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Contrary to ultra-processed foods, the consumption of unprocessed or minimally processed foods is associated with favorable patterns of protein intake, diet quality and lower cardiometabolic risk in French adults (INCA3)

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Abstract

Purpose

While the consumption of ultra-processed foods is steadily increasing, there is a growing interest in more sustainable diets that would include more plant protein. We aimed to study associations between the degree of food processing, patterns of protein intake, diet quality and cardiometabolic risk.

Methods

Using the NOVA classification, we assessed the proportion of energy from unprocessed/minimally processed foods (MPFp), processed foods (PFp) and ultra-processed foods (UPFp) in the diets of 1774 adults (18–79 years) from the latest cross-sectional French national survey (INCA3, 2014–2015). We studied the associations between MPFp, PFp and UPFp with protein intakes, diet quality (using the PANDiet scoring system, the global (PDI), healthful (hPDI) and unhealthful (uPDI) plant-based diet indices) and risk of cardiometabolic death (using the EpiDiet model).

Results

MPFp was positively associated with animal protein intake and plant protein diversity, whereas PFp was positively associated with plant protein intake and negatively with plant protein diversity. The PANDiet was positively associated with MPFp (β = 0.14, P < 0.0001) but negatively with UPFp (β = − 0.05, P < 0.0001). These associations were modified by adjustment for protein intakes and plant protein diversity. As estimated with comparative risk assessment modeling between extreme tertiles of intake, mortality from cardiometabolic diseases would be decreased with higher MPFp (e.g. by 31% for ischemic heart diseases) and increased with higher UPFp (by 42%) and PFp (by 11%).

Conclusions

In the French population, in contrast with UPFp, higher MPFp was associated with higher animal protein intake, better plant protein diversity, higher diet quality and markedly lower cardiometabolic risk.

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Availability of data and materials

The datasets of the INCA3 survey are available at data.gouv.fr. Data sets generated during the current study are available from the corresponding author on reasonable request.

Code availability

Custom code are available from the corresponding author on reasonable request.

Abbreviations

ANSES:

French Agency for Food, Environmental and Occupational Health and Safety

AS:

Adequacy Subscore

BI:

Berry-Index

BI-ABF:

Berry-Index-animal-based families

BI-PBF:

Berry-Index-plant-based families

CIQUAL:

French Centre for Information on Food Quality

EpiDiet:

Evaluate the Potential Impact of a Diet

hPDI:

Healthful Plant-based Diet Index

INCA3:

Third Individual and National Study on Food Consumption Survey

MPF:

Unprocessed/minimally processed foods

MPFp:

Proportion of total energy intake from MPF

MS:

Moderation subscore

PANDiet:

Probability of adequate nutrient intake

PDI:

Plant-based diet Index

PF:

Processed foods

PFp:

Proportion of total energy intake from PF

uPDI:

Unhealthful plant-based DIET Index

UI:

Uncertainty interval

UPF:

Ultra-processed foods

UPFp:

Proportion of total energy intake from UPF

References

  1. Monteiro CA, Cannon G, Levy R et al (2016) NOVA. The star shines bright. [Food classification Puclib health]. World Nutr 7:28–38

    Google Scholar 

  2. Monteiro CA, Cannon G, Levy RB et al (2019) Ultra-processed foods: what they are and how to identify them. Public Health Nutr 22:936–941. https://doi.org/10.1017/S1368980018003762

    Article  PubMed  Google Scholar 

  3. Martínez Steele E, Baraldi LG, da Louzada MLC et al (2016) Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open 6:e009892. https://doi.org/10.1136/bmjopen-2015-009892

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rauber F, da Costa Louzada ML, Steele E et al (2018) Ultra-processed food consumption and chronic non-communicable diseases-related dietary nutrient profile in the UK (2008–2014). Nutrients 10:587. https://doi.org/10.3390/nu10050587

    Article  CAS  PubMed Central  Google Scholar 

  5. Monteiro CA, Moubarac J-C, Cannon G et al (2013) Ultra-processed products are becoming dominant in the global food system: ultra-processed products: global dominance. Obes Rev 14:21–28. https://doi.org/10.1111/obr.12107

    Article  PubMed  Google Scholar 

  6. Monteiro CA, Cannon G, Moubarac J-C et al (2018) The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr 21:5–17. https://doi.org/10.1017/S1368980017000234

    Article  PubMed  Google Scholar 

  7. Martínez Steele E, Raubenheimer D, Simpson SJ et al (2018) Ultra-processed foods, protein leverage and energy intake in the USA. Public Health Nutr 21:114–124. https://doi.org/10.1017/S1368980017001574

