Abstract
Objectives
To quantify the mediating role of leisure time physical activity (LTPA) and five dietary behaviours on educational differences in 13-year body mass index (BMI) gain across adulthood.
Methods
Participants from the Melbourne Collaborative Cohort Study (4791 women; 3103 men) who maintained or gained BMI over 1990–1994 to 2003–2007 and met our inclusion criteria were selected. Education, potential mediators and confounders (age, alcohol, and smoking) were measured at baseline. We conducted sex-specific multiple mediation analyses using MacKinnon’s product of coefficients method.
Results
A higher educational attainment was associated with a 0.27 kg m−2 (95% CI 0.14, 0.39) lesser 13-year BMI gain among women only. We observed significant indirect effects of educational attainment on 13-year BMI gain through LTPA and nutrient-rich foods (each associated with a higher educational attainment and lesser 13-year BMI gain) and diet soft drink (associated with a lower educational attainment and greater 13-year BMI gain), which mediated 10, 15 and 20% of this relationship, respectively (45% in total).
Conclusions
Nutrient-rich foods, LTPA and diet soft drink may represent effective public health targets to reduce inequities in excess weight across adulthood.
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References
Australian Bureau of Statistics (2011–2012) Australian Health Survey, Nutrition and Physical Activity, 2011–12, Table Builder. Findings based on use of ABS TableBuilder data
Australian Bureau of Statistics (2014) Australian health survey: nutrition first results - foods and nutrients, 2011–12. Australian Bureau of Statistics, Canberra
Backholer K et al (2012a) Projected socioeconomic disparities in the prevalence of obesity among Australian adults. Aust N Z J Public Health 36:557–563. https://doi.org/10.1111/j.1753-6405.2012.00885.x
Backholer K, Pasupathi K, Wong E, Hodge A, Stevenson C, Peeters A (2012b) The relationship between body mass index prior to old age and disability in old age. Int J Obes (Lond) 36:1180–1186. https://doi.org/10.1038/ijo.2012.99
Ball K, Crawford D (2005) Socioeconomic status and weight change in adults: a review. Soc Sci Med 60:1987–2010
Beenackers MA, Kamphuis CB, Giskes K, Brug J, Kunst AE, Burdorf A, van Lenthe FJ (2012) Socioeconomic inequalities in occupational, leisure-time, and transport related physical activity among European adults: a systematic review. Int J Behav Nutr Phys Act 9:116. https://doi.org/10.1186/1479-5868-9-116
Burke MV, Small DM (2015) Physiological mechanisms by which non-nutritive sweeteners may impact body weight and metabolism. Physiol Behav 152:381–388. https://doi.org/10.1016/j.physbeh.2015.05.036
Cancer Epidemiology Centre (2008) Melbourne collaborative cohort study databook. The Cancer Council Victoria, Melbourne
Compernolle S et al (2016) Mediating role of energy-balance related behaviors in the association of neighborhood socio-economic status and residential area density with BMI: the SPOTLIGHT study. Prev Med 86:84–91. https://doi.org/10.1016/j.ypmed.2016.01.005
Darmon N, Drewnowski A (2008) Does social class predict diet quality? Am J Clin Nutr 87:1107–1117. https://doi.org/10.1093/ajcn/87.5.1107
Ding P, Vanderweele TJ (2016) Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding. Biometrika 103:483–490. https://doi.org/10.1093/biomet/asw012
Feng X, Wilson A (2015) Getting Bigger, Quicker? Gendered socioeconomic trajectories in Body Mass Index across the adult lifecourse: a longitudinal study of 21,403 Australians. PLoS One 10:e0141499. https://doi.org/10.1371/journal.pone.0141499
Forouzanfar MH et al (2016) Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388:1659–1724. https://doi.org/10.1016/S0140-6736(16)31679-8
Fowler SP (2016) Low-calorie sweetener use and energy balance: results from experimental studies in animals, and large-scale prospective studies in humans. Physiol Behav 164:517–523. https://doi.org/10.1016/j.physbeh.2016.04.047
Fowler SP, Williams K, Resendez RG, Hunt KJ, Hazuda HP, Stern MP (2008) Fueling the obesity epidemic? Artificially sweetened beverage use and long-term weight gain Obesity (Silver Spring) 16:1894–1900. https://doi.org/10.1038/oby.2008.284
Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G (2006) Indicators of socioeconomic position (part 1). J Epidemiol Community Health 60:7–12. https://doi.org/10.1136/jech.2004.023531
Gearon E, Backholer K, Hodge A, Peeters A (2013) The mediating role of dietary factors and leisure time physical activity on socioeconomic inequalities in body mass index among Australian adults. BMC Public Health 13:1214. https://doi.org/10.1186/1471-2458-13-1214
