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Food Compass is a nutrient profiling system using expanded characteristics for assessing healthfulness of foods


Nutrient profiling systems (NPS) aim to discriminate the healthfulness of foods for front-of-package labelling, warning labels, taxation, company ratings and more. Existing NPS often assess relatively few nutrients and ingredients, use inconsistent criteria across food categories and have not incorporated the newest science. Here, we developed and validated an NPS, the Food Compass, to incorporate a broader range of food characteristics, attributes and uniform scoring principles. We scored 54 attributes across 9 health-relevant domains: nutrient ratios, vitamins, minerals, food ingredients, additives, processing, specific lipids, fibre and protein, and phytochemicals. The domain scores were summed into a final Food Compass Score (FCS) ranging from 1 (least healthy) to 100 (most healthy) for all foods and beverages. Content validity was confirmed by assessing nutrients, food ingredients and other characteristics of public health concern; face validity was confirmed by assessing the FCS for 8,032 foods and beverages reported in NHANES/FNDDS 2015–16; and convergent and discriminant validity was confirmed from comparisons with the NOVA food processing classification, the Health Star Rating and the Nutri-Score. The FCS differentiated food categories and food items well, with mean ± s.d. ranging from 16.4 ± 17.7 for savoury snacks and sweet desserts to 78.6 ± 17.4 for legumes, nuts and seeds. In many food categories, the FCS provided important discrimination of specific foods and beverages as compared with NOVA, the Health Star Rating or the Nutri-Score. On the basis of demonstrated content, convergent and discriminant validity, the Food Compass provides an NPS scoring a broader range of attributes and domains than previous systems with uniform and transparent principles. This publicly available tool will help guide consumer choice, research, food policy, industry reformulations and mission-focused investment decisions.

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Fig. 1: Domains of the Food Compass.
Fig. 2: FCS for 8,032 unique foods and beverages consumed by US adults, based on NHANES 2015–16.
Fig. 3: FCS according to HSR category for 8,032 unique foods and beverages consumed in the United States (NHANES/FNDDS 2015–16).
Fig. 4: FCS according to Nutri-Score category for 8,032 unique foods and beverages consumed in the United States (NHANES/FNDDS 2015–16).

Data availability

The attribute and domain scoring algorithm used to generate the Food Compass is available in the Supplementary Information. The NHANES data are publicly available at The statistical coding is not available. The generated Food Compass, HSR and NOVA food processing classification scores for each of the 8,032 food items in the dataset are available in the Supplementary Information.


  1. 1.

    GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).

  2. 2.

    Herforth, A. et al. A global review of food-based dietary guidelines. Adv. Nutr. 10, 590–605 (2019).

    Article  Google Scholar 

  3. 3.

    Nutrient Profiling: Report of a Technical Meeting (World Health Organization, 2010);

  4. 4.

    Labonte, M. E. et al. Nutrient profile models with applications in government-led nutrition policies aimed at health promotion and noncommunicable disease prevention: a systematic review. Adv. Nutr. 9, 741–788 (2018).

    Article  Google Scholar 

  5. 5.

    El-Abbadi, N. H., Taylor, S. F., Micha, R. & Blumberg, J. B. Nutrient profiling systems, front of pack labeling, and consumer behavior. Curr. Atheroscler. Rep. 22, 36 (2020).

    Article  Google Scholar 

  6. 6.

    Lim, J. H., Rishika, R., Janakiraman, R. & Kannan, P. K. Competitive effects of front-of-package nutrition labeling adoption on nutritional quality: evidence from facts up front–style labels. J. Mark. 84, 3–21 (2020).

    Article  Google Scholar 

  7. 7.

    Dreano-Trecant, L. et al. Performance of the front-of-pack nutrition label Nutri-Score to discriminate the nutritional quality of foods products: a comparative study across 8 European countries. Nutrients (2020).

  8. 8.

    McColl, K., Lobstein, T. & Brinsden, H. Nutrient profiling could be used to transform food systems and support health-promoting food policies. Public Health Panor. 3, 586–597 (2017).

