The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Differential DNA methylation clock ages across buffy coat (BC), peripheral blood mononuclear cells (PBMC), and saliva in individuals in early-to-mid adulthood
Open accessPublished version of a preprint
Abstract
Abstract Epigenetic aging prior to midlife is gaining interest as an intervenable period to address health and cognitive aging. Epigenetic changes may index DNA methylation aging rates, but methylation profiles may not be substitutable across tissues. We compared DNA methylation clocks and age acceleration in saliva, buffy coat (BC), and peripheral blood mononuclear cells (PBMC) collected in 91 individuals (7 unpaired, 20 siblings, 64 twins; 18 monozygotic (MZ), 14 dizygotic (DZ) pairs) from the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife1; Mean age=30.90 years [range=28.07–41.13]; 50.5% female). Across 15 DNA methylation clocks, chronological age and DNA methylation ages were moderately associated (mean Spearman correlations: r =0.37, Saliva; r =0.41, BC; r =0.38, PBMC). In mixed-effects models, saliva showed higher DNA methylation ages ( B =3.83–16.46 years vs BC, p<0.001), whereas PBMC and BC were comparable ( B =-0.06–0.39 years vs BC, p > 0.447). The exception was the next-generation clock DunedinPace showing comparability ( p =0.486). Similar patterns were observed for age acceleration estimates. Altogether, MZ pairs (meta-analytic r= 0.49, 95%CI=0.32,0.66) and DZ pairs (meta-analytic r= 0.38, 95%CI=0.25,0.52) were moderately correlated (Spearman), but MZ pairs showed heterogeneity across tissues ( p <0.020): saliva was lower (mean r= 0.33, SD =0.25) than BC (mean r= 0.64, SD =0.10) and PBMC (mean r= 0.49, SD =0.18). Next-generation PCGrimAge and DunedinPace clocks showed consistent zygosity correlations across tissues, while multi-tissue clocks (e.g., ZhangQ) showed comparable MZ-DZ correlations. While saliva-based DNA methylation is not a direct substitute for blood-based DNA methylation, BC and PBMC show comparability; nevertheless, all tissue types may be appropriate for DNA methylation aging studies when compared within tissues. Key Policy Highlights Saliva-based methylation is not a direct substitute for blood-based methylation. Saliva-based DNA methylation clocks demonstrated relatively older methylation ages and faster age accelerations relative to blood-based DNA methylation clocks. Blood-based DNA methylation clocks were comparable across buffy coat and peripheral blood mononuclear cell tissues. Twin similarity, particularly among identical twins, was higher for blood-based DNA methylation clocks and newer generation DNA methylation clocks. Three clocks (one first-generation multi-tissue and two next-generation) showed consistency and/or comparability across tissues and showed moderate to strong correlations among identical and fraternal twins, supporting the importance of tool development.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A data-driven approach to preprocessing Illumina 450K methylation array data
Pidsley, R., Y Wong, C. C., Volta, M., Lunnon, K., Mill, J., & Schalkwyk, L. C. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-293 · Cited by 1,143
The central role of DNA damage in the ageing process
Schumacher, B., Pothof, J., Vijg, J., & Hoeijmakers, J. H. J. (2021) · Nature · doi:10.1038/s41586-021-03307-7 · Cited by 907
Horvath, S., Oshima, J., Martin, G. M., Lu, A. T., Quach, A., Cohen, H., Felton, S., Matsuyama, M., Lowe, D., Kabacik, S., et al. (2018) · Aging · doi:10.18632/aging.101508 · Cited by 815
