Systematic underestimation of the epigenetic clock and age acceleration in older subjects

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Abstract

Background

The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate “age acceleration” in various tissues and environments.

Results

The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways.

Conclusions

The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate.

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Most cited references 50

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DNA methylation age of human tissues and cell types

Steve Horvath, Ilias Lagkouvardos (2014)

Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.

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An epigenetic biomarker of aging for lifespan and healthspan

Morgan E Levine, Ake T. Lu, Austin Quach … (2018)

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

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Genome-wide methylation profiles reveal quantitative views of human aging rates.

Gregory Hannum, Justin Guinney, Ling Zhao … (2013)

The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual's methylome ages, which we show is impacted by gender and genetic variants. We also show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Moreover, we show how our aging model is upheld in other human tissues and reveals an advanced aging rate in tumor tissue. Our model highlights specific components of the aging process and provides a quantitative readout for studying the role of methylation in age-related disease. Copyright © 2013 Elsevier Inc. All rights reserved.

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Author and article information

Contributors

Louis Y. El Khoury: elkhoury.louis@mayo.edu

Tyler Gorrie-Stone: tgorri@essex.ac.uk

Melissa Smart: mcsmar@essex.ac.uk

Amanda Hughes: amanda.hughes@bristol.ac.uk

Yanchun Bao: ybaoa@essex.ac.uk

Alexandria Andrayas: aandra@essex.ac.uk

Joe Burrage: J.Burrage@exeter.ac.uk

Eilis Hannon: E.J.Hannon@exeter.ac.uk

Meena Kumari: mkumari@essex.ac.uk

Jonathan Mill: J.Mill@exeter.ac.uk

Leonard C. Schalkwyk:

ORCID: http://orcid.org/0000-0001-7030-5756

lschal@essex.ac.uk

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 17 December 2019

Publication date PMC-release: 17 December 2019

Publication date Collection: 2019

Volume: 20

Electronic Location Identifier: 283

Affiliations

[1 ]ISNI 0000 0001 0942 6946, GRID grid.8356.8, School of Life Sciences, , University of Essex, ; Colchester, UK

[2 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Present Address: Department of Molecular Pharmacology and Experimental Therapeutics, , Mayo Clinic, ; Rochester, MN USA

[3 ]ISNI 0000 0001 0942 6946, GRID grid.8356.8, Institute for Social and Economic Research, , University of Essex, ; Colchester, UK

[4 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, MRC Integrative Epidemiology Unit - University of Bristol, ; Bristol, UK

[5 ]ISNI 0000 0004 1936 8024, GRID grid.8391.3, Medical School, , University of Exeter, ; Exeter, UK

Author information

Leonard C. Schalkwyk http://orcid.org/0000-0001-7030-5756

Article

Publisher ID: 1810

DOI: 10.1186/s13059-019-1810-4

PMC ID: 6915902

PubMed ID: 31847916

SO-VID: ea9b22b2-3c73-49d3-8fd1-0f8e6fab0b64

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 20 August 2018

Date accepted : 3 September 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: R01 AG036039

Award Recipient : Jonathan Mill

Funded by: FundRef http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;

Award ID: ES/N00812X/1

Award Recipient : Meena Kumari

Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;

Award ID: K013807

Award ID: MR/R005176/1

Award Recipient : Jonathan Mill

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ScienceOpen disciplines: Genetics

Keywords: dna methylation,epigenetic clock,age acceleration

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ScienceOpen disciplines: Genetics

Keywords: dna methylation, epigenetic clock, age acceleration

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Systematic underestimation of the epigenetic clock and age acceleration in older subjects

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Genomic Prediction

Most cited references 50

DNA methylation age of human tissues and cell types

An epigenetic biomarker of aging for lifespan and healthspan

Genome-wide methylation profiles reveal quantitative views of human aging rates.

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Cited by 40

Most referenced authors 1,113