BACKGROUND: Bioelectrical impedance analysis (BIA) is an attractive method of measuring pediatric body composition in the field, but the applicability of existing equations to diverse populations has been questioned. OBJECTIVE: The objectives were to evaluate the performance of 13 published pediatric BIA-based predictive equations for total body water (TBW) and fat-free mass (FFM) and to refit the best-performing models. DESIGN: We used TBW by deuterium dilution, FFM by dual-energy X-ray absorptiometry, and BIA-derived variables to evaluate BIA models in a cross-sectional study of 1291 pediatric subjects aged 4-18 y, from several ethnic backgrounds, including 54 children with HIV infection and 627 females. The best-performing models were refitted according to criterion values from this population, cross-validated, and assessed for performance. Additional variables were added to improve the predictive accuracy of the equations. RESULTS: The correlation between predicted and criterion values was high for all models tested, but bias and precision improved with the refitted models. The 95% limits of agreement between predicted and criterion values were 16% and 11% for TBW and FFM, respectively. Bias was significant for some subgroups, and there was greater loss of precision in specific age groups and pubertal stages. The models with additional variables eliminated bias, but the limits of agreement and the loss of precision persisted. CONCLUSION: This study confirms that BIA prediction models may not be appropriate for individual evaluation but are suitable for population studies. Additional variables may be necessary to eliminate bias for specific subgroups.
BACKGROUND: Bioelectrical impedance analysis (BIA) is an attractive method of measuring pediatric body composition in the field, but the applicability of existing equations to diverse populations has been questioned. OBJECTIVE: The objectives were to evaluate the performance of 13 published pediatric BIA-based predictive equations for total body water (TBW) and fat-free mass (FFM) and to refit the best-performing models. DESIGN: We used TBW by deuterium dilution, FFM by dual-energy X-ray absorptiometry, and BIA-derived variables to evaluate BIA models in a cross-sectional study of 1291 pediatric subjects aged 4-18 y, from several ethnic backgrounds, including 54 children with HIV infection and 627 females. The best-performing models were refitted according to criterion values from this population, cross-validated, and assessed for performance. Additional variables were added to improve the predictive accuracy of the equations. RESULTS: The correlation between predicted and criterion values was high for all models tested, but bias and precision improved with the refitted models. The 95% limits of agreement between predicted and criterion values were 16% and 11% for TBW and FFM, respectively. Bias was significant for some subgroups, and there was greater loss of precision in specific age groups and pubertal stages. The models with additional variables eliminated bias, but the limits of agreement and the loss of precision persisted. CONCLUSION: This study confirms that BIA prediction models may not be appropriate for individual evaluation but are suitable for population studies. Additional variables may be necessary to eliminate bias for specific subgroups.
Authors: R Colin Carter; Joseph L Jacobson; Christopher D Molteno; Hongyu Jiang; Ernesta M Meintjes; Sandra W Jacobson; Christopher Duggan Journal: Alcohol Clin Exp Res Date: 2012-08-15 Impact factor: 3.455
Authors: Marsha Dowda; Sharon E Taverno Ross; Kerry L McIver; Rodney K Dishman; Russell R Pate Journal: Child Obes Date: 2016-12-08 Impact factor: 2.992
Authors: Marcia Wong; Stephanie Shiau; Michael T Yin; Renate Strehlau; Faeezah Patel; Ashraf Coovadia; Lisa K Micklesfield; Louise Kuhn; Stephen M Arpadi Journal: J Pediatr Date: 2016-02-26 Impact factor: 4.406
Authors: Lorrene D Ritchie; Sushma Sharma; Joanne P Ikeda; Rita A Mitchell; Aarthi Raman; Barbara S Green; Mark L Hudes; Sharon E Fleming Journal: Trials Date: 2010-05-21 Impact factor: 2.279
Authors: Analiza M Silva; Steven B Heymsfield; Dympna Gallagher; Jeanine Albu; Xavier F Pi-Sunyer; Richard N Pierson; Jack Wang; Stanley Heshka; Luis B Sardinha; Zimian Wang Journal: Am J Clin Nutr Date: 2008-08 Impact factor: 7.045
Authors: Sushma Sharma; Lindsay S Roberts; Mark L Hudes; Robert H Lustig; Sharon E Fleming Journal: Nutr Metab (Lond) Date: 2009-10-13 Impact factor: 4.169