Gearon E et al (2015) Trends in body mass index according to educational attainment for urban Australian adults between 1980 and 2007. Int J Obes (Lond) 39:1019–1026. https://doi.org/10.1038/ijo.2015.27
Government Office for Science (2007) Foresight. http://www.bis.gov.uk/assets/foresight/docs/obesity/11.pdf
Hodge AM, Jenkins AJ, English DR, O’Dea K, Giles GG (2011) NMR-determined lipoprotein subclass profile is associated with dietary composition and body size. Nutr Metab Cardiovasc Dis 21:603–609. https://doi.org/10.1016/j.numecd.2009.12.003
Hu FB (2013) Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases. Obes Rev 14:606–619. https://doi.org/10.1111/obr.12040
Ireland P et al (1994) Development of the Melbourne FFQ: a food frequency questionnaire for use in an Australian prospective study involving an ethnically diverse cohort. Asia Pac J Clin Nutr 3:19–31
Kavanagh A, Bentley RJ, Turrell G, Shaw J, Dunstan D, Subramanian SV (2010) Socioeconomic position, gender, health behaviours and biomarkers of cardiovascular disease and diabetes. Soc Sci Med 71:1150–1160. https://doi.org/10.1016/j.socscimed.2010.05.038
Lohman TG, Roche AF, Martorell R (1988) Anthropometric standardization reference manual. Human Kinetics Books, Champaign
Lowe MR (2015) Dieting: proxy or cause of future weight gain? Obes Rev 16(Suppl 1):19–24. https://doi.org/10.1111/obr.12252
MacInnis RJ, English DR, Hopper JL, Haydon AM, Gertig DM, Giles GG (2004) Body size and composition and colon cancer risk in men. Cancer Epidemiol Biomark Prev 13:553–559
MacKinnon DP (2008) Introduction to statistical mediation analysis. Multivariate applications. Psychology Press, New York
Milne RL et al (2017) Cohort profile: the Melbourne Collaborative Cohort Study (Health 2020). Int J Epidemiol https://doi.org/10.1093/ije/dyx085
Miura K, Turrell G (2014) Reported consumption of takeaway food and its contribution to socioeconomic inequalities in body mass index. Appetite 74:116–124. https://doi.org/10.1016/j.appet.2013.12.007
Molarius A (2003) The contribution of lifestyle factors to socioeconomic differences in obesity in men and women—a population-based study in Sweden. Eur J Epidemiol 18:227–234
Moore CJ, Cunningham SA (2012) Social position, psychological stress, and obesity: a systematic review. J Acad Nutr Diet 112:518–526 https://doi.org/10.1016/j.jand.2011.12.001
Mozaffarian D (2016) Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation 133:187–225. https://doi.org/10.1161/CIRCULATIONAHA.115.018585
Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB (2011) Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med 364:2392–2404. https://doi.org/10.1056/NEJMoa1014296
Murray S, Tulloch A, Criscitelli K, Avena NM (2016) Recent studies of the effects of sugars on brain systems involved in energy balance and reward: relevance to low calorie sweeteners. Physiol Behav 164:504–508. https://doi.org/10.1016/j.physbeh.2016.04.004
National Health and Medical Research Council (2009) Australian guidelines to reduce health risks from drinking alcohol. Canberra, Australia
Peeters A, Backholer K (2015) Reducing socioeconomic inequalities in obesity: the role of population prevention. Lancet Diabetes Endocrinol 3:838–840. https://doi.org/10.1016/S2213-8587(15)00373-3
Peeters A, Blake MRC (2016) Socioeconomic Inequalities in Diet Quality: from Identifying the problem to implementing solutions. Curr Nutri Rep 5:150–159. https://doi.org/10.1007/s13668-016-0167-5
Prentice AM, Jebb SA (2001) Beyond body mass index. Obes Rev 2:141–147
Proper KI, Cerin E, Brown WJ, Owen N (2007) Sitting time and socio-economic differences in overweight and obesity. Int J Obes (Lond) 31:169–176. https://doi.org/10.1038/sj.ijo.0803357
Prynne CJ, Paul AA, Mishra GD, Greenberg DC, Wadsworth ME (2005) Changes in intake of key nutrients over 17 years during adult life of a British birth cohort. Br J Nutr 94:368–376
PwC (2015) Weighing the costs of obesity: a case for action. https://www.pwc.com.au/pdf/weighing-the-cost-of-obesity-final.pdf
Rogers PJ et al (2016) Does low-energy sweetener consumption affect energy intake and body weight? A systematic review, including meta-analyses, of the evidence from human and animal studies. Int J Obes (Lond) 40:381–394. https://doi.org/10.1038/ijo.2015.177
Sanders T, Feng X, Fahey PP, Lonsdale C, Astell-Burt T (2015) Greener neighbourhoods, slimmer children? Evidence from 4423 participants aged 6 to 13 years in the Longitudinal Study of Australian children. Int J Obes (Lond) 39:1224–1229. https://doi.org/10.1038/ijo.2015.69
Shearer J, Swithers SE (2016) Artificial sweeteners and metabolic dysregulation: lessons learned from agriculture and the laboratory. Rev Endocr Metab Disord 17:179–186. https://doi.org/10.1007/s11154-016-9372-1
