Bioelectrical Impedance Analysis Estimates Research Articles

Estimating fat-free mass in recreationally resistance-trained young men: Longitudinal and cross-sectional validation of different methods

Several techniques exist to measure fat-free mass (FFM). Accordingly, this study is based on data from our recent trial comparing the sensitivity of the main field methods available with that of dual-energy X-ray absorptiometry (DXA) as reference and analyzing the cross-sectional accuracy of these field methods in recreationally resistance-trained males. We hypothesized that the use of these techniques would lead to varying estimates of FFM compared with DXA. Participants (N=23; 21.4 ± 3.3 years) completed a 10-week resistance training plus diet intervention designed to optimize hypertrophy. FFM was determined by bioelectrical impedance analysis (BIA), 23 anthropometric equations, and DXA. After the intervention, FFM increased significantly according to BIA and most anthropometric estimates, but this increase was not detected by 2 anthropometric equations or by DXA. Only 1 of these 2 equations showed significant correlation with DXA and no standardized or significant differences to this reference method, although it did display significant heteroscedasticity. In our cross-sectional analysis, only 1 anthropometric equation gave rise to good accuracy as confirmed by DXA. Our findings indicate that the use of different techniques to assess FFM gains in response to a hypertrophic intervention yields different results. BIA with general embedded equations should not be used to monitor a young male adult's body composition. To monitor FFM over time, we would recommend the Dunne etal. equation (2) as the most sensitive field method, and to assess FFM cross-sectionally, equation (1) of these authors is the most accurate field method.

Adipose tissue estimation of foraging and nesting green turtles Chelonia mydas using bioelectrical impedance analysis

Adipose tissue is the main energy store in sea turtles and fluctuates in response to dietary conditions and external stressors. Monitoring programmes commonly use body condition indices (BCIs) to infer the nutritional and health status of sea turtle populations. However, BCIs have poor predictive power for estimating adipose tissue. We introduce the use of bioelectrical impedance analysis (BIA) as a portable technique to estimate adipose tissue in green turtles Chelonia mydas. The aims of this study were to estimate adipose tissue of green turtles on the Great Barrier Reef (Australia), and to examine whether adipose tissue is a more sensitive indicator than BCI. Turtles (n = 250) were sampled at 3 foraging sites and at a nesting beach with differing levels of anthropogenic impact. Differences in adipose tissue, Fulton’s BCI, and body mass across study sites and life stages were assessed by conducting linear mixed effects models. BIA estimates of mean adipose tissue revealed significant differences across life stages and sampling sites, that were not found using BCI data. Mean adipose tissue was estimated to be 4.6 ± 0.6% (% body mass ± SD) and was not correlated with mean BCI (1.2 ± 0.1). Adipose tissue was not reduced in turtles foraging at sites with a high level of anthropogenic impact. Adult turtles had significantly higher adipose tissue values than juveniles and subadults. Adult females measured during and shortly before nesting season had the highest adipose tissue values (%). BIA is a practical method for estimating adipose tissue, and we recommend this technique for consideration in sea turtle monitoring programmes.

Comparison of body fat percentage assessments by bioelectrical impedance analysis, anthropometrical prediction equations, and dual-energy X-ray absorptiometry in older women.

