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Determinants of obesity in Latin America

Abstract

Obesity rates are increasing almost everywhere in the world, although the pace and timing for this increase differ when populations from developed and developing countries are compared. The sharp and more recent increase in obesity rates in many Latin American countries is an example of that and results from regional characteristics that emerge from interactions between multiple factors. Aware of the complexity of enumerating these factors, we highlight eight main determinants (the physical environment, food exposure, economic and political interest, social inequity, limited access to scientific knowledge, culture, contextual behaviour and genetics) and discuss how they impact obesity rates in Latin American countries. We propose that initiatives aimed at understanding obesity and hampering obesity growth in Latin America should involve multidisciplinary, global approaches that consider these determinants to build more effective public policy and strategies, accounting for regional differences and disease complexity at the individual and systemic levels.

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Fig. 1: Percentage (current and projections) of people with obesity by age and sex in countries at different levels of economic development.
Fig. 2: Determinants of obesity and their interactions.
Fig. 3: Scholarly output versus the percentage of adults with obesity (BMI ≥ 30 kg/m2) in countries of different geographical regions and at different levels of income.

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References

  1. Pi-Sunyer, X. The medical risks of obesity. Postgrad. Med. 121, 21–33 (2009).

    PubMed  PubMed Central  Google Scholar 

  2. Obesity Atlas 2023 | World Obesity Federation Global Obesity Observatory. https://data.worldobesity.org/publications/?cat=19

  3. The World Bank. World Development Report 2022: FINANCE for an Equitable Recovery. https://www.worldbank.org/en/publication/wdr2022

  4. Hruby, A. et al. Determinants and consequences of obesity. Am. J. Public Health 106, 1656–1662 (2016).

    PubMed  PubMed Central  Google Scholar 

  5. Ng, M. 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 384, 766–781 (2014).

    PubMed  PubMed Central  Google Scholar 

  6. Hossain, P., Kawar, B. & El Nahas, M. Obesity and diabetes in the developing world—a growing challenge. N. Engl. J. Med. 356, 213–215 (2007).

    CAS  PubMed  Google Scholar 

  7. Bhurosy, T. & Jeewon, R. Overweight and obesity epidemic in developing countries: a problem with diet, physical activity, or socioeconomic status? ScientificWorldJournal 2014, 964236 (2014).

  8. Monteiro, C. A., Moura, E. C., Conde, W. L. & Popkin, B. M. Socioeconomic status and obesity in adult populations of developing countries: a review. Bull. World Health Organ. 82, 940–946 (2004).

    PubMed  Google Scholar 

  9. Reinehr, T. Type 2 diabetes mellitus in children and adolescents. World J. Diabetes 4, 270–281 (2013).

    PubMed  PubMed Central  Google Scholar 

  10. Singh, R. B. et al. Prevalence of obesity, physical inactivity and undernutrition, a triple burden of diseases during transition in a developing economy. The Five City Study Group. Acta Cardiol. 62, 119–127 (2007).

    PubMed  Google Scholar 

  11. Knopp, R. H. et al. Gender differences in lipoprotein metabolism and dietary response: basis in hormonal differences and implications for cardiovascular disease. Curr. Atheroscler. Rep. 7, 472–479 (2005).

    CAS  PubMed  Google Scholar 

  12. Alves, J. G., Falcão, R. W., Pinto, R. A. & Correia, J. B. Obesity patterns among women in a slum area in Brazil. J. Health Popul. Nutr. 29, 286–289 (2011).

    PubMed  PubMed Central  Google Scholar 

  13. Batis, C., Mazariegos, M., Martorell, R., Gil, A. & Rivera, J. A. Malnutrition in all its forms by wealth, education and ethnicity in Latin America: who are more affected? Public Health Nutr. 23, s1–s12 (2020).

    PubMed  PubMed Central  Google Scholar 

  14. Misra, A. & Khurana, L. Obesity and the metabolic syndrome in developing countries. J. Clin. Endocrinol. Metab. 93, S9–S30 (2008).

    CAS  PubMed  Google Scholar 

  15. World Inequality Database. https://wid.world/

  16. Campos-Nonato, I., Galván-Valencia, O., Hernández-Barrera, L., Oviedo-Solís, C. & Barquera, S. Prevalencia de obesidad y factores de riesgo asociados en adultos mexicanos: resultados de la Ensanut 2022. Salud Publica Mex. 65, s238–s247 (2023).

    PubMed  Google Scholar 

  17. Estivaleti, J. M. et al. Time trends and projected obesity epidemic in Brazilian adults between 2006 and 2030. Sci. Rep. 12, 12699 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. ENSIN: Encuesta Nacional de Situación Nutricional | Portal ICBF - Instituto Colombiano de Bienestar Familiar ICBF. https://www.icbf.gov.co/bienestar/nutricion/encuesta-nacional-situacion-nutricional#ensin3/

  19. Banco de Recursos de Comunicación del Ministerio de Salud de la Nación | 2° Encuesta Nacional de Nutrición y Salud - Indicadores priorizados. https://bancos.salud.gob.ar/recurso/2deg-encuesta-nacional-de-nutricion-y-salud-indicadores-priorizados/

  20. Informes Encuestas - EPI - Departamento de Epidemiologia. http://epi.minsal.cl/resultados-encuestas/

  21. Shamah-Levy, T. et al. Prevalencias de sobrepeso y obesidad en población escolar y adolescente de México. Ensanut Continua 2020–2022. Salud Publica Mex. 65, s218–s224 (2023).

    PubMed  Google Scholar 

  22. Flores, L. S., Gaya, A. R., Petersen, R. D. S. & Gaya, A. Trends of underweight, overweight, and obesity in Brazilian children and adolescents. J. Pediatr. 89, 456–461 (2013).

    Google Scholar 

  23. Estadísticas – SOCHOB. https://www.sochob.cl/web1/estadisticas/

  24. Organización de las Naciones Unidas para la Alimentación y la Agricultura y la Organización Panamericana de la Salud. América Latina y el Caribe: Panorama de la seguridad alimentaria y nutricional. Sistemas alimentarios sostenibles para poner fin al hambre y la malnutrición, 2016. Us1.1 163 (2017).

  25. Hernández-Valero, M. A. et al. Higher risk for obesity among Mexican-American and Mexican immigrant children and adolescents than among peers in Mexico. J. Immigr. Minor. Health 14, 517–522 (2012).

    PubMed  PubMed Central  Google Scholar 

  26. Encuesta Nacional de Salud y Nutrición. https://ensanut.insp.mx/encuestas/ensanutcontinua2020/informes.php

  27. Dommarco, J. A. R. et al. Situación nutricional de la población en México durante los últimos 120 años. Cuernavaca, México: Instituto Nacional de Salud Pública https://spmediciones.mx/libro/situacion-nutricional-de-la-poblacion-en-mexico-durante-los-ultimos-120-anos_147842/(2023).

  28. Shamah, L. T. et al. ENSANUT 2018-19. Resultados Nacionales. Instituto Nacional de Salud Pública 268 (2020).

  29. Monteiro, C. A., Conde, W. L. & Popkin, B. M. Is obesity replacing or adding to undernutrition? Evidence from different social classes in Brazil. Public Health Nutr. 5, 105–112 (2002).

    PubMed  Google Scholar 

  30. Bloch, K. V. et al. ERICA: prevalences of hypertension and obesity in Brazilian adolescents. Rev. Saude Publica 50, 9s (2016).

    PubMed  PubMed Central  Google Scholar 

  31. Santos, F. D. P., Silva, E. A. F., Baeta, C. L. V., Campos, F. S. & Campos, H. O. Prevalence of childhood obesity in Brazil: a systematic review. J. Trop. Pediatr. 69, fmad017 (2023).