    Article  PubMed  Google Scholar 

  8. Gerber PJ, Food and Agriculture Organization of the United Nations (2013) Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations, Rome

    Google Scholar 

  9. Wu G, Fanzo J, Miller DD et al (2014) Production and supply of high-quality food protein for human consumption: sustainability, challenges, and innovations: sustainability, challenge and innovations. Ann NY Acad Sci 1321:1–19. https://doi.org/10.1111/nyas.12500

    Article  CAS  PubMed  Google Scholar 

  10. Springmann M, Clark M, Mason-D’Croz D et al (2018) Options for keeping the food system within environmental limits. Nature 562:519–525. https://doi.org/10.1038/s41586-018-0594-0

    Article  CAS  PubMed  Google Scholar 

  11. Willett W, Rockström J, Loken B et al (2019) Food in the anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet 393:447–492. https://doi.org/10.1016/S0140-6736(18)31788-4

    Article  Google Scholar 

  12. World Cancer Research Fund, American Institute for Cancer Research. (2018) Diet, nutrition, physical activity and cancer: a global perspective. The third Expert Report

  13. Mariotti F (2019) Animal and plant protein sources and cardiometabolic health. Adv Nutr 10:S351–S366. https://doi.org/10.1093/advances/nmy110

    Article  PubMed  PubMed Central  Google Scholar 

  14. Song M, Fung TT, Hu FB et al (2016) Association of animal and plant protein intake with all-cause and cause-specific mortality. JAMA Intern Med 176:1453. https://doi.org/10.1001/jamainternmed.2016.4182

    Article  PubMed  PubMed Central  Google Scholar 

  15. Satija A, Bhupathiraju SN, Spiegelman D et al (2017) Healthful and unhealthful plant-based diets and the risk of coronary heart disease in U.S. Adults J Am Coll Cardiol 70:411–422. https://doi.org/10.1016/j.jacc.2017.05.047

    Article  PubMed  Google Scholar 

  16. Satija A, Bhupathiraju SN, Rimm EB et al (2016) Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies. PLoS Med 13:e1002039. https://doi.org/10.1371/journal.pmed.1002039

    Article  PubMed  PubMed Central  Google Scholar 

  17. Gehring J, Touvier M, Baudry J et al (2020) Consumption of ultra-processed foods by pesco-vegetarians, vegetarians, and vegans: associations with duration and age at diet initiation. J Nutr 151:120–131. https://doi.org/10.1093/jn/nxaa196

    Article  Google Scholar 

  18. Moodie R, Stuckler D, Monteiro C et al (2013) Profits and pandemics: prevention of harmful effects of tobacco, alcohol, and ultra-processed food and drink industries. The Lancet 381:670–679. https://doi.org/10.1016/S0140-6736(12)62089-3

    Article  Google Scholar 

  19. Vandevijvere S, Jaacks LM, Monteiro CA et al (2019) Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev 20:10–19. https://doi.org/10.1111/obr.12860

    Article  PubMed  Google Scholar 

  20. Monteiro CA, Cannon G, Lawrence M et al (2019) Ultra-processed foods, diet quality, and health using the NOVA classification system. FAO, Rome

    Google Scholar 

  21. Fiolet T, Srour B, Sellem L et al (2018) Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. https://doi.org/10.1136/bmj.k322

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mendonça R de D, Lopes ACS, Pimenta AM et al (2017) Ultra-processed food consumption and the incidence of hypertension in a mediterranean cohort: the Seguimiento Universidad de Navarra Project. Am J Hypertens 30:358–366. https://doi.org/10.1093/ajh/hpw137

    Article  CAS  Google Scholar 

  23. Nardocci M, Leclerc B-S, Louzada M-L et al (2019) Consumption of ultra-processed foods and obesity in Canada. Can J Public Health 110:4–14. https://doi.org/10.17269/s41997-018-0130-x

    Article  PubMed  Google Scholar 

  24. Blanco-Rojo R, Sandoval-Insausti H, López-Garcia E et al (2019) Consumption of ultra-processed foods and mortality: a National Prospective Cohort in Spain. Mayo Clin Proc 94:2178–2188. https://doi.org/10.1016/j.mayocp.2019.03.035

    Article  PubMed  Google Scholar 

  25. Kim H, Hu EA, Rebholz CM (2019) Ultra-processed food intake and mortality in the USA: results from the Third National Health and Nutrition Examination Survey (NHANES III, 1988–1994). Public Health Nutr 22:1777–1785. https://doi.org/10.1017/S1368980018003890

