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Prevalence of processed foods in major US grocery stores

Nature Food volume 6, pages 296–308 (2025)Cite this article

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Abstract

The offering of grocery stores is a strong driver of consumer decisions. While highly processed foods such as packaged products, processed meat and sweetened soft drinks have been increasingly associated with unhealthy diets, information on the degree of processing characterizing an item in a store is not straightforward to obtain, limiting the ability of individuals to make informed choices. GroceryDB, a database with over 50,000 food items sold by Walmart, Target and Whole Foods, shows the degree of processing of food items and potential alternatives in the surrounding food environment. The extensive data gathered on ingredient lists and nutrition facts enables a large-scale analysis of ingredient patterns and degrees of processing, categorized by store, food category and price range. Furthermore, it allows the quantification of the individual contribution of over 1,000 ingredients to ultra-processing. GroceryDB makes this information accessible, guiding consumers toward less processed food choices.

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Fig. 1: Degrees of food processing in three categories.
Fig. 2: Food processing in grocery stores.
Fig. 3: Price and food processing.
Fig. 4: The difference between stores in terms of processing.
Fig. 5: Ingredient trees.
Fig. 6: IgFPro.

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Data availability

The data in GroceryDB was scraped from Walmart, Target and Whole Foods in 2021. GroceryDB is available to the public and consumers at https://www.TrueFood.tech/. The data are also openly available on MongoDB servers with a read-only key available via BarabasiLab GitHub repository at https://github.com/Barabasi-Lab/GroceryDB/. The USDA FNDDS dataset is available via the same GitHub repository. Source data are provided with this paper.

Code availability

All code generated for the analysis are available via the BarabasiLab GitHub repository at https://github.com/Barabasi-Lab/GroceryDB/. The analysis was done in Python==3.11.7 with the following packages: jupyter notebook==6.5.4, pymongo==4.8.0, pandas==2.1.4, numpy==1.26.4, seaborn==0.12.2, statsmodels==0.14.0, scipy==1.11.4, matlabplot==3.8.0, plotly==5.9.0 and certifi==2024.6.2.

References

  1. Seferidi, P. et al. The neglected environmental impacts of ultra-processed foods. Lancet Planet. Health 4, e437–e438 (2020).

    Article  PubMed  Google Scholar 

  2. Fardet, A. & Rock, E. Ultra-processed foods and food system sustainability: what are the links? Sustainability 12, 6280 (2020).

    Article  MATH  Google Scholar 

  3. Macdiarmid, J. I. The food system and climate change: are plant-based diets becoming unhealthy and less environmentally sustainable? Proc. Nutr. Soc. 81, 162–167 (2022).

    Article  CAS  PubMed  Google Scholar 

  4. Ambikapathi, R. et al. Global food systems transitions have enabled affordable diets but had less favourable outcomes for nutrition, environmental health, inclusion and equity. Nat. Food 3, 764–779 (2022).

    Article  PubMed  MATH  Google Scholar 

  5. Lane, M. M. et al. Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. BMJ 384, e077310 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lustig, R. H. Processed food—an experiment that failed. JAMA Pediatr. 171, 212–214 (2017).

    Article  PubMed  MATH  Google Scholar 

  7. Milanlouei, S. et al. A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease. Nat. Commun. 11, 1–14 (2020).

    Article  Google Scholar 

  8. Martínez Steele, E., Popkin, B. M., Swinburn, B. & Monteiro, C. A. The share of ultra-processed foods and the overall nutritional quality of diets in the US: evidence from a nationally representative cross-sectional study. Popul. Health Metr. 15, 6 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Monteiro, C. A. et al. NOVA. The star shines bright. World Nutr. J. 7, 28–38 (2016).

    MATH  Google Scholar 

  10. Steele, E. M. et al. Ultra-processed foods and added sugars in the U.S. diet: evidence from a nationally representative cross-sectional study. BMJ Open 6, e009892 (2016).

    Article  Google Scholar 

  11. Steele, E. M. & Monteiro, C. A. Association between dietary share of ultra-processed foods and urinary concentrations of phytoestrogens in the US. Nutrients 9, 209 (2017).

    Article  MATH  Google Scholar 

  12. Adjibade, M. et al. Prospective association between ultra-processed food consumption and incident depressive symptoms in the French NutriNet-Santé cohort. BMC Med. 17, 1–13 (2019).

    Article  Google Scholar 

  13. Fiolet, T. et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ 360, k322 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Srour, B. et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). BMJ 365, l1451 (2019).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  15. 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, 1–11 (2019).

    Article  Google Scholar 

  16. Martínez Steele, E., Khandpur, N., da Costa Louzada, M. L. & Monteiro, C. A. Association between dietary contribution of ultra-processed foods and urinary concentrations of phthalates and bisphenol in a nationally representative sample of the US population aged 6 years and older. PLoS ONE 15, 1–21 (2020).

