Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-26T14:29:49.927Z Has data issue: false hasContentIssue false
  • English
  • Français

Consumption of ultra-processed foods and IL-6 in two cohorts from high- and middle-income countries

Published online by Cambridge University Press:  21 February 2022

Francine Silva dos Santos Open the ORCID record for Francine Silva dos Santos [Opens in a new window] ,
Gicele Costa Mintem ,
Isabel Oliveira de Oliveira Open the ORCID record for Isabel Oliveira de Oliveira [Opens in a new window] ,
Bernardo Lessa Horta ,
Elisabete Ramos ,
Carla Lopes  and
Denise Petrucci Gigante
Francine Silva dos Santos*
Affiliation:
Programa de pós-graduação em Epidemiologia, Universidade Federal de Pelotas, Pelotas, Brasil
Gicele Costa Mintem
Affiliation:
Programa de pós-graduação em Nutrição e Alimentos, Universidade Federal de Pelotas, Pelotas, Brasil
Isabel Oliveira de Oliveira
Affiliation:
Programa de pós-graduação em Epidemiologia, Universidade Federal de Pelotas, Pelotas, Brasil
Bernardo Lessa Horta
Affiliation:
Programa de pós-graduação em Epidemiologia, Universidade Federal de Pelotas, Pelotas, Brasil
Elisabete Ramos
Affiliation:
EPIUnit – Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
Carla Lopes
Affiliation:
EPIUnit – Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
Denise Petrucci Gigante
Affiliation:
Programa de pós-graduação em Epidemiologia, Universidade Federal de Pelotas, Pelotas, Brasil Programa de pós-graduação em Nutrição e Alimentos, Universidade Federal de Pelotas, Pelotas, Brasil
*
*Corresponding author: Francine Silva dos Santos, email nutrifrancinesantos@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This study evaluated the association between ultra-processed foods (UPF) on serum IL-6 and to investigate the mediation role of adiposity. Participants were 524 adults from the EPITeen Cohort (Porto, Portugal) and 2888 participants from the 1982 Pelotas Birth Cohort (Pelotas, Brazil). Dietary intake was collected using FFQ when participants were 21 years of age in the EPITeen and 23 years in the Pelotas Cohort. Serum IL-6 and body fat mass were evaluated when participants were 27 and 30 years old in the EPITeen and Pelotas, respectively. Generalised linear models were fitted to test main associations. Mediation of body fat mass was estimated using G-computation. After adjustment for socio-economic and behaviour variables, among females from the EPITeen, the concentration of IL-6 (pg/ml) increased with increasing intake of UPF from 1·31 (95 % CI 0·95, 1·82) in the first UPF quartile to 2·20 (95 % CI 1·60, 3·01) and 2·64 (95 % CI 1·89, 3·69) for the third and fourth UPF quartiles, respectively. A similar result was found among males in the Pelotas Cohort, IL-6 increased from 1·40 (95 % CI 1·32, 1·49) in the first UPF quartile to 1·50 (95 % CI 1·41, 1·59) and 1·59 (95 % CI 1·49, 1·70) in the two highest UPF quartiles. The P-value for the linear trend was < 0·01 in both findings. The indirect effect through fat mass was NS. Our findings suggest that the consumption of UPF was associated with an increase in IL-6 concentration; however, this association was not explained by adiposity.

Keywords

Food consumptionInflammationIL-6AdultsCohort studies
Type
Research Article
Information
British Journal of Nutrition , Volume 129 , Issue 9 , 14 May 2023 , pp. 1552 - 1562
Check for updates
Copyright
© The Author(s), 2022. 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

