Adherence to the 2017 French dietary guidelines and adult weight gain: A cohort study

Autoři: Dan Chaltiel aff001;  Chantal Julia aff001;  Moufidath Adjibade aff001;  Mathilde Touvier aff001;  Serge Hercberg aff001;  Emmanuelle Kesse-Guyot aff001
Působiště autorů: Nutritional Epidemiology Research Team (EREN), Sorbonne Paris Cité Centre of Research in Epidemiology and Statistics (CRESS), Conservatoire National des Arts et Métiers, Paris 13 University, Bobigny, France aff001;  Public Health Department, Avicenne Hospital, Assistance Publique–Hôpitaux de Paris, Bobigny, France aff002
Vyšlo v časopise: Adherence to the 2017 French dietary guidelines and adult weight gain: A cohort study. PLoS Med 16(12): e32767. doi:10.1371/journal.pmed.1003007
Kategorie: Research Article
doi: 10.1371/journal.pmed.1003007



The French dietary guidelines were updated in 2017, and an adherence score to the new guidelines (Programme National Nutrition Santé Guidelines Score 2 [PNNS-GS2]) has been developed and validated recently. Since overweight and obesity are key public health issues and have been related to major chronic conditions, this prospective study aimed to measure the association between PNNS-GS2 and risk of overweight and obesity, and to compare these results with those for the modified Programme National Nutrition Santé Guidelines Score (mPNNS-GS1), reflecting adherence to 2001 guidelines.

Methods and findings

Participants (N = 54,089) were recruited among French adults (≥18 years old, mean baseline age = 47.1 [SD 14.1] years, 78.3% women) in the NutriNet-Santé web-based cohort. Mean (SD) score was 1.7 (3.3) for PNNS-GS2 and 8.2 (1.6) for mPNNS-GS1. Selected participants were those included between 2009 and 2014 and followed up to September 2018 (median follow-up = 6 years). Collected data included at least three 24-hour dietary records over a 2-year period following inclusion, baseline sociodemographics, and anthropometric data over time. In Cox regression models, PNNS-GS2 was strongly and linearly associated with a lower risk of overweight and obesity (HR for quintile 5 versus quintile 1 [95% CI] = 0.48 [0.43–0.54], p < 0.001, and 0.47 [0.40–0.55], p < 0.001, for overweight and obesity, respectively). These results were much weaker for mPNNS-GS1 (HR for quintile 5 versus quintile 1 = 0.90 [0.80–0.99], p = 0.03, and 0.98 [0.84–1.15], p = 0.8, for overweight and obesity, respectively). In multilevel models, PNNS-GS2 was negatively associated with baseline BMI and BMI increase over time (β for a 1-SD increase in score [95% CI] = −0.040 [−0.041; −0.038], p < 0.001, and −0.00080 [−0.00094; −0.00066], p < 0.001, respectively). In “direct comparison” models, PNNS-GS2 was associated with a lower risk of overweight and obesity, lower baseline BMI, and lower BMI increase over time than mPNNS-GS1. Study limitations include possible selection bias, reliance on participant self-report, use of arbitrary cutoffs in data analyses, and residual confounding, but robustness was tested in sensitivity analyses.


Our findings suggest that adherence to the 2017 French dietary guidelines is associated with a lower risk of overweight and obesity. The magnitude of the association and the results of the direct comparison reinforced the validity of the updated recommendations.

Trial registration

The NutriNet-Santé Study (NCT03335644)

Klíčová slova:

Alcohol consumption – Body Mass Index – Food consumption – Legumes – Meat – Obesity – Physical activity


1. Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15(5):288–98. doi: 10.1038/s41574-019-0176-8 30814686

2. Santé Publique France. Étude de santé sur l’environnement, la biosurveillance, l’activité physique et la nutrition (ESTEBAN 2014–2016). Paris: Santé Publique France; 2017 Jun [cited 2019 Dec 9]. Available from:

3. Trésor direction générale. Obésité: quelles conséquences pour l'économie et comment les limiter? Report no. 179. Trésor-Éco. 2016 Sep [cited 2019 Mar 13]. Available from:

4. Hruby A, Manson JE, Qi L, Malik VS, Rimm EB, Sun Q, et al. Determinants and consequences of obesity. Am J Public Health. 2016;106(9):1656–62. doi: 10.2105/AJPH.2016.303326 27459460

