DNA methylation age is associated with mortality in a longitudinal Danish twin study

Lene Christiansen, Adam Lenart, Qihua Tan, James W Vaupel, Abraham Aviv, Matt McGue, Kaare Christensen, Lene Christiansen, Adam Lenart, Qihua Tan, James W Vaupel, Abraham Aviv, Matt McGue, Kaare Christensen

Abstract

An epigenetic profile defining the DNA methylation age (DNAm age) of an individual has been suggested to be a biomarker of aging, and thus possibly providing a tool for assessment of health and mortality. In this study, we estimated the DNAm age of 378 Danish twins, age 30-82 years, and furthermore included a 10-year longitudinal study of the 86 oldest-old twins (mean age of 86.1 at follow-up), which subsequently were followed for mortality for 8 years. We found that the DNAm age is highly correlated with chronological age across all age groups (r = 0.97), but that the rate of change of DNAm age decreases with age. The results may in part be explained by selective mortality of those with a high DNAm age. This hypothesis was supported by a classical survival analysis showing a 35% (4-77%) increased mortality risk for each 5-year increase in the DNAm age vs. chronological age. Furthermore, the intrapair twin analysis revealed a more-than-double mortality risk for the DNAm oldest twin compared to the co-twin and a 'dose-response pattern' with the odds of dying first increasing 3.2 (1.05-10.1) times per 5-year DNAm age difference within twin pairs, thus showing a stronger association of DNAm age with mortality in the oldest-old when controlling for familial factors. In conclusion, our results support that DNAm age qualifies as a biomarker of aging.

Keywords: DNA methylation; biological age; biomarker; epigenetic clock; mortality; twins.

© 2015 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

Figures

Figure 1
Figure 1
Horvath DNAm age against chronological age. (a) Correlation between the methylation age using the Horvath model. The relation is visualized by a smoothing spline. (b) DNAm age trajectories of each individual in the longitudinal study of oldest‐olds. The lines are colored by intervals of slow to fast agers, and the histogram indicates the distribution within the intervals.
Figure 2
Figure 2
Mortality of the oldest twins according to intrapair DNAm age difference at follow‐up and difference in change over 10 years. The plots display the proportion of twins where the DNAm oldest twins died first (Proportion dead), for all twins and divided into groups with increasing intrapair difference. The numbers of twin pairs in each group are given in brackets, and the P‐values are shown below the lines on the left panel and above the lines on the right.

