Abstract
Purpose
The increasing burden of noncommunicable diseases (NCDs) attributable to high body mass index (BMI) represents both a threat and an opportunity for intervention. Estimates of the global latest trend of high BMI-related NCDs and its association with socioeconomic status can facilitate strategic intervention and inform further research.
Methods
This global burden of disease study extracted global, regional, and national data on death and disability-adjusted life years (DALYs) attributable to high BMI-related NCDs from the GBD Study 2017. Secondary analyses were performed by year, age, sex, and specific causes of death and DALYs. The 2017 Socio-demographic Index (SDI) was used as an indicator of national socioeconomic status. The association between age-standardized death or DALYs rate and socioeconomic status were analyzed.
Results
Worldwide, 4.7 million deaths and 147.7 million DALYs of NCDs were related to high BMI in 2017, with a projection to 5.5 million deaths and 176.9 million DALYs in 2025. Globally, high BMI-related burden showed an increasing trend with males being more heavily impacted overall. The trend and magnitude of high BMI-related disease burden varied substantially in different geographical and socioeconomic regions. Specifically, the low-middle, middle, and high-middle SDI countries were associated with a higher burden. The leading three causes of DALYs attributable to high BMI in 2017 were ischemic heart diseases, stroke, and diabetes mellitus.
Conclusions
High BMI-related burden of NCDs is worsening, particularly in develo** countries. Our findings may enhance public awareness of interventions to reduce the diseases burden caused by high BMI.
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References
M. Ng, T. Fleming, M. Robinson, B. Thomson, N. Graetz, C. Margono et al. Global, regional and national prevalence of overweight and obesity in children and adults 1980–2013: a systematic analysis. Lancet 384(9945), 766–781 (2014). https://doi.org/10.1016/S0140-6736(14)60460-8
WHO expert consultation., Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363(9412), 157–163 (2004). https://doi.org/10.1016/S0140-6736(03)15268-3
S. Kent, J. Green, G. Reeves, V. Beral, A. Gray, S.A. Jebb et al. Hospital costs in relation to body-mass index in 1.1 million women in England: a prospective cohort study. Lancet Public Health 2(5), e214–e222 (2017). https://doi.org/10.1016/S2468-2667(17)30062-2
D.T. Chu, N.T. Minh Nguyet, T.C. Dinh, N.V. Thai Lien, K.H. Nguyen, V.T. Nhu Ngoc et al. An update on physical health and economic consequences of overweight and obesity. Diabetes Metab. Syndr. Clin. Res. Rev 12(6), 1095–1100 (2018). https://doi.org/10.1016/j.dsx.2018.05.004
L. Yang, G.A. Colditz, Prevalence of overweight and obesity in the United States, 2007–2012. JAMA Intern. Med. 175(8), 1412–1413 (2015). https://doi.org/10.1001/jamainternmed.2015.2405
Y.J. Mi, B. Zhang, H.J. Wang, J. Yan, W. Han, J. Zhao et al. Prevalence and secular trends in obesity among Chinese adults, 1991–2011. Am. J. Prev. Med. 49(5), 661–669 (2015). https://doi.org/10.1016/j.amepre.2015.05.005
A. Afshin, M.H. Forouzanfar, M.B. Reitsma, P. Sur, K. Estep, A. Lee et al. Health Effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 377(1), 13–27 (2017). https://doi.org/10.1056/NEJMoa1614362
World Health Organization. Global Health Observatory (GHO) data.https://www.who.int/gho/ncd/risk_factors/overweight/en/. Accessed 1 June 2019
S.L. Marrero, D.E. Bloom, E.Y. Adashi, Noncommunicable diseases: A global health crisis in a new world order. JAMA 307(19), 2037–2038 (2012). https://doi.org/10.1001/jama.2012.3546
Mohsen Naghavi, H.W, Rafael Lozano, Adrian Davis, **aofeng Liang, Maigeng Zhou, SteinEmil Vollset et al. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 385(9963), 117–171 (2015). https://doi.org/10.1016/S0140-6736(14)61682-2
World Health Organization. Global Action Plan for the Prevention and Control of NCDs 2013–2020 (2013). http://www.who.int/nmh/events/ncd_action_plan/en/. Accessed 1 June 2019
J. Pearson-Stuttard, B. Zhou, V. Kontis, J. Bentham, M.J. Gunter, M. Ezzati, Worldwide burden of cancer attributable to diabetes and high body-mass index: a comparative risk assessment. Lancet Diabetes Endocrinol 6(6), e6–e15 (2018). https://doi.org/10.1016/S2213-8587(18)30150-5
Y. Lu, K. Hajifathalian, M. Ezzati, M. Woodward, E.B. Rimm, G. Danaei et al. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: A pooled analysis of 97 prospective cohorts with 1.8 million participants. Lancet 383(9921), 970–983 (2014). https://doi.org/10.1016/S0140-6736(13)61836-X