Individuals with high body fat have a higher risk of mortality. Numerous anthropometric-based predictive equations are available for body composition assessments; furthermore, bioelectrical impedance analysis (BIA) estimates are available. However, in older adults, the validity of body fat estimates requires further investigation. To assess the agreement between percentage body fat (BF%) estimates by BIA and five predictive equations based on anthropometric characteristics using dual X-ray absorptiometry (DXA) as reference method. A secondary objective was to identify whether excluding short-stature women improves the agreement of BF% estimates in a group of community-dwelling, older Mexican women. A concordance analysis of BF% was performed. A total of 121 older women participated in the study. Anthropometric information, BIA, and DXA body composition estimates were obtained. Five equations using anthropometric data were evaluated in order to determine body fat percentage (BF%) using DXA as reference method. Paired t-test comparisons and standard error of estimates (SEE) were obtained. The Bland-Altman plot with 95% limits of agreement and the concordance correlation coefficient (CCC) were used to evaluate the BF% prediction equations and BIA estimates. The mean age of the study participants was 73.7 (±5.8) years old. BIA and the anthropometric based equations examined showed mean significant differences when tested in the entire sample. For the taller women (height > 145 cm), no significant difference in the paired comparison was found between DXA and BIA of BF% estimates. The mean BF% was 40.3 (±4.8) and 40.7 (±6.2) for DXA and BIA, respectively. The concordance between methods was good (CCC 0.814), (SEE 2.62). Also, in the taller women subset, the Woolcott equation using waist-to-height ratio presented no significant difference in the paired comparison; however, the error of the estimates was high (SEE 3.37) and the concordance was moderate (CCC 0.693). This study found that BIA yielded good results in the estimation of BF% among women with heights over 145 cm. Also, in this group, the Woolcott predictive equation based on waist circumference and height ratio showed no significant differences compared to DXA in the paired comparison; however, the large error of estimates observed may limit its application. In older women, short stature may impact the validity of the body fat percentage estimates of anthropometric-based predictive equations.

Influence of Upper-Extremity and Lower-Extremity Resistance Exercise on Segmental Body Composition and Body Fluid Estimates.

Rodriguez, C, Florez, CM, Prather, J, Zaragoza, J, Tinnin, M, Brennan, KL, Taylor, L, and Tinsley, GM. Influence of upper-extremity and lower-extremity resistance exercise on segmental body composition and body fluid estimates. J Strength Cond Res 37(5): 1042-1051, 2023-The purpose of this analysis was to determine if acute, localized resistance exercise (RE) artificially influences total and regional estimates of body composition from dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA). Recreationally active male ( n = 14) and female ( n = 18) subjects completed 3 testing visits: rest (R), upper-extremity RE (U), and lower-extremity RE (L). Dual-energy X-ray absorptiometry scans were completed before exercise and 60 minutes after exercise. Bioelectrical impedance analysis was completed immediately before and after exercise and at 15, 30, and 60 minutes after exercise. Subjects were not allowed to intake fluid during the exercise session or during the postexercise assessment period. The effects of the acute RE session on DXA and BIA estimates were analyzed using linear mixed-effects models with a random intercept for subject. Condition by time interactions were observed for most BIA outcomes. Relative to the reference model (i.e., R condition at baseline), total body water and fat-free mass estimates were, on average, approximately 1 and approximately 1.2 kg higher, in the U condition. In contrast, lower-extremity RE exerted little or no impact on most BIA variables. Some DXA estimates exhibited time main effects, but the magnitude of changes was negligible. An acute bout of localized RE, particularly upper-extremity RE, can artificially influence BIA body fluid and composition estimates, whereas DXA may be robust to the acute biological error introduced by RE. Although body composition assessments should ideally be conducted under standardized conditions, DXA may be suitable in less standardized situations. In addition, BIA is differentially influenced by upper-extremity and lower-extremity resistance exercise.

Explaining Segmental Lean Soft Tissue Discrepancies Between Bioelectrical Impedance Analysis And Dual-Energy X-Ray Absorptiometry

