Prospective association between ultra-processed food consumption and incident depressive symptoms in the French NutriNet-Santé cohort

Moufidath Adjibade, Chantal Julia, Benjamin Allès, Mathilde Touvier, Cédric Lemogne, Bernard Srour, Serge Hercberg, Pilar Galan, Karen E Assmann, Emmanuelle Kesse-Guyot, Moufidath Adjibade, Chantal Julia, Benjamin Allès, Mathilde Touvier, Cédric Lemogne, Bernard Srour, Serge Hercberg, Pilar Galan, Karen E Assmann, Emmanuelle Kesse-Guyot

Abstract

Background: Ultra-processed food (UPF) consumption has increased over the last decades in Westernized countries. Our objective was to investigate for the first time the association between the proportion of UPF (%UPF) in the diet and incident depressive symptoms in the NutriNet-Santé cohort.

Methods: The sample included 20,380 women and 6350 men (aged 18-86 years) without depressive symptoms at the first Center for Epidemiologic Studies Depression Scale (CES-D) measurement, using validated cut-offs (CES-D score ≥ 17 for men and ≥ 23 for women). The proportion of UPF in the diet was computed for each subject using the NOVA classification applied to dietary intakes collected by repeated 24-h records (mean = 8; SD = 2.3). The association between UPF and depressive symptoms was evaluated using multivariable Cox proportional hazards models.

Results: Over a mean follow-up of 5.4 years, 2221 incident cases of depressive symptoms were identified. After accounting for a wide range of potential confounders, an increased risk of depressive symptoms was observed with an increased %UPF in the diet. In the main model adjusted for sociodemographic characteristics, body mass index, and lifestyle factors, the estimated hazard ratio for a 10% increase in UPF was 1.21 (95% confidence interval = 1.15-1.27). Considering %UPF in food groups, the association was significant only for beverages and sauces or added fats.

Conclusion: Overall, UPF consumption was positively associated with the risk of incident depressive symptoms, suggesting that accounting for this non-nutritional aspect of the diet could be important for mental health promotion.

Keywords: Depression; Mental health; Prospective cohort; Ultra-processed food.

Conflict of interest statement

Ethics approval and consent to participate

The NutriNet-Santé study is conducted in accordance with the Declaration of Helsinki and was approved by the ethics committee of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and by the National Commission on Informatics and Liberty (CNIL no. 908450 and no. 909216). Electronic informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

Cédric Lemogne has received honoraria for board membership from Lundbeck and for speaking at invited symposia from Janssen, Lundbeck and Otsuka Pharmaceuticals.

The other authors declare that they have no conflict of interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Flow chart of participant selection. CES-D Center for Epidemiologic Studies Depression Scale
Fig. 2
Fig. 2
Association between ultra-processed food intake and incident depressive symptoms in population subgroups. Values are hazard ratios (HR) and 95% confidence intervals (95% CI). BMI body max index; BMR basal metabolic rate; CES-D Center for Epidemiologic Studies Depression Scale; EI energy intake. Model was adjusted for sex, age, marital status, educational level, occupational categories, household income per consumption unit, residential area, number of 24-h dietary records, inclusion month, energy intake without alcohol, alcohol intake, body max index, smoking status, and physical activity (main model)