    PubMed  Google Scholar 

  32. Ferreira, S. R. G. et al. Disturbances of glucose and lipid metabolism in first and second generation Japanese-Brazilians. Diabetes Res Clin. Pract. 34, S59–S63 (1996).

    PubMed  Google Scholar 

  33. Siqueira, A. F. A. et al. Macrovascular disease in a Japanese-Brazilian population of high prevalence of metabolic syndrome: associations with classical and non-classical risk factors. Atherosclerosis 195, 160–166 (2007).

    CAS  PubMed  Google Scholar 

  34. Gimeno, S. G. A., Osiro, K., Matsumura, L., Massimino, F. C. & Ferreira, S. R. G. Glucose intolerance and all-cause mortality in Japanese migrants. Diabetes Res Clin. Pract. 68, 147–154 (2005).

    PubMed  Google Scholar 

  35. Almeida-Pittito, B., Hirai, A. T., Sartorelli, D. S., Gimeno, S. G. A. & Ferreira, S. R. G. Impact of a 2-year intervention program on cardiometabolic profile according to the number of goals achieved. Braz. J. Med. Biol. Res. 43, 1088–1094 (2010).

    CAS  PubMed  Google Scholar 

  36. Damião, R. et al. Dietary intakes associated with metabolic syndrome in a cohort of Japanese ancestry. Br. J. Nutr. 96, 532–538 (2006).

    PubMed  Google Scholar 

  37. Levitsky, D. A. & Pacanowski, C. R. Free will and the obesity epidemic. Public Health Nutr. 15, 126–141 (2012).

    PubMed  Google Scholar 

  38. World Health Organization. Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight/

  39. UNDRR ROAMC: regional assessment report on disaster risk in Latin America and the Caribbean (RAR, 2021). https://www.undrr.org/publication/undrr-roamc-regional-assessment-report-disaster-risk-latin-america-and-caribbean-rar/

  40. Bell, M. L., Davis, D. L., Gouveia, N., Borja-Aburto, V. H. & Cifuentes, L. A. The avoidable health effects of air pollution in three Latin American cities: Santiago, São Paulo, and Mexico City. Environ. Res. 100, 431–440 (2006).

    CAS  PubMed  Google Scholar 

  41. Gouveia, N. et al. Ambient fine particulate matter in Latin American cities: Levels, population exposure, and associated urban factors. Sci. Total Environ. 772, 145035 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. United Nations. Economic Commission for Latin America and the Caribbean. & United Nations Environment Programme. Oficina Regional para América Latina y el Caribe. The sustainability of development in Latin America and the Caribbean: challenges and opportunities (ECLAC, 2002).

  43. Souza, M. C. O. et al. Legacy and emerging pollutants in Latin America: a critical review of occurrence and levels in environmental and food samples. Sci. Total Environ. 848, 157774 (2022).

    CAS  PubMed  Google Scholar 

  44. Hernández, J. R. Society, environment, vulnerability, and climate change in latin america. Lat. Am. Perspect. 43, 29–42 (2016).

    Google Scholar 

  45. World Health Organization. Air quality database 2016. https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database/2016/

  46. Xu, Z. et al. Ambient particulate air pollution induces oxidative stress and alterations of mitochondria and gene expression in brown and white adipose tissues. Part. Fibre Toxicol. 8, 20 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu, C. et al. Air pollution–mediated susceptibility to inflammation and insulin resistance: influence of CCR2 pathways in mice. Environ. Health Perspect. 122, 17–26 (2014).

    PubMed  Google Scholar 

  48. Toledo-Corral, C. M. et al. Effects of air pollution exposure on glucose metabolism in Los Angeles minority children. Pediatr. Obes. 13, 54–62 (2018).

    CAS  PubMed  Google Scholar 

  49. Rao, X., Patel, P., Puett, R. & Rajagopalan, S. Air pollution as a risk factor for type 2 diabetes. Toxicol. Sci. 143, 231–241 (2015).

    PubMed  Google Scholar 

  50. Yu, G. et al. Fine particular matter and its constituents in air pollution and gestational diabetes mellitus. Environ. Int. 142, 105880 (2020).

    CAS  PubMed  Google Scholar 

  51. An, R., Zhang, S., Ji, M. & Guan, C. Impact of ambient air pollution on physical activity among adults: a systematic review and meta-analysis. Perspect. Public Health 138, 111–121 (2018).

    PubMed  Google Scholar 

  52. An, R., Ji, M., Yan, H. & Guan, C. Impact of ambient air pollution on obesity: a systematic review. Int J. Obes. 42, 1112–1126 (2018).

    CAS  Google Scholar 

  53. Sargis, R. M. & Simmons, R. A. Environmental neglect: endocrine disruptors as underappreciated but potentially modifiable diabetes risk factors. Diabetologia 62, 1811–1822 (2019).

    PubMed  PubMed Central  Google Scholar 

  54. Darbre, P. D. Endocrine disruptors and obesity. Curr. Obes. Rep. 6, 18–27 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. Myers, S., Fanzo, J., Wiebe, K., Huybers, P. & Smith, M. Food security, climate change, and health: current guidance underestimates risk of global environmental change to food security. BMJ 378, e071533 (2022).

    PubMed  PubMed Central  Google Scholar 

  56. Ebi, K. L. & Loladze, I. Elevated atmospheric CO2 concentrations and climate change will affect our food’s quality and quantity. Lancet Planet Health 3, e283–e284 (2019).

    PubMed  Google Scholar 

  57. Nepstad, D. et al. Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science 344, 1118–1123 (2014).

    CAS  PubMed  Google Scholar 

  58. Hall, C. M. et al. Deforestation reduces fruit and vegetable consumption in rural Tanzania. Proc. Natl Acad. Sci. USA 119, e2112063119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Lapola, D. M. et al. The drivers and impacts of Amazon forest degradation. Science 379, eabp8622 (2023).

    CAS  PubMed  Google Scholar 

  60. Boakye, K. et al. Urbanization and physical activity in the global Prospective Urban and Rural Epidemiology study. Sci. Rep. 13290 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Konttinen, H. Emotional eating and obesity in adults: the role of depression, sleep and genes. Proc. Nutr. Soc. 79, 283–289 (2020).

    CAS  PubMed  Google Scholar 

  62. Blüher, M. Obesity: global epidemiology and pathogenesis. Nat. Rev. Endocrinol. 15, 288–298 (2019).

    PubMed  Google Scholar 

  63. World Obesity Federation. Calculating the costs of the consequences of obesity. https://www.worldobesity.org/resources/resource-library/calculating-the-costs-of-the-consequences-of-obesity/

  64. Monteiro, C. A. et al. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 21, 5–17 (2018).

    PubMed  Google Scholar 

  65. Juul, F., Martinez-Steele, E., Parekh, N., Monteiro, C. A. & Chang, V. W. Ultra-processed food consumption and excess weight among US adults. Br. J. Nutr. 120, 90–100 (2018).

    CAS  PubMed  Google Scholar 

  66. Askari, M., Heshmati, J., Shahinfar, H., Tripathi, N. & Daneshzad, E. Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. Int. J. Obes. 44, 2080–2091 (2020).

    Google Scholar 

  67. Beslay, M. et al. Ultra-processed food intake in association with BMI change and risk of overweight and obesity: a prospective analysis of the French NutriNet-Santé cohort. PLoS Med. 17, e1003256 (2020).

    PubMed  PubMed Central  Google Scholar 

  68. Lane, M. M. et al. Ultraprocessed food and chronic noncommunicable diseases: a systematic review and meta-analysis of 43 observational studies. Obes. Rev. 22, e0144408 (2021).