Interest in evaluating the composition of specific anatomical regions has become commonplace in a variety of settings. Appendicular lean soft tissue (ALST) estimates are considered in the diagnosis of sarcopenia. While dual-energy x-ray absorptiometry (DXA) is viewed as a reference method for regional assessments, its availability is limited. Thus, explaining discrepancies in regional body composition estimates between DXA and the more accessible bioelectrical impedance analysis (BIA) is of utmost importance. PURPOSE: To assess the anthropometric and physiological predictors of variations between BIA and DXA segmental lean soft tissue (LST) estimates. METHODS: During a single visit, 179 participants (103 females, 76 males; Mean ± SD: 33.6 ± 15.3 years; 73.4 ± 16.2 kg; 171.2 ± 9.2 cm; 28.2 ± 8.9% DXA body fat %) underwent body composition assessments via DXA and 8-point single-frequency BIA. Potential predictors of discrepancies between DXA and BIA LST estimates were obtained from these methods and additional laboratory techniques. Specifically, air displacement plethysmography, 3-dimensional optical scanning, and bioimpedance spectroscopy were used to estimate body volume, anthropometrics, and hydration variables, respectively. Significant predictors (p ≤ 0.05) of the mean difference between DXA and BIA estimates of trunk LST (TLST) and ALST were established using ordinary least squares regression. Standardized model coefficients, p-values for coefficients, and R2 values were generated. RESULTS: For both TLST and ALST, extracellular fluid percentage, LST hydration, height, total LST mass, the male sex, and racial identification as Black significantly predicted discrepancies between DXA and BIA. Additional predictors for TLST discrepancies were DXA total fat mass (FM) to LST ratio and DXA TLST, while additional predictors of ALST discrepancies included DXA ALST, DXA FM to LST ratio of the legs, DXA appendicular FM, and DXA-derived volume of the arms and legs. Regression models including these significant predictor variables produced R2 values of 0.92 and 0.95 for TLST and ALST, respectively. CONCLUSIONS: Hydration variables, the quantity of LST in the region of interest, and height were the most influential predictor variables for discrepancies between DXA and BIA segmental LST estimates.

Single‐frequency and multi‐frequency bioimpedance analysis: What is the difference?

Bioelectrical impedance analysis is a promising method in determining the body compartments in haemodialysis patients. In this study, we aimed to investigate the agreement between two widely used methods: the single-frequency and multi-frequency bioelectrical impedance analyses. Maintenance haemodialysis patients were enrolled in the study. Single-frequency and multi-frequency bioelectrical impedance analyses were performed consecutively before haemodialysis. A second bioelectrical impedance analysis was performed right after the haemodialysis session. A third bioelectrical impedance analysis was performed one hour after haemodialysis. We used weight change as a measure of fluid removal during haemodialysis session. Bioelectrical impedance analysis estimates from both devices had significant differences. Best agreement was observed between single frequency and multifrequency devices' extracellular water estimates immediately after haemodialysis (mean difference 0.076 L). We found the best agreement between weight change and extracellular water change using single-frequency bioimpedance analysis. Moreover, one hour waiting time did not improve the agreement between weight and extracellular water changes for both devices. Different estimates seem to be caused by different raw impedance data measured by both devices and device-specific equations. There are significant differences among bioelectrical impedance measurements performed with different bioelectrical impedance analyzers. Using open source software might be an important step forward in the development of standardized measurements.

Validity of bioelectrical impedance analysis in estimation of fat-free mass in colorectal cancer patients

