#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Ultra-processed food intake in association with BMI change and risk of overweight and obesity: A prospective analysis of the French NutriNet-Santé cohort


Autoři: Marie Beslay aff001;  Bernard Srour aff001;  Caroline Méjean aff002;  Benjamin Allès aff001;  Thibault Fiolet aff001;  Charlotte Debras aff001;  Eloi Chazelas aff001;  Mélanie Deschasaux aff001;  Méyomo Gaelle Wendeu-Foyet aff001;  Serge Hercberg aff001;  Pilar Galan aff001;  Carlos A. Monteiro aff004;  Valérie Deschamps aff005;  Giovanna Calixto Andrade aff001;  Emmanuelle Kesse-Guyot aff001;  Chantal Julia aff001;  Mathilde Touvier aff001
Působiště autorů: Sorbonne Paris Nord University, Inserm, INRAE, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center–University of Paris (CRESS), Bobigny, France aff001;  MOISA, Univ Montpellier, CIRAD, CIHEAM-IAMM, INRAE, Montpellier SupAgro, Montpellier, France aff002;  Public Health Department, Avicenne Hospital, AP-HP, Bobigny, France aff003;  Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil aff004;  Santé Publique France (The French Public Health Agency), Nutritional Epidemiology Surveillance Team (ESEN) aff005;  Department of Preventive Medicine, Medical School, University of São Paulo, São Paulo, Brazil aff006
Vyšlo v časopise: Ultra-processed food intake in association with BMI change and risk of overweight and obesity: A prospective analysis of the French NutriNet-Santé cohort. PLoS Med 17(8): e32767. doi:10.1371/journal.pmed.1003256
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pmed.1003256

Souhrn

Background

Ultra-processed food (UPF) consumption has increased drastically worldwide and already represents 50%–60% of total daily energy intake in several high-income countries. In the meantime, the prevalence of overweight and obesity has risen continuously during the last century. The objective of this study was to investigate the associations between UPF consumption and the risk of overweight and obesity, as well as change in body mass index (BMI), in a large French cohort.

Methods and findings

A total of 110,260 adult participants (≥18 years old, mean baseline age = 43.1 [SD 14.6] years; 78.2% women) from the French prospective population-based NutriNet-Santé cohort (2009–2019) were included. Dietary intakes were collected at baseline using repeated and validated 24-hour dietary records linked to a food composition database that included >3,500 different food items, each categorized according to their degree of processing by the NOVA classification. Associations between the proportion of UPF in the diet and BMI change during follow-up were assessed using linear mixed models. Associations with risk of overweight and obesity were assessed using Cox proportional hazard models. After adjusting for age, sex, educational level, marital status, physical activity, smoking status, alcohol intake, number of 24-hour dietary records, and energy intake, we observed a positive association between UPF intake and gain in BMI (β Time × UPF = 0.02 for an absolute increment of 10 in the percentage of UPF in the diet, P < 0.001). UPF intake was associated with a higher risk of overweight (n = 7,063 overweight participants; hazard ratio (HR) for an absolute increase of 10% of UPFs in the diet = 1.11, 95% CI: 1.08–1.14; P < 0.001) and obesity (n = 3,066 incident obese participants; HR10% = 1.09 (1.05–1.13); P < 0.001). These results remained statistically significant after adjustment for the nutritional quality of the diet and energy intake. Study limitations include possible selection bias, potential residual confounding due to the observational design, and a possible item misclassification according to the level of processing. Nonetheless, robustness was tested and verified using a large panel of sensitivity analyses.

Conclusions

In this large observational prospective study, higher consumption of UPF was associated with gain in BMI and higher risks of overweight and obesity. Public health authorities in several countries recently started to recommend privileging unprocessed/minimally processed foods and limiting UPF consumption.

Trial registration

ClinicalTrials.gov NCT03335644 (https://clinicaltrials.gov/ct2/show/NCT03335644)

Klíčová slova:

Body Mass Index – Cancer risk factors – Diet – Food – Medical risk factors – Obesity – Overweight – Weight gain


Zdroje

1. World Health Organization. Factsheet on Obesity and overweight. In: WHO [Internet]. 16 Feb 2018 [cited 3 Dec 2019]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.

