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Cross-sectional comparisons of dietary indexes underlying nutrition labels: nutri-score, Canadian ‘high in’ labels and Diabetes Canada Clinical Practices (DCCP)

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To assess the cross-sectional association between dietary indexes (DI) that underlie, respectively, the Nutri-score (NS), the proposed Canadian ‘High In’ Symbol (CHIL) and the Diabetes Canada Clinical Practice Guidelines (DCCP) with food consumption, nutrient intakes and metabolic markers.


1836 adults (18–74 years) participating in the representative ESTEBAN study, conducted in mainland France in 2014–2016, were included in the analysis. Food consumption was assessed with three repeated 24 h dietary recalls. Anthropometric measurements and biomarkers of metabolic risk (cholesterol—total, LDL (Low Density Lipoprotein), HDL (High Density Lipoprotein)—triglycerides, glucose) were obtained through a clinical examination and fasting blood draw. The DI were assessed for their association with food consumption, dietary intakes and metabolic biomarkers as quintiles and continuous variables using multi-adjusted linear regression. Heathier diets were assigned to lower scores.


Correlations between scores ranged from + 0.62 between CHIL-DI and NS-DI to + 0.75 between NS-DI and DCCP-DI. All DIs discriminated individuals according to the nutritional quality of their diets through food consumption and nutrient intakes (healthier diets were associated with lower intakes of energy, added sugars and saturated fat; and with higher intakes of fiber, vitamins and minerals). NS-DI was associated with blood glucose (adjusted mean in Q1 = 5 vs. Q5 = 5.46 mmol/dl, ptrend = 0.001) and DCCP-DI was associated with BMI (Q1 = 24.8 kg/m2 vs. Q5 = 25.8 kg/m2, ptrend = 0.025), while CHIL showed no significant association with any anthropometric measures or biomarkers.


This study provides elements supporting the validity of the nutrient profiling systems underlying front-of-package nutrition labellings (FOPLs) to characterize the healthiness of diets.

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  1. Micha R, Shulkin ML, Peñalvo JL et al (2017) 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.

    Article  CAS  Google Scholar 

  2. Afshin A, Sur PJ, Fay KA et al (2019) Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the global burden of disease study 2017. The Lancet 393:1958–1972.

    Article  Google Scholar 

  3. D’Agostino RB, Pencina MJ, Massaro JM, Coady S (2013) Cardiovascular disease risk assessment: insights from Framingham. Glob Heart 8:11–23.

    Article  Google Scholar 

  4. Strilchuk L, Cincione RI, Fogacci F, Cicero AFG (2020) Dietary interventions in blood pressure lowering: current evidence in 2020. Kardiol Pol 78:659–666.

    Article  Google Scholar 

  5. Feingold KR (2000) The effect of diet on cardiovascular disease and lipid and lipoprotein levels. In: Feingold KR, Anawalt B, Boyce A, et al (eds) Endotext., Inc., South Dartmouth (MA)

  6. Sievenpiper JL, Chan CB, Dworatzek PD et al (2018) Nutrition therapy diabetes Canada clinical practice guidelines expert committee. Can J Diabetes 42(Suppl 1):S64–S79.

    Article  Google Scholar 

  7. Temple NJ (2020) Front-of-package food labels: a narrative review. Appetite 144:104485.

    Article  Google Scholar 

  8. (2020) Report from the commission to the European parliament and the council regarding the use of additional forms of expression and presentation of the nutrition declaration

  9. HCSP (2015) Information sur la qualité nutritionnelle des produits alimentaires. Haut Conseil de la Santé Publique, Paris

    Google Scholar 

  10. Nutri-Score. Accessed 19 Aug 2021

  11. Reyes M, Garmendia ML, Olivares S et al (2019) Development of the Chilean front-of-package food warning label. BMC Public Health 19:906.

    Article  Google Scholar 

  12. Canada H (2018) Summary of proposed amendments published in Canada Gazette, Part I: nutrition symbols, other labelling provisions, partially hydrogenated oils and vitamin D. Accessed 19 Aug 2021

  13. Government of Canada PW and GSC (2018) Canada Gazette – Regulations amending certain regulations made under the food and drugs act (Nutrition symbols, other labelling provisions, partially hydrogenated oils and vitamin D). Accessed 6 Sep 2021

  14. Mavra Ahmed, Jennifer Lee, Madyson Weippert, et al (2021) TECHNICAL DOCUMENT - Diabetes Canada Clinical Practice Guidelines Nutrient Profiling Model

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

    Article  CAS  Google Scholar 

  16. Julia C, Méjean C, Touvier M et al (2016) Validation of the FSA nutrient profiling system dietary index in French adults-findings from SUVIMAX study. Eur J Nutr 55:1901–1910.

    Article  CAS  Google Scholar 

  17. Julia C, Touvier M, Méjean C et al (2014) Development and validation of an individual dietary index based on the British food standard agency nutrient profiling system in a French context. J Nutr 144:2009–2017.

    Article  CAS  Google Scholar 

  18. Julia C, Ducrot P, Lassale C et al (2015) 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.

    Article  Google Scholar 

  19. Basto-Abreu A, Torres-Alvarez R, Reyes-Sánchez F et al (2020) Predicting obesity reduction after implementing warning labels in Mexico: a modeling study. PLoS Med.

    Article  Google Scholar 

  20. Balicco A, Oleko A, Szego E et al (2017) Protocole Esteban : une Étude transversale de santé sur l’environnement, la biosurveillance, l’activité physique et la nutrition (2014–2016). Toxicol Anal Clin 29:517–537.