Bioelectrical impedance analysis (BIA) is an accessible and cheap method to measure fat-free mass (FFM). However, BIA estimates are subject to uncertainty in patient populations with altered body composition and hydration. The aim of the current study was to validate a whole-body and a segmental BIA device against dual-energy X-ray absorptiometry (DXA) in colorectal cancer (CRC) patients, and to investigate the ability of different empiric equations for BIA to predict DXA FFM (FFMDXA). Forty-three non-metastatic CRC patients (aged 50-80 years) were enrolled in this study. Whole-body and segmental BIA FFM estimates (FFMwhole-bodyBIA, FFMsegmentalBIA) were calculated using 14 empiric equations, including the equations from the manufacturers, before comparison to FFMDXA estimates. Strong linear relationships were observed between FFMBIA and FFMDXA estimates for all equations (R2=0.94-0.98 for both devices). However, there were large discrepancies in FFM estimates depending on the equations used with mean differences in the ranges-6.5-6.8kg and-11.0-3.4kg for whole-body and segmental BIA, respectively. For whole-body BIA, 77% of BIA derived FFM estimates were significantly different from FFMDXA, whereas for segmental BIA, 85% were significantly different. For whole-body BIA, the Schols* equation gave the highest agreement with FFMDXA with mean difference ±SD of-0.16±1.94kg (p=0.582). The manufacturer's equation gave a small overestimation of FFM with 1.46±2.16kg (p<0.001) with a tendency towards proportional bias (r=0.28, p=0.066). For segmental BIA, the Heitmann* equation gave the highest agreement with FFMDXA (0.17±1.83kg (p=0.546)). Using the manufacturer's equation, no difference in FFM estimates was observed (-0.34±2.06kg (p=0.292)), however, a clear proportional bias was detected (r=0.69, p<0.001). Both devices demonstrated acceptable ability to detect low FFM compared to DXA using the optimal equation. In a population of non-metastatic CRC patients, mostly consisting of Caucasian adults and with a wide range of body composition measures, both the whole-body BIA and segmental BIA device provide FFM estimates that are comparable to FFMDXA on a group level when the appropriate equations are applied. At the individual level (i.e. in clinical practice) BIA may be a valuable tool to identify patients with low FFM as part of a malnutrition diagnosis.

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 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.

Fatness of female field hockey players: Comparison of estimates with different methods

The aim of the study was to compare relative body fat (% fat) in female field hockey players using several methods with dual-energy X-ray absorptiometry (DXA) as the reference. Participants were 31 Polish hockey players 16–30 years of age, 17 national and 14 youth level. Percent body fat was estimated by DXA (reference method), conventional and segmental bioelectrical impedance analysis (BIA), and predicted from skinfolds (SKF). National and youth team members did not differ in estimated body fat. Correlations between BIA and skinfold estimates of % fat and DXA % fat though significant, were moderate. Both % fat SKF and % fat SBIA differed significantly from % fat DXA, while estimated % fat BIA and % fat DXA did not differ. Limits of agreement were narrow for conventional BIA (−1.20 to 1.71% fat), followed by segmental BIA (3.72–6.09% fat) and broadest for SKF (5.97–9.28% fat). Differences between DXA % fat and estimated % fat with SKF and SBIA increased from the leanest to fattest athletes, whereas conventional BIA overestimated % fat relative to DXA in the small sample of individuals with low relative fatness and underestimated % fat in individuals with elevated relative fatness. Estimated % fat from conventional BIA most closely approximated DXA % fat in this sample of female field hockey players suggesting that the method may be suitable for field surveys to monitor body composition during the season.

Smart Multi-Frequency Bioelectrical Impedance Spectrometer for BIA and BIVA Applications.

Bioelectrical impedance analysis (BIA) is a noninvasive and commonly used method for the assessment of body composition including body water. We designed a small, portable and wireless multi-frequency impedance spectrometer based on the 12 bit impedance network analyzer AD5933 and a precision wide-band constant current source for tetrapolar whole body impedance measurements. The impedance spectrometer communicates via Bluetooth with mobile devices (smart phone or tablet computer) that provide user interface for patient management and data visualization. The export of patient measurement results into a clinical research database facilitates the aggregation of bioelectrical impedance analysis and biolectrical impedance vector analysis (BIVA) data across multiple subjects and/or studies. The performance of the spectrometer was evaluated using a passive tissue equivalent circuit model as well as a comparison of body composition changes assessed with bioelectrical impedance and dual-energy X-ray absorptiometry (DXA) in healthy volunteers. Our results show an absolute error of 1% for resistance and 5% for reactance measurements in the frequency range of 3 kHz to 150 kHz. A linear regression of BIA and DXA fat mass estimations showed a strong correlation (r(2)=0.985) between measures with a maximum absolute error of 6.5%. The simplicity of BIA measurements, a cost effective design and the simple visual representation of impedance data enables patients to compare and determine body composition during the time course of a specific treatment plan in a clinical or home environment.

