A simplified approach to analysing bio-electrical impedance data in epidemiological surveys

Abstract

Background: Bio-electrical impedance analysis (BIA) is widely used to estimate body composition. It is simple, quick and cheap, but less accurate than other methods. It has potential epidemiological value, but has conventionally required validation before application. Aims: To develop a simple method of expressing weight, height and impedance data that avoids the need for populationspecific validation equations in order to facilitate epidemiological application. Methods: Body composition was measured using the four-component model in young adults (43 males, 90 females). Impedance (R) was measured hand-foot and foot-foot. Lean mass and fat mass were adjusted for height to give lean mass index (LMI) and fat mass index (FMI). Based on theoretical principles, we generated the index 1/R, which provides an index of body water adjusted for height. Sex-specific regression models were used to investigate the relationships between (a) 1/R and LMI, and (b) body mass index (BMI) adjusted for 1/R and FMI. The success of this approach was evaluated in relation to the conventional BIA approach, using correlation analysis. Results: 1/R was a highly significant predictor of LMI. BMI adjusted for 1/R was a significant predictor of FMI. Our approach performed as well as the conventional approach for LMI, but not for FMI. Discussion: Direct use of BIA data, rather than their combination with population-specific equations for the prediction of total body water, proved successful at ranking individuals of both sexes in terms of LMI and FMI. The index 1/R may prove particularly valuable in epidemiological studies where ranking of LMI is required.

Bioimpedance in 7-Year-Old Children: Validation by Dual X-Ray Absorptiometry - Part 1: Assessment of Whole Body Composition

“…Although fat mass estimations showed a nonhomogeneous distribution of bias, the ability to properly rank individuals in the highest FMI percentiles was high. Our results are consistent with those from Wells et al [17], who proved that direct use of body composition from BIA was successful at ranking individuals in terms of LMI and FMI.…”

Correlation Between Birth Weight and Maternal Body Composition

“…[9][10][11][12] Recent technical advances with eight-electrode multifrequency bioelectric impedance analysis (multifrequency bioelectric impedance analysis) mean that bioelectric impedance analysis can be applied in large numbers of patients to analyze body composition and also to analyze the distribution of fat and fat-free mass without recourse to the supine position. [10][11][12] We have demonstrated previously that in early pregnancy the use of multifrequency bioelectric impedance analysis is feasible and reproducible, and that it correlates strongly with clinical and endocrine markers of maternal adiposity. 11,12 The purpose of this prospective observational study was to estimate whether maternal fat mass measured in the first trimester correlated better with birth weight than did fat-free mass, and to identify which maternal body composition parameters correlated best with birth weight.…”

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (MAFOI) generated by the association between body fat and body mass index.

“…Hence, the BIA measures the body composition indirectly and is based on the electrical-conductivity principle and its stable relation with the body's liquid. It also uses the resistance, the reactance, and the phase angle as bioelectrical parameters 48,[50][51][52][53][54][55] . Resistance is the opposition offered by the body content to the alternated electrical current and is inversely proportional to the quantity of water and electrolytes present in the tissues.…”

“…The BIA's predictive accuracy can be influenced by the degree of body fat, age, gender, ethnic characteristics, diseases that alter the body type and factors that modify the hydroelectrolytic composition 53 . In order to clarify conflicting results, we used predictive equations that were adequate to the population under study 50,[48][49][50][51][52][53][54][55] .…”