ESPE2023 Poster Category 1 Fat, Metabolism and Obesity (97 abstracts)
Clinical usefulness of bioimpedance analysis in children and adolescents with severe obesity. Preliminary results of the Polish-German study project on severe early-onset obesity.
Ewa Kostrzeba 1 , Mirosław Bik-Multanowski 2 , Stephanie Brandt 3 , Bertram Flehmig 4 , Ewa Małecka-Tendera 5 , Artur Mazur 6 , Michael B. Ranke 7 , Martin Wabitsch 3 , Małgorzata Wójcik 8 , Agnieszka Zachurzok 9 & Elżbieta Petriczko 1
1Department of Pediatrics, Endocrinology, Diabetology, Metabolic Disorders and Cardiology of Developmental Age, Pomeranian Medical University, Szczecin, Poland. 2Department of Medical Genetics, Faculty of Medicine, Jagiellonian University Medical College, Cracow, Poland. 3Center for Rare Endocrine Diseases, Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics and Adolescent Medicine, Ulm, Germany. 4Mediagnost GmbH, Reutlingen, Germany. 5Department of Pediatrics and Pediatric Endocrinology, Medical University of Silesia, School of Medicine in Katowice, Katowice, Poland. 6Department of Pediatrics, Pediatric Endocrinology and Diabetes, Medical Faculty, University of Rzeszów, Rzeszów, Poland. 7Children’s Hospital, University of Tübingen, Tübingen, Germany. 8Department of Pediatric and Adolescent Endocrinology, Pediatric Institute, Jagiellonian University Medical College, Cracow, Poland. 9Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Zabrze, Poland
Aim: Childhood obesity has become extremely important issue worldwide. The aim of this study was to determine associations between data from bioimpedance analysis with the antrophometric measurements and biochemical results obtained from children and adolescents with severe obesity.
Study population and Methods: Study group consisted of 128 children recruited in four regional reference centers including 70 girls (54.7%) and 58 boys (45.3%) aged 9-18 years with severe obesity defined as BMI>35 kg/m2 in children aged 6-14 years and BMI>40 kg/m2 in adolescents> 14 years. Derived parameters were: BMI 41.7 (range 35-63.8, SD=4.7), BMI Z-score 3.7 (range 2.6-6.3), WHR 0.9 (range 0.7-1.2, SD=0.1), WHtR 0.7 (range 0.6-1, SD=0.07). Blood samples levels of ALT, AST, glucose and insulin concentrations were measured in fasting. Derived HOMA IR was calculated 6.9 (range 1.6-19.1, SD=3.7). Bioimpedance measurements (performed on TANITA MC-580 M S MDD, TANITA MC-780MA-N, TANITA MC-780 P MA) provided information about fat (FM, kg) and fat-free mass (FFM, kg). Spearman's rank correlation was used to estimate described associations. The threshold of statistical significance was P=0.05.
Results: FM was positively correlated with BMI (P=0.8), WHtR (P=0.3), SBP (P=0.3) and HOMA IR (P=0.1). FFM was associated with SBP (P=0.4), BMI (P=0.3), ALT and AST (P=0.3 for both), WHR (P=0.3) and triglycerides (P=0.1). BMI was positively correlated with systolic blood pressure (SBP) (P=0.3) and HOMA IR (P=0.2). WHtR was strongly correlated with BMI (P=0.6). There was no statistically significant correlation between WHR and BMI. WHR was positively associated with the liver enzymes (P=0.4) and triglycerides (P=0.2). ALT and AST were positively associated with triglycerides (P=0.3 and P=0.2 respectively). There was a positive correlation between WHtR and SBP (P=0.2).
Conclusions: Results have shown that bioimpedance is a useful tool for determining cardiometabolic risk factors in examined cohort. Associations between FM, FFM and cardiometabolic risk factors were found to be stronger, than when only BMI was considered. Increased BMI and FM were associated with the greater risk of AH and carbohydrate metabolism disorders. Increased FFM was associated with the greater risk of AH, MAFLD and hypertriglycerydemia development. WHtR seemed to be a better indicator of severe obesity than WHR, probably due to the obesity hormones enhancing growth rate. WHR was found to be a better predictor of MAFLD and hypertriglycerydemia, while WHtR was found to be a better predictor of AH.
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