    Article  Google Scholar 

  21. Golubic R, May AM, Benjaminsen Borch K et al (2014) Validity of electronically administered recent physical activity questionnaire (RPAQ) in ten European countries. PLoS ONE 9:e92829.

    Article  CAS  Google Scholar 

  22. Touvier M, Kesse-Guyot E, Méjean C et al (2011) Comparison between an interactive web-based self-administered 24 h dietary record and an interview by a dietitian for large-scale epidemiological studies. Br J Nutr 105:1055–1064.

    Article  CAS  Google Scholar 

  23. Validation du manuel-photos utilisé pour l’enquête alimentaire de l’étude SU.VI.MAX – ScienceOpen. Accessed 2 Oct 2021

  24. (2013) Publication de la Table de composition nutritionnelle des aliments. In: Salle Presse Inserm. Accessed 25 May 2022

  25. Friedewald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:499–502

    Article  CAS  Google Scholar 

  26. EUR-Lex - 32011R1169 - EN - EUR-Lex. Accessed 20 Nov 2021

  27. 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.

    Article  Google Scholar 

  28. Black AE (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 Relat Metab Disord J Int Assoc Study Obes 24:1119–1130.

    Article  CAS  Google Scholar 

  29. Hemphill JF (2003) Interpreting the magnitudes of correlation coefficients. Am Psychol 58:78–79.

    Article  Google Scholar 

  30. Desquilbet L, Mariotti F (2010) Dose-response analyses using restricted cubic spline functions in public health research. Stat Med 29:1037–1057.

    Article  Google Scholar 

  31. Egnell M, Seconda L, Neal B et al (2021) Prospective associations of the original food standards agency nutrient profiling system and three variants with weight gain, overweight and obesity risk: results from the French NutriNet-Santé cohort. Br J Nutr 125:902–914.

    Article  CAS  Google Scholar 

  32. Frisoli TM, Schmieder RE, Grodzicki T, Messerli FH (2011) Beyond salt: lifestyle modifications and blood pressure. Eur Heart J 32:3081–3087.

    Article  Google Scholar 

  33. Energy balance and body fatness. In: WCRF Int. Accessed 17 Aug 2021

  34. Nimptsch K, Konigorski S, Pischon T (2019) Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine. Metabolism 92:61–70.

    Article  CAS  Google Scholar 

  35. Togo P, Osler M, Sørensen TI, Heitmann BL (2001) Food intake patterns and body mass index in observational studies. Int J Obes Relat Metab Disord J Int Assoc Study Obes 25:1741–1751.

    Article  CAS  Google Scholar 

  36. Julia C, Péneau S, Andreeva VA et al (2014) Weight-loss strategies used by the general population: how are they perceived? PLoS ONE 9:e97834.

    Article  CAS  Google Scholar 

  37. Murakami K (2017) Nutritional quality of meals and snacks assessed by the food standards agency nutrient profiling system in relation to overall diet quality, body mass index, and waist circumference in British adults. Nutr J 16:57.

    Article  CAS  Google Scholar 

  38. Nettleton JA, Hivert M-F, Lemaitre RN et al (2013) Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts. Am J Epidemiol 177:103–115.

    Article  Google Scholar 

  39. Ojo O, Ojo OO, Adebowale F, Wang X-H (2018) The effect of dietary glycaemic index on glycaemia in patients with type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials. Nutrients 10:373.

    Article  CAS  Google Scholar 

  40. Howell WH, McNamara DJ, Tosca MA et al (1997) Plasma lipid and lipoprotein responses to dietary fat and cholesterol: a meta-analysis. Am J Clin Nutr 65:1747–1764.

    Article  CAS  Google Scholar 

  41. Grundy SM, Denke MA (1990) Dietary influences on serum lipids and lipoproteins. J Lipid Res 31:1149–1172

    Article  CAS  Google Scholar 

  42. Vincent MJ, Allen B, Palacios OM et al (2019) Meta-regression analysis of the effects of dietary cholesterol intake on LDL and HDL cholesterol. Am J Clin Nutr 109:7–16.

    Article  Google Scholar 

  43. Julia C, Fézeu LK, Ducrot P et al (2015) 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.

    Article  CAS  Google Scholar 

  44. Ma Y, Olendzki BC, Pagoto SL et al (2009) Number of 24-hour diet recalls needed to estimate energy intake. Ann Epidemiol 19:553–559.

    Article  Google Scholar 

  45. Waijers PMCM, Feskens EJM, Ocké MC (2007) A critical review of predefined diet quality scores. Br J Nutr 97:219–231.

    Article  CAS  Google Scholar 

  46. Jacobs DR, Gross MD, Tapsell LC (2009) Food synergy: an operational concept for understanding nutrition. Am J Clin Nutr 89:1543S-1548S.

    Article  CAS  Google Scholar 

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The authors gratefully acknowledge the dieticians who collected the data, the participants to the study. and “Santé Publique France”, as the main promoter and supporter, for access to the Esteban database and support documentation.


This investigation within the ESTEBAN cross-sectional study was funded by the Sanofi-Pasteur-University of Toronto-Université Paris-Descartes International Collaborative Research Pilot and Feasibility Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Correspondence to Laura Paper.

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Paper, L., Ahmed, M., Lee, J.J. et al. Cross-sectional comparisons of dietary indexes underlying nutrition labels: nutri-score, Canadian ‘high in’ labels and Diabetes Canada Clinical Practices (DCCP). Eur J Nutr 62, 261–274 (2023).

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