D.K. Tobias, A. Pan, C.L. Jackson, E.J. O’Reilly, E.L. Ding, W.C. Willett et al. Body-Mass index and mortality among adults with incident type 2 diabetes. N. Engl. J. Med. 370(3), 233–244 (2014). https://doi.org/10.1056/NEJMoa1304501
J.H. Ryoo, S.K. Park, S. Ye, J.M. Choi, C.M. Oh, S.Y. Kim et al. Estimation of risk for diabetes according to the metabolically healthy status stratified by degree of obesity in Korean men. Endocrine 50(3), 650–658 (2015). https://doi.org/10.1007/s12020-015-0635-5
D. Dicker, G. Nguyen, D. Abate, K.H. Abate, S.M. Abay, C. Abbafati et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1684–1735 (2018). https://doi.org/10.1016/S0140-6736(18)31891-9
H.H. Kyu, D. Abate, K.H. Abate, S.M. Abay, C. Abbafati, N. Abbasi et al. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392(10159), 1859–1922 (2018). https://doi.org/10.1016/S0140-6736(18)32335-3
J.D. Stanaway, A. Afshin, E. Gakidou, S.S. Lim, D. Abate, K.H. Abate et al. 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 392(10159), 1923–1994 (2018). https://doi.org/10.1016/S0140-6736(18)32225-6
Global Health Data Exchange. http://ghdx.healthdata.org/gbd-results-tool. Institute for Health Metrics and Evaluation (IHME). Accessed 1 June 2019
S.L. James, D. Abate, K.H. Abate, S.M. Abay, C. Abbafati, N. Abbasi et al. 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 392(10159), 1789–1858 (2018). https://doi.org/10.1016/S0140-6736(18)32279-7
D.N. Juurlink, M.M. Mamdani, D.S. Lee, A. Kopp, P.C. Austin, A. Laupacis et al. Rates of hyperkalemia after publication of the randomized aldactone evaluation study. N. Engl. J. Med. 351(6), 543–551 (2004). https://doi.org/10.1056/nejmoa040135
K.J. Foreman, N. Marquez, A. Dolgert, K. Fukutaki, N. Fullman, M. McGaughey et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet 392(10159), 2052–2090 (2018). https://doi.org/10.1016/S0140-6736(18)31694-5
R.G. Kavasseri, K. Seetharaman, Day-ahead wind speed forecasting using f-ARIMA models. Renew. Energy 34(5), 1388–1393 (2009). https://doi.org/10.1016/j.renene.2008.09.006
L. McLaren, Socioeconomic status and obesity. Epidemiol. Rev. 29(1), 29–48 (2007). https://doi.org/10.1093/epirev/mxm001
G.A.V.V. Murphy, G. Asiki, K. Ekoru, R.N. Nsubuga, J. Nakiyingi-miiro, E.H. Young et al. Sociodemographic distribution of non-communicable disease risk factors in rural Uganda: a cross-sectional study. Int. J. Epidemiol. 42(6), 1740–1753 (2013). https://doi.org/10.1093/ije/dyt184
M. Elaine, Power determinants of healthy eating among low-income Canadians. Can. J. Public Heal 96, S20–S26 (2005). https://doi.org/10.17269/CJPH.96.1504
J. Brug, Determinants of healthy eating: motivation, abilities and environmental opportunities. Fam. Pract 25, 50–55 (2009). https://doi.org/10.1093/fampra/cmn063
R.A. Shaikh, M. Siahpush, G.K. Singh, M. Tibbits, Socioeconomic status, smoking, alcohol use, physical activity, and dietary behavior as determinants of obesity and body mass index in the United States: findings from the National Health Interview Survey. Int. J. MCH AIDS 4(1), 22–34 (2015)
A. Misra, L. Khurana, Obesity and the metabolic syndrome in develo** countries. J. Clin. Endocrinol. Metab. 93(11), 9–30 (2008). https://doi.org/10.1210/jc.2008-1595
B.M. Popkin, L.S. Adair, S.W. Ng, Global nutrition transition and the pandemic of obesity in develo** countries. Nutr. Rev 70(1), 3–21 (2012). https://doi.org/10.1111/j.1753-4887.2011.00456.x
A. Tchernof, A. Desmeules, C. Richard, P. Laberge, M. Daris, J. Mailloux et al. Ovarian hormone status and abdominal visceral adipose tissue metabolism. J. Clin. Endocrinol. Metab. 89(7), 3425–3430 (2004). https://doi.org/10.1210/jc.2003-031561
B.H. Goodpaster, S. Krishnaswami, T.B. Harris, A. Katsiaras, S.B. Kritchevsky, E.M. Simonsick et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch. Intern. Med. 165(7), 777–783 (2005). https://doi.org/10.1001/archinte.165.7.777
J.P. Després, Body fat distribution and risk of cardiovascular disease: An update. Circulation 126(10), 1301–1313 (2012). https://doi.org/10.1161/CIRCULATIONAHA.111.067264
D.T. Duncan, K.Y. Wolin, M. Scharoun-Lee, E.L. Ding, E.T. Warner, G.G. Bennett, Does perception equal reality? Weight misperception in relation to weight-related attitudes and behaviors among overweight and obese US adults. Int. J. Behav. Nutr. Phys. Act 8, 1–9 (2011). https://doi.org/10.1186/1479-5868-8-20
