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The trajectory of the blood DNA methylome ageing rate is largely set before adulthood: evidence from two longitudinal studies

AGE volume 38, Article number: 65 (2016) Cite this article

Abstract

The epigenetic clock, defined as the DNA methylome age (DNAmAge), is a candidate biomarker of ageing. In this study, we aimed to characterize the behaviour of this marker during the human lifespan in more detail using two follow-up cohorts (the Young Finns study, calendar age i.e. cAge range at baseline 15–24 years, 25-year-follow-up, N = 183; The Vitality 90+ study, cAge range at baseline 19–90 years, 4-year-follow-up, N = 48). We also aimed to assess the relationship between DNAmAge estimate and the blood cell distributions, as both of these measures are known to change as a function of age. The subjects’ DNAmAges were determined using Horvath’s calculator of epigenetic cAge. The estimate of the DNA methylome age acceleration (Δ-cAge-DNAmAge) demonstrated remarkable stability in both cohorts: the individual rank orders of the DNAmAges remained largely unchanged during the follow-ups. The blood cell distributions also demonstrated significant intra-individual correlation between the baseline and follow-up time points. Interestingly, the immunosenescence-associated features (CD8+CD28− and CD4+CD28− cell proportions and the CD4/CD8 cell ratio) were tightly associated with the estimate of the DNA methylome age. In summary, our data demonstrate that the general level of Δ-cAge-DNAmAge is fixed before adulthood and appears to be quite stationary thereafter, even in the oldest-old ages. Moreover, the blood DNAmAge estimate seems to be tightly associated with ageing-associated shifts in blood cell composition, especially with those that are the hallmarks of immunosenescence. Overall, these observations contribute to the understanding of the longitudinal aspects of the DNAmAge estimate.

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Abbreviations

cAge:

Calendar age

DNAmAge:

DNA methylome age

FACS:

Fluorescence-activated cell sorting analysis

MAD:

The median of the differences between DNAmAge and cAge

PBMC:

Peripheral blood mononuclear cell

PCA:

Principal component analysis

WBL:

White blood leukocyte

YFS:

Young Finns study

V90:

Vitality 90+ study

Δ-cAge-DNAmAge:

The difference between calendar age and DNA methylome age

References

  • Akerblom HK, Viikari J, Rasanen L, Kuusela V, Uhari M, Lautala P (1989) Cardiovascular risk in young Finns, results from the second follow-up study. Ann Med 21:223–225

    CAS  Article  PubMed  Google Scholar 

  • Arnold CR, Wolf J, Brunner S, Herndler-Brandstetter D, Grubeck-Loebenstein B (2011) Gain and loss of T cell subsets in old age–age-related reshaping of the T cell repertoire. J Clin Immunol 31:137–146. doi:10.1007/s10875-010-9499-x

    Article  PubMed  Google Scholar 

  • Benetos A, Okuda K, Lajemi M, Kimura M, Thomas F, Skurnick J, Labat C, Bean K, Aviv A (2001) Telomere length as an indicator of biological aging: the gender effect and relation with pulse pressure and pulse wave velocity. Hypertension 37:381–385

    CAS  Article  PubMed  Google Scholar 

  • Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, Delano D, Zhang L, Schroth GP, Gunderson KL, et al. (2011) High density DNA methylation array with single CpG site resolution. Genomics 98:288–295. doi:10.1016/j.ygeno.2011.07.007

    CAS  Article  PubMed  Google Scholar 

  • Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, Gunderson KL (2009) Genome-wide DNA methylation profiling using Infinium(R) assay. Epigenomics 1:177–200. doi:10.2217/epi.09.14

    CAS  Article  PubMed  Google Scholar 

  • Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW, Wu B, Doucet D, Thomas NJ, Wang Y, Vollmer E, et al. (2006) High-throughput DNA methylation profiling using universal bead arrays. Genome Res 16:383–393