Zhang, Q., Vallerga, C. L., Walker, R. M., Lin, T., Henders, A. K., Montgomery, G. W., He, J., Fan, D., Fowdar, J., Kennedy, M., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0667-1 · Cited by 487
Flagged first, then by citations
Research works printed but not linked: 55
- 10.1182/blood.V93.12.4059 · DOI
- 10.1016/j.tig.2022.03.010 · DOI
- 10.1016/j.mrfmmm.2008.08.006 · DOI
- 10.1111/acel.12349 · DOI
- 10.3389/fgene.2023.1125967 · DOI
- 10.1038/nrg1655 · DOI · Paper page
- 10.1186/s13072-018-0191-3 · DOI · Paper page
- 10.1111/bcpt.12721 · DOI
- 10.1080/15592294.2017.1417710 · DOI
- 10.3389/fbioe.2021.782843 · DOI
- 10.3390/epigenomes1030014 · DOI
- 10.1038/s41576-024-00807-w · DOI
- 10.3390/cancers14194882 · DOI
- 10.1001/jamapediatrics.2024.3669 · DOI
- 10.1002/ajmg.b.32278 · DOI · Paper page
- 10.1038/s41598-018-25311-0 · DOI
- 10.3390/ijms252011329 · DOI
- 10.1186/s12865-019-0286-z · DOI
- 10.1038/s43856-024-00652-2 · DOI
- 10.1158/1055-9965.EPI-12-0859 · DOI
- 10.1371/journal.pbio.1000533 · DOI
- 10.1007/s11357-014-9648-x · DOI
- 10.1371/journal.pone.0044213 · DOI
- 10.1038/s41467-023-40389-5 · DOI
- 10.1007/s11357-023-00871-w · DOI
- 10.1007/s10522-025-10265-4 · DOI
- 10.7554/eLife.51507 · DOI · Paper page
- 10.1038/s42003-025-08021-y · DOI
- 10.1186/s13148-025-01868-2 · DOI
- 10.1111/acel.14451 · DOI
- 10.1037/amp0000600 · DOI
- 10.7554/eLife.73420 · DOI · Paper page
- 10.18632/aging.101684 · DOI · Paper page
- 10.1016/j.ebiom.2019.01.040 · DOI
- 10.1111/acel.13197 · DOI
- 10.1017/thg.2019.49 · DOI
- 10.1023/B:BEGE.0000038492.50446.25 · DOI
- 10.1093/bioinformatics/btu049 · DOI
- 10.1093/bioinformatics/btw691 · DOI
- 10.1080/15592294.2021.1890874 · DOI
- 10.1186/s12864-021-07675-2 · DOI
- 10.1093/brain/awaa334 · DOI
- 10.1016/j.molcel.2012.10.016 · DOI · Paper page
- 10.1186/gb-2013-14-10-r115 · DOI · Paper page
- 10.1038/s43587-022-00248-2 · DOI · Paper page
- 10.18632/aging.101414 · DOI · Paper page
- 10.1037/met0000079 · DOI · Paper page
- Knopik VS, Neiderhiser JM, DeFries JC, et al. Behavioral genetics. 7th ed. New York (NY): Worth Publishers; 2017.
- Hagenbeek FA, van Dongen J, Pool R, et al. Twins and omics: the role of twin studies in multi-omics. In: Tarnoki AD, Tarnoki DL, Harris JR, Segal NL, editors. Twin research for everyone. Elsevier; 2022. p. 547–584. · DOI
- 10.3758/s13428-013-0386-2 · DOI
- 10.18637/jss.v036.i03 · DOI · Paper page
- 10.1093/biomet/75.2.383 · DOI
- 10.1186/s12864-025-11713-8 · DOI
- 10.3390/ijms25021168 · DOI
- 10.1089/bio.2025.0043 · DOI
Metadata
- Authors
- Bruellman, R., Evans, D., Smolen, A., Evans, L. M., & Reynolds, C. A.
- Affiliations
- University of California, Riverside (US)
- University of Colorado
- Corresponding author
- Reynolds, Chandra A.
- Venue
- Epigenetics
- Publisher
- Informa UK Limited
- Published
- 2026-04-15
- Volume
- 21
- Issue
- 1
- Pages
- 2653960-2653960
- ISSN
- 1559-2294 (ISSN-L)
- 1559-2308
- PMC
- PMC12458201
- OpenAlex
- W7154492183
- Cited by
- 1
- References
- 4
- Citations by year
- 2026: 1
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Epigenetics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging of the National Institutes of Health (R01AG046938)
- American Federation for Aging Research Hevolution/AFAR New Investigator Awards in Aging Biology
- Geroscience Research (HEV~NI23013)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Keywords
- DNA methylation clocks, epigenetic aging, accelerated aging, tissue-specific methylation, saliva, buffy coat, peripheral blood mononuclear cells, monozygotic and dizygotic twins, zygosity, DunedinPACE, epigenetic age prediction, cognitive aging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review