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Population Trends and Variation in Body Mass Index from 1971 to 2008 in the Framingham Heart Study Offspring Cohort
Open accessAbstract
OBJECTIVE: We examined body mass index (BMI) across place and time to determine the pattern of BMI mean and standard deviation trajectories. METHODS: We included participants in the Framingham Heart Study (FHS) Offspring Cohort over eight waves of follow-up, from 1971 to 2008. After exclusions, the final sample size was 4569 subjects with 28,625 observations. We used multi-level models to examine population means and variation at the individual and neighborhood (census tracts) levels across time with measured BMI as the outcome, controlling for individual demographics and behaviors and neighborhood poverty. Because neighborhoods accounted for limited BMI variance, we removed this level as a source of variation in final models. We examined sex-stratified models with all subjects and models stratified by sex and baseline weight classification. RESULTS: Mean BMI increased from 24.0 kg/m(2) at Wave 1 to 27.7 at Wave 8 for women and from 26.6 kg/m(2) to 29.0 for men. In final models, BMI variation also increased from Waves 1 to 8, with the standard deviation increasing from 4.18 kg/m(2) to 6.15 for women and 3.31 kg/m(2) to 4.73 for men. BMI means increased in parallel across most baseline BMI weight classifications, except for more rapid increases through middle-age for obese women followed by declines in the last wave. BMI standard deviations also increased in parallel across baseline BMI classifications for women, with greater divergence of BMI variance for obese men compared to other weight classifications. CONCLUSION: Over nearly 40 years, BMI mean and variation increased in parallel across most baseline weight classifications in our sample. Individual-level characteristics, especially baseline BMI, were the primary factors in rising BMI. These findings have important implications not only for understanding the sources of the obesity epidemic in the United States but also for the targeting of interventions to address the epidemic.
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It is well-established that obesity is related to menstrual disorders, infertility, miscarriage, obstetric complications, live birth rate and can affect the success of assisted reproductive technology.
“It is well-established that obesity is related to menstrual disorders, infertility, miscarriage, obstetric complications, live birth rate and can affect the success of assisted reproductive technology.29–32”
From Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles (2019) · cites this paper as [32]
The Results and Discussion address BMI trends and weight gain in the Framingham cohort, not reproductive outcomes. Searches for menstrual disorders, infertility, miscarriage, obstetric complications, live birth, and assisted reproductive technology found no passage addressing the claimed relationships. (LLM, conf=0.76)
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Cites
Prevalence and Trends in Obesity Among US Adults, 1999-2008
Flegal, K. M. (2010) · JAMA · doi:10.1001/jama.2009.2014 · Cited by 7,580
Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010.
Flegal, K., Carroll, M., Kit, B., & Ogden, C. (2012) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.2012.39 · Cited by 5,523
Mean body weight, height, and body mass index, United States 1960-2002.
Ogden, C. L., Fryar, C. D., Carroll, M. D., & Flegal, K. M. (2004) · PubMed · Cited by 717
Morbid obesity rates continue to rise rapidly in the United States
Sturm, R., & Hattori, A. (2012) · International Journal of Obesity · doi:10.1038/ijo.2012.159 · Cited by 620
Ogden, C. L., & Carroll, M. D. (2010) · PsycEXTRA Dataset · doi:10.1037/e582042012-001 · Cited by 593
Fryar, C. D., Carroll, M. D., & Ogden, C. L. (2012) · Cited by 575
A User's Guide to MLwiN version 2.0
Rasbash, J., Steele, F., Browne, W. J., & Goldstein, H. (2004) · Bristol Research (University of Bristol) · Cited by 457
A multilevel analysis of race, community disadvantage, and body mass index among adults in the US
Robert, S., & Reither, E. N. (2004) · Social Science & Medicine · doi:10.1016/j.socscimed.2004.03.034 · Cited by 365
Merlo, J., Ohlsson, H., Lynch, K. F., Chaix, B., & Subramanian, S. V. (2009) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2009.088310 · Cited by 166
Self-citationBrown, W. V. (2011) · Journal of clinical lipidology · doi:10.1016/j.jacl.2011.08.003 · Cited by 143
King, T. L., Kavanagh, A. M., Jolley, D. J., Turrell, G., & Crawford, D. A. (2005) · International Journal of Obesity · doi:10.1038/sj.ijo.0803176 · Cited by 140
Heterogeneity Is Not Always Noise
Davidoff, F. (2009) · JAMA · doi:10.1001/jama.2009.1845 · Cited by 117
Young adult weight trajectories through midlife by body mass category
Malhotra, R., Østbye, T., Riley, C. M., & Finkelstein, E. A. (2013) · Obesity · doi:10.1002/oby.20318 · Cited by 115
Harrington, D. W., & Elliott, S. J. (2008) · Social Science & Medicine · doi:10.1016/j.socscimed.2008.11.021 · Cited by 113
Deprivation and the Development of Obesity
Stafford, M., Brunner, E. J., Head, J. A., & Ross, N. A. (2010) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.03.021 · Cited by 101
Explaining Variance; Or, Stuck in a Moment We Can't Get Out Of
Braumoeller, B. F. (2006) · Political Analysis · doi:10.1093/pan/mpj009 · Cited by 93