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Bocklandt S, Lin W, Sehl ME, Sanchez FJ, Sinsheimer JS, Horvath S, Vilain E (2011) Epigenetic predictor of age. PLoS One 6:e14821. doi:10.1371/journal.pone.0014821

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Boks MP, van Mierlo HC, Rutten BP, Radstake TR, De Witte L, Geuze E, Horvath S, Schalkwyk LC, Vinkers CH, Broen JC, et al. (2015) Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder. Psychoneuroendocrinology 51:506–512. doi:10.1016/j.psyneuen.2014.07.011

    CAS  Article  PubMed  Google Scholar 

  • Broux B, Markovic-Plese S, Stinissen P, Hellings N (2012) Pathogenic features of CD4+CD28- T cells in immune disorders. Trends Mol Med 18:446–453. doi:10.1016/j.molmed.2012.06.003

    CAS  Article  PubMed  Google Scholar 

  • Florath I, Butterbach K, Muller H, Bewerunge-Hudler M, Brenner H (2014) Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum Mol Genet 23:1186–1201. doi:10.1093/hmg/ddt531

    CAS  Article  PubMed  Google Scholar 

  • Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, Di Blasio AM, Gentilini D, Vitale G, Collino S, et al. (2012) Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell 11:1132–1134. doi:10.1111/acel.12005

    CAS  Article  PubMed  Google Scholar 

  • Goebeler S, Jylha M, Hervonen A (2003) Medical history, cognitive status and mobility at the age of 90. A population-based study in Tampere, Finland. Aging Clin Exp Res 15:154–161

    Article  PubMed  Google Scholar 

  • Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan J, Gao Y, et al. (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49:359–367. doi:10.1016/j.molcel.2012.10.016

    CAS  Article  PubMed  Google Scholar 

  • Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115

    Article  PubMed  PubMed Central  Google Scholar 

  • Horvath S, Levine AJ (2015) HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis 212:1563–1573

    Article  PubMed  PubMed Central  Google Scholar 

  • Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schonfels W, Ahrens M, Heits N, Bell JT, Tsai PC, Spector TD, et al. (2014) Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci U S A 111:15538–15543. doi:10.1073/pnas.1412759111

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Horvath S, Garagnani P, Bacalini MG, Pirazzini C, Salvioli S, Gentilini D, Di Blasio AM, Giuliani C, Tung S, Vinters HV, et al. (2015a) Accelerated epigenetic aging in Down syndrome. Aging Cell 14:491–495. doi:10.1111/acel.12325

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Horvath S, Mah V, Lu AT, Woo JS, Choi OW, Jasinska AJ, Riancho JA, Tung S, Coles NS, Braun J, et al. (2015b) The cerebellum ages slowly according to the epigenetic clock. Aging (Albany NY) 7:294–306

    Article  Google Scholar 

  • Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinf 13:86. doi:10.1186/1471-2105-13-86

    Article  Google Scholar 

  • Jaffe AE, Irizarry RA (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol 15:R31. doi:10.1186/gb-2014-15-2-r31

    Article  PubMed  PubMed Central  Google Scholar 

  • Kananen L, Marttila S, Nevalainen T, Jylhava J, Mononen N, Kahonen M, Raitakari OT, Lehtimaki T, Hurme M (2016) Aging-associated DNA methylation changes in middle-aged individuals: the Young Finns study. BMC Genomics 17:103. doi:10.1186/s12864-016-2421-z

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Lam LL, Emberly E, Fraser HB, Neumann SM, Chen E, Miller GE, Kobor MS (2012) Factors underlying variable DNA methylation in a human community cohort. Proc Natl Acad Sci U S A 109(Suppl 2):17253–17260. doi:10.1073/pnas.112124910

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, Harris SE, Gibson J, Henders AK, Redmond P, Cox SR, et al. (2015a) DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol 16:25. doi:10.1186/s13059-015-0584-6

    Article  PubMed  PubMed Central  Google Scholar 

  • Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, Gibson J, Redmond P, Cox SR, Pattie A, 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

