Iris Iglesia1, Inge Huybrechts2, Theodora Mouratidou3, Javier Santabárbara4, Juan M Fernández-Alvira5, Alba M Santaliestra-Pasías6, Yannis Manios7, Alejandro De la O Puerta8, Anthony Kafatos9, Frédéric Gottrand10, Ascensión Marcos11, Stefania Sette12, Maria Plada9, Peter Stehle13, Dénes Molnár14, Kurt Widhalm15, Mathilde Kersting16, Stefaan De Henauw17, Luis A Moreno6, Marcela González-Gross18. 1. Growth, Exercise, NUtrition and Development (GENUD) Research group, Universidad de Zaragoza, Zaragoza, Spain; Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Aragón, Spain; Red de Salud Materno-infantil y del Desarrollo (SAMID), Barakaldo, Spain. Electronic address: Iglesia@unizar.es. 2. Department of Public Health, Ghent University, Ghent, Belgium; International Agency for Research on Cancer (IARC), Lyon, France. 3. Growth, Exercise, NUtrition and Development (GENUD) Research group, Universidad de Zaragoza, Zaragoza, Spain. 4. Department of Preventive Medicine and Public Health, Universidad de Zaragoza, Zaragoza, Spain. 5. Growth, Exercise, NUtrition and Development (GENUD) Research group, Universidad de Zaragoza, Zaragoza, Spain; Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain. 6. Growth, Exercise, NUtrition and Development (GENUD) Research group, Universidad de Zaragoza, Zaragoza, Spain; Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Aragón, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Madrid, Spain. 7. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece. 8. Department of Physiology, School of Medicine, University of Granada, Granada, Spain. 9. University of Crete School of Medicine, Crete, Greece. 10. Université de Lille, Clinical Investigation Centre, Lille, France. 11. Immunonutrition Research Group, Department of Metabolism and Nutrition, Institute of Food Science, Technology and Nutrition (ICTAN), Spanish National Research Council (CSIC), Madrid, Spain. 12. CREA, Research Centre for Food and Nutrition, Rome, Italy. 13. Department of Nutrition and Food Science, University of Bonn, Bonn, Germany. 14. Department of Pediatrics, University of Pécs, Pécs, Hungary. 15. Division of Clinical Nutrition and Prevention, Department of Pediatrics, Medical University of Vienna, Vienna, Austria. 16. Research Institute of Child Nutrition, Pediatric University Clinic, Ruhr University, Bochum, Germany. 17. Department of Public Health, Ghent University, Ghent, Belgium. 18. ImFINE Research Group, Department of Health and Human Performance, Universidad Politécnica de Madrid, Madrid, Spain.
Abstract
OBJECTIVES: To determine dietary patterns (DPs) and explain the highest variance of vitamin B6, folate, and B12 intake and related concentrations among European adolescents. METHODS: A total of 2173 adolescents who participated in the Healthy Lifestyle in Europe by Nutrition in Adolescence study met the eligibility criteria for the vitamin B intake analysis (46% boys) and 586 adolescents for the biomarkers analysis (47% boys). Two non-consecutive, 24-h, dietary recalls were used to assess the mean intakes. Concentrations were measured by chromatography and immunoassay testing. A reduced rank regression was applied to elucidate the combined effect of food intake of vitamin B and related concentrations. RESULTS: The identified DPs (one per vitamin B intake and biomarker and by sex) explained a variability between 34.2% and 23.7% of the vitamin B intake and between 17.2% and 7% of the biomarkers. In the reduced rank regression models, fish, eggs, cheese, whole milk and buttermilk intakes were loaded positively for vitamin B intake in both sexes; however, soft drinks and chocolate were loaded negatively. For the biomarkers, a higher variability was observed in the patterns in terms of food loads such as alcoholic drinks, sugars, and soft drinks. Some food items were loaded differently between intakes and biomarkers such as fish products, which was loaded positively for intakes but negatively for plasma folate in girls. CONCLUSIONS: The identified DPs explained up to 34.2% and 17.2% of the variability of the vitamin B intake and plasma concentrations, respectively, in European adolescents. Further studies are needed to elucidate the factors that determine such patterns.
OBJECTIVES: To determine dietary patterns (DPs) and explain the highest variance of vitamin B6, folate, and B12 intake and related concentrations among European adolescents. METHODS: A total of 2173 adolescents who participated in the Healthy Lifestyle in Europe by Nutrition in Adolescence study met the eligibility criteria for the vitamin B intake analysis (46% boys) and 586 adolescents for the biomarkers analysis (47% boys). Two non-consecutive, 24-h, dietary recalls were used to assess the mean intakes. Concentrations were measured by chromatography and immunoassay testing. A reduced rank regression was applied to elucidate the combined effect of food intake of vitamin B and related concentrations. RESULTS: The identified DPs (one per vitamin B intake and biomarker and by sex) explained a variability between 34.2% and 23.7% of the vitamin B intake and between 17.2% and 7% of the biomarkers. In the reduced rank regression models, fish, eggs, cheese, whole milk and buttermilk intakes were loaded positively for vitamin B intake in both sexes; however, soft drinks and chocolate were loaded negatively. For the biomarkers, a higher variability was observed in the patterns in terms of food loads such as alcoholic drinks, sugars, and soft drinks. Some food items were loaded differently between intakes and biomarkers such as fish products, which was loaded positively for intakes but negatively for plasma folate in girls. CONCLUSIONS: The identified DPs explained up to 34.2% and 17.2% of the variability of the vitamin B intake and plasma concentrations, respectively, in European adolescents. Further studies are needed to elucidate the factors that determine such patterns.
Authors: Zoi Toumpakari; Russell Jago; Laura D Howe; Hazreen Abdul Majid; Angeliki Papadaki; Shooka Mohammadi; Muhammad Yazid Jalaludin; Maznah Dahlui; Mohd Nahar Azmi Mohamed; Tin Tin Su; Laura Johnson Journal: Int J Environ Res Public Health Date: 2019-11-22 Impact factor: 3.390