Razak, F. A., Corsi, D. J., & Subramanian, S. V. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001367 · Cited by 76
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Downs, G. W., & Rocke, D. M. (1979) · American Journal of Political Science · doi:10.2307/2110809 · Cited by 59
de Kroon, M. L. A., Renders, C. M., Wouwe, J. P. V., van Buuren, S., & Hirasing, R. A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013966 · Cited by 55
Sund, E. R., Jones, A., & Midthjell, K. (2010) · Social Science & Medicine · doi:10.1016/j.socscimed.2010.01.007 · Cited by 45
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Regidor, E., Gutiérrez-Fisac, J. L., Ronda, E., Calle, M. E., Martínez, D., & Domı́nguez, V. (2008) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2006.059360 · Cited by 19
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Bull, J. R., Rowland, S. P., Scherwitzl, E. B., Scherwitzl, R., Danielsson, K. G., & Harper, J. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0152-7 · Cited by 404
Subramanian, S. V., Kim, R., & Christakis, N. A. (2018) · Social Science & Medicine · doi:10.1016/j.socscimed.2018.04.027 · Cited by 50
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Zhang, Q., Marioni, R. E., Robinson, M. R., Higham, J., Sproul, D., Wray, N. R., Deary, I. J., McRae, A. F., & Visscher, P. M. (2018) · Genome Medicine · doi:10.1186/s13073-018-0585-7 · Cited by 49
Hamilton, O. K., Zhang, Q., McRae, A. F., Walker, R. M., Morris, S. W., Redmond, P. H., Campbell, A., Murray, A. D., Porteous, D. J., Evans, K. L., et al. (2019) · International Journal of Obesity · doi:10.1038/s41366-018-0262-3 · Cited by 50
Wang, W., Yang, J., Xu, J., Yu, H., Liu, Y., Wang, R., Ho, R., Ho, C. S. H., & Pan, F. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.11.015 · Cited by 46
Magnitude of Nonalcoholic Fatty Liver Disease: Western Perspective
Samji, N. S., Verma, R., & Satapathy, S. K. (2019) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2019.05.001 · Cited by 40
Life-long body mass index trajectories and mortality in two generations
Zheng, H., Echave, P., Mehta, N. K., & Myrskylä, M. (2021) · Annals of Epidemiology · doi:10.1016/j.annepidem.2021.01.003 · Cited by 40
Oleoylethanolamide and Human Neural Responses to Food Stimuli in Obesity
Grosshans, M., Schwarz, E., Bumb, J. M., Schaefer, C., Rohleder, C., Vollmert, C., Vollstädt‐Klein, S., Tost, H., Meyer‐Lindenberg, A., Kiefer, F., et al. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1215 · Cited by 37
Control-theory models of body-weight regulation and body-weight-regulatory appetite
Geary, N. (2019) · Appetite · doi:10.1016/j.appet.2019.104440 · Cited by 39
Jukic, A. M. Z., Upson, K., Harmon, Q. E., & Baird, D. D. (2016) · Fertility and Sterility · doi:10.1016/j.fertnstert.2016.03.004 · Cited by 35
Kahn, J., Grupp, U., & Maurer, M. H. (2013) · European Journal of Radiology · doi:10.1016/j.ejrad.2013.10.002 · Cited by 33
Scavone, G., Caltabiano, D. C., Gulino, F., Raciti, M. V., Giarrizzo, A., Biondi, A., Piazza, L., & Scavone, A. (2020) · Updates in Surgery · doi:10.1007/s13304-020-00743-4 · Cited by 30
Razak, F. A., Smith, G. D., & Subramanian, S. V. (2016) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.127357 · Cited by 26
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Concurrent trajectories of BMI and mental health patterns in emerging adulthood
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Porter, C., Wechsler, A. M., Naschold, F., Hime, S. J., & Fox, L. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-022731 · Cited by 16
Mayer, M. A., Finlayson, G., Fischman, D., de Paz, C., Telleriarte, M. R., Ferrero, A. J., Bobillo, C., & Fernández, B. E. (2014) · Food & Function · doi:10.1039/c3fo60631g · Cited by 14
Bruno, V. D., Chivasso, P., Rapetto, F., Guida, G. A., Tommaso, E. D., Chau, H. M., & Vohra, H. A. (2019) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2019.02.015 · Cited by 13
Siordia, C. (2014) · Journal of Sociological Research · doi:10.5296/jsr.v4i2.4883 · Cited by 12
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Text messaging as an intervention for weight loss in emerging adults
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Metadata
- Authors
- Block, J. P., Subramanian, S. V., Christakis, N. A., & O’Malley, A. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Block, Jason P.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-05-10
- Volume
- 8
- Issue
- 5
- Pages
- e63217
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3651246
- OpenAlex
- W2085927068
- Cited by
- 35
- References
- 22
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 7 · 2018: 4 · 2017: 3 · 2016: 4 · 2014: 6 · 2013: 4
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Robert Wood Johnson Foundation
- National Institutes of Health (n01hc25195-41-0-1, 5k25hl081275-05)
- National Institute on Aging (P01AG031093)
- National Heart, Lung, and Blood Institute (N01‐HC25195, P30HL101312, K25 HL081275), N01-HC-025195, HL109263, R01HL109263)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- body mass index, obesity epidemic, secular trends, sex differences, Framingham Offspring cohort
- MeSH terms
- Adult, Aged, Female, Humans, Male, Middle Aged, Obesity, Population Surveillance, Risk Factors, United States, Cohort Studies, Body Mass Index, History, 20th Century, History, 21st Century
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