References

    1. GBD 2016 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Lond Engl. 2017;390:1211–1259. doi: 10.1016/S0140-6736(17)32154-2.
    1. WHO Fact sheet - Depression. World Health Organization. 2018. . Accessed 7 June 2018.
    1. Post RM. Heading off depressive illness evolution and progression to treatment resistance. Dialogues Clin Neurosci. 2015;17:105–109.
    1. Rahe C, Unrath M, Berger K. Dietary patterns and the risk of depression in adults: a systematic review of observational studies. Eur J Nutr. 2014;53:997–1013. doi: 10.1007/s00394-014-0652-9.
    1. Lai JS, Hiles S, Bisquera A, Hure AJ, McEvoy M, Attia J. A systematic review and meta-analysis of dietary patterns and depression in community-dwelling adults. Am J Clin Nutr. 2014;99:181–197. doi: 10.3945/ajcn.113.069880.
    1. Li Y, Lv M-R, Wei Y-J, Sun L, Zhang J-X, Zhang H-G, et al. Dietary patterns and depression risk: a meta-analysis. Psychiatry Res. 2017;253:373–382. doi: 10.1016/j.psychres.2017.04.020.
    1. Molendijk M, Molero P, Ortuno Sanchez-Pedreno F, Van der Does W, Angel Martinez-Gonzalez M. Diet quality and depression risk: A systematic review and dose-response meta-analysis of prospective studies. J Affect Disord. 2018;226:346–354. doi: 10.1016/j.jad.2017.09.022.
    1. Adjibade Moufidath, Lemogne Cédric, Julia Chantal, Hercberg Serge, Galan Pilar, Assmann Karen E., Kesse-Guyot Emmanuelle. Prospective association between adherence to dietary recommendations and incident depressive symptoms in the French NutriNet-Santé cohort. British Journal of Nutrition. 2018;120(03):290–300. doi: 10.1017/S0007114518000910.
    1. Wang Jian, Zhou Yao, Chen Kang, Jing Yuntian, He Jiaan, Sun Hongxiao, Hu Xinhua. Dietary inflammatory index and depression: a meta-analysis. Public Health Nutrition. 2018;22(04):654–660. doi: 10.1017/S1368980018002628.
    1. Luiten CM, Steenhuis IH, Eyles H, Ni Mhurchu C, 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;19:530–538. doi: 10.1017/S1368980015002177.
    1. Martínez Steele E, Baraldi LG, da C Louzada ML, Moubarac J-C, 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.
    1. Monteiro CA, Moubarac J-C, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev. 2013;14:21–28. doi: 10.1111/obr.12107.
    1. Baraldi LG, Martinez Steele E, Canella DS, Monteiro CA. Consumption of ultra-processed foods and associated sociodemographic factors in the USA between 2007 and 2012: evidence from a nationally representative cross-sectional study. BMJ Open. 2018;8:e020574. doi: 10.1136/bmjopen-2017-020574.
    1. Julia Chantal, Martinez Lucien, Allès Benjamin, Touvier Mathilde, Hercberg Serge, Méjean Caroline, Kesse-Guyot Emmanuelle. Contribution of ultra-processed foods in the diet of adults from the French NutriNet-Santé study. Public Health Nutrition. 2017;21(01):27–37. doi: 10.1017/S1368980017001367.
    1. Ludwig DS. Technology, diet, and the burden of chronic disease. JAMA. 2011;305:1352–1353. doi: 10.1001/jama.2011.380.
    1. 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.
    1. Clapp M, Aurora N, Herrera L, Bhatia M, Wilen E, Wakefield S. Gut microbiota’s effect on mental health: the gut-brain axis. Clin Pract. 2017;7:987. doi: 10.4081/cp.2017.987.
    1. da C Louzada ML, Baraldi LG, Steele EM, APB M, Canella DS, Moubarac J-C, et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev Med. 2015;81:9–15. doi: 10.1016/j.ypmed.2015.07.018.
    1. de D Mendonça R, ACS L, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M. Ultra-Processed Food Consumption and the Incidence of Hypertension in a Mediterranean Cohort: The Seguimiento Universidad de Navarra Project. Am J Hypertens. 2017;30:358–366.
    1. Tavares LF, Fonseca SC, Garcia Rosa ML, Yokoo EM. Relationship between ultra-processed foods and metabolic syndrome in adolescents from a Brazilian Family Doctor Program. Public Health Nutr. 2012;15:82–87. doi: 10.1017/S1368980011001571.
    1. 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.
    1. 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;10:242. doi: 10.1186/1471-2458-10-242.
    1. Führer R, Rouillon F. The French version of the Center for Epidemiologic Studies-Depression Scale. 4, 163-166. Psychiatr Psychobiol. 1989;4:163–166.
    1. Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401. doi: 10.1177/014662167700100306.
    1. 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;105:1055–1064. doi: 10.1017/S0007114510004617.
    1. 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.
    1. 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. JAcadNutr Diet. 2016;116:427–438.
