IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i17p3141-d261880.html
   My bibliography  Save this article

Education and Lifestyle Factors Are Associated with DNA Methylation Clocks in Older African Americans

Author

Listed:
  • Wei Zhao

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA)

  • Farah Ammous

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA)

  • Scott Ratliff

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA)

  • Jiaxuan Liu

    (Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA)

  • Miao Yu

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA)

  • Thomas H. Mosley

    (Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS 39126, USA)

  • Sharon L. R. Kardia

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA)

  • Jennifer A. Smith

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
    Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA)

Abstract

DNA methylation (DNAm) clocks are important biomarkers of cellular aging and are associated with a variety of age-related chronic diseases and all-cause mortality. Examining the relationship between education and lifestyle risk factors for age-related diseases and multiple DNAm clocks can increase the understanding of how risk factors contribute to aging at the cellular level. This study explored the association between education or lifestyle risk factors for age-related diseases and the acceleration of four DNAm clocks, including intrinsic (IEAA) and extrinsic epigenetic age acceleration (EEAA), PhenoAge acceleration (PhenoAA), and GrimAge acceleration (GrimAA) in the African American participants of the Genetic Epidemiology Network of Arteriopathy. We performed both cross-sectional and longitudinal analyses. In cross-sectional analyses, gender, education, BMI, smoking, and alcohol consumption were all independently associated with GrimAA, whereas only some of them were associated with other clocks. The effect of smoking and education on GrimAA varied by gender. Longitudinal analyses suggest that age and BMI continued to increase GrimAA, and that age and current smoking continued to increase PhenoAA after controlling DNAm clocks at baseline. In conclusion, education and common lifestyle risk factors were associated with multiple DNAm clocks. However, the association with each risk factor varied by clock, which suggests that different clocks may capture adverse effects from different environmental stimuli.

Suggested Citation

  • Wei Zhao & Farah Ammous & Scott Ratliff & Jiaxuan Liu & Miao Yu & Thomas H. Mosley & Sharon L. R. Kardia & Jennifer A. Smith, 2019. "Education and Lifestyle Factors Are Associated with DNA Methylation Clocks in Older African Americans," IJERPH, MDPI, vol. 16(17), pages 1-18, August.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:17:p:3141-:d:261880
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/17/3141/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/17/3141/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriella Conti & James Heckman & Sergio Urzua, 2010. "The Education-Health Gradient," American Economic Review, American Economic Association, vol. 100(2), pages 234-238, May.
    2. Cutler, David M. & Lleras-Muney, Adriana, 2010. "Understanding differences in health behaviors by education," Journal of Health Economics, Elsevier, vol. 29(1), pages 1-28, January.
    3. Simone Wahl & Alexander Drong & Benjamin Lehne & Marie Loh & William R. Scott & Sonja Kunze & Pei-Chien Tsai & Janina S. Ried & Weihua Zhang & Youwen Yang & Sili Tan & Giovanni Fiorito & Lude Franke &, 2017. "Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity," Nature, Nature, vol. 541(7635), pages 81-86, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bergvall, Sanna & Fernström, Clara & Ranehill, Eva & Sandberg, Anna, 2024. "The Impact of PhD Studies on Mental Health—A Longitudinal Population Study," Working Papers in Economics 846, University of Gothenburg, Department of Economics.
    2. Michael Keane & Elena Capatina & Shiko Maruyama, 2019. "Health Shocks and the Evolution of Earnings over the Life-Cycle," Discussion Papers 2018-14a, School of Economics, The University of New South Wales.
    3. Strulik, Holger, 2018. "The return to education in terms of wealth and health," The Journal of the Economics of Ageing, Elsevier, vol. 12(C), pages 1-14.
    4. Ji, Sisi & Zhu, Zheyi, 2022. "Does higher education matter for health?," Cardiff Economics Working Papers E2022/4, Cardiff University, Cardiff Business School, Economics Section.
    5. Naci Mocan & Duha Altindag, 2014. "Education, cognition, health knowledge, and health behavior," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(3), pages 265-279, April.
    6. Xianhua Dai & Wolfgang Karl Härdle & Keming Yu, 2016. "Do maternal health problems influence child's worrying status? Evidence from the British Cohort Study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(16), pages 2941-2955, December.
    7. Henry Saffer, 2014. "Self-regulation and Health," NBER Working Papers 20483, National Bureau of Economic Research, Inc.
    8. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory & Urzua, Sergio, 2014. "Education, Health and Wages," IZA Discussion Papers 8027, Institute of Labor Economics (IZA).
    9. Hania Wu & Tony Tam, 2015. "Economic Development and Socioeconomic Inequality of Well-Being: A Cross-Sectional Time-Series Analysis of Urban China, 2003–2011," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 401-425, November.
    10. Giorgio Brunello & Margherita Fort & Nicole Schneeweis & Rudolf Winter‐Ebmer, 2016. "The Causal Effect of Education on Health: What is the Role of Health Behaviors?," Health Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 314-336, March.
    11. Miller, Ray & Bairoliya, Neha & Canning, David, 2019. "Health disparities and the socioeconomic gradient in elderly life-cycle consumption," The Journal of the Economics of Ageing, Elsevier, vol. 14(C).
    12. Sze Yan Liu & Jennifer J Manly & Benjamin D Capistrant & M Maria Glymour, 2015. "Historical Differences in School Term Length and Measured Blood Pressure: Contributions to Persistent Racial Disparities among US-Born Adults," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.
    13. Peter A. Savelyev & Kegon T. K. Tan, 2019. "Socioemotional Skills, Education, and Health-Related Outcomes of High-Ability Individuals," American Journal of Health Economics, MIT Press, vol. 5(2), pages 250-280, Spring.
    14. Laura Crespo & Borja López-Noval & Pedro Mira, 2013. "Compulsory Schooling, Education and Mental Health: New Evidence from SHARELIFE," Working Papers wp2013_1304, CEMFI.
    15. Joshua Ping Ang & Tim Murray, 2021. "Education in Mathematics and the Spread of COVID-19," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 47(4), pages 571-589, October.
    16. Owen O'Donnell & Eddy Van Doorslaer & Tom Van Ourti, 2013. "Health and Inequality," Tinbergen Institute Discussion Papers 13-170/V, Tinbergen Institute.
    17. Matthew A. Andersson & Vida Maralani & Renae Wilkinson, 2022. "Origins and Destinations, but How Much and When? Educational Disparities in Smoking and Drinking Across Adolescence and Young Adulthood," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(2), pages 521-558, April.
    18. Bello, Piera & Rocco, Lorenzo, 2022. "Education and COVID-19 excess mortality," Economics & Human Biology, Elsevier, vol. 47(C).
    19. David W. Johnston & Claryn S. J. Kung & Michael A. Shields, 2021. "Who is resilient in a time of crisis? The importance of financial and non‐financial resources," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3051-3073, December.
    20. Jared C. Carbone & Snorre Kverndokk, 2016. "Individual Investments in Education and Health: Policy Responses and Interactions," CESifo Working Paper Series 6154, CESifo.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:17:p:3141-:d:261880. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.