World Health Organization. Non communicable diseases fact sheet [Internet]. 2023 [cité 20 déc 2023]. Disponible sur: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases

Willett W. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet Br. 2019;393(10170):447–92.

Article  Google Scholar 

World Health Organization, FAO, éditeurs. Diet, nutrition, and the prevention of chronic diseases: report of a WHO-FAO Expert Consultation; [Joint WHO-FAO Expert Consultation on Diet, Nutrition, and the Prevention of Chronic Diseases, 2002, Geneva, Switzerland]. Geneva: World Health Organization. 2003. 149 p. (WHO technical report series).

GBD protocol| The Institute for Health Metrics and Evaluation [Internet]. [cité 20 déc 2023]. Disponible sur: https://www.healthdata.org/research-analysis/about-gbd/protocol

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. oct 2020;17(10258):1223–49.

Schwingshackl L, Bogensberger B, Hoffmann G. Diet Quality as assessed by the healthy eating index, alternate healthy eating Index, Dietary approaches to stop hypertension score, and Health outcomes: an updated systematic review and Meta-analysis of Cohort studies. J Acad Nutr Diet janv. 2018;118(1):74–e10011.

Article  Google Scholar 

Morze J, Danielewicz A, Przybyłowicz K, Zeng H, Hoffmann G, Schwingshackl L. An updated systematic review and meta-analysis on adherence to mediterranean diet and risk of cancer. Eur J Nutr avr. 2021;60(3):1561–86.

Article  CAS  Google Scholar 

Morze J, Danielewicz A, Hoffmann G, Schwingshackl L. Diet Quality as assessed by the healthy eating index, alternate healthy eating Index, Dietary approaches to stop hypertension score, and Health outcomes: a second update of a systematic review and Meta-analysis of Cohort studies. J Acad Nutr Dietetics 1 déc. 2020;120(12):1998–e203115.

Article  Google Scholar 

Jayedi A, Soltani S, Abdolshahi A, Shab-Bidar S. Healthy and unhealthy dietary patterns and the risk of chronic disease: an umbrella review of meta-analyses of prospective cohort studies. Br J Nutr 14 déc. 2020;124(11):1133–44.

Article  CAS  Google Scholar 

Waijers PMCM, Feskens EJM, Ocké MC. A critical review of predefined diet quality scores. Br J Nutr févr. 2007;97(2):219–31.

Article  CAS  Google Scholar 

Ocké MC. Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis. Proceedings of the Nutrition Society. mai. 2013;72(2):191–9.

Burggraf C, Teuber R, Brosig S, Meier T. Review of a priori dietary quality indices in relation to their construction criteria. Nutr Rev. 2018;01(10):747–64.

Article  Google Scholar 

Fouillet H, Dussiot A, Perraud E, Wang J, Huneau JF, Kesse-Guyot E, et al. Plant to animal protein ratio in the diet: nutrient adequacy, long-term health and environmental pressure. Front Nutr. 2023;10:1178121.

Article  PubMed  PubMed Central  Google Scholar 

Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiol Sept. 2000;11(Print):1044–3983.

Google Scholar 

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 11 mai 2010;10.

Moullec N, Deheeger M, Preziosi P, Monteiro P, Valeix P, Rolland-Cachera M et al. Validation du manuel-photos utilisé pour l’enquête alimentaire de l’étude SU.VI.MAX. Nutrition Clinique Et Metabolisme [Internet]. 1996 [cité 19 déc 2023]; Disponible sur: https://www.semanticscholar.org/paper/Validation-du-manuel-photos-utilis%C3%A9-pour-l%27enqu%C3%AAte-Moullec-Deheeger/3e18905fb9584e0766662ae6a052cac245eb1807

Etude Nutrinet-Santé. Table de composition des aliments de l’étude Nutrinet-Santé (Nutrinet-Santé Study Food Composition Database). Paris: Economica; 2013.

Google Scholar 

Touvier M, Méjean C, Kesse-Guyot E, Vergnaud AC, Péneau S, Hercberg S, et al. Variations in compliance with starchy food recommendations and consumption of types of starchy foods according to sociodemographic and socioeconomic characteristics. Br J Nutr Mai. 2010;103(10):1485–92.

Article  CAS  Google Scholar 

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 mars. 2016;116(3):427–e4385.

Article  Google Scholar 

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. 28 mars. 2015;113(6):953–62.

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. IntJ Obes Relat Metab Disord Sept. 2000;24(9):1119–30.

Article  CAS  Google Scholar 

Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr Sept. 2006;9(Print):1368–9800.

Google Scholar 

Robins JM, Hernan J. Causal Inference: What If (the book) [Internet]. Chapman&Hall/CRC. Boca Raton; 2020. Disponible sur: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Hernan MA, Robins JM. Estimating causal effects from epidemiological data. JEpidemiolCommunity Health Juill. 2006;60:0143–X005.

Google Scholar 

Naimi AI, Moodie EEM, Auger N, Kaufman JS. Constructing inverse probability weights for continuous exposures: a comparison of methods. Epidemiol mars. 2014;25(2):292–9.