    Article  Google Scholar 

  17. Nerín, C., Aznar, M. & Carrizo, D. Food contamination during food process. Trends Food Sci. Technol. 48, 63–68 (2016).

    Article  Google Scholar 

  18. Rather, I. A., Koh, W. Y., Paek, W. K. & Lim, J. The sources of chemical contaminants in food and their health implications. Front. Pharmacol. 8, 830 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Arisseto, A. P. Furan in processed foods. In Food Hygiene and Toxicology in Ready-to-Eat Foods (ed. Kotzekidou, P.) Ch. 21, 383–396 (Academic, 2016).

  20. Buckley, J. P., Kim, H., Wong, E. & Rebholz, C. M. Ultra-processed food consumption and exposure to phthalates and bisphenols in the US National Health and Nutrition Examination Survey, 2013–2014. Environ. Int. 131, 105057 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mozaffarian, D., Fleischhacker, S. & Andrés, J. R. Prioritizing nutrition security in the US. JAMA 325, 1605–1606 (2021).

    Article  PubMed  MATH  Google Scholar 

  22. Livings, M. S. et al. Food and nutrition insecurity: experiences that differ for some and independently predict diet-related disease, Los Angeles County, 2022. J. Nutr. 154, 2566–2574 (2024).

    Article  CAS  PubMed  MATH  Google Scholar 

  23. Food and Nutrition Security (USDA, 2024); https://www.usda.gov/about-usda/general-information/priorities/food-and-nutrition-security

  24. Volpp, K. G. et al. Food is medicine: a presidential advisory from the American Heart Association. Circulation 148, 1417–1439 (2023).

    Article  PubMed  MATH  Google Scholar 

  25. Mozaffarian, D., Andrés, J. R., Cousin, E., Frist, W. H. & Glickman, D. R. The White House Conference on Hunger, Nutrition and Health is an opportunity for transformational change. Nat. Food 3, 561–563 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mozaffarian, D., Rosenberg, I. & Uauy, R. History of modern nutrition science-implications for current research, dietary guidelines, and food policy. BMJ 361, k2392 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sadler, C. R. et al. Processed food classification: conceptualisation and challenges. Trends Food Sci. Technol. 112, 149–162 (2021).

    Article  CAS  MATH  Google Scholar 

  28. Gibney, M. J. & Forde, C. G. Nutrition research challenges for processed food and health. Nat. Food 3, 104–109 (2022).

    Article  PubMed  MATH  Google Scholar 

  29. Lacy-Nichols, J. & Freudenberg, N. Opportunities and limitations of the ultra-processed food framing. Nat. Food 3, 975–977 (2022).

    Article  PubMed  MATH  Google Scholar 

  30. Braesco, V. et al. Ultra-processed foods: how functional is the NOVA system? Eur. J. Clin. Nutr. 76, 1245–1253 (2022).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  31. Data Crunch Report: The Impact of Bad Data on Profits and Customer Service in the UK Grocery Industry (accessed April 4, 2022) (GS1 UK and Cranfield University School of Management, 2009); https://dspace.lib.cranfield.ac.uk/bitstream/handle/1826/4135/Data_crunch_report.pdf

  32. THE 17 GOALS ∣ Sustainable Development (United Nations, 2020); https://sdgs.un.org/goals

  33. Methods and Standards (Food and Agriculture Organization of the United Nations, 2021); https://www.fao.org/statistics/methods-and-standards/en/

  34. Sarku, R., Clemen, U. A. & Clemen, T. The application of artificial intelligence models for food security: a review. Agriculture 13, 2037 (2023).

    Article  MATH  Google Scholar 

  35. Hu, G., Ahmed, M. & L’Abbé, M. R. Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods. Am. J. Clin. Nutr. 117, 553–563 (2023).

    Article  CAS  PubMed  MATH  Google Scholar 

  36. Impact Initiative (AI for Good, 2019); https://aiforgood.itu.int/

  37. Menichetti, G., Ravandi, B., Mozaffarian, D. & Barabási, A.-L. Machine learning prediction of the degree of food processing. Nat. Commun. 14, 2312 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen, X. et al. Consumption of ultra-processed foods and health outcomes: a systematic review of epidemiological studies. Nutr. J. 19, 86 (2020).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  39. Mendoza, K. et al. Ultra-processed foods and cardiovascular disease: analysis of three large US prospective cohorts and a systematic review and meta-analysis of prospective cohort studies. Lancet Reg. Health Am. 37, 100859 (2024).

