Are changes in depressive symptoms, general health and residential area socio-economic status associated with trajectories of waist circumference and body mass index?

Autoři: Theo Niyonsenga aff001;  Suzanne J. Carroll aff001;  Neil T. Coffee aff001;  Anne W. Taylor aff003;  Mark Daniel aff001
Působiště autorů: Health Research Institute, Faculty of Health, University of Canberra, Canberra, Australia aff001;  School of Architecture and Built Environment, Healthy Cities Research Group, The University of Adelaide, South Australia, Australia aff002;  Discipline of Medicine, The University of Adelaide, South Australia, Australia aff003;  Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Fitzroy, Australia aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227029



This study sought to assess whether changes in depressive symptoms, general health, and area-level socio-economic status (SES) were associated to changes over time in waist circumference and body mass index (BMI).


A total of 2871 adults (18 years or older), living in Adelaide (South Australia), were observed across three waves of data collection spanning ten years, with clinical measures of waist circumference, height and weight. Participants completed the Centre for Epidemiologic Studies Depression (CES-D) and Short Form 36 health questionnaires (SF-36 general health domain). An area-level SES measure, relative location factor, was derived from hedonic regression models using residential property features but blind to location. Growth curve models with latent variables were fitted to data.


Waist circumference, BMI and depressive symptoms increased over time. General health and relative location factor decreased. Worsening general health and depressive symptoms predicted worsening waist circumference and BMI trajectories in covariate-adjusted models. Diminishing relative location factor was negatively associated with waist circumference and BMI trajectories in unadjusted models only.


Worsening depressive symptoms and general health predict increasing adiposity and suggest the development of unhealthful adiposity might be prevented by attention to negative changes in mental health and overall general health.

Klíčová slova:

Body mass index – Built environment – Depression – Health informatics – Mental health and psychiatry – Obesity – Socioeconomic aspects of health – Walking


1. WHO. Global Health Observatory Data: Overweight and obesity Geneva: World Health Organization; 2017 [cited 2017 19 December]. Available from:

2. OECD. Health at a Glance 2015. Paris: OECD Publishing; 2015.

3. AIHW. Risk factor trends: age patterns in key health risk factors over time. In: Welfare AIoHa, editor. Canberra: Australian Institute of Health and Welfare; 2015.

4. ABS. National Health Survey: First Results, 2014–15. In: Statistics ABo, editor. Canberra: Australian Bureau of Statistics; 2015.

5. Peeters A, Magliano DJ, Backholer K, Zimmet P, Shaw JE. Changes in the rates of weight and waist circumference gain in Australian adults over time: a longitudinal cohort study. BMJ Open. 2014;4(1):e003667. Epub 2014/01/21. doi: 10.1136/bmjopen-2013-003667 24440794; PubMed Central PMCID: PMC3902308.

6. Sarlio-Lahteenkorva S, Silventoinen K, Lahti-Koski M, Laatikainen T, Jousilahti P. Socio-economic status and abdominal obesity among Finnish adults from 1992 to 2002. International Journal of Obesity. 2006;30(11):1653–60. doi: 10.1038/sj.ijo.0803319 WOS:000241732100010. 16607386

7. Pavela G, Lewis D, Locher J, Allison D. Socioeconomic Status, Risk of Obesity, and the Importance of Albert J. Stunkard. Curr Obes Rep. 2016; 5(1):132–9. doi: 10.1007/s13679-015-0185-4 26746415

8. Drewnowski A, Moudon AV, Jiao J, Aggarwal A, Charreire H, Chaix B. Food environment and socioeconomic status influence obesity rates in Seattle and in Paris. International journal of obesity. 2014;38(2):306. doi: 10.1038/ijo.2013.97 23736365

9. Drewnowski A, Aggarwal A, Tang W, Moudon AV. Residential property values predict prevalent obesity but do not predict 1‐year weight change. Obesity. 2015;23(3):671–6. doi: 10.1002/oby.20989 25684713

10. Fahrenkamp AJ, Darling KE, Ruzicka EB, Sato AF. Maternal Depressive Symptoms Mediate the Association between Socio-economic Status and Adolescent Weight Outcomes: A Longitudinal Analysis. Maternal and child health journal. 2018;22(10):1462–9. doi: 10.1007/s10995-018-2541-y 29948764

11. Mansur RB, Brietzke E, McIntyre RS. Is there a “metabolic-mood syndrome”? A review of the relationship between obesity and mood disorders. Neuroscience & Biobehavioral Reviews. 2015;52:89–104.

12. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. 2010;67(3):220–9. Epub 2010/03/03. doi: 10.1001/archgenpsychiatry.2010.2 20194822.

13. Markowitz S, Friedman M, Arent S, Rutgers. Understanding the relation between obesity and depression: causal mechanisms and implications for treatment. Clinical Psychology: Science & Practice 2008. 2008;15:1–20.

14. Atlantis E, Baker M. Obesity effects on depression: systematic review of epidemiological studies. International Journal Of Obesity. 2008;32:881. doi: 10.1038/ijo.2008.54 18414420

15. Mannan M, Mamun A, Doi S, Clavarino A. Is there a bi-directional relationship between depression and obesity among adult men and women? Systematic review and bias-adjusted meta analysis. Asian journal of psychiatry. 2016;21:51–66. doi: 10.1016/j.ajp.2015.12.008 27208458

16. Coffee NT, Lockwood T, Rossini P, Niyonsenga T, McGreal S. Composition and context drivers of residential property location value as a socioeconomic status measure. Environment and Planning B: Urban Analytics and City Science. 2018:2399808318805489.

17. Daniel M, Moore S, Kestens Y. Framing the biosocial pathways underlying associations between place and cardiometabolic disease. Health & place. 2008;14(2):117–32.

18. Daniel M, Lekkas P, Cargo M, Stankov I, Brown A. Environmental risk conditions and pathways to cardiometabolic diseases in Indigenous populations. Annual Review of Public Health. 2011;32:327–47. doi: 10.1146/annurev.publhealth.012809.103557 21219157

19. Diez Roux AV, Mair C. Neighborhoods and health. Annals of the New York Academy of Sciences. 2010;1186(1):125–45. doi: 10.1111/j.1749-6632.2009.05333.x 20201871

20. Renalds A, Smith TH, Hale PJ. A systematic review of built environment and health. Family & community health. 2010;33(1):68–78.

21. Roof K, Oleru N. Public health: Seattle and King County’s push for the built environment. Journal of environmental health. 2008;71(1):24–7. 18724501

22. Black J, Macinko J. Neighborhoods and obesity. Nutrition Reviews. 2008;66(1):2–20. doi: 10.1111/j.1753-4887.2007.00001.x 18254880

23. Janssen I, Boyce WF, Simpson K, Pickett W. Influence of individual-and area-level measures of socioeconomic status on obesity, unhealthy eating, and physical inactivity in Canadian adolescents. The American journal of clinical nutrition. 2006;83(1):139–45. doi: 10.1093/ajcn/83.1.139 16400062

24. Halpern MT, Arena LC, Royce RA, Soler RE, Munoz B, Hennessy CM. Neighborhood and Individual Sociodemographic Characteristics Associated with Disparities in Adult Obesity and Perceptions of the Home Food Environment. Health equity. 2017;1(1):139–49. doi: 10.1089/heq.2017.0010 29167837

25. Niyonsenga T, Trepka MJ, Lieb S, Maddox LM. Measuring socioeconomic inequality in the incidence of AIDS: rural–urban considerations. AIDS and Behavior. 2013;17(2):700–9. doi: 10.1007/s10461-012-0236-8 22711226

26. ABS. Information Paper, Census of Population and Housing: Socio-Economic Index for Areas. In: Statistics ABo, editor. Canberra: Australian Bureau of Statistics; 2003.

27. Berry T, Spence J, Blanchard C, Cutumisu N, Edwards J, Nykiforuk C. Changes in BMI over 6 years: the role of demographic and neighborhood characteristics. International journal of obesity. 2010;34(8):1275. doi: 10.1038/ijo.2010.36 20157324

28. Powell-Wiley TM, Ayers C, Agyemang P, Leonard T, Berrigan D, Ballard-Barbash R, et al. Neighborhood-level socioeconomic deprivation predicts weight gain in a multi-ethnic population: longitudinal data from the Dallas Heart Study. Preventive medicine. 2014;66:22–7. doi: 10.1016/j.ypmed.2014.05.011 24875231

29. Coogan PF, Cozier YC, Krishnan S, Wise LA, Adams‐Campbell LL, Rosenberg L, et al. Neighborhood Socioeconomic Status in Relation to 10‐Year Weight Gain in the Black Women's Health Study. Obesity. 2010;18(10):2064–5. doi: 10.1038/oby.2010.69 20360755

30. Thurber KA, Joshy G, Korda R, Eades SJ, Wade V, Bambrick H, et al. Obesity and its association with sociodemographic factors, health behaviours and health status among Aboriginal and non-Aboriginal adults in New South Wales, Australia. J Epidemiol Community Health. 2018;72(6):491–8. doi: 10.1136/jech-2017-210064 29514925

31. Coffee N, Lockwood T, Hugo G, Paquet C, Howard N, Daniel M. Relative residential property value as a socio-economic status indicator for health research. International Journal Health Geography. 2013;12(22).

