Esther María González-Gil1,2,3,4, Gianluca Tognon5, Lauren Lissner5, Timm Intemann6, Valeria Pala7, Claudio Galli8, Maike Wolters6, Alfonso Siani9, Toomas Veidebaum10, Nathalie Michels11, Denes Molnar12, Jaakko Kaprio13, Yannis Kourides14, Arno Fraterman15, Licia Iacoviello16, Catalina Picó17,18, Juan Miguel Fernández-Alvira19,20, Luis Alberto Moreno Aznar19,21,22,17. 1. GENUD (Growth, Exercise, NUtrition and Development) Research Group, Faculty of Health Sciences, Universidad de Zaragoza, C/ Pedro Cerbuna, 12, 50009, Zaragoza, Spain. esthergg@unizar.es. 2. Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain. esthergg@unizar.es. 3. Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain. esthergg@unizar.es. 4. Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Madrid, Spain. esthergg@unizar.es. 5. Section for Epidemiology and Social Medicine (EPSO), Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 6. Leibniz Institute for Prevention Research and Epidemiology, Bremen, Germany. 7. Department of Preventive Medicine, Nutritional Epidemiology Unit, Fondazione IRCSS Istituto Nazionale dei Tumori, Milan, Italy. 8. Department of Pharmacological Sciences, University of Milan, Milan, Italy. 9. Unit of Epidemiology and Population Genetics, Institute of Food Sciences, National Research Council, Avellino, Italy. 10. National Institute for Health Development, Estonian Centre of Behavioral and Health Sciences, Tallinn, Estonia. 11. Department of Public Health, Ghent University, Ghent, Belgium. 12. Department of Paediatrics, Medical Faculty, University of Pécs, Pécs, Hungary. 13. Department of Public Health and Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland. 14. Research and Education Institute of Child health, REF, Strovolos, Cyprus. 15. Laboratoriumsmedizin Dortmund, Eberhard & Partner, Dortmund, Germany. 16. IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Molise, Italy. 17. Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Madrid, Spain. 18. Laboratory of Molecular Biology, Nutrition and Biotechnology, University of the Balearic Islands, Palma de Mallorca, Spain. 19. GENUD (Growth, Exercise, NUtrition and Development) Research Group, Faculty of Health Sciences, Universidad de Zaragoza, C/ Pedro Cerbuna, 12, 50009, Zaragoza, Spain. 20. Fundación Centro Nacional de Investigaciones Cardiovasculares (CNIC) Carlos III, Madrid, Spain. 21. Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain. 22. Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain.
Abstract
PURPOSE: This prospective study explores high sensitivity C-reactive protein (hs-CRP) levels in relation to dietary patterns at two time points in European children. METHODS: Out of the baseline sample of the IDEFICS study (n = 16,228), 4020 children, aged 2-9 years at baseline, with available hs-CRP levels and valid data from a food frequency questionnaire (FFQ) at baseline (T0) and 2 years later (T1) were included. K-means clustering algorithm based on the similarities between relative food consumption frequencies of the FFQ was applied. hs-CRP was dichotomized according to sex-specific cutoff points. Multilevel logistic regression was performed to assess the relationship between dietary patterns and hs-CRP adjusting for covariates. RESULTS: Three consistent dietary patterns were found at T0 and T1: 'animal protein and refined carbohydrate', 'sweet and processed' and 'healthy'. Children allocated to the 'protein' and 'sweet and processed' clusters at both time points had significantly higher odds of being in the highest category of hs-CRP (OR 1.47; 95% CI 1.03-2.09 for 'animal protein and refined carbohydrate' and OR 1.44; 95% CI 1.08-1.92 for 'sweet and processed') compared to the 'healthy' cluster. The odds remained significantly higher for the 'sweet and processed' pattern (OR 1.39; 95% CI 1.05-1.84) when covariates were included. CONCLUSIONS: A dietary pattern characterized by frequent consumption of sugar and processed products and infrequent consumption of vegetables and fruits over time was independently related with inflammation in European children. Efforts to improve the quality of the diet in childhood may prevent future diseases related with chronic inflammation.
PURPOSE: This prospective study explores high sensitivity C-reactive protein (hs-CRP) levels in relation to dietary patterns at two time points in European children. METHODS: Out of the baseline sample of the IDEFICS study (n = 16,228), 4020 children, aged 2-9 years at baseline, with available hs-CRP levels and valid data from a food frequency questionnaire (FFQ) at baseline (T0) and 2 years later (T1) were included. K-means clustering algorithm based on the similarities between relative food consumption frequencies of the FFQ was applied. hs-CRP was dichotomized according to sex-specific cutoff points. Multilevel logistic regression was performed to assess the relationship between dietary patterns and hs-CRP adjusting for covariates. RESULTS: Three consistent dietary patterns were found at T0 and T1: 'animal protein and refined carbohydrate', 'sweet and processed' and 'healthy'. Children allocated to the 'protein' and 'sweet and processed' clusters at both time points had significantly higher odds of being in the highest category of hs-CRP (OR 1.47; 95% CI 1.03-2.09 for 'animal protein and refined carbohydrate' and OR 1.44; 95% CI 1.08-1.92 for 'sweet and processed') compared to the 'healthy' cluster. The odds remained significantly higher for the 'sweet and processed' pattern (OR 1.39; 95% CI 1.05-1.84) when covariates were included. CONCLUSIONS: A dietary pattern characterized by frequent consumption of sugar and processed products and infrequent consumption of vegetables and fruits over time was independently related with inflammation in European children. Efforts to improve the quality of the diet in childhood may prevent future diseases related with chronic inflammation.
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