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Epidemiology and Population Health

Birth cohort effects on abdominal obesity in the United States: the Silent Generation, Baby Boomers and Generation X

Abstract

BACKGROUND:

Abdominal obesity predicts a wide range of adverse health outcomes. Over the past several decades, prevalence of abdominal obesity has increased markedly in industrialized countries like the United States No previous analyses, however, have evaluated whether there are birth cohort effects for abdominal obesity. Estimating cohort effects is necessary to forecast future health trends and understand the past population-level trends.

METHODS:

This analysis evaluated whether there were birth cohort effects for abdominal obesity for the Silent Generation (born 1925–1945), children of the Great Depression; Baby Boomers (born 1946–1964); or Generation X (born 1965–1980). Cohort effects for prevalence of abdominal obesity were estimated using the median polish method with data collected from the National Health and Nutrition Examination Survey (NHANES) between 1988 and 2008. Respondents were aged 20–74 years.

RESULTS:

After taking into account age effects and ubiquitous secular changes, the Silent Generation and Generation X had higher cohort-specific prevalence of abdominal obesity than the Baby Boomers. Effects were more pronounced in women than men.

CONCLUSIONS:

This work presents a novel finding: evidence that the birth cohorts of the post-World War II Baby Boom appeared to have uniquely low cohort effects on abdominal obesity. The growing prosperity of the post-World War II US may have exposed the baby-boom generation to lower levels of psychosocial and socioeconomic stress than the previous or subsequent generations. By identifying factors associated with the Baby Boomers’ low cohort-specific sensitivity to the obesogenic environment, the obesity prevention community can identify early-life factors that can protect future generations from excess weight gain.

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References

  1. Janssen I, Katzmarzyk PT, Ross R . Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr 2004; 79: 379–384.

    Article  CAS  PubMed  Google Scholar 

  2. Li C, Ford ES, McGuire LC, Mokdad AH . Increasing trends in waist circumference and abdominal obesity among US adults. Obesity (Silver Spring) 2007; 15: 216–224.

    Article  Google Scholar 

  3. Bouchard C . BMI, fat mass, abdominal adiposity and visceral fat: where is the ‘beef’? Int J Obes 2007; 31: 1552–1553.

    Article  CAS  Google Scholar 

  4. Diabetes Prevention Program Research Group. Relationship of body size and shape to the development of diabetes in the diabetes prevention program. Obesity (Silver Spring) 2006; 14: 2107–2117.

    Article  Google Scholar 

  5. Frey WH, Abresch B, Yeasting J . America by the Numbers: A Field Guide to the US Population. 1st edn (New Press: New York, USA, 2001).

    Google Scholar 

  6. Komlos J, Brabec M . The trend of mean BMI values of US adults, birth cohorts 1882-1986 indicates that the obesity epidemic began earlier than hitherto thought. Am J Hum Biol 2010; 22: 631–638.

    Article  PubMed  Google Scholar 

  7. Komlos J, Brabec M . The trend of BMI values of US adults by deciles, birth cohorts 1882-1986 stratified by gender and ethnicity. Econ Hum Biol 2011; 9: 234–250.

    Article  PubMed  Google Scholar 

  8. Gluckman P, Hanson M . Developmental and epigenetic pathways to obesity: an evolutionary-developmental perspective. Int J Obes 2008; 32: S62–S71.

    Article  CAS  Google Scholar 

  9. National Center for Health Statistics. Plan and operation of the health and nutrition examination survey. United States-1971–1973. Vital Health Stat 1978; 32, pp 1–407.

    Google Scholar 

  10. National Center for Health Statistics. Plan and operation of the third national health and nutrition examination survey, 1988-94. Series 1: programs and collection procedures. Vital Health Stat 1994; 10, pp 1–46.

    Google Scholar 

  11. National Center for Health Statistics. Analytic and reporting guidelines: the national health and nutrition examination survey (NHANES) 2005.

  12. Flegal KM, Carroll MD, Ogden CL, Curtin LR . Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010. : 2009.2014; 303: 235–241.

    Article  CAS  PubMed  Google Scholar 

  13. National Institutes of Health. “Lung and Blood Institute". Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults-the evidence report. Obes Res 1998; 6 (Suppl 2): S51–209.

