Skip to main content

Advertisement

SpringerLink
Log in

Retirement and Epigenetic age Acceleration Among Older U.S. Adults

  • Research
  • Published:
Adaptive Human Behavior and Physiology Aims and scope Submit manuscript

Abstract

Purpose

This study examined associations of older men’s and women’s retired status with their biological age acceleration, and mediation of these linkages by depressive symptoms.

Methods

Data were from the 2010–2016 waves of the Health and Retirement Study, nationally representative of older U.S. adults. Age acceleration was proxied through newly available epigenetic measures. Doubly robust estimation was used to establish baseline linkages, and heterogenous treatment effect models to examine variations in effects by one’s increasing propensity to be retired. Mediation analysis was through a recently developed regression-with-residuals (RWR) approach for structural nested mean models.

Results

Six years after treatment assessment, women retired at baseline showed faster aging than those fully employed. Retired men’s subsequent depressive symptoms were lower, with sparse results also supporting their slower senescence. Associations did not significantly change with increasing propensity for being retired, for either gender.

Conclusion

Results provide novel evidence for retirement’s gender-specific senescence effects. Potential lifestyle mechanisms remain unexplored. Individual and policy implications are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data Availability

The HRS data that support the findings of this study are available from the Institute for Social Research at the University of Michigan: https://hrs.isr.umich.edu/data-products.

Data Availability

The HRS data that support the findings of this study are available from the Institute for Social Research at the University of Michigan: https://hrs.isr.umich.edu/data-products.

References

  • Abeliansky, A. L., & Strulik, H. (2021). Health and aging before and after retirement. CESifo Working Papers. Munich, Germany: Munich Society for the Promotion of Economic Research.

  • Andresen, M. E. (2018). Exploring marginal treatment effects: Flexible estimation using Stata. Stata Journal, 18, 118–158.

    Article  Google Scholar 

  • Ayyagari, P. (2016). The impact of retirement on smoking behavior. Eastern Economic Journal, 42, 270–287.

    Article  Google Scholar 

  • Barnett, I., van Sluijs, E., Oglivie, D., et al. (2014). Changes in household, transport and recreational physical activity and television viewing time across the transition to retirement. Journal of Epidemiology and Community Health, 68, 747–753.

    Article  Google Scholar 

  • Bellés Obrero, C., Jimenez-Martin, S., & Ye, H. (2022). The effect of removing early retirement on mortality. IZA DP No. 15577. Bonn, Germany: Institute for Labor Economics.

    Google Scholar 

  • Belsky, D. W., Caspi, A., Arseneault, L., Corcoran, D., Hannon, E., Harrington, H. L., & Moffitt, T. E. (2020). Quantification of the pace of biological aging in humans through a blood test: A DNA methylation algorithm. eLife, 9, e54870.

    Article  Google Scholar 

  • Beydoun, M. A., Hossain, S., Chitrala, K. N., Tajuddin, S. M., Beydoun, H. A., Evans, M. K., & Zonderman, A. B. (2019). Association between epigenetic age acceleration and depressive symptoms in a prospective cohort study of urban-dwelling adults. Journal of Affective Disorders, 257, 64–73.

    Article  Google Scholar 

  • Blau, F. D., Simpson, P., & Anderson, D. (1998). Continuing progress? Trends in occupational segregation in the United States over the 1970s and 1980s. Feminist Economics, 4, 29–71.

    Article  Google Scholar 

  • Bloemen, H., Hochguertel, S., & Zweerink, J. (2017). The causal effect of retirement on mortality: Evidence from targeted incentives to retire early. Health Economics (United Kingdom), 26, e204–e218.

    Google Scholar 

  • Bozack, A. K., Rifas-Shiman, S. L., Gold, D. R., Laubach, Z. M., Perng, W., Hivert, M. F., & Cardenas, A. (2023). DNA methylation age at birth and childhood: Performance of epigenetic clocks and characteristics associated with epigenetic age acceleration in the Project Viva cohort. Clinical Epigenetics, 15, art. no. 62.

