MON-104 The Relationship Between Metabolic Syndrome Indicators and Body Composition Measured by Bioelectrical Impedance Analysis Methods in Obese Children

Purpose: This study aimed to compare obesity indices with impedance analyzed body composition data, and to investigate the association between impedance analyzed body composition data and the prevalence of metabolic syndrome. Methods: 123 prepubertal children (49% girls 3-to-8- year-old, 51% boys 3-to-9-year-old) who are below or equal to body mass index (BMI, kg/m(2)) 85(th) percentile were retrospectively reviewed. Height, weight, waist circumference, blood pressure, serum lipid profiles, fasting plasma glucose and serum insulin were measured. Body fat percentile (BFP), fat-free mass (FFM) were measured by BIA and fat mass index (FMI), fat-free mass index (FFMI) were calculated. We investigated the relationship between metabolic syndrome indicators and body composition measured by BIA. Metabolic syndrome (MetS) was defined as including more than or equal to three of the metabolic abnormalities according to the modified National Cholesterol Education Program Adult Treatment Panel III. Results: The overall prevalence of MetS was found to be 15.4%(19/123). The prevalence of MetS, MetS indicators, and body composition measured by BIA were not significantly different between males and females. BMI z-score was positively correlated with BFP, FMI and FFMI (r=0.51, P=0.001; r=0.63, P=0.001; r=0.29, P=0.001, respectively), so was waist-to-height ratio (WHR) (r=0.57, P=0.001; r=0.70, P=0.001; r=0.33, P=0.001). Homeostatic model assessment for insulin resistance (HOMA-IR) index was associated to BFP, FFM, FMI, and FFMI (r=0.305, P=0.003; r=0.359, P=0.001; r=0.331, P=0.001; r=0.24, P=0.018, respectively). Regression analysis showed chronological age (CA) and BMI z-score affect HOMA-IR (β=0.61, P=0.001; β=0.93, P=0.002, respectively) and CA was considered as a potential risk factor of MetS (Odd ratio of 3.09 and 95 % confidence interval of 1.25–7.65). Conclusion: BIA seems to be a good tools for measuring obesity but not a good tool for predicting complications of obesity in prepubertal children. Further study is needed on the risk factors for complications of obesity in prepubertal children.