Skip to main content Accessibility help
×
Home
Hostname: page-component-8bbf57454-47mcc Total loading time: 0.446 Render date: 2022-01-26T06:26:02.168Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

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

Published online by Cambridge University Press:  03 September 2020

Manon Egnell*
Affiliation:
Sorbonne Paris Nord University, Inserm, Inrae, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Centre – University of Paris (CRESS), 93017 Bobigny, France
Louise Seconda
Affiliation:
Sorbonne Paris Nord University, Inserm, Inrae, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Centre – University of Paris (CRESS), 93017 Bobigny, France ADEME (Agence de l’Environnement et de la Maîtrise de l’Energie), 49004 Angers, France
Bruce Neal
Affiliation:
The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Sydney, NSW 2006, Australia The Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia Division of Epidemiology and Biostatistics, Imperial College London, London SW7 2BU, UK
Cliona Ni Mhurchu
Affiliation:
The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Sydney, NSW 2006, Australia National Institute for Health Innovation, University of Auckland, Auckland 1072, New Zealand
Mike Rayner
Affiliation:
Nuffield Department of Population Health, Centre on Population Approaches for Non-Communicable Disease Prevention, University of Oxford, Oxford OX3 7BN, UK
Alexandra Jones
Affiliation:
The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Sydney, NSW 2006, Australia The Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
Mathilde Touvier
Affiliation:
Sorbonne Paris Nord University, Inserm, Inrae, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Centre – University of Paris (CRESS), 93017 Bobigny, France
Emmanuelle Kesse-Guyot
Affiliation:
Sorbonne Paris Nord University, Inserm, Inrae, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Centre – University of Paris (CRESS), 93017 Bobigny, France
Serge Hercberg
Affiliation:
Sorbonne Paris Nord University, Inserm, Inrae, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Centre – University of Paris (CRESS), 93017 Bobigny, France Public Health Department, Avicenne Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), 93000 Bobigny, France
Chantal Julia
Affiliation:
Sorbonne Paris Nord University, Inserm, Inrae, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Centre – University of Paris (CRESS), 93017 Bobigny, France Public Health Department, Avicenne Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), 93000 Bobigny, France
*
*Corresponding author: Manon Egnell, email m.egnell@eren.smbh.univ-paris13.fr

Abstract

Nutrient profiling systems (NPS) are used to classify foods according to their nutritional composition. However, investigating their prospective associations with health is key to their validation. The study investigated the associations of the original Food Standards Agency (FSA)-NPS and three variants (Food Standards Australia New Zealand Nutrient Profiling Scoring Criterion (NPSC), Health Star Rating NPS and the French High Council of Public Health NPS (HCSP-NPS)), with weight status. Individual dietary indices based on each NPS at the food level were computed to characterise the dietary quality of 71 403 French individuals from the NutriNet-Santé cohort. Associations of these indices with weight gain were assessed using mixed models and with overweight and obesity risks using Cox models. Participants with a higher dietary index (reflecting lower diet nutritional quality) were more likely to have a significant increase in BMI over time (β-coefficients positive) and an increased risk of overweight (hazard ratio (HR) T3 v. T1 = 1·27 (95 % CI 1·17, 1·37)) for the HCSP-Dietary Index, followed by the original FSA-Dietary Index (HR T3 v. T1 = 1·18 (95 % CI 1·09, 1·28)), the NPSC-Dietary Index (HR T3 v. T1 = 1·14 (95 % CI 1·06, 1·24)) and the Health Star Rating-Dietary Index (HR T3 v. T1 = 1·12 (95 % CI 1·04, 1·21)). Whilst differences were small, the HCSP-Dietary Index appeared to show significantly greater association with overweight risk. Overall, these results show the validity of NPS derived from the FSA-NPS, supporting their use in public policies for chronic disease prevention.

Type
Full Papers
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Nutrition Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