    Google Scholar 

  69. Liu, J. et al. Consumption of ultraprocessed foods and body fat distribution among US adults. Am. J. Prev. Med. https://doi.org/10.1016/J.AMEPRE.2023.03.012 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Pagliai, G. et al. Consumption of ultra-processed foods and health status: a systematic review and meta-analysis. Br. J. Nutr. 125, 308–318 (2021).

    CAS  PubMed  Google Scholar 

  71. Neri, D. et al. Ultraprocessed food consumption and dietary nutrient profiles associated with obesity: a multicountry study of children and adolescents. Obes. Rev. 23, e13387 (2022).

    PubMed  Google Scholar 

  72. Srour, B. et al. Ultra-processed foods and human health: from epidemiological evidence to mechanistic insights. Lancet Gastroenterol. Hepatol. 7, 1128–1140 (2022).

    PubMed  Google Scholar 

  73. Marrón-Ponce, J. A., Sánchez-Pimienta, T. G., Da Costa Louzada, M. L. & Batis, C. Energy contribution of NOVA food groups and sociodemographic determinants of ultra-processed food consumption in the Mexican population. Public Health Nutr. 21, 87–93 (2018).

    PubMed  Google Scholar 

  74. Pan American Health Organization. Ultra-processed food and drink products in Latin America: trends, impact on obesity, policy implications. https://www3.paho.org/hq/index.php?option=com_content&view=article&id=11153:ultra-processed-food-and-drink-products&Itemid=0&lang=fr#gsc.tab=0/

  75. Swinburn, B. A. et al. Theglobal syndemic of obesity, undernutrition, and climate change: the Lancet Commission report. Lancet 393, 791–846 (2019).

    PubMed  Google Scholar 

  76. Scrinis, G. & Monteiro, C. From ultra-processed foods to ultra-processed dietary patterns. Nat. Food 3, 671–673 (2022).

    PubMed  Google Scholar 

  77. Marrón-Ponce, J. A., Sánchez-Pimienta, T. G., Rodríguez-Ramírez, S., Batis, C. & Cediel, G. Ultra-processed foods consumption reduces dietary diversity and micronutrient intake in the Mexican population. J. Hum. Nutr. Diet. 36, 241–251 (2023).

    PubMed  Google Scholar 

  78. Hall, K. D. et al. Ultra-Processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metab. 30, 67–77 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Edwin Thanarajah, S. et al. Habitual daily intake of a sweet and fatty snack modulates reward processing in humans. Cell Metab. 35, 571–584 (2023).

    CAS  PubMed  Google Scholar 

  80. Kelly, A. L., Baugh, M. E., Oster, M. E. & DiFeliceantonio, A. G. The impact of caloric availability on eating behavior and ultra-processed food reward. Appetite 178, 106274 (2022).

    PubMed  PubMed Central  Google Scholar 

  81. Barr, S. B. & Wright, J. C. Postprandial energy expenditure in whole-food and processed-food meals: implications for daily energy expenditure. Food Nutr. Res. 54, (2010).

  82. Speakman, J. R. et al. Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure. Nat. Metab. 5, 579–588 (2023).

    PubMed  PubMed Central  Google Scholar 

  83. Eshriqui, I., Folchetti, L. D., Valente, A. M. M., De Almeida-Pititto, B. & Ferreira, S. R. G. Breastfeeding duration is associated with offspring’s adherence to prudent dietary pattern in adulthood: results from the Nutritionist’s Health Study. J. Dev. Orig. Health Dis. 11, 136–145 (2020).

    PubMed  Google Scholar 

  84. Lippert, R. N. et al. Maternal high-fat diet during lactation reprograms the dopaminergic circuitry in mice. J. Clin. Invest. 130, 3761–3776 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Park, S. et al. Maternal low-calorie sweeteners consumption rewires hypothalamic melanocortin circuits via a gut microbial co-metabolite pathway. JCI Insight 8, e156397 (2023).

    PubMed  PubMed Central  Google Scholar 

  86. Viennois, E. et al. Dietary emulsifiers directly impact adherent-invasive E. coli gene expression to drive chronic intestinal inflammation. Cell Rep. 33, 108229 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. García-Montero, C. et al. Nutritional components in western diet versus mediterranean diet at the gut microbiota–immune system interplay. Implications for health and disease. Nutrients 13, 699 (2021).

    PubMed  PubMed Central  Google Scholar 

  88. Zhu, C. et al. Human gut microbiome composition and tryptophan metabolites were changed differently by fast food and Mediterranean diet in 4 days: a pilot study. Nutr. Res. 77, 62–72 (2020).

    CAS  PubMed  Google Scholar 

  89. Handakas, E. et al. Metabolic profiles of ultra-processed food consumption and their role in obesity risk in British children. Clin. Nutr. 41, 2537–2548 (2022).

    CAS  PubMed  Google Scholar 

  90. Gentile, C. L. & Weir, T. L. The gut microbiota at the intersection of diet and human health. Science 362, 776–780 (2018).

    CAS  PubMed  Google Scholar 

  91. Bäckhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004).

    PubMed  PubMed Central  Google Scholar 

  92. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

    PubMed  Google Scholar 

  93. Koponen, K. K. et al. Associations of healthy food choices with gut microbiota profiles. Am. J. Clin. Nutr. 114, 605–616 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Ritchie, H., Rosado, P. & Roser, M. Diet Compositions. Our World in Data https://ourworldindata.org/diet-compositions/ (2017).

  95. OECDiLibrary. OECD-FAO Agricultural Outlook 2022–2031. https://doi.org/10.1787/F1B0B29C-EN (2022).

  96. Lara-Castor, L. et al. Sugar-sweetened beverage intakes among adults between 1990 and 2018 in 185 countries. Nat. Commun. 14, 5957 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. O’Hearn, M. et al. Incident type 2 diabetes attributable to suboptimal diet in 184 countries. Nat. Med. 29, 982–995 (2023).

    PubMed  PubMed Central  Google Scholar 

  98. Koya. Dracula, blood banks…and getting serious about malnutrition. http://koya.org.uk/dracula-blood-banksand-getting-serious-about-malnutrition/

  99. Lauber, K., Rutter, H. & Gilmore, A. B. Big food and the World Health Organization: a qualitative study of industry attempts to influence global-level non-communicable disease policy. BMJ Glob. Health 6, e005216 (2021).

    PubMed  PubMed Central  Google Scholar 

  100. Stuckler, D. & Nestle, M. Big food, food systems, and global health. PLoS Med. 9, e1001242 (2012).

    PubMed  PubMed Central  Google Scholar 

  101. Hernandez-Aguado, I. & Zaragoza, G. A. Support of public–private partnerships in health promotion and conflicts of interest. BMJ Open 6, e009342 (2016).

    PubMed  PubMed Central  Google Scholar 

  102. Friel, S. et al. Commercial determinants of health: future directions. Lancet 401, 1229–1240 (2023).

    PubMed  Google Scholar 

  103. UNICEF. Front-of-pack nutrition warning labels in Latin America and the Caribbean. https://www.unicef.org/lac/en/reports/front-pack-nutrition-warning-labels-in-latin-america-and-caribbean/

  104. Barquera, S. & Rivera, J. A. Obesity in Mexico: rapid epidemiological transition and food industry interference in health policies. Lancet Diabetes Endocrinol. 8, 746–747 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. UK Health Forum. Public health and the food and drinks industry: The governance and ethics of interaction. Lessons from research, policy and practice (UKHF, 2018).