    Article  PubMed  PubMed Central  Google Scholar 

  • Marttila S, Kananen L, Hayrynen S, Jylhava J, Nevalainen T, Hervonen A, Jylha M, Nykter M, Hurme M (2015) Ageing-associated changes in the human DNA methylome: genomic locations and effects on gene expression. BMC Genomics 16. doi:10.1186/s12864-015-1381-z

  • Mather KA, Jorm AF, Parslow RA, Christensen H (2011) Is telomere length a biomarker of aging? A review. J Gerontol A Biol Sci Med Sci 66:202–213. doi:10.1093/gerona/glq180

    Article  PubMed  Google Scholar 

  • Nuotio J, Oikonen M, Magnussen CG, Jokinen E, Laitinen T, Hutri-Kahonen N, Kahonen M, Lehtimaki T, Taittonen L, Tossavainen P, et al. (2014) Cardiovascular risk factors in 2011 and secular trends since 2007: the Cardiovascular Risk in Young Finns Study. Scand J Public Health 42:563–571. doi:10.1177/1403494814541597

    Article  PubMed  Google Scholar 

  • Pathai S, Bajillan H, Landay AL, High KP (2014) Is HIV a model of accelerated or accentuated aging? J Gerontol Ser A Biol Med Sci 69:833–842. doi:10.1093/gerona/glt168

    Article  Google Scholar 

  • Pawelec G, Larbi A, Derhovanessian E (2010) Senescence of the human immune system. J Comp Pathol 142(Suppl 1):S39–S44. doi:10.1016/j.jcpa.2009.09.005

    CAS  Article  PubMed  Google Scholar 

  • Raitakari OT, Juonala M, Ronnemaa T, Keltikangas-Jarvinen L, Rasanen L, Pietikainen M, Hutri-Kahonen N, Taittonen L, Jokinen E, Marniemi J, et al. (2008) Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol 37:1220–1226. doi:10.1093/ije/dym225

    Article  PubMed  Google Scholar 

  • Simpkin AJ, Hemani G, Suderman M, Gaunt TR, Lyttleton O, Mcardle WL, Ring SM, Sharp GC, Tilling K, Horvath S, et al. (2016) Prenatal and early life influences on epigenetic age in children: a study of mother-offspring pairs from two cohort studies. Hum Mol Genet 25:191–201. doi:10.1093/hmg/ddv456

    Article  PubMed  Google Scholar 

  • Steegenga WT, Boekschoten MV, Lute C, Hooiveld GJ, de Groot PJ, Morris TJ, Teschendorff AE, Butcher LM, Beck S, Muller M (2014) Genome-wide age-related changes in DNA methylation and gene expression in human PBMCs. Age (Dordr) 36:9648. doi:10.1007/s11357-014-9648-x

    Article  Google Scholar 

  • Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, Bauerschlag DO, Jockel KH, Erbel R, Muhleisen TW, et al. (2014) Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol 15:R24. doi:10.1186/gb-2014-15-2-r24

    Article  PubMed  PubMed Central  Google Scholar 

  • Weiskopf D, Weinberger B, Grubeck-Loebenstein B (2009) The aging of the immune system. Transpl Int 22:1041–1050. doi:10.1111/j.1432-2277.2009.00927.x

    CAS  Article  PubMed  Google Scholar 

  • Zampieri M, Ciccarone F, Calabrese R, Franceschi C, Burkle A, Caiafa P (2015) Reconfiguration of DNA methylation in aging. Mech Ageing Dev 151:60–70

    CAS  Article  PubMed  Google Scholar 

  • Zilbauer M, Rayner TF, Clark C, Coffey AJ, Joyce CJ, Palta P, Palotie A, Lyons PA, Smith KG (2013) Genome-wide methylation analyses of primary human leukocyte subsets identifies functionally important cell-type-specific hypomethylated regions. Blood 122:e52–e60. doi:10.1182/blood-2013-05-503201

