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Aging (Albany NY). 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.

Author information

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.

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

PMID:
27690265
PMCID:
PMC5076441
DOI:
10.18632/aging.101020
[Indexed for MEDLINE]
Free PMC Article

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.

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