2. World Health Organization. Global Strategy on Diet, Physical Activity and Health. Geneva: WHO Technical Report; 2004. [cited 2020 Feb 4]. Available from: http://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf.

3. Équipe de surveillance et d’épidémiologie nutritionnelle (Esen). Étude de santé sur l’environnement, la biosurveillance, l’activité physique et la nutrition (Esteban), 2014–2016. Volet Nutrition. Chapitre Consommations alimentaires. Saint-Maurice: Santé Publique France; 2018 Sep p. 193. [cited 2020 Jan 15]. Available from: www.santepubliquefrance.fr.

4. Latino-Martel P, Cottet V, Druesne-Pecollo N, Pierre FHF, Touillaud M, Touvier M, et al. Alcoholic beverages, obesity, physical activity and other nutritional factors, and cancer risk: A review of the evidence. Crit Rev Oncol Hematol. 2016;99: 308–323. doi: 10.1016/j.critrevonc.2016.01.002 26811140

5. WCRF/AICR. Diet, Nutrition, Physical Activity and Cancer: a Global Perspective. Continuous Update Project Expert Report 2018. Recommendations and public health and policy implications. 2018.

6. Williams EP, Mesidor M, Winters K, Dubbert PM, Wyatt SB. Overweight and Obesity: Prevalence, Consequences, and Causes of a Growing Public Health Problem. Curr Obes Rep. 2015;4: 363–370. doi: 10.1007/s13679-015-0169-4 26627494

7. Swinburn BA, Caterson I, Seidell JC, James WPT. Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutr. 2004;7: 123–146. doi: 10.1079/phn2003585 14972057

8. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. The Lancet. 2011;378: 804–814. doi: 10.1016/S0140-6736(11)60813-1

9. Swinburn BA, Kraak VI, Allender S, Atkins VJ, Baker PI, Bogard JR, et al. The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report. The Lancet. 2019;393: 791–846. doi: 10.1016/S0140-6736(18)32822-8

10. Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med. 1999;29: 563–570. doi: 10.1006/pmed.1999.0585 10600438

11. Cutler DM, Glaeser EL, Shapiro JM. Why Have Americans Become More Obese? J Econ Perspect. 2003;17: 93–118. doi: 10.1257/089533003769204371

12. Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev. 2013;14 Suppl 2: 21–28. doi: 10.1111/obr.12107 24102801

13. Adams J, White M. Characterisation of UK diets according to degree of food processing and associations with socio-demographics and obesity: cross-sectional analysis of UK National Diet and Nutrition Survey (2008–12). Int J Behav Nutr Phys Act. 2015;12: 160. doi: 10.1186/s12966-015-0317-y 26684833

14. Martinez SE, Baraldi LG, Louzada ML, Moubarac JC, Mozaffarian D, Monteiro CA. Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open. 2016;6: e009892. doi: 10.1136/bmjopen-2015-009892 26962035

15. Moubarac JC, Batal M, Louzada ML, Martinez SE, Monteiro CA. Consumption of ultra-processed foods predicts diet quality in Canada. Appetite. 2017;108: 512–520. doi: 10.1016/j.appet.2016.11.006 27825941

16. Machado PP, Steele EM, Levy RB, Sui Z, Rangan A, Woods J, et al. Ultra-processed foods and recommended intake levels of nutrients linked to non-communicable diseases in Australia: evidence from a nationally representative cross-sectional study. BMJ Open. 2019;9. doi: 10.1136/bmjopen-2019-029544 31462476

17. Luiten CM, Steenhuis IH, Eyles H, Ni MC, Waterlander WE. Ultra-processed foods have the worst nutrient profile, yet they are the most available packaged products in a sample of New Zealand supermarkets. Public Health Nutr. 2016;19: 539. doi: 10.1017/S1368980015002840 26419699

