Evidence map›Paper›PMID 40269264›Full record

ArticleCommunications biology2025

DNAm age differences between infinium methylationEPICv1 vs EPICv2 in buffy coat, PBMC, and saliva samples.

Jian Hua Tay, Yi Ern Chew, Weilan Wang, Zhi Meng Lim, Lihuan Guan, Rajkumar Dorajoo, Brian K Kennedy, Robert Brooke, Juozas Gordevicius, Steve Horvath and 2 more

Abstract read
In one paragraph

Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

12 authors.

Jian Hua TayHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
Yi Ern ChewHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
Weilan WangHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0002-9009-8077
Zhi Meng LimHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
Lihuan GuanHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0003-0497-8519
Rajkumar DorajooGenome Institute of Singapore, Agency for Science, Technology and Research, Singapore, 138672, Singapore.ORCID http://orcid.org/0000-0001-6608-2051
Brian K KennedyHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0002-5754-1874
Robert BrookeEpigenetic Clock Development Foundation, Torrance, CA, USA.
Juozas GordeviciusEpigenetic Clock Development Foundation, Torrance, CA, USA.
Steve HorvathEpigenetic Clock Development Foundation, Torrance, CA, USA.ORCID http://orcid.org/0000-0002-4110-3589
Elena SandalovaHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
Andrea B MaierHealthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore. a.b.maier@vu.nl.ORCID http://orcid.org/0000-0001-7206-1724

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to evaluate differences between Infinium MethylationEPIC (EPICv1) and Infinium MethylationEPICv2 (EPICv2) arrays in estimating DNAm age with eleven DNAm clocks using buffy coat, peripheral blood mononuclear cell (PBMC), and saliva from 16 healthy middle-aged individuals. DNAm ages were estimated using six principal component-based (PC) clocks (PCHorvath1, PCHorvath2, PCHannum, PCPhenoAge, PCGrimAge, and PCDNAmTL) and five non-PC clocks (DunedinPACE, DNAmFit, YingCausAge, YingAdaptAge, and YingDamAge) across all biological samples. Agreement between arrays was assessed using Spearman correlation, Bland-Altman plots, and Wilcoxon Signed-Rank test. The 16 individuals with median age of 48 [43.5;53.8] years, were predominantly female, Chinese and non-smokers. High correlations (ρ > 0.8) were observed between EPICv1 and EPICv2 except for DunedinPACE, YingDamAge and YingAdaptAge. PC-based clocks showed lower systematic bias (MAPE:0.118-8.98%) compared to non-PC-based clocks (MAPE:5.31-21.2%). Saliva samples demonstrated greatest variability between arrays. EPICv2 introduces systematic biases especially in non-PC-based clocks and between different biological samples.

Indexed as

AgingBlood Buffy CoatDNA MethylationLeukocytes, MononuclearSalivaAdultFemaleHumansMaleMiddle Aged

Identifiers

PMID40269264
PMCPMC12019316

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.