Smith JD, Hou T, Hu FB, Rimm EB, Spiegelman D, Willett WC, Mozaffarian D (2015) A comparison of different methods for evaluating diet, physical activity, and long-term weight gain in 3 prospective cohort studies. J Nutr 145:2527–2534. https://doi.org/10.3945/jn.115.214171
Suez J et al (2014) Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514:181–186. https://doi.org/10.1038/nature13793
Textor J, Hardt J, Knuppel S (2011) DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology 22:745. https://doi.org/10.1097/EDE.0b013e318225c2be
Tolppanen AM, Ngandu T, Kareholt I, Laatikainen T, Rusanen M, Soininen H, Kivipelto M (2014) Midlife and late-life body mass index and late-life dementia: results from a prospective population-based cohort. J Alzheimers Dis 38:201–209. https://doi.org/10.3233/JAD-130698
Turrell G, Bentley R, Thomas LR, Jolley D, Subramanian S, Kavanagh AM (2009) A multilevel study of area socio-economic status and food purchasing behaviour. Public Health Nutr 12:2074–2083. https://doi.org/10.1017/S1368980009004911
Turrell G, Haynes M, Wilson LA, Giles-Corti B (2013) Can the built environment reduce health inequalities? A study of neighbourhood socioeconomic disadvantage and walking for transport. Health Place 19:89–98. https://doi.org/10.1016/j.healthplace.2012.10.008
Wamala SP, Wolk A, Orth-Gomer K (1997) Determinants of obesity in relation to socioeconomic status among middle-aged Swedish women. Prev Med 26:734–744. https://doi.org/10.1006/pmed.1997.0199
Ward H, Tarasuk V, Mendelson R, McKeown-Eyssen G (2007) An exploration of socioeconomic variation in lifestyle factors and adiposity in the Ontario Food Survey through structural equation modeling. Int J Behav Nutr Phys Act 4:8. https://doi.org/10.1186/1479-5868-4-8
Winship C, Mare RD (1983) Structural equations and path analysis for discrete data. Am J Sociol 89:54–110
World Cancer Research Fund/American Institute for Cancer Research (2007) Food, nutrition, physical activity, and the prevention of cancer: a global perspective. American Institute for Cancer Research, Washington DC
World Health Organization (2003) Joint WHO/FAO expert report on diet, nutrition and the prevention of chronic disease. World Health Organization, Geneva
Xian H et al (2017) Steeper change in body mass across four decades predicts poorer cardiometabolic outcomes at midlife. Obesity (Silver Spring) 25:773–780. https://doi.org/10.1002/oby.21791
Zheng Y et al (2017) Associations of weight gain from early to middle adulthood with major health outcomes later in life. JAMA 318:255–269. https://doi.org/10.1001/jama.2017.7092
Zimberg IZ et al (2012) Short sleep duration and obesity: mechanisms and future perspectives. Cell Biochem Funct 30:524–529. https://doi.org/10.1002/cbf.2832
Acknowledgements
This work was supported by an Australian Research Council (ARC) Linkage grant (LP120100418) and in part by the Victorian Government’s Operational Infrastructure Support (OIS) Program. EG was supported by an Australian Government Research Training Program (RTP) Scholarship, AP was supported by a National Health and Medical Research Council Career Development Fellowship (1045456) and is a researcher within the NHMRC Centre for Research Excellence in Obesity Policy and Food Systems (APP1041020) and Deakin University, WLN is supported by a Monash Graduate Scholarship, a Monash International Post-graduate Research Scholarship and a Baker Bright Sparks Top-Up Scholarship, and KB was supported by a National Heart Foundation of Australia Post-Doctoral Fellowship (PH 12M6824). The MCCS study was made possible by the contribution of many people, including the original investigators and the diligent team who recruited the participants and completed follow-up. We would like to express our gratitude to the many thousands of Melbourne residents who continue to participate in the study. MCCS Data used in this research were obtained from Cancer Council Victoria. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414 and by infrastructure provided by Cancer Council Victoria. These funding sources had no input into study design, or collection, analysis and interpretation of the data.
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Gearon, E., Peeters, A., Ng, W. et al. Diet and physical activity as possible mediators of the association between educational attainment and body mass index gain among Australian adults. Int J Public Health 63, 883–893 (2018). https://doi.org/10.1007/s00038-018-1100-z
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DOI: https://doi.org/10.1007/s00038-018-1100-z