The global burden of disease attributable to high body mass index in 195 countries and territories, 1990–2017: An analysis of the Global Burden of Disease Study


Autoři: Haijiang Dai aff001;  Tariq A. Alsalhe aff003;  Nasr Chalghaf aff004;  Matteo Riccò aff006;  Nicola Luigi Bragazzi aff002;  Jianhong Wu aff002
Působiště autorů: Department of Cardiology, Third Xiangya Hospital, Central South University, Changsha, China aff001;  Laboratory for Industrial and Applied Mathematics, Centre for Disease Modelling, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada aff002;  College of Sport Sciences and Physical Activity, King Saud University, Riyadh, Saudi Arabia aff003;  Group for the Study of Development and Social Environment, Faculty of Letters and Human Sciences of Sfax, University of Sfax, Sfax, Tunisia aff004;  Higher Institute of Sport and Physical Education of Sfax, University of Sfax, Sfax, Tunisia aff005;  Occupational Health and Safety Services, Department of Public Health, AUSL-IRCCS di Reggio Emilia, Reggio Emilia, Italy aff006
Vyšlo v časopise: The global burden of disease attributable to high body mass index in 195 countries and territories, 1990–2017: An analysis of the Global Burden of Disease Study. PLoS Med 17(7): e32767. doi:10.1371/journal.pmed.1003198
Kategorie: Research Article
doi: 10.1371/journal.pmed.1003198

Souhrn

Background

Obesity represents an urgent problem that needs to be properly addressed, especially among children. Public and global health policy- and decision-makers need timely, reliable quantitative information to develop effective interventions aimed at counteracting the burden generated by high body mass index (BMI). Few studies have assessed the high-BMI-related burden on a global scale.

Methods and findings

Following the methodology framework and analytical strategies used in the Global Burden of Disease Study (GBD) 2017, the global deaths and disability-adjusted life years (DALYs) attributable to high BMI were analyzed by age, sex, year, and geographical location and by Socio-demographic Index (SDI). All causes of death and DALYs estimated in GBD 2017 were organized into 4 hierarchical levels: level 1 contained 3 broad cause groupings, level 2 included more specific categories within the level 1 groupings, level 3 comprised more detailed causes within the level 2 categories, and level 4 included sub-causes of some level 3 causes. From 1990 to 2017, the global deaths and DALYs attributable to high BMI have more than doubled for both females and males. However, during the study period, the age-standardized rate of high-BMI-related deaths remained stable for females and only increased by 14.5% for males, and the age-standardized rate of high-BMI-related DALYs only increased by 12.7% for females and 26.8% for males. In 2017, the 6 leading GBD level 3 causes of high-BMI-related DALYs were ischemic heart disease, stroke, diabetes mellitus, chronic kidney disease, hypertensive heart disease, and low back pain. For most GBD level 3 causes of high-BMI-related DALYs, high-income North America had the highest attributable proportions of age-standardized DALYs due to high BMI among the 21 GBD regions in both sexes, whereas the lowest attributable proportions were observed in high-income Asia Pacific for females and in eastern sub-Saharan Africa for males. The association between SDI and high-BMI-related DALYs suggested that the lowest age-standardized DALY rates were found in countries in the low-SDI quintile and high-SDI quintile in 2017, and from 1990 to 2017, the age-standardized DALY rates tended to increase in regions with the lowest SDI, but declined in regions with the highest SDI, with the exception of high-income North America. The study’s main limitations included the use of information collected from some self-reported data, the employment of cutoff values that may not be adequate for all populations and groups at risk, and the use of a metric that cannot distinguish between lean and fat mass.

Conclusions

In this study, we observed that the number of global deaths and DALYs attributable to high BMI has substantially increased between 1990 and 2017. Successful population-wide initiatives targeting high BMI may mitigate the burden of a wide range of diseases. Given the large variations in high-BMI-related burden of disease by SDI, future strategies to prevent and reduce the burden should be developed and implemented based on country-specific development status.

