DNA methylation-based measures of biological age: meta-analysis predicting time to death

doi:10.18632/aging.101020

Meta-Analysis

. 2016 Sep 28;8(9):1844-1865.

doi: 10.18632/aging.101020.

DNA methylation-based measures of biological age: meta-analysis predicting time to death

Brian H Chen^{1

2

3}, Riccardo E Marioni^{4

5

6}, Elena Colicino⁷, Marjolein J Peters⁸, Cavin K Ward-Caviness⁹, Pei-Chien Tsai¹⁰, Nicholas S Roetker¹¹, Allan C Just⁷, Ellen W Demerath¹¹, Weihua Guan¹², Jan Bressler¹³, Myriam Fornage^{13

14}, Stephanie Studenski¹, Amy R Vandiver¹⁵, Ann Zenobia Moore¹, Toshiko Tanaka¹, Douglas P Kiel^{16

17}, Liming Liang^{18

19}, Pantel Vokonas¹⁸, Joel Schwartz¹⁸, Kathryn L Lunetta^{2

20}, Joanne M Murabito^{2

21}, Stefania Bandinelli²², Dena G Hernandez²³, David Melzer²⁴, Michael Nalls²³, Luke C Pilling²⁴, Timothy R Price²³, Andrew B Singleton²³, Christian Gieger^{9

25}, Rolf Holle²⁶, Anja Kretschmer^{9

25}, Florian Kronenberg²⁷, Sonja Kunze^{9

25}, Jakob Linseisen⁹, Christine Meisinger⁹, Wolfgang Rathmann²⁸, Melanie Waldenberger^{9

25}, Peter M Visscher^{4

6

29}, Sonia Shah^{6

29}, Naomi R Wray⁶, Allan F McRae^{6

29}, Oscar H Franco³⁰, Albert Hofman^{18

30}, André G Uitterlinden^{8

30}, Devin Absher³¹, Themistocles Assimes³², Morgan E Levine³³, Ake T Lu³³, Philip S Tsao^{32

34}, Lifang Hou^{35

36}, JoAnn E Manson³⁷, Cara L Carty³⁸, Andrea Z LaCroix³⁹, Alexander P Reiner^{40

41}, Tim D Spector¹⁰, Andrew P Feinberg^{15

3}, Daniel Levy^{2

42}, Andrea Baccarelli^{7

43}, Joyce van Meurs⁸, Jordana T Bell¹⁰, Annette Peters⁹, Ian J Deary^{4

44}, James S Pankow¹¹, Luigi Ferrucci¹, Steve Horvath^{33

44}

Affiliations

¹ Longitudinal Studies Section, Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
² The NHLBI's Framingham Heart Study, Framingham, MA 01702, USA.
³ Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 01702, USA.
⁴ Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
⁵ Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
⁶ Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
⁷ Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences and Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
⁸ Department of Internal Medicine, Erasmus University Medical Centre, Rotterdam, 3000 CA, The Netherlands.
⁹ Institute of Epidemiology II, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
¹⁰ Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK.
¹¹ Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN 55455, USA.
¹² Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, 55455, ; USA.
¹³ Human Genetics Center, School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX, ; USA.
¹⁴ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, ; USA.
¹⁵ Center for Epigenetics, Johns Hopkins University, Baltimore, MD 21205, ; USA.
¹⁶ Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, ; USA.
¹⁷ Institute for Aging Research, Hebrew Senior Life, Boston, MA 02215, USA.
¹⁸ Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
¹⁹ Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
²⁰ Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.
²¹ Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.
²² Geriatric Unit, Usl Centro Toscana Florence, Italy.
²³ Laboratory of Neurogenetics, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA.
²⁴ Epidemiology and Public Health, Medical School, University of Exeter, RILD, Exeter EX2 5DW, ; UK.
²⁵ Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
²⁶ Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
²⁷ Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck 6020, Austria.
²⁸ Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, 40225 Düsseldorf, Germany.
²⁹ University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia.
³⁰ Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, 3015 CN, The Netherlands.
³¹ HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA.
³² Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, ; Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
³³ VA Palo Alto Health Care System, Palo Alto CA 94304, USA.
³⁴ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA.
³⁵ Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA.
³⁶ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, and the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
³⁷ Center for Translational Science Children's National Medical Center, George Washington University, Washington, DC 20010, USA.
³⁸ Department of Family Medicine and Public Health, University of California-San Diego, La Jolla, CA 92093-0725, ; USA.
³⁹ Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98195, USA.
⁴⁰ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, ; USA.
⁴¹ Departments of Medicine, Molecular Biology/Genetics, Oncology, and Biostatistics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
⁴² Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁴³ Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
⁴⁴ Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA.

