Research Paper Volume 9, Issue 2 pp 419—446
Epigenetic clock analysis of diet, exercise, education, and lifestyle factors
- 1 Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- 2 Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD 21224, USA
- 3 Department of Neurology, UCLA School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- 4 Department of Epidemiology, UCLA Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
- 5 Geriatric Unit, Azienda Sanitaria Firenze (ASF), Florence, Italy
- 6 Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA
- 7 Department of Medicine, New York University, New York, NY 10016, USA
- 8 Department of Epidemiology, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA
- 9 Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- 10 VA Palo Alto Health Care System, Palo Alto CA 94304, USA
- 11 HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
- 12 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
- 13 Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
- 14 Department. of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
- 15 Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL 60611, USA
- 16 Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University Chicago, IL 60611, USA
- 17 Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
- 18 Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
- 19 Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
Received: November 11, 2016 Accepted: January 25, 2017 Published: February 14, 2017
https://doi.org/10.18632/aging.101168How to Cite
Abstract
Behavioral and lifestyle factors have been shown to relate to a number of health-related outcomes, yet there is a need for studies that examine their relationship to molecular aging rates. Toward this end, we use recent epigenetic biomarkers of age that have previously been shown to predict all-cause mortality, chronic conditions, and age-related functional decline. We analyze cross-sectional data from 4,173 postmenopausal female participants from the Women's Health Initiative, as well as 402 male and female participants from the Italian cohort study, Invecchiare nel Chianti.
Extrinsic epigenetic age acceleration (EEAA) exhibits significant associations with fish intake (p=0.02), moderate alcohol consumption (p=0.01), education (p=3x10-5), BMI (p=0.01), and blood carotenoid levels (p=1x10-5)—an indicator of fruit and vegetable consumption, whereas intrinsic epigenetic age acceleration (IEAA) is associated with poultry intake (p=0.03) and BMI (p=0.05). Both EEAA and IEAA were also found to relate to indicators of metabolic syndrome, which appear to mediate their associations with BMI. Metformin—the first-line medication for the treatment of type 2 diabetes—does not delay epigenetic aging in this observational study. Finally, longitudinal data suggests that an increase in BMI is associated with increase in both EEAA and IEAA.
Overall, the epigenetic age analysis of blood confirms the conventional wisdom regarding the benefits of eating a high plant diet with lean meats, moderate alcohol consumption, physical activity, and education, as well as the health risks of obesity and metabolic syndrome.