Evidence map›Paper›PMID 39060678›Full record

ArticleGeroScience2025

Biological aging of different blood cell types.

Saara Marttila, Sonja Rajić, Joanna Ciantar, Jonathan K L Mak, Ilkka S Junttila, Laura Kummola, Sara Hägg, Emma Raitoharju, Laura Kananen

Abstract read
In one paragraph

Article in GeroScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Exploring the association between epigenetic age and intensive care unit delirium: a pilot feasibility study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
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

9 authors.

Saara MarttilaMolecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. saara.marttila@tuni.fi.ORCID 0000-0003-3071-3784
Sonja RajićMolecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID 0000-0002-0497-3577
Joanna CiantarMolecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID 0000-0002-3715-480X
Jonathan K L MakDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Ilkka S JunttilaFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID 0000-0002-9830-0823
Laura KummolaFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Sara HäggDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Emma RaitoharjuMolecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID 0000-0002-7023-8706
Laura KananenGerontology Research Center, Tampere University, Tampere, Finland. laura.kananen@ki.se.ORCID 0000-0003-3742-8927

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological age (BA) captures detrimental age-related changes. The best-known and most-used BA indicators include DNA methylation-based epigenetic clocks and telomere length (TL). The most common biological sample material for epidemiological aging studies, whole blood, is composed of different cell types. We aimed to compare differences in BAs between blood cell types and assessed the BA indicators' cell type-specific associations with chronological age (CA). An analysis of DNA methylation-based BA indicators, including TL, methylation level at cg16867657 in ELOVL2, as well as the Hannum, Horvath, DNAmPhenoAge, and DunedinPACE epigenetic clocks, was performed on 428 biological samples of 12 blood cell types. BA values were different in the majority of the pairwise comparisons between cell types, as well as in comparison to whole blood (p < 0.05). DNAmPhenoAge showed the largest cell type differences, up to 44.5 years and DNA methylation-based TL showed the lowest differences. T cells generally had the "youngest" BA values, with differences across subsets, whereas monocytes had the "oldest" values. All BA indicators, except DunedinPACE, strongly correlated with CA within a cell type. Some differences such as DNAmPhenoAge-difference between naïve CD4 + T cells and monocytes were constant regardless of the blood donor's CA (range 20-80 years), while for DunedinPACE they were not. In conclusion, DNA methylation-based indicators of BA exhibit cell type-specific characteristics. Our results have implications for understanding the molecular mechanisms underlying epigenetic clocks and underscore the importance of considering cell composition when utilizing them as indicators for the success of aging interventions.

Indexed as

AgingBlood CellsAdultAgedAged, 80 and overDNA MethylationEpigenesis, GeneticFatty Acid ElongasesFemaleHumansMaleMiddle AgedYoung AdultELOVL2 protein, humanFatty Acid ElongasesBiological ageBiological agingBlood cell subtypesDNA methylationEpigenetic clockTelomere length

Identifiers

PMID39060678
PMCPMC11872950

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.