Klíčová slova:

Age groups – Body mass index – Cardiovascular diseases – Death rates – Diabetes mellitus – Global health – Obesity – Public and occupational health


Zdroje

1. Ding D, Rogers K, van der Ploeg H, Stamatakis E, Bauman AE. Traditional and Emerging Lifestyle Risk Behaviors and All-Cause Mortality in Middle-Aged and Older Adults: Evidence from a Large Population-Based Australian Cohort. PLoS Med. 2015;12(12):e1001917. doi: 10.1371/journal.pmed.1001917 26645683

2. Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med. 2020;18(1):5.

3. Singh-Manoux A, Fayosse A, Sabia S, Tabak A, Shipley M, Dugravot A, et al. Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study. PLoS Med. 2018;15(5):e1002571. doi: 10.1371/journal.pmed.1002571 29782486

4. Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 2009;6(4):e1000058. doi: 10.1371/journal.pmed.1000058 19399161

5. Tsai AG, Williamson DF, Glick HA. Direct medical cost of overweight and obesity in the USA: a quantitative systematic review. Obes Rev. 2011;12(1):50–61. doi: 10.1111/j.1467-789X.2009.00708.x 20059703

6. Di Cesare M, Soric M, Bovet P, Miranda JJ, Bhutta Z, Stevens GA, et al. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med. 2019;17(1):212. doi: 10.1186/s12916-019-1449-8 31760948

7. Hivert MF, Arena R, Forman DE, Kris-Etherton PM, McBride PE, Pate RR, et al. Medical Training to Achieve Competency in Lifestyle Counseling: An Essential Foundation for Prevention and Treatment of Cardiovascular Diseases and Other Chronic Medical Conditions: A Scientific Statement From the American Heart Association. Circulation. 2016;134(15):e308–e27. doi: 10.1161/CIR.0000000000000442 27601568

8. Calitz C, Pollack KM, Millard C, Yach D. National Institutes of Health funding for behavioral interventions to prevent chronic diseases. Am J Prev Med. 2015;48(4):462–71. doi: 10.1016/j.amepre.2014.10.015 25576496

9. Wilfley DE, Hayes JF, Balantekin KN, Van Buren DJ, Epstein LH. Behavioral interventions for obesity in children and adults: Evidence base, novel approaches, and translation into practice. Am Psychol. 2018;73(8):981–93. doi: 10.1037/amp0000293 30394777

10. GBD 2015 Obesity Collaborators. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377(1):13–27. doi: 10.1056/NEJMoa1614362 28604169

11. GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1923–94. doi: 10.1016/S0140-6736(18)32225-6 30496105

12. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858. doi: 10.1016/S0140-6736(18)32279-7 30496104

13. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736–88. doi: 10.1016/S0140-6736(18)32203-7 30496103

14. Institute for Health Metrics and Evaluation. Protocol for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2018. 2018 Feb 26 [Cited 2020 June 3]. Available from: http://www.healthdata.org/sites/default/files/files/Projects/GBD/GBD_Protocol.pdf

15. Zou Z, Cini K, Dong B, Ma Y, Ma J, Burgner DP, et al. Time Trends in Cardiovascular Disease Mortality Across the BRICS: An Age-Period-Cohort Analysis of Key Nations With Emerging Economies Using the Global Burden of Disease Study 2017. Circulation. 2020;141(10):790–9. doi: 10.1161/CIRCULATIONAHA.119.042864 31941371

16. Global Burden of Disease Cancer Collaboration. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2017;3(4):524–48. doi: 10.1001/jamaoncol.2016.5688 27918777

17. Lang JJ, Alam S, Cahill LE, Drucker AM, Gotay C, Kayibanda JF, et al. Global Burden of Disease Study trends for Canada from 1990 to 2016. CMAJ. 2018;190(44):E1296–E304. doi: 10.1503/cmaj.180698 30397156

18. Liu Z, Jiang Y, Yuan H, Fang Q, Cai N, Suo C, et al. The trends in incidence of primary liver cancer caused by specific etiologies: Results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention. J Hepatol. 2019;70(4):674–83. doi: 10.1016/j.jhep.2018.12.001 30543829

19. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81. doi: 10.1016/S0140-6736(14)60460-8 24880830

20. Alexander S, Hayes S. Viewing Health Policy through a Gender Lens: Highlights from Several U.S. Communities. Womens Health Issues. 2017;27 Suppl 1:S2–S5.