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hall KD, Ayuketah A, Brychta R et al (2019) 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.e3. https://doi.org/10.1016/j.cmet.2019.05.008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. French Agency for Food, Environmental and Occupational Health & Safety (ANSES) (2017) Opinion of the French Agency for Food, Environmental and Occupational Health & Safety on “the Third Individual and National Survey on Food Consumption (INCA3 survey)”. ANSES. Available at: https://www.anses.fr/en/system/files/NUT2014SA0234EN.pdf

  28. Dubuisson C, Dufour A, Carrillo S et al (2019) The Third French Individual and National Food Consumption (INCA3) Survey 2014–2015: method, design and participation rate in the framework of a European harmonization process. Public Health Nutr 22:584–600. https://doi.org/10.1017/S1368980018002896

    Article  PubMed  Google Scholar 

  29. Henry C (2005) Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 8:1133–1152. https://doi.org/10.1079/PHN2005801

    Article  CAS  PubMed  Google Scholar 

  30. Black A (2000) Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes 24:1119–1130. https://doi.org/10.1038/sj.ijo.0801376

    Article  CAS  Google Scholar 

  31. Slimani N, Freisling H, Huybrechts I, et al (2013) Food Consumption Data Collection Methodology for the EU Menu Survey (EMP‐PANEU) Final Report. EFSA supporting publication

  32. French Agency for Food, Environmental and Occupational Health & Safety (ANSES) (2016) ANSES-CIQUAL French food composition table version 2016

  33. Salomé M, de Gavelle E, Dufour A et al (2020) Plant-protein diversity is critical to ensuring the nutritional adequacy of diets when replacing animal with plant protein: observed and modeled diets of French adults (INCA3). J Nutr 150:536–545. https://doi.org/10.1093/jn/nxz252

    Article  PubMed  Google Scholar 

  34. Drescher LS, Thiele S, Mensink GBM (2007) A new index to measure healthy food diversity better reflects a healthy diet than traditional measures. J Nutr 137:647–651. https://doi.org/10.1093/jn/137.3.647

    Article  CAS  PubMed  Google Scholar 

  35. de Oliveira Otto MC, Padhye NS, Bertoni AG et al (2015) Everything in moderation—dietary diversity and quality, central obesity and risk of diabetes. PLoS ONE 10:e0141341. https://doi.org/10.1371/journal.pone.0141341

    Article  CAS  PubMed Central  Google Scholar 

  36. Verger EO, Mariotti F, Holmes BA et al (2012) Evaluation of a Diet Quality Index based on the probability of adequate nutrient intake (PANDiet) using National French and US Dietary Surveys. PLoS ONE 7:e42155. https://doi.org/10.1371/journal.pone.0042155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. de Gavelle E, Huneau J-F, Fouillet H, Mariotti F (2019) The initial dietary pattern should be considered when changing protein food portion sizes to increase nutrient adequacy in french adults. J Nutr 149:488–496. https://doi.org/10.1093/jn/nxy275

    Article  PubMed  Google Scholar 

  38. French Agency for Food, Environmental and Occupational Health & Safety (ANSES) (2016) Actualisation des repères du PNNS : élaboration des références nutritionnelles. Anses. Available at: https://www.anses.fr/fr/system/files/NUT2012SA0103Ra-2.pdf

  39. French Agency for Food, Environmental and Occupational Health & Safety (Anses) (2019) Opinion of the French Agency for Food, Environmental and Occupation Health & Safety on the updating of the PNNS dietary guidelines for women from menopause and men over 65 years of age. Anses. Available at: https://www.anses.fr/en/system/files/NUT2017SA0143EN.pdf

  40. Murray CJL, Lopez AD (2013) Measuring the global burden of disease. N Engl J Med 369:448–457. https://doi.org/10.1056/NEJMra1201534

    Article  CAS  PubMed  Google Scholar 

  41. Kesse-Guyot E, Chaltiel D, Wang J et al (2020) Sustainability analysis of French dietary guidelines using multiple criteria. Nat Sustain 3:377–385. https://doi.org/10.1038/s41893-020-0495-8

    Article  Google Scholar 

  42. Aune D, Giovannucci E, Boffetta P et al (2017) Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality—a systematic review and dose-response meta-analysis of prospective studies. Int J Epidemiol 46:1029–1056. https://doi.org/10.1093/ije/dyw319

    Article  PubMed  PubMed Central  Google Scholar 

  43. Micha R, Peñalvo JL, Cudhea F et al (2017) Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States. JAMA 317:912. https://doi.org/10.1001/jama.2017.0947