M. Cluskey, D. Grobe, College weight gain and behavior transitions: male and female differences. J. Am. Diet Assoc. 109(2), 325–329 (2009). https://doi.org/10.1016/j.jada.2008.10.045
S. Lemieux, J.P. Despr, S. Moorjani, A. Nadeau, G. Th, D. Prud et al. Are gender differences in cardiovascular disease risk factors explained by the level of visceral adipose tissue? Diabetologia 37, 757–764 (1994)
World Health Organization. Noncommunicable diseases country profiles 2018 (2018). https://apps.who.int/iris/bitstream/handle/10665/274512/9789241514620-eng.pdf. Accessed 1 June 2019
I.O.O. Ajayi, C. Adebamowo, H.O. Adami, S. Dalal, M.B. Diamond, F. Bajunirwe et al. Urban-rural and geographic differences in overweight and obesity in four sub-Saharan African adult populations: a multi-country cross-sectional study. BMC Public Health 16(1), 1–13 (2016). https://doi.org/10.1186/s12889-016-3789-z
N. Shapira, Women’s higher health risks in the obesogenic environment: a gender nutrition approach to metabolic dimorphism with predictive, preventive, and personalised medicine. EPMA J 4(1), 1 (2013). https://doi.org/10.1186/1878-5085-4-1
S.S. Khan, H. Ning, J.T. Wilkins, N. Allen, M. Carnethon, J.D. Berry et al. Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA Cardiol 3(4), 280–287 (2018). https://doi.org/10.1001/jamacardio.2018.0022
P. Nordström, N.L. Pedersen, Y. Gustafson, K. Michaëlsson, A. Nordström, Risks of myocardial infarction, death, and diabetes in identical twin pairs with different body mass indexes. JAMA Intern. Med. 176(10), 1522–1529 (2016). https://doi.org/10.1001/jamainternmed.2016.4104
G. Bray, I. Culbert, C. Champagne, M. Crow, L. Dawson, B. Eberhardt et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N. Engl. J. Med. 346(6), 393–403 (2002)
G. Li, P. Zhang, J. Wang, E.W. Gregg, W. Yang, Q. Gong et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet 371(9626), 1783–1789 (2008). https://doi.org/10.1016/S0140-6736(08)60766-7
L. Jiang, A. Johnson, K. Pratte, J. Beals, A. Bullock, S.M. Manson, Long-term outcomes of lifestyle intervention to prevent diabetes in American Indian and Alaska native communities: The special diabetes program for Indians diabetes prevention program. Diabetes Care 41(7), 1462–1470 (2018). https://doi.org/10.2337/dc17-2685
M. Arnold, N. Pandeya, G. Byrnes, A.G. Renehan, G.A. Stevens, M. Ezzati et al. Global burden of cancer attributable to high body-mass index in 2012: a population-based study. Lancet Oncol. 16(1), 36–46 (2015). https://doi.org/10.1016/S1470-2045(14)71123-4
U.P. Gujral, E. Vittinghoff, M. Mongraw-Chaffin, D. Vaidya, N.R. Kandula, M. Allison et al. Cardiometabolic abnormalities among normal-weight persons from five racial/ethnic groups in the United States: a cross-sectional analysis of two cohort studies. Ann. Intern. Med. 166(9), 628–636 (2017). https://doi.org/10.7326/M16-1895.Cardiometabolic
S. Heymsfield, M. Russell, R.N. Pierson, J. Wang, J.C. Thornton, S. Burastero, Asians have lower body mass index (BMI) but higher percent body fat than do whites: comparisons of anthropometric measurements. Am. J. Clin. Nutr. 60(1), 23–28 (1994). https://doi.org/10.1093/ajcn/60.1.23
L.T. Ho-Pham, T.Q. Lai, N.D. Nguyen, E. Barrett-Connor, T.V. Nguyen, Similarity in percent body fat between White and Vietnamese Women: implication for a Universal Definition of Obesity. Obesity 18(6), 1242–1246 (2010). https://doi.org/10.1038/oby.2010.19
Acknowledgements
The estimates used in this manuscript based on the GBD data and methodologies. We appreciate the visionary global health leadership of the Institute for Health Metrics and Evaluation (IHME) in Seattle, Washington, and the contribution of all anonymous collaborators, without whom this report would not be possible.
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P.F.S. received grants from the National Natural Science Foundation of China (81670744, 81870564), Science Technology Department of Zhejiang Province of China (No. 2017C33037), and Y.F.X. received grants from China Postdoctoral Science Foundation (2019M652107). The authors declare that there is no other potential conflict of interests.
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Lin, X., Xu, Y., Xu, J. et al. Global burden of noncommunicable disease attributable to high body mass index in 195 countries and territories, 1990–2017. Endocrine 69, 310–320 (2020). https://doi.org/10.1007/s12020-020-02352-y
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DOI: https://doi.org/10.1007/s12020-020-02352-y