21. Briggs AM, Woolf AD, Dreinhofer K, Homb N, Hoy DG, Kopansky-Giles D, et al. Reducing the global burden of musculoskeletal conditions. Bull World Health Organ. 2018;96(5):366–8. doi: 10.2471/BLT.17.204891 29875522

22. Park Y, Peterson LL, Colditz GA. The Plausibility of Obesity Paradox in Cancer-Point. Cancer Res. 2018;78(8):1898–903. doi: 10.1158/0008-5472.CAN-17-3043 29654151

23. Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S. Agricultural sustainability and intensive production practices. Nature. 2002;418(6898):671–7. doi: 10.1038/nature01014 12167873

24. Kearney J. Food consumption trends and drivers. Philos Trans R Soc Lond B Biol Sci. 2010;365(1554):2793–807. doi: 10.1098/rstb.2010.0149 20713385

25. Baker P, Friel S. Food systems transformations, ultra-processed food markets and the nutrition transition in Asia. Global Health. 2016;12(1):80. doi: 10.1186/s12992-016-0223-3 27912772

26. Pirgon O, Aslan N. The Role of Urbanization in Childhood Obesity. J Clin Res Pediatr Endocrinol. 2015;7(3):163–7. doi: 10.4274/jcrpe.1984 26831548

27. Igel U, Romppel M, Baar J, Brahler E, Grande G. Association between parental socio-economic status and childhood weight status and the role of urbanicity. Public Health. 2016;139:209–11. doi: 10.1016/j.puhe.2016.06.013 27423417

28. Hruby A, Hu FB. The Epidemiology of Obesity: A Big Picture. Pharmacoeconomics. 2015;33(7):673–89. doi: 10.1007/s40273-014-0243-x 25471927

29. Rao M, Afshin A, Singh G, Mozaffarian D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open. 2013;3(12):e004277. doi: 10.1136/bmjopen-2013-004277 24309174

30. Cizza G, Rother KI. Beyond fast food and slow motion: weighty contributors to the obesity epidemic. J Endocrinol Invest. 2012;35(2):236–42. doi: 10.3275/8182 22183119

31. Luczynski W, Glowinska-Olszewska B, Bossowski A. Empowerment in the Treatment of Diabetes and Obesity. J Diabetes Res. 2016;2016:5671492. doi: 10.1155/2016/5671492 28090541

32. Gopalan A, Makelarski JA, Garibay LB, Escamilla V, Merchant RM, Wolfe MB, Sr., et al. Health-Specific Information and Communication Technology Use and Its Relationship to Obesity in High-Poverty, Urban Communities: Analysis of a Population-Based Biosocial Survey. J Med Internet Res. 2016;18(6):e182. doi: 10.2196/jmir.5741 27352770

33. Laxer RE, Cooke M, Dubin JA, Brownson RC, Chaurasia A, Leatherdale ST. Behavioural patterns only predict concurrent BMI status and not BMI trajectories in a sample of youth in Ontario, Canada. PLoS ONE. 2018;13(1):e0190405. doi: 10.1371/journal.pone.0190405 29293654

34. McKinnon RA, Orleans CT, Kumanyika SK, Haire-Joshu D, Krebs-Smith SM, Finkelstein EA, et al. Considerations for an obesity policy research agenda. Am J Prev Med. 2009;36(4):351–7. doi: 10.1016/j.amepre.2008.11.017 19211215

35. Lyn R, Aytur S, Davis TA, Eyler AA, Evenson KR, Chriqui JF, et al. Policy, systems, and environmental approaches for obesity prevention: a framework to inform local and state action. J Public Health Manag Pract. 2013;19(3 Suppl 1):S23–33. doi: 10.1097/PHH.0b013e3182841709 23529052

36. Clarke B, Swinburn B, Sacks G. The application of theories of the policy process to obesity prevention: a systematic review and meta-synthesis. BMC Public Health. 2016;16(1):1084. doi: 10.1186/s12889-016-3639-z 27737707


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2020 Číslo 7

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