Advertisement

Patterns of DNA methylation as an indicator of biological aging: State of the science and future directions in precision health promotion

      Highlights

      • The genome and the exposome affect epigenetic age and health over time.
      • Epigenetic age surpasses chronologic age in health and disease prediction.
      • Precision prevention may benefit from targeting biological indicators of aging.

      Abstract

      Background

      A rapidly expanding literature suggests that individuals of the same chronological age show significant variation in biological age.

      Purpose

      The purpose of this article is to review the literature surrounding epigenetic age as estimated by DNA methylation, involving the addition or removal of methyl groups to DNA that can alter gene expression without changing the DNA sequence.

      Methods

      This state of the science literature review summarizes current approaches in epigenetic age determination and applications of aging algorithms.

      Findings

      A number of algorithms estimate epigenetic age using DNA methylation markers, primarily among adults. Algorithm application has focused on determining predictive value for risk of disease and death and identifying antecedents to age acceleration. Several studies have incorporated epigenetic age to evaluate intervention effectiveness.

      Discussion

      As the research community continues to refine aging algorithms, there may be opportunity to promote health from a precision health perspective.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Nursing Outlook
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Allis C.D.
        • Jenuwein T.
        The molecular hallmarks of epigenetic control.
        Nature Reviews Genetics. 2016; 17: 487-500https://doi.org/10.1038/nrg.2016.59
        • Andersen G.B.
        • Tost J.
        A summary of the biological processes, disease-associated changes, and clinical applications of DNA methylation.
        Methods in Molecular Biology (Clifton, N.J.). 2018; 1708: 3-30https://doi.org/10.1007/978-1-4939-7481-8_1
        • Austin M.K.
        • Chen E.
        • Ross K.M.
        • McEwen L.M.
        • Maclsaac J.L.
        • Kobor M.S.
        • et al.
        Early-life socioeconomic disadvantage, not current, predicts accelerated epigenetic aging of monocytes.
        Psychoneuroendocrinology. 2018; 97 (doi:S0306-4530(17)31549-4): 131-134
        • Barter J.D.
        • Foster T.C.
        Aging in the brain: New roles of epigenetics in cognitive decline.
        The Neuroscientist. 2018; 24: 516-525https://doi.org/10.1177/1073858418780971
        • Belsky D.W.
        • Huffman K.M.
        • Pieper C.F.
        • Shalev I.
        • Kraus W.E.
        Change in the rate of biological aging in response to caloric restriction: CALERIE biobank analysis.
        Journals of Gerontology Series A, Biological Sciences and Medical Sciences. 2017; 73: 4-10https://doi.org/10.1093/gerona/glx096
        • Bocklandt S.
        • Lin W.
        • Sehl M.E.
        • Sanchez F.J.
        • Sinsheimer J.S.
        • Horvath S.
        • et al.
        Epigenetic predictor of age.
        PloS One. 2011; 6: e14821https://doi.org/10.1371/journal.pone.0014821
        • Chen B.H.
        • Marioni R.E.
        • Colicino E.
        • Peters M.J.
        • Ward-Caviness C.K.
        • Tsai P.C.
        • et al.
        DNA methylation-based measures of biological age: Meta-analysis predicting time to death.
        Aging. 2016; 8: 1844-1865https://doi.org/10.18632/aging.101020
        • Chen L.
        • Dong Y.
        • Bhagatwala J.
        • Raed A.
        • Huang Y.
        • Zhu H.
        Effects of vitamin D3 supplementation on epigenetic aging in overweight and obese african americans with suboptimal vitamin D status: A randomized clinical trial.
        The Journals of Gerontology Series A, Biological Sciences and Medical Sciences. 2019; 74: 91-98https://doi.org/10.1093/gerona/gly223
        • Collins F.S.
        • Varmus H.
        A new initiative on precision medicine.
        The New England Journal of Medicine. 2015; 372: 793-795https://doi.org/10.1056/NEJMp1500523
        • Drucker E.