References

    1. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST (2012) Age‐associated DNA methylation in pediatric populations. Genome Res. 22, 623–632.
    1. Bell JT, Tsai PC, Yang TP, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G, Zhang F, Valdes A, Shin SY, Dempster EL, Murray RM, Grundberg E, Hedman AK, Nica A, Small KS, Mu TC, Dermitzakis ET, McCarthy MI, Mill J, Spector TD, Deloukas P (2012) Epigenome‐wide scans identify differentially methylated regions for age and age‐related phenotypes in a healthy ageing population. PLoS Genet. 8, e1002629.
    1. Bocklandt S, Lin W, Sehl ME, Sanchez FJ, Sinsheimer JS, Horvath S, Vilain E (2011) Epigenetic predictor of age. PLoS ONE 6, e14821.
    1. Christensen K, Holm NV, McGue M, Corder L, Vaupel JW (1999) A Danish population‐based twin study on general health in the elderly. J. Aging Health 11, 49–64.
    1. Christensen K, McGue M, Petersen I, Jeune B, Vaupel JW (2008) Exceptional longevity does not result in excessive levels of disability. Proc. Natl Acad. Sci. USA 105, 13274–13279.
    1. 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.
    1. Frost M, Petersen I, Brixen K, Beck‐Nielsen H, Holst JJ, Christiansen L, Hojlund K, Christensen K (2012) Adult glucose metabolism in extremely birthweight‐discordant monozygotic twins. Diabetologia 55, 3204–3212.
    1. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K (2013) Genome‐wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367.
    1. Holstege H, Pfeiffer W, Sie D, Hulsman M, Nicholas TJ, Lee CC, Ross T, Lin J, Miller MA, Ylstra B, Meijers‐Heijboer H, Brugman MH, Staal FJ, Holstege G, Reinders MJ, Harkins TT, Levy S, Sistermans EA (2014) Somatic mutations found in the healthy blood compartment of a 115‐yr‐old woman demonstrate oligoclonal hematopoiesis. Genome Res. 24, 733–742.
    1. Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol. 14, R115.
    1. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schonfels W, Ahrens M, Heits N, Bell JT, Tsai PC, Spector TD, Deloukas P, Siebert R, Sipos B, Becker T, Rocken C, Schafmayer C, Hampe J (2014) Obesity accelerates epigenetic aging of human liver. Proc. Natl Acad. Sci. USA 111, 15538–15543.
    1. Horvath S, Garagnani P, Bacalini MG, Pirazzini C, Salvioli S, Gentilini D, Di Blasio AM, Giuliani C, Tung S, Vinters HV, Franceschi C (2015a) Accelerated epigenetic aging in Down syndrome. Aging Cell 14, 491–495.
    1. Horvath S, Mah V, Lu AT, Woo JS, Choi OW, Jasinska AJ, Riancho JA, Tung S, Coles NS, Braun J, Vinters HV, Coles LS (2015b) The cerebellum ages slowly according to the epigenetic clock. Aging (Albany NY) 7, 294–306.
    1. 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 Bioinformatics 13, 86.
    1. Huidobro C, Fernandez AF, Fraga MF (2013) Aging epigenetics: causes and consequences. Mol. Aspects Med. 34, 765–781.
    1. Koch CM, Wagner W (2011) Epigenetic‐aging‐signature to determine age in different tissues. Aging (Albany NY) 3, 1018–1027.
    1. Maksimovic J, Gordon L, Oshlack A (2012) SWAN: Subset‐quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 13, R44.
    1. Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, Harris SE, Gibson J, Henders AK, Redmond P, Cox SR, Pattie A, Corley J, Murphy L, Martin NG, Montgomery GW, Feinberg AP, Fallin MD, Multhaup ML, Jaffe AE, Joehanes R, Schwartz J, Just AC, Lunetta KL, Murabito JM, Starr JM, Horvath S, Baccarelli AA, Levy D, Visscher PM, Wray NR, Deary IJ (2015a) DNA methylation age of blood predicts all‐cause mortality in later life. Genome Biol. 16, 25.
    1. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz‐Terrera G, Harris SE, Gibson J, Redmond P, Cox SR, Pattie A, Corley J, Taylor A, Murphy L, Starr JM, Horvath S, Visscher PM, Wray NR, Deary IJ (2015b) The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int. J. Epidemiol. 44, 1388–1396.
    1. Maunakea AK, Chepelev I, Zhao K (2010) Epigenome mapping in normal and disease States. Circ. Res. 107, 327–339.
    1. McGue M, Christensen K (2007) Social activity and healthy aging: a study of aging Danish twins. Twin Res. Hum. Genet. 10, 255–265.
    1. Miller SA, Dykes DD, Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 16, 1215.
    1. Murphy TM, Mill J (2014) Epigenetics in health and disease: heralding the EWAS era. Lancet 383, 1952–1954.
    1. Skytthe A, Christiansen L, Kyvik KO, Bodker FL, Hvidberg L, Petersen I, Nielsen MM, Bingley P, Hjelmborg J, Tan Q, Holm NV, Vaupel JW, McGue M, Christensen K (2013) The Danish Twin Registry: linking surveys, national registers, and biological information. Twin Res. Hum. Genet. 16, 104–111.
    1. Vaupel JW (2010) Biodemography of human ageing. Nature 464, 536–542.
    1. Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, Bauerschlag DO, Jockel KH, Erbel R, Muhleisen TW, Zenke M, Brummendorf TH, Wagner W (2014) Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 15, R24.

Source: PubMed

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