  106. Connectas. Las fichas de Coca Cola. https://www.connectas.org/especiales/las-fichas-de-coca-cola/en.html#landing/

  107. Thompson, R. C., Moore, C. J., Saal, F. S. V. & Swan, S. H. Plastics, the environment and human health: current consensus and future trends. Philos. Trans. R. Soc. B Biol. Sci. 364, 2153–2166 (2009).

    Google Scholar 

  108. Our World in Data. Share of consumer expenditure spent on food vs. total consumer expenditure, 2021. https://ourworldindata.org/grapher/food-expenditure-share-gdp/

  109. World Economic Forum. This map shows how much each country spends on food. https://www.weforum.org/agenda/2016/12/this-map-shows-how-much-each-country-spends-on-food/

  110. Mayén, A. L., Marques-Vidal, P., Paccaud, F., Bovet, P. & Stringhini, S. Socioeconomic determinants of dietary patterns in low- and middle-income countries: a systematic review. Am. J. Clin. Nutr. 100, 1520–1531 (2014).

    PubMed  Google Scholar 

  111. Chrousos, G. P. The role of stress and the hypothalamic–pituitary–adrenal axis in the pathogenesis of the metabolic syndrome: neuro-endocrine and target tissue-related causes. Int. J. Obes. 24, S50–S55 (2000).

    CAS  Google Scholar 

  112. Lovasi, G. S., Hutson, M. A., Guerra, M. & Neckerman, K. M. Built environments and obesity in disadvantaged populations. Epidemiol. Rev. 31, 7–20 (2009).

    PubMed  Google Scholar 

  113. Jiwani, S. S. et al. The shift of obesity burden by socioeconomic status between 1998 and 2017 in Latin America and the Caribbean: a cross-sectional series study. Lancet Glob. Health 7, e1644–e1654 (2019).

    PubMed  PubMed Central  Google Scholar 

  114. Jaacks, L. M. et al. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 7, 231–240 (2019).

    PubMed  PubMed Central  Google Scholar 

  115. Aitsi-Selmi, A., Bell, R., Shipley, M. J. & Marmot, M. G. Education modifies the association of wealth with obesity in women in middle-income but not low-income countries: an interaction study using seven national datasets, 2005–2010. PLoS ONE 9, e90403 (2014).

  116. Arrighi, E. et al. Scoping health literacy in Latin America. 29, 78–87 https://doi.org/10.1177/17579759211016802 (2021).

  117. International Monetary Fund. Economic Issues No. 33 - Educating Children in Poor Countries. https://www.imf.org/external/pubs/ft/issues/issues33/

  118. United States Census Bureau. https://www.census.gov/

  119. Ikegami, N. et al. Japanese universal health coverage: evolution, achievements, and challenges. Lancet 378, 1106–1115 (2011).

    PubMed  Google Scholar 

  120. Blümel, M. et al. Germany: health system summary, 2022. European Observatory on Health Systems and Policies 1–20 (2022).

  121. Balarajan, Y., Selvaraj, S. & Subramanian, S. Health care and equity in India. Lancet 377, 505–515 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Demo, M. L. O., Orth, L. C. & Marcon, C. E. M. Brazil’s health-care system. Lancet 394, 1992 (2019).

    PubMed  Google Scholar 

  123. Barber, R. M. et al. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of Disease Study 2015. Lancet 390, 231–266 (2017).

    Google Scholar 

  124. Haakenstad, A. et al. Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet Glob. Health 10, e1715–e1743 (2022).

    Google Scholar 

  125. Bixby, H. et al. Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 569, 260–264 (2019).

    Google Scholar 

  126. Monteiro, C. A., Conde, W. L. & Popkin, B. M. Independent effects of income and education on the risk of obesity in the brazilian adult population. J. Nutr. 131, 881S–886S (2001).

    CAS  PubMed  Google Scholar 

  127. Mazariegos, M. et al. Educational inequalities in obesity: a multilevel analysis of survey data from cities in Latin America. Public Health Nutr. 25, 1790–1798 (2021).

    Google Scholar 

  128. Modlinska, K., Adamczyk, D., Maison, D. & Pisula, W. Gender differences in attitudes to vegans/vegetarians and their food preferences, and their implications for promoting sustainable dietary patterns–a systematic review. Sustainability 12, 6292 (2020).

    Google Scholar 

  129. Jensen, K. O. D. & Holm, L. Preferences, quantities and concerns: socio-cultural perspectives on the gendered consumption of foods. Eur. J. Clin. Nutr. 53, 351–359 (1999).

    Google Scholar 

  130. Azevedo, M. R. et al. Gender differences in leisure-time physical activity. Int. J. Public Health 52, 8–15 (2007).

    PubMed  Google Scholar 

  131. International Labour Organization. World employment and social outlook: trends for women 2017. https://www.ilo.org/global/research/global-reports/weso/trends-for-women2017/lang--en/index.htm

  132. Perreira, K. M. & Telles, E. E. The color of health: skin color, ethnoracial classification, and discrimination in the health of Latin Americans. Soc. Sci. Med 116, 241–250 (2014).

    PubMed  Google Scholar 

  133. Chor, D., Faerstein, E., Kaplan, G. A., Lynch, J. W. & Lopes, C. S. Association of weight change with ethnicity and life course socioeconomic position among Brazilian civil servants. Int. J. Epidemiol. 33, 100–106 (2004).

    PubMed  Google Scholar 

  134. Araujo, M. C., Baltar, V. T., Yokoo, E. M. & Sichieri, R. The association between obesity and race among Brazilian adults is dependent on sex and socio-economic status. Public Health Nutr. 21, 2096–2102 (2018).

    PubMed  PubMed Central  Google Scholar 

  135. Sohail, M. et al. Mexican Biobank advances population and medical genomics of diverse ancestries. Nature 622, 775–783 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. Kowaltowski, A., Naslavsky, M. & Zatz, M. Open access: Brazilian scientists denied waivers and discounts. Nature 603, 793 (2022).

    CAS  PubMed  Google Scholar 

  137. Kwon, D. Open-access publishing fees deter researchers in the global south. Nature https://doi.org/10.1038/D41586-022-00342-W (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Godlee, F., Pakenham-Walsh, N., Ncayiyana, P. D., Cohen, B. & Packer, A. Can we achieve health information for all by 2015? Lancet 364, 295–300 (2004).

    PubMed  Google Scholar 

  139. Oh, S. S. et al. Diversity in clinical and biomedical research: a promise yet to be fulfilled. PLoS Med. 12, e1001918 (2015).

    PubMed  PubMed Central  Google Scholar 

  140. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Cohen, J. et al. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat. Genet. 37, 161–165 (2005).

    CAS  PubMed  Google Scholar 

  142. Miller, J. E. et al. Evaluation of drug trials in high-, middle-, and low-income countries and local commercial availability of newly approved drugs. JAMA Netw. Open 4, e217075 (2021).

    PubMed  PubMed Central  Google Scholar 

  143. Downing, N. S., Zhang, A. D. & Ross, J. S. Regulatory review of new therapeutic agents - FDA versus EMA, 2011–2015. N. Engl. J. Med. 376, 1386–1387 (2017).

    PubMed  Google Scholar 

  144. Zerhouni, E. & Hamburg, M. The need for global regulatory harmonization: a public health imperative. Sci. Transl. Med. 8, 338ed6 (2016).

    PubMed  Google Scholar 

  145. ClinicalTrials.gov. https://clinicaltrials.gov/

  146. REBEC. https://ensaiosclinicos.gov.br/

  147. Clinical Trials - Worldwide Clinical Research Trials. https://inclinicaltrials.com/

  148. Situación nutricional de la población en México durante los últimos 120 años. https://www.insp.mx/novedades-editoriales/situacion-nutricional-de-la-poblacion-en-mexico-durante-los-ultimos-120-anos

  149. Aceves-Martins, M. et al. Cultural factors related to childhood and adolescent obesity in Mexico: a systematic review of qualitative studies. Obes. Rev. 23, e13461 (2022).