    1. Le Moullec N, Deheeger M, Preziosi P, Monteiro P, Valeix P, Rolland-Cachera M, et al. Validation of the photo manual used for the collection of dietary data in the SU.VI.MAX study. Cah Nutr Diététique. 1996;31:158-64.
    1. NutriNet-Santé coordination . Table de composition des aliments - Etude NutriNet-Santé. Paris: Economica; 2013.
    1. 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. 2000;24:1119–1130. doi: 10.1038/sj.ijo.0801376.
    1. Monteiro CA, Cannon G, Moubarac J-C, Levy RB, Louzada MLC, Jaime PC. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018;21:5–17. doi: 10.1017/S1368980017000234.
    1. Vergnaud A-C, Touvier M, Méjean 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-Santé study. Int J Public Health. 2011;56:407–417. doi: 10.1007/s00038-011-0257-5.
    1. INSEE . Definitions and methods. 2009.
    1. 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.
    1. Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9:755–762. doi: 10.1079/PHN2005898.
    1. Derouesné C. Empirical evaluation of the “Cognitive difficulties scale” for assessment of memory complaints in general practice: a study of 1628 cognitively normal subjects aged 45-75 years. Int J Geriatr Psychiatry. 1993;8:599–607. doi: 10.1002/gps.930080712.
    1. McNair D, Kahn R. Self-assessment of cognitive deficits. In Assessment in Geriatr Psychopharmacol, Crook T, Ferris A, Baltus R Eds., Mark Powley Associates, New Canaan, CT. 1983;137–43.
    1. Andridge RR, Little RJA. A review of hot deck imputation for survey non-response. Int Stat Rev Rev Int Stat. 2010;78:40–64. doi: 10.1111/j.1751-5823.2010.00103.x.
    1. Willett W. Nutritional epidemiology. USA: OUP; 2012.
    1. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39(Suppl 1):5–41.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;57:289–300.
    1. Moubarac J-C, Batal M, Louzada ML, Martinez Steele E, 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.
    1. Djupegot IL, Nenseth CB, Bere E, Bjørnarå HBT, Helland SH, Øverby NC, et al. The association between time scarcity, sociodemographic correlates and consumption of ultra-processed foods among parents in Norway: a cross-sectional study. BMC Public Health. 2017;17:447. doi: 10.1186/s12889-017-4408-3.
    1. Martínez Steele E, Popkin BM, Swinburn B, Monteiro CA. The share of ultra-processed foods and the overall nutritional quality of diets in the US: evidence from a nationally representative cross-sectional study. Popul Health Metrics. 2017;15:6. doi: 10.1186/s12963-017-0119-3.
    1. Akbaraly TN, Brunner EJ, Ferrie JE, Marmot MG, Kivimaki M, Singh-Manoux A. Dietary pattern and depressive symptoms in middle age. Br J Psychiatry. 2009;195:408–413. doi: 10.1192/bjp.bp.108.058925.
    1. Sánchez-Villegas A, Toledo E, de Irala J, Ruiz-Canela M, Pla-Vidal J, Martínez-González MA. Fast-food and commercial baked goods consumption and the risk of depression. Public Health Nutr. 2012;15:424–432. doi: 10.1017/S1368980011001856.
    1. Jacka FN, Cherbuin N, Anstey KJ, Butterworth P. Dietary patterns and depressive symptoms over time: examining the relationships with socioeconomic position, health behaviours and cardiovascular risk. PLoS One. 2014;9:e87657. doi: 10.1371/journal.pone.0087657.
    1. Cenit MC, Sanz Y, Codoñer-Franch P. Influence of gut microbiota on neuropsychiatric disorders. World J Gastroenterol. 2017;23:5486–5498. doi: 10.3748/wjg.v23.i30.5486.
    1. Choudhary AK, Lee YY. Neurophysiological symptoms and aspartame: what is the connection? Nutr Neurosci. 2018;21:306–316. doi: 10.1080/1028415X.2017.1288340.
    1. Lohner S, Toews I, Meerpohl JJ. Health outcomes of non-nutritive sweeteners: analysis of the research landscape. Nutr J. 2017;16:55. doi: 10.1186/s12937-017-0278-x.
    1. Zinöcker MK, Lindseth IA. The Western diet–microbiome-host interaction and its role in metabolic disease. Nutrients. 2018;10:365. doi: 10.3390/nu10030365.
    1. Grissa I, Guezguez S, Ezzi L, Chakroun S, Sallem A, Kerkeni E, et al. The effect of titanium dioxide nanoparticles on neuroinflammation response in rat brain. Environ Sci Pollut Res Int. 2016;23:20205–20213. doi: 10.1007/s11356-016-7234-8.
    1. Quines CB, Rosa SG, Da Rocha JT, Gai BM, Bortolatto CF, Duarte MMMF, et al. Monosodium glutamate, a food additive, induces depressive-like and anxiogenic-like behaviors in young rats. Life Sci. 2014;107:27–31. doi: 10.1016/j.lfs.2014.04.032.
    1. Campos-Sepúlveda AE, Martínez Enríquez ME, Rodríguez Arellanes R, Peláez LE, Rodríguez Amézquita AL, Cadena RA. Neonatal monosodium glutamate administration increases aminooxyacetic acid (AOA) susceptibility effects in adult mice. Proc West Pharmacol Soc. 2009;52:72–74.
    1. 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.

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