Isabel Iguacel1, Nathalie Michels2, Wolfgang Ahrens3, Karin Bammann4, Gabriele Eiben5, Juan M Fernández-Alvira6, Staffan Mårild7, Dénes Molnár8, Lucia Reisch9, Paola Russo10, Michael Tornaritis11, Toomas Veidebaum12, Maike Wolters13, Luis A Moreno14, Claudia Börnhorst13. 1. GENUD (Growth, Exercise, NUtrition and Development) Research Group, Faculty of Health Sciences, University of Zaragoza, Edificio del SAI, C/Pedro Cerbuna s/n, 50009 Zaragoza, Spain; Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain; Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Zaragoza, Spain. Electronic address: iguacel@unizar.es. 2. Department of Public Health, Ghent University, Ghent, Belgium. 3. Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany; Institute of Statistics, University of Bremen, Bremen, Germany. 4. Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany; Institute for Public Health and Nursing Sciences (IPP), University of Bremen, Bremen, Germany. 5. Section for Epidemiology and Social Medicine (EPSO), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Biomedicine and Public Health, School of Health and Education, University of Skövde, Skövde, Sweden. 6. GENUD (Growth, Exercise, NUtrition and Development) Research Group, Faculty of Health Sciences, University of Zaragoza, Edificio del SAI, C/Pedro Cerbuna s/n, 50009 Zaragoza, Spain; Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain. 7. Section for Epidemiology and Social Medicine (EPSO), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 8. Department of Paediatrics, University of Pécs, Pécs, Hungary. 9. Copenhagen Business School, Copenhagen, Denmark. 10. Institute of Food Sciences, National Research Council, Avellino, Italy. 11. Research and Education Institute of Child Health, Strovolos, Cyprus. 12. Department of Chronic Diseases, National Institute for Health Development, Tallinn, Estonia. 13. Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany. 14. GENUD (Growth, Exercise, NUtrition and Development) Research Group, Faculty of Health Sciences, University of Zaragoza, Edificio del SAI, C/Pedro Cerbuna s/n, 50009 Zaragoza, Spain; Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain; Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Zaragoza, Spain.
Abstract
OBJECTIVE: Socioeconomic disadvantages during childhood are hypothesised to have negative implications for health. We aimed to investigate the association between socioeconomic disadvantages and children's total metabolic syndrome (MetS) score at baseline and follow-up and the extent to which socioeconomic disadvantages over time and the accumulation of these socioeconomic disadvantages can affect children's MetS risk. METHODS: The two-year longitudinal IDEFICS study included 2401 European children (aged 2.0-9.9) with complete information of the 16,229 participating at baseline. Sociodemographic variables, psychosocial factors and lifestyle were proxy-reported via questionnaires. Socioeconomically disadvantaged groups included children from families with low income, low education, migrant origin, unemployed parents, parents who lacked a social network, and from non-traditional families. MetS risk score was calculated as the sum of z-scores of waist circumference, blood pressure, lipids and insulin resistance. Linear mixed-effects models were used to study the association between social disadvantages and MetS risk. Models were adjusted for sex, age, well-being and lifestyle (fruit and vegetables consumption, physical activity, screen time). RESULTS: At both time points, children from low-income families (0.20 [0.03-0.37]); (β estimate and 99% confidence interval), children from non-traditional families (0.14 [0.02-0.26]), children whose parents were unemployed (0.31 [0.05-0.57]) and children who accumulated >3 disadvantages (0.21 [0.04-0.37]) showed a higher MetS score compared to non-socioeconomically disadvantaged groups. CONCLUSION: Children from socioeconomically disadvantaged families are at high metabolic risk independently of diet, physical activity, sedentary behaviours and well-being. Interventions focusing on these socioeconomically disadvantaged groups should be developed to tackle health disparities.
OBJECTIVE: Socioeconomic disadvantages during childhood are hypothesised to have negative implications for health. We aimed to investigate the association between socioeconomic disadvantages and children's total metabolic syndrome (MetS) score at baseline and follow-up and the extent to which socioeconomic disadvantages over time and the accumulation of these socioeconomic disadvantages can affect children's MetS risk. METHODS: The two-year longitudinal IDEFICS study included 2401 European children (aged 2.0-9.9) with complete information of the 16,229 participating at baseline. Sociodemographic variables, psychosocial factors and lifestyle were proxy-reported via questionnaires. Socioeconomically disadvantaged groups included children from families with low income, low education, migrant origin, unemployed parents, parents who lacked a social network, and from non-traditional families. MetS risk score was calculated as the sum of z-scores of waist circumference, blood pressure, lipids and insulin resistance. Linear mixed-effects models were used to study the association between social disadvantages and MetS risk. Models were adjusted for sex, age, well-being and lifestyle (fruit and vegetables consumption, physical activity, screen time). RESULTS: At both time points, children from low-income families (0.20 [0.03-0.37]); (β estimate and 99% confidence interval), children from non-traditional families (0.14 [0.02-0.26]), children whose parents were unemployed (0.31 [0.05-0.57]) and children who accumulated >3 disadvantages (0.21 [0.04-0.37]) showed a higher MetS score compared to non-socioeconomically disadvantaged groups. CONCLUSION:Children from socioeconomically disadvantaged families are at high metabolic risk independently of diet, physical activity, sedentary behaviours and well-being. Interventions focusing on these socioeconomically disadvantaged groups should be developed to tackle health disparities.
Keywords:
Children; Family structure; Lack of social support; Metabolic syndrome; Migrants; Modifiable lifestyle indicators; Obesity; Social vulnerabilities; Socioeconomic disadvantages; Socioeconomic status
Authors: Elida Sina; Christoph Buck; Toomas Veidebaum; Alfonso Siani; Lucia Reisch; Hermann Pohlabeln; Valeria Pala; Luis A Moreno; Dénes Molnar; Lauren Lissner; Yiannis Kourides; Stefaan De Henauw; Gabriele Eiben; Wolfgang Ahrens; Antje Hebestreit Journal: Int J Behav Nutr Phys Act Date: 2021-10-18 Impact factor: 6.457