    PubMed  PubMed Central  Google Scholar 

  150. The New York Times. In Town With Little Water, Coca-Cola Is Everywhere. So Is Diabetes. https://www.nytimes.com/2018/07/14/world/americas/mexico-coca-cola-diabetes.html

  151. World Health Organization. Obesity. https://www.who.int/health-topics/obesity#tab=tab_2/

  152. Luli, M. et al. The implications of defining obesity as a disease: a report from the Association for the Study of Obesity 2021 annual conference. EClinicalMedicine 58, 101962 (2023).

    PubMed  PubMed Central  Google Scholar 

  153. Renzaho, A. M. N. Fat rich and beautiful: Changing socio-cultural paradigms associated with obesity risk, nutritional status and refugee children from sub-Saharan Africa. Health Place 10, 105–113 (2004).

    PubMed  Google Scholar 

  154. Soltero, E. G. et al. Associations between screen-based activities, physical activity, and dietary habits in Mexican schoolchildren. Int J. Environ. Res. Public Health 18, 6788 (2021).

    PubMed  PubMed Central  Google Scholar 

  155. Cartanyà-Hueso, À. et al. Association between leisure screen time and junk food intake in a nationwide representative sample of spanish children (1–14 years): a cross-sectional study. Healthcare 9, 228 (2021).

    PubMed  PubMed Central  Google Scholar 

  156. Chou, S. Y., Rashad, I. & Grossman, M. Fast-food restaurant advertising on television and its influence on childhood obesity. J. Law Econ. 51, 599–618 (2008).

    Google Scholar 

  157. World Health Organization EMRO. Physical inactivity | Causes | NCDs. https://www.emro.who.int/noncommunicable-diseases/causes/physical-inactivity.html

  158. World Health Organization. Global action plan on physical activity 2018–2030: more active people for a healthier world. https://www.who.int/publications/i/item/9789241514187(2018).

  159. ElectronicsHub. The average screen time and usage by country. https://www.electronicshub.org/the-average-screen-time-and-usage-by-country/

  160. Schaan, C. W. et al. Prevalence of excessive screen time and TV viewing among Brazilian adolescents: a systematic review and meta-analysis. J. Pediatr. 95, 155–165 (2019).

    Google Scholar 

  161. DataReportal. Digital 2023: Global overview report—global digital insights. https://datareportal.com/reports/digital-2023-global-overview-report/

  162. Teixeira, I. P. et al. Built environments for physical activity: a longitudinal descriptive analysis of Sao Paulo city. Braz. Cities Health 7, 137–147 (2023).

    Google Scholar 

  163. Hernández, E. D., Cobo, E. A., Cahalin, L. P. & Seron, P. Impact of environmental interventions based on social programs on physical activity levels: a systematic review. Front. Public Health 11, 1095146 (2023).

    PubMed  PubMed Central  Google Scholar 

  164. Simões, E. J. et al. Effectiveness of a scaled up physical activity intervention in Brazil: a natural experiment. Prev. Med. 103S, S66–S72 (2017).

    PubMed  Google Scholar 

  165. Torres, A. et al. Assessing the effect of physical activity classes in public spaces on leisure-time physical activity: ‘Al Ritmo de las Comunidades’ a natural experiment in Bogota, Colombia. Prev. Med. 103S, S51–S58 (2017).

    PubMed  Google Scholar 

  166. Hilmers, A., Hilmers, D. C. & Dave, J. Neighborhood disparities in access to healthy foods and their effects on environmental justice. Am. J. Public Health 102, 1644–1654 (2012).

    PubMed  PubMed Central  Google Scholar 

  167. Babey, S. H., et al. Designed for disease: the link between local food environments and obesity and diabetes. https://escholarship.org/uc/item/9zc7p54b/ (2008).

  168. Story, M., Nanney, M. S. & Schwartz, M. B. Schools and obesity prevention: creating school environments and policies to promote healthy eating and physical activity. Milbank Q 87, 71–100 (2009).

    PubMed  PubMed Central  Google Scholar 

  169. Shaw, S. C., Ntani, G., Baird, J. & Vogel, C. A. A systematic review of the influences of food store product placement on dietary-related outcomes. Nutr. Rev. 78, 1030–1045 (2020).

    PubMed  PubMed Central  Google Scholar 

  170. Daniel, C. Is healthy eating too expensive? How low-income parents evaluate the cost of food. Soc. Sci. Med 248, 112823 (2020).

    PubMed  PubMed Central  Google Scholar 

  171. Hardcastle, S. J. & Blake, N. Influences underlying family food choices in mothers from an economically disadvantaged community. Eat. Behav. 20, 1–8 (2016).

    PubMed  Google Scholar 

  172. Ragelienė, T. & Grønhøj, A. The influence of peers′ and siblings′ on children’s and adolescents′ healthy eating behavior. A systematic literature review. Appetite 148, 104592 (2020).

    PubMed  Google Scholar 

  173. OECD. Average annual hours actually worked per worker. https://stats.oecd.org/index.aspx?DataSetCode=ANHRS

  174. Chaput, J. P. et al. The role of insufficient sleep and circadian misalignment in obesity. Nat. Rev. Endocrinol. 19, 82–97 (2022).

    PubMed  PubMed Central  Google Scholar 

  175. Thomas, C., Hyppönen, E. & Power, C. Obesity and type 2 diabetes risk in midadult life: the role of childhood adversity. Pediatrics 121, e1240–e1249 (2008).

    PubMed  Google Scholar 

  176. Valderhaug, T. G. & Slavich, G. M. assessing life stress: a critical priority in obesity research and treatment. Obesity 28, 1571–1573 (2020).

    PubMed  Google Scholar 

  177. Felitti, V. J. et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: the adverse childhood experiences (ACE) study. Am. J. Prev. Med 14, 245–258 (1998).

    CAS  PubMed  Google Scholar 

  178. Schroeder, K., Schuler, B. R., Kobulsky, J. M. & Sarwer, D. B. The association between adverse childhood experiences and childhood obesity: a systematic review. Obes. Rev. 22, e13204 (2021).

    PubMed  PubMed Central  Google Scholar 

  179. Hughes, K. et al. The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet Public Health 2, e356–e366 (2017).

    PubMed  Google Scholar 

  180. Flores-Torres, M. H. et al. Impact of adverse childhood experiences on cardiovascular disease risk factors in adulthood among Mexican women. Child Abuse Negl. 99, 104175 (2020).

    PubMed  Google Scholar 

  181. Elks, C. E. et al. Variability in the heritability of body mass index: a systematic review and meta-regression. Front. Endocrinol. 3, 29 (2012).

    Google Scholar 

  182. Hales, C. N. & Barker, D. J. P. The thrifty phenotype hypothesis. Br. Med. Bull. 60, 5–20 (2001).

    CAS  PubMed  Google Scholar 

  183. Yang, W., Kelly, T. & He, J. Genetic epidemiology of obesity. Epidemiol. Rev. 29, 49–61 (2007).

    PubMed  Google Scholar 

  184. Farooqi, I. S. & O’Rahilly, S. Monogenic obesity in humans. Annu. Rev. Med. 56, 443–458 (2005).

    CAS  PubMed  Google Scholar 

  185. Montague, C. T. et al. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 387, 903–908 (1997).

    CAS  PubMed  Google Scholar 

  186. Clément, K. et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392, 398–401 (1998).

    PubMed  Google Scholar 

  187. Jackson, R. S. et al. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nat. Genet. 16, 303–306 (1997).

    CAS  PubMed  Google Scholar 

  188. Krude, H. et al. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat. Genet. 19, 155–157 (1998).

    CAS  PubMed  Google Scholar 

  189. Yeo, G. S. H. et al. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat. Genet. 20, 111–112 (1998).

    CAS  PubMed  Google Scholar 

  190. Friedel, S. et al. Mutation screen of the brain derived neurotrophic factor gene (BDNF): identification of several genetic variants and association studies in patients with obesity, eating disorders, and attention-deficit/hyperactivity disorder. Am. J. Med. Genet. B Neuropsychiatr. Genet. 132B, 96–99 (2005).

