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Epidemiology and Population Health

Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies

Abstract

Background

Numerous studies have reported the association of ultra-processed foods with excess body weight; however, the nature and extent of this relation has not been clearly established. This systematic review was conducted to analyze the currently documented evidence regarding the association between ultra-processed food with overweight and obesity.

Methods

A literature search was performed using multiple literature databases for relevant articles published prior to November 2019. Random effects model, namely the DerSimonian–Laird method, was applied to pool effect sizes. The potential sources of heterogeneity across studies were explored using the Cochrane Q test.

Results

Fourteen studies (one cohort study and thirteen cross-sectional studies) were included in this review. A significant association was identified between ultra-processed food intake and overweight (pooled effect size: 1.02; 95% confidence interval (95% CI): 1.01, 1.03, p < 0.001) and obesity (pooled effect size: 1.26; 95% CI: 1.13, 1.41, p < 0.001).

Conclusion

Our findings revealed a positive association between ultra-processed foods and excess body weight. Future studies with longitudinal designs and adequate control for confounding factors are required to clarify whether ultra-processed food intake alters anthropometric parameters and leads to obesity.

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Fig. 1: Literature search flow chart.
Fig. 2: Forest plot of the association between Ultra-processed food consumption and overweight using a random-effects model.
Fig. 3: Forest plot of the association between Ultra-processed food consumption and obesity using a random-effects model.

References

  1. 1.

    Moulder R, Schvartz D, Goodlett DR, Dayon L. Proteomics of diabetes, obesity, and related disorders. Proteom Clin Appl. 2018;12:1600134.

    Google Scholar 

  2. 2.

    Forse RA, Betancourt-Garcia MM, Kissee MC. Epidemiology and discrimination in obesity. In: The ASMBS textbook of bariatric surgery. Springer; 2020. p. 3–14.

  3. 3.

    Forouzanfar MH, Afshin A, Alexander LT, Anderson HR, Bhutta ZA, Biryukov S, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1659–724.

    Google Scholar 

  4. 4.

    Sepidarkish M, Maleki-Hajiagha A, Maroufizadeh S, Rezaeinejad M, Almasi-Hashiani A, Razavi M. The effect of body mass index on sperm DNA fragmentation: a systematic review and meta-analysis. Int J Obes. 2020;44:549–58.

    CAS  Google Scholar 

  5. 5.

    Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin BJOR. Ultra-processed products are becoming dominant in the global food system. Obes Rev. 2013;14:21–8.

    Google Scholar 

  6. 6.

    Monteiro C, Cannon G, Levy R, Moubarac J, Jaime P, Martins A, et al. Food classification. Public Health. 2016;7:28–38.

    Google Scholar 

  7. 7.

    Monteiro CA, Cannon G, Moubarac J-C, Levy RB, Louzada MLC, Jaime PC. The UN decade of nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018;21:5–17.

    Google Scholar 

  8. 8.

    Monteiro CA, Cannon G, Levy R, Moubarac J-C, Jaime P, Martins AP, et al. NOVA. The star shines bright. World Nutr. 2016;7:28–38.

    Google Scholar 

  9. 9.

    Bielemann RM, Motta JVS, Minten GC, Horta BL, Gigante DP. Consumption of ultra-processed foods and their impact on the diet of young adults. Revista de saude publica. 2015;49:28.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Martínez Steele E, Monteiro CA. Association between dietary share of ultra-processed foods and urinary concentrations of phytoestrogens in the US. Nutrients. 2017;9:209.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Mendonça RDD, Lopes ACS, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M.Ultra-processed food consumption and the incidence of hypertension in a Mediterranean cohort: the Seguimiento Universidad de Navarra Project.Am J Hypertens.2017;30:358–66.

    Google Scholar 

  12. 12.

    Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C, et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. 2018;360:k322.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lavigne-Robichaud M, Moubarac J-C, Lantagne-Lopez S, Johnson-Down L, Batal M, Sidi EAL, et al. Diet quality indices in relation to metabolic syndrome in an Indigenous Cree (Eeyouch) population in northern Québec, Canada. 2018;21:172–80.

  14. 14.

    Tavares LF, Fonseca SC, Rosa MLG, Yokoo EM. Relationship between ultra-processed foods and metabolic syndrome in adolescents from a Brazilian Family Doctor Program. Public Health Nutr. 2012;15:82–7.

    Google Scholar 

  15. 15.

    Nasreddine L, Tamim H, Itani L, Nasrallah MP, Isma’eel H, Nakhoul NF, et al. A minimally processed dietary pattern is associated with lower odds of metabolic syndrome among Lebanese adults. Public Health Nutr. 2018;21:160–71.

    Google Scholar 

  16. 16.