32. Lockwood T, Coffee NT, Rossini P, Niyonsenga T, McGreal S. Does where you live influence your socio-economic status? Land Use Policy. 2018;72:152–60.

33. Leonard T, Powell-Wiley TM, Ayers C, Murdoch JC, Yin W, Pruitt SL. Property values as a measure of neighborhoods: an application of hedonic price theory. Epidemiology (Cambridge, Mass). 2016;27(4):518.

34. Drewnowski A, Buszkiewicz J, Aggarwal A, Cook A, Moudon A. A new method to visualize obesity prevalence in Seattle‐King County at the census block level. Obesity science & practice. 2018;4(1):14–9.

35. Daniel M, Carroll SJ, Niyonsenga T, Piggott EJ, Taylor A, Coffee NT. Concurrent assessment of urban environment and cardiometabolic risk over 10 years in a middle‐aged population‐based cohort. Geographical Research. 2019;57(1):98–110.

36. Hirsch JA, Moore KA, Barrientos‐Gutierrez T, Brines SJ, Zagorski MA, Rodriguez DA, et al. Built environment change and change in BMI and waist circumference: Multi‐ethnic Study of Atherosclerosis. Obesity. 2014;22(11):2450–7. doi: 10.1002/oby.20873 25136965

37. Daniel M, Kestens Y, Paquet C. Demographic and urban form correlates of healthful and unhealthful food availability in Montréal, Canada. Canadian Journal of Public Health/Revue Canadienne de Sante'e Publique. 2009:189–93.

38. Carroll SJ. The contributions of compositional and contextual features of local residential areas to the evolution of cardiometabolic risk over ten years in a population-based biomedical cohort [Doctoral Thesis]. Adelaide: University of South Australia; 2017.

39. Barrientos-Gutierrez T, Moore KA, Auchincloss AH, Mujahid MS, August C, Sanchez BN, et al. Neighborhood physical Environment and changes in body mass index: results from the Multi-Ethnic Study of Atherosclerosis. American Journal of Epidemiology. 2017;186(11):1237–45. doi: 10.1093/aje/kwx186 29206987

40. Grant JF, Chittleborough CR, Taylor AW, Dal Grande E, Wilson DH, Phillips PJ, et al. The North West Adelaide Health Study: detailed methods and baseline segmentation of a cohort for selected chronic diseases. Epidemiologic Perspectives & Innovations. 2006;3(1):4.

41. Grant JF, Taylor AW, Ruffin RE, Wilson DH, Phillips PJ, Adams RJ, et al. Cohort Profile: The North West Adelaide Health Study (NWAHS). Int J Epidemiol. 2009;38(6):1479–86. Epub 2008/12/17. doi: 10.1093/ije/dyn262 19074192.

42. Devins G, Orme C, Costello C, Binik Y, Frizzell B, Stam H, et al. Measuring depressive symptoms in illness populations: Psychometric properties of the Center for Epidemiologic Studies Depression (CES-D) Scale. Psychology & Health. 1988;2:139–56.

43. Shafer A. Meta-analysis of the factor structures of fours depression questionnaires: Beck, CES-D. Hamilton, and Zung. Journal of Clinical Psychology. 2005;62(1):123–46.

44. Ware J, Gandek B. Methods for Testing Data Quality, Scaling Assumptions, and Reliability: The IQOLA Project Approach. Journal of Clinical Epidemiology. 1998;51(11):945–52. doi: 10.1016/s0895-4356(98)00085-7 9817111

45. Anagnostopoulos F, Niakas D, Pappa E. Construct validation of the Greek SF-36 health survey. Quality of Life Research. 2005;14(8):1959–65. doi: 10.1007/s11136-005-3866-8 16155784

46. Ware J, Snow K, Kosinski M, Gandek B, Center. NEM. Health Institute SF-36 Health Survey: Manual and interpretation guide. Boston, MA: The Health Institute New England Medical Center; 1993.