18. Cediel G, Reyes M, da Costa Louzada ML, Martinez SE, Monteiro CA, Corvalan C, et al. Ultra-processed foods and added sugars in the Chilean diet (2010). Public Health Nutr. 2017; 1–9. doi: 10.1017/S1368980017001161 28625223

19. Costa Louzada ML, Martins AP, Canella DS, Baraldi LG, Levy RB, Claro RM, et al. Ultra-processed foods and the nutritional dietary profile in Brazil. Rev Saude Publica. 2015;49: 38. doi: 10.1590/S0034-8910.2015049006132 26176747

20. Moubarac JC, Martins AP, Claro RM, Levy RB, Cannon G, Monteiro CA. Consumption of ultra-processed foods and likely impact on human health. Evidence from Canada. Public Health Nutr. 2013;16: 2240–2248. doi: 10.1017/S1368980012005009 23171687

21. Poti JM, Mendez MA, Ng SW, Popkin BM. Is the degree of food processing and convenience linked with the nutritional quality of foods purchased by US households? Am J Clin Nutr. 2015;101: 1251–1262. doi: 10.3945/ajcn.114.100925 25948666

22. Slimani N, Deharveng G, Southgate DA, Biessy C, Chajes V, van Bakel MM, et al. Contribution of highly industrially processed foods to the nutrient intakes and patterns of middle-aged populations in the European Prospective Investigation into Cancer and Nutrition study. Eur J Clin Nutr. 2009;63 Suppl 4: S206–S225. doi: 10.1038/ejcn.2009.82 19888275

23. Louzada ML, Martins AP, Canella DS, Baraldi LG, Levy RB, Claro RM, et al. Impact of ultra-processed foods on micronutrient content in the Brazilian diet. Rev Saude Publica. 2015;49: 45. doi: 10.1590/S0034-8910.2015049006211 26270019

24. Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM, et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). BMJ. 2019;365: l1451. doi: 10.1136/bmj.l1451 31142457

25. Monteiro CA, Cannon G, Levy RB, Moubarac J-C, Louzada ML, Rauber F, et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019;22: 936–941. doi: 10.1017/S1368980018003762 30744710

26. Monteiro CA, Cannon G, Lawrence M, Costa Louzada ML da, Pereira Machado P. Ultra-processed foods,diet quality, and health using the NOVA classification system. Rome FAO. 2019 [cited 4 Sep 2019]. Available from: http://www.fao.org/3/ca5644en/ca5644en.pdf.

27. Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C, et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. 2018;360: k322. doi: 10.1136/bmj.k322 29444771

28. Adjibade M, Julia C, Allès B, Touvier M, Lemogne C, Srour B, et al. Prospective association between ultra-processed food consumption and incident depressive symptoms in the French NutriNet-Santé cohort. In: BMC Medicine [Internet]. Dec 2019 [cited 15 May 2019]. doi: 10.1186/s12916-019-1312-y 30982472

29. Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Debras C, Druesne-Pecollo N, et al. Ultraprocessed Food Consumption and Risk of Type 2 Diabetes Among Participants of the NutriNet-Santé Prospective Cohort. JAMA Intern Med. 2019. doi: 10.1001/jamainternmed.2019.5942 31841598

30. Schnabel L, Kesse-Guyot E, Alles B, Touvier M, Srour B, Hercberg S, et al. Association Between Ultraprocessed Food Consumption and Risk of Mortality Among Middle-aged Adults in France. JAMA Intern Med. 2019. doi: 10.1001/jamainternmed.2018.7289 30742202

31. Al-Goblan AS, Al-Alfi MA, Khan MZ. Mechanism linking diabetes mellitus and obesity. Diabetes Metab Syndr Obes Targets Ther. 2014;7: 587–591. doi: 10.2147/DMSO.S67400 25506234

32. Paul Poirier, Giles Thomas D., Bray George A., Yuling Hong, Stern Judith S., Xavier Pi-Sunyer F, et al. Obesity and Cardiovascular Disease: Pathophysiology, Evaluation, and Effect of Weight Loss. Circulation. 2006;113: 898–918. doi: 10.1161/CIRCULATIONAHA.106.171016 16380542