    Rauber F, Campagnolo P, Hoffman DJ, Vitolo MR. Consumption of ultra-processed food products and its effects on children’s lipid profiles: a longitudinal study. Nutri Metabol Cardiovascular Dis. 2015;25:116–22.

    CAS  Google Scholar 

  17. 17.

    Steele EM, Juul F, Neri D, Rauber F, Monteiro CA. Dietary share of ultra-processed foods and metabolic syndrome in the US adult population. Prev Med. 2019;125:40–8.

    Google Scholar 

  18. 18.

    Canella DS, Levy RB, Martins APB, Claro RM, Moubarac J-C, Baraldi LG, et al. Ultra-processed food products and obesity in Brazilian households (2008–2009). PLoS ONE. 2014;9:e92752.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Djupegot IL, Nenseth CB, Bere E, Bjørnarå HBT, Helland SH, Øverby NC, et al. The association between time scarcity, sociodemographic correlates and consumption of ultra-processed foods among parents in Norway: a cross-sectional study. BMC Public Health. 2017;17:447.

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Juul F, Martinez-Steele E, Parekh N, Monteiro CA, Chang VW. Ultra-processed food consumption and excess weight among US adults. Br J Nutr. 2018;120:90–100.

    CAS  Google Scholar 

  21. 21.

    Mendonça RDD, Pimenta AM, Gea A, de la Fuente-Arrillaga C, Martinez-Gonzalez MA, Lopes ACS, et al. Ultraprocessed food consumption and risk of overweight and obesity: the University of Navarra Follow-Up (SUN) cohort study. Am J Clin Nutr. 2016;104:1433–40.

    Google Scholar 

  22. 22.

    Nardocci M, Leclerc B-S, Louzada M-L, Monteiro CA, Batal M, Moubarac J-C. Consumption of ultra-processed foods and obesity in Canada. Can J Public Health. 2019;110:4–14.

    Google Scholar 

  23. 23.

    Sartorelli DS, Crivellenti LC, Zuccolotto DCC, Franco LJ. Relationship between minimally and ultra-processed food intake during pregnancy with obesity and gestational diabetes mellitus. Cad Saude Publica. 2019;35:e00049318.

    Google Scholar 

  24. 24.

    Silva FM, Giatti L, de Figueiredo RC, Molina MDCB, de Oliveira Cardoso L, Duncan BB, et al. Consumption of ultra-processed food and obesity: cross sectional results from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) cohort (2008–2010). Public Health Nutr. 2018;21:2271–9.

    Google Scholar 

  25. 25.

    Silveira JACd, Meneses SS, Quintana PT, Santos VDS. Association between overweight and consumption of ultra-processed food and sugar-sweetened beverages among vegetarians. Revista de Nutrição. 2017;30:431–41.

    Google Scholar 

  26. 26.

    Sparrenberger K, Friedrich RR, Schiffner MD, Schuch I, Wagner MB. Ultra-processed food consumption in children from a Basic Health Unit. J Pediatria. 2015;91:535–42.

    Google Scholar 

  27. 27.

    Monteiro CA, Moubarac J-C, Levy RB, Canella DS, da Costa Louzada ML, Cannon G. Household availability of ultra-processed foods and obesity in nineteen European countries. Public Health Nutr. 2018;21:18–26.

    Google Scholar 

  28. 28.

    Asfaw A. Does consumption of processed foods explain disparities in the body weight of individuals? The case of Guatemala. Health Econ. 2011;20:184–95.

    Google Scholar 

  29. 29.

    da Costa Louzada ML, Baraldi LG, Steele EM, Martins APB, Canella DS, Moubarac J-C, et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev Med. 2015;81:9–15.

    Google Scholar 

  30. 30.

    de Melo ISV, Costa CACB, dos Santos JVL, dos Santos AF, Florêncio TMDMT, Bueno NB. Consumption of minimally processed food is inversely associated with excess weight in adolescents living in an underdeveloped city. PLoS ONE. 2017;12:e0188401.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Adams J, White M. Characterisation of UK diets according to degree of food processing and associations with socio-demographics and obesity: cross-sectional analysis of UK National Diet and Nutrition Survey (2008–12). Int J Behav Nutr Phys Activity. 2015;12:160.

    Google Scholar 

  32. 32.

    Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;151:264–9.

    Google Scholar 

  33. 33.

    Stang AJEJOE. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur. J. Epidemiol. 2010;25:603–5.

    Google Scholar 

  34. 34.

    Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics Med. 2002;21:1539–58.

    Google Scholar 

  35. 35.

    Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56:455–63.

    CAS  Google Scholar 

  36. 36.

    Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, et al. Ultra-processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metab. 2019;30:67–77. e63.

    CAS  Google Scholar 

  37. 37.