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The YFS has been financially supported by the Academy of Finland (grants 286284 (T.L), 132704 (M.H.), 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), 41071 (Skidi) and 250602 (Coctel to M.J.); the Social Insurance Institution of Finland; Kuopio, Tampere and Turku University Hospital Medical Funds (grant X51001 for T.L.); the Juho Vainio Foundation; the Paavo Nurmi Foundation; the Finnish Foundation of Cardiovascular Research (T.L.); the Tampere Tuberculosis Foundation (T.L., M.H., I.J.); the Emil Aaltonen Foundation (T.L., L.Ku.); the Finnish Cultural Foundation, Pirkanmaa Regional fund (N.M); and the Yrjö Jahnsson Foundation (T.L., M.H.) This work was also supported by grants from the Competitive Research Fund of Pirkanmaa Hospital District (9M017, 9N013 to M.H. and 9P002 to M.J.), Sigrid Juselius Foundation (I.J.), Finnish Medical Association (I.J.) and Competitive Research Fund of Fimlab Laboratories (X51409, I.J.). We thank Nina Peltonen, Sinikka Repo-Koskinen, Eeva Timonen, Janette Hinkka and Kristiina Lehtinen for their excellent technical assistance.

Authors’ contributions

LK processed the data, performed the analyses and was responsible for writing the manuscript. LK, SM, JJ, LKu, NM and TN were responsible for performing the experiments. MH, MK, IJ and TL provided reagents and materials. LK, SM, TL, JJ and MH contributed to the design of the study. MH, MJ, AH, OTR, MK and TL were responsible for recruiting the subjects for the study. All authors contributed to the writing of the manuscript and read and approved the final manuscript.

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Affiliations

  1. Department of Microbiology and Immunology, School of Medicine, University of Tampere, Tampere, Finland

    L. Kananen, S. Marttila, T. Nevalainen, M. Hurme & J. Jylhävä

  2. Gerontology Research Center, Tampere, Finland

    L. Kananen, S. Marttila, T. Nevalainen, A. Hervonen, M. Jylhä, M. Hurme & J. Jylhävä

  3. School of Medicine, University of Tampere, Tampere, Finland

    L. Kummola & I. Junttila

  4. Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland

    I. Junttila, N. Mononen, T. Lehtimäki & M. Hurme

  5. Department of Clinical Physiology, Tampere University Hospital and University of Tampere School of Medicine, Tampere, Finland

    M. Kähönen

  6. Research Centre of Applied and Preventive Cardiovascular Medicine and the Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland

    O. T. Raitakari

  7. School of Health Sciences, University of Tampere, Tampere, Finland

    A. Hervonen & M. Jylhä

  8. Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland

    T. Lehtimäki

Authors
  1. L. Kananen
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  2. S. Marttila
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  3. T. Nevalainen
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  4. L. Kummola
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  5. I. Junttila
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  6. N. Mononen
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  7. M. Kähönen
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  8. O. T. Raitakari
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  9. A. Hervonen
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  10. M. Jylhä
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  11. T. Lehtimäki
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  12. M. Hurme
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  13. J. Jylhävä
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Corresponding author

Correspondence to L. Kananen.

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The authors declare that they have no competing interests.

Electronic supplementary material

Additional file 1

Supporting data of this manuscript (DNAmAges and other fenotypes) (XLSX 90 kb)

Additional file 2

More detailed summary information of the DNAmAges (PDF 354 kb)

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Kananen, L., Marttila, S., Nevalainen, T. et al. The trajectory of the blood DNA methylome ageing rate is largely set before adulthood: evidence from two longitudinal studies. AGE 38, 65 (2016). https://doi.org/10.1007/s11357-016-9927-9

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  • DOI: https://doi.org/10.1007/s11357-016-9927-9

Keywords

  • DNA methylation
  • DNAmAge
  • Epigenetic clock
  • Follow-up
  • Immunosenescence