33. Li K, Hüsing A, Kaaks R. Lifestyle risk factors and residual life expectancy at age 40: a German cohort study. BMC Med. 2014;12: 59. doi: 10.1186/1741-7015-12-59 24708705

34. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, et al. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019. doi: 10.1016/j.cmet.2019.05.008 31105044

35. Canella DS, Levy RB, Martins AP, Claro RM, Moubarac JC, Baraldi LG, et al. Ultra-processed food products and obesity in Brazilian households (2008–2009). PLoS ONE. 2014;9: e92752. doi: 10.1371/journal.pone.0092752 24667658

36. Juul F, Martinez-Steele E, Parekh N, Monteiro CA, Chang VW. Ultra-processed food consumption and excess weight among US adults. Br J Nutr. 2018;120: 90–100. doi: 10.1017/S0007114518001046 29729673

37. Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac J-C, Girling‐Butcher M, Lee AC, et al. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 0. doi: 10.1111/obr.12860 31099480

38. Nardocci M, Leclerc B-S, Louzada M-L, Monteiro CA, Batal M, Moubarac J-C. Consumption of ultra-processed foods and obesity in Canada. Can J Public Health Rev Can Sante Publique. 2019;110: 4–14. doi: 10.17269/s41997-018-0130-x 30238324

39. Julia C, Martinez L, Alles B, Touvier M, Hercberg S, Mejean C, et al. Contribution of ultra-processed foods in the diet of adults from the French NutriNet-Sante study. Public Health Nutr. 2017; 1–11. doi: 10.1017/S1368980017001367 28703085

40. Mendonca RD, Pimenta AM, Gea A, Fuente-Arrillaga C, Martinez-Gonzalez MA, Lopes AC, et al. Ultraprocessed food consumption and risk of overweight and obesity: the University of Navarra Follow-Up (SUN) cohort study. Am J Clin Nutr. 2016;104: 1433–1440. doi: 10.3945/ajcn.116.135004 27733404

41. Canhada SL, Luft VC, Giatti L, Duncan BB, Chor D, Fonseca M de JM da, et al. Ultra-processed foods, incident overweight and obesity, and longitudinal changes in weight and waist circumference: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. undefined/ed; 1–11. doi: 10.1017/S1368980019002854 31619309

42. Hercberg S, Castetbon K, Czernichow S, Malon A, Mejean C, Kesse E, et al. The Nutrinet-Santé 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: 242. doi: 10.1186/1471-2458-10-242 20459807

43. Kesse-Guyot E, Andreeva V, Castetbon K, Vernay M, Touvier M, Méjean C, et al. Participant profiles according to recruitment source in a large Web-based prospective study: experience from the Nutrinet-Santé study. J Med Internet Res. 2013;15: e205. doi: 10.2196/jmir.2488 24036068

44. Arnault N, Caillot L, Castetbon K, et al. Table de composition des aliments, Etude NutriNet-Santé. [Food composition table, NutriNet-Santé study] (in French). Paris: Les éditions INSERM/Economica,. 2013.

45. 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. IntJObesRelat Metab Disord. 2000;24: 1119–1130.

46. Touvier M, Kesse-Guyot E, Mejean 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. BrJNutr. 2011;105: 1055–1064.

47. 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. JAcadNutrDiet. 2016;116: 427–438.

48. 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: 953–962. doi: 10.1017/S0007114515000057 25772032

49. Monteiro CA, Cannon G, Levy RB, Moubarac JC, Jaime PC, Martins AP, et al. NOVA. The star shines bright. World Nutr. 2016;7: 28–38.