        • Krapfenbauer K.
        Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine.
        The EPMA Journal. 2013; 4: 7https://doi.org/10.1186/1878-5085-4-7
        • Feero W.G.
        • Guttmacher A.E.
        • Collins F.S.
        Genomic medicine–an updated primer.
        The New England Journal of Medicine. 2010; 362: 2001-2011https://doi.org/10.1056/NEJMra0907175
        • Feinberg A.P.
        • Tycko B.
        The history of cancer epigenetics.
        Nature Reviews Cancer. 2004; 4: 143-153https://doi.org/10.1038/nrc1279
        • Flatt T.
        • Partridge L.
        Horizons in the evolution of aging.
        BMC Biology. 2018; 16: 93https://doi.org/10.1186/s12915-018-0562-z
        • Florath I.
        • Butterbach K.
        • Muller H.
        • Bewerunge-Hudler M.
        • Brenner H.
        Cross-sectional and longitudinal changes in DNA methylation with age: An epigenome-wide analysis revealing over 60 novel age-associated CpG sites.
        Human Molecular Genetics. 2014; 23: 1186-1201https://doi.org/10.1093/hmg/ddt531
        • Fransquet P.D.
        • Wrigglesworth J.
        • Woods R.L.
        • Ernst M.E.
        • Ryan J.
        The epigenetic clock as a predictor of disease and mortality risk: A systematic review and meta-analysis.
        Clinical Epigenetics. 2019; 11: 62https://doi.org/10.1186/s13148-019-0656-7
        • Freire-Aradas A.
        • Phillips C.
        • Giron-Santamaria L.
        • Mosquera-Miguel A.
        • Gomez-Tato A.
        • Casares de Cal M.A.
        • et al.
        Tracking age-correlated DNA methylation markers in the young.
        Forensic Science International Genetics. 2018; 36 (doi:S1872-4973(18)30206-0 [pii]): 50-59
        • Gillman M.W.
        • Hammond R.A.
        Precision treatment and precision prevention: Integrating “below and above the skin”.
        JAMA Pediatrics. 2016; 170: 9-10https://doi.org/10.1001/jamapediatrics.2015.2786
        • Gonzalez-Angulo A.M.
        • Hennessy B.T.
        • Mills G.B.
        Future of personalized medicine in oncology: A systems biology approach.
        Journal of Clinical Oncology. 2010; 28: 2777-2783https://doi.org/10.1200/JCO.2009.27.0777
        • Goronzy J.J.
        • Hu B.
        • Kim C.
        • Jadhav R.R.
        • Weyand C.M.
        Epigenetics of T cell aging.
        Journal of Leukocyte Biology. 2018; 104: 691-699
        • Hannum G.
        • Guinney J.
        • Zhao L.
        • Zhang L.
        • Hughes G.
        • Sadda S.
        • et al.
        Genome-wide methylation profiles reveal quantitative views of human aging rates.
        Molecular Cell. 2013; 49: 359-367https://doi.org/10.1016/j.molcel.2012.10.016
        • Hong S.R.
        • Jung S.E.
        • Lee E.H.
        • Shin K.J.
        • Yang W.I.
        • Lee H.Y.
        DNA methylation-based age prediction from saliva: High age predictability by combination of 7 CpG markers.
        Forensic Science International Genetics. 2017; 29 (doi:S1872-4973(17)30090-X [pii]): 118-125
        • Hong S.R.
        • Shin K.J.
        • Jung S.E.
        • Lee E.H.
        • Lee H.Y.
        Platform-independent models for age prediction using DNA methylation data.
        Forensic Science International Genetics. 2019; 38 (doi:S1872-4973(18)30241-2 [pii]): 39-47
        • Horvath S.
        DNA methylation age of human tissues and cell types.
        Genome Biology. 2013; 14 (R115-2013-14-10-r115. doi:gB-2013-14-10-r115 [pii])
        • Institute of Medicine Committee on Qualification of Biomarkers and Surrogate Endpoints in Chronic Disease
        Perspectives on biomarker and surrogate endpoint evaluation: Discussion forum summary.
        National Academy Press, Washington DC2011 (doi:NBK209568 [bookaccession])
        • Jung S.E.
        • Lim S.M.
        • Hong S.R.
        • Lee E.H.
        • Shin K.J.
        • Lee H.Y.
        DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples.
        Forensic Science International Genetics. 2019; 38 (doi:S1872-4973(18)30238-2 [pii]): 1-8
        • Jylhava J.
        • Pedersen N.L.
        • Hagg S.
        Biological age predictors.
        EBioMedicine. 2017; 21 (doi:S2352-3964(17)30142-1 [pii]): 29-36