    CAS  PubMed  Google Scholar 

  191. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  192. Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  193. Winkler, T. W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 11, e1005378 (2015).

    PubMed  PubMed Central  Google Scholar 

  194. Loos, R. J. F. & Yeo, G. S. H. The genetics of obesity: from discovery to biology. Nat. Rev. Genet. 23, 120–133 (2022).

    CAS  PubMed  Google Scholar 

  195. De Souza, C. T. et al. Consumption of a fat-rich diet activates a proinflammatory response and induces insulin resistance in the hypothalamus. Endocrinology 146, 4192–4199 (2005).

    PubMed  Google Scholar 

  196. Van De Sande-Lee, S. et al. Partial reversibility of hypothalamic dysfunction and changes in brain activity after body mass reduction in obese subjects. Diabetes 60, 1699–1704 (2011).

    PubMed  PubMed Central  Google Scholar 

  197. Thaler, J. P. et al. Obesity is associated with hypothalamic injury in rodents and humans. J. Clin. Invest. 122, 153–162 (2012).

    CAS  PubMed  Google Scholar 

  198. Van Der Klaauw, A. A. & Farooqi, I. S. The hunger genes: pathways to obesity. Cell 161, 119–132 (2015).

    PubMed  Google Scholar 

  199. Engel, D. F. & Velloso, L. A. The timeline of neuronal and glial alterations in experimental obesity. Neuropharmacology 208, 108983 (2022).

    CAS  PubMed  Google Scholar 

  200. Halaas, J. L. et al. Physiological response to long-term peripheral and central leptin infusion in lean and obese mice. Proc. Natl Acad. Sci. USA 94, 8878–8883 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  201. Ramalho, A. F. et al. Dietary fats promote functional and structural changes in the median eminence blood/spinal fluid interface-the protective role for BDNF. J. Neuroinflammation 15, 10 (2018).

    PubMed  PubMed Central  Google Scholar 

  202. Souza, G. F. P. et al. Defective regulation of POMC precedes hypothalamic inflammation in diet-induced obesity. Sci. Rep. 6, 29290 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  203. de Araujo, T. M. et al. The partial inhibition of hypothalamic IRX3 exacerbates obesity. EBioMedicine 39, 448–460 (2019).

    PubMed  Google Scholar 

  204. GWAS Central.https://www.gwascentral.org/

  205. GWAS Catalog. https://www.ebi.ac.uk/gwas/

  206. Costa-Urrutia, P. et al. Genome-wide association study of body mass index and body fat in Mexican-Mestizo children. Genes 10, 945 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  207. Justice, A. E. et al. Genome-wide association study of body fat distribution traits in Hispanics/Latinos from the HCHS/SOL. Hum. Mol. Genet 30, 2190–2204 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  208. Fernández-Rhodes, L. et al. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits–The Hispanic/Latino Anthropometry Consortium. HGG Adv. 3, 100099 (2022).

    PubMed  PubMed Central  Google Scholar 

  209. Piaggi, P. et al. Exome sequencing identifies a nonsense variant in DAO associated with reduced energy expenditure in american indians. J. Clin. Endocrinol. Metab. 105, e3989–e4000 (2020).

    PubMed  PubMed Central  Google Scholar 

  210. Piaggi, P. et al. A genome-wide association study using a custom genotyping array identifies variants in GPR158 associated with reduced energy expenditure in american indians. Diabetes 66, 2284–2295 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  211. Bian, L. et al. MAP2K3 is associated with body mass index in American Indians and Caucasians and may mediate hypothalamic inflammation. Hum. Mol. Genet 22, 4438–4449 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  212. Gillman, M. W. Developmental origins of health and disease. N. Engl. J. Med. 353, 1848–1850 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  213. Barker, D. J. P. The origins of the developmental origins theory. J. Intern Med. 261, 412–417 (2007).

    CAS  PubMed  Google Scholar 

  214. Mandy, M. & Nyirenda, M. Developmental origins of health and disease: the relevance to developing nations. Int. Health 10, 66–70 (2018).

    PubMed  PubMed Central  Google Scholar 

  215. Vézina-Im, L. A., Nicklas, T. A. & Baranowski, T. Intergenerational effects of health issues among women of childbearing age: a review of the recent literature. Curr. Nutr. Rep. 7, 274–285 (2018).

    PubMed  Google Scholar 

  216. Hanafi, M. Y., Saleh, M. M., Saad, M. I., Abdelkhalek, T. M. & Kamel, M. A. Transgenerational effects of obesity and malnourishment on diabetes risk in F2 generation. Mol. Cell. Biochem. 412, 269–280 (2016).

    CAS  PubMed  Google Scholar 

  217. Sales, V. M., Ferguson-Smith, A. C. & Patti, M. E. Epigenetic mechanisms of transmission of metabolic disease across generations. Cell Metab. 25, 559–571 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  218. Ling, C. & Rönn, T. Epigenetics in human obesity and type 2 diabetes. Cell Metab. 29, 1028–1044 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  219. Stewart-Morgan, K. R., Petryk, N. & Groth, A. Chromatin replication and epigenetic cell memory. Nat. Cell Biol. 22, 361–371 (2020).

    CAS  PubMed  Google Scholar 

  220. Fitz-James, M. H. & Cavalli, G. Molecular mechanisms of transgenerational epigenetic inheritance. Nat. Rev. Genet. 23, 325–341 (2022).

    CAS  PubMed  Google Scholar 

  221. Ribaroff, G. A., Wastnedge, E., Drake, A. J., Sharpe, R. M. & Chambers, T. J. G. Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis. Obes. Rev. 18, 673–686 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  222. Godfrey, K. M. et al. Influence of maternal obesity on the long-term health of offspring. Lancet Diabetes Endocrinol. 5, 53–64 (2017).

    PubMed  Google Scholar 

  223. Fernandez-Twinn, D. S., Hjort, L., Novakovic, B., Ozanne, S. E. & Saffery, R. Intrauterine programming of obesity and type 2 diabetes. Diabetologia 62, 1789–1801 (2019).

    PubMed  PubMed Central  Google Scholar 

  224. Kruse, M. et al. High-fat diet during mouse pregnancy and lactation targets GIP-regulated metabolic pathways in adult male offspring. Diabetes 65, 574–584 (2016).

    CAS  PubMed  Google Scholar 

  225. Sharp, G. C. et al. Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium. Hum. Mol. Genet. 26, 4067–4085 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  226. Gohir, W., Ratcliffe, E. M. & Sloboda, D. M. Of the bugs that shape us: maternal obesity, the gut microbiome, and long-term disease risk. Pediatr. Res. 77, 196–204 (2015).

    PubMed  Google Scholar 

  227. Kimura, I. et al. Maternal gut microbiota in pregnancy influences offspring metabolic phenotype in mice. Science 367, eaaw8429 (2020).

    CAS  PubMed  Google Scholar 

  228. Vega-Tapia, F. et al. Maternal obesity is associated with a sex-specific epigenetic programming in human neonatal monocytes. Epigenomics 12, 1999–2018 (2020).