50. 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: e152. doi: 10.2196/jmir.2575 23928492

51. Vergnaud AC, Touvier M, Mejean C, Kesse-Guyot E, Pollet C, Malon A, et al. Agreement between web-based and paper versions of a socio-demographic questionnaire in the NutriNet-Sante study. Int J Public Health. 2011;56: 407–417. doi: 10.1007/s00038-011-0257-5 21538094

52. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35: 1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB 12900694

53. Rubin DB. Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons; 2004.

54. Ahn S, Lim J, Paik MC, Sacco RL, Elkind MS. Cox model with interval-censored covariate in cohort studies. Biom J Biom Z. 2018;60: 797–814. doi: 10.1002/bimj.201700090 29775990

55. Monteiro CA, Moubarac J-C, Levy RB, Canella DS, Louzada ML da C, Cannon G. Household availability of ultra-processed foods and obesity in nineteen European countries. Public Health Nutr. 2018;21: 18–26. doi: 10.1017/S1368980017001379 28714422

56. Wahlqvist ML. Food structure is critical for optimal health. Food Funct. 2016;7: 1245–1250. doi: 10.1039/c5fo01285f 26667120

57. Kyriazis GA, Soundarapandian MM, Tyrberg B. Sweet taste receptor signaling in beta cells mediates fructose-induced potentiation of glucose-stimulated insulin secretion. Proc Natl Acad Sci U S A. 2012;109: E524–532. doi: 10.1073/pnas.1115183109 22315413

58. Roca-Saavedra P, Mendez-Vilabrille V, Miranda JM, Nebot C, Cardelle-Cobas A, Franco CM, et al. Food additives, contaminants and other minor components: effects on human gut microbiota-a review. J Physiol Biochem. 2018;74: 69–83. doi: 10.1007/s13105-017-0564-2 28488210

59. Bhattacharyya S, O-Sullivan I, Katyal S, Unterman T, Tobacman JK. Exposure to the common food additive carrageenan leads to glucose intolerance, insulin resistance and inhibition of insulin signalling in HepG2 cells and C57BL/6J mice. Diabetologia. 2012;55: 194–203. doi: 10.1007/s00125-011-2333-z 22011715

60. Bhattacharyya S, Feferman L, Tobacman JK. Carrageenan Inhibits Insulin Signaling through GRB10-mediated Decrease in Tyr(P)-IRS1 and through Inflammation-induced Increase in Ser(P)307-IRS1. J Biol Chem. 2015;290: 10764–10774. doi: 10.1074/jbc.M114.630053 25784556

61. Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444: 840–846. doi: 10.1038/nature05482 17167471

62. Gourd E. Ultra-processed foods might increase cancer risk. Lancet Oncol. 2018;19: e186. doi: 10.1016/S1470-2045(18)30184-0 29478712

63. Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Willett WC. Trans Fatty Acids and Cardiovascular Disease. N Engl J Med. 2006;354: 1601–1613. doi: 10.1056/NEJMra054035 16611951

64. Thompson AK, Minihane A-M, Williams CM. Trans fatty acids and weight gain. Int J Obes 2005. 2011;35: 315–324. doi: 10.1038/ijo.2010.141 20644558

65. Dorfman SE, Laurent D, Gounarides JS, Li X, Mullarkey TL, Rocheford EC, et al. Metabolic Implications of Dietary Trans-fatty Acids. Obesity. 2009;17: 1200–1207. doi: 10.1038/oby.2008.662 19584878

66. European CHemical Agency (ECHA). Member State Committee support document for identification of 4,4’-isopropylidenediphenol (bisphenol a) as a substance of very high concern because of its toxic for reproduction (Article 57 c) properties. Adopted on 2 December 2016. [cited 2020 Jan 5]. Available from: https://echa.europa.eu/documents/10162/b10d6a00-8e47-9b14-4f61-c779a8dc8450.