        • Kabacik S.
        • Horvath S.
        • Cohen H.
        • Raj K.
        Epigenetic ageing is distinct from senescence-mediated ageing and is not prevented by telomerase expression.
        Aging. 2018; 10: 2800-2815https://doi.org/10.18632/aging.101588
        • Kananen L.
        • Marttila S.
        • Nevalainen T.
        • Kummola L.
        • Junttila I.
        • Mononen N.
        • et al.
        The trajectory of the blood DNA methylome ageing rate is largely set before adulthood: Evidence from two longitudinal studies.
        Age (Dordrecht, Netherlands). 2016; 38 (65-016-9927-9. Epub 2016 Jun 14)https://doi.org/10.1007/s11357-016-9927-9
        • Knight A.K.
        • Craig J.M.
        • Theda C.
        • Baekvad-Hansen M.
        • Bybjerg-Grauholm J.
        • Hansen C.S.
        • et al.
        An epigenetic clock for gestational age at birth based on blood methylation data.
        Genome Biology. 2016; 17: 206https://doi.org/10.1186/s13059-016-1068-z
        • Levine M.E.
        • Lu A.T.
        • Quach A.
        • Chen B.H.
        • Assimes T.L.
        • Bandinelli S.
        • et al.
        An epigenetic biomarker of aging for lifespan and healthspan.
        Aging. 2018; 10: 573-591https://doi.org/10.18632/aging.101414
        • Li C.
        • Gao W.
        • Gao Y.
        • Yu C.
        • Lv J.
        • Lv R.
        • et al.
        Age prediction of children and adolescents aged 6-17 years: An epigenome-wide analysis of DNA methylation.
        Aging. 2018; 10: 1015-1026https://doi.org/10.18632/aging.101445
        • Lin Q.
        • Weidner C.I.
        • Costa I.G.
        • Marioni R.E.
        • Ferreira M.R.
        • Deary I.J.
        • et al.
        DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy.
        Aging. 2016; 8 (doi:100908 [pii]): 394-401
        • Lu A.T.
        • Quach A.
        • Wilson J.G.
        • Reiner A.P.
        • Aviv A.
        • Raj K.
        • et al.
        DNA methylation GrimAge strongly predicts lifespan and healthspan.
        Aging. 2019; 11: 303-327https://doi.org/10.18632/aging.101684
        • Lyko F.
        The DNA methyltransferase family: A versatile toolkit for epigenetic regulation.
        Nature Reviews Genetics. 2018; 19: 81-92https://doi.org/10.1038/nrg.2017.80
        • Maegawa S.
        • Lu Y.
        • Tahara T.
        • Lee J.T.
        • Madzo J.
        • Liang S.
        • et al.
        Caloric restriction delays age-related methylation drift.
        Nature Communications. 2017; 8 (539-017-00607-3)https://doi.org/10.1038/s41467-017-00607-3
        • Mari-Alexandre J.
        • Diaz-Lagares A.
        • Villalba M.
        • Juan O.
        • Crujeiras A.B.
        • Calvo A.
        • et al.
        Translating cancer epigenomics into the clinic: Focus on lung cancer.
        Translational Research. 2017; 189 (doi:S1931-5244(17)30208-6 [pii]): 76-92
        • Marioni R.E.
        • Harris S.E.
        • Shah S.
        • McRae A.F.
        • von Zglinicki T.
        • Martin-Ruiz C.
        • et al.
        The epigenetic clock and telomere length are independently associated with chronological age and mortality.
        International Journal of Epidemiology. 2016; 45 (doi:dYw041 [pii]): 424-432
        • Marioni R.E.
        • Shah S.
        • McRae A.F.
        • Chen B.H.
        • Colicino E.
        • Harris S.E.
        • et al.
        DNA methylation age of blood predicts all-cause mortality in later life.
        Genome Biology. 2015; 16 (25-015-0584-6)https://doi.org/10.1186/s13059-015-0584-6
        • Muezzinler A.
        • Zaineddin A.K.
        • Brenner H.
        A systematic review of leukocyte telomere length and age in adults.
        Ageing Research Reviews. 2013; 12: 509-519https://doi.org/10.1016/j.arr.2013.01.003
        • Niccoli T.
        • Partridge L.
        Ageing as a risk factor for disease.
        Current Biology: CB. 2012; 22: R741-R752https://doi.org/10.1016/j.cub.2012.07.024
        • Obeid R.
        • Hubner U.
        • Bodis M.
        • Graeber S.
        • Geisel J.
        Effect of adding B-vitamins to vitamin D and calcium supplementation on CpG methylation of epigenetic aging markers.
