Epigenetic clock analysis of diet, exercise, education, and lifestyle factors

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-caus...

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Published in:	Aging (Albany, NY.) Vol. 9; no. 2; p. 419
Main Authors:	Quach, Austin, Levine, Morgan E, Tanaka, Toshiko, Lu, Ake T, Chen, Brian H, Ferrucci, Luigi, Ritz, Beate, Bandinelli, Stefania, Neuhouser, Marian L, Beasley, Jeannette M, Snetselaar, Linda, Wallace, Robert B, Tsao, Philip S, Absher, Devin, Assimes, Themistocles L, Stewart, James D, Li, Yun, Hou, Lifang, Baccarelli, Andrea A, Whitsel, Eric A, Horvath, Steve
Format:	Journal Article
Language:	English
Published:	United States 14.02.2017
Subjects:	Aged Aged, 80 and over Aging - genetics Aging - metabolism Cohort Studies Cross-Sectional Studies Diet Educational Status Epigenesis, Genetic Exercise Female Humans Life Style Middle Aged DNA methylation alcohol intake aging diet fish intake epigenetic clock lifestyle
ISSN:	1945-4589, 1945-4589
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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 ), BMI (p=0.01), and blood carotenoid levels (p=1x10 )-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.
AbstractList	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 ), BMI (p=0.01), and blood carotenoid levels (p=1x10 )-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. 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.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.
Author	Neuhouser, Marian L Beasley, Jeannette M Assimes, Themistocles L Hou, Lifang Tanaka, Toshiko Bandinelli, Stefania Li, Yun Levine, Morgan E Wallace, Robert B Baccarelli, Andrea A Tsao, Philip S Horvath, Steve Snetselaar, Linda Chen, Brian H Ritz, Beate Whitsel, Eric A Absher, Devin Stewart, James D Quach, Austin Ferrucci, Luigi Lu, Ake T
Author_xml	– sequence: 1 givenname: Austin surname: Quach fullname: Quach, Austin organization: Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA – sequence: 2 givenname: Morgan E surname: Levine fullname: Levine, Morgan E organization: Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA – sequence: 3 givenname: Toshiko surname: Tanaka fullname: Tanaka, Toshiko organization: Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD 21224, USA – sequence: 4 givenname: Ake T surname: Lu fullname: Lu, Ake T organization: Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA – sequence: 5 givenname: Brian H surname: Chen fullname: Chen, Brian H organization: Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD 21224, USA – sequence: 6 givenname: Luigi surname: Ferrucci fullname: Ferrucci, Luigi organization: Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD 21224, USA – sequence: 7 givenname: Beate surname: Ritz fullname: Ritz, Beate organization: Department of Epidemiology, UCLA Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA – sequence: 8 givenname: Stefania surname: Bandinelli fullname: Bandinelli, Stefania organization: Geriatric Unit, Azienda Sanitaria Firenze (ASF), Florence, Italy – sequence: 9 givenname: Marian L surname: Neuhouser fullname: Neuhouser, Marian L organization: Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA – sequence: 10 givenname: Jeannette M surname: Beasley fullname: Beasley, Jeannette M organization: Department of Medicine, New York University, New York, NY 10016, USA – sequence: 11 givenname: Linda surname: Snetselaar fullname: Snetselaar, Linda organization: Department of Epidemiology, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA – sequence: 12 givenname: Robert B surname: Wallace fullname: Wallace, Robert B organization: Department of Epidemiology, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA – sequence: 13 givenname: Philip S surname: Tsao fullname: Tsao, Philip S organization: VA Palo Alto Health Care System, Palo Alto CA 94304, USA – sequence: 14 givenname: Devin surname: Absher fullname: Absher, Devin organization: HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA – sequence: 15 givenname: Themistocles L surname: Assimes fullname: Assimes, Themistocles L organization: Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA – sequence: 16 givenname: James D surname: Stewart fullname: Stewart, James D organization: Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA – sequence: 17 givenname: Yun surname: Li fullname: Li, Yun organization: Department. of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA – sequence: 18 givenname: Lifang surname: Hou fullname: Hou, Lifang organization: Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University Chicago, IL 60611, USA – sequence: 19 givenname: Andrea A surname: Baccarelli fullname: Baccarelli, Andrea A organization: Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA – sequence: 20 givenname: Eric A surname: Whitsel fullname: Whitsel, Eric A organization: Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA – sequence: 21 givenname: Steve surname: Horvath fullname: Horvath, Steve organization: Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28198702$$D View this record in MEDLINE/PubMed
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Snippet	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...
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SubjectTerms	Aged Aged, 80 and over Aging - genetics Aging - metabolism Cohort Studies Cross-Sectional Studies Diet Educational Status Epigenesis, Genetic Exercise Female Humans Life Style Middle Aged
Title	Epigenetic clock analysis of diet, exercise, education, and lifestyle factors
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