Bioelectrical Impedance Analysis to Estimate Lipid Content in Atlantic Salmon Parr as Influenced by Temperature, PIT Tags, and Instrument Precision and Application in Field Studies

AbstractThe objective of this study was to investigate the effectiveness of bioelectrical impedance analysis (BIA) in field studies monitoring Atlantic Salmon Salmo salar parr, as related to temperature corrections, instrument precision, and passive integrated transponder (PIT) tags. Currently, BIA studies are restricted to laboratory settings where water temperature is controlled to decrease error in BIA predictions caused by fish body temperature. We compared models of predicted total and percent lipids with and without temperature corrections and found that temperature corrections reduced error caused by temperature. Without temperature corrections, an 8°C increase in temperature increased the predicted total lipids by 55%. After temperature corrections were added, the predicted total lipid only increased by 2.55%. Repeated measurements were collected on 40 salmon parr (56–115 mm FL) in four separate time trials (1 min, 1.5 h, 3 h, and 6 h), and we found that lipid content predictions between measurements were not significantly different; however, the variability within longer time trials was moderate (6.43% error). No significant differences were found in the predicted lipid value before or after PIT tags were removed from the body cavity, suggesting PIT tags do not affect BIA readings. On average, the difference between predicted total lipids after tag removal was 0.0023 and 0.02 g for 12.5‐mm and 22‐mm PIT tags, respectively. We also observed that increases in fish body temperature caused by handing resulted in increased variability in BIA estimates, indicating the need for temperature corrections.Received March 10, 2014; accepted September 22, 2014

Bioelectrical impedance analysis to estimate body composition in surgical and oncological patients: a systematic review.

Bioelectrical impedance analysis (BIA) is a commonly used method for the evaluation of body composition. However, BIA estimations are subject to uncertainties.The aim of this systematic review was to explore the variability of empirical prediction equations used in BIA estimations and to evaluate the validity of BIA estimations in adult surgical and oncological patients. Studies developing new empirical prediction equations and studies evaluating the validity of BIA estimations compared with a reference method were included. Only studies using BIA devices measuring the entire body were included. Studies that included patients with altered body composition or a disturbed fluid balance and studies written in languages other than English were excluded.To illustrate variability between equations, fixed normal reference values of resistance values were entered into the existing empirical prediction equations of the included studies and the results were plotted in figures. The validity was expressed by the difference in means between BIA estimates and the reference method, and relative difference in %. Substantial variability between equations for groups (including men and women) was found for total body water (TBW) and fat free mass (FFM). The gender-specific existing general equations assume less variability for TBW and FFM. BIA mainly underestimated TBW (range relative difference -18.8% to +7.2%) and FFM (range relative differences -15.2% to +3.8%). Estimates of the fat mass (FM) demonstrated large variability (range relative difference -15.7 to +43.1%). Application of equations validated in healthy subjects to predict body composition performs less well in oncologic and surgical patients. We suggest that BIA estimations, irrespective of the device, can only be useful when performed longitudinally and under the same standard conditions.

Tracking fat-free mass changes in elderly men and women using single-frequency bioimpedance and dual-energy X-ray absorptiometry: a four-compartment model comparison

To compare single estimations of fat-free mass (FFM) and to track FFM using single-frequency bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) compared with a four-compartment (4C) model in healthy elderly Americans. Thirty-four men and thirty-eight women (Caucasian, ≥ 65 years) were included in the study. Subjects participated in either the control group or the exercise group. All testing and training took place during the 21-week investigation. Body composition assessments using nine BIA equations, DXA and a 4C model were performed during weeks 1, 12 and 24 of the study. Single estimations for DXA and BIA produced high r values (0.79-0.95) and low standard error of estimate values (1.62-3.3 kg), producing subjective ratings of 'ideal' for men and 'excellent' for women. Both DXA and two BIA equations revealed the same significance when comparing groups and times with the 4C model. Individual accuracy for tracking changes was similar among BIA equations and DXA compared with the 4C model, with a total agreement of 25% for BIA and 27% for DXA compared with the 4C model. The current data in combination with the reliability errors for both BIA and DXA FFM estimations suggest that individual results should be interpreted with caution if FFM changes are <5 kg. However, DXA and BIA are both valid methods that can be used interchangeably to estimate FFM at a single time point or for tracking changes in FFM in small groups (15-22) of healthy American older adults.