Monteiro, CA, Cannon, G, Levy, RB, et al. (2019) Ultra-processed foods: what they are and how to identify them. Public Health Nutr 22, 936–941.CrossRefGoogle Scholar
Louzada, ML, Martins, APB, Canella, DS, et al. (2015) Impact of ultra-processed foods on micronutrient content in the Brazilian diet. Rev Saude Publica 49, 45.Google ScholarPubMed
Da Costa Louzada, ML, Ricardo, CZ, Steele, EM, et al. (2018) The share of ultra-processed foods determines the overall nutritional quality of diets in Brazil. Public Health Nutr 21, 94–102.CrossRefGoogle Scholar
Martínez Steele, E, Raubenheimer, D, Simpson, SJ, et al. (2018) Ultra-processed foods, protein leverage and energy intake in the USA. Public Health Nutr 21, 114–124.CrossRefGoogle ScholarPubMed
Moubarac, JC, Batal, M, Louzada, ML, et al. (2017) Consumption of ultra-processed foods predicts diet quality in Canada. Appetite 108, 512–520.CrossRefGoogle ScholarPubMed
de Miranda, RC, Rauber, F, de Moraes, MM, et al. (2020) Consumption of ultra-processed foods and non-communicable diseases-related nutrient profile in Portuguese adults and elderly (2015–2016): the Upper project. Br J Nutr 125, 1177–1187.CrossRefGoogle Scholar
Louzada, ML, Martins, APB, Canella, DS, et al. (2015) Ultra-processed foods and the nutritional dietary profile in Brazil. Rev Saude Publica 49, 1–11.CrossRefGoogle Scholar
Zinöcker, MK & Lindseth, IA (2018) The western diet–microbiome–host interaction and its role in metabolic disease. Nutrients 10, 1–15.CrossRefGoogle ScholarPubMed
Cuevas-Sierra, A, Milagro, FI, Aranaz, P, et al. (2021) Gut microbiota differences according to ultra-processed food consumption in a Spanish population. Nutrients 13, 2710.CrossRefGoogle Scholar
Calder, PC, Ahluwalia, N, Brouns, F, et al. (2011) Dietary factors and low-grade inflammation in relation to overweight and obesity. Br J Nutr 106, S5–S78.CrossRefGoogle ScholarPubMed
Calder, PC, Ahluwalia, N, Albers, R, et al. (2013) A consideration of biomarkers to be used for evaluation of inflammation in human nutritional studies. Br J Nutr 109, S1–S34.CrossRefGoogle Scholar
Sarwar, N, Butterworth, AS, Freitag, DF, et al. (2012) Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet 379, 1205–1213.Google ScholarPubMed
Swerdlow, DI, Holmes, MV, Kuchenbaecker, KB, et al. (2012) The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet 379, 1214–1224.Google ScholarPubMed
Word Health Organization (2018) HEARTS Technical Package for Cardiovascular Disease Management in Primary Health Care: Healthy-Lifestyle Counselling. Geneva: WHO.Google Scholar
Barbaresko, J, Koch, M, Schulze, MB, et al. (2013) Dietary pattern analysis and biomarkers of low-grade inflammation: a systematic literature review. Nutr Rev 71, 511–527.CrossRefGoogle ScholarPubMed
Defago, MD, Elorriaga, N, Irazola, VE, et al. (2014) Influence of food patterns on endothelial biomarkers: a systematic review. J Clin Hypertens 16, 907–913.CrossRefGoogle ScholarPubMed
Smidowicz, A & Regula, J (2015) Effect of nutritional status and dietary patterns on human serum C-reactive protein and interleukin-6 concentrations. Adv Nutr 6, 738–747.CrossRefGoogle ScholarPubMed
Schwingshackl, L & Hoffmann, G (2014) Mediterranean dietary pattern, inflammation and endothelial function: a systematic review and meta-analysis of intervention trials. Nutr Metab Cardiovasc Dis 24, 929–939.CrossRefGoogle ScholarPubMed
Lopes, AE, Araújo, LF, Levy, RB, et al. (2019) Association between consumption of ultra-processed foods and serum c-reactive protein levels: cross-sectional results from the ELSA-Brasil study. Sao Paulo Med J 137, 169–176.CrossRefGoogle ScholarPubMed
Hall, KD, Ayuketah, A, Brychta, R, et al. (2019) Ultra-processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metab 30, 67–77.CrossRefGoogle ScholarPubMed
Mendonca, RD, Pimenta, AM, Gea, A, et al. (2016) Ultraprocessed food consumption and risk of overweight and obesity: the University of Navarra Follow-Up (SUN) cohort study. Am J Clin Nutr 104, 1433–1440.CrossRefGoogle ScholarPubMed
Beslay, M, Srour, B, Méjean, C, et al. (2020) 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, 1–19.CrossRefGoogle ScholarPubMed
Menezes, AMB, Oliveira, PD, Wehrmeister, FC, et al. (2018) Association between interleukin-6, C-reactive protein and adiponectin with adiposity: findings from the 1993 pelotas (Brazil) birth cohort at 18 and 22 years. Cytokine 110, 44–51.CrossRefGoogle ScholarPubMed