WHO (2013) World Health Organization Global Action Plan for the Prevention and Control of NCDs 2013–2020. https://www.who.int/nmh/publications/ncd-action-plan/en/ (accessed February 2019).Google Scholar
World Health Organization (2018) Obesity and Overweight – Key Facts. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed December 2018).Google Scholar
Lim, SS, Vos, T, Flaxman, AD, et al. (2012) A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancetl 380, 22242260.CrossRefGoogle ScholarPubMed
World Health Organization (2004) Global Strategy on Diet, Physical Activity and Health. Geneva: WHO.Google Scholar
Scarborough, P, Rayner, M & Stockley, L (2007) Developing nutrient profile models: a systematic approach. Public Health Nutr 10, 330336.CrossRefGoogle ScholarPubMed
Rayner, M (2017) Nutrient profiling for regulatory purposes. Proc Nutr Soc 76, 230236.CrossRefGoogle ScholarPubMed
Sacks, G, Rayner, M, Stockley, L, et al. (2011) Applications of nutrient profiling: potential role in diet-related chronic disease prevention and the feasibility of a core nutrient-profiling system. Eur J Clin Nutr 65, 298306.CrossRefGoogle ScholarPubMed
Labonté, M-È, Poon, T, Gladanac, B, et al. (2018) Nutrient profile models with applications in government-led nutrition policies aimed at health promotion and noncommunicable disease prevention: a systematic review. Adv Nutr 9, 741788.CrossRefGoogle ScholarPubMed
Rayner, M, Scarborough, P & Lobstein, T (2009) The UK Ofcom Nutrient Profiling Model – Defining ‘Healthy’ and ‘Unhealthy’ Food and Drinks for TV Advertising to Children. https://www.ndph.ox.ac.uk/cpnp/files/about/uk-ofcom-nutrient-profile-model.pdf (accessed August 2017).Google Scholar
Julia, C, Kesse-Guyot, E, Touvier, M, et al. (2014) Application of the British Food Standards Agency nutrient profiling system in a French food composition database. Br J Nutr 112, 16991705.CrossRefGoogle Scholar
Arambepola, C, Scarborough, P & Rayner, M (2008) Validating a nutrient profile model. Public Health Nutr 11, 371378.CrossRefGoogle ScholarPubMed
Scarborough, P, Boxer, A, Rayner, M, et al. (2007) Testing nutrient profile models using data from a survey of nutrition professionals. Public Health Nutr 10, 337345.CrossRefGoogle ScholarPubMed
Australian Government – Federal Register of Legislation (2017) Health Australia New Zealand Food Standards Code – Standard 1.2.7 – Nutrition, Health and Related Claims. http://www.legislation.gov.au/Details/F2017C01048/Html/Text (accessed February 2019).Google Scholar
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, 20092017.CrossRefGoogle Scholar
Adriouch, S, Julia, C, Kesse-Guyot, E, et al. (2017) 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, 2227.CrossRefGoogle ScholarPubMed
Adriouch, S, Julia, C, Kesse-Guyot, E, et al. (2016) Prospective association between a dietary quality index based on a nutrient profiling system and cardiovascular disease risk. Eur J Prev Cardiol 23, 16691676.CrossRefGoogle ScholarPubMed
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, 23552361.CrossRefGoogle ScholarPubMed
Donnenfeld, M, Julia, C, Kesse-Guyot, E, et al. (2015) Prospective association between cancer risk and an individual dietary index based on the British Food Standards Agency Nutrient Profiling System. Br J Nutr 114, 17021710.CrossRefGoogle Scholar
Deschasaux, M, Julia, C, Kesse-Guyot, E, et al. (2017) Are self-reported unhealthy food choices associated with an increased risk of breast cancer? Prospective cohort study using the British Food Standards Agency nutrient profiling system. BMJ Open 7, e013718.CrossRefGoogle ScholarPubMed
Deschasaux, M, Huybrechts, I, Murphy, N, et al. (2018) 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.CrossRefGoogle ScholarPubMed
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, 189194.CrossRefGoogle ScholarPubMed
Hercberg, S, Castetbon, K, Czernichow, S, et al. (2010) The Nutrinet-Sante Study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status. BMC Public Health 10, 242.CrossRefGoogle ScholarPubMed
Craig, CL, Marshall, AL, Sjöström, M, et al. (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35, 13811395.CrossRefGoogle ScholarPubMed
Vergnaud, A-C, Touvier, M, Méjean, C, et al. (2011) Agreement between web-based and paper versions of a socio-demographic questionnaire in the NutriNet-Santé study. Int J Public Health 56, 407417.CrossRefGoogle ScholarPubMed
Touvier, M, Mejean, C, Kesse-Guyot, E, et al. (2010) Comparison between web-based and paper versions of a self-administered anthropometric questionnaire. Eur J Epidemiol 25, 287296.CrossRefGoogle ScholarPubMed
Lassale, C, Peneau, S, Touvier, M, et al. (2013) Validity of web-based self-reported weight and height: results of the Nutrinet-Sante study. J Med Internet Res 15, e152.CrossRefGoogle ScholarPubMed
World Health Organization (2000) Obesity: Preventing and Managing the Global Epidemic. Geneva: World Health Organization.Google Scholar
