Christopher Nuñez

Bioimpedance analysis : evaluation of leg-to-leg system based on pressure contact foot-pad electrodes

Conventional single frequency bioimpedance analysis (BIA) systems require technician placement of arm and leg gel electrodes, a suitable location for recumbent measurements, and a separate measurement of body weight. The aim of this study was to evaluate a new single frequency 50 KHz leg-to-leg bioimpedance analysis (BIA) system combined with a digital scale that employs stainless steel pressure-contact foot pad electrodes for standing impedance and body weight measurements. Healthy adults were evaluated for 1) electrode validity and 2) potential for body component estimation. Pressure-contact foot-pad electrode measured impedance was highly correlated with (N = 9, r = 0.99, P < 0.001) impedance measured using conventional gel electrodes applied to the plantar surface of both lower extremities; mean (+/-SD) impedance was systematically higher by about 15 ohms for pressure contact electrodes (526 +/- 56 ohms vs 511 +/- 59 ohms; P < 0.001). Second, the relationship between stature-adjusted leg-to-leg impedance (H2/Z) measured by the new system and two body composition components (total body water by 3H2O dilution (N = 144); and fat-free body mass, by underwater weighing and dual x-ray absorptiometry (N = 231)) was modeled using multiple regression analysis. Correlation coefficients for H2/Z alone versus body composition components were lower for leg-to-leg BIA than for arm-to-leg BIA; correlation coefficients and SEEs became similar for the leg-to-leg and arm-to-leg BIA systems with addition of three covariates (age, gender, and waist/hip circumference ratio) to regression models. The leg-to-leg pressure contact electrode BIA system has overall performance characteristics for impedance measurement and body composition analysis similar to conventional arm-to-leg gel electrode BIA and offers the advantage of increased speed and ease of measurement.

Evidence for Independent Genetic Influences on Fat Mass and Body Mass Index in a Pediatric Twin Sample

Objective. Insight into genetic and environmental influences on fat mass, independent of body mass index (BMI; kg/m2), is expected to enhance methods for treating pediatric obesity. However, few studies have estimated the heritability of fat mass in pediatric samples, and those conducted have relied primarily on BMI measurements. Present Study. Using bioimpedance analysis, the present study tested a series of hypotheses predicting significant genetic and environmental influences on percent body fat (PBF) above and beyond BMI. Subjects were 66 pairs of twins, including 41 monozygotic and 25 dizygotic pairs, from 3 to 17 years of age. Structural equation modeling tested hypotheses, adjusting for demographic variables. Results. Analyses indicated significant genetic influences on PBF, with genes estimated to account for 75% to 80% of the phenotypic variation. The remaining variation was attributable to nonshared environmental influences. Multivariate analyses revealed sizable genetic correlations and environmental correlations between BMI and PBF (rg = .74 andre = .67, respectively), suggesting that some genes and environmental experiences influence both phenotypes. However, analyses confirmed genetic and environmental influences on PBF above and beyond BMI. For example, 62.5% of the total genetic variation in PBF was attributable to genes that influenced PBF but not BMI. Conclusion. There seems to be a substantial genetic contribution to fat mass distinct from BMI in a sample of children and adolescents. Studies testing putative genetic or environmental determinants of pediatric obesity might be strengthened further by including research-based body composition methods. pediatric obesity, twin design, heritability, nonshared environment, bioimpedance analysis, body mass index, body composition.

Appendicular skeletal muscle mass : prediction from multiple frequency segmental bioimpedance analysis

Objectives: Bioimpedance analysis (BIA) methods have potential to predict appendicular skeletal muscle mass (SM), although available 50 kHz prediction models include, in addition to impedance (Z), an independent age term. An age term in models is undesirable as it reflects incomplete understanding of underlying conduction physiology. This study tested the hypothesis, based on fluid distribution models related to aging, that appendicular SM bioimpedance analysis (BIA) prediction models would no longer include an independent age term, after first controlling for stature-adjusted appendicular impedance (height2/Z), at injected frequencies greater than 50 kHz.Design: Cross-sectional evaluation of adults who had segmental Z and phase angle (Φ) measured with multiple frequency BIA, and arm and leg SM with dual-energy X-ray absorptiometry (DXA). Skeletal muscle prediction models were developed with appendicular SM as dependent variable and height2/Z, gender, age and Φ as potential independent variables.Results: Examination of hypothesis in 49 subjects indicated: both arm and leg SM were highly correlated with height2/segmental Z at frequencies ranging from 1–300 kHz; gender was significant covariate in prediction models only at 1 kHz; age remained a significant covariate after controlling for height2/segmental Z at all frequencies; Φ did not add significantly to models; and SM prediction models gave maximum R2 at 50 kHz for arm but R2 continued to rise up to 300 kHz for leg.Conclusion: Although multifrequency BIA did not eliminate SM prediction model age term, our findings suggest injected frequencies up to 300 kHz may have advantages for evaluating leg SM over conventional 50 kHz method.Sponsorship: This study was in-part supported by NIH Grant RO1-NIDDK 42618 and a Scholarship awarded to Dr Pietrobelli from University of Milan, H San Raffaele, Italy.

Bioimpedance analysis: potential for measuring lower limb skeletal muscle mass.

