Abstract
The development of valid and reliable biomarkers of aging has become a major initiative in Geroscience research. Our ability to distinguish biological from chronological age will enable identification of accelerated versus decelerated agers, and could also provide a valuable tool for assessing intervention efficacy. While various types of data can be used to quantify “biological age”, perhaps the most successful applications have been using DNA methylation data. To date nearly a dozen different “epigenetic clocks” have been develop—most of which track age in a variety of tissues and cell types. Nevertheless, while they seem to share some characteristics, such as robust age prediction, their associations with age-related outcomes vary substantially. In moving forward, the utility of such measures will depend on our understanding of (1) why specific CpG dinucleotides exhibit such consistent DNAm changes over time, (2) what pathways or hallmarks are driven and/or reflected by alterations of DNAm, and (3) perhaps most importantly, whether these patterns of aging are amenable to intervention.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahuja N, Issa JP (2000) Aging, methylation and cancer. Histol Histopathol 15:835–842. https://doi.org/10.14670/hh-15.835
Ambatipudi S et al (2017) DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility. Eur J Cancer 75:299–307. https://doi.org/10.1016/j.ejca.2017.01.014
Aunan JR, Cho WC, Soreide K (2017) The biology of aging and cancer: a brief overview of shared and divergent molecular hallmarks. Aging Dis 8:628–642. https://doi.org/10.14336/AD.2017.0103
Belsky DW et al (2018) Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: do they measure the same thing? Am J Epidemiol 187:1220–1230. https://doi.org/10.1093/aje/kwx346
Bocklandt S et al (2011) Epigenetic predictor of age. PLoS One 6. https://doi.org/10.1371/journal.pone.0014821
Boltzmann L (1878) Zur Theorie der elastischen Nachwirkung. Ann Phys 241:430–432. https://doi.org/10.1002/andp.18782411107
Bortz WM 2nd (1986) Aging as entropy. Exp Gerontol 21:321–328
Chen J et al (2012) Maternal deprivation in rats is associated with corticotrophin-releasing hormone (CRH) promoter hypomethylation and enhances CRH transcriptional responses to stress in adulthood. J Neuroendocrinol 24:1055–1064. https://doi.org/10.1111/j.1365-2826.2012.02306.x
Chen BH et al (2016) DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging (Albany NY) 8:1844–1865. https://doi.org/10.18632/aging.101020
de Magalhães JP (2012) Programmatic features of aging originating in development: aging mechanisms beyond molecular damage? FASEB J 26:4821–4826. https://doi.org/10.1096/fj.12-210872
Ferrucci L, Levine Morgan E, Kuo P-L, Simonsick Eleanor M (2018) Time and the metrics of aging. Circ Res 123:740–744. https://doi.org/10.1161/CIRCRESAHA.118.312816
Florath I, Butterbach K, Müller H, Bewerunge-Hudler M, Brenner H (2013) Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Doi: D—NLM: PMC3919014; EDAT—2013/10/29 06:00; MHDA—2014/10/16 06:00; CRDT—2013/10/29 06:00; PHST—2013/10/29 06:00 [entrez]; PHST—2013/10/29 06:00 [pubmed]; PHST—2014/10/16 06:00 [medline]; AID—ddt531 [pii]; AID—https://doi.org/10.1093/hmg/ddt531 [doi]; PST—ppublish
Frenk S, Houseley J (2018) Gene expression hallmarks of cellular ageing. Biogerontology 19:547–566. https://doi.org/10.1007/s10522-018-9750-z
Hannum G et al (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49:359–367. https://doi.org/10.1016/j.molcel.2012.10.016
Hayflick L (2007) Entropy explains aging, genetic determinism explains longevity, and undefined terminology explains misunderstanding both. PLoS Genet 3:e220. https://doi.org/10.1371/journal.pgen.0030220
Hofstatter EW et al (2018) Increased epigenetic age in normal breast tissue from luminal breast cancer patients. Clin Epigenet 10:112. https://doi.org/10.1186/s13148-018-0534-8
Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115. https://doi.org/10.1186/gb-2013-14-10-r115
Horvath S, Levine AJ (2015) HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis 212:1563–1573. https://doi.org/10.1093/infdis/jiv277
Horvath S, Raj K (2018) DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet 19:371–384. https://doi.org/10.1038/s41576-018-0004-3
Horvath S et al (2015a) Accelerated epigenetic aging in down syndrome. Aging Cell 14. https://doi.org/10.1111/acel.12325
Horvath S et al (2015b) Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging (Albany NY) 7. https://doi.org/10.18632/aging.100861
Horvath S et al (2018) Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging (Albany NY) 10:1758–1775. https://doi.org/10.18632/aging.101508
Hudgins AD et al (2018) Age- and tissue-specific expression of senescence biomarkers in mice. Front Genet 9:59–59. https://doi.org/10.3389/fgene.2018.00059
Jabbari K, Bernardi G (2004) Cytosine methylation and CpG, TpG (CpA) and TpA frequencies. Gene 333:143–149. https://doi.org/10.1016/j.gene.2004.02.043
Jenkinson G, Pujadas E, Goutsias J, Feinberg AP (2017) Potential energy landscapes identify the information-theoretic nature of the epigenome. Nat Genet 49:719–729. https://doi.org/10.1038/ng.3811
Joehanes R et al (2016) Epigenetic signatures of cigarette smoking. Circ Cardiovasc Genet 9:436–447. https://doi.org/10.1161/CIRCGENETICS.116.001506