5. World Health Organization. ICD-10 version: 2016. Geneva: World Health Organization; 2016 [cited 2019 Mar 26]. Available from:

6. World Health Organization. ICD-11 for mortality and morbidity statistics. Geneva: World Health Organization; 2019 Apr [cited 2019 Dec 9]. Available from:

7. Haidar YM, Cosman BC. Obesity epidemiology. Clin Colon Rectal Surg. 2011;24(4):205–10. doi: 10.1055/s-0031-1295684 23204935

8. Ladabaum U, Mannalithara A, Myer PA, Singh G. Obesity, abdominal obesity, physical activity, and caloric intake in US adults: 1988 to 2010. Am J Med. 2014;127(8):717–27.e12.

9. World Health Organization. Obesity: preventing and managing the global epidemic. Geneva: World Health Organization; 2000 [cited 2019 Jan 3]. Available from:

10. Kesse-Guyot E, Baudry J, Assmann KE, Galan P, Hercberg S, Lairon D. Prospective association between consumption frequency of organic food and body weight change, risk of overweight or obesity: results from the NutriNet-Santé Study. Br J Nutr. 2017;117(02):325–34.

11. Thayer KA, Heindel JJ, Bucher JR, Gallo MA. Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review. Environ Health Perspect. 2012;120(6):779–89. doi: 10.1289/ehp.1104597 22296744

12. Schlesinger S, Neuenschwander M, Schwedhelm C, Hoffmann G, Bechthold A, Boeing H, et al. Food groups and risk of overweight, obesity, and weight gain: a systematic review and dose-response meta-analysis of prospective studies. Adv Nutr. 2019;10(2):205–18. doi: 10.1093/advances/nmy092 30801613

13. Estaquio C, Kesse-Guyot E, Deschamps V, Bertrais S, Dauchet L, Galan P, et al. Adherence to the French Programme National Nutrition Santé Guideline Score is associated with better nutrient intake and nutritional status. J Am Diet Assoc. 2009;109(6):1031–41. doi: 10.1016/j.jada.2009.03.012 19465185

14. Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, et al. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr. 2012;142(6):1009–18. doi: 10.3945/jn.111.157222 22513989

15. Guenther PM, Kirkpatrick SI, Krebs-Smith SM, Reedy J, Buckman DW, Dodd KW, et al. Evaluation of the Healthy Eating Index–2010 (HEI-2010). FASEB J. 2013;27(Suppl 1):230.5.

16. Hansen SH, Overvad K, Hansen CP, Dahm CC. Adherence to national food-based dietary guidelines and incidence of stroke: a cohort study of Danish men and women. PLoS ONE. 2018;13(10):e0206242. doi: 10.1371/journal.pone.0206242 30356304

17. Voortman T, Kiefte-de Jong JC, Ikram MA, Stricker BH, van Rooij FJA, Lahousse L, et al. Adherence to the 2015 Dutch dietary guidelines and risk of non-communicable diseases and mortality in the Rotterdam Study. Eur J Epidemiol. 2017;32(11):993–1005. doi: 10.1007/s10654-017-0295-2 28825166

18. Russell J, Flood V, Rochtchina E, Gopinath B, Allman-Farinelli M, Bauman A, et al. Adherence to dietary guidelines and 15-year risk of all-cause mortality. Br J Nutr. 2013;109(3):547–55. doi: 10.1017/S0007114512001377 22571690

19. Kurotani K, Akter S, Kashino I, Goto A, Mizoue T, Noda M, et al. Quality of diet and mortality among Japanese men and women: Japan Public Health Center based prospective study. BMJ. 2016;352:i1209. doi: 10.1136/bmj.i1209 27005903

20. Al Thani M, Al Thani AA, Al-Chetachi W, Al Malki B, Khalifa SAH, Bakri AH, et al. Adherence to the Qatar dietary guidelines: a cross-sectional study of the gaps, determinants and association with cardiometabolic risk amongst adults. BMC Public Health. 2018;18(1):503. doi: 10.1186/s12889-018-5400-2 29661175

21. High Council for Public Health. Statement related to the update of the French Nutrition and Health Programme’s dietary guidelines for adults for the period 2017–2021. Paris: High Council for Public Health; 2017 Feb [cited 2019 Dec 5]. Available from:

22. Chaltiel D, Adjibade M, Deschamps V, Touvier M, Hercberg S, Julia C, et al. PNNS-GS2—development and validation of a diet quality score reflecting the 2017 French dietary guidelines. Br J Nutr. 2019;122(3):331–42. doi: 10.1017/S0007114519001181 31342885

23. Hercberg S, Castetbon K, Czernichow S, Malon A, Mejean C, Kesse E, et al. 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. 2010;10(1):242.