47. Sullivan M, Karlsson J, Ware J. The Swedish SF-36 Health Survey: Evaluation of data quality, scaling assumptions, reliability, and construct validity across general populations in Sweden Social Sciences and Medicine. 1995;41:1349–58.

48. Coffee N, Howard N, Paquet C, Hugo G, Daniel M. Is walkability associated with a lower cardiometabolic risk? Health Place. 2013;21:163–9. doi: 10.1016/j.healthplace.2013.01.009 23501378

49. Paquet C, Coffee NT, Haren MT, Howard NJ, Adams RJ, Taylor AW, et al. Food environment, walkability, and public open spaces are associated with incident development of cardio-metabolic risk factors in a biomedical cohort. Health & Place. 2014;28:173–6.

50. Bohannon RW. Comfortable and maximum walking speed of adults aged 20–79 years: reference values and determinants. Age and Ageing. 1997;26(1):15–9. doi: 10.1093/ageing/26.1.15 9143432

51. Leslie E, Coffee N, Frank L, Owen N, Bauman A, Hugo G. Walkability of local communities: using geographic information systems to objectively assess relevant environmental attributes. Health and Place. 2007;13(1):111–22. doi: 10.1016/j.healthplace.2005.11.001 16387522

52. Daker M, Pieters J, Coffee NT. Validating and measuring public open space is not a walk in the park. Australian Planner. 2016;53(2):143–51.

53. Muthen LK, Muthen BO. Mplus User's Guide. 8th ed. Los Angeles, CA: Muthen & Muthen; 1998–2017.

54. Singer JD, Willet JB. Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press; 2003.

55. Bollen KA, Curran PJ. Latent curve models: A structural equation perspective: John Wiley & Sons; 2006.

56. Curran PJ, Obeidat K, Losardo D. Twelve frequently asked questions about growth curve modeling. Journal of cognition and development. 2010;11(2):121–36. doi: 10.1080/15248371003699969 21743795

57. Graham JW. Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling. 2003;10(1):80–100.

58. Bentler PM, Chou C-P. Practical issues in structural modeling. Sociological Methods & Research. 1987;16(1):78–117.

59. Preacher K. Latent growth curve models. The reviewer's guide to quantitative methods in the social sciences. 2010;1:185–98.

60. Hooper D, Coughlan J, Mullen MR. Structural equation modelling: Guidelines for determining model fit. Electronic journal of business research methods. 2008;6(1):53–60.

61. Wu W, West SG, Taylor AB. Evaluating model fit for growth curve models: Integration of fit indices from SEM and MLM frameworks. Psychological methods. 2009;14(3):183. doi: 10.1037/a0015858 19719357

62. Miller GE, Freedland KE, Carney RM, Stetler CA, Banks WA. Pathways linking depression, adiposity, and inflammatory markers in healthy young adults. Brain, Behavior, and Immunity. 2003;17(4):276–85. doi: 10.1016/s0889-1591(03)00057-6 12831830

63. Shelton RC, Miller AH. Inflammation in depression: is adiposity a cause? Dialogues in Clinical Neuroscience. 2011;13(1):41–53. PMC3181969. 21485745

64. Cameron A, Magliano D, Dunstan D, Zimmet P, Hesketh K, Peeters A, et al. A bi-directional relationship between obesity and health-related quality of life: evidence from the longitudinal AusDiab study. International journal of obesity. 2012;36(2):295. doi: 10.1038/ijo.2011.103 21556045

65. Zaninotto P, Pierce M, Breeze E, Oliveira C, Kumari M. BMI and Waist Circumference as Predictors of Well‐being in Older Adults: Findings From the English Longitudinal Study of Ageing. Obesity. 2010;18(10):1981–7. doi: 10.1038/oby.2009.497 20075853

66. Kwarteng J, Schulz A, Mentz G, Israel B, Shanks T, Perkins D. Neighbourhood poverty, perceived discrimination and central adiposity in the USA: independent associations in a repeated measures analysis. Journal of Biosocial Science. 2016;48(6):709–22. doi: 10.1017/S0021932016000225 27238086

67. Drewnowski A, Aggarwal A, Rehm CD, Cohen-Cline H, Hurvitz PM, Moudon AV. Environments perceived as obesogenic have lower residential property values. American journal of preventive medicine. 2014;47(3):260–74. doi: 10.1016/j.amepre.2014.05.006 25049218