BACKGROUND Ambulation, balance, and lower extremity bone mass and strength are all partially dependent on lower limb skeletal muscle mass. At present, both research and clinical methods of evaluating lower limb skeletal muscle mass as a component of nutrition assessment are limited. One potential simple and inexpensive method is lower extremity bioimpedance analysis (BIA). The present study had two objectives: to examine the determinants of lower limb resistance, with the underlying hypothesis that fluid-containing muscle is the main electrical conductor of the lower limbs; and to establish if a correlation of equivalent magnitude and similar covariates is observed when height squared (H2) is used instead of lower limb length squared (L2) in multiple regression models relating resistance to independent variables. METHODS Lower limb resistance was measured using a contact-electrode BIA system, and lower limb fat and skeletal muscle were estimated by dual-energy x-ray absorptiometry in healthy adults. A physical BIA model was developed in the form of a regression equation with path-length (as L2 and H2)-adjusted resistance as dependent variables and lower limb skeletal muscle, fat, age, and gender as potential independent variables. RESULTS There were 94 subjects, 34 men and 60 women, with a mean (-/+SD) age of 41.5+/-17.8 years. Strong associations were observed between L2/resistance and lower limb skeletal muscle, although for both men and women, age entered into the model as a significant covariate (total R2, men = .79 and women = .72; both p < .001). Similar models were observed with H2/resistance as dependent variable. Additional analyses showed a significantly lower resistance in lower limb skeletal muscle and height-matched old vs young subjects. CONCLUSIONS Strong associations exist between measured lower limb resistance and lower limb muscle mass, adjusting for electrical path length either by L2 or H2. These observations suggest the potential of predicting skeletal muscle using BIA-measured lower limb resistance adjusted for stature. Age is also an independent variable in lower limb resistance-skeletal muscle associations, suggesting the need to establish underlying mechanisms of age-related resistance effects and to consider subject age when developing BIA prediction models.

New electrode system for rapid whole-body and segmental bioimpedance assessment

Skeletal muscle is a clinically important body composition component which at present is difficult to quantify in vivo. Previous studies suggest that measured appendicular resistance at 50 kHz can be used to predict extremity skeletal muscle mass, although accurate technician placement of multiple gel electrodes is required. In the present study we developed a new bioimpedance analysis (BIA) electrode stand designed for rapid whole-body and segmental resistance and reactance measurements. The new system incorporates stainless steel hand and foot contact electrodes in place of gel electrodes and employs a previously reported lead placement algorithm for deriving extremity resistances without the need for placing conventional proximal limb gel electrodes. This report describes the new electrode systems design and examines the relationships between contact and gel electrode-measured resistance and between appendicular resistance measured with the recently reported lead placement algorithm and conventionally placed segmental electrodes. Results in healthy adults demonstrate high correlations between contact and gel electrodes (e.g., hand-to-hand, N = 12, r = 0.994, P < 0.001) and between segmental resistance measured by the recently reported approach and conventionally-measured segmental resistance (e.g., right arm, N = 13; r = 0.997, P < 0.001). These results strongly support the validity of the new electrode systems resistance measurements and suggests the feasibility of developing a BIA system for rapidly measuring extremity skeletal muscle mass.

Upper extremity skeletal muscle mass: potential of measurement with single frequency bioimpedance analysis.

This study examined the potential of single frequency (50 kHz) BIA for estimation of upper extremity skeletal muscle (SM) mass. Subjects (n = 50) were weight stable adults varying in age (X +/- SD, 51.6 +/- 17 yr) and body mass index (27.2 +/- 5.9 kg/m2). Determinants of arm to arm impedance index (length L; L2/Z) were examined using multiple regression analysis. A good correlation was observed between L2/Z and arm SM estimated by dual-energy X-ray absorptiometry (r = 0.88, p < 0.001). Additional significant model covariates were arm fat mass (p < 0.05), bone mass (p < 0.01), and age (p < 0.001). These findings suggest that upper extremity SM may be rapidly and easily quantified using a simple and inexpensive BIA system combined with appropriate age-adjusted impedance prediction equations.

Assessment by bioimpedance of forearm cell mass: a new approach to calibration.

Objective: Changes in skeletal muscle mass are involved in several important clinical disorders including sarcopenia and obesity. Unlike body fat, skeletal muscle is difficult to quantify in vivo, particularly without highly specialized equipment. The present study had a two-fold aim: to develop a regional 40K counter for non-invasively estimating cell mass in the arm, mainly skeletal muscle cell mass, without radiation exposure; and to test the hypothesis that cell mass in the arm is highly correlated with electrical impedance after adjusting for the arms length.Methods: Forearm cell mass was estimated using a rectangular lead-shielded 40K counter with 4-NaI crystals; impedance of the arm was measured at multiple frequencies using a segmental bioimpedance analysis (BIA) system. The systems within- and between-day coefficient of variation (CV) for 40K-derived elemental potassium averaged 1.8±1.3 and 5.8±1.2%, respectively. The corresponding BIA systems CVs were 1.0±0.4 and 2.1±1.0%, respectively.Subjects and results: Participants in the study were 15 healthy adults (eight females, seven males; age 39±2.8 y, BMI 22.9±4.5 kg/m2). The right arms K (5.2±1.7 g) was highly correlated with length-adjusted impedance (r2=0.81, 0.82, and 0.83 for 5, 50 and 300 kHz, respectively; all P<0.001); multiple regression analysis showed no additional improvement by adding age or sex to the prediction models.Conclusion: These results demonstrate the feasibility of calibrating BIA-measured electrical properties of the arm against estimates of arm cell mass, mainly of skeletal muscle, obtained by regional 40K counting. This simple and practical approach should facilitate the development of BIA-based regional cell mass prediction formulas.Sponsorship: National Institutes of Health grants RR00645 and NIDDK 42618.