    Google Scholar 

  14. Yang Y, Land KC . A mixed models approach to age-period-cohort analysis of repeated cross-section surveys: trends in verbal test scores. Sociol Methodol 2006; 36: 75–97.

    Article  Google Scholar 

  15. Eggebeen DJ, Lichter DT . Race family structure, and changing poverty among American children. Am Sociol Rev 1991; 56: 801–817.

    Article  Google Scholar 

  16. World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 1995; 854: 1–452.

  17. Ryder NB . The cohort as a concept in the study of social change. Am Sociol Rev 1965; 30: 843–861.

    Article  CAS  PubMed  Google Scholar 

  18. Keyes KM, Li G . Age-period-cohort analysis in injury epidemiology. In: Li G, Baker SP (eds) Injury Research: Theories, Methods, Approaches. Springer: New York, 2011.

    Google Scholar 

  19. Keyes KM, Schulenberg JE, O'Malley PM, Johnston LD, Bachman JG, Li G et al. The social norms of birth cohorts and adolescent marijuana use in the United States, 1976-2007. Addiction 2011; 106: 1790–1800.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Keyes KM, Li G . A multiphase method for estimating cohort effects in age-period contingency table data. Ann Epidemiol 2010; 20: 779–785.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yang Y . Aging, cohorts, and methods. In: The Handbook of Aging and the Social Sciences Binstock RH, George LK (eds) 7th edn. Academic Press: London; Burlington, MA; San Diego, CA, USA, pp 17–30 2010.

    Google Scholar 

  22. Tu YK, Kramer N, Lee WC . Addressing the identification problem in age-period-cohort analysis: a tutorial on the use of partial least squares and principal components analysis. Epidemiology 2012; 23: 583–593.

    Article  PubMed  Google Scholar 

  23. Reither EN, Olshansky SJ, Yang Y . New forecasting methodology indicates more disease and earlier mortality ahead for today's younger Americans. Health Aff (Millwood) 2011; 30: 1562–1568.

    Article  Google Scholar 

  24. Selvin S . Statistical Analysis of Epidemiologic Data. 2nd edn (Oxford University Press: New York, 1996).

    Google Scholar 

  25. Keyes KM, Utz RL, Robinson W, Li G . What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971-2006. Soc Sci Med 2010; 70: 1100–1108.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Robinson WR, Keyes KM, Utz RL, Martin CL, Yang Y . Birth cohort effects among US-born adults born in the 1980s: foreshadowing future trends in US obesity prevalence. Int J Obes; e-pub ahead of print 1 May 2012; doi:10.1038/ijo.2012.66.

    Article  PubMed  Google Scholar 

  27. Hernan MA, Hernandez-Diaz S, Robins JM . A structural approach to selection bias. Epidemiology 2004; 15: 615–625.

    Article  PubMed  Google Scholar 

  28. Rubin DB . For objective causal inference, design trumps analysis. Ann Appl Stat 2008; 2: 808–840.

    Article  Google Scholar 

  29. Suzuki E . Time changes, so do people. Soc Sci Med 2012.

  30. Brenner DR, Tepylo K, Eny KM, Cahill LE, El-Sohemy A . Comparison of body mass index and waist circumference as predictors of cardiometabolic health in a population of young Canadian adults. Diabetol Metab Syndr 2010; 2: 28.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Faeh D, Bopp M . Increase in the prevalence of obesity in Switzerland 1982-2007: birth cohort analysis puts recent slowdown into perspective. Obesity (Silver Spring) 2010; 18: 644–646.

    Article  Google Scholar 

  32. Keith SW, Redden DT, Katzmarzyk PT, Boggiano MM, Hanlon EC, Benca RM et al. Putative contributors to the secular increase in obesity: exploring the roads less traveled. Int J Obes 2006; 30: 1585–1594.

    Article  CAS  Google Scholar 

  33. Lawlor DA, Timpson NJ, Harbord RM, Leary S, Ness A, McCarthy MI et al. Exploring the developmental overnutrition hypothesis using parental-offspring associations and FTO as an instrumental variable. PLoS Med 2008; 5: e33.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Gluckman PD, Hanson MA . The developmental origins of the metabolic syndrome. Trends Endocrinol Metab 2004; 15: 183–187.