    Google Scholar 

  9. 9.

    Pomeranz, J., Mozaffarian, D. & Micha, R. Mandating front-of-package food labels in the U.S.—what are the First Amendment obstacles?. Food Policy 86, 101722 (2019).

    Article  Google Scholar 

  10. 10.

    Monteiro, C. A. et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 22, 936–941 (2019).

    Article  Google Scholar 

  11. 11.

    Nutrient Profiling (World Health Organization, 2010);

  12. 12.

    Nutrition Labelling: Who is For and Against a Harmonised EU-Wide Approach? (Food Navigator, 2020);

  13. 13.

    Arambepola, C., Scarborough, P. & Rayner, M. Validating a nutrient profile model. Public Health Nutr. 11, 371–378 (2008).

    Article  Google Scholar 

  14. 14.

    Townsend, M. S. Where is the science? What will it take to show that nutrient profiling systems work? Am. J. Clin. Nutr. 91, 1109S–1115S (2010).

    CAS  Article  Google Scholar 

  15. 15.

    Labonte, M. E. et al. Comparison of global nutrient profiling systems for restricting the commercial marketing of foods and beverages of low nutritional quality to children in Canada. Am. J. Clin. Nutr. 106, 1471–1481 (2017).

    CAS  Article  Google Scholar 

  16. 16.

    Contreras-Manzano, A. et al. Comparative analysis of the classification of food products in the Mexican market according to seven different nutrient profiling systems. Nutrients (2018).

  17. 17.

    Poon, T. et al. Comparison of nutrient profiling models for assessing the nutritional quality of foods: a validation study. Br. J. Nutr. 120, 567–582 (2018).

    CAS  Article  Google Scholar 

  18. 18.

    Micha, R. et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS ONE 12, e0175149 (2017).

    Article  Google Scholar 

  19. 19.

    2015-2020 Dietary Guidelines for Americans 8th edn (USDHHS, USDA, 2017);

  20. 20.

    Comprehensive Implementation Plan on Maternal, Infant, and Young Child Nutrition (World Health Organization, 2019);

  21. 21.

    Elizabeth, L., Machado, P., Zinocker, M., Baker, P. & Lawrence, M. Ultra-processed foods and health outcomes: a narrative review. Nutrients (2020).

  22. 22.

    WCRF/AICR Systematic Literature Review Continuous Update Project Report: The Associations between Food, Nutrition and Physical Activity and the Risk of Colorectal Cancer (World Cancer Research Fund/American Institute for Cancer Research, 2011);

  23. 23.

    Scientific Report of the 2015 Dietary Guidelines Advisory Committee (Dietary Guidelines Advisory Committee, 2015);

  24. 24.

    Gibney, M. J. Ultra-processed foods: definitions and policy issues. Curr. Dev. Nutr. 3, nzy077 (2019).

    CAS  Article  Google Scholar 

  25. 25.

    Julia, C. et al. Prospective associations between a dietary index based on the British Food Standard Agency nutrient profiling system and 13-year weight gain in the SU.VI.MAX cohort. Prev. Med. 81, 189–194 (2015).

    Article  Google Scholar 

  26. 26.

    Julia, C. et al. The nutrient profile of foods consumed using the British Food Standards Agency nutrient profiling system is associated with metabolic syndrome in the SU.VI.MAX cohort. J. Nutr. 145, 2355–2361 (2015).

    CAS  Article  Google Scholar 

  27. 27.

    Adriouch, S. et al. Association between a dietary quality index based on the Food Standard Agency nutrient profiling system and cardiovascular disease risk among French adults. Int. J. Cardiol. 234, 22–27 (2017).

    Article  Google Scholar 

  28. 28.

    Deschasaux, M. et al. Nutritional quality of food as represented by the FSAm-NPS nutrient profiling system underlying the Nutri-Score label and cancer risk in Europe: results from the EPIC prospective cohort study. PLoS Med. 15, e1002651 (2018).