    Article  PubMed  PubMed Central  Google Scholar 

  44. Scarborough P, Harrington RA, Mizdrak A et al (2014) The preventable risk integrated ModEl and its use to estimate the health impact of public health policy scenarios. Scientifica 2014:1–21. https://doi.org/10.1155/2014/748750

    Article  Google Scholar 

  45. Inserm CépiDC Classification internationale des maladies (CIM). In: CépiDC Internet. https://www.cepidc.inserm.fr/causes-medicales-de-deces/classification-internationale-des-maladies-cim. Accessed 26 Feb 2019

  46. Insee Évolution et structure de la population en 2014. In: Insee Internet. https://www.insee.fr/fr/statistiques/2862200. Accessed 26 Feb 2019

  47. CépiDc (2019) Causes Médicales des Décès en 2014. https://www.cepidc.inserm.fr/causes-medicales-de-deces/interroger-les-donnees-de-mortalite . Accessed 26 Feb 2019

  48. Thiébaut A, Kesse E, Com-Nougué C et al (2004) Ajustement sur l’apport énergétique dans l’évaluation des facteurs de risque alimentaires. Rev Épidémiol Santé Publ 52:539–557. https://doi.org/10.1016/S0398-7620(04)99093-1

    Article  Google Scholar 

  49. Willett WC, Howe GR, Kushi LH (1997) Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 65:1220S-1228S. https://doi.org/10.1093/ajcn/65.4.1220S

    Article  CAS  PubMed  Google Scholar 

  50. Saltelli A, Ratto M, Andres T et al (2008) Global sensitivity analysis: the primer. Wiley, Chichester

    Google Scholar 

  51. Adams J, White M (2015) 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 Act 12:160. https://doi.org/10.1186/s12966-015-0317-y

    Article  PubMed  PubMed Central  Google Scholar 

  52. Julia C, Martinez L, Allès B et al (2018) Contribution of ultra-processed foods in the diet of adults from the French NutriNet-Santé study. Public Health Nutr 21:27–37. https://doi.org/10.1017/S1368980017001367

    Article  PubMed  Google Scholar 

  53. Baraldi LG, Martinez Steele E, Canella DS, Monteiro CA (2018) Consumption of ultra-processed foods and associated sociodemographic factors in the USA between 2007 and 2012: evidence from a nationally representative cross-sectional study. BMJ Open 8:e020574. https://doi.org/10.1136/bmjopen-2017-020574

    Article  PubMed  PubMed Central  Google Scholar 

  54. Machado PP, Steele EM, da Louzada MLC et al (2019) Ultra-processed food consumption drives excessive free sugar intake among all age groups in Australia. Eur J Nutr. https://doi.org/10.1007/s00394-019-02125-y

    Article  PubMed  Google Scholar 

  55. Baker P, Machado P, Santos T, et al (2020) Ultra‐processed foods and the nutrition transition: Global, regional and national trends, food systems transformations and political economy drivers. Obesity Reviews 21. https://doi.org/10.1111/obr.13126

  56. de Gavelle E, Huneau J-F, Mariotti F (2018) Patterns of protein food intake are associated with nutrient adequacy in the general French adult population. Nutrients 10:226. https://doi.org/10.3390/nu10020226

    Article  CAS  PubMed Central  Google Scholar 

  57. Gazan R, Béchaux C, Crépet A et al (2016) Dietary patterns in the French adult population: a study from the second French national cross-sectional dietary survey (INCA2) (2006–2007). Br J Nutr 116:300–315. https://doi.org/10.1017/S0007114516001549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. da Louzada MLC, Ricardo CZ, Steele EM et al (2018) The share of ultra-processed foods determines the overall nutritional quality of diets in Brazil. Public Health Nutr 21:94–102. https://doi.org/10.1017/S1368980017001434

    Article  PubMed  Google Scholar 

  59. Martínez Steele E, Popkin BM, Swinburn B, Monteiro CA (2017) 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 Metrics 15:6. https://doi.org/10.1186/s12963-017-0119-3

    Article  Google Scholar 

  60. Rauber F, da Louzada MLC, Martinez Steele E et al (2019) Ultra-processed foods and excessive free sugar intake in the UK: a nationally representative cross-sectional study. BMJ Open 9:e027546. https://doi.org/10.1136/bmjopen-2018-027546

    Article  PubMed  PubMed Central  Google Scholar 

  61. Rauber F, Chang K, Vamos EP et al (2020) Ultra-processed food consumption and risk of obesity: a prospective cohort study of UK Biobank. Eur J Nutr. https://doi.org/10.1007/s00394-020-02367-1

