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Priority Research Paper|Volume 8, Issue 9|pp 1844—1865

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

Brian H. Chen1,2,3, Riccardo E. Marioni4,5,6, 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 Fornage13,14, Stephanie Studenski1, Amy R. Vandiver15, Ann Zenobia Moore1, Toshiko Tanaka1, Douglas P. Kiel16,17, Liming Liang18,19, Pantel Vokonas18, Joel Schwartz18, Kathryn L. Lunetta2,20, Joanne M. Murabito2,21, Stefania Bandinelli22, Dena G. Hernandez23, David Melzer24, Michael Nalls23, Luke C. Pilling24, Timothy R. Price23, Andrew B. Singleton23, Christian Gieger9,25, Rolf Holle26, Anja Kretschmer9,25, Florian Kronenberg27, Sonja Kunze9,25, Jakob Linseisen9, Christine Meisinger9, Wolfgang Rathmann28, Melanie Waldenberger9,25, Peter M. Visscher4,6,29, Sonia Shah6,29, Naomi R. Wray6, Allan F. McRae6,29, Oscar H. Franco30, Albert Hofman18,30, André G. Uitterlinden8,30, Devin Absher31, Themistocles Assimes32, Morgan E. Levine33, Ake T. Lu33, Philip S. Tsao32,34, Lifang Hou35,36, JoAnn E. Manson37, Cara L. Carty38, Andrea Z. LaCroix39, Alexander P. Reiner40,41, Tim D. Spector10, Andrew P. Feinberg15,42, Daniel Levy2,43, Andrea Baccarelli7,44, Joyce van Meurs8, Jordana T. Bell10, Annette Peters9, Ian J. Deary4,45, James S. Pankow11, Luigi Ferrucci1, Steve Horvath33,45
  • 1Longitudinal Studies Section, Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
  • 2The NHLBI’s Framingham Heart Study, Framingham, MA 01702, USA
  • 3Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 01702, USA
  • 4Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
  • 5Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
  • 6Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
  • 7Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences and Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
  • 8Department of Internal Medicine, Erasmus University Medical Centre, Rotterdam, 3000 CA, The Netherlands
  • 9Institute of Epidemiology II, Helmholtz Zentrum München, 85764 Neuherberg, Germany
  • 10Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
  • 11Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN 55455, USA
  • 12Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, 55455, ; USA
  • 13Human Genetics Center, School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX, ; USA
  • 14Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, ; USA
  • 15Center for Epigenetics, Johns Hopkins University, Baltimore, MD 21205, ; USA
  • 16Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, ; USA
  • 17Institute for Aging Research, Hebrew Senior Life, Boston, MA 02215, USA
  • 18Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
  • 19Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA
  • 20Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
  • 21Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
  • 22Geriatric Unit, Usl Centro Toscana Florence, Italy
  • 23Laboratory of Neurogenetics, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
  • 24Epidemiology and Public Health, Medical School, University of Exeter, RILD, Exeter EX2 5DW, ; UK
  • 25Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
  • 26Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, 85764 Neuherberg, Germany
  • 27Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck 6020, Austria
  • 28Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, 40225 Düsseldorf, Germany
  • 29University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
  • 30Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, 3015 CN, The Netherlands
  • 31HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
  • 32Department 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
  • 33VA Palo Alto Health Care System, Palo Alto CA 94304, USA
  • 34Department of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA
  • 35Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA
  • 36Department 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
  • 37Center for Translational Science Children’s National Medical Center, George Washington University, Washington, DC 20010, USA
  • 38Department of Family Medicine and Public Health, University of California-San Diego, La Jolla, CA 92093-0725, ; USA
  • 39Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98195, USA
  • 40Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, ; USA
  • 41Departments of Medicine, Molecular Biology/Genetics, Oncology, and Biostatistics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
  • 42Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 01702, USA
  • 43Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
  • 44Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
  • 45Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA

* * Equal contribution

Received: July 1, 2016Accepted: August 18, 2016Published: September 28, 2016

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.