PMID: 27690265
PMCID: PMC5076441
DOI: 10.18632/aging.101020

Meta-Analysis

DNA methylation-based measures of biological age: meta-analysis predicting time to death

Brian H Chen et al. Aging (Albany NY). 2016.

. 2016 Sep 28;8(9):1844-1865.

doi: 10.18632/aging.101020.

Authors

Affiliations

¹ Longitudinal Studies Section, Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
² The NHLBI's Framingham Heart Study, Framingham, MA 01702, USA.
³ Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 01702, USA.
⁴ Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
⁵ Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
⁶ Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
⁷ Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences and Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
⁸ Department of Internal Medicine, Erasmus University Medical Centre, Rotterdam, 3000 CA, The Netherlands.
⁹ Institute of Epidemiology II, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
¹⁰ Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK.
¹¹ Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN 55455, USA.
¹² Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, 55455, ; USA.
¹³ Human Genetics Center, School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX, ; USA.
¹⁴ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, ; USA.
¹⁵ Center for Epigenetics, Johns Hopkins University, Baltimore, MD 21205, ; USA.
¹⁶ Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, ; USA.
¹⁷ Institute for Aging Research, Hebrew Senior Life, Boston, MA 02215, USA.
¹⁸ Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
¹⁹ Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
²⁰ Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.
²¹ Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.
²² Geriatric Unit, Usl Centro Toscana Florence, Italy.
²³ Laboratory of Neurogenetics, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA.
²⁴ Epidemiology and Public Health, Medical School, University of Exeter, RILD, Exeter EX2 5DW, ; UK.
²⁵ Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
²⁶ Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
²⁷ Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck 6020, Austria.
²⁸ Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, 40225 Düsseldorf, Germany.
²⁹ University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia.
³⁰ Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, 3015 CN, The Netherlands.
³¹ HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA.
³² Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, ; Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
³³ VA Palo Alto Health Care System, Palo Alto CA 94304, USA.
³⁴ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA.
³⁵ Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA.
³⁶ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, and the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
³⁷ Center for Translational Science Children's National Medical Center, George Washington University, Washington, DC 20010, USA.
³⁸ Department of Family Medicine and Public Health, University of California-San Diego, La Jolla, CA 92093-0725, ; USA.
³⁹ Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98195, USA.
⁴⁰ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, ; USA.
⁴¹ Departments of Medicine, Molecular Biology/Genetics, Oncology, and Biostatistics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
⁴² Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁴³ Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
⁴⁴ Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA.

PMID: 27690265
PMCID: PMC5076441
DOI: 10.18632/aging.101020

Abstract

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10^-9), independent of chronological age, even after adjusting for additional risk factors (p<5.4x10^-4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5x10^-43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

Keywords: DNA methylation; all-cause mortality; epigenetic clock; epigenetics; lifespan; mortality.

PubMed Disclaimer

Conflict of interest statement

The Regents of the University of California is the sole owner of a provisional patent application directed at the invention of measures of epigenetic age acceleration for which SH is a named inventor. The other authors declare no conflicts of interest.

Figures

**Figure 1. Epigenetic age acceleration in blood versus that in breast or saliva**
(A-D) Epigenetic age acceleration in healthy female breast tissue (y-axis) versus various measures of epigenetic age acceleration in blood: (A) universal measure of age acceleration in blood, (B) intrinsic epigenetic age acceleration based on the Horvath estimate of epigenetic age, (C) extrinsic epigenetic age acceleration, (D) intrinsic epigenetic age acceleration based on the Hannum estimate of epigenetic age. (E-H) analogous plots for epigenetic age acceleration in saliva (y-axis) and (E) *AgeAccel*, (F) *IEAA* based on Horvath, (G) *EEAA*, (H) *IEAA* based on the Hannum estimate. The y-axis of each plot represents the universal measure of age acceleration defined as the raw residual resulting from regressing epigenetic age (based on Horvath) on chronological age.

**Figure 2. Univariate Cox regression meta-analysis of all-cause mortality**
A univariate Cox regression model was used to relate the censored survival time (time to all-cause mortality) to (A) the universal measure of age acceleration (*AgeAccel*), (B) intrinsic epigenetic age acceleration (*IEAA*), (C) extrinsic epigenetic age acceleration (*EEAA*). The rows correspond to the different cohorts. Each row depicts the hazard ratio and a 95% confidence interval. The coefficient estimates from the respective studies were meta-analyzed using a fixed-effect model weighted by inverse variance (implemented in the *metafor* R package [34]). It is not appropriate to compare the hazard ratios and confidence intervals of the different measures directly because the measures have different scales/distributions. However, it is appropriate to compare the meta analysis p values (red sub-title of each plot). The p-value of the heterogeneity test (Cochran's Q-test) is significant if the cohort-specific estimates differed substantially.