  • Charles, M. (2003). Deciphering sex segregation: Vertical and horizontal inequalities in ten national labor markets. Acta Sociologica, 46, 267–287.

    Article  Google Scholar 

  • Charles, M. (2011). A world of difference: International trends in women’s economic status. Annual Review of Sociology, 37, 355–371.

    Article  Google Scholar 

  • Chen, B. H., Marioni, R. E., Colicino, E., Peters, M. J., Ward-Caviness, C. K., Tsai, P. C., & Horvath, S. (2016). DNA methylation-based measures of biological age: Meta-analysis predicting time to death. Aging (Albany Ny), 8, 1844–1865.

    Article  Google Scholar 

  • Cinelli, C., Forney, A., & Pearl, J. (2020). A crash course in good and bad controls Retrieved from University of California, Los Angeles Samueli Computer Science website: https://ftp.cs.ucla.edu/pub/stat_ser/r493.pdf.

  • Crimmins, E., Faul, J., Thyagarajan, B., & Weir, D. (2017). Venous blood collection and assay protocol in the 2016 Health and Retirement Study. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan.

    Google Scholar 

  • Crimmins, E., Faul, J., Kim, J. K., Thyagarajan, B., & Weir, D. (2020). HRS 2016 VBS – innovative sub sample assays: Homocysteine, clusterin, brain-derived neurotrophic factor (BDNF), and mtDNA copy number. Ann Arbor, MI: Survey Research Center, University of Michigan.

    Google Scholar 

  • Crimmins, E. M., Thyagarajan, B., Levine, M. E., Weir, D. R., & Faul, J. (2021). Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. sample: The Health and Retirement Study. Journals of Gerontology - Series A Biological Sciences and Medical Sciences, 76, 1117–1123.

    Article  Google Scholar 

  • Dahl, S., Nilsen, Ø. A., & Vaage, K. (2003). Gender differences in early retirement behaviour. European Sociological Review, 19, 179–198.

    Article  Google Scholar 

  • Diniz, B. S. (2023). Psychosocial determinants of biological aging in older adults: What is next? The American Journal of Geriatric Psychiatry, 31, 110–111.

    Article  Google Scholar 

  • Dugué, P. A., Bassett, J. K., Joo, J. E., Baglietto, L., Jung, C. H., Wong, E. M., & Milne, R. L. (2018). Association of DNA methylation-based biological age with health risk factors and overall and cause-specific mortality. American Journal of Epidemiology, 187, 529–538.

    Article  Google Scholar 

  • Eibich, P. (2015). Understanding the effect of retirement on health: Mechanisms and heterogeneity. Journal of Health Economics, 43, 1–12.

    Article  Google Scholar 

  • Ferrucci, L., Gonzalez-Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & de Cabo, R. (2020). Measuring biological aging in humans: A quest. Aging Cell, 19, art. no. e13080.

  • Fitzpatrick, M. D., & Moore, T. J. (2018). The mortality effects of retirement: Evidence from Social Security eligibility at age 62. Journal of Public Economics, 157, 121–137.

    Article  Google Scholar 

  • Forrester, S. N., Whitfield, K. E., Kiefe, C. I., & Thorpe, R. J. (2022). Navigating black aging: The biological consequences of stress and depression. The Journals of Gerontology Series B, 77, 2101–2112. Psychological Sciences and Social Sciences.

    Article  Google Scholar 

  • Fransquet, P. D., Wrigglesworth, J., Woods, R. L., Ernst, M. E., & Ryan, J. (2019). The epigenetic clock as a predictor of disease and mortality risk: A systematic review and meta-analysis. Clinical Epigenetics, 11, artno62.

    Article  Google Scholar 

  • Hallberg, D., Johansson, P., & Josephson, M. (2015). Is an early retirement offer good for your health? Quasi-experimental evidence from the army. Journal of Health Economics, 44, 274–285.

    Article  Google Scholar 

  • Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., & Zhang, K. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular Cell, 49, 359–367.

    Article  Google Scholar 

  • Heller-Sahlgren, G. (2017). Retirement blues. Journal of Health Economics, 54, 66–78.

