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Demography

, Volume 55, Issue 2, pp 387–402 | Cite as

Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades

  • Morgan E. Levine
  • Eileen M. Crimmins
Article

Abstract

Increasing life expectancy has been interpreted as improving health of a population. However, mortality is not always a reliable proxy for the pace of aging and could instead reflect achievement in keeping ailing people alive. Using data from NHANES III (1988–1994) and NHANES IV (2007–2010), we examined how biological age, relative to chronological age, changed in the United States between 1988 and 2010, while estimating the contribution of changes in modifiable health behaviors. Results suggest that biological age is lower for more recent periods; however, the degree of improvement varied across age and sex groups. Overall, older adults experienced the greatest improvement or decreases in biological age. Males, especially those in the youngest and oldest groups, experienced greater declines in biological age than females. These differences were partially explained by age- and sex-specific changes in behaviors, such as smoking, obesity, and medication use. Slowing the pace of aging, along with increasing life expectancy, has important social and economic implications; thus, identifying modifiable risk factors that contribute to cohort differences in health and aging is essential.

Keywords

Biomarkers Aging Time trends Obesity Smoking 

References

  1. Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., . . . Moffitt, T. E. (2015). Quantification of biological aging in young adults. Proceedings of the National Academy of Sciences, 112, E4104–4110.Google Scholar
  2. Butler, R. N., Sprott, R., Warner, H., Bland, J., Feuers, R., Forster, M., . . . Wolf, N. (2004). Biomarkers of aging: From primitive organisms to humans. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 59, 560–567.Google Scholar
  3. Cho, I. H., Park, K. S., & Lim, C. J. (2010). An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI). Mechanisms of Ageing and Development, 131, 69–78.Google Scholar
  4. Cohen, J. D., Cziraky, M. J., Cai, Q., Wallace, A., Wasser, T., Crouse, J. R., & Jacobson, T. A. (2010). 30-year trends in serum lipids among United States adults: Results from the National Health and Nutrition Examination Surveys II, III, and 1999–2006. American Journal of Cardiology, 106, 969–975.Google Scholar
  5. Comfort, A. (1969). Test-battery to measure ageing-rate in man. Lancet, 294, 1411–1415.Google Scholar
  6. Crimmins, E. M., & Beltran-Sanchez, H. (2011). Mortality and morbidity trends: Is there compression of morbidity? Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 66, 75–86.Google Scholar
  7. Crimmins, E. M., & Finch, C. E. (2006). Infection, inflammation, height, and longevity. Proceedings of the National Academy of Sciences, 103, 498–503.Google Scholar
  8. Daviglus, M. L., Liu, K., Yan, L. L., Pirzada, A., Manheim, L., Manning, W., . . . Stamler, J. (2004). Relation of body mass index in young adulthood and middle age to Medicare expenditures in older age. JAMA, 292, 2743–2749.Google Scholar
  9. Fee, E. (1991). Save the babies: American public health reform and the prevention of infant mortality, 1850–1929. Medical History, 35, 374–375.Google Scholar
  10. Finch, C. E., & Crimmins, E. M. (2004). Inflammatory exposure and historical changes in human life-spans. Science, 305, 1736–1739.Google Scholar
  11. Finch, C. E., & Kirkwood, T. B. L. (2000). Chance, development, and aging. New York, NY: Oxford University Press.Google Scholar
  12. Finucane, M. M., Stevens, G. A., Cowan, M. J., Danaei, G., Lin, J. K., Paciorek, C. J., . . . Ezzati, M. (2011). National, regional, and global trends in body-mass index since 1980: Systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet, 377, 557–567.Google Scholar
  13. Fries, J. F. (1983). The compression of morbidity. Milbank Memorial Fund Quarterly: Health and Society, 61, 397–419.Google Scholar
  14. Goetzel, R. Z. (2009). Do prevention or treatment services save money? The wrong debate. Health Affairs, 28, 37–41.Google Scholar
  15. Hajjar, I., & Kotchen, T. A. (2003). Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988–2000. JAMA, 290, 199–206.Google Scholar
  16. Hayward, M. D., & Gorman, B. K. (2004). The long arm of childhood: The influence of early-life social conditions on men’s mortality. Demography, 41, 87–107.Google Scholar