Vella, CA & Allison, MA (2019) Associations of abdominal intermuscular adipose tissue and inflammation: the Multi-Ethnic Study of Atherosclerosis. Obes Res Clin Pract 12, 534–540.CrossRefGoogle Scholar
Bielemann, RM, Santos Motta, JV, Minten, GC, et al. (2015) Consumption of ultra-processed foods and their impact on the diet of young adults. Rev Saude Publica 49, 1–10.CrossRefGoogle ScholarPubMed
dos Santos Costa, C, Assunção, MCF, dos Santos Vaz, J, et al. (2021) Consumption of ultra-processed foods at 11, 22 and 30 years at the 2004, 1993 and 1982 Pelotas Birth Cohorts. Public Health Nutr 24, 299–308.CrossRefGoogle ScholarPubMed
Schnabel, L, Kesse-Guyot, E, Allès, B, et al. (2019) Association between ultraprocessed food consumption and risk of mortality among middle-aged adults in France. JAMA Intern Med 179, 490–498.CrossRefGoogle ScholarPubMed
Baraldi, LG, Martinez Steele, E, Canella, DS, et al. (2018) 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 8, e020574.CrossRefGoogle ScholarPubMed
Djupegot, IL, Nenseth, CB, Bere, E, et al. (2017) The association between time scarcity, sociodemographic correlates and consumption of ultra-processed foods among parents in Norway: a cross-sectional study. BMC Public Health 17, 1–8.CrossRefGoogle ScholarPubMed
Brion, MJA, Lawlor, DA, Matijasevich, A, et al. (2011) What are the causal effects of breastfeeding on IQ, obesity and blood pressure? Evidence from comparing high-income with middle-income cohorts. Int J Epidemiol 40, 670–680.CrossRefGoogle ScholarPubMed
Ramos, E & Barros, H (2007) Family and school determinants of overweight in 13-year-old Portuguese adolescents. Acta Paediatr Int J Paediatr 96, 281–286.CrossRefGoogle ScholarPubMed
Ridker, PM, Rifai, N, Stampfer, MJ, et al. (2000) Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Circulation 101, 1767–1772.CrossRefGoogle ScholarPubMed
Amaral, WZ, Krueger, RF, Ryff, CD, et al. (2016) Genetic and environmental determinants of population variation in interleukin-6, its soluble receptor and C-reactive protein: insights from identical and fraternal Caucasian twins. Brain Behav Immun 49, 171–181.CrossRefGoogle Scholar
Rico-Campà, A, Martínez-González, MA, Alvarez-Alvarez, I, et al. (2019) Association between consumption of ultra-processed foods and all cause mortality: SUN prospective cohort study. BMJ 365, l1949.CrossRefGoogle ScholarPubMed
Hornung, RW & Reed, LD (1990) Estimation of average concentration in the presence of Nondetectable values. Appl Occup Environ Hyg 5, 46–51.CrossRefGoogle Scholar
Barros, FC, Victora, CG, Horta, BL, et al. (2008) Methodology of the Pelotas birth cohort study from 1982 to 2004-5, Southern Brazil. Revista de Saúde Pública 42, 7–15.CrossRefGoogle ScholarPubMed
Horta, BL, Gigante, DP, Gonçalves, H, et al. (2015) Cohort profile update: the 1982 Pelotas (Brazil) Birth Cohort Study. Int J Epidemiol 44, 441–441e.CrossRefGoogle ScholarPubMed
Victora, CG & Barros, FC (2006) Cohort profile: the 1982 Pelotas (Brazil) Birth Cohort Study. Int J Epidemiol 35, 237–242.CrossRefGoogle ScholarPubMed
Willett, WC, Sampson, L, Stampfer, MJ, et al. (1985) Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 122, 51–65.CrossRefGoogle ScholarPubMed
Lopes, C, Aro, A, Azevedo, A, et al. (2007) Intake and adipose tissue composition of fatty acids and risk of myocardial infarction in a male Portuguese community sample. J Am Dietetic Assoc 107, 276–286.CrossRefGoogle Scholar
Lopes, C (2000) Reproducibility and Validity of Semiquantitative Food Frequency Questionnaire. In Diet and Myocardial Infarction: A Community-Based Case-Control Study (PhD Thesis). University of Porto. https://repositorio-aberto.up.pt/handle/10216/9938 (accessed February 2022).Google Scholar
Sichieri, R & Everhart, JE (1998) Validity of a Brazilian food frequency questionnaire against dietary recalls and estimated energy intake. Rev Bras Epidemiol 18, 1649–1659.Google Scholar
Monteiro, CA, Cannon, G, Moubarac, JC, et al. (2018) The un decade of nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr 21, 5–17.CrossRefGoogle ScholarPubMed
Monteiro, CA, Cannon, G, Levy, R, et al. (2016) The food system. Food classification. Public health. NOVA. The star shines bright. World Nutr 7, 28–38.Google Scholar
Bazzocchi, A, Ponti, F, Albisinni, U, et al. (2016) DXA: technical aspects and application. Eur J Radiol 85, 1481–1492.CrossRefGoogle ScholarPubMed