    Article  Google Scholar 

  • Hernaes, E., Markussen, S., Pigott, J., & Vestad, O. (2013). Does retirement age impact mortality? Journal of Health Economics, 32, 586–598.

    Article  Google Scholar 

  • Hillary, R. F., Stevenson, A. J., McCartney, D. L., Campbell, A., Walker, R. M., Howard, D. M., & Marioni, R. E. (2020). Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden. Clinical Epigenetics, 12, art. no. 115.

  • Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14, art. no. R115.

  • Horvath, S., Oshima, J., Martin, G. M., Lu, A. T., Quach, A., Cohen, H., & Raj, K. (2018). Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging (Albany Ny), 10, 1758–1775.

    Article  Google Scholar 

  • Huber, M. (2014). Treatment evaluation in the presence of sample selection. Econometric Reviews, 33, 869–905.

    Article  Google Scholar 

  • Imai, K., & Kim, I. S. (2019). When should we use unit fixed effects regression models for causal inference with longitudinal data? American Journal of Political Science, 63, 467–490.

    Article  Google Scholar 

  • Imai, K., & Kim, I. S. (2021). On the use of two-way fixed effects regression models for causal inference with panel data. Political Analysis, 29, 405–415.

    Article  Google Scholar 

  • Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., & Sierra, F. (2014). Geroscience: Linking aging to chronic disease. Cell, 159, 709–713.

    Article  Google Scholar 

  • Kim, J. E., & Moen, P. (2002). Retirement transitions, gender, and psychological well-being: A life-course, ecological model. Journals of Gerontology - Series B Psychological Sciences and Social Sciences, 57, 212–222.

    Article  Google Scholar 

  • Kolodziej & García-Gómez. (2019). Saved by retirement: Beyond the mean effect on mental health. Social Science & Medicine, 225, 85–97.

    Article  Google Scholar 

  • Kuhn, A., Wuellrich, J. P., & Zweimüller, J. (2015). Fatal attraction? Access to early retirement and mortality. mimeo, University of Zurich.

  • Leimer, B., & van Ewijk, R. (2022). No “honeymoon phase”: Whose health benefits from retirement and when. Economics and Human Biology, 47, art. no. 101171.

  • Levine, M. E., Ake, T. L., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., & Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany Ny), 10, 573–591.

    Article  Google Scholar 

  • Lewis, J. (2001). The decline of the male breadwinner model: Implications for work and care. Social Politics, 8, 152–169.

    Article  Google Scholar 

  • Li, X., Ploner, A., Wang, Y., Magnusson, P. K. E., Reynolds, C., Finkel, D., & Hägg, S. (2020). Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. eLife, 9, art. no. e51507.

  • Li, W., Ye, X., Zhu, D., & He, P. (2021). The longitudinal association between retirement and depression: A systematic review and meta-analysis. American Journal of Epidemiology, 190, 2220–2230.

    Article  Google Scholar 

  • Lim, S., Nzegwu, D., & Wright, M. L. (2022). The impact of psychosocial stress from life trauma and racial discrimination on epigenetic aging—A systematic review. Biological Research For Nursing, 24, 202–215.

    Article  Google Scholar 

  • Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., & Horvath, S. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany Ny), 11, 303–327.

    Article  Google Scholar 

  • McCrory, C., Fiorito, G., McLoughlin, S., Polidoro, S., Cheallaigh, C. N., Bourke, N., & Kenny, R. A. (2020). Epigenetic clocks and allostatic load reveal potential sex-specific drivers of biological aging. Journals of Gerontology - Series A Biological Sciences and Medical Sciences, 75, 495–503.

    Google Scholar 

  • Mendelson, M. M. (2018). Epigenetic age acceleration: A biological doomsday clock for cardiovascular disease? Circulation. Genomic and Precision Medicine, 11, e002089.

    Article  Google Scholar 

  • Milner, A., Page, A., & Lamontagne, A. D. (2014). Cause and effect in studies on unemployment, mental health and suicide: A meta-analytic and conceptual review. Psychological Medicine, 44, 909–917.