Body composition in athletes and sports nutrition: an examination of the bioimpedance analysis technique

The purpose of the current review was to evaluate how body composition can be utilised in athletes, paying particular attention to the bioelectrical impedance analysis (BIA) technique. Various body composition methods are discussed, as well as the unique characteristics of athletes that can lead to large errors when predicting fat mass (FM) and fat-free mass (FFM). Basic principles of BIA are discussed, and past uses of the BIA technique in athletes are explored. Single-prediction validation studies and studies tracking changes in FM and FFM are discussed with applications for athletes. Although extensive research in the area of BIA and athletes has been conducted, there remains a large gap in the literature pertaining to a single generalised athlete equation developed using a multiple-compartment model that includes total body water (TBW). Until a generalised athlete-specific BIA equation developed from a multiple-compartment is published, it is recommended that generalised equations such as those published by Lukaski and Bolonchuk and Lohman be used in athletes. However, BIA equations developed for specific athletes may also produce acceptable values and are still acceptable for use until more research is conducted. The use of a valid BIA equation/device should produce values similar to those of hydrostatic weighing and dual-energy X-ray absorptiometry. However, researchers and practitioners need to understand the individual variability associated with BIA estimations for both single assessments and repeated measurements. Although the BIA method shows promise for estimating body composition in athletes, future research should focus on the development of general athlete-specific equations using a TBW-based three- or four-compartment model.

PP114-MON HIGH PROPORTION OF DIETARY FAT INTAKE INCREASES RISK OF BEING MALNOURISHED 10 YEARS LATER

Rationale: Position of the body and consequent distribution of body water between body compartments affect the accuraccy of bioelectrical impedance analysis (BIA) and may lead to incorrect information about an individual’s body composition. This study aimed to compare the accuracy of body fat estimates obtained with standing leg-to-leg (LL) and supine hand-to-leg (HL) BIA techniques relative to anthropometry, and to evaluate the effects of body position on BIA-determined body fat percentage (BFP). Methods: The BFP of young males and females (n = 40) was determined with LL (adjusted for standing position) and HL (adjusted for supine position) BIA analysers, as well as from gender-specific Durnin and Womersly anthropometric equations used as the reference method for BIA body fat estimation. Each subject was measured with LL analyser in a standing position, before and after 20 minutes of supine rest, and with HL analyser in a standing and a supine position, before and after 20 minutes of supine rest. All measurements were taken under constant and controlled conditions. Significance of differences was assessed by means of one-way ANOVA for repeated measures. Results: The results demonstrate that the BFP as determined with different BIA analysers in different body positions differ significantly according to the procedure used (P< 0.01). In general, BIA values of BFP obtained after 20 minutes of supine rest tend to overestimate, and values obtained in upright position underestimate the actual BFP. Conclusion: The results of this study demonstrate that BIA estimations of BFP depend on the position of the body during the measurement, likely due to the posturerelated distribution of water in the body. Consequently, the use of different metods can classify one subject into different body fat content categories, which calls for further standardisation. Disclosure of Interest: None Declared.