  17. Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312, 1900–1902.Google Scholar
  18. Kirkwood, T. B. (2002). Evolution of ageing. Mechanisms of Ageing and Development, 123, 737–745.Google Scholar
  19. Klemera, P., & Doubal, S. (2006). A new approach to the concept and computation of biological age. Mechanisms of Ageing and Development, 127, 240–248.Google Scholar
  20. Levine, M. E. (2013). Modeling the rate of senescence: Can estimated biological age predict mortality more accurately than chronological age? Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 68, 667–674.Google Scholar
  21. Levine, M. E., & Crimmins, E. M. (2014a). A comparison of methods for assessing mortality risk. American Journal of Human Biology, 26, 768–776.Google Scholar
  22. Levine, M. E., & Crimmins, E. M. (2014b). Evidence of accelerated aging among African Americans and its implications for mortality. Social Science & Medicine, 118, 27–32.Google Scholar
  23. Mooradian, A. D. (1990). Biomarkers of aging: Do we know what to look for? Journal of Gerontology, 45, B183–B186.Google Scholar
  24. National Center for Health Statistics (NCHS). (2011). Health, United States, 2010: With special feature on death and dying. Hyattsville, MD: NCHS.Google Scholar
  25. National Research Council. (2011). Explaining divergent levels of longevity in high-income countries. (E. M. Crimmins, S. H. Preston, & B. D. Cohen, Eds.).Washington, DC: National Academies Press.Google Scholar
  26. Oeppen, J., & Vaupel, J. W. (2002). Demography—Broken limits to life expectancy. Science, 296, 1029–1031.Google Scholar
  27. Phoenix, C., & de Grey, A. D. N. J. (2007). A model of aging as accumulated damage matches observed mortality patterns and predicts the life-extending effects of prospective interventions. Age, 29, 133–189.Google Scholar
  28. Preston, S. H., & Wang, H. D. (2006). Sex mortality differences in the United States: The role of cohort smoking patterns. Demography, 43, 631–646.Google Scholar
  29. Psaty, B. M., Manolio, T. A., Smith, N. L., Heckbert, S. R., Gottdiener, J. S., Burke, G. L., . . . Furberg, C. D. (2002). Time trends in high blood pressure control and the use of antihypertensive medications in older adults: The Cardiovascular Health Study. Archives of Internal Medicine, 162, 2325–2332.Google Scholar
  30. Rosen, M., & Haglund, B. (2005). From healthy survivors to sick survivors: Implications for the twenty-first century. Scandanavian Journal of Public Health, 33, 151–155.Google Scholar
  31. Schaefer, J. D., Caspi, A., Belsky, D. W., Harrington, H., Houts, R., Israel, S., . . . Moffitt, T. E. (2016). Early-life intelligence predicts midlife biological age. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 71, 968–977.Google Scholar
  32. Singh, G. K., & Yu, S. M. (1996). US childhood mortality, 1950 through 1993: Trends and socioeconomic differentials. American Journal of Public Health, 86, 505–512.Google Scholar
  33. Smith, D. W., & Bradshaw, B. S. (2006). Variation in life expectancy during the twentieth century in the United States. Demography, 43, 647–657.Google Scholar
  34. Vaupel, J. W. (2010). Biodemography of human ageing. Nature, 464, 536–542.Google Scholar
  35. Wang, H. D., & Preston, S. H. (2009). Forecasting United States mortality using cohort smoking histories. Proceedings of the National Academy of Sciences, 106, 393–398.Google Scholar
  36. Wang, Y., & Beydoun, M. A. (2007). The obesity epidemic in the United States—Gender, age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta-regression analysis. Epidemiologic Reviews, 29, 6–28.Google Scholar
  37. Yashin, A. I., Arbeev, K. G., Wu, D., Arbeeva, L. S., Kulminski, A., Akushevich, I., . . . Ukraintseva, S. V. (2013). How lifespan associated genes modulate aging changes: Lessons from analysis of longitudinal data. Frontiers in Genetics, 4, 3.  https://doi.org/10.3389/fgene.2013.00003
  38. Yashin, A. I., Ukraintseva, S. V., Boiko, S. I., & Arbeev, K. G. (2002). Individual aging and mortality rate: How are they related? Social Biology, 49, 206–217.Google Scholar

Copyright information

© Population Association of America 2018

Authors and Affiliations

  1. 1.Department of PathologyYale School of MedicineNew HavenUSA
  2. 2.Davis School of GerontologyUniversity of Southern CaliforniaLos AngelesUSA

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