that can predict chronological age with a surprisingly high degree of accuracy appear to do so independently of tissue and cell-type, suggesting that a component of epigenetic drift is cell-type independent. However, the relative amount of age-associated DNAm changes that are specific to a cell or tissue type versus the amount that occurs independently of cell or tissue type is unclear and a matter of debate, with a recent study concluding that most epigenetic drift is tissue-specific. Here, we perform a novel comprehensive statistical analysis, including matched multi cell-type and multi-tissue DNA methylation profiles from the same individuals and adjusting for cell-type heterogeneity, demonstrating that a substantial amount of epigenetic drift, possibly over 70%, is shared between significant numbers of different tissue/cell types. We further show that ELOVL2 is not unique and that many other CpG sites, some mapping to genes in the Wnt and glutamate receptor signaling pathways, are altered with age across at least 10 different cell/tissue types. We propose that while most age-associated DNAm changes are shared between cell-types that the putative functional effect is likely to be tissue-specific.

Indexed as

DNA MethylationEpigenesis, GeneticAgingBrainCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCervix UteriCheekFemaleFibroblastsHumansLiverMaleDNA methylationepigenetic clockepigenetic drift

Identifiers

PMID30482885
PMCPMC6286821

What OpenQuestion holds

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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.