Magalhães, A, Severo, M, Autran, R, et al. (2017) Validation of a single question for the evaluation of physical activity in adolescents. Int J Sport Nutr Exerc Metab 27, 361–369.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, 1381–1395.CrossRefGoogle ScholarPubMed
Daniel, RM, de Stavola, BL & Cousens, SN (2011) Gformula: estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula. Stata J 11, 479–517.CrossRefGoogle Scholar
De Stavola, BL, Daniel, RM, Ploubidis, GB, et al. (2015) Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens. Am J Epidemiol 181, 64–80.CrossRefGoogle ScholarPubMed
Akbaraly, TN, Shipley, MJ, Ferrie, JE, et al. (2015) Long-term adherence to healthy dietary guidelines and chronic inflammation in the prospective Whitehall II study. Am J Med 128, 152–160.e4.CrossRefGoogle ScholarPubMed
Klein, SL & Flanagan, KL (2016) Sex differences in immune responses. Nat Rev Immunol 16, 626–638.CrossRefGoogle ScholarPubMed
Zmora, N, Suez, J & Elinav, E (2019) You are what you eat: diet, health and the gut microbiota. Nat Rev Gastroenterol Hepatol 16, 35–56.CrossRefGoogle ScholarPubMed
Shi, Z (2019) Gut microbiota: an important link between western diet and chronic diseases. Nutrients 11, 2287.CrossRefGoogle ScholarPubMed
Dos Santos Costa, C, Formoso Assunção, MC, Dos Santos Vaz, J, et al. (2020) Consumption of ultra-processed foods at 11, 22 and 30 years at the 2004, 1993 and 1982 Pelotas Birth Cohorts. Public Health Nutr 24, 299–308.CrossRefGoogle ScholarPubMed
Simões, BD, Cardoso, LD, Benseñor, IJ, et al. (2018) Consumption of ultra-processed foods and socioeconomic position: a cross-sectional analysis of the Brazilian Longitudinal Study of Adult Health. Cadernos Saude Publica 34, 1–13.CrossRefGoogle ScholarPubMed
Khandpur, N, Cediel, G, Obando, DA, et al. (2020) Sociodemographic factors associated with the consumption of ultra-processed foods in Colombia. Rev Saude Publica 54, 1–12.CrossRefGoogle ScholarPubMed
Ahluwalia, N, Andreeva, VA, Kesse-Guyot, E, et al. (2013) Dietary patterns, inflammation and the metabolic syndrome. Diabetes Metab 39, 99–110.CrossRefGoogle ScholarPubMed
Food and Agriculture Organization of the United Nations (2019) Ultra-Processed Foods, Diet Quality, and Health Using the NOVA Classification System. Rome: FAO.Google Scholar
Buckley, JP, Kim, H, Wong, E, et al. (2019) Ultra-processed food consumption and exposure to phthalates and bisphenols in the US National Health and Nutrition Examination Survey, 2013–2014. Environ Int 131, 105057.CrossRefGoogle ScholarPubMed
Ferguson, KK, Loch-Caruso, R & Meeker, JD (2011) Urinary phthalate metabolites in relation to biomarkers of inflammation and oxidative stress: NHANES 1999–2006. Environ Res 111, 718–726.CrossRefGoogle Scholar
Ferguson, KK, Cantonwine, DE, Rivera-González, LO, et al. (2014) Urinary phthalate metabolite associations with biomarkers of inflammation and oxidative stress across pregnancy in Puerto Rico. Environ Sci Technol 48, 7018–7025.CrossRefGoogle Scholar
Willett, W (1989) An overview of issues related to the correction of non-differential exposure measurement error in epidemiologic studies. Stat Med 8, 1031–1040.CrossRefGoogle Scholar
Instituto Brasileiro de Geografia (2020) Consumer Expenditure Survey 2017-2018: analysis of Personal Food Consumption in Brazil. Rio Janeiro: IBGE.Google Scholar
Costa, CD, Assunção, MC, Loret de Mola, C, et al. (2020) Role of ultra-processed food in fat mass index between 6 and 11 years of age: a cohort study. Int J Epidemiol 50, 1–10.Google Scholar
Hoevenaar-Blom, MP, Spijkerman, AMW, Boshuizen, HC, et al. (2014) Effect of using repeated measurements of a Mediterranean style diet on the strength of the association with cardiovascular disease during 12 years: the Doetinchem Cohort Study. Eur J Nutr 53, 1209–1215.CrossRefGoogle ScholarPubMed
Sempos, CT, Flegal, KM, Johnson, CL, et al. (1993) Issues in the Long-Term Evaluation of Diet in Longitudinal Studies. J Nutr 123, S406–S412.CrossRefGoogle ScholarPubMed
Dossus, L, Becker, S, Achaintre, D, et al. (2009) Validity of multiplex-based assays for cytokine measurements in serum and plasma from ‘non-diseased’ subjects: comparison with ELISA. J Immunol Meth 350, 125–132.CrossRefGoogle ScholarPubMed
Supplementary material: File

Silva dos Santos et al. supplementary material

Tables S1-S6 and Figure S1

Download Silva dos Santos et al. supplementary material(File)
File 116.5 KB