Lassale, C, Castetbon, K, Laporte, F, et al. (2016) Correlations between fruit, vegetables, fish, vitamins, and fatty acids estimated by web-based nonconsecutive dietary records and respective biomarkers of nutritional status. J Acad Nutr Diet 116, 427438.CrossRefGoogle ScholarPubMed
Lassale, C, Castetbon, K, Laporte, F, et al. (2015) Validation of a web-based, self-administered, non-consecutive-day dietary record tool against urinary biomarkers. Br J Nutr 113, 953962.CrossRefGoogle ScholarPubMed
Le Moullec, N, Deheeger, M, Preziosi, P, et al. (1996) Validation du manuel photo utilisé pour l’enquête alimentaire de l’étude SU.VI.MAX (Validation of the food portion size booklet used in the SU.VI.MAX study). Cah Nutr Diet 31, 158164.Google Scholar
Arnault, N, Caillot, L, Castetbon, K, et al. (2013) Table de composition des aliments, étude NutriNet-Santé (Food Composition Table, NutriNet-Santé Study). Paris: Éditions Inser.Google Scholar
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 24, 11191130.CrossRefGoogle ScholarPubMed
Australian Government – Federal Register of Legislation (2017) Australia New Zealand Food Standards Code – Standard 1.2.7 – Nutrition, health and related claims. https://www.legislation.gov.au/Series/F2015L00394 (accessed September 2018).Google Scholar
Haut Conseil de la santé publique (2015) On Information Regarding the Nutritional Quality of Foodstuffs. https://www.hcsp.fr/explore.cgi/avisrapportsdomaine?clefr=524 (accessed September 2018).Google Scholar
Willett, W & Stampfer, MJ (1986) Total energy intake: implications for epidemiologic analyses. Am J Epidemiol 124, 1727.CrossRefGoogle ScholarPubMed
Benjamini, Y & Hochberg, Y (1995) Controlling the false discovery rate – a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57, 289300.CrossRefGoogle Scholar
Korn, EL, Graubard, BI & Midthune, D (1997) Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale. Am J Epidemiol 145, 7280.Google ScholarPubMed
Ahn, S, Lim, J, Paik, MC, et al. (2018) Cox model with interval-censored covariate in cohort studies. Biom J Biom Z 60, 797814.CrossRefGoogle ScholarPubMed
Chiuve, SE, Fung, TT, Rimm, EB, et al. (2012) Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 142, 10091018.CrossRefGoogle ScholarPubMed
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, 517.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Jéquier, E (2001) Is fat intake a risk factor for fat gain in children? J Clin Endocrinol Metab 86, 980983.CrossRefGoogle ScholarPubMed
Roberts, SB, McCrory, MA & Saltzman, E (2002) The influence of dietary composition on energy intake and body weight. J Am Coll Nutr 21, 140S145S.CrossRefGoogle ScholarPubMed
WCRF (2018) Energy Balance and Body Fatness. World Cancer Research Fund. https://www.wcrf.org/dietandcancer/energy-balance-body-fatness (accessed February 2019).Google Scholar
Giskes, K, Avendaňo, M, Brug, J, et al. (2010) A systematic review of studies on socioeconomic inequalities in dietary intakes associated with weight gain and overweight/obesity conducted among European adults. Obes Rev 11, 413429.CrossRefGoogle ScholarPubMed
Townsend, MS (2010) Where is the science? What will it take to show that nutrient profiling systems work? Am J Clin Nutr 91, 1109S1115S.CrossRefGoogle Scholar
Mytton, OT, Forouhi, NG, Scarborough, P, et al. (2018) Association between intake of less-healthy foods defined by the United Kingdom’s nutrient profile model and cardiovascular disease: a population-based cohort study. PLOS Med 15, e1002484.CrossRefGoogle ScholarPubMed
Chiuve, SE, Sampson, L & Willett, WC (2011) The association between a nutritional quality index and risk of chronic disease. Am J Prev Med 40, 505513.CrossRefGoogle ScholarPubMed
Deschasaux, M, Huybrechts, I, Murphy, N, et al. (2018) 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.CrossRefGoogle ScholarPubMed
Andrianasolo, RM, Julia, C, Varraso, R, et al. (2019) Association between an individual dietary index based on the British Food Standard Agency Nutrient Profiling System and asthma symptoms. Br J Nutr 122, 6370.CrossRefGoogle ScholarPubMed
Andreeva, VA, Egnell, M, Galan, P, et al. (2019) Association of the dietary index underpinning the Nutri-Score label with oral health: preliminary evidence from a large, population-based sample. Nutrients 11, 1998.CrossRefGoogle ScholarPubMed
Mozaffarian, D, Hao, T, Rimm, EB, et al. (2011) Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med 364, 23922404.CrossRefGoogle ScholarPubMed
Malhotra, R, Østbye, T, Riley, CM, et al. (2013) Young adult weight trajectories through midlife by body mass category. Obesity 21, 19231934.Google ScholarPubMed
Supplementary material: File

Egnell et al. supplementary material

Egnell et al. supplementary material

Download Egnell et al. supplementary material(File)
File 45 KB
3
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

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
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

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
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *