ESPE Abstracts (2014) 82 P-D-1-2-122

aLeipzig University Medical Center, IFB AdiposityDiseases, Leipzig, Germany; bClinical Trial Centre, Leipzig, Germany; cInstitute of Human Genetics, Jena University Hospital, Friedrich-Schiller-University, Jena, Germany; dDepartment of Pediatric Endocrinology and Diabetology, Charité Universitätsmedizin Berlin, Berlin, Germany; eEastern Swiss Children’s Hospital, St Gallen, Switzerland; fPediatric Endocrinology and Diabetology, University Children’s Hospital, University of Tuebingen, Tuebingen, Germany; gPediatric Endocrinology and Diabetology, Centre for Child and Adolescent Health, University of Freiburg, Freiburg, Germany; hChildren’s Hospital Sylt, Westerland, Germany; iDepartment of Women and Child Health, University Hospital of Leipzig, Leipzig, Germany; jChildren’s Hospital Wilhelmsstift, Hamburg, Germany; kSANA Hospital Lichtenberg, Berlin, Germany; lInstitute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany

Background: Weight status in children is commonly defined using BMI (SDS), but this measure is problematic due to the skewness of the BMI distribution and its age-dependant increase. In addition, it is difficult for physicians or parents alike to grasp what a certain value means. Excess body weight (EBW) is frequently used in adult patients in the context of bariatric surgery.

Objective and hypotheses: An appropriate definition for the paediatric population is not available. A simple definition for EBW in children/adolescents is introduced with median weight as a function of height, age and gender as a robust reference point. The relationships between EBW, BMI-SDS, waist to height ratio (WHtR) and metabolic parameters are examined.

Method: EBW(%)=100×(weight-median weight)/median weight. Correlations are analyzed in 14,362 children aged 11–18 (7553 overweight/obese children from the APV data base which collects data from German/Swiss/Austrian obesity outpatient centres; 6809 representative German children from the KiGGS survey covering all weight ranges).

Results: In both cohorts, BMI-SDS correlates strongly with EBW (linear correlation coefficients≥0.93) and to a lower extent with WHtR (linear correlation coefficients≥0.76). The relationships of all three measures with metabolic (triglycerides, HDL-cholesterol, fasting glucose) and clinical (systolic/diastolic blood pressure) parameters are quite similar to each other, and the strongest linear correlation of all measures can be found with HDL-cholesterol and systolic blood pressure. BMD-SDS, EBW and WHtR are similar in terms of their ability to predict metabolic risks, based on area under the curve from receiver operating characteristic (ROC) analyses.

Conclusion: EBW is a novel four-dimensional marker, comparing individual weight to a gender, age and height related ideal weight. BMI-SDS, WHtR and EBW have similar predictive values for metabolic comorbidities in the pediatric population. As EBW is valid even in extremely obese patients, it would make a very useful addition to existing anthropometric tools in paediatric obesity.

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