Anthropometric indexes outperform bioelectrical impedance analysis-derived estimates of body composition in identification of metabolic abnormalities in morbid obesity

BackgroundThe validity of anthropometric indexes in ascertaining the body composition (BC) in morbidly obese (MO) subjects has been questioned. Our objective was to evaluate, in MO subjects, whether bioelectrical impedance analysis (BIA) of BC is more closely associated with the metabolic syndrome (MS) and insulin resistance (IR) than are classic anthropometric measurements. The setting was a university hospital. MethodsThe association between anthropometric (body mass index, waist circumference [WC]) and BIA (total fat mass [FM] [percentage of FM], truncal FM, android FM) estimates of BC, MS, and IR was evaluated in 784 white MO subjects (212 men and 572 women). BIA estimates were calculated using equations specific for MO subjects developed by our own group and validated against dual energy x-ray absorptiometry. ResultsThe prevalence of the MS and IR was 78.6% and 88.6%, respectively. The body mass index was greater in women with the MS (P <.001) or IR (P <.001), and the WC was larger in subjects of both genders with the MS or IR (P <.001). Moreover, the WC correlated significantly with all the MS components (P <.05). In contrast, the percentage of FM, truncal FM, and android FM were significantly associated with the MS only in women. Stepwise logistic regression analysis demonstrated the WC as the only significant predictor of the MS or IR (both P <.001). Furthermore, receiver operating curve analysis showed WC was the most accurate BC parameter for the identification of subjects with the MS (area under the curve, WC = .681, P <.001) or IR (area under the curve, WC = .753, P <.001). ConclusionIn MO subjects, the BIA-derived indexes of total and central adiposity were not better predictors of the MS or IR than were traditional anthropometric measurements.

Ethnic variation in body composition assessment in a sample of adolescent girls

To compare body composition assessment by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) in a multiethnic sample of adolescent girls. Data were from a physical activity intervention study among 254 14-20-year-old sedentary American girls, including 69 whites, 74 blacks, 42 Hispanics, and 69 Asians. Height and weight were objectively measured. Body composition was assessed using a foot-to-foot BIA and a fan-beam DXA. We calculated ethnic-specific estimates of percentage body fat (BF%), fat mass (FM), fat mass index (FMI), fat-free mass (FFM), and fat-free mass index (FFMI) from BIA and DXA. We used Bland-Altman plots to examine ethnic-specific agreement between BIA and DXA, and used linear regression to test whether the BIA-DXA difference varied across the mean. Compared to DXA, BIA estimates of fat measures (BF%, FM, and FMI) were lower and lean tissue measures (FFM and FFMI) higher. For example, the BIA-estimated BF% was lower than the DXA estimate by between 4.9% (95% CI: -5.9, -3.9) in blacks and 8.7% (-7.0, -5.0) in Asians, with large limits of agreement (-15.4 to -5.4 in blacks and -16.8 to -0.4 in Asians). Regression analysis showed that BIA-DXA differences were not constant across means for any body composition measure among Asians or for any measure except BF% among whites. Compared to DXA, BIA yielded lower estimates of adiposity in a multiethnic adolescent sample. BIA-DXA differences varied by ethnicity, and across mean body composition values for some ethnicities.

Assessing change in body composition in children with Duchenne muscular dystrophy: Anthropometry and bioelectrical impedance analysis versus dual-energy X-ray absorptiometry

To compare the ability of bioelectrical impedance analysis (BIA) and skinfold thickness (ST) measurements to estimate changes in body composition in Duchenne muscular dystrophy (DMD). A secondary analysis was performed on 26 ambulatory DMD boys aged 3-11 y selected for a randomised trial of glutamine supplementation. We assessed fat free mass (FFM) and percentage fat mass (%FM) by BIA (monofrequency (50kHz) unit), ST measurements and a criterion method, dual-energy X-ray absorptiometry (DXA), and repeated these measures 5 mo later at 3 outpatient clinical investigation centers in France. When compared with DXA reference method, ST overestimated change in FFM (P<0.01), whereas BIA estimates did not differ from DXA. Concordance plots revealed that when compared with DXA, ST overestimated the increase in FFM (mean: 0.6 kg; 95% CI: 0.17 to 0.99) which led to an underestimation in %FM change (mean: -1.4%; 95% CI: -2.6 to -0.2), whereas BIA estimated change in FFM (mean: -0.05 kg; 95% CI: -0.39 to 0.29) and %FM (mean: 1.3%; 95% CI: -0.06 to 2.7) more accurately. BIA method can be used to follow changes in nutritional status of ambulatory DMD children or to evaluate treatment efficacy.