Article  Google Scholar 

Chesnaye NC, Stel VS, Tripepi G, Dekker FW, Fu EL, Zoccali C, et al. An introduction to inverse probability of treatment weighting in observational research. Clin Kidney J 12 janv. 2022;15(1):14–20.

Article  Google Scholar 

Syriopoulou E, Wästerlid T, Lambert PC, Andersson TML. Standardised survival probabilities: a useful and informative tool for reporting regression models for survival data. Br J Cancer Nov. 2022;127(10):1808–15.

Article  Google Scholar 

Sjölander A. Estimation of causal effect measures with the R-package stdReg. Eur J Epidemiol Sept. 2018;33(9):847–58.

Article  Google Scholar 

Lescinsky H, Afshin A, Ashbaugh C, Bisignano C, Brauer M, Ferrara G, et al. Health effects associated with consumption of unprocessed red meat: a Burden of Proof study. Nat Med oct. 2022;28(10):2075–82.

Article  CAS  Google Scholar 

GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 18 mai 2024;403(10440):2162–203.

ANSES. Etude individuelle nationale des consommations alimentaires 3 (INCA 3) [Internet]. 2017 juin. Disponible sur: https://www.anses.fr/fr/system/files/NUT2014SA0234Ra.pdf

Schwingshackl L, Schwedhelm C, Hoffmann G, Lampousi AM, Knüppel S, Iqbal K, et al. Food groups and risk of all-cause mortality: a systematic review and meta-analysis of prospective studies. Am J Clin Nutr juin. 2017;105(6):1462–73.

Article  CAS  Google Scholar 

Schwingshackl L, Hoffmann G, Lampousi AM, Knuppel S, Iqbal K, Schwedhelm C, et al. Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol Mai. 2017;32(5):363–75.

Article  Google Scholar 

Bechthold A, Boeing H, Schwedhelm C, Hoffmann G, Knüppel S, Iqbal K, et al. Food groups and risk of coronary heart disease, stroke and heart failure: a systematic review and dose-response meta-analysis of prospective studies. Crit Rev Food Sci Nutr. oct 2017;17:1–20.

Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association between Dietary Factors and Mortality from Heart Disease, Stroke, and type 2 diabetes in the United States. JAMA. 2017;07(9):912–24.

Article  Google Scholar 

Aune D, Ursin G, Veierod MB. Meat consumption and the risk of type 2 diabetes: a systematic review and meta-analysis of cohort studies. Diabetologia Nov. 2009;52(11):2277–87.

Article  CAS  Google Scholar 

Aune D, Keum N, Giovannucci E, Fadnes LT, Boffetta P, Greenwood DC et al. Whole grain consumption and risk of cardiovascular disease, cancer, and all cause and cause specific mortality: systematic review and dose-response meta-analysis of prospective studies. BMJ. 14 juin 2016;353:i2716.

Jiang B, Pang J, Li J, Mi L, Ru D, Feng J et al. The effects of organic food on human health: a systematic review and meta-analysis of population-based studies. Nutr Rev 31 oct 2023;nuad124.

Lane MM, Davis JA, Beattie S, Gómez-Donoso C, Loughman A, O’Neil A, et al. Ultraprocessed food and chronic noncommunicable diseases: a systematic review and meta-analysis of 43 observational studies. Obes Rev mars. 2021;22(3):e13146.

Article  Google Scholar 

Miller V, Micha R, Choi E, Karageorgou D, Webb P, Mozaffarian D. Evaluation of the Quality of Evidence of the Association of Foods and nutrients with Cardiovascular Disease and Diabetes: a systematic review. JAMA Netw Open 3 févr. 2022;5(2):e2146705.

Article  Google Scholar 

Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021. (GBD 2021) Results [Internet]. Seattle, United States: Institute for Health Metrics and Evaluation (IHME); 2022 [cité 30 sept 2024]. Disponible sur: https://vizhub.healthdata.org/gbd-results/

Zhao J, Li Z, Gao Q, Zhao H, Chen S, Huang L et al. A review of statistical methods for dietary pattern analysis. Nutr J. 19 avr. 2021;20(1):37.

Wirt A, Collins CE. Diet quality–what is it and does it matter? Public Health Nutr déc. 2009;12(Electronic):1475–2727.

Google Scholar 

Wirfalt E, Drake I, Wallstrom P. What do review papers conclude about food and dietary patterns? Food NutrRes. 2013;57(1654-661X (Electronic)).

Andreeva VA, Deschamps V, Salanave B, Castetbon K, Verdot C, Kesse-Guyot E, et al. Comparison of Dietary Intakes between a large online cohort study (etude NutriNet-Santé) and a nationally Representative Cross-sectional Study (Etude Nationale Nutrition Santé) in France: addressing the issue of Generalizability in E-Epidemiology. Am J Epidemiol 1 nov. 2016;184(9):660–9.

Article  Google Scholar 

Hutcheon JA, Chiolero A, Hanley JA. Random measurement error and regression dilution bias. BMJ 23 juin. 2010;340:c2289.

Google Scholar 

Srour B, Chazelas E, Druesne-Pecollo N, Esseddik Y, de Edelenyi F, Agaesse C et al. Dietary exposure to nitrites and nitrates in association with type 2 diabetes risk: results from the NutriNet-Sante population-based cohort study. PLOS Med janv 2023;20(1).