    Article  CAS  PubMed  Google Scholar 

  35. Ravelli AC, van Der Meulen JH, Osmond C, Barker DJ, Bleker OP . Obesity at the age of 50 y in men and women exposed to famine prenatally. Am J Clin Nutr 1999; 70: 811–816.

    Article  CAS  PubMed  Google Scholar 

  36. Diouf I, Charles M, Ducimetière P, Basdevant A, Eschwege E, Heude B . Evolution of obesity prevalence in France: an age-period-cohort analysis. Epidemiology 2010; 21: 360.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Reither EN, Hauser RM, Yang Y . Do birth cohorts matter? Age-period-cohort analyses of the obesity epidemic in the United States. Soc Sci Med 2009; 69: 1439–1448.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Allman-Farinelli MA, Chey T, Bauman AE, Gill T, James WP . Age, period and birth cohort effects on prevalence of overweight and obesity in Australian adults from 1990 to 2000. Eur J Clin Nutr 2008; 62: 898–907.

    Article  CAS  PubMed  Google Scholar 

  39. Bjorntorp P . Do stress reactions cause abdominal obesity and comorbidities? Obes Rev (an official journal of the International Association for the Study of Obesity) 2001; 2: 73–86.

    Article  CAS  Google Scholar 

  40. Dallman MF, Pecoraro NC, la Fleur SE . Chronic stress and comfort foods: self-medication and abdominal obesity. Brain Behav Immun 2005; 19: 275–280.

    Article  PubMed  Google Scholar 

  41. Franzini L, Ribble JC, Keddie AM . Understanding the hispanic paradox. Ethn Dis 2001; 11: 496–518.

    CAS  PubMed  Google Scholar 

  42. Federici N, Mason KO, Sogner S (eds) Women's Position and Demographic Change. Clarendon Press: Oxford, UK, pp 190–212 1993.

    Google Scholar 

  43. Kaestner R, Xin X . Title IX girls' sports participation, and adult female physical activity and weight. Eval Rev 2010; 34: 52–78.

    Article  PubMed  Google Scholar 

  44. Poore KR, Boullin JP, Cleal JK, Newman JP, Noakes DE, Hanson MA et al. Sex- and age-specific effects of nutrition in early gestation and early postnatal life on hypothalamo-pituitary-adrenal axis and sympathoadrenal function in adult sheep. J Physiol 2010; 588: 2219–2237.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dunn GA, Morgan CP, Bale TL . Sex-specificity in transgenerational epigenetic programming. Horm Behav 2011; 59: 290–295.

    Article  PubMed  Google Scholar 

  46. Case A, Menendez A . Sex differences in obesity rates in poor countries: evidence from South Africa. Econ Hum Biol 2009; 7: 271–282.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Hult M, Tornhammar P, Ueda P, Chima C, Edstedt Bonamy AK, Ozumba B et al. Hypertension, diabetes and overweight: looming legacies of the biafran famine. PLoS One 5: e13582.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Park J, Myers D, Kao D, Min S . Immigrant obesity and unhealthy assimilation: alternative estimates of convergence or divergence, 1995-2005. Soc Sci Med 2009; 69: 1625–1633.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Houle B . Obesity disparities among disadvantaged men: national adult male inmate prevalence pooled with non-incarcerated estimates, United States, 2002-2004. Soc Sci Med 2011; 72: 1667–1673.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Dr Robinson would like to thank the National Cancer Institute (1K01CA172717-01) and University Cancer Research Funds at the Lineberger Cancer Center at UNC-Chapel Hill for their financial support. Dr Keyes would like to thank the Columbia University Department of Epidemiology and New York State Psychiatric Institute for their financial support. Dr Utz would like to thank the University of Utah, Department of Sociology and NCI P01-CA13837 for current financial support. Ms Martin would like to thank the University of North Carolina at Chapel Hill and grant no. 5-T32-HD052468-04 for current financial support. Dr Yang is supported by NIA grant no. 1K01AG036745-01 and University Cancer Research Funds (UCRF) at the Lineberger Cancer Center at UNC-Chapel Hill.

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Correspondence to W R Robinson.

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Robinson, W., Utz, R., Keyes, K. et al. Birth cohort effects on abdominal obesity in the United States: the Silent Generation, Baby Boomers and Generation X. Int J Obes 37, 1129–1134 (2013). https://doi.org/10.1038/ijo.2012.198

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