68. Drewnowski A, Aggarwal A, Tang W, Hurvitz PM, Scully J, Stewart O, et al. Obesity, diet quality, physical activity, and the built environment: the need for behavioral pathways. BMC public health. 2016;16(1):1153. doi: 10.1186/s12889-016-3798-y 27832766

69. Stark JH, Neckerman K, Lovasi GS, Quinn J, Weiss CC, Bader MD, et al. The impact of neighborhood park access and quality on body mass index among adults in New York City. Preventive medicine. 2014;64:63–8. doi: 10.1016/j.ypmed.2014.03.026 24704504

70. Li F, Harmer P, Cardinal BJ, Bosworth M, Johnson-Shelton D. Obesity and the built environment: does the density of neighborhood fast-food outlets matter? American Journal of Health Promotion. 2009;23(3):203–9. doi: 10.4278/ajhp.071214133 19149426

71. Li F, Harmer P, Cardinal BJ, Bosworth M, Johnson-Shelton D, Moore JM, et al. Built environment and 1-year change in weight and waist circumference in middle-aged and older adults: Portland Neighborhood Environment and Health Study. American journal of epidemiology. 2009;169(4):401–8. doi: 10.1093/aje/kwn398 19153214

72. Sugiyama T, Koohsari MJ, Mavoa S, Owen N. Activity-friendly built environment attributes and adult adiposity. Current obesity reports. 2014;3(2):183–98. doi: 10.1007/s13679-014-0096-9 26626602

73. Graham JW. Missing data analysis: Making it work in the real world. Annual review of psychology. 2009;60:549–76. doi: 10.1146/annurev.psych.58.110405.085530 18652544

74. Uhrig SN. The nature and causes of attrition in the British Household Panel Survey: Institute for Social and Economic Research, University of Essex Colchester …; 2008.

75. Carroll SJ, Niyonsenga T, Coffee NT, Taylor AW, Daniel M. Does physical activity mediate the associations between local-area descriptive norms, built environment walkability, and glycosylated hemoglobin? International journal of environmental research and public health. 2017;14(9):953.

76. Smith KR, Hanson HA, Brown BB, Zick CD, Kowaleski-Jones L, Fan JX. Movers and stayers: how residential selection contributes to the association between female body mass index and neighborhood characteristics. International Journal of Obesity. 2016;40(9):1384. doi: 10.1038/ijo.2016.78 27133620

77. Mayne DJ, Morgan GG, Jalaludin BB, Bauman AE. Area-level walkability and the geographic distribution of high body mass in Sydney, Australia: a spatial analysis using the 45 and up study. International journal of environmental research and public health. 2019;16(4):664.

78. Davillas A, Benzeval M, Kumari M. Association of adiposity and mental health functioning across the lifespan: findings from understanding society (The UK Household Longitudinal Study). PloS one. 2016;11(2):e0148561. doi: 10.1371/journal.pone.0148561 26849046

79. Kivimaki M, Lawlor DA, Singh-Manoux A, Batty GD, Ferrie JE, Shipley MJ, et al. Common mental disorder and obesity-insight from four repeat measures over 19 years: prospective Whitehall II cohort study. Brit Med J. 2009;339:b3765. ARTN b3765 10.1136/bmj.b3765. WOS:000270719100002. doi: 10.1136/bmj.b3765 19808765

80. Jagielski AC, Brown A, Hosseini-Araghi M, Thomas GN, Taheri S. The association between adiposity, mental well-being, and quality of life in extreme obesity. PloS one. 2014;9(3):e92859. doi: 10.1371/journal.pone.0092859 24671197

81. Britt H, Miller GC, Charles J, Henderson J, Bayram C, Pan Y, et al. General practice activity in Australia 2008–09. General practice series. 2009;(25).

82. Niyonsenga T, Coffee N, Del Fante P, Høj S, Daniel M. Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma. BMC health services research. 2018;18(1):897. doi: 10.1186/s12913-018-3714-5 30477507

83. Fitzpatrick SL, Wischenka D, Appelhans BM, Pbert L, Wang M, Wilson DK, et al. An evidence-based guide for obesity treatment in primary care. The American journal of medicine. 2016;129(1):115. e1-. e7.

Článek vyšel v časopise


2020 Číslo 1