24. Le Moullec N, Deheeger M, Preziosi P, Monteiro P, Valeix P, Rolland-Cachera M-F, et al. Validation du manuel-photos utilisé pour l’enquête alimentaire de l’étude SU. VI. MAX. Cah Nutr Diététique. 1996;31(3):158–64.

25. Etude NutriNet-Santé. Table de composition des aliments de l’étude NutriNet-Santé. Paris: Economica; 2013.

26. Black AE. 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. 2000;24(9):1119–30. doi: 10.1038/sj.ijo.0801376 11033980

27. Touvier M, Kesse-Guyot E, Méjean C, Pollet C, Malon A, Castetbon K, et al. 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. 2011;105(7):1055–64. doi: 10.1017/S0007114510004617 21080983

28. Lassale C, Castetbon K, Laporte F, Camilleri GM, Deschamps V, Vernay M, et al. Validation of a web-based, self-administered, non-consecutive-day dietary record tool against urinary biomarkers. Br J Nutr. 2015;113(06):953–62.

29. Lassale C, Castetbon K, Laporte F, Deschamps V, Vernay M, Camilleri GM, et al. 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. 2016;116(3):427–38.e5.

30. Baudry J, Méjean C, Péneau S, Galan P, Hercberg S, Lairon D, et al. Health and dietary traits of organic food consumers: results from the NutriNet-Santé study. Br J Nutr. 2015;114(12):2064–73. doi: 10.1017/S0007114515003761 26429066

31. Touvier M, Méjean C, Kesse-Guyot E, Pollet C, Malon A, Castetbon K, et al. Comparison between web-based and paper versions of a self-administered anthropometric questionnaire. Eur J Epidemiol. 2010;25(5):287–96. doi: 10.1007/s10654-010-9433-9 20191377

32. Lassale C, Péneau S, Touvier M, Julia C, Galan P, Hercberg S, et al. Validity of web-based self-reported weight and height: results of the Nutrinet-Santé study. J Med Internet Res. 2013;15(8):e152. doi: 10.2196/jmir.2575 23928492

33. Hallal PC, Victora CG. Reliability and validity of the International Physical Activity Questionnaire (IPAQ). Med Sci Sports Exerc. 2004;36(3):556. doi: 10.1249/01.mss.0000117161.66394.07 15076800

34. National Institute of Statistics and Economic Studies. Consumption unit: definition. Paris: National Institute of Statistics and Economic Studies; 2016 [cited 2018 Jul 27]. Available from:

35. Santé Publique France. Avis d’experts relatif à l’évolution du discours public en matière de consommation d’alcool en France organisé par Santé publique France et l’Institut national du cancer. Paris: Santé Publique France; 2017 May.

36. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9 Suppl):S498–504.

37. National Institute of Statistics and Economic Studies. Consulter la PCS 2003—professions et catégories socioprofessionnelles. Paris: National Institute of Statistics and Economic Studies; 2003 [cited 2019 Sep 20]. Available from:

38. Lamarca R, Alonso J, Gómez G, Muñoz A. Left-truncated data with age as time scale: an alternative for survival analysis in the elderly population. J Gerontol A Biol Sci Med Sci. 1998;53(5):M337–43. doi: 10.1093/gerona/53a.5.m337 9754138

39. Korn EL, Graubard BI, Midthune D. Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale. Am J Epidemiol. 1997;145(1):72–80. doi: 10.1093/oxfordjournals.aje.a009034 8982025

40. Harrell FEJ. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. New York: Springer; 2015. 598 p.

41. Harrell FE. rms: regression modeling strategies. Version 5.1–4. Vienna: Comprehensive R Archive Network; 2011 [cited 2019 Dec 5]. Available from:

42. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515–26.