    Article  Google Scholar 

  29. 29.

    Andrianasolo, R. M. et al. Association between an individual dietary index based on the British Food Standard Agency Nutrient Profiling System and asthma symptoms. Br. J. Nutr. 122, 63–70 (2019).

    CAS  Article  Google Scholar 

  30. 30.

    Pan, X. F. et al. Seventeen-year associations between diet quality defined by the Health Star Rating and mortality in Australians: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Curr. Dev. Nutr. 4, nzaa157 (2020).

    Article  Google Scholar 

  31. 31.

    Gomez-Donoso, C. et al. Association between the nutrient profile system underpinning the Nutri-Score front-of-pack nutrition label and mortality in the SUN project: a prospective cohort study. Clin. Nutr. 40, 1085–1094 (2021).

    CAS  Article  Google Scholar 

  32. 32.

    Health Star Rating System: Five Year Review Report (mpconsulting, 2019);$File/Health-Star-Rating-System-Five-Year-Review-Draft-Report.pdf

  33. 33.

    What We Eat in America, NHANES 2015–2016 (USDA, 2020);

  34. 34.

    European Food Safety Authority. The food classification and description system FoodEx 2 (revision 2). EFSA Support. Publ. 12, 804E (2015).

    Google Scholar 

  35. 35.

    Neal, B., Sacks, G., Shahid, M., Taylor, F. & Huffman, M. FoodSwitch: State of the Food Supply (George Institute, 2019);

  36. 36.

    Classification of Individual Consumption According to Purpose (COICOP) (UN DESA Secretariat & Statistics Division, 2018);

  37. 37.

    Mozaffarian, D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation 133, 187–225 (2016).

    CAS  Article  Google Scholar 

  38. 38.

    Global Index 2018 (Access to Nutrition Initiative, 2018);

  39. 39.

    Chantal, J. & Hercberg, S. World Health Organization, Regional Office for Europe. Development of a new front-of-pack nutrition label in France: the five-colour Nutri-Score. Public Health Panor. 3, 712–725 (2017).

    Google Scholar 

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This research was supported by Danone (N.H.E.-A., W.A.M., P.J., P.S., J.B.B. and R.M.) and the National Heart, Lung, and Blood Institute, National Institutes of Health (grant nos R01 HL130735 (R.M.) and 2 R01 HL115189 (D.M.)). The funders had no role in study design, data collection, data analysis or interpretation, drafting of the manuscript, or decision to submit the manuscript for publication. We thank S. Gerber (Tufts University) for helpful insights on processing attributes.

Author information




D.M., N.H.E.-A., W.A.M., P.J., J.B.B. and R.M. conceived and designed the work. D.M. and N.H.E.-A. acquired the data. N.H.E.-A., M.O., J.E.-M. and P.S. analysed the data. All authors interpreted the data. D.M. drafted the manuscript, and all authors substantively revised the manuscript. In addition, all authors approved the submitted version and have agreed both to be personally accountable for their own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated and resolved and the resolution documented in the literature.

Corresponding author

Correspondence to Dariush Mozaffarian.

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Competing interests

D.M. receives personal fees from Acasti Pharma, Barilla, Cleveland Clinic Foundation, Danone and Motif FoodWorks; is on the scientific advisory boards of Brightseed, Calibrate, DayTwo (ended June 2020), Elysium Health, Filtricine, Foodome, HumanCo, January Inc., Perfect Day, Season and Tiny Organics; and receives chapter royalties from UpToDate. J.B.B. reports personal fees from Guiding Stars Licensing Company. All other authors declare no competing interests.

Additional information

Peer review information Nature Food thanks Mike Rayner, Cliona Ni Mhurchu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Text, Tables 1–7 and Figs. 1–3.

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Mozaffarian, D., El-Abbadi, N.H., O’Hearn, M. et al. Food Compass is a nutrient profiling system using expanded characteristics for assessing healthfulness of foods. Nat Food (2021).

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