    Article  Google Scholar 

  • Moen, P. (1996). A life course perspective on retirement, gender, and well-being. Journal of Occupational Health Psychology, 1, 131–144.

    Article  Google Scholar 

  • Moffitt, T. E. (2020). Behavioral and social research to accelerate the geroscience translation agenda. Ageing Research Reviews, 63, art. no. 101146.

  • Nishimura, Y., Oikawa, M., & Motegi, H. (2018). What explains the difference in the effect of retirement on health? Evidence from global aging data. Journal of Economic Surveys, 32, 792–847.

    Article  Google Scholar 

  • Odone, A., Gianfredi, V., Vigezzi, G. P., Amerio, A., Ardito, C., D’Errico, A., Stuckler, D., & Costa, G. (2021). Does retirement trigger depressive symptoms? A systematic review and meta-analysis. Epidemiology and Psychiatric Sciences, 30, art. no. e77.

  • Pearce, N., Checkoway, H., & Kriebel, D. (2007). Bias in occupational epidemiology studies. Occupational and Environmental Medicine, 64, 562–568.

    Article  Google Scholar 

  • Pearl, J. (2009). Causality: Models, reasoning, and inference. New York: Cambridge University Press.

    Book  Google Scholar 

  • Perez, N. B., Vorderstrasse, A. A., Yu, G., Melkus, G. D., Wright, F., Ginsberg, S. D., & Taylor, J. Y. (2022). Associations between DNA methylation age acceleration, depressive symptoms, and cardiometabolic traits in african american mothers from the InterGEN study. Epigenetics Insights, 15, 1–10.

    Article  Google Scholar 

  • Philibert, R., Beach, S. R. H., Lei, M. K., Gibbons, F. X., Gerrard, M., Simons, R. L., & Dogan, M. V. (2020). Array-based epigenetic aging indices may be racially biased. Genes, 11, art. no. 685.

  • Raffington, R., Belsky, D. W., Malanchini, M., Tucker-Drob, E. M., & Harden, K. P. (2020). Analysis of socioeconomic disadvantage and pace of aging measured in saliva DNA methylation of children and adolescents. bioRxiv. https://doi.org/10.1101/2020.06.04.134502.

  • Ryan, C. P. (2021). “Epigenetic clocks”: Theory and applications in human biology. American Journal of Human Biology, 33, art. no. e23488.

  • Schaap, R., de Wind, A., Coenen, P., Proper, K., & Boot, C. (2018). The effects of exit from work on health across different socioeconomic groups: A systematic literature review. Social Science and Medicine, 198, 36–45.

    Article  Google Scholar 

  • Sewdas, R., De Wind, A., Stenholm, S., Coenen, P., Louwerse, I., Boot, C., & Van Der Beek, A. (2020). Association between retirement and mortality: Working longer, living longer? A systematic review and meta-analysis. Journal of Epidemiology and Community Health, 74, 473–480.

    Article  Google Scholar 

  • Sun, B., Perkins, N. J., Cole, S. R., Harel, O., Mitchell, E. M., Schisterman, E. F., & Tchetgen Tchetgen, E. J. (2018). Inverse-probability-weighted estimation for monotone and nonmonotone missing data. American Journal of Epidemiology, 187, 585–591.

    Article  Google Scholar 

  • Tamman, A. J. F., Nagamatsu, S., Krystal, J. H., Gelernter, J., Montalvo-Ortiz, J. L., & Pietrzak, R. H. (2023). Psychosocial factors associated with accelerated GrimAge in male U.S. military veterans. The American Journal of Geriatric Psychiatry, 31, 97–109.

    Article  Google Scholar 

  • Tennant, P. W. G., Arnold, K. F., Ellison, G. T. H., & Gilthorpe, M. S. (2021). Analyses of ‘change scores’ do not estimate causal effects in observational data. International Journal of Epidemiology, 51, 1604–1615.