        Nutrition, Metabolism, and Cardiovascular Diseases: NMCD. 2018; 28 (doi:S0939-4753(17)30320-4 [pii]): 411-417
        • Pan Y.
        • Liu G.
        • Zhou F.
        • Su B.
        • Li Y.
        DNA methylation profiles in cancer diagnosis and therapeutics.
        Clinical and Experimental Medicine. 2018; 18: 1-14https://doi.org/10.1007/s10238-017-0467-0
        • Ryan J.
        • Wrigglesworth J.
        • Loong J.
        • Fransquet P.D.
        • Woods R.L.
        A systematic review and meta-analysis of environmental, lifestyle and health factors associated with DNA methylation age.
        The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. 2019; (doi:gLz099 [pii]. [epub ahead of print])
        • Schmidt K.T.
        • Chau C.H.
        • Price D.K.
        • Figg W.D.
        Precision oncology medicine: The clinical relevance of patient-specific biomarkers used to optimize cancer treatment.
        Journal of Clinical Pharmacology. 2016; 56: 1484-1499https://doi.org/10.1002/jcph.765
        • Song S.
        • Johnson F.B.
        Epigenetic mechanisms impacting aging: A focus on histone levels and telomeres.
        Genes. 2018; 9 (doi:E201 [pii])https://doi.org/10.3390/genes9040201
        • Szostak J.W.
        • Blackburn E.H.
        Cloning yeast telomeres on linear plasmid vectors.
        Cell. 1982; 29 (doi:0092-8674(82)90109-X [pii]): 245-255
        • Taylor S.M.
        • Jones P.A.
        Multiple new phenotypes induced in 10T1/2 and 3T3 cells treated with 5-azacytidine.
        Cell. 1979; 17 (doi:0092-8674(79)90317-9 [pii]): 771-779
        • Theunissen T.W.
        • Jaenisch R.
        Mechanisms of gene regulation in human embryos and pluripotent stem cells.
        Development (Cambridge, England). 2017; 144: 4496-4509https://doi.org/10.1242/dev.157404
        • Turner K.J.
        • Vasu V.
        • Griffin D.K.
        Telomere biology and human phenotype.
        Cells. 2019; 8 (doi:E73 [pii])https://doi.org/10.3390/cells8010073
        • Vargas A.J.
        • Harris C.C.
        Biomarker development in the precision medicine era: Lung cancer as a case study.
        Nature Reviews Cancer. 2016; 16: 525-537https://doi.org/10.1038/nrc.2016.56
        • Vidaki A.
        • Ballard D.
        • Aliferi A.
        • Miller T.H.
        • Barron L.P.
        • Syndercombe Court D.
        DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing.
        Forensic Science International Genetics. 2017; 28 (doi:S1872-4973(17)30038-8 [pii]): 225-236
        • Vidal-Bralo L.
        • Lopez-Golan Y.
        • Gonzalez A.
        Simplified assay for epigenetic age estimation in whole blood of adults.
        Frontiers in Genetics. 2016; 7: 126https://doi.org/10.3389/fgene.2016.00126
        • Weidner C.I.
        • Lin Q.
        • Koch C.M.
        • Eisele L.
        • Beier F.
        • Ziegler P.
        • et al.
        Aging of blood can be tracked by DNA methylation changes at just three CpG sites.
        Genome Biology. 2014; 15 (R24-2014-15-2-r24)https://doi.org/10.1186/gb-2014-15-2-r24
        • Wild C.P.
        Complementing the genome with an “exposome”: The outstanding challenge of environmental exposure measurement in molecular epidemiology.
        Cancer Epidemiology, Biomarkers & Prevention. 2005; 14 (doi:14/8/1847 [pii]): 1847-1850
        • Wolf E.J.
        • Maniates H.
        • Nugent N.
        • Maihofer A.X.
        • Armstrong D.
        • Ratanatharathorn A.
        • et al.
        Traumatic stress and accelerated DNA methylation age: A meta-analysis.
        Psychoneuroendocrinology. 2018; 92 (doi:S0306-4530(17)31260-X [pii]): 123-134
        • Wu H.
        • Caffo B.
        • Jaffee H.A.
        • Irizarry R.A.
        • Feinberg A.P.
        Redefining CpG islands using hidden markov models.
        Biostatistics (Oxford, England). 2010; 11: 499-514https://doi.org/10.1093/biostatistics/kxq005
        • Xu Y.
        • Li X.
        • Yang Y.
        • Li C.
        • Shao X.
        Human age prediction based on DNA methylation of non-blood tissues.
        Computer Methods and Programs in Biomedicine. 2019; 171 (doi:S0169-2607(18)31663-8 [pii]): 11-18