Utility of specific bioelectrical impedance vector analysis for the assessment of body composition in children. Issue 3 (March 2021)

• Record Type: Journal Article
• Authors: Wells, Jonathan CK.; Williams, Jane E.; Ward, Leigh C.; Fewtrell, Mary S.
• Is Part Of: Clinical nutrition. Volume 40:Issue 3(2021)
• Page Start: 1147
• Page End: 1154
• Publication Date: 2021-03
• Subjects: Body composition -- Bioelectrical impedance analysis -- Children -- Adolescents -- Bioelectrical impedance vector analysis; Critically ill -- Nutrition -- Periodicals; Diet therapy -- Periodicals; Parenteral feeding -- Periodicals; Enteral feeding -- Periodicals; Enteral Nutrition -- Periodicals; Parenteral Nutrition -- Periodicals; Metabolism -- Periodicals; Diétothérapie -- Périodiques; Alimentation parentérale -- Périodiques; Alimentation entérale -- Périodiques; Nutrition -- Périodiques; Diet therapy; Enteral feeding; Nutrition; Parenteral feeding; Electronic journals; Periodicals; Electronic journals; 615.854
• DOI: 10.1016/j.clnu.2020.07.022
• Languages: English
• ISSNs: 0261-5614

Abstract

Summary: Background & aims: Bioelectrical impedance analysis (BIA) is widely considered a body composition technique suitable for routine application. However, its utility in sick or malnourished children is complicated by variability in hydration. A BIA variant termed vector analysis (BIVA) aims to resolve this, by differentiating hydration from cell mass. However, the model was only partially supported by children's data. To improve accuracy, further adjustment for body shape variability has been proposed, known as specific BIVA (BIVAspecific ). Methods: We re-analysed body composition data from 281 children and adolescents (46% male) aged 4–20 years of European ancestry. Measurements included anthropometry, conventional BIA, BIVA outcomes adjusted either for height (BIVAconventional ), or for height and body cross-sectional area (BIVAspecific ), and fat-free mass (FFM) and fat mass (FM) by the criterion 4-component model. Graphic analysis and regression analysis were used to evaluate different BIA models for predicting FFM and FM. Results: Age was strongly correlated with BIVAconventional parameters, but weakly with BIVAspecific parameters. FFM correlated more strongly with BIVAconventional than with BIVAspecific parameters, whereas the opposite pattern was found for FM. In multiple regression analyses, the best prediction models combined conventional BIA with BIVAspecific parameters, explaining 97.0% and 89.8% of the variance in FFM and FM respectively. These models could be further improved by incorporating body weight. Conclusions: The prediction of body composition can be improved by combining two different theoretical models, each of which appears to provide different information about the two components FFM and FM. Further work should test the utility of this approach in pediatric patients.