Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons

The assessment of body composition has important applications in the evaluation of nutritional status and estimating potential health risks. Bioelectrical impedance analysis (BIA) is a valid method for the assessment of body composition. BIA is an alternative to more invasive and expensive methods l...

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Bibliographic Details
Published in:Aging clinical and experimental research 2017-08, Vol.29 (4), p.591-597
Main Authors: Sergi, Giuseppe, De Rui, Marina, Stubbs, Brendon, Veronese, Nicola, Manzato, Enzo
Format: Article
Language:English
Subjects:
Aging - physiology
Body composition
Body Composition - physiology
Electric Impedance
Geriatrics/Gerontology
Humans
Medicine
Medicine & Public Health
Muscle, Skeletal - pathology
Nutritional Status
Regression Analysis
Reproducibility of Results
Review
Thinness - physiopathology
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Summary:The assessment of body composition has important applications in the evaluation of nutritional status and estimating potential health risks. Bioelectrical impedance analysis (BIA) is a valid method for the assessment of body composition. BIA is an alternative to more invasive and expensive methods like dual-energy X-ray absorptiometry, computerized tomography, and magnetic resonance imaging. Bioelectrical impedance analysis is an easy-to-use and low-cost method for the estimation of fat-free mass (FFM) in physiological and pathological conditions. The reliability of BIA measurements is influenced by various factors related to the instrument itself, including electrodes, operator, subject, and environment. BIA assumptions beyond its use for body composition are the human body is empirically composed of cylinders, FFM contains virtually all the water and conducting electrolytes in the body, and its hydration is constant. FFM can be predicted by BIA through equations developed using reference methods. Several BIA prediction equations exist for the estimation of FFM, skeletal muscle mass (SMM), or appendicular SMM. The BIA prediction models differ according to the characteristics of the sample in which they have been derived and validated in addition to the parameters included in the multiple regression analysis. In choosing BIA equations, it is important to consider the characteristics of the sample in which it has been developed and validated, since, for example, age- and ethnicity-related differences could sensitively affect BIA estimates.
ISSN:1720-8319
1594-0667
1720-8319
DOI:10.1007/s40520-016-0622-6