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
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
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
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
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
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
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
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
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
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
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
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
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
Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115
Horvath S, Levine AJ (2015) HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis 212:1563–1573
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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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