    PubMed  PubMed Central  MATH  Google Scholar 

  40. Slimani, N. et al. Contribution of highly industrially processed foods to the nutrient intakes and patterns of middle-aged populations in the European prospective investigation into cancer and nutrition study. Eur. J. Clin. Nutr. 63, S206–S225 (2009).

    Article  CAS  PubMed  MATH  Google Scholar 

  41. Poti, J. M., Mendez, M. A., Ng, S. W. & Popkin, B. M. Is the degree of food processing and convenience linked with the nutritional quality of foods purchased by US households? Am. J. Clin. Nutr. 101, 1251–1262 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Davidou, S., Christodoulou, A., Fardet, A. & Frank, K. The holistico-reductionist SIGA classification according to the degree of food processing: an evaluation of ultra-processed foods in French supermarkets. Food Funct. 11, 2026–2039 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. U.S. Population: Consumption of Breakfast Cereals (Cold) from 2011 to 2024 (accessed February 2022) (Statista, 2021); https://www.statista.com/statistics/281995/us-households-consumption-of-breakfast-cereals-cold-trend/

  44. Bray, G. A., Nielsen, S. J. & Popkin, B. M. Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. Am. J. Clin. Nutr. 79, 537–543 (2004).

    Article  CAS  PubMed  Google Scholar 

  45. Guidance for Industry: Food Labeling Guide (accessed 1 November 2021) (USFDA, 2021); https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-industry-food-labeling-guide

  46. Igoe, R. S. Dictionary of Food Ingredients (Springer Science & Business Media, 2011).

  47. Goyal, A., Sharma, V., Upadhyay, N., Gill, S. & Sihag, M. Flax and flaxseed oil: an ancient medicine & modern functional food. J. Food Sci. Technol. 51, 1633–1653 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hashempour-Baltork, F., Torbati, M., Azadmard-Damirchi, S. & Savage, G. P. Vegetable oil blending: a review of physicochemical, nutritional and health effects. Trends Food Sci. Technol. 57, 52–58 (2016).

    Article  CAS  Google Scholar 

  49. Whole Foods Mission and Values (accessed 1 March 2022) (2012); https://www.WholeFoodsmarket.com/mission-values

  50. Walmart History (accessed 1 March 2022) (2022); https://corporate.walmart.com/about/history

  51. Gupta, S., Hawk, T., Aggarwal, A. & Drewnowski, A. Characterizing ultra-processed foods by energy density, nutrient density, and cost. Front. Nutr. 6, 70 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Zenk, S. N., Tabak, L. A. & Pérez-Stable, E. J. Research opportunities to address nutrition insecurity and disparities. JAMA 327, 1953–1954 (2022).

    Article  PubMed  Google Scholar 

  53. Venkataramani, A. S., O’Brien, R., Whitehorn, G. L. & Tsai, A. C. Economic influences on population health in the United States: toward policymaking driven by data and evidence. PLoS Med. 17, e1003319 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Erndt-Marino, J., O’Hearn, M. & Menichetti, G. An integrative analytical framework to identify healthy, impactful, and equitable foods: a case study on 100% orange juice. Int. J. Food Sci. Nutr. 74, 668–684 (2023).

    Article  PubMed  Google Scholar 

  55. Coletro, H. N. et al. The combined consumption of fresh/minimally processed food and ultra-processed food on food insecurity: COVID Inconfidentes, a population-based survey. Public Health Nutr. 26, 1414–1423 (2023).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  56. Hutchinson, J. & Tarasuk, V. The relationship between diet quality and the severity of household food insecurity in Canada. Public Health Nutr. 25, 1013–1026 (2022).

    Article  PubMed  MATH  Google Scholar 

  57. Griffith, R., Jenneson, V., James, J. & Taylor, A. The Impact of a Tax on Added Sugar and Salt. Tech. Rep., IFS Working Paper (IFS, 2021); http://hdl.handle.net/10419/242920

  58. Mozaffarian, D., Blanck, H. M., Garfield, K. M., Wassung, A. & Petersen, R. A Food is Medicine approach to achieve nutrition security and improve health. Nat. Med. 28, 2238–2240 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. The National Food Strategy: The Plan (accessed 23 March 2022) (2019); https://www.nationalfoodstrategy.org/

  60. True Cost of Food: Measuring What Matters to Transform the U.S. Food System (The Rockefeller Foundation, 2021); https://www.rockefellerfoundation.org/report/true-cost-of-food-measuring-what-matters-to-transform-the-u-s-food-system/

  61. Nasirian, F. & Menichetti, G. Molecular interaction networks and cardiovascular disease risk: the role of food bioactive small molecules. Arterioscler. Thromb. Vasc. Biol. 43, 813–823 (2023).