43. Vang A, Singh PN, Lee JW, Haddad EH, Brinegar CH. Meats, processed meats, obesity, weight gain and occurrence of diabetes among adults: findings from Adventist Health Studies. Ann Nutr Metab. 2008;52(2):96–104. doi: 10.1159/000121365 18349528

44. Wang Y, Beydoun MA. Meat consumption is associated with obesity and central obesity among US adults. Int J Obes (Lond). 2009;33(6):621–8.

45. Rouhani MH, Salehi‐Abargouei A, Surkan PJ, Azadbakht L. Is there a relationship between red or processed meat intake and obesity? A systematic review and meta-analysis of observational studies. Obes Rev. 2014;15(9):740–8. doi: 10.1111/obr.12172 24815945

46. Jackson CL, Hu FB. Long-term associations of nut consumption with body weight and obesity. Am J Clin Nutr. 2014;100(Suppl 1):408–11S.

47. Babio N, Bulló M, Basora J, Martínez-González MA, Fernández-Ballart J, Márquez-Sandoval F, et al. Adherence to the Mediterranean diet and risk of metabolic syndrome and its components. Nutr Metab Cardiovasc Dis. 2009;19(8):563–70. doi: 10.1016/j.numecd.2008.10.007 19176282

48. Martínez-González MA, García-Arellano A, Toledo E, Salas-Salvadó J, Buil-Cosiales P, Corella D, et al. A 14-item Mediterranean diet assessment tool and obesity indexes among high-risk subjects: the PREDIMED trial. PLoS ONE. 2012;7(8):e43134. doi: 10.1371/journal.pone.0043134 22905215

49. American Institute for Cancer Research, World Cancer Research Fund. Diet, nutrition and physical activity: energy balance and body fatness. London: World Cancer Research Fund; 2018.

50. Liu S, Willett WC, Manson JE, Hu FB, Rosner B, Colditz G. Relation between changes in intakes of dietary fiber and grain products and changes in weight and development of obesity among middle-aged women. Am J Clin Nutr. 2003;78(5):920–7. doi: 10.1093/ajcn/78.5.920 14594777

51. Swithers SE. Artificial sweeteners produce the counterintuitive effect of inducing metabolic derangements. Trends Endocrinol Metab. 2013;24(9):431–41. doi: 10.1016/j.tem.2013.05.005 23850261

52. Toews I, Lohner S, Gaudry DK de, Sommer H, Meerpohl JJ. Association between intake of non-sugar sweeteners and health outcomes: systematic review and meta-analyses of randomised and non-randomised controlled trials and observational studies. BMJ. 2019;364:k4718. doi: 10.1136/bmj.k4718 30602577

53. Kesse-Guyot E, Castetbon K, Estaquio C, Czernichow S, Galan P, Hercberg S. Association between the French nutritional guideline-based score and 6-year anthropometric changes in a French middle-aged adult cohort. Am J Epidemiol. 2009;170(6):757–65. doi: 10.1093/aje/kwp174 19656810

54. Lassale C, Fezeu L, Andreeva VA, Hercberg S, Kengne A-P, Czernichow S, et al. Association between dietary scores and 13-year weight change and obesity risk in a French prospective cohort. Int J Obes (Lond). 2012;36(11):1455–62.

55. Assmann KE, Lassale C, Galan P, Hercberg S, Kesse-Guyot E. Dietary quality and 6-year anthropometric changes in a sample of French middle-aged overweight and obese adults. PLoS ONE. 2014;9(2):e87083. doi: 10.1371/journal.pone.0087083 24516542

56. Rothman KJ. BMI-related errors in the measurement of obesity. Int J Obes (Lond). 2008;32(Suppl 3):S56–9.

57. Meeuwsen S, Horgan GW, Elia M. The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr Edinb Scotl. 2010;29(5):560–6.

58. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage—a cross-sectional study in American adult individuals. Sci Rep. 2018;8(1):10980. doi: 10.1038/s41598-018-29362-1 30030479

59. Wang Y, Chen X. How much of racial/ethnic disparities in dietary intakes, exercise, and weight status can be explained by nutrition- and health-related psychosocial factors and socioeconomic status among US adults? J Am Diet Assoc. 2011;111(12):1904–11. doi: 10.1016/j.jada.2011.09.036 22117667

Interní lékařství

Článek vyšel v časopise

PLOS Medicine

2019 Číslo 12

Nejčtenější v tomto čísle
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.


Nemáte účet?  Registrujte se