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Body composition by whole-body bioelectrical impedance and prediction of clinically relevant outcomes: overvalued or underused?

Abstract

Background/Objectives:

Body composition calculated using whole-body bioelectrical impedance analysis (BIA), almost invariably with height (H) and often with weight (W), can help patient management and predict clinical outcomes. This study aimed to examine the merits of this approach compared with simple anthropometry (W+H).

Subjects/Methods:

Use was made of original data and validation studies based on reference body composition methods: water dilution, densitometry, dual-energy X-ray absorptiometry, and more robust methods. Prediction of clinical outcomes, including mortality and length of hospital stay, was examined in six studies of chronic obstructive pulmonary disease and a study with multiple patient groups. Vector analysis, phase angle, multi-frequency BIA and segmental impedance were not considered.

Results:

In a broad range of study populations, from neonates to older people, in health and disease, body composition calculated using BIA with simple anthropometry frequently offered no advantage over W+H alone, but in some situations it was superior and in others inferior. In predicting clinically relevant outcomes, the fat-free mass index (FFMI), established using BIA, had comparable and sometimes greater power than body mass index (BMI), but none of the reviewed papers used FFMI calculated from W+H or BMI to predict clinical outcomes.

Conclusions:

A variable and generally weak evidence base was found to suggest that BIA with anthropometry is better at predicting body composition than simple anthropometry alone. No evidence was found from the reviewed studies that FFMI calculated from BIA and anthropometry was better at predicting clinical outcomes than FFMI calculated by simple anthropometry alone.

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Publication of this article was supported by a grant from seca Gmbh & Co. KG, Hamburg, Germany.

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Elia, M. Body composition by whole-body bioelectrical impedance and prediction of clinically relevant outcomes: overvalued or underused?. Eur J Clin Nutr 67 (Suppl 1), S60–S70 (2013). https://doi.org/10.1038/ejcn.2012.166

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