    CAS  PubMed  Google Scholar 

  229. Öst, A. et al. Paternal diet defines offspring chromatin state and intergenerational obesity. Cell 159, 1352–1364 (2014).

    PubMed  Google Scholar 

  230. Ng, S. F. et al. Chronic high-fat diet in fathers programs β-cell dysfunction in female rat offspring. Nature 467, 963–966 (2010).

    CAS  PubMed  Google Scholar 

  231. Donkin, I. et al. Obesity and bariatric surgery drive epigenetic variation of spermatozoa in humans. Cell Metab. 23, 369–378 (2016).

    CAS  PubMed  Google Scholar 

  232. Denham, J., O’Brien, B. J., Harvey, J. T. & Charchar, F. J. Genome-wide sperm DNA methylation changes after 3 months of exercise training in humans. Epigenomics 7, 717–731 (2015).

    CAS  PubMed  Google Scholar 

  233. Kusuyama, J., Alves-Wagner, A. B., Makarewicz, N. S. & Goodyear, L. J. Effects of maternal and paternal exercise on offspring metabolism. Nat. Metab. 2, 858–872 (2020).

    PubMed  PubMed Central  Google Scholar 

  234. Barrès, R. & Zierath, J. R. The role of diet and exercise in the transgenerational epigenetic landscape of T2DM. Nat. Rev. Endocrinol. 12, 441–451 (2016).

    PubMed  Google Scholar 

  235. Qi, Y. et al. Associations between parental adherence to healthy lifestyles and risk of obesity in offspring: a prospective cohort study in China. Lancet Glob. Health 11, S6 (2023).

    PubMed  Google Scholar 

  236. Heijmans, B. T. et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc. Natl Acad. Sci. USA 105, 17046–17049 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  237. Roseboom, T., de Rooij, S. & Painter, R. The Dutch famine and its long-term consequences for adult health. Early Hum. Dev. 82, 485–491 (2006).

    PubMed  Google Scholar 

  238. Ravelli, A. C. J. et al. Glucose tolerance in adults after prenatal exposure to famine. Lancet 351, 173–177 (1998).

    CAS  PubMed  Google Scholar 

  239. Lumey, L. H., Khalangot, M. D. & Vaiserman, A. M. Association between type 2 diabetes and prenatal exposure to the Ukraine famine of 1932–33: a retrospective cohort study. Lancet Diabetes Endocrinol. 3, 787–794 (2015).

    CAS  PubMed  Google Scholar 

  240. Zimmet, P., Shi, Z., El-Osta, A. & Ji, L. Epidemic T2DM, early development and epigenetics: implications of the Chinese famine. Nat. Rev. Endocrinol. 14, 738–746 (2018).

    PubMed  Google Scholar 

  241. Stein, A. D., Zybert, P. A., van de Bor, M. & Lumey, L. H. Intrauterine famine exposure and body proportions at birth: the Dutch Hunger Winter. Int. J. Epidemiol. 33, 831–836 (2004).

    PubMed  Google Scholar 

  242. Li, C. & Lumey, L. H. Exposure to the Chinese famine of 1959–61 in early life and long-term health conditions: a systematic review and meta-analysis. Int. J. Epidemiol. 46, 1157–1170 (2017).

    PubMed  Google Scholar 

  243. Del Rosario, M. C. et al. Potential epigenetic dysregulation of genes associated with MODY and type 2 diabetes in humans exposed to a diabetic intrauterine environment: An analysis of genome-wide DNA methylation. Metabolism 63, 654–660 (2014).

    PubMed  PubMed Central  Google Scholar 

  244. Watkins, A. J. et al. Paternal diet programs offspring health through sperm- and seminal plasma-specific pathways in mice. Proc. Natl Acad. Sci. USA 115, 10064–10069 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  245. Dunford, A. R. & Sangster, J. M. Maternal and paternal periconceptional nutrition as an indicator of offspring metabolic syndrome risk in later life through epigenetic imprinting: a systematic review. Diabetes Metab. Syndr. 11, S655–S662 (2017).

    PubMed  Google Scholar 

  246. de Oliveira Nascimento Freitas, R. G. B. et al. Parental body mass index and maternal gestational weight gain associations with offspring body composition in young women from the Nutritionists’ Health Study. Arch. Endocrinol. Metab. 67, 101–110 (2022).

    PubMed  Google Scholar 

  247. Araujo, W. R. M. et al. Brazilian cohorts with potential for life-course studies: a scoping review. Rev. Saude Publica 54, 48–48 (2020).

    PubMed  PubMed Central  Google Scholar 

  248. Eshriqui, I., Folchetti, L. D., Valente, A. M. M., de Almeida-Pititto, B. & Ferreira, S. R. G. Early life feeding and current dietary patterns are associated with biomarkers of glucose and lipid metabolism in young women from the Nutritionist’s Health Study. Eur. J. Clin. Nutr. 74, 509–517 (2020).

    CAS  PubMed  Google Scholar 

  249. Tamashiro, K. L. K., Terrillion, C. E., Hyun, J., Koenig, J. I. & Moran, T. H. Prenatal stress or high-fat diet increases susceptibility to diet-induced obesity in rat offspring. Diabetes 58, 1116–1125 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  250. Paternain, L. et al. Postnatal maternal separation modifies the response to an obesogenic diet in adulthood in rats. Dis. Model Mech. 5, 691–697 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  251. Tate, E. B., Wood, W., Liao, Y. & Dunton, G. F. Do stressed mothers have heavier children? A meta-analysis on the relationship between maternal stress and child body mass index. Obes. Rev. 16, 351–361 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  252. World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO Consultation on Obesity, Geneva, 3–5 June 1997. https://apps.who.int/iris/handle/10665/63854/

  253. Pan American Health Organization. Plan of Action for the Prevention of Obesity in Children and Adolescents. https://www.paho.org/en/documents/plan-action-prevention-obesity-children-and-adolescents/ (2014).

  254. World Health Organization. Seventy-fifth World Health Assembly WHO. Follow-up to the political declaration of the third high-level meeting of the General Assembly on the prevention and control of non-communicable diseases. https://apps.who.int/gb/ebwha/pdf_files/WHA75/A75_10Add6-en.pdf

  255. World Health Organization. Seventy-sixth World Health Assembly WHO. Political declaration of the third high-level meeting of the General Assembly on the prevention and control of non-communicable diseases, and mental health. https://apps.who.int/gb/ebwha/pdf_files/WHA76/A76_7Add1Rev1-en.pdf

  256. World Health Organization. WHO Discussion Paper: Draft recommendations for the prevention and management of obesity over the life course, including potential targets. https://www.who.int/publications/m/item/who-discussion-paper-draft-recommendations-for-the-prevention-and-management-of-obesity-over-the-life-course-including-potential-targets

  257. Palacios, C. et al. Obesity in Latin America, a scoping review of public health prevention strategies and an overview of their impact on obesity prevention. Public Health Nutr. 24, 5142–5155 (2021).

    PubMed  Google Scholar 

  258. Pan American Health Organization. Regional Overview of Food Security and Nutrition – Latin America and the Caribbean 2022. Regional Overview of Food Security and Nutrition – Latin America and the Caribbean 2022. https://doi.org/10.4060/CC3859EN (2023).

  259. Popkin, B. M. et al. Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol. 9, 462–470 (2021).

    PubMed  PubMed Central  Google Scholar 

  260. Melo, G. et al. Structural responses to the obesity epidemic in Latin America: what are the next steps for food and physical activity policies? Lancet Reg. Health Am. 21, 100486 (2023).

    PubMed  PubMed Central  Google Scholar 

  261. Headey, D. D. & Alderman, H. H. The relative caloric prices of healthy and unhealthy foods differ systematically across income levels and continents. J. Nutr. 149, 2020–2033 (2019).

