W Ahrens1, L A Moreno2, S Mårild3, D Molnár4, A Siani5, S De Henauw6, J Böhmann7, K Günther8, C Hadjigeorgiou9, L Iacoviello10, L Lissner11, T Veidebaum12, H Pohlabeln8, I Pigeot1. 1. 1] Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany [2] Institute of Statistics, Faculty of Mathematics and Computer Science, Bremen University, Bremen, Germany. 2. GENUD (Growth, Exercise, Nutrition and Development) Research Group, University of Zaragoza, Zaragoza, Spain. 3. Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 4. Department of Paediatrics, Medical Faculty, University of Pécs, Pécs, Hungary. 5. Institute of Food Sciences, Unit of Epidemiology & Population Genetics, National Research Council, Avellino, Italy. 6. Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium. 7. Paediatric Clinic Delmenhorst, Delmenhorst, Germany. 8. Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany. 9. Research and Education Institute of Child Health, Strovolos, Cyprus. 10. Department of Epidemiology and Prevention, Unit of Molecular and Nutritional Epidemiology, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy. 11. Department of Public Health and Community Medicine, University of Gothenburg, Gothenburg, Sweden. 12. National Institute for Health Development, Tallinn, Estonia.
Abstract
OBJECTIVE: To estimate the prevalence of the metabolic syndrome (MetS) using reference standards obtained in European children and to develop a quantitative MetS score and describe its distribution in children. DESIGN AND METHODS: Population-based survey in eight European countries, including 18745 children 2.0 to 10.9 years, recruited during a second survey. Anthropometry (weight, height and waist circumference), blood pressure and serum-fasting triglycerides, HDL cholesterol, glucose and insulin were measured. We applied three widely accepted definitions of the pediatric MetS and we suggest a new definition, to guide pediatricians in decisions about close monitoring or even intervention (values of at least three of the MetS components exceeding the 90th or 95th percentile, respectively). We used a z-score standardisation to calculate a continuous score combining the MetS components. RESULTS: Among the various definitions of MetS, the highest prevalence (5.5%) was obtained with our new definition requiring close observation (monitoring level). Our more conservative definition, requiring pediatric intervention gives a prevalence of 1.8%. In general, prevalences were higher in girls than in boys. The prevalence of metabolic syndrome is highest among obese children. All definitions classify a small percentage of thin or normal weight children as being affected. The metabolic syndrome score shows a positive trend with age, particularly regarding the upper percentiles of the score. CONCLUSIONS: According to different definitions of pediatric MetS, a non-negligible proportion of mostly prepubertal children are classified as affected. We propose a new definition of MetS that should improve clinical guidance. The continuous score developed may also serve as a useful tool in pediatric obesity research. It has to be noted, however, that the proposed cutoffs are based on a statistical definition that does not yet allow to quantify the risk of subsequent disease.
OBJECTIVE: To estimate the prevalence of the metabolic syndrome (MetS) using reference standards obtained in European children and to develop a quantitative MetS score and describe its distribution in children. DESIGN AND METHODS: Population-based survey in eight European countries, including 18745 children 2.0 to 10.9 years, recruited during a second survey. Anthropometry (weight, height and waist circumference), blood pressure and serum-fasting triglycerides, HDL cholesterol, glucose and insulin were measured. We applied three widely accepted definitions of the pediatric MetS and we suggest a new definition, to guide pediatricians in decisions about close monitoring or even intervention (values of at least three of the MetS components exceeding the 90th or 95th percentile, respectively). We used a z-score standardisation to calculate a continuous score combining the MetS components. RESULTS: Among the various definitions of MetS, the highest prevalence (5.5%) was obtained with our new definition requiring close observation (monitoring level). Our more conservative definition, requiring pediatric intervention gives a prevalence of 1.8%. In general, prevalences were higher in girls than in boys. The prevalence of metabolic syndrome is highest among obesechildren. All definitions classify a small percentage of thin or normal weight children as being affected. The metabolic syndrome score shows a positive trend with age, particularly regarding the upper percentiles of the score. CONCLUSIONS: According to different definitions of pediatric MetS, a non-negligible proportion of mostly prepubertal children are classified as affected. We propose a new definition of MetS that should improve clinical guidance. The continuous score developed may also serve as a useful tool in pediatric obesity research. It has to be noted, however, that the proposed cutoffs are based on a statistical definition that does not yet allow to quantify the risk of subsequent disease.
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