**Figure 3. Multivariate Cox regression meta-analysis adjusted for clinical covariates**
A multivariate Cox regression model was used to relate the censored survival time (time to all-cause mortality) to (A) the universal measure of age acceleration (*AgeAccel*), (B) intrinsic epigenetic age acceleration (*IEAA*), (C) extrinsic epigenetic age acceleration (*EEAA*). The multivariate Cox regression model included the following additional covariates: chronological age, body mass index (category), educational level (category), alcohol intake, smoking pack years, prior history of diabetes, prior history of cancer, hypertension status, recreational physical activity (category). The rows correspond to the different cohorts. Each row depicts the hazard ratio and a 95% confidence interval. The coefficient estimates from the respective studies were meta-analyzed using a fixed-effect model weighted by inverse variance (implemented in the *metafor* R package [34]). The sub-title of each plot reports the meta-analysis p-value and a heterogeneity test p-value (Cochran's Q-test).

**Figure 4. Hazard ratio of death versus cohort characteristics**
Each circle corresponds to a cohort (data set). Circle sizes correspond to the square root of the number of observed deaths, because the statistical power of a Cox model is determined by the number of observed deaths. (A-C) The y-axis of each panel corresponds to the natural log of the hazard ratio (ln HR) of a univariate Cox regression model for all-cause mortality. Each panel corresponds to a different measure of epigenetic age acceleration (A) universal age acceleration, (B) intrinsic age acceleration, (C) extrinsic age acceleration. Panels (D-F) are analogous to those in A-C but the x-axis corresponds to the median age of the subjects at baseline (Table 1). The title of each panel reports the Wald test statistic (T) and corresponding p-value resulting from a weighted linear regression model (y regressed on x) where each point (data set) is weighted by the square root of the number of observed deaths. The dotted red line represents the regression line. The black solid line represents the line of identify (i.e., no association).

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References

1. Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, Vilain E. Epigenetic predictor of age. PLoS One. 2011;6:e14821. doi: 10.1371/journal.pone.0014821. - DOI - PMC - PubMed
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, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359–67. doi: 10.1016/j.molcel.2012.10.016. - DOI - PMC - PubMed
1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115. doi: 10.1186/gb-2013-14-10-r115. - DOI - PMC - PubMed
1. Lin Q, Weidner CI, Costa IG, Marioni RE, Ferreira MR, Deary IJ, Wagner W. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging (Albany NY) 2016;8:394–401. doi: 10.18632/aging.100908. - DOI - PMC - PubMed
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, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015;16:25. doi: 10.1186/s13059-015-0584-6. - DOI - PMC - PubMed

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[1] Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, Vilain E. Epigenetic predictor of age. PLoS One. 2011;6:e14821. doi: 10.1371/journal.pone.0014821. - DOI - PMC - PubMed

[2] Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, Vilain E. Epigenetic predictor of age. PLoS One. 2011;6:e14821. doi: 10.1371/journal.pone.0014821. - DOI - PMC - PubMed

[3] 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, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359–67. doi: 10.1016/j.molcel.2012.10.016. - DOI - PMC - PubMed

[4] 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, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359–67. doi: 10.1016/j.molcel.2012.10.016. - DOI - PMC - PubMed

[5] Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115. doi: 10.1186/gb-2013-14-10-r115. - DOI - PMC - PubMed

[6] Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115. doi: 10.1186/gb-2013-14-10-r115. - DOI - PMC - PubMed

[7] Lin Q, Weidner CI, Costa IG, Marioni RE, Ferreira MR, Deary IJ, Wagner W. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging (Albany NY) 2016;8:394–401. doi: 10.18632/aging.100908. - DOI - PMC - PubMed

[8] Lin Q, Weidner CI, Costa IG, Marioni RE, Ferreira MR, Deary IJ, Wagner W. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging (Albany NY) 2016;8:394–401. doi: 10.18632/aging.100908. - DOI - PMC - PubMed

[9] 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, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015;16:25. doi: 10.1186/s13059-015-0584-6. - DOI - PMC - PubMed

[10] 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, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015;16:25. doi: 10.1186/s13059-015-0584-6. - DOI - PMC - PubMed

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DNA methylation-based measures of biological age: meta-analysis predicting time to death

Affiliations

DNA methylation-based measures of biological age: meta-analysis predicting time to death

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Abstract

Conflict of interest statement

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