    PubMed  PubMed Central  Google Scholar 

  262. Batis, C. et al. Adoption of healthy and sustainable diets in Mexico does not imply higher expenditure on food. Nat. Food 2, 792–801 (2021).

    PubMed  Google Scholar 

  263. Navarrete, J. A. M. et al. Effectiveness of educational interventions conducted in latin america for the prevention of overweight and obesity in scholar children from 6–17 years old; a systematic review. Nutr. Hosp. 31, 102–114 (2014).

    Google Scholar 

  264. Willett, W. et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).

    PubMed  Google Scholar 

  265. Monteiro, C. A. et al. The need to reshape global food processing: a call to the United Nations Food Systems Summit. BMJ Glob. Health 6, e006885 (2021).

    PubMed  PubMed Central  Google Scholar 

  266. Wang, P. et al. Optimal dietary patterns for prevention of chronic disease. Nat. Med. 29, 719–728 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  267. World Health Organization. Use of non-sugar sweeteners: WHO guideline. https://www.who.int/publications/i/item/9789240073616/

  268. Bays, H. E., Bindlish, S. & Clayton, T. L. Obesity, diabetes mellitus, and cardiometabolic risk: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023. Obes. Pillars 5, 100056 (2023).

    PubMed  PubMed Central  Google Scholar 

  269. World Health Organization. Healthy diet. https://www.who.int/news-room/fact-sheets/detail/healthy-diet/

  270. Kozlov, M. FDA to require diversity plan for clinical trials. Nature https://doi.org/10.1038/D41586-023-00469-4 (2023).

    Article  PubMed  Google Scholar 

  271. Organization of American States. Democracy for peace, security, and development (2009).

  272. World Health Organization. Human rights. https://www.who.int/news-room/fact-sheets/detail/human-rights-and-health/

  273. Boudry, C. et al. Worldwide inequality in access to full text scientific articles: the example of ophthalmology. PeerJ 2019, e7850 (2019).

    Google Scholar 

  274. Kowaltowski, A. J. & Oliveira, M. F. Plan S: unrealistic capped fee structure. Science 363, 461 (2019).

    PubMed  Google Scholar 

  275. Eckmann, P. & Bandrowski, A. PreprintMatch: a tool for preprint to publication detection shows global inequities in scientific publication. PLoS ONE 18, e0281659 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  276. Taillie, L. S. et al. Changes in food purchases after the Chilean policies on food labelling, marketing, and sales in schools: a before and after study. Lancet Planet Health 5, e526–e533 (2021).

    PubMed  PubMed Central  Google Scholar 

  277. Vargas-Meza, J., Jaúregui, A., Contreras-Manzano, A., Nieto, C. & Barquera, S. Acceptability and understanding of front-of-pack nutritional labels: an experimental study in Mexican consumers. BMC Public Health 19, 1751 (2019).

    PubMed  PubMed Central  Google Scholar 

  278. Basto-Abreu, A. et al. Predicting obesity reduction after implementing warning labels in Mexico: a modeling study. PLoS Med. 17, e1003221 (2020).

    PubMed  PubMed Central  Google Scholar 

  279. Crosbie, E. et al. A policy study on front–of–pack nutrition labeling in the Americas: emerging developments and outcomes. Lancet Reg. Health Am. 18, 100400 (2023).

    PubMed  Google Scholar 

  280. Arantxa Colchero, M. et al. Changes in prices after an excise tax to sweetened sugar beverages was implemented in mexico: evidence from urban areas. PLoS ONE 10, e0144408 (2015).

    PubMed  PubMed Central  Google Scholar 

  281. Hernández-F, M., Batis, C., Rivera, J. A. & Colchero, M. A. Reduction in purchases of energy-dense nutrient-poor foods in Mexico associated with the introduction of a tax in 2014. Prev. Med. 118, 16–22 (2019).

    PubMed  Google Scholar 

  282. Andreyeva, T., Marple, K., Marinello, S., Moore, T. E. & Powell, L. M. Outcomes following taxation of sugar-sweetened beverages: a systematic review and meta-analysis. JAMA Netw. Open 5, e2215276 (2022).

    PubMed  PubMed Central  Google Scholar 

  283. Malik, V. S. & Hu, F. B. The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nat. Rev. Endocrinol. 18, 205–218 (2022).

    PubMed  PubMed Central  Google Scholar 

  284. Pedraza, L. S. et al. The caloric and sugar content of beverages purchased at different store-types changed after the sugary drinks taxation in Mexico. Int. J. Behav. Nutr. Phys. Act. 16, 103 (2019).

    PubMed  PubMed Central  Google Scholar 

  285. Pan American Health Organization. No más grasas trans en México - OPS/OMS | Organización Panamericana de la Salud. https://www.paho.org/es/campanas/no-mas-grasas-trans-mexico/

  286. UNICEF. Prácticas de lactancia materna en México. https://www.unicef.org/mexico/informes/pr%C3%A1cticas-de-lactancia-materna-en-m%C3%A9xico/

  287. Encuesta Nacional de Salud y Nutrición. Reports from the National Health and Nutrition Survey - 2012. https://ensanut.insp.mx/encuestas/ensanut2012/informes.php

  288. González-Castell, L. D., Unar-Munguía, M., Bonvecchio-Arenas, A., Ramírez-Silva, I. & Lozada-Tequeanes, A. L. Prácticas de lactancia materna y alimentación complementaria en menores de dos años de edad en México. Salud Publica Mex. 65, s204–s210 (2023).

    PubMed  Google Scholar 

  289. Gobierno de Mexico. Impulsa Gobierno de México un sistema agroalimentario justo, saludable, sustentable y competitivo. | Procuraduría Agraria | Gobierno | gob.mx. https://www.gob.mx/pa/articulos/impulsa-gobierno-de-mexico-un-sistema-agroalimentario-justo-saludable-sustentable-y-competitivo/

  290. Barquera, S. et al. Obesidad en México, prevalencia y tendencias en adultos. Ensanut 2018–19. Salud Publica Mex. 62, 682–692 (2020).

    PubMed  Google Scholar 

  291. Monteiro, C. A. et al. Dietary guidelines to nourish humanity and the planet in the twenty-first century. A blueprint from Brazil. Public Health Nutr. 18, 2311–2322 (2015).

    PubMed  PubMed Central  Google Scholar 

  292. World Food Programme. 2017 - Smart school meals - Nutrition-sensitive national programmes in Latin America and the Caribbean. https://www.wfp.org/publications/smart-school-meals-nutrition-sensitive-national-programmes-latin-america-and-caribbean/

  293. UNICEF. Convention on the rights of the child text. https://www.unicef.org/child-rights-convention/convention-text#

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Acknowledgements

We thank D. de Moraes for helping with the figures. We received funding from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; 2021/08354-2 to M.A.M.; 2013/07607-8 to M.A.M. and L.A.V.), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (306193/2022-1 to M.A.M.), the Chan Zuckerberg Initiative (to M.A.M.), AstraZeneca (to M.A.M.), the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT; 284771 to Y.M.) and the UNAM DGAPA-PAPIIT (IN207321 to Y.M.).

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S.R.G.F., Y.M., L.A.V. and M.A.M. wrote the manuscript and approved the final version.

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Correspondence to Marcelo A. Mori.

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M.A.M. received funding from AstraZeneca to perform research related to obesity. The other authors declare no competing interests.

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Ferreira, S.R.G., Macotela, Y., Velloso, L.A. et al. Determinants of obesity in Latin America. Nat Metab 6, 409–432 (2024). https://doi.org/10.1038/s42255-024-00977-1

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