Johansson A, Enroth S, Gyllensten U (2013) Continuous aging of the human DNA methylome throughout the human lifespan. Doi: D—NLM: PMC3695075; EDAT—2013/07/05 06:00; MHDA—2013/07/05 06:01; CRDT—2013/07/05 06:00; PHST—2013/01/09 00:00 [received]; PHST—2013/05/16 00:00 [accepted]; PHST—2013/07/05 06:00 [entrez]; PHST—2013/07/05 06:00 [pubmed]; PHST—2013/07/05 06:01 [medline]; AID—https://doi.org/10.1371/journal.pone.0067378 [doi]; AID—PONE-D-13-02458 [pii]; PST—epublish
Jylhävä J, Pedersen NL, Hägg S (2017) Biological age predictors. EBioMedicine 21:29–36. https://doi.org/10.1016/j.ebiom.2017.03.046
Kannel WB, McGee D, Gordon T (1976) A general cardiovascular risk profile: the Framingham Study. Am J Cardiol 38:46–51
Kennedy BK et al (2014) Geroscience: linking aging to chronic disease. Cell 159:709–713. https://doi.org/10.1016/j.cell.2014.10.039
Kirkwood TBL (1977) Evolution of ageing. Nature 270:301. https://doi.org/10.1038/270301a0
Klutstein M, Nejman D, Greenfield R, Cedar H (2016) DNA methylation in cancer and aging. Can Res 76:3446–3450. https://doi.org/10.1158/0008-5472.Can-15-3278
Kowald A, Kirkwood TBL (2016) Can aging be programmed? A critical literature review. Aging Cell 15:986–998. https://doi.org/10.1111/acel.12510
Kumsta R et al (2016) Severe psychosocial deprivation in early childhood is associated with increased DNA methylation across a region spanning the transcription start site of CYP2E1. Transl Psychiatry 6:e830. https://doi.org/10.1038/tp.2016.95
Kurdyukov S, Bullock M (2016) DNA methylation analysis: choosing the right method. Biology 5:3. https://doi.org/10.3390/biology5010003
Levine ME et al (2015a) DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative. Aging (Albany NY) 7:690–700. https://doi.org/10.18632/aging.100809
Levine M, Lu A, Bennett D, Horvath S (2015b) Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging (Albany NY)
Levine ME et al (2016) Menopause accelerates biological aging. Proc Natl Acad Sci U S A 113:9327–9332. https://doi.org/10.1073/pnas.1604558113
Levine ME et al (2018) An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10:573–591. https://doi.org/10.18632/aging.101414
Lin Q, Wagner W (2015) Epigenetic aging signatures are coherently modified in cancer. PLoS Genet 11:e1005334. https://doi.org/10.1371/journal.pgen.1005334
Maegawa S et al (2017) Caloric restriction delays age-related methylation drift. Nat Commun 8:539. https://doi.org/10.1038/s41467-017-00607-3
Marioni R et al (2015a) DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol 16:25
Marioni RE et al (2015b) The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int J Epidemiol 44:1388–1396. https://doi.org/10.1093/ije/dyu277
Rakyan VK et al (2010) Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res 20:434–439. https://doi.org/10.1101/gr.103101.109
Rockwood K, Mitnitski AB, MacKnight C (2002) Some mathematical models of frailty and their clinical implications. Rev Clin Gerontol 12:109–117. https://doi.org/10.1017/S0959259802012236
Seeman TE, McEwen BS, Rowe JW, Singer BH (2001) Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proc Natl Acad Sci U S A 98:4770–4775. https://doi.org/10.1073/pnas.081072698
Skuladottir GV, Nilsson EK, Mwinyi J, Schiöth HB (2016) One-night sleep deprivation induces changes in the DNA methylation and serum activity indices of stearoyl-CoA desaturase in young healthy men. Lipids Health Dis 15:137. https://doi.org/10.1186/s12944-016-0309-1
Smith ZD, Meissner A (2013) DNA methylation: roles in mammalian development. Nat Rev Genet 14:204. https://doi.org/10.1038/nrg3354
St-Cyr S, McGowan PO (2017) Adaptation or pathology? The role of prenatal stressor type and intensity in the developmental programing of adult phenotype. Neurotoxicol Teratol. https://doi.org/10.1016/j.ntt.2017.12.003
Teschendorff AE et al (2010) Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 20:440–446
Tomasetti C, Vogelstein B (2015) Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347:78–81. https://doi.org/10.1126/science.1260825
Vidal-Bralo L, Lopez-Golan Y, Gonzalez A (2016) Simplified assay for epigenetic age estimation in whole blood of adults. Front Genet 7:126. https://doi.org/10.3389/fgene.2016.00126
Weidner CI et al (2014) Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol 15:R24. https://doi.org/10.1186/gb-2014-15-2-r24
Yang Z et al (2016) Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol 17:205. https://doi.org/10.1186/s13059-016-1064-3
Zhang Y et al (2017) DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat Commun 8:14617. https://doi.org/10.1038/ncomms14617
Zheng Y et al (2016) Blood epigenetic age may predict cancer incidence and mortality. EBioMedicine 5:68–73. https://doi.org/10.1016/j.ebiom.2016.02.008
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Levine, M.E. (2019). Epigenetic Biomarkers of Aging. In: Moskalev, A. (eds) Biomarkers of Human Aging. Healthy Ageing and Longevity, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-24970-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-24970-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24969-4
Online ISBN: 978-3-030-24970-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)