Skip to main content
    • Aa
    • Aa

Uses of nutrient profiling to address public health needs: from regulation to reformulation

  • Adam Drewnowski (a1)

Nutrient profiling (NP) models rate the nutritional quality of individual foods, based on their nutrient composition. Their goal is to identify nutrient-rich foods, generally defined as those that contain more nutrients than calories and are low in fat, sugar and salt. NP models have provided the scientific basis for evaluating nutrition and health claims and regulating marketing and advertising to children. The food industry has used NP methods to reformulate product portfolios. To help define what we mean by healthy foods, NP models need to be based on published nutrition standards, mandated serving sizes and open-source nutrient composition databases. Specifically, the development and testing of NP models for public health should follow the seven decision steps outlined by the European Food Safety Authority. Consistent with this scheme, the nutrient-rich food (NRF) family of indices was based on a variable number of qualifying nutrients (from six to fifteen) and on three disqualifying nutrients (saturated fat, added sugar, sodium). The selection of nutrients and daily reference amounts followed nutrient standards for the USA. The base of calculation was 418·4 kJ (100 kcal), in preference to 100 g, or serving sizes. The NRF algorithms, based on unweighted sums of percent daily values, subtracted negative (LIM) from positive (NRn) subscores (NRn – LIM). NRF model performance was tested with respect to energy density and independent measures of a healthy diet. Whereas past uses of NP modelling have been regulatory or educational, voluntary product reformulation by the food industry may have most impact on public health.

Corresponding author
Corresponding author: A. Drewnowski, fax (206) 685-1696, email
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

1. A Drewnowski & VL Fulgoni III (2014) Nutrient density: principles and evaluation tools. Am J Clin Nutr 99, 1223s1228s.

2. TA Nicklas , A Drewnowski & CE O'Neil (2014) The nutrient density approach to healthy eating: challenges and opportunities. Public Health Nutr 17, 26262636.

4. G Maschkowski , M Hartmann & J Hoffmann (2014) Health-related on-pack communication and nutritional value of ready-to-eat breakfast cereals evaluated against five nutrient profiling schemes. BMC Public Health 14, 1178.

6. MA Royo-Bordonada , K Leon-Flandez , J Damian (2016) The extent and nature of food advertising to children on Spanish television in 2012 using an international food-based coding system and the UK nutrient profiling model. Public Health 137, 8894.

9. C Batis , JA Rivera , BM Popkin (2016) First-year evaluation of Mexico's tax on nonessential energy-dense foods: an observational study. PLoS Med 13, e1002057.

10. N Darmon , M Darmon , M Maillot (2005) A nutrient density standard for vegetables and fruits: nutrients per calorie and nutrients per unit cost. J Am Diet Assoc 105, 18811887.

11. A Vlassopoulos , G Masset , F Leroy (2017) A nutrient profiling system for the (re)formulation of a global food & beverage portfolio. Eur J Nutr 56, 11051122.

12. G Masset , KC Mathias , A Vlassopoulos (2016) Modeled dietary impact of pizza reformulations in US children and adolescents. PLoS ONE 11, e0164197.

13. A Vlassopoulos , G Masset , VR Charles (2017) A nutrient profiling system for the (re)formulation of a global food and beverage portfolio. Eur J Nutr 56, 11051122.

15. A Drewnowski & V Fulgoni (2008) Nutrient profiling of foods: creating a nutrient-rich food index. Nutr rev 66, 2339.

16. A. Drewnowski (2009) Defining nutrient density: development and validation of the nutrient rich foods index. J Am Coll Nutr 28, 421s426s.

17. A Drewnowski , M Maillot & N Darmon (2009) Testing nutrient profile models in relation to energy density and energy cost. Eur J Clin Nutr 63, 674683.

19. EB Fern , H Watzke , DV Barclay (2015) The nutrient balance concept: a new quality metric for composite meals and diets. PLoS ONE 10, e0130491.

20. A Drewnowski , M Maillot & N Darmon (2009) Should nutrient profiles be based on 100 g, 100 kcal or serving size? Eur J Clin Nutr 63, 898904.

22. V Azais-Braesco , C Goffi & E Labouze (2006) Nutrient profiling: comparison and critical analysis of existing systems. Public Health Nutr 9, 613622.

23. E Labouze , C Goffi , L Moulay (2007) A multipurpose tool to evaluate the nutritional quality of individual foods: nutrimap. Public Health Nutr 10, 690700.

24. P Scarborough , A Boxer , M Rayner (2007) Testing nutrient profile models using data from a survey of nutrition professionals. Public Health Nutr 10, 337345.

25. C Arambepola , P Scarborough & M Rayner (2008) Validating a nutrient profile model. Public Health Nutr 11, 371378.

26. N Darmon , F Vieux , M Maillot (2009) Nutrient profiles discriminate between foods according to their contribution to nutritionally adequate diets: a validation study using linear programming and the SAIN,LIM system. Am J Clin Nutr 89, 12271236.

27. A Francou , P Hebel , V Braesco (2015) Consumption patterns of fruit and vegetable juices and dietary nutrient density among French children and adults. Nutrients 7, 60736087.

28. D Sluik , MT Streppel , L van Lee (2015) Evaluation of a nutrient-rich food index score in the Netherlands. J Nutr Sci 4, e14.

29. MT Streppel , D Sluik , JF van Yperen (2014) Nutrient-rich foods, cardiovascular diseases and all-cause mortality: the Rotterdam study. Eur J Clin Nutr 68, 741747.

30. TA O'Sullivan , AP Bremner , HK Bremer (2015) Dairy product consumption, dietary nutrient and energy density and associations with obesity in Australian adolescents. J Hum Nutr Diet 28, 452464.

31. Z Zhou , W Hu , M Li (2014) Development and validation of a new model of desirable dietary pattern (N-DDP) score for Chinese diets. Public Health Nutr 17, 519528.

40. U Fahmida , O Santika , R Kolopaking (2014) Complementary feeding recommendations based on locally available foods in Indonesia. Food Nutr Bull 35, S174S179.

45. P Trumbo , AA Yates , S Schlicker (2001) Dietary reference intakes: vitamin A, vitamin K, arsenic, boron, chromium, copper, iodine, iron, manganese, molybdenum, nickel, silicon, vanadium, and zinc. J Am Diet Assoc 101, 294301.

46. P Trumbo , S Schlicker , AA Yates Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids. J Am Diet Assoc 102, 16211630.

48. A Drewnowski & CD Rehm (2013) Vegetable cost metrics show that potatoes and beans provide most nutrients per penny. PLoS ONE 8, e63277.

50. C Julia , C Méjean , S Péneau (2016) The 5-CNL front-of-pack nutrition label appears an effective tool to achieve food substitutions towards healthier diets across dietary profiles. PLoS ONE 11, e0157545.

51. A Mendoza , AE Perez , A Aggarwal (2017) Energy density of foods and diets in Mexico and their monetary cost by socioeconomic strata: analyses of ENSANUT data 2012. J Epidemiol Commun Health [Epublication ahead of print version].

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Proceedings of the Nutrition Society
  • ISSN: 0029-6651
  • EISSN: 1475-2719
  • URL: /core/journals/proceedings-of-the-nutrition-society
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Altmetric attention score

Full text views

Total number of HTML views: 6
Total number of PDF views: 82 *
Loading metrics...

Abstract views

Total abstract views: 483 *
Loading metrics...

* Views captured on Cambridge Core between 9th June 2017 - 22nd September 2017. This data will be updated every 24 hours.