    Article  CAS  PubMed  MATH  Google Scholar 

  62. Adams, J. Rebalancing the marketing of healthier versus less healthy food products. PLoS Med. 19, e1003956 (2022).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  63. 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 

  64. Shepherd, R. Resistance to changes in diet. Proc. Nutr. Soc. 61, 267–272 (2002).

    Article  PubMed  MATH  Google Scholar 

  65. Kelly, M. P. & Barker, M. Why is changing health-related behaviour so difficult? Public Health 136, 109–116 (2016).

    Article  PubMed  Google Scholar 

  66. Barabási, A. L., Menichetti, G. & Loscalzo, J. The unmapped chemical complexity of our diet. Nat. Food 1, 33–37 (2020).

    Article  MATH  Google Scholar 

  67. Menichetti, G., Barabasi, A.-L. & Loscalzo, J. Decoding the Foodome: molecular networks connecting diet and health. Annu. Rev. Nutr. 44, 257–288 (2024).

    Article  CAS  PubMed  Google Scholar 

  68. Davidou, S., Christodoulou, A., Frank, K. & Fardet, A. A study of ultra-processing marker profiles in 22,028 packaged ultra-processed foods using the Siga classification. J. Food Compos. Anal. 99, 103848 (2021).

    Article  CAS  Google Scholar 

  69. Menichetti, G. & Barabási, A.-L. Nutrient concentrations in food display universal behaviour. Nat. Food 3, 375–382 (2022).

    Article  PubMed  MATH  Google Scholar 

  70. Hooton, F., Menichetti, G. & Barabási, A. L. Exploring food contents in scientific literature with FoodMine. Sci. Rep. 10, 16191 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Chatterjee, A. et al. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nat. Commun. 14, 1989 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  72. Seabold, S. & Perktold, J. Statsmodels: Econometric and statistical modeling with Python. In Proc. 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 57–61 (2010).

  73. Huber, P. J. Robust regression: asymptotics, conjectures and Monte Carlo. Ann. Stat. 1, 799–821 (1973).

    Article  MathSciNet  MATH  Google Scholar 

  74. Croux, C. & Rousseeuw, P. J. Time-efficient algorithms for two highly robust estimators of scale. In Computational Statistics, 411–428 (Springer, 1992).

  75. Brown, G. G. & Rutemiller, H. C. Means and variances of stochastic vector products with applications to random linear models. Manag. Sci. 24, 210–216 (1977).

    Article  MATH  Google Scholar 

  76. Beel, J., Gipp, B., Langer, S. & Breitinger, C. Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17, 305–338 (2016).

    Article  Google Scholar 

  77. Substances Added to Food (FDA, accessed 1 November 2021); https://www.hfpappexternal.fda.gov/scripts/fdcc/index.cfm?set=FoodSubstances

  78. Substances Added to Food (FDA, accessed 1 November 2021) (2003); https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=172

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Acknowledgements

We thank D. Shanbhag at Northeastern University for his help on data collection and cleaning. We thank D. Koshkina for help in designing the figures. A.-L.B. is partially supported by National Institutes of Health grant 1P01HL132825, American Heart Association grant 151708 and European Research Council grant 810115-DYNASET. G.M. is supported by National Institutes of Health/National Heart, Lung, and Blood Institute K25HL173665 and American Heart Association 24MERIT1185447.

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Author notes

  1. These authors contributed equally: Babak Ravandi, Gordana Ispirova, Michael Sebek, Giulia Menichetti.

Authors and Affiliations

  1. Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA

    Babak Ravandi, Michael Sebek, Albert-László Barabási & Giulia Menichetti

  2. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Gordana Ispirova, Albert-László Barabási & Giulia Menichetti

  3. Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark

    Peter Mehler

  4. Department of Network and Data Science, Central European University, Budapest, Hungary

    Albert-László Barabási

  5. Harvard Data Science Initiative, Harvard University, Boston, MA, USA

    Giulia Menichetti

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Contributions

G.M., B.R. and A.-L.B. conceived and designed the research. B.R. performed data collection, data modelling, statistical analysis, and data querying and integration and contributed to the writing of the manuscript. G.I. and M.S. performed data cleaning, data curation, code cleaning and optimization, and fact checking and contributed to the writing of the manuscript. P.M. performed data cleaning and data integration and contributed to the writing of the manuscript. G.M. and A.-L.B. wrote the manuscript and contributed to the conceptual and statistical design of the study.

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Correspondence to Giulia Menichetti.

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Competing interests

A.-L.B. is the founder of Scipher Medicine and Naring Health, companies that explore the use of network-based tools in health and food, and Datapolis, which focuses on urban data. All other authors declare no competing interests.

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Ravandi, B., Ispirova, G., Sebek, M. et al. Prevalence of processed foods in major US grocery stores. Nat Food 6, 296–308 (2025). https://doi.org/10.1038/s43016-024-01095-7

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