Assessing body fat changes during moderate weight loss with anthropometry and bioelectrical impedance

BACKGROUND/OBJECTIVES: Monitoring changes in total fat mass and abdominal adiposity are important in understanding the impact of different types of weight loss interventions on health risks. Our objective was to assess the usefulness of anthropometry and bioelectrical impedance analysis (BIA) in predicting fat mass changes during moderate weight loss. SUBJECTS/METHODS: Fat mass changes were assessed in 34 overweight adults (24 females, 10 males) after a 12-week supervised weight loss induced by caloric restriction (-30% of requirement) using BIA and DXA. Agreement between BIA and DXA measurements were assessed by Bland-Altman plots. Linear regression modeling was used to predict body and truncal fat mass from anthropometric measures. RESULTS: Diet intervention resulted in a significant decrease in body weight (- 7.86 ± 2.87 kg), body mass index (BMI - 2.69 ± 0.98 kg/m(2)), total body fat (- 5.22 ± 2.32 kg), truncal fat (- 2.80 ± 1.94 kg) and waist circumference (- 5.52 ± 3.57 cm). BMI and body weight were highly correlated with body fat (0.83 and 0.92 in females and 0.94 and 0.92 in males respectively) and truncal fat (0.75 and 0.87 in females; 0.90 and 0.84 in males respectively) during weight loss. Waist circumference was more correlated with truncal fat in males than females (0.94 vs. 0.85 in females). Compared to DXA, BIA underestimated total body fat changes in males (- 8.8 kg, p<0.001) and overestimated total body fat changes in females (+ 2.1 kg, p< 0.001). CONCLUSIONS: Body mass index, body weight, and waist circumference provide simple and more accurate than BIA estimates of relative changes in total and truncal fat during moderate weight loss in adults.

Effects of Nine Weeks of β-Hydroxy-β- Methylbutyrate Supplementation on Strength and Body Composition in Resistance Trained Men

The dietary supplement beta-hydroxy-beta-methylbutyrate (HMB) is claimed to increase strength, lean body mass, and decrease fat mass when used in conjunction with resistance training. Although there is some support for these claims, the evidence is not conclusive, and it is even less so for resistance trained individuals. Therefore, we aimed to further elucidate the effects of HMB supplementation in trained men. A randomized, double-blind, controlled study design was used to investigate the effects of supplementing 22 resistance trained men with 3 g.d of HMB or corn starch placebo for 9 weeks with resistance training. The effect of HMB on strength was determined using the 1-repetition maximum (1RM) method for the lower body (leg extension) and upper body (bench press, bicep preacher curl) at baseline and after the supplementation period. Body composition was assessed by skinfolds and bioelectrical impedance analysis (BIA). Overall, 9 weeks' HMB supplementation resulted in a clear-cut, trivial increase in combined averaged strength measures of 1.6% (90% confidence limits: +/-4.3%). When considered in isolation, however, leg extension 1RM increased by a substantial 9.1% (90% confidence limits: +/-7.5%), but the effect on upper-body strength was inconclusive (bench press: -1.9 +/- 9.3%; bicep curl: -1.7 +/- 4.7%). Based on BIA estimates, HMB had a decreasing (although inconclusive) influence on fat mass of -9 +/- 14%, but it had a clear, trivial effect on fat-free mass of 0.2 +/- 2.2%. The magnitude of change in body mass was trivial, but the probability of substantial reductions in skinfold thicknesses ranged from negligible to likely. In previously trained men, supplementation of HMB in conjunction with resistance training provides a substantial benefit to lower-body strength, but it has negligible effects on body composition.