    Article  PubMed  PubMed Central  Google Scholar 

  62. Salas-Salvadó J, Becerra-Tomás N, García-Gavilán JF et al (2018) Mediterranean diet and cardiovascular disease prevention: what do we know? Prog Cardiovasc Dis 61:62–67. https://doi.org/10.1016/j.pcad.2018.04.006

    Article  PubMed  Google Scholar 

  63. Anand SS (2015) Food consumption and its impact on cardiovascular disease: importance of solutions focused on the globalized food system: a report from the workshop convened by the World Heart Federation. Cardiovasc Dis 66:25

    Google Scholar 

  64. Petersen KS, Flock MR, Richter CK et al (2017) Healthy dietary patterns for preventing cardiometabolic disease: the role of plant-based foods and animal products. Curr Dev Nutr 1:cdn.117.001289. https://doi.org/10.3945/cdn.117.001289

    Article  PubMed  PubMed Central  Google Scholar 

  65. Kim H, Caulfield LE, Garcia-Larsen V et al (2019) Plant-based diets are associated with a lower risk of incident cardiovascular disease, cardiovascular disease mortality, and all-cause mortality in a general population of middle-aged adults. JAHA 8:e012865. https://doi.org/10.1161/JAHA.119.012865

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Springmann M, Wiebe K, Mason-D’Croz D et al (2018) Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. The Lancet Planetary Health 2:e451–e461. https://doi.org/10.1016/S2542-5196(18)30206-7

    Article  PubMed  PubMed Central  Google Scholar 

  67. Tian S, Xu Q, Jiang R et al (2017) Dietary protein consumption and the risk of type 2 diabetes: a systematic review and meta-analysis of cohort studies. Nutrients 9:982. https://doi.org/10.3390/nu9090982

    Article  CAS  PubMed Central  Google Scholar 

  68. Sanchez-Sabate R, Badilla-Briones Y, Sabaté J (2019) Understanding attitudes towards reducing meat consumption for environmental reasons. A qualitative synthesis review. Sustainability 11:6295. https://doi.org/10.3390/su11226295

    Article  Google Scholar 

  69. Malek L, Umberger WJ, Goddard E (2019) Committed vs. uncommitted meat eaters: understanding willingness to change protein consumption. Appetite 138:115–126. https://doi.org/10.1016/j.appet.2019.03.024

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank Benjamin Allès (Nutritional Epidemiology Research Team (EREN) at Université Paris 13, France) for his scientific support in constructing the NOVA database and Emmanuelle Kesse-Guyot (Nutritional Epidemiology Research Team (EREN) at Université Paris 13, France) for her scientific contribution to the constitution of the disease mortality database for the EpiDiet model.

Funding

M. Salomé’s PhD fellowship is currently being funded by a research contract with Terres Univia, the French Interbranch organization for plant oils and proteins. F Mariotti is the scientific leader of this contract.

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Author notes

  1. Marion Salomé and Laura Arrazat contributed equally to this work.

Authors and Affiliations

  1. Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 16 rue Claude Bernard, 75005, Paris, France

    Marion Salomé, Laura Arrazat, Juhui Wang, Jean-François Huneau & François Mariotti

  2. Risk Assessment Department, Methodology and Survey Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 14 rue Pierre et Marie Curie, 94701, Maisons-Alfort Cedex, France

    Ariane Dufour, Carine Dubuisson & Jean-Luc Volatier

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Contributions

MS, LA and FM designed research; MS, LA and JW conducted research; AD, CD and J-LV provided the databases essential for the research; J-FH provided methodological support. MS and LA analyzed data and performed statistical analysis; MS, LA, JW and FM interpreted the results; MS, LA, JW and FM wrote paper; MS and FM had primary responsibility for the final content and all authors read and approved the final manuscript.

Corresponding author

Correspondence to François Mariotti.

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Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The INCA3 study was carried out in accordance with the Declaration of Helsinki guidelines and was approved by the ‘Comité Consultatif sur le Traitement de l’Information en matière de Recherche dans le domaine de la Santé’ (Advisory Committee on Information Processing in Health Research).

Consent to participate

For the data collection of the INCA3 survey, oral consent was obtained, witnessed and formally recorded from participants.

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Not applicable.

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Salomé, M., Arrazat, L., Wang, J. et al. Contrary to ultra-processed foods, the consumption of unprocessed or minimally processed foods is associated with favorable patterns of protein intake, diet quality and lower cardiometabolic risk in French adults (INCA3). Eur J Nutr 60, 4055–4067 (2021). https://doi.org/10.1007/s00394-021-02576-2

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