    Article  Google Scholar 

  • Trappe, H., Pollmann-Schult, M., & Schmitt, C. (2015). The rise and decline of the male breadwinner model: Institutional underpinnings and future expectations. European Sociological Review, 31, 230–242.

    Article  Google Scholar 

  • Tsai, S. P., Wendt, J. K., Donnelly, R. P., De Jong, G., & Ahmed, F. S. (2005). Age at retirement and long term survival of an industrial population: Prospective cohort study. British Medical Journal, 331, 995–997.

    Article  Google Scholar 

  • Turvey, C. L., Wallace, R. B., & Herzog, R. (1999). A revised CES-D measure of depressive symptoms and a DSM-based measure of major depressive episodes in the elderly. International Psychogeriatrics/IPA, 11, 139–148.

    Article  Google Scholar 

  • Vanderweele, T. J., Vansteelandt, S., & Robins, J. M. (2014). Effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology (Cambridge, Mass.), 25, 300–306.

    Article  Google Scholar 

  • Vaupel, J. W. (2010). Biodemography of human ageing. Nature, 464, 536–542.

    Article  Google Scholar 

  • Wagner, N. L., Berger, J., Flesch-Janys, D., Koch, P., Köchel, A., Peschke, M., & Ossenbach, T. (2006). Mortality and life expectancy of professional fire fighters in Hamburg, Germany: A cohort study 1950–2000. Environmental Health: A Global Access Science Source, 5, art. no. 27.

  • Wang, M., Henkens, K., & van Solinge, H. (2011). Retirement adjustment: A review of theoretical and empirical advancements. American Psychologist, 66, 204–213.

    Article  Google Scholar 

  • Weinstein, G. S., & Levin, B. (1989). Effect of crossover on the statistical power of randomized studies. Annals of Thoracic Surgery, 48, 490–495.

    Article  Google Scholar 

  • Weuve, J., Tchetgen Tchetgen, E. J., Glymour, M. M., Beck, T. L., Aggarwal, N. T., Wilson, R. S., et al. (2012). Accounting for bias due to selective attrition: The example of smoking and cognitive decline. Epidemiology (Cambridge, Mass.), 23, 119–128.

    Article  Google Scholar 

  • White, A. J., Kresovich, J. K., Keller, J. P., Xu, Z., Kaufman, J. D., Weinberg, C. R., & Sandler, D. P. (2019). Air pollution, particulate matter composition and methylation-based biologic age. Environment International, 132, 105071.

    Article  Google Scholar 

  • Wodtke, G. T., Alaca, Z., & Zhou, X. (2020). Regression-with-residuals estimation of marginal effects. Journal of the Royal Statistical Society Series A, 183, 311–332.

    Article  Google Scholar 

  • Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.

    Google Scholar 

  • Wu, C., Odden, M. C., Fisher, G. G., & Stawski, R. S. (2016). Association of retirement age with mortality: A population-based longitudinal study among older adults in the USA. Journal of Epidemiology and Community Health, 70, 917–923.

    Article  Google Scholar 

  • Xie, Y., Brand, J. E., & Jann, B. (2012). Estimating heterogeneous treatment effects with observational data. Sociological Methodology, 42, 314–347.

    Article  Google Scholar 

  • Zhou, X., & Xie, Y. (2020). Heterogeneous treatment effects in the presence of self-selection: A propensity score perspective. Sociological Methodology, 50, 350–385.

    Article  Google Scholar 

Download references

Funding

None. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Sociology, McGill University, Room 712, Leacock Building, 855 Sherbrooke Street West, Montreal, QC, H3A 2T7, Canada

    Aniruddha Das

Authors
  1. Aniruddha Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All work on this manuscript was done by Aniruddha Das.

Corresponding author

Correspondence to Aniruddha Das.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Ethical Approval

Not applicable. No human subjects were involved in this study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, A. Retirement and Epigenetic age Acceleration Among Older U.S. Adults. Adaptive Human Behavior and Physiology 9, 264–283 (2023). https://doi.org/10.1007/s40750-023-00221-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40750-023-00221-2

Keywords

Search

Navigation