Bioelectrical impedance analysis models for prediction of total body water and fat-free mass in healthy and HIV-infected children and adolescents

• Published in: The American journal of clinical nutrition 2002-11, Vol.76 (5), p.991-999
• Main Authors: Horlick, Mary, Arpadi, Stephen M, Bethel, James, Wang, Jack, Moye, Jack Jr, Cuff, Patricia, Pierson, Richard N. Jr, Kotler, Donald
• Format: Article
• Language: English
• Subjects: Adolescent; Analysis; Biological and medical sciences; Body Composition; Body Water - metabolism; Child; Child, Preschool; Children & youth; Cross-Sectional Studies; Electric Impedance; Female; Forecasting; HIV; HIV Infections - metabolism; HIV Infections - pathology; Human immunodeficiency virus; Human viral diseases; Humans; Infectious diseases; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Miscellaneous. Technology; Models, Biological; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Reference Values; Viral diseases; Viral diseases of the lymphoid tissue and the blood. Aids; Water
• ISSN: 0002-9165; 1938-3207
• DOI: 10.1093/ajcn/76.5.991

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.