67. Casals-Casas C, Desvergne B. Endocrine disruptors: from endocrine to metabolic disruption. Annu Rev Physiol. 2011;73: 135–162. doi: 10.1146/annurev-physiol-012110-142200 21054169

68. Buckley JP, Kim H, Wong E, Rebholz CM. Ultra-processed food consumption and exposure to phthalates and bisphenols in the US National Health and Nutrition Examination Survey, 2013–2014. Environ Int. 2019;131: 105057. doi: 10.1016/j.envint.2019.105057 31398592

69. Vafeiadi M, Myridakis A, Roumeliotaki T, Margetaki K, Chalkiadaki G, Dermitzaki E, et al. Association of Early Life Exposure to Phthalates With Obesity and Cardiometabolic Traits in Childhood: Sex Specific Associations. Front Public Health. 2018;6: 327. doi: 10.3389/fpubh.2018.00327 30538977

70. Lee H-W, Pyo S. Acrylamide induces adipocyte differentiation and obesity in mice. Chem Biol Interact. 2019;298: 24–34. doi: 10.1016/j.cbi.2018.10.021 30409764

71. Hobbs M, Radley D. Obesogenic environments and obesity: a comment on ‘Are environmental area characteristics at birth associated with overweight and obesity in school-aged children? Findings from the SLOPE (Studying Lifecourse Obesity PrEdictors) population-based cohort in the south of England.’ BMC Med. 2020;18: 59. doi: 10.1186/s12916-020-01538-5 32183849

72. Tyrrell J, Wood AR, Ames RM, Yaghootkar H, Beaumont RN, Jones SE, et al. Gene–obesogenic environment interactions in the UK Biobank study. Int J Epidemiol. 2017;46: 559–575. doi: 10.1093/ije/dyw337 28073954

73. Andreeva VA, Salanave B, Castetbon K, Deschamps V, Vernay M, Kesse-Guyot E, et al. Comparison of the sociodemographic characteristics of the large NutriNet-Santé e-cohort with French Census data: the issue of volunteer bias revisited. J Epidemiol Community Health. 2015;69: 893–898. doi: 10.1136/jech-2014-205263 25832451

74. 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. 2016;184: 660–669. doi: 10.1093/aje/kww016 27744386

75. Rothman KJ. BMI-related errors in the measurement of obesity. Int J Obes 2005. 2008;32 Suppl 3: S56–59. doi: 10.1038/ijo.2008.87 18695655

76. Flom P. Why BMI is a bad measure of obesity (and what is better). In: Medium [Internet]. 3 Aug 2018 [cited 29 May 2020]. Available from: https://medium.com/peter-flom-the-blog/why-bmi-is-a-bad-measure-of-obesity-and-what-is-better-f8a62fc9ca49.

77. Woolcott OO, Bergman RN. Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999–2014. Int J Obes 2005. 2020. doi: 10.1038/s41366-019-0516-8 31911664

78. Gibney MJ, Nutrition Society (Great Britain). Public health nutrition. Oxford, UK; Ames, Iowa: Blackwell Science; 2004. [cited 2020 Feb 20]. Available from: http://www.123library.org/book_details/?id=64307.

79. Willett W. Nutritional Epidemiology. Oxford University Press; 2012. doi: 10.1097/EDE.0b013e31825afb0b

80. Haut Conseil de la Santé Publique. Avis relatif à la révision des repères alimentaires pour les adultes du futur Programme National Nutrition Santé 2017–2021. 2017 février. [cited 2019 Oct 5]. Available from: http://www.hcsp.fr/Explore.cgi/Telecharger?NomFichier=hcspa20170216_reperesalimentairesactua2017.pdf.


Článek vyšel v časopise

PLOS Medicine


2020 Číslo 8
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autoři: MUDr. Tomáš Ürge, PhD.

Střevní příprava před kolonoskopií
Autoři: MUDr. Klára Kmochová, Ph.D.

Závislosti moderní doby – digitální závislosti a hypnotika
Autoři: MUDr. Vladimír Kmoch

Aktuální možnosti diagnostiky a léčby AML a MDS nízkého rizika
Autoři: MUDr. Natália Podstavková

Jak diagnostikovat a efektivně léčit CHOPN v roce 2024
Autoři: doc. MUDr. Vladimír Koblížek, Ph.D.

Všechny kurzy
Přihlášení
Zapomenuté heslo

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

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#