    Lam MCL, Adams J. Association between home food preparation skills and behaviour, and consumption of ultra-processed foods: cross-sectional analysis of the UK National Diet and nutrition survey (2008–2009). Int J Behav Nutr Phys Activity. 2017;14:68.

    Google Scholar 

  38. 38.

    Costa CS, Del-Ponte B, Assunção MCF, Santos IS. Consumption of ultra-processed foods and body fat during childhood and adolescence: a systematic review. Public Health Nutr. 2018;21:148–59.

    Google Scholar 

  39. 39.

    Zobel EH, Hansen TW, Rossing P, von Scholten BJ. Global changes in food supply and the obesity epidemic. Curr Obes Rep. 2016;5:449–55.

    Google Scholar 

  40. 40.

    Monteiro CA, Levy RB, Claro RM, de Castro IRR, Cannon G. Increasing consumption of ultra-processed foods and likely impact on human health: evidence from Brazil. Public Health Nutr. 2010;14:5–13.

    Google Scholar 

  41. 41.

    Hall K. A review of the carbohydrate–insulin model of obesity. Eur J Clin Nutr. 2017;71:323.

    CAS  Google Scholar 

  42. 42.

    Schulte EM, Avena NM, Gearhardt AN. Which foods may be addictive? The roles of processing, fat content, and glycemic load. PLoS ONE. 2015;10:e0117959.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Carter A, Hendrikse J, Lee N, Yücel M, Verdejo-Garcia A, Andrews ZB, et al. The neurobiology of “food addiction” and its implications for obesity treatment and policy. Annu Rev Nutr. 2016;36:105–28.

    CAS  Google Scholar 

  44. 44.

    Fardet A. Minimally processed foods are more satiating and less hyperglycemic than ultra-processed foods: a preliminary study with 98 ready-to-eat foods. Food Funct. 2016;7:2338–46.

    CAS  Google Scholar 

  45. 45.

    N Gearhardt A, Davis C, Kuschner R, D Brownell K. The addiction potential of hyperpalatable foods. Curr. Drug Abuse Rev. 2011;4:140–5.

    Google Scholar 

  46. 46.

    Boyland EJ, Nolan S, Kelly B, Tudur-Smith C, Jones A, Halford JC, et al. Advertising as a cue to consume: a systematic review and meta-analysis of the effects of acute exposure to unhealthy food and nonalcoholic beverage advertising on intake in children and adults, 2. Am J Clin Nutr. 2016;103:519–33.

    CAS  Google Scholar 

  47. 47.

    Sadeghirad B, Duhaney T, Motaghipisheh S, Campbell N, Johnston B. Influence of unhealthy food and beverage marketing on children’s dietary intake and preference: a systematic review and meta-analysis of randomized trials. Obes Rev. 2016;17:945–59.

    CAS  Google Scholar 

  48. 48.

    Robinson E, Aveyard P, Daley A, Jolly K, Lewis A, Lycett D, et al. Eating attentively: a systematic review and meta-analysis of the effect of food intake memory and awareness on eating. Am J Clin Nutr. 2013;97:728–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Robinson E, Almiron-Roig E, Rutters F, de Graaf C, Forde CG, Tudur Smith C, et al. A systematic review and meta-analysis examining the effect of eating rate on energy intake and hunger. Am J Clin Nutr. 2014;100:123–51.

    CAS  Google Scholar 

  50. 50.

    Barr S, Wright J. Postprandial energy expenditure in whole-food and processed-food meals: implications for daily energy expenditure. Food Nutr Res. 2010;54:5144.

    Google Scholar 

  51. 51.

    Astrup A, Dyerberg J, Selleck M, Stender S. Nutrition transition and its relationship to the development of obesity and related chronic diseases. Obes Rev. 2008;9:48–52.

    Google Scholar 

  52. 52.

    Nettleton JA, Lutsey PL, Wang Y, Lima JA, Michos ED, Jacobs DR. Diet soda intake and risk of incident metabolic syndrome and type 2 diabetes in the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care. 2009;32:688–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Payne A, Chassard C, Lacroix C. Gut microbial adaptation to dietary consumption of fructose, artificial sweeteners and sugar alcohols: implications for host–microbe interactions contributing to obesity. Obes Rev. 2012;13:799–809.

    CAS  Google Scholar 

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Contributions

MA, ED designed the study. MA and ED independently carried out the literature search and screening of articles; ED analyzed the data; MA, HSH, and JH wrote the manuscript. NT and ED edited the writing. All authors read and approved the final manuscript.

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Correspondence to Elnaz Daneshzad.

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Askari, M., Heshmati, J., Shahinfar